nepal hazard risk assessment - adpc · 2.5.7 industrial sector ... 3.5.1 agriculture sector ......

Nepal Hazard Risk Assessment

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Nepal Hazard Risk Assessment Part 2: Exposure, Vulnerability and Risk Assessment

Asian Disaster Preparedness Center (ADPC)

Norwegian Geotechnical Institute (NGI)

Centre for International Studies and Cooperation (CECI)

Supported by


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TABLE OF CONTENTS Table of Contents ........................................................................................................................................ ii List of Figures ............................................................................................................................................. iv List of Tables .............................................................................................................................................. vi Contributors .............................................................................................................................................. vii Acknowledgements ..................................................................................................................................... ix Preface .......................................................................................................................................................... x Acronyms .................................................................................................................................................... xi Executive Summary .................................................................................................................................. xii 1 Exposure, Vulnerability and Risk Assessment (EVRA) .................................................................... 16

1.1 Overview ..................................................................................................................................... 16 1.2 Application of EVRA .................................................................................................................. 16 1.3 Key Issues of EVRA ................................................................................................................... 16 1.4 Methodology for EVRA .............................................................................................................. 17

1.4.1 Exposure Assessment (EA) .................................................................................................. 17 1.4.2 Vulnerability Assessment .................................................................................................... 18 1.4.3 Risk Assessment .................................................................................................................. 18 1.4.4 Conclusion ........................................................................................................................... 18

2 Earthquake Exposure, Vulnerability and Risk Assessment ............................................................... 19 2.1 Overview ..................................................................................................................................... 19 2.2 Earthquake Exposure Assessment ............................................................................................... 19 2.3 Methodology for Earthquake Exposure Assessment ................................................................... 20 2.4 How to read and analyze the exposure results ............................................................................ 20 2.5 Analysis of Exposure Assessment ............................................................................................... 21

2.5.1 Population ............................................................................................................................ 21 2.5.2 Housing Sector ..................................................................................................................... 22 2.5.3 Education Sector .................................................................................................................. 24 2.5.4 Health Sector ........................................................................................................................ 25 2.5.5 Transport Sector ................................................................................................................... 28 2.5.6 Power and Electricity Sector ................................................................................................ 30 2.5.7 Industrial Sector ................................................................................................................... 30

2.6 Earthquake Vulnerability and Risk Assessment (VRA) ............................................................. 33 2.7 Earthquake Vulnerability Assessment ......................................................................................... 33

2.7.1 Population casualty modeling .............................................................................................. 33 2.7.2 Sectoral vulnerability model ................................................................................................ 34

2.8 Analysis of Earthquake Vulnerability and Risk Assessment ...................................................... 35 2.8.1 Population ............................................................................................................................ 35 2.8.2 Housing Sector .................................................................................................................... 39 2.8.3 Education Sector .................................................................................................................. 41 2.8.4 Health Sector ....................................................................................................................... 43 2.8.5 Transportation Sector .......................................................................................................... 46 2.8.6 Power and Electricity Sector ............................................................................................... 49 2.8.7 Industrial Sector ................................................................................................................... 52

2.9 Scenario Building for Significant Past Earthquakes ................................................................... 53 2.9.1 Hazard Review .................................................................................................................... 54 2.9.2 Risk Analysis ....................................................................................................................... 55 2.9.3 Analysis of Damage Scenario Profile .................................................................................. 56

2.10 Conclusion .................................................................................................................................. 56 3 Flood Exposure, Vulnerability and Risk Assessment ....................................................................... 58

3.1 Overview ..................................................................................................................................... 58 3.2 Application of Flood Exposure Assessment ............................................................................... 58 3.3 Methodology for Flood Exposure Assessment ........................................................................... 59 3.4 How to read and analyze the exposure results ............................................................................ 59 3.5 Analysis of Exposure Assessment .............................................................................................. 59

3.5.1 Agriculture Sector ................................................................................................................ 59 3.5.2 Housing Sector .................................................................................................................... 61 3.5.3 Education ............................................................................................................................. 64 3.5.4 Health ................................................................................................................................... 65 3.5.5 Population ............................................................................................................................ 67

3.6 Flood Vulnerability and Risk Assessment .................................................................................. 68 3.7 Methodology for Flood Vulnerability and Risk Assessment ...................................................... 68 3.8 Analysis of Flood Vulnerability and Risk Assessment ............................................................... 70

3.8.1 Agriculture Sector ................................................................................................................ 70 3.8.2 Housing Sector .................................................................................................................... 73 3.8.3 Education Sector .................................................................................................................. 75 3.8.4 Health Sector ....................................................................................................................... 76

3.9 Conclusion .................................................................................................................................. 77 4 Drought Exposure, Vulnerability and Risk Assessment ................................................................... 79


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4.1 Overview ..................................................................................................................................... 79 4.2 Application of Drought Exposure Assessment ........................................................................... 79 4.3 Methodology for Drought Exposure Assessment ....................................................................... 80 4.4 How to read and analyze the exposure results ............................................................................ 80 4.5 Analysis of Exposure Assessment ............................................................................................... 80

4.5.1 Agriculture Sector ................................................................................................................ 80 4.6 Drought Vulnerability and Risk Assessment .............................................................................. 84 4.7 Methodology for Drought Vulnerability and Risk Assessment .................................................. 84 4.8 Analysis of Drought Vulnerability and Risk Assessment ........................................................... 86

4.8.1 Agriculture Sector ................................................................................................................ 86 4.9 Conclusion ................................................................................................................................... 90

5 Assessing the economic impacts of disasters in Nepal ...................................................................... 91 5.1 The Burden of Disasters in Nepal ............................................................................................... 91

5.1.1 The direct burden of natural disasters .................................................................................. 91 5.1.2 Socioeconomic characteristics ............................................................................................. 92 5.1.3 Indirect effects and developmental implications ................................................................. 93

5.2 Assessing and modelling economic disaster risk ........................................................................ 93 5.2.1 Classifying economic impacts ............................................................................................. 93 5.2.2 Observing economic impacts of disasters ............................................................................ 94 5.2.3 Modeling economic disaster risk and impacts ..................................................................... 94

5.3 Policy Options for Managing Disaster Risk ................................................................................ 95 5.3.1 Overview .............................................................................................................................. 95 5.3.2 Further categorizing options ................................................................................................ 96 5.3.3 Assessing, planning and financing economic risk ............................................................... 97 5.3.4 The relevance of risk for assessing options ......................................................................... 98

5.4 Assessing and planning for Economic Risk: The CATSIM model ............................................. 98 5.4.1 Methodology ........................................................................................................................ 99 5.4.2 CATSIM steps ...................................................................................................................... 99

5.5 Results ....................................................................................................................................... 101 5.5.1 Step 1: Assessment of direct, asset risks ............................................................................ 101 5.5.2 Step 2: Estimation of the fiscal resilience of the public sector .......................................... 104 5.5.3 Step 3: Fiscal Vulnerability and the “fiscal gap” .............................................................. 104 5.5.4 Step 4: Mainstreaming disaster risk into macroeconomic and development planning ...... 106

5.6 Discussion and Conclusions ...................................................................................................... 108 6 National strategy for Disaster Risk Reduction in Nepal .................................................................. 110

6.1 Overview ................................................................................................................................... 110 6.2 Policy, Institutional Mandates and Institutional Development ................................................. 110 6.3 National Disaster Management Act .......................................................................................... 110

6.3.1 Review and formalize institutional mandates for line agencies to perform disaster related activities ........................................................................................................................................... 110 6.3.2 Developing Institutional mandates and capacities ............................................................. 111 6.3.3 Formulation of CBDRM Policy ........................................................................................ 111 6.3.4 Enforcement of Policies ..................................................................................................... 111

6.4 Hazard, vulnerability and Risk Assessment .............................................................................. 112 6.4.1 Natural Hazard, Vulnerability and Risk Assessment ........................................................ 112 6.4.2 Database Management Systems ........................................................................................ 113 6.4.3 HVRA capacity building for focal agencies ...................................................................... 113 6.4.4 Science and technology in HVRA ..................................................................................... 113

6.5 Establishment of National Early Warning Center of Nepal ...................................................... 114 6.5.1 Establishment of national EW center of Nepal ................................................................. 114 6.5.2 Improvement of meteorological observations and prediction capabilities ........................ 114 6.5.3 Improvements in landslide prediction and EW capabilities .............................................. 114 6.5.4 Develop long and short-term drought forecasting and monitoring systems for agricultural and associated sectors ...................................................................................................................... 115

6.6 Preparedness and response plan ................................................................................................ 115 6.6.1 Hazard specific response plans .......................................................................................... 115 6.6.2 National rapid response team ............................................................................................ 115 6.6.3 Emergency Operation Center ............................................................................................ 116 6.6.4 Hazard specific contingency planning ............................................................................... 116 6.6.5 Emergency service networking ......................................................................................... 116 6.6.6 Knowledge management systems ...................................................................................... 117 6.6.7 Health sector preparedness and response mechanism ....................................................... 117 6.6.8 Private sector preparedness for disaster response .............................................................. 117 6.6.9 Capacity-building of local authorities for emergency response ........................................ 118 6.6.10 Provision of facilities for storage of emergency reserves and resource needs .................. 118 6.6.11 Establish a nation-wide emergency communication system ............................................. 118

6.7 Integration of DRR into development planning ........................................................................ 119 6.8 Community Based Disaster Management ................................................................................. 122 6.9 Public awareness, education and training ................................................................................. 123

Reference ................................................................................................................................................. 124


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LIST OF FIGURES Figure 2.1 Methodology of Earthquake Exposure, Vulnerability and Risk Assessment ........................... 19 Figure 2.2 District wide population exposure in a very high hazard zone for a 500 year earthquake return period ......................................................................................................................................................... 21 Figure 2.3 District wide population exposure in a high hazard zone for a 100 year earthquake return period ......................................................................................................................................................... 22 Figure 2.4 Type of housing exposed to a very high risk zone in a 500 year earthquake return period ..... 23 Figure 2.5 Type of housing exposed to a high risk zone in a 100 year earthquake return period ............. 23 Figure 2.6 Education infrastructures exposed to earthquakes in districts for a 500 year earthquake return period ......................................................................................................................................................... 24 Figure 2.7 Education infrastructures exposed to earthquakes in districts for a 100 year earthquake return period ......................................................................................................................................................... 25 Figure 2.8 Health post infrastructure exposed to earthquakes in districts for a 500 year earthquake return period ......................................................................................................................................................... 26 Figure 2.9 Health post infrastructure exposed to earthquakes in districts for a 100 year earthquake return period ......................................................................................................................................................... 26 Figure 2.10 Hospitals exposed to earthquakes in districts for a 500 year earthquake return period ......... 27 Figure 2.11 Hospitals exposed to earthquakes in districts for a 100 year earthquake return period ......... 27 Figure 2.12 Road length exposure to earthquakes in districts for a 500 year earthquake return period .... 28 Figure 2.13 Road length exposure to earthquakes in districts for a 100 year earthquake return period .... 29 Figure 2.14 Bridge length exposure to earthquakes in districts for a 500 year return period earthquake . 29 Figure 2.15 Bridge length exposure to earthquakes in districts for a 100 year earthquake return period . 30 Figure 2.16 High electric line exposure in districts for a 500 year earthquake return period .................... 31 Figure 2.17 High electric line exposure in districts for a 100 year earthquake return period .................... 31 Figure 2.18 Electric transformer exposure in districts for a 500 year earthquake return period ............... 32 Figure 2.19 Electric transformer exposure in districts for a 100 year earthquake return period ............... 32 Figure 2.20 Industry exposure in districts for 500 and 100 year earthquake return periods ..................... 33 Figure 2.21 Map of life casualty for a 500 year earthquake return period (day and night scenario) ......... 36 Figure 2.22 District-wide casualties for a 500 year earthquake return period (daytime scenario) ............ 37 Figure 2.23 District-wide casualties for a 500 year return period earthquake (nighttime scenario) .......... 37 Figure 2.24 Map of casualties for a 100 year earthquake return period (day and night scenario) ............. 38 Figure 2.25 District-wide casualties for a 100 year earthquake return period (daytime scenario) ............ 38 Figure 2.26 District-wide casualties for a 100 year earthquake return period (nighttime scenario) .......... 39 Figure 2.27 Housing risk zones for 500 and 100 year earthquakes return periods .................................... 40 Figure 2.28 Housing risk profile for a 500 year earthquake return period scenario .................................. 40 Figure 2.29 Housing risk profile for a 100 year earthquake return period scenario .................................. 41 Figure 2.30 School damage risk for 500 and 100 year earthquake return periods .................................... 42 Figure 2.31 School damage risk for a 500 year earthquake return period scenario ................................... 42 Figure 2.32 School damage risk for a 100 year earthquake return period scenario ................................... 43 Figure 2.33 Health post damage risk distribution for 500 and 100 year earthquake return periods .......... 44 Figure 2.34 Health post damage for a 500 year earthquake return period scenario .................................. 44 Figure 2.35 Health post damage for a 100 year earthquake return period scenario .................................. 45 Figure 2.36 Hospital damage for a 500 year earthquake return period scenario ....................................... 45 Figure 2.37 Hospital damage for a 100 year earthquake return scenario .................................................. 46 Figure 2.38 Map of the most prominent road damage zones for 500 and 100 year earthquake return periods ........................................................................................................................................................ 47

Figure 2.39 Road damage for a 500 year earthquake return period scenario ............................................ 47 Figure 2.40 Road damage for a 100 year earthquake return period scenario ............................................ 48 Figure 2.41 Bridge damage for a 500 and 100 year earthquake return period scenario ............................ 48 Figure 2.42 Map of the most prominent high electric line damage zones for 500 and 100 year earthquake return periods ............................................................................................................................................. 49 Figure 2.43 High electric line damage for a 500 year earthquake return period scenario ......................... 50 Figure 2.44 High electric line damage for a 100 year earthquake return period scenario ......................... 50 Figure 2.45 Electric Transformer damage for a 500 year earthquake return period scenario ................... 51 Figure 2.46 Electric Transformer damage for a 100 year earthquake return period scenario ................... 51 Figure 2.47 Map of Industry damage for 500 and 100 year earthquake return period scenarios .............. 52 Figure 2.48 Industry damage for 500 and 100 year earthquake return period scenarios .......................... 53 Figure 2.49. MMI zone for the Bihar 1934 earthquake (Bilham, 1995) ................................................... 54 Figure 2.50. MMI zone for the 1833 earthquake (Bilham, 1995) ............................................................. 55 Figure 3.1 Methodology of Flood Exposure, Vulnerability and Risk Assessment ................................... 58 Figure 3.2 Percentage of agricultural land affected by flood in a 10 year return period ........................... 60 Figure 3.3 Percentage of agricultural land affected by a 100 year flood return period ............................. 60 Figure 3.4 Percentage of permanent housing exposed to flooding in a 10 year return period .................. 62 Figure 3.5 Percentage of semi-permanent housing exposed to flooding in a 10 year return period ......... 62 Figure 3.6 Percentage of temporary housing exposed to flooding in a 10 year return period .................. 62 Figure 3.7 Percentage of other types of housing exposed to flooding in a 10 year return period ............. 62 Figure 3.8 Percentage of permanent housing exposed to flooding in a 100 year return period ................ 63 Figure 3.9 Percentage of semi-permanent housing exposed to flooding in a 100 year return period ....... 63 Figure 3.10 Percentage of temporary housing exposed to flooding in a 100 year return period .............. 63 Figure 3.11 Percentage of other types of housing exposed to flooding in a 100 year return period ......... 63 Figure 3.12 Percentage of schools exposed to flooding in a 10 year return period ................................... 64 Figure 3.13 Percentage of schools exposed to flooding in a 100 year return period ................................. 65 Figure 3.14 Percentage of health posts and hospitals exposed to flooding in a 10 year return period ...... 66 Figure 3.15 Percentage of health posts and hospitals exposed to flooding in a 100 year return period .... 66 Figure 3.16 Population by gender exposed to flooding ............................................................................. 67 Figure 3.17 Age of population exposed to flooding in a 10 years return period ....................................... 68 Figure 3.18 Age of population exposed to flooding in a 100 year return period ...................................... 68 Figure 3.19 Modified damage depth ratio for buildings (based on HAZUS depth damage ratio) ............ 70 Figure 3.20 Percentage of paddy crop that survives after being submerged ............................................. 71 Figure 3.21 Paddy yield and affected area ................................................................................................ 71 Figure 3.22 Estimated loss of production at various stages of growth (Mt) due to flooding from the Babai River Basin ................................................................................................................................................ 71 Figure 3.23 Estimated loss of production at various stages of growth (Mt) due to flooding from the Bagmati River Basin .................................................................................................................................. 72 Figure 3.24 Estimated loss of production at various stages of growth (Mt) due to flooding from the Rapti River Basin ................................................................................................................................................ 72 Figure 3.25 Estimated loss of production at various stages of growth (Mt) due to flooding from the Kankai River Basin .................................................................................................................................... 72 Figure 3.26 Estimated loss of production at various stages of growth (Mt) due to flooding from the Kamala River Basin ................................................................................................................................... 72 Figure 3.27 Estimated loss of production at various stages of growth (Mt) due to flooding from the Tinau River Basin ................................................................................................................................................ 73


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Figure 3.28 Estimated loss of production at various stages of growth (Mt) due to flooding from the Narayani River Basin ................................................................................................................................. 73 Figure 3.29 Housing sector at risk of flooding from the Babai River Basin ............................................. 73 Figure 3.30 Housing sector at risk of flooding from the Bagmati River Basin ......................................... 74 Figure 3.31 Housing sector at risk of flooding from the Kamala River Basin .......................................... 74 Figure 3.32 Housing sector at risk of flooding from the Kankai River Basin ........................................... 74 Figure 3.33 Housing sector at risk of flooding from the Narayani River Basin ........................................ 75 Figure 3.34 Housing sector at risk of flooding from the Rapti River Basin .............................................. 75 Figure 3.35 Housing sector at risk of flooding from the Tinau River Basin ............................................. 75 Figure 3.36 Number of educational institutions at risk of flooding for a 10 year return period ................ 76 Figure 3.37 Number of educational institutions at risk of flooding for a 100 year return period .............. 76 Figure 3.38 Number of health posts at risk of flooding for a 10 year return period .................................. 77 Figure 3.39 Number of hospitals at risk of flooding for a 10 year return period ....................................... 77 Figure 3.40 Number of health post at risk of flooding for a 10 year return period ................................... 77 Figure 3.41 Number of hospitals at risk of flooding for a 10 year return period ....................................... 77 Figure 4.1 Methodology of Drought Exposure, Vulnerability and Risk Assessment ................................ 79 Figure 4.2 Wheat crop areas exposed to drought during winter A)Geographical zones B) Development zones ........................................................................................................................................................... 81 Figure 4.3 Wheat crop areas exposed to drought during the pre-monsoon season A) Geographical zones, B) Development zones ............................................................................................................................... 81 Figure 4.4 Barley crop areas exposed to drought during winter A) Geographical zones, B) Development zones ........................................................................................................................................................... 82 Figure 4.5 Barley crop areas exposed to drought during the pre-monsoon season A) Geographical zones, B) Development zones ............................................................................................................................... 82 Figure 4.6 Paddy crop areas exposed to drought during the monsoon season A) Geographical zones, B) Development zones .................................................................................................................................... 83 Figure 4.7 Paddy crop areas exposed to drought during the post-monsoon season A) Geographical zones, B) Development zones ............................................................................................................................... 83 Figure 4.8 Maize crop areas exposed to drought during the monsoon season A) Geographical zones, B) Development zones .................................................................................................................................... 84 Figure 4.9 Drought vulnerability coefficient for different stages of crop growth ..................................... 85 Figure 4.10 Drought risk on wheat production losses in winter A) Geographical zones, B) Development zones ........................................................................................................................................................... 86 Figure 4.11 Drought risk on wheat losses in pre-monsoon drought A) Geographical zones, B) Development zones .................................................................................................................................... 87 Figure 4.12 Drought risk on barley production losses in winter A) Geographical zones, B) Development zones ........................................................................................................................................................... 87 Figure 4.13 Drought risk on barley production losses in the pre-monsoon season A) Geographical zones, B) Development zones ............................................................................................................................... 88 Figure 4.14 Drought risk on paddy production losses in the monsoon season A) Geographical zones, B) Development zones .................................................................................................................................... 88 Figure 4.15 Drought risk on paddy production losses in the post-monsoon season A) Geographical zones, B) Development zones .................................................................................................................... 89 Figure 4.16 Drought risk on maize production losses in the monsoon season A) Geographical zones, B) Development zones .................................................................................................................................... 89 Figure 5.1 Regional distribution of damages (Source: Upreti, 2006) ........................................................ 92

Figure 5.2 Comparison of key development and economic indicators for Nepal with the low-income group (Source: World Bank, 2009) ........................................................................................................... 92 Figure 5.3 Composition of 2008 debt (USD milion) (Source: World Bank, 2009) .................................. 93 Figure 5.4 Foreign assistance for disaster relief spending(Source: Upreti, 2006) .................................... 93 Figure 5.5 Natural disaster risk and categories of potential disaster impacts ............................................ 93 Figure 5.6 Fiscal and economics effects of hurricanes on Grenada. (Source: OECS, 2004, 2005) .......... 94 Figure 5.7 Planning for disaster risks. (Source: Bettencourt et al., 2006) ................................................. 97 Figure 5.8 The layering approach for risk reduction and risk financing ................................................... 98 Figure 5.9 CATSIM framework for assessing fiscal vulnerability and the management of extreme event risk ............................................................................................................................................................. 99 Figure 5.10 Estimating fiscal vulnerability ............................................................................................. 100 Figure 5.11 Outline of the Social Accounting Matrix approach ............................................................. 101 Figure 5.12 Damages in constant 2000 USD for past earthquake and flood events. (source: CRED, 2010) ................................................................................................................................................................. 102 Figure 5.13 Damage-frequency distribution for EQ, flood and joint damage distribution ..................... 102 Figure 5.14 Fitted GEV for the given return periods .............................................................................. 103 Figure 5.15 Fiscal vulnerability and fiscal gap for flood risk (central estimate) ..................................... 104 Figure 5.16 Fiscal vulnerability and fiscal gap: flood (minimum and maximum cases) ........................ 104 Figure 5.17 Fiscal vulnerability and fiscal gap: earthquake (central estimate) ....................................... 105 Figure 5.18 Fiscal vulnerability and fiscal gap for the joint risk distribution (central estimate) ............ 105 Figure 5.19 Potential fiscal impacts due to the joint risk of flood and earthquake. ................................ 106 Figure 5.20 Potential GDP impacts due to joint risk of flood and earthquake ........................................ 107 Figure 5.21 Primary and higher order losses for a 1934 scenario earthquake ......................................... 108 Figure 5.22 Income effects for an earthquake of the size of the 1934 event ........................................... 108


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LIST OF TABLES Table 1.1 Scope of EVRA for various hazards with their severity ............................................................ 16 Table 2.1 Impact on sectors affected - identified in the exposure assessment ........................................... 20 Table 2.2 Parameters for calculation of casualty model ............................................................................ 34 Table 2.3 A comparison of the characteristics of buildings in Nepal ........................................................ 34 Table 2.4 Building response to earthquake intensity scale ........................................................................ 35 Table 2.5 Damage probability matrix for house, education, health sector ................................................ 35 Table 2.6 Cost estimates for several factors as a result of the 1934 earthquake ........................................ 55 Table 2.7 Cost estimates for several factors as a result of the 1833 earthquake ........................................ 55 Table 3.1 Impact on sectors affected - identified in the Exposure assessment .......................................... 59 Table 3.2 Vulnerability Assessment Matrix .............................................................................................. 69 Table 3.3 Inundated areas in affected VDC ............................................................................................... 70 Table 3.4 Inundated paddy areas in affected VDCs ................................................................................... 70 Table 4.1 Season used for drought indices analysis ................................................................................... 84 Table 4.2 Major agricultural crops considered for drought impact analysis ............................................. 84 Table 4.3 Crop Yield .................................................................................................................................. 85 Table 5.1 Top 10 natural disasters in terms of killed, number affected and direct damages. .................... 91 Table 5.2 Socio-economic indicators for Nepal (year 2008) ..................................................................... 92 Table 5.3 Economics models for assessing the effects of disaster risk ...................................................... 95 Table 5.4 Overview of disaster risk management measures ..................................................................... 95 Table 5.5 Further categorizing policy options for dealing with extreme events ........................................ 96 Table 5.6 Government liabilities and disaster risk ..................................................................................... 97 Table 5.7 Return periods and damages due to flooding ........................................................................... 101 Table 5.8 Return periods and damages for earthquake risk ..................................................................... 101 Table 5.9 Elements exposed to risk ......................................................................................................... 102 Table 5.10 Potential damages due to flood risk ....................................................................................... 103 Table 5.11 Potential damages due to earthquake risk (central estimate case) ......................................... 103 Table 5.12 Potential total damages due to combined flood and earthquake risk ..................................... 104 Table 5.13 Sources for financing of disaster damages ............................................................................. 104 Table 5.14 Parameters of the production function ................................................................................... 106 Table 5.15 Indicators for investigation of disaster risk for government (in year 10 of the modeling time horizon) .................................................................................................................................................... 106 Table 5.16 Primary and higher order losses of a scenario earthquake of the severity of the 1934 scenario earthquake current (million USD) ............................................................................................................ 107 Table 6.1 DRR component within specified sectors ................................................................................ 119


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CONTRIBUTORS

Project Management Team

Name of organization Name Designation

Asian Disaster Preparedness Center, Bangkok

MNSI Arambepola Team Leader(Project), Director ADPC, Bangkok

Amit Kumar Project Manager (Project), Senior Project Manager, ADPC, Bangkok

Rendy Dwi Katiko Ex. GIS and Database Specialist, ADPC Bangkok

Kittiphong Phongsapan GIS and Database Specialist, ADPC, Bangkok

Amin Budiarjo Ex. GIS and Database Specialist, ADPC, Bangkok

Peeranan Towashiraporn Program Manager, ADPC, Bangkok

Nirmala Fernando Project Manager, ADPC, Bangkok

Anis ur Rahman Project Manager, ADPC, Bangkok

Khaled Mushfiq Structural Engineer and Project Coordinator, ADPC Dhaka

Norwegian Geotechnical Institute, Norway

Farrokh Nadim Hazard and Vulnerability Assessment Expert, Director, International Centre for Geohazards, NGI, Norway

Bjørn Kalsnes Hazard and Vulnerability Assessment Expert, Dy. Director, International Centre for Geohazards, NGI, Norway

Helge Christian Smebye GIS and Database Specialist, NGI Norway

Center for international studies and cooperation, Nepal

Prahlad K. Thapa Hazard and Vulnerability Assessment Expert, Center for International Studies and Cooperation, Nepal

Sushma Shreshtha Project Administrative Support, Center for International Studies and Cooperation, Nepal

Sony Baral

Project Technical Support, Center for International Studies and Cooperation, Nepal

International Institute of Applied System Analysis, Austria

Reinhard Mechler Economic Risk Assessment Expert , International Institute of Applied System Analysis, Austria

Stefan Hochrainer Economic Risk Assessment Expert, International Institute of Applied System Analysis, Austria

Nakano Kazuyoshi Economic Risk Assessment Expert, Kyoto University, Japan

The World Bank Shyam S Ranjitkar Project Manager, The World Bank , Nepal

Tashi Tenzing Project Manager, The World Bank , Nepal

Silva Shreshtha Project research Analyst, The World Bank, Nepal

Project Contribution (Arranged in Alphabetic order)

Name Designation

Achyut Lamichhang Department of Health Services, Ministry of Health and Population, Government of Nepal

Ajay Kumar Mull Senior Divisional Engineer, Foreign Cooperation Branch, Department of Roads, Ministry of Physical Planning and Works, Government of Nepal

Amrit Man Tuladhar Senior Divisional Engineer, Department of Urban Development and Building Construction, Ministry of Physical Planning and Works, Government of Nepal

Basistha Raj Adhikari Senior Divisional Engineer, Department of Water Induced Disaster Prevention, Ministry of Water Resources Government of Nepal

Birendra Piya Senior Divisional Geologist, Department of Mines and Geology, Ministry of Industry, Commerce and Supplies, Government of Nepal

Dharanidhar Gautam Dy. Director, Epidemiology and Disease Control Division, Department of Health Services, Ministry of Health and Population, Government of Nepal

Dilip Kumar Gautam Senior Divisional Hydrologist, Department of Hydrology and Meteorology, Ministry of Environment , Science and Technology, Government of Nepal

Dinesh Nepali Department of Mines and Geology, Ministry of Industry, Commerce and Supplies, Government of Nepal

Fulgen Pradhan Director General, Department of Agriculture, Government of Nepal

Gauri Shankar Babsi Department of Water Induced Disaster Prevention, Department of Water induced Disaster Prevention, Ministry of Water Resources Government of Nepal

Govind Prasad Kusum Chairman of Project Advisory Committee and Secretary, Ministry of Home Affairs, Government of Nepal

Hira Nanda Jha Director General, Department of Water Induced Disaster Prevention, Department of Water induced Disaster Prevention, Ministry of Water Resources Government of Nepal

Indu Sharma Dhakal Dy. Director, Department of Roads, Ministry of Physical Planning and Works, Government of Nepal

Kamal P. Budhathoki Senior Divisional Meteorologist, Department of Hydrology and Meteorology, Ministry of Environment , Science and Technology, Government of Nepal

Keshav P. Sharma Dy. Director General, Department of Hydrology and Meteorology, Ministry of Environment , Science and Technology, Government of Nepal


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Name Designation

Lilanath Rimal Senior Divisional Geologist, Department of Mines and Geology, Ministry of Industry, Commerce and Supplies, Government of Nepal

Madhu Sudan Paudel Director General, Department of Irrigation, Ministry of Water Resources, Government of Nepal

Mahendra B. Gurung

Member of Project Advisory Committee and Director General, Department of Water Induced Disaster Prevention, Department of Water induced Disaster Prevention, Ministry of Water Resources Government of Nepal

Mohan Prasad Wagle Under Secretary, Ministry of Home Affairs, Government of Nepal

Mr. Mahesh Nakarmi Consultant, Disaster Risk Management, Nepal Mr. Surya N. Shreshtha Executive Director, NSET, Nepal

Nandalal K D W Professor & Head Department of Civil Engineering, University of Peradeniya, Sri Lanka

Nirmal Hari Rajbhandari Member of Project Advisory Committee and Director General, Department of Hydrology and Meteorology, Ministry of Environment , Science and Technology, Government of Nepal

Pranay Upadhyay Department of Health Services, Ministry of Health and Population, Government of Nepal

Purna Kadariya Secretary, Ministry of Physical Planning and Works, Government of Nepal

Rakesh Thakur Department of Health Services, Ministry of Health and Population, Government of Nepal

Ram Prasad Pathak Unit Chief, Senior Divisional Engineer, Department of Roads, Ministry of Physical Planning and Works, Government of Nepal

Rameshwor Dangal Undersecretary, Ministry of Home Affairs, Government of Nepal

Rudra Khadka Undersecretary, Ministry of Home Affairs, Government of Nepal

Saraju K. Baidya Department of Hydrology and Meteorology, Ministry of Environment , Science and Technology, Government of Nepal

Sarbajeet Prasad Mahato Member of Project Advisory Committee and Director General, Department of Mines and Geology, Ministry of Industry, Commerce and Supplies, Government of Nepal

Senendra Raj Upreti Director, Epidemiology and Disease control Division, Department of Health Services, Ministry of Health and Population, Government of Nepal

Shankar P. Kharel Under Secretary, National Planning Commission, Government of Nepal

Shankar P. Koirala Joint Secretary and Vice Chairman of Project Advisory Committee, Ministry of Home Affairs, Government of Nepal

Name Designation

Shanmukhesh C. Amatya Senior Divisional Hydrologist, Department of Water Induced Disaster Prevention, Ministry of Water Resources Government of Nepal

Shreekamal Dwivedi Department of Water induced Disaster Prevention, Ministry of Water Resources Government of Nepal

SomaNath Sapkota Chief National Seismological Center, Department of Mines and Geology, Ministry of Industry, Commerce and Supplies, Government of Nepal

Suresh P Acharya Joint Secretary, Chairperson, Rural Housing Co. Ltd, Ministry of Physical Planning and Works, Government of Nepal

Thir Bahadur GC Undersecretary, Ministry of Home Affairs, Government of Nepal

Vijoy Kumar Mallick Director General, Department of Agriculture, Government of Nepal


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ACKNOWLEDGEMENTS

The project team owes its deepest gratitude to the Ministry of Home Affairs (MoHA), Government of Nepal for its immense support during the implementation of this project. MoHA facilitated the project through constitution of the National Project Advisory Committee (PAC), encompassing all national technical departments including the Department of Health, the Department of Mines and Geology, the Department of Water induced Disaster Prevention, the Department of Hydrology and Meteorology, the Department of Agriculture and the National Planning Commission. The members of PAC provided full support throughout the project implementation in terms of data provision and the development of the methodology. The project team extends warm gratitude to the Global Forum for Disaster Risk Reduction and the World Bank Nepal for providing the necessary financial support to implement this project and develop the national risk profile of Nepal. This report will be one of the essential guiding tools for the implementation of disaster risk reduction measures in Nepal.

The project team extends the greatest thanks to its project partners, the Norwegian Geotechnical Institute and the International Centre for Cooperation and Studies (CECI), for providing all technical support needed to complete the project. This mutual technical cooperation and collaboration has rendered immense benefit to project outcomes, as well as furthering institutions in building their professional capacity. The International Institute of Applied System Analysis (IIASA) has rendered its services in the development of economic modeling for disaster impacts. The team extends its heartiest thanks to the IIASA team for their support in developing the economic model for Nepal.

The team would like to thank all stakeholders with whom the project team consulted with during the course of the implementation of this project. These interactions have greatly enriched the quality of the project outcomes. We would like to thank all technical consultants and advisors, particularly Dr. KWD Nandalal for the floods hazard and risk assessment, Mr. Kamal Budhathoki for the drought hazard assessment, Mr. Dharanidhar Gautam for the epidemic and disease mapping, Mr. Mahesh Nakarmi for the scoping the project needs and Mr. Ramhari Gaihare for the data collection and analysis.

The project team would like to thank all ADPC colleagues and staff who provided the necessary support for the development of the GIS based database, data analysis, report preparation and financial and administrative support. The team extends gratitude to Ms. Emma Lovell and Ms. Katerina Telonis for all editorial support in bringing the report to its current format.


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PREFACE

It is my great honor to present this report on “Nepal Hazard and Risk Assessment”. This report received overwhelming support from the Government of Nepal in close collaboration with a number of national focal, technical and scientific agencies related to disaster risk reduction. The presented methodology was developed after rigorous discussion and consultation with all leading national government and non-governmental agencies. The project is financially supported by the Global Forum for Disaster Risk Reduction and the World Bank.

The project highlights the regional experience intrinsic to the work of ADPC, NGI and CECI in disaster risk assessment and mapping in the region. The project objectives included the development of a synthesis report on Nepal's major hazard risks through the completion of a desk review of existing reports, studies, analyses and assessments regarding vulnerability and hazards carried out at the national and sub-national levels; an overview presentation of the vulnerability assessment for Nepal; the development of a detailed economic analysis using the loss probability modeling of Nepal’s risks in conjunction with projected economic losses from forecasted hazards; and the mapping of high risk geographic regions.

Upon fulfilling the objectives of the project, the project team produced a two-part report. Part one covers an overview of the project, a description of the baseline data, and hazard assessment and mapping for earthquakes, floods, droughts, landslides and epidemics/disease hazards all at the national level. Thus, part one results in the development of a multi-hazard risk map for Nepal. Part two of the report discusses the methodology for exposure, vulnerability and risk assessment for various hazards. In addition, part two provides an analysis of the risk assessments as well as necessary interventions for disaster risk reduction.

The project has further developed modeling for the economic effects of disaster risk in Nepal. Macro aspects of economic impacts are discussed in detail. This has led to an analysis of the country’s economic capacity to deal with impending disasters. Finally, the project provides recommendations for disaster risk reduction based on the completed pragmatic and scientific analysis. The recommendations are segmented into eight sections namely policy; institutional mandates and institutional development; hazard, vulnerability and risk assessment; multi-hazard early warning systems; preparedness and response plans; the integration of DRR into development planning; community-based DRM; and public awareness, education and training.

I hope that this report will prove to be useful to the Ministry of Home Affairs, the Government of Nepal, the National Planning Commission, the Department of Water Induced Disaster Prevention, the Department of Health Services, the Department of Hydrology and Meteorology, the Department of Mines and Geology, the Department of Agriculture among others through its provision of sectorally-based disaster management planning. The report is formatted in a user-friendly manner such that all disciplines are able apply the information and recommendations provided in the report for the safe and sustainable development of each respective sector. The ultimate goal is for the two volumes within the report to serve as a practical guide for disaster managers, as well as enable them to use the information provided effectively for the benefits of the millions of people living in disaster prone areas of Nepal.

I would like to extend my sincere gratitude to Government of Nepal, the World Bank, national stakeholders involved and project partners for partnership, consultation, support and advices.

Dr. Bhichit Rattakul

Executive Director, ADPC, Bangkok


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ACRONYMS ADPC Asian Disaster Preparedness Center ATC Applied Technology Council CATBONDS Catastrophe Bonds CATSIM Catastrophic Simulation Model CBDRM Community-Based Disaster Risk Reduction CBO Community-Based Organization CDRC Central Disaster Relief Committee CECI Center for International Studies and Cooperation CGE Computable General Equilibrium CRED Centre for Research on the Epidemiology of Disasters DD Depth Damage DEM Digital Elevation Models DM Disaster Management DMH Department of Meteorology and Hydrology DoA Department of Agriculture DOS Department of Survey DRM Disaster Risk Management DRR Disaster Risk Reduction DVI Disaster Victim Identification DWIDP Department of Water Induced Disaster Prevention EA Exposure Assessment EM-DAT Emergency Events Database by CRED EOC Emergency Operations Center EVRA Exposure, Vulnerability and Risk Assessment EW Early Warning EWS Early Warning System GDP Gross Domestic Product GESI Global Earthquake Safety Initiative GEV Generalized Extreme Value GIS Geographic Information System GPD Generalized Pareto HAZUS Hazards United States HECRAS Hydraulic Engineering Centers River Analysis System HVRA Hazard, Vulnerability and Risk Assessment ICS Incident Command System IDB International Development Bank IIASA International Institute of Applied System Analysis I-O Input-Output JICA Japan International Cooperation Agency

KVERMP Kathmandu Valley Earthquake Risk Management Project LDC Least Developed Country MC Municipal Council MMI Modified Mercalli Intensity MOAC Ministry of Agriculture and Cooperatives MOHA Ministry of Home Affairs NDMA National Disaster Management Act NDMP National Disaster Management Plan NGI Norwegian Geotechnical Institute NGO Non-governmental Organization NOH Number of Houses NPC National Planning Commission NSET National Society for Earthquake Technology – Nepal OAS Organization of American States OECS Organization of Eastern Caribbean States PGA Peak Ground Acceleration RA Risk Assessment RADIUS Risk Assessment Tool for Diagnosis of Urban areas against Seismic disasters SAM Social Accounting Matrix SOP Standard Operating Procedure UNDP United Nations Development Program UNECLAC United Nations Economic Commission for Latin America and the Caribbean USACE United State Army Corps of Engineer UC Urban Council VA Vulnerability Assessment VDC Village Development Committee VRA Vulnerability and Risk Assessment


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EXECUTIVE SUMMARY

Background

Nepal has witnessed several major natural disasters in the last two centuries. A number of those highlighted are the 1833 and 1934 earthquakes, the Koshi floods and the GLOF events in the higher Himalayas. Apart from these major disasters, Nepal also faces frequent landslides during monsoon season, thunder lightning, storms and regular seasonal flooding in terai areas. The combination of these multiple hazard events poses a severe threat to national development processes. The national government is committed to take the necessary measures to avert these threats in the development process. In line with this commitment, the Ministry of Home Affairs, Government of Nepal initiated the process of developing a national hazard and risk profile for the country. The project was financially supported by the Global Forum of Disaster Risk Reduction and the World Bank. The project was implemented by the Asian Disaster Preparedness Center (ADPC), the Norwegian Geotechnical Institute (NGI) and the Center for International Studies and Cooperation (CECI). The project was technically supported by the International Institute of Applied System Analysis (IIASA). The project was largely implemented by consultation and coordination with all focal government departments including the Department of Hydrology and Meteorology, the Department of Mines and Geology, the Department of Health Services, the Department of Agriculture, the Department of Water Induced Disaster Prevention and the National Planning Commission. The project process was further coordinated with several focal agencies, departments and institutions.

Project Objectives

The project objectives include the development of a synthesis report on Nepal's major hazard risks by carrying out a review of existing vulnerability and hazard reports, studies, analyses and assessments at the national and sub-national levels; an overview presentation of the vulnerability assessment for the country; the development of a detailed economic analysis using the loss probability modeling of Nepal’s risks in conjunction with projected economic losses from forecasted hazards; and the mapping of high risk geographic regions.

Scope of the Project

The scope of the project included collecting and analyzing existing data and reports of historical losses due to catastrophic events in Nepal; mapping the natural hazard risks for Nepal; detailing exposure to droughts, floods, landslides, earthquakes and other hazards; analyzing and quantifying the projected losses in absence of mitigation investments; and identifying possible information gaps and outlining the need for further analytical work to develop a comprehensive quantitative risk assessment for Nepal.

Project Methodology

The methodology has been compartmentalized into several sections. The methodology is presented in a flowchart as presented in Figure 1. The project methodology incorporated data collected from existing

hazard and vulnerability studies, risk assessment reports, disaster databases, the national economic assets database etc. In addition, hazard mapping was conducted by modeling for earthquakes, floods, landslides, epidemics and drought. The vulnerability functions of various assets were characterized with respect to each hazard, as well as the estimation of associated risk and the economic implication of disasters. Lastly, national disaster risk reduction recommendations were evolved. The linkages between various components can be seen in Figure 1.

The project began by studying a number of past disasters and their impacts in Nepal. Thanks to a strong national commitment to alleviate disaster suffering, the impacts of several types of disasters are reducing every day. However, there will always be an impending threat of major disasters in the future. There are several important sectors which govern the national growth of Nepal on which disasters can have serious impacts. It is thus necessary to assess such impacts on these identified sectors. Key sectors such as housing, health, education, transportation, agriculture, tourism, mines and query, power, industry, irrigation, fisheries and trade are considered in this report. However there remain several other important sectors in Nepal which could not be considered in this project due to the unavailability of data and time constraints.

The hazard assessment and hazard mapping has been carried out for earthquakes, floods, landslides, droughts and epidemics. A variety of well established scientific tools and techniques have been used to assess the hazards and mapping accordingly. For earthquakes, hazard mapping is done for 50 year, 100 year, 250 year and 500 year return periods. For flooding, the most flood prone rivers and catchments is considered in the flood hazard assessment. The flood hazard mapping presents flood severity in terms of inundation depth and area. It was carried out for the Kamala, Kankai, Bagmati, Tapti, Tinau, Babai and Narayani rivers and considered 10 year, 25 year, 50 year and 100 year return periods. The landslide hazard mapping is carried out according to two triggering factors, namely earthquake-induced and rainfall-induced. Landslide prone areas are classified as low, moderate and high prone areas. The drought hazard mapping consisted of an analysis for the whole of Nepal using the standard precipitation index. Drought hazards were classified into moderate, severe and extreme conditions. Furthermore, the drought assessment was carried out for four distinct seasons, namely winter, pre-monsoon, monsoon and post-monsoon. Hazard assessment and mapping is conducted for several epidemics and diseases including leprosy, malaria, tuberculosis (TB), sexually transmitted infections (STI), diarrhea, acute respiratory infections (ARI), filariasis and kalazar, typhoid, influenza and ARI, gastroenteritis and hepatitis.

The hazards assessment concludes with a multi-hazard assessment and mapping based on two hazard severity scenarios. Scenario one considers a 500 year return period earthquake, earthquake-induced landslides, rainfall-induced landslides, 10 year return period floods, droughts occurring in the winter season, and health hazards susceptibility. Scenario two considers a 250 year return period earthquake, rainfall-induced landslides, 10 year return period floods, droughts occurring in the winter season, and health hazards susceptibility. Scenario one reveals that three districts Sindhuli, Salyan and Rolpa are having five types of Geological, Hydro-meteorological hazards. Ilam, Lalitpur, Dhading, Kaski, Syangja, Mustang, Rukum,Makawanpur, Dang, Jajarkot, Dailekh, Kalikot and Accham are prone to four


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types of Geological, Hydro-meteorological hazards. Similarly second scenario further discusses about hazard prone districts.

The next component of the report is the exposure, vulnerability and risk assessment. This is essential in policy-making, planning and strategy development. Each hazard has specific impacts on particular sectors. For example, earthquakes largely impact the population, housing, education, health, industry, transportation and power sectors. Floods largely impact the agriculture, housing, population, education, health and transportation sectors. Droughts largely affect the agriculture sector. Landslides typically occur in hilly areas and primarily affects the road sector. At the national scale, the damage caused by landslides is negligible in comparison to that caused by earthquakes, floods and droughts. These three disasters impact large geographical areas, covering almost all parts of the topography of Nepal. In light of this fact, only earthquakes, floods and droughts are considered for detailed exposure, vulnerability and risk assessment.

The next component of the project was the modeling of the economic effects of disaster risk in Nepal. IIASA has tailored their CATSIM model specifically to the needs of Nepal and its disaster profile. The macro economic analysis utilized an economic growth framework as well as an Input-Output analysis by way of a Social Accounting Matrix (SAM). Implications for developing a national strategy for risk reduction and management have been developed based on the analysis of the CATSIM. The analysis shows that the economic and fiscal risks posed by natural disasters are large for Nepal, and there is a clear case for specifically considering these impacts in economic and fiscal planning.

Finally, the project provides recommendations for using the economic risk and loss modeling for disaster risk reduction based on the completed scientific analysis. This discussion is divided into eight sections namely policy; institutional mandates and institutional development; hazard, vulnerability and risk assessment; multi-hazard early warning systems; preparedness and response plans; the integration of DRR into development planning; community-based DRM; and public awareness, education and training. Each section considers the geographic locations where projects should be implemented, the associated activities and expected outcomes of these projects, and the ministries, departments and agencies responsible for project implementation.

Applications of the Project Report

1 At present, several agencies have carried out risk assessments for various parts of Nepal at different scales. This study has developed a comprehensive risk assessment profile for the whole of Nepal at the district level. It will serve to enhance the qualitative and quantitative aspects of the work previously conducted by other agencies.

2 Tools for physical vulnerability assessments of various assets at the district level have been developed. These will aid in the identification of the most vulnerable sectors and the measures necessary to reduce disaster impacts.

3 The economic projection of losses will aid concerned stakeholders in prioritization within disaster risk reduction strategies.

4 The study will bring out existing gaps in disaster risk reduction strategies. In addition, it will recommend measures to build decision-making capacities.

5 This report is will be a useful tool in mainstreaming disaster risk reduction into various sectors at all levels.

6 The assessment will help district and regional decision-makers, policy-makers and development agencies in preparing disaster risk reduction planning.

7 Based on the outcomes of this study, the government may take actions toward capacity building for disaster risk reduction.

8 The study developed a robust methodology for hazard, vulnerability and risk assessment in close collaboration with national technical departments and agencies. These models may now be replicated in other countries and regions.

9 Developing an economic model of losses based on the impacts of major hazards for a number of physical and social assets will guide budget allocation for disaster risk reduction of vital infrastructure.

10 Ideally, the study will encourage the financial support of international organizations for measures and actions that will reduce the risk associated with natural hazards in Nepal.

Report Chapter Organization

The project report is divided into two parts. The first part covers the project profile and background, as well as the sectoral database and hazards assessment and mapping. The second part of the report contains the exposure, vulnerability and risk assessments for earthquakes, floods and droughts, the economic modeling of losses, and recommendations for the development of a national disaster risk reduction strategy.

Chapter organization (Part One)

• Chapter 1 presents the backgrounds of the project. It describes the relationship between disaster management cycle and risk assessment process. Mostly risk assessment process is carried out in pre-disaster phases, which provides diagnosis of disasters for a specific geographic location. This helps significantly in developing the basis for DRM. The chapter further discusses purpose of hazard and risk assessment mapping at national level and necessary assumption framework. The chapter also describes project implementation agencies and mode of partnership for project implementation, Project objectives, scopes, adopted methodology and expected outcomes. The chapter shows the flowchart of the project methodology.

• Chapter 2 presents brief profile of natural hazards, and their respective trend in the country, characteristics of secondary data from various sources and various map information including administrative boundaries, Landuse characteristics, housing, education, health, fisheries, irrigation, agriculture, transportation, industry, power and tourism, mining and trade and financial institutions sectors. The chapter further explains brief profile of each sector with their distribution in all districts.

• Chapter 3 provides detailed understanding of hazard assessment, mapping and analysis of five major hazards in Nepal. It includes earthquake, floods, landslides, epidemics and drought. The chapter presents the overview of hazard assessment approach, earthquake hazard analysis, respective mapping for 50 years, 100 years, 250 years and 500 years return period, spatial distribution of hazards in Nepal. The landslide hazard mapping has been presented with two classes of triggering factors i.e., earthquake and heavy precipitation. The next section discusses about flood hazards assessment for seven major rivers. The flood hazard assessment has been discussed with 10 years, 25


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years, 100 years and 500 years return period floods. The drought hazard mapping is carried out using Standard Precipitation Index. The chapter discusses the severity of drought in various part of the country. The hazard assessment further discusses implication of epidemics in the country, their distribution and respective causative factors. The chapter is concluded with developing multi-hazard scenarios for the country.

Part Two: The whole report is divided into six chapters.

• Chapter 1 discusses the concept, applications, process and challenges of exposure, vulnerability and risk assessment. The methodology adopted under the project is discussed in details.

• Chapter 2 discusses about earthquake exposure, vulnerability and risk assessment. The chapter presents the methodology for exposure assessment, analysis of exposure considering population, housing, education, health, roads, industrial and power sectors. It further elaborates the methodology for vulnerability assessment along with risk assessment of various assets due to 100 and 500 years return period.

• Chapter 3 presents flood exposure, vulnerability and risk assessment. The chapter reflects the methodology for floods exposure, vulnerability and risk assessment. Implication of floods hazards due to 10 years and 100 years return period are discussed with respect to various important physical assets including agriculture.

• Chapter 4 elaborates on drought risk assessment, which is presented in terms of potential losses to various important crops in the country. The chapter discusses the methodology for drought risk assessment along with detailed analysis of crop losses and recommendations for enhancing the risk modeling and mitigation measures.

• Chapter 5 introduces the economic risk and loss modeling approach for key hazards in Nepal. Such analysis is necessary for international, national, regional and district level decision makers to understand the economic implications of disasters. The IIASA CATSIM model was used both for economic modeling and the further analyze the results. The chapter then further simplified the modeling outcome so as to be simply understood by decision makers and DRR professionals.

• Chapter 6 concludes the project outcomes and renders realistic recommendations for national DRR planning.


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Flowchart showing Methodology for Hazard, Vulnerability and Risk Assessment


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1 EXPOSURE, VULNERABILITY AND RISK ASSESSMENT (EVRA)

1.1 OVERVIEW

Chapter 1 provides information on the exposure, vulnerability and risk assessment (EVRA) framework and methodology. Different hazards are analyzed using the EVRA, followed by recommendations for implementing DRR planning strategies.

1.2 APPLICATION OF EVRA

• The EVRA provides a basic framework which can be used to help/understand the linkages between hazard, exposure, vulnerability and risk of various physical, social and infrastructural assets existing in various geographical and development zones of the country.

• The exposure assessment (EA) identifies the elements at risk in the hazard prone area. Due to the potential major damage and losses caused by earthquakes, floods and drought, these hazards have been analyzed in a more detailed manner.

• The vulnerability assessment (VA) diagnoses the characteristics of the physical and social elements with respect to a specific hazard’s severity, which reflects the strength and weaknesses of the assets. Thus, VAs develops a basic understanding about the sector’s vulnerability and provides evidence based approach for disaster risk reduction (DRR). Subsequent chapters discuss VAs of various essential sectors, which will further help the decision cum policy makers and planners for safer sectoral development.

• The risk assessment (RA) will provide details of sectoral elements at risk of various geological and hydro-meteorological hazards. This will further enable policy makers and decision makers to understand potential damage and losses to the specific sectors. RA is an essential tool for apex planning bodies like National Planning Commission for the allocation of funds and resources for DRR.

• The economic RA will provide sensitivity of economic growth with respect to disaster events. The Recent Kosi flood in 2008 has had severe impacts on the country’s development and Gross Domestic Product (GDP). The economic RA will help to show the economic losses to various sectors including housing, education, health, industry, human lives, power, transportation, population etc.

• The EVRA will bring out the existing gap in sectoral development. This will help ensure that correcting factors can be introduced for sustainable development.

• The EVRA will lead to recommendations for national DRR planning.

1.3 KEY ISSUES OF EVRA

• Essential elements of the EVRA: The EVRA comprises essential elements of hazard, exposure, vulnerability and risk. The EVRA will help to further analyze economic risk caused by earthquake, flood, and drought events. The details of economic risk are shown in subsequent chapters.

• The EVRA has been developed based on comprehensive hazard assessment, details of which can be found in part 1 of this report. The vulnerability and risk assessment (VRA) has been carried out on earthquakes, floods and drought of varying severity. Though the hazard assessment produces scenarios for various return periods, the EVRA provides two scenarios: most frequent and extreme frequent hazards. These can be illustrated in Table 1.1 below:

Table 1.1 Scope of EVRA for various hazards with their severity

Type of Hazard Most frequent Extreme Frequent

Earthquake 100 years 500 Years

Floods 10 years 100 years

Drought Winter drought

• EVRA outcomes depend largely upon the availability of data. The project aims to develop an EVRA profile based on available secondary information. The data is mostly collected from reliable government and international sources. The EVRA is based on data collected from the Bureau of Statistics, Government of Nepal 2001; data is projected to 2011, as and when it is feasible and needed. The precision of EVRA will largely depend upon the quality of the data. The respective departments and ministries may carry out further detailed assessments based on the suggested methodology. The Asian Disaster Preparedness Centre (ADPC) may work with the Ministry of Home Affairs (MOHA) to help improve and build their capacity to work in the area of EVRA.

• Characterizing the vulnerability of various assets requires extensive technical and scientific inputs. Although significant work has been carried out internationally in the past regarding how to characterize the vulnerability of housing, human beings and other physical infrastructure, limited work has been carried out in the Nepalese context. There is little literature available on the vulnerability functions for earthquake, floods and landslides in Nepal. Consequently, the literature that is available for similar geographical and cultural locations must be applied when determining the vulnerability functions. The vulnerability functions are also drawn from experts and field based judgment.

• Triggered hazards are showing different impacts on specific sectors. In the case of earthquakes, great impact on people’s lives and physical development has been observed. In case of floods, severe impacts are witnessed on agriculture, lives and housing. Other sectors like education, health and


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industries are comparatively less impacted by floods. In the case of drought, larger impacts have been observed on the agricultural sector. In the case of epidemics, human lives are the most affected. All of these factors are considered whilst carrying out an EVRA.

• EVRA involves specific recommendations for major development sectors. The process brings out specific recommendations for housing, agriculture, education, health, social sectors, industries, and other important sectors. The details are provided in subsequent chapters.

• The results are represented in simple terminology so as to be understood by a range of stakeholders. This report will be largely used by policy-makers, decision makers, planners, communities and non-governmental agencies working on DRR planning. The MOHA, Government of Nepal will upload the project outcomes on their website. In addition the report will be translated into the Nepali language and printed for large-scale distribution.

• The scale of EVRA is limited to the district, regional and national level. The focal department will be able to work further at the Village Development Committee (VDC) or local level.

1.4 METHODOLOGY FOR EVRA

The EVRA methodology is comprised of three major components: exposure, vulnerability and risk. The methodology is further presented in a flowchart which is presented in the following chapters.

1.4.1 EXPOSURE ASSESSMENT (EA)

Exposure1 is the total value of elements at risk. It is expressed as the number of human lives and the value of the properties or assets that can potentially be affected by hazards. Exposure is a function of the geographic location of the elements at risk. EA is an intermediate stage of EVRA, which links hazard assessment with assets under consideration for EVRA.

For the last several years multiple attempts have been made to define and establish the methodology for EA. Much work has been carried out relating the exposure and impact of chemical hazards on human kinds and on the environment. With regards to environmental hazards, EA is focused on characterizing the nature of geophysical processes that pose risks, including the magnitude, frequency, spatial dispersion, duration, speed of onset, timing, and temporal spacing of physical conditions.

The United States Environment Protection Agency2 has explained the tools and methods for EA and analysis. The tools include initial priority setting tools, screening and higher tier tools. The initial priority tools include a source ranking database and use cluster scoring systems. The screening tools include chemical screening tools for exposure and environmental releases, exposure-fate assessment screening tests, estimate program interface suit, pesticide inert RA tools etc. The higher tier tools include

1 UNDP (2004): Reducing Disaster Risk: a challenge for development. A global report (M. Pelling, A. Maskrey, P. Ruiz, L. Hall, eds.). John S. Swift Co., USA, 146 pp, 2 Exposure assessment and models, website reference http://www.epa.gov/opptintr/exposure/ referred on 4th August 2010

geographical exposure modeling systems. However these systems and tools are largely used for chemical or toxic hazard EA.

The objective of the EA under this project is to create an extensive national level database of various assets relating to the major economy sectors. It also includes a quantification of a number of assets lying in hazard prone areas, the development of asset profiles and an analysis of their proneness to various natural hazards. The EA will provide inputs to vulnerability and EVRAs. The scope of the EA includes the following:

• The EA collects all data related to the various economic sectors’ assets from nodal and focal departments. Major sectors include agriculture, industry, transportation, communication, electricity, housing, education and hospitals. The analysis is carried out for the sectors which have the potential to be significantly affected and whose detailed data is available. Updated data and information from various relevant sources are collected. The sources are primarily from the Bureau of Statistics, Survey Department, Department of Road and other focal departments.

• The hazards assessment identifies the geographical coverage of hazard and susceptibility of earthquakes, landslides, drought and floods. The spatial sectoral information is overlaid on hazard / susceptibility maps. By applying the GIS tools the exposed assets are quantified.

• The analysis provides the national sectoral profile located in the hazard zones. Due to a limitation of time and resources, it is difficult to collect data from district or VDC level. The analysis has been carried out based on national and district data available.

Limitation of Exposure Assessment

• The data available at the national scale is limited. The survey department is mandated to collect and compile the data from VDC, district level and divisional level. The data is available from the 2001 census, but after this time, several datasets have not been updated. Provincial and district governments update the data regularly but the data is not updated in a spatial format. The project team is therefore finding it difficult to update the data in the necessary spatial format.

• The national scale EA requires detailed updated information. At present some of the sectoral information is available at the national level but not at the district level. The EA and analysis are thus restricted at the district level.


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1.4.2 VULNERABILITY ASSESSMENT

Vulnerability Assessment3: VA is a systematic examination of building elements, facilities, population groups or components of the economy, which helps to identify features that are susceptible to damage from the effects of natural hazards. Vulnerability is a function of the existing hazards and the characteristics and quality of resources or population exposed to those effects. Vulnerability can be estimated for individual structures, for specific sectors or for selected geographic areas, e.g. areas with the greatest development potential or already developed areas in hazardous zones. UNISDR4 defines vulnerability as “the conditions determined by physical, social, economic, and environmental factors or processes, which increase the susceptibility of a community to the impact of hazards”. Under the project the VA aims to diagnose parameters governing the weakness and strength of the elements at risk.

Vulnerability Assessment Methodology:

The United Nations Economic Commission for Latin America and the Caribbean (UNECLAC)5 has extensive expertise in post-disaster impact assessment. Many of the techniques developed for post-disaster assessments apply to pre-disaster VAs. The Organization of American States (OAS) and the US National Oceanographic and Atmospheric Administration have collaborated to organize a series of workshops on VA techniques6. The purpose of these workshops is to create networking opportunities and dialogues to explore new ideas and potential partnerships in the development and application of VAs and indexing. VA can be carried out for critical facilities, environmental societal and economic assets.

Under the project the VA has assessed human lives, housing, education, health, industry, power and transport sectors affected by different hazards. Precedent from other countries, similar to Nepal, was used to guide the VA in this project. In this project report, vulnerability and risk assessments (VRA) are carried out simultaneously. The VRA has been carried out for earthquakes, floods and drought. The methodologies are discussed in detail in subsequent chapters. The earthquake VRA includes the housing, education, health, population, industry and transport sectors which can be found in Chapter 2. The earthquake VRA has been developed based on a Modified Mercalli Intensity (MMI) scale. The flood VRA has been carried out for the housing, population, health, education, industry and agricultural sectors. The vulnerability functions have been developed based on the “damage-depth ratio”, which can be found in Chapter 3. The drought vulnerability functions have been developed to assess causative functions for damage to the agricultural sector. Landslide and epidemic VRAs have not been considered in this project, as landslide events tend to be localized and direct damage and losses are comparatively non-significant at the national scale of VRA.

3 Vulnerability Assessment, Website reference http://www.cdera.org/projects/champ/mitiplcy/vulnerb.shtml downloaded on 15 August 2010 4 Terminology: Basic terms of disaster risk reduction, Web reference http://www.unisdr.org/eng/library/lib-terminology-engpercent20home.htm 5 Manual for Estimating the Socio-Economic Effects of Natural Disasters, Website reference www.eclac.cl/publicaciones/xml/8/7818/partone.pdf downloaded on 15th August 2010 6 Vulnerability Assessment Techniques and Applications (VATA), web reference: http://www.csc.noaa.gov/vata/rvas.html referred on 15th August 2010

1.4.3 RISK ASSESSMENT

The RA is the final step and involves input of realistic disaster management (DM) planning. The RA assesses the hazard, vulnerability and exposure factors. The risk factor can be further represented in the following equation:

The RA estimates the level of risk of the sectors under consideration. Under this project RA is estimated based on two aspects: first a general RA and second an economic RA.

General RA: The general RA is based on the hazard assessment outcomes and vulnerability functions of an area where risk has been quantified. The outcomes are primarily represented in terms of distribution of damage under varying hazard severity.

Economic RA: Chapter 5 presents the methodology for the economic RA. Macroeconomic RA models such as Catastrophic Simulation Model (CATSIM) and Social Accounting Matrix (SAM) have been used. Natural disaster events will impact on national sectoral growth and therefore both these must be taken into account. Past major earthquakes, floods and drought events have been considered in scenario building and RA.

1.4.4 CONCLUSION

Chapter 1 explains the terminology for exposure, vulnerability and risk assessments, and the scope of these terminologies within the project. This chapter provides information about the broader EVRA approach used for assessing earthquakes, floods and drought. Further information on EVRA is explained in the following chapters.


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2 EARTHQUAKE EXPOSURE, VULNERABILITY AND RISK ASSESSMENT

2.1 OVERVIEW

Overview

In the past, Nepal has witnessed several mega-earthquakes, with the most recent occurring in 1833 and 1934. These earthquakes had a widespread impact both on human life and physical infrastructure. In light of both past and more recent earthquake events in Nepal it is necessary to study the distribution of earthquake hazard, vulnerability and risk factors, as well as the exposure of social and physical infrastructure. Chapter 2 discusses the approach taken in this project to assess earthquake exposure, vulnerability and risk in Nepal. The approach is supported by quantitative evidence identifying the exposure and risk of a number of vital sectors. The sectors studied are human life, housing, education, health, power, transportation and industry. The chapter further analyzes the degree of earthquake risk present in particularly risk-prone districts. The analysis has been carried out for both 500 and 100 year earthquake return period scenarios. In addition, a scenario has been built upon precedent set by significant earthquakes in the past. The impacts of the mega-earthquakes of 1833 and 1934 have been considered according to currently existing physical infrastructure. This scenario is analyzed in terms of the human life, housing, education, health, power, transportation and industrial sectors. The earthquake risk assessment analysis has been presented in a series of user-friendly charts and graphs. The analysis should be easily understood by policy-makers and sectoral development officials.

2.2 EARTHQUAKE EXPOSURE ASSESSMENT

Earthquake EA aims to identify the physical and social elements at risk. Quantifying the exposure of sectoral assets illustrates the proportion of assets that are located in hazard prone areas. This provides understanding about the stock of asset which may be vulnerable to different earthquake hazard severity.

The assessment provides information to policy makers, decision makers and planners about assets which may need mitigation intervention. Nevertheless it does not characterize the performance of assets on varying hazard intensities. Thus EA aims to initiate the process of the VRA.

The impact profiles of hazards on different assets are distinctive; they vary depending upon the characteristics of the sectoral assets. For example earthquakes primarily affect physical infrastructure, followed by other secondary sectors. The project aims to estimate primary physical infrastructure including housing, education, health, power, industry and transport systems. Apart from these, the population is also considered for earthquake impacts. Population is further classified into age, gender and dependents. There are other sectors which are impacted by earthquakes however the effects are comparatively low and therefore not considered in this study.

Figure 2.1 Methodology of Earthquake Exposure, Vulnerability and Risk Assessment


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2.3 METHODOLOGY FOR EARTHQUAKE EXPOSURE ASSESSMENT

The identification of sectors for EVRA is based on past impacts. Primary affected sectors are identified in the EA. Table 2.1Error! Reference source not found. provides the level of effect on various sectors.

Table 2.1 Impact on sectors affected - identified in the exposure assessment Table 2.1 Impact on sectors affected - identified in the exposure assessment

Type of Hazard Affected Sectors Primary Secondary Others

Earthquake

Housing Tourism Agriculture Education Trade Real estate

Hospital Irrigation infrastructure Financial institutions

Industry Power (Electricity)

Population

Data Collection: Data relating to the primary sector is collected from a number of reliable sources. The details of the data may be found in Chapter 2, Part 1 of this project report. The data is structured in Geographic Information Systems (GIS) format and is created at the district level.

Application of GIS tools for EA: Chapter 3, Part 1 comprehensibly discusses the hazard maps whilst considering the different severity of the hazard. GIS tools facilitate overlaying susceptibility / hazard maps within the identified sectors. The overlapping areas of the hazard maps and sectoral data allow for the identification of different elements at risk. This report quantified the number of houses, their classes, and the number of schools, hospitals, health posts, industries and power infrastructures falling in the earthquake hazard prone areas.

The earthquake hazard maps have been developed for return periods of 500 years, 250 years, 100 years and 50 years. The EA has been carried out for all return periods. However this report will only be addressing the EA for frequent (100 years) and extreme return (500 years) periods.

Analysis of EA: The analysis of the EA provides information about the stock of assets in hazard prone areas. The application of EA has been discussed in brief in paragraph 2.2.

2.4 HOW TO READ AND ANALYZE THE EXPOSURE RESULTS

Information on how to use the EA and how to analyze the data can be found in the following paragraphs.

The analysis of the EA is illustrated through the use of comprehensive exposure graph plots. The national and district development authorities may refer back to the graph to understand the extent of physical asset exposure present.


21

2.5 ANALYSIS OF EXPOSURE ASSESSMENT

2.5.1 POPULATION

The exposure of the population in a very high hazard zone (the most damaging zone) for a 500 year return period earthquake is presented in Figure 2.2. Analysis for the population exposure is based on age distribution in each district. Kathmandu has the highest population; a high proportion of the population falls in the productive age group (age 15-59 year). There are also a high proportion of children. Several districts have been given a null value because they are not exposed to a very high hazard zone and are therefore classified as living in a safe zone. Figure 2.2b shows the distribution of the population living in very high earthquake hazard zones. The analysis reveals that around 10 percent of the elderly population in more than 60 districts is exposed to very high earthquake hazards. Similarly 60 percent of adults are exposed to very high earthquake hazard zones in 40 districts. Around 40 percent of the children living in 35 districts are prone to a very high earthquake hazard zone.

The exposure of the population to a high hazard zone for a 100 year return period earthquake is presented in Figure 2.3. Analysis for the population exposure is based on age distribution for each district. Kathmandu followed by Dhanusa, Siraha has the highest percentage of people who are categorized as being in the productive age group (age 15-59 year). There are also a high proportion of children. Several districts are not included in this project because they are not exposed to a high hazard zone and are therefore classified as living in a safer zone.

Figure 2.3b shows the distribution of the population living in a high earthquake hazard zone. The analysis reveals that around 10 percent of the elderly people living in more than 55 districts are exposed to a high earthquake hazard. Similarly, 60 percent of adults and 40 percent of children are exposed to a high earthquake hazard zone in 28 districts.

Figure 2.2 District wide population exposure in a very high hazard zone for a 500 year earthquake return period

0 5 10 15

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum

SankhuwasabhaBhojpur

SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha

SindhupalchokKabhrepalanchok

LalitpurBhaktapur

KathmanduNuwakotRasuwaDhading

MakawanpurRautahat

BaraParsa

ChitawanGorkha LamjungManang

KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa

NawalparasiRupandehiKapilbastu

ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang

BankeBardiyaSurkhetJajarkotDailekh

DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali

KanchanpurDadeldhura

BaitadiDarchula

x 100000 people

District wide population exposurein a very high hazard zone

for a 500 year earthquake return period

Children

aged 15‐59

Old People

a)

0

10

20

30

40

50

60

70

10% 20% 30% 40% 50% 60% 70%

Num

ber o

f Dis

tric

t

District wide population percentage

Child

Adult (age 15-59)Old

b)


22

Figure 2.3 District wide population exposure in a high hazard zone for a 100 year earthquake return period

2.5.2 HOUSING SECTOR

Part 1 of Chapter 2 describes the classification of buildings in Nepal. There are four types of buildings included: permanent, semi-permanent, temporary and other. The EA identifies the number of houses in each hazard prone area. Figure 2.4 shows the graphical presentation of the number of houses in a very high seismic risk zone (500 years return period hazard map). The district wide distribution of house type exposure is presented in Figure 2.4a; this presents four different colors that represent each type of housing. As a highly concentrated urban area, Kathmandu has the highest proportion of permanent houses exposed to a very high hazard zone in this graph. Based on the histogram showed in Figure 2.4b, on average 35 percent of permanent houses in Nepalese districts are exposed to a very high hazard earthquake zone. Baitadi, Darchula, Kathmandu, Baglung, Doti are the five districts with the highest percentage of permanent houses exposed to a very high hazard zone. Similarly the distribution of other types of housing can be observed in Figure 2.4c, d, and e.

The analysis has also been carried out for a 100 year return period hazard map.

Figure 2.4 shows the graphical presentation of the number of houses lying in a high seismic risk zone (100 year return period hazard map). The district wide distribution of house type exposure is presented in Figure 2.4a, which presents different colors for each type of housing. Kathmandu, Lalitpur and Tanahu have the highest proportion of exposed permanent houses due to the concentration of urban areas in these districts. Based on the histogram shown in Figure 2.4b, on average 40 percent of permanent housing in all Nepalese districts are exposed to a high hazard earthquake zone. Baitadi, Dharchula, Dadeldhula, Kathmandu and Baglung are the five districts with the highest percentage of permanent housing exposed to a high hazard zone area. Similarly the distribution of other types of housing can be found in Figures 2.5 c, d, and e.

0 5 10 15

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

x 100000 people

District wide population exposurein a high hazard zone


Children

aged 15‐59

Old People

a)

0

10

20

30

40

50

60

70

0% 10% 20% 30% 40% 50% 60% 70%

Num

ber o

f Dis

tric

t

District wide population percentage

ChildAdult (age 15-59)

Old

b)


23

Figure 2.4 Type of housing exposed to a very high risk zone in a 500 year earthquake return period

Figure 2.5 Type of housing exposed to a high risk zone in a 100 year earthquake return period

50000 100000 150000 200000 250000

TAPLEJUNGPANCHTHAR

ILAMJHAPA

MORANGSUNSARI

DHANKUTATERHATHUM

SANKHUWASAB…BHOJPUR

SOLUKHUMBUKHOTANG

OKHALDHUNGAUDAYAPUR

SAPTARISIRAHA

DHANUSHAMAHOTTARI

SARLAHISINDHULI

RAMECHHAPDOLAKHA

SINDHUPALCHOKKABHREPALAN…

LALITPURBHAKTAPUR

KATHMANDUNUWAKOT

RASUWADHADING

MAKAWANPURRAUTAHAT

BARAPARSA

CHITAWANGORKHA

LAMJUNGMANANG

KASKITANAHU

SYANGJAPARBAT

BAGLUNGMYAGDI

MUSTANGPALPA

NAWALPARASIRUPANDEHI

KAPILBASTUARGHAKHANCHI

GULMIRUKUM

SALYANROLPA

PYUTHANDANG

BANKEBARDIYASURKHET

JAJARKOTDAILEKH

DOLPAJUMLA

KALIKOTMUGU

HUMLABAJHANG

BAJURAACHHAM

DOTIKAILALI

KANCHANPURDADELDHURA

BAITADIDARCHULA

Number of Houses

Type of housing exposed to very high hazard zone in 100 year earthquake return period

House Permanent

House SemiPermanent

House Temporary

House Other

a)

5 HighestPercentage

Mean % value = 39.74%

District affectedby very high haza rd zone = 58

% of permanent house to total housein each dist rict

Nu

mb

er o

f dis

trict

House Permanent

House Semi Permanent



Num

ber

of d

istri

ct

% of semi pe rmanent house to total housein each dist rict

5 HighestPercentage

DolpaJumlaSyangjaOkhaldhungaSolukhumbu

% of other house to total housein each dist rict

Num

ber

of d

istri

ct



House Other

5 HighestPercentage

DhankutaUdayapurSarlahiSaptariSolukhumbu

House Temporary

5 HighestPercentage

Nu

mb

er o

f dis

trict



% of temporary house to total housein each dist rict

SaptariSirahaUdayapurSarlahiDhankuta

b)

c)

d)

e)

f)


24

2.5.3 EDUCATION SECTOR

Chapter 2 identifies the schools in all the districts of Nepal. The EA for earthquake hazard maps has been carried out for a 500 and 100 year return period. The details are shown in Table 2.3.

Figure 2.6 shows the district wide distribution and exposure of schools due in a 500 year earthquake return period in very high and moderate earthquake hazard zones. Analysis reveals that schools from all 39 districts are located in a very high earthquake hazard zone. Figure 2.6b shows that 84.8 percent of schools in the districts of Nepal are exposed to very high hazard zone areas. The most exposed schools are found in Bara, Parsa, Kanchanpur, Illam and Rautahat; these schools are located in the highest earthquake hazard zone areas (Figure 2.6c). The exposure profile of schools in moderate earthquake hazard zone areas are presented in Figure 2.6d.

Analysis has been carried out for a 100 year return period. In 12 districts all the schools are located in a high hazard zone area. In around 20 districts schools are located in a moderate earthquake hazard zone.

Figure 2.6 Education infrastructures exposed to earthquakes in districts for a 500 year earthquake return period

0 50 100 150 200 250 300

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Education infrastructure exposed to earthquake in districts


Moderate

High

Very High Mean % value = 84.8%


Very High Hazard Zone

% school building exposurein each dist rict

Nu

mb

er o

f dis

trict

39 districts are 100% exposedto very high hazard

Moderate Hazard Zone


Num

ber

of d

istri

ct


District affectedby moderate hazard zone = 12

KapilbastuBankeRupandehiMorangJhapa

5 highestPercentage

Mean % value = 44.82%District affectedby hazard zone = 36

High Hazard Zone

Nu

mb

er o

f dis

trict


5 highestPercentage

BaraParsaKanchanpurIlamRautahat

a)

b)

c)

d)


25

Figure 2.7 Education infrastructures exposed to earthquakes in districts for a 100 year earthquake return period

2.5.4 HEALTH SECTOR

Health post infrastructure exposure for a 500 year earthquake return period is presented in Figure 2.8. Dhanusha, Accham, Kaski, Sindhuplachok districts have the highest number of health posts exposed to a very high hazard zone. There are 41 districts that are exposed entirely to a very high hazard zone area, as illustrated in Figure 2.8b; this means that there are also a large number of health posts exposed. Illam, Rautahat, Parsa, Bara and Kanchanpur are the most exposed districts. A histogram illustrating the health posts found in a high hazard or moderate hazard zone area is presented in Figure 2.8c and d; the proportion of health posts found in a high hazard zone area is higher than in a moderate hazard zone area.

Health post exposure for a 100 year earthquake return period is presented in Figure 2.89. 20 districts have 100 percent health post exposure to a high hazard zone area (Figure 2.9b). Figure 2.9c demonstrates that another 19 districts have health post exposure to a moderate hazard zone area.

The EA has also been carried out for a 500 year earthquake return period. The exposure of hospitals is presented in Figure 2.10. Hospitals were found to be less common in Nepal than health posts. The profile shows that in Kathmandu, Gorakha and Chitwan districts more than 4 hospitals are exposed to a very high earthquake hazard zone area. More than 60 districts have over 100 percent of their hospitals located in a very high earthquake hazard zone area.

The analysis has also been carried out for a 100 year return period. Figure 2.11 illustrates the profile of hospitals located in a moderate or high earthquake hazard zone area in this time period.

0 50 100 150 200 250 300

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Education infrastructure exposed toearthquake in districtsfor 100 year earthquake

return period

Moderate

High



Num

ber

of d

istri

ct



6 districts are 100% exposedto Moderate hazardDhankutaPalpaNawalparasiRupandehiArghakhanchiHumla

Mean % value = 71.30%District affectedby hazard zone = 56

High Hazard Zone

Num

ber

of d

istri

ct


17 districts are 100% exposedto high hazard


26

Figure 2.8 Health post infrastructure exposed to earthquakes in districts for a 500 year earthquake return period

Figure 2.9 Health post infrastructure exposed to earthquakes in districts for a 100 year earthquake return period

0 5 10 15 20 25 30 35 40 45 50

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Healthpost infrastructure exposed to earthquakes in districtsfor a 500 year earthquake

return period

Moderate

High

Very high

a)



Very High Hazard Zone

% healthpost building exposurein each dist rict

Num

ber

of d

istri

ct

41 districts are 100% exposedto very high hazard


High Hazard Zone

Num

ber

of d

istri

ct


5 district are 100 %in High hazard zone

IlamRautahatBaraParsaKanchanpur



Num

ber

of d

istri

ct



5 highestPercentage

KapilbastuBankeRupandehiJhapaMorang

b)

c)

d)


27

Figure 2.10 Hospitals exposed to earthquakes in districts for a 500 year earthquake return period

Figure 2.11 Hospitals exposed to earthquakes in districts for a 100 year earthquake return period

0 1 2 3 4 5 6 7 8

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Hospital exoposed to earthquakes in districtsfor a 100 year earthquake return period

Moderate

High


28

2.5.5 TRANSPORT SECTOR

The EA has been carried out for the transport sector. Three types of road are considered within this sector: national highways, district highways and other roads; these are combined for the EA found in Figure 2.12. Data and source information may be found in Chapter 2, Part 1 of this project report. Figure 2.12 shows that district roads in Kathmandu, Lalitpur, Bhaktapur and Nuwakot, which are densely populated, are also located in a high hazard zone area. The histogram in Figure 2.12b shows that more than in 20 districts roads are located in a very high hazard zone area. Based on the data provided by the Nepal Department of Survey (DOS), several districts don’t have the three types of road classified above, resulting in a blank value.

Figure 2.13 shows that in more than 15 districts 100 percent of the roads are located in a high earthquake hazard zone area, with roads in more than 23 districts located in a moderate earthquake hazard zone area.

Bridges exposed in each district are highlighted in Figure 2.14 for a 500 year earthquake return period. In over 25 districts, the bridges are located in a very high hazard zone area. The analysis has also been carried out for a 100 year return period; Figure 2.15 shows the profile of bridges located in a high and moderate earthquake hazard zone area. In 24 districts 100 percent of the bridges are located in a high earthquake hazard zone area.

Figure 2.12 Road length exposure to earthquakes in districts for a 500 year earthquake return period

0 200 400 600 800 1000 1200 1400 1600 1800

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Road length exposure (in km)to earthquakes in districts


Road Moderate

Road High

Road Very High

0

5

10

15

20

25

30

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Num

ber o

f Dis

tric

t

District wise percentage

Moderate

High

Very High

a)

b)

District wide percentage


29

Figure 2.13 Road length exposure to earthquakes in districts for a 100 year earthquake return period

Figure 2.14 Bridge length exposure to earthquakes in districts for a 500 year return period earthquake

0 200 400 600 800 1000 1200 1400 1600 1800

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Road length exposure (in km)to earthquakes in districts


Moderate

High

0

5

10

15

20

25

30

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Num

ber o

f Dis

tric

t


Moderate High

wide

a)

b)

0 5 10 15 20 25 30 35 40 45 50

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Bridges length (in km) exposureto earthquakes in districts


Moderate

High

Very High

0

5

10

15

20

25

30

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Num

ber o

f Dis

tric

t


Moderate

High

Very High



30

Figure 2.15 Bridge length exposure to earthquakes in districts for a 100 year earthquake return period

2.5.6 POWER AND ELECTRICITY SECTOR

Figure 2.16 illustrates that the high electricity lines in Nepal are usually fewer than 100 KMs in length per district. High electricity lines in more than 35 districts are located in a very high earthquake hazard zone area.

The analysis has been carried out for a 100 year return period, as illustrated in Figure 2.17. In more than 25 districts high electricity lines are exposed to a high earthquake hazard zone area. Similarly more than 20 districts have high electricity lines located in a moderate earthquake hazard zone area.

EA has also been carried out on electric transformers (an important part of the power sector). EA has been carried out for a 500 year earthquake return period. The analysis reveals that Kathmandu, due to the high concentration of urban infrastructure and industry, has the highest exposure of electric transformers. The exposure profile is presented in Figure 2.18. The electric transformers of Ilam, Jhapa, Sunsari, Rautahat, Bara, Parsa, Nawalparasi, Rupandehi, Kapilabastu, Dang, Banke, Surkhet, Humla, Kailali, Kachanpur have less exposure and are not located in a very high hazard zone area. Based on data provided from the DOS, there are some districts that do not have electric transformers, as reflected in Figure 2.18.

The EA has been carried out for a 100 year return period, the results of which are presented in Figure 2.19.

2.5.7 INDUSTRIAL SECTOR

The number of industries exposed to 500 and 100 year earthquake return periods are presented in Figure 2.20. Kathmandu has by far the highest number of industries; 1460 of which are fully exposed to a very high hazard zone area in a 500 year return period and a high hazard zone area in a 100 year return period. The histogram for a 500 year return period shows that industries in more than 25 districts are completely exposed to a very high hazard zone area. Meanwhile for a 100 year return period industries in more than 10 districts are found to be in a high hazard zone area.


31

Figure 2.16 High electric line exposure in districts for a 500 year earthquake return period

Figure 2.17 High electric line exposure in districts for a 100 year earthquake return period

0 50 100 150 200 250 300

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

High Electric Line (km) Exposure for 100 yr return period earthquake

Moderate

High

0

5

10

15

20

25

30

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Num

ber o

f Dis

tric

t


Moderate High

0 50 100 150 200 250 300

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

High electric line (in km) exposure for a 500 yr earthquake return period

Moderate

High

Very High

0

5

10

15

20

25

30

35

40

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%N

umbe

r of D

istr

ict


Moderate

High

Very High


0 50 100 150 200 250 300

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

High electric line (in km) exposurefor a 100 yr earthquake return period

Moderate

High

0

5

10

15

20

25

30

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Num

ber o

f Dis

tric

t


Moderate High



32

Figure 2.18 Electric transformer exposure in districts for a 500 year earthquake return period

Figure 2.19 Electric transformer exposure in districts for a 100 year earthquake return period

0 2 4 6 8 10 12 14 16 18

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Number of Electric Transformer Exposure for 100 yr return period earthquake

Moderate

High

0 2 4 6 8 10 12 14 16 18

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Electrict transformer exposure to earthquake in districts for a 500 year earthquake return period

Moderate

High

Very High

0 2 4 6 8 10 12 14 16 18

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Electric transformer exposure to earthquakein districts for a 500 year earthquake return period

Moderate

High


33

Figure 2.20 Industry exposure in districts for 500 and 100 year earthquake return periods

2.6 EARTHQUAKE VULNERABILITY AND RISK ASSESSMENT (VRA)

Sections 2.7 and 2.8 discuss the approach and analysis of VRA for earthquakes.

2.7 EARTHQUAKE VULNERABILITY ASSESSMENT

There are several ways to approach VAs for earthquakes. VAs identify the social, physical and environmental elements in a society and their resilience and susceptibility to hazard. The scope of this project focuses on a physical and direct assessment of sectoral vulnerability. The precision of a VA depends upon the classification of building functions, the material of construction, the age and usages of the building, its, physical characteristics, the seismic intensity of the earthquake.

The physical VA of this project considers buildings used for housing, education, health and industry purposes. Three categories of roads will be considered in the transport sector, namely national highways, district highways, and other road types and bridges. Power transmission and transformers are considered under the electricity supply system. The population residing in earthquake prone areas is considered separately in the VA.

2.7.1 POPULATION CASUALTY MODELING

The methodology for estimating loss of life and injury is based on the Lethality ratio developed by Coburn, Spence and Pomonis, (1992). The number of people killed (K) can be expressed as:

M = Noh (M1*M2*M3*(M4+M5))

The model is developed on the basis of five major factors i.e., Population per Building (M1), Occupancy at Time of Earthquake (M2)), Occupants Trapped by Collapse (M3) and Injury Distribution at Collapse (M4), Post collapse mortality (M5) and Number of houses (Noh)

The parameters for calculation of death and injury are worked out based on the following rationale:

Population per Building (M1) is calculated based on a field survey. The M1 factor for the study area is based on the population per building in each district. This value is calculated from the total population divided by the total number of houses in the district. Occupancy at Time of Earthquake (M2) is divided by occupancy at day time and night time, as shown in Table 2.2.

Occupants Trapped by Collapse (M3) is based on studies conducted by Okada and Pomonis (1990). The research is based on data from developing countries on the number of people trapped in collapsed buildings due to earthquakes.

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

Tapl

ejun

gP

anch

thar

Ilam

Jhap

aM

oran

gS

unsa

riD

hank

uta

Terh

athu

mSa

nkhu

was

abha

Bho

jpur

Sol

ukhu

mbu

Kho

tang

Uda

yapu

rS

apta

riS

iraha

Dha

nush

aM

ahot

tari

Sar

lahi

Ram

echh

apD

olak

haSi

ndhu

palc

hok

Kab

hrep

alan

chok

Lalit

pur

Bha

kta p

urK

athm

andu

Nuw

akot

Ras

uwa

Dha

ding

Mak

awan

pur

Rau

taha

tBa

raP

arsa

Chi

taw

anG

orkh

aLa

mju

ngK

aski

Tana

huS

yang

jaB

aglu

ngM

usta

ngP

alpa

Naw

alpa

rasi

Rup

ande

hiK

apilb

astu

Ar g

hakh

anch

iG

ulm

iS

alya

nR

olpa

Dan

gB

anke

Bar

diya

Sur

khet

Dol

paH

umla

Ach

ham

Kaila

liK

anch

anpu

rD

adel

dhur

aNumber of Industry Exposure

for 500 yr return period Earthquake

Very High

High

Moderate

0

50

100

150

200

250

300

350

Tapl

ejun

gPa

ncht

har

Ilam

Jhap

aM

oran

gSu

nsar

iD

hank

uta

Terh

athu

mS

ankh

uwas

abha

Bhoj

pur

Solu

khum

buK

hota

ngU

daya

pur

Sapt

ari

Sira

haD

hanu

sha

Mah

otta

riS

arla

hiR

amec

hhap

Dol

akha

Sind

hupa

lcho

kK

abhr

epal

anch

okLa

litpu

rBh

akta

pur

Kath

man

duN

uwak

otR

asuw

aD

hadi

ngM

akaw

anpu

rR

auta

hat

Bar

aPa

rsa

Chi

taw

anG

orkh

aLa

mju

ngKa

ski

Tana

huSy

angj

aB

aglu

ngM

usta

ngP

alpa

Naw

alpa

rasi

Rup

ande

hiK

apilb

astu

Argh

akha

nchi

Gul

mi

Saly

anR

olpa

Dan

gBa

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Bard

iya

Surk

het

Dol

paH

umla

Achh

amKa

ilali

Kanc

hanp

urD

adel

dhur

a

Number of Industry Exposurefor 100yr return period Earthquake

High

Moderate

Kathmandu

= 1460

Kathmandu

= 1460

0

5

10

15

20

25

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Num

ber o

f Dis

trict


Moderate High

0

5

10

15

20

25

30

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Num

ber o

f Dis

trict


Moderate

High

Very High



Industry exposure in districtsfor a 500 year earthquake return period

Industry exposure in districtsfor a 100 year earthquake return period


34

Table 2.2 Parameters for calculation of casualty model

Factor Value Description

Population per building (M1) based on each district

Population per building (M2) Time Occupancy Day time 40%Night time 95%

Occupants Trapped by Collapse (M3)

Intensity Value VI NilVII 5%VII 30%

Injury severity scale (M4)

Category Value Dead 20%Life threatening 30%

Hospitalized injury 30%Light injury 20%

Post collapse mortality (M5) 95%

The causes of death and injury of those trapped inside damaged structures varies considerably. In masonry buildings a primary cause of death is suffocation from the weight and dust of collapsed walls or roofing. Noji (1989) proposes a number of injury severity scales. One of the simplest and most useful is the four point standard triage categorization of injuries (M4). The last factor considered the equation is post-collapse mortality (M5), which has value of around 95 percent.

2.7.2 SECTORAL VULNERABILITY MODEL

VA of physical sectors at the national level depends upon the classification of buildings according to their materials and structure. In Nepal, housing has been categorized into four classes, namely permanent, semi-permanent, temporary and other. This classification was used in the national census as well. However, issues arose when using census data as the data does not differentiate between buildings according to building material used and building load paths.

Several fragility curves are available for determining the fragility of buildings according to varying earthquake severity. The most commonly used fragility functions are Applied Technology Council (ATC), Risk Assessment Tool for Diagnosis of Urban areas against Seismic disasters (RADIUS), Global

Earthquake Safety Initiative (GESI), ATC-13 and Arya et al. (1997). These fragility functions are based on data from California and cover the many types of masonry and frame structures. Further fragility curves have been developed for various essential lifelines. RADIUS fragility functions have been developed for Latin American countries. However, housing categories differ from California and Latin America to Nepal. Global Earthquake Safety Initiative (GESI) has developed fragility curves based on building design, the quality of materials used and building code regulation. However, it is difficult to apply the attributes used in this fragility curve to a large-scale geographical area as they are too specific to the area for which they were developed.

The building stock and building practices in Nepal are similar to those in South and South-East Asia. Much research has been conducted in correlating housing stock classifications with the damage sustained at varying hazard intensity. An effort has been made to sync the housing classifications used in this study with already established classes and methodologies. Table 2.3 shows the classifications used to categorize buildings in Nepal, define their respective characteristics and determine their similarity with ATC-13, RADIUS and Arya (1997).

Table 2.3 A comparison of the characteristics of buildings in Nepal

S. No Type of House Characteristics of Housing ATC-13 Class RADIUS class Arya’s Class

1 Permanent House Both wall and roof are made with permanent material (i.e. Concrete, bricks, woods)

FC No 1 Res-4 Type C

2 Semi Permanent House Either walls or roof are made with permanent material

FC No 75/76 Res -3 Type A and B

3 Temporary House Both wall and roof are made with temporary material (i.e. adobe, thatch etc)

FC No 75/76 Res -1 Type A

4 Other type house Light weight material for walls and roof

FC No 2, 75/76 Res 1 Type A

The Damage Probability Matrix (DPM) shows the ratio of damage to each different category of house. Arya (1997) characterized building response for the South Asian region. In addition, he developed potential building damage for the 1905 Kangra earthquake for the Indian region of South Asia. This assessment is based on Arya (1997). The building response table is displayed in Table 2.4 and the DPM in Table 2.5. The damage levels in the DPM are (D1) slightly damaged, (D2) moderate damage, (D3) severe damage, and (D4) completely collapsed.

Health infrastructures for this assessment consider health posts and hospitals. Health posts in Nepal are classified as permanent and semi-permanent structures, while hospitals are permanent infrastructure.


35

Table 2.4 Building response to earthquake intensity scale

Building Type Intensity VII Intensity VIII Intensity IX Intensity X A) Mud and Adobe houses, random stone construction

Most have large deep cracks Few suffer partial collapse

Most suffer partial collapse Few suffer complete collapse

Most suffer complete collapse, Few partial collapse, remaining deep cracks

Most suffer complete collapse, Few partial collapse, remaining deep cracks

Ordinary brick buildings or large blocks and prefab type, poor half timbered houses

Many have small cracks in walls

Most have large and deep cracks Few partial collapse

Many show partial collapse Few completely collapse Few minor cracks

Most suffer complete collapse, Few partial Collapse, remaining deep cracks

Reinforced buildings well built wooden buildings

Many have fine plaster cracks

Most may have small cracks in walls Few may have large deep cracks

Many may have large and deep cracks Few may have partial collapse

Most suffer complete collapse, Few partial Collapse, remaining deep cracks

Transport infrastructure in Nepal consists of roads and bridges. The vulnerability damage matrix was derived from the ATC-13 fragility curve as transport infrastructure design is largely the same in different countries. The road infrastructure classifications can be seen in Table 2.5. A small fraction of roads will be completely damaged in very high hazard zones (± 5%). However, this small fraction can affect the entire transport system if the damage has occurred at a critical juncture within the system as a whole. Bridges are more vulnerable than roads due to the inherent nature of the structure itself. Transformer and electricity line damage usually falls into the moderate (D2) and severe (D3) categories of damage. This is due to the fact that vital infrastructure such as this is typically built under stricter design codes that consider earthquake response. In Nepal industrial structures are classified as permanent buildings and are considered in the same manner as the permanent class of housing.

Table 2.5 Damage probability matrix for house, education, health sector

Type of Asset Medium = 6 High = 7 Very High = 8 &moreD1 D2 D3 D4 D1 D2 D3 D4 D1 D2 D3 D4

House

Permanent 75% 5%

20% 30% 45% 5%

Semi Permanent 50%

45% 50% 5% 5% 40% 50% 5% Temporary 20% 75% 5% 5% 15% 75% 5% 20% 5% 75% Other 45% 5% 50% 20% 5% 75% 20% 5% 75%

Education Building

Permanent 75% 5% 20% 30% 45% 5% Semi Permanent 50% 45% 50% 5% 5% 40% 50% 5%

Health Infrastructure

Permanent Health post 75% 5% 20% 30% 45% 5%

Semi Permanent Health post 50% 45% 50% 5% 5% 40% 50% 5% Hospital 75% 5% 20% 30% 45% 5%

Transportation Road 80% 20% 30% 70% 15% 70% 10% 5% Bridges 12% 86% 71% 29% 71% 29%

Electricity High electric line 84% 14% 52% 48% 3% 96% 1% Transformer 11% 87% 3% 2% 64% 34% 17% 83%

Industry Permanent 75% 5% 20% 30% 45% 5%

2.8 ANALYSIS OF EARTHQUAKE VULNERABILITY AND RISK ASSESSMENT

2.8.1 POPULATION

Human life should be considered above all other physical sectors when conducting a RA. Casualty grades are assigned to the population of earthquake risk zones in the VRA for daytime and nighttime scenarios. The analysis has been carried out for both 500 and 100 year return period earthquakes.

The analysis reveals that the nighttime scenario results in more casualties than the daytime scenario. The Coburn (1992) fragility model has been adopted to calculate the population at risk. Figure 2.21 shows the casualty distribution during mid-day and mid-night scenarios for 500 years and 100 year return periods. Terai districts such as Siraha, Dhanusha and Mahottari show very high mortality rates. The terai and hill regions have a comparatively high population density combined with regular seismic activity, the population living at the Nepal – India border at very high risk. The analysis further reveals that western and far-western terai and mountain districts are comparatively safer than other regions. Figures 2.22 and 2.23 show the distribution of casualty grades for all districts for both daytime and nighttime scenarios.


36

Figure 2.24 shows a similar analysis as carried out for a 100 year return period earthquake. The analysis shows that the casualty rate is comparatively low for a 100 year return period. Figures 2.25 and 2.26 show casualty and injury grades as the portion of the population death or suffering life threatening, hospitalized and light injuries.

Casualties in terai zones can reach up to 45,000 in the daytime scenario and more than 100,000 in nighttime scenario in a 500 year return period earthquake. The casualties are comparatively lower for a 100 year return period earthquake at less than 600 in the daytime scenario and less than 1400 in the night time scenario in the Dhanusha district.

Figure 2.21 Map of life casualty for a 500 year earthquake return period (day and night scenario)


37

Figure 2.22 District-wide casualties for a 500 year earthquake return period (daytime scenario)

Figure 2.23 District-wide casualties for a 500 year return period earthquake (nighttime scenario)

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

District wide casualties for a 500 year earthquake return period(daytime scenario)

Dead

Life Threatening

Hospitalized Injury

Light Injury

0 20,000 40,000 60,000 80,000 100,000

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

District wide casualties for a 500 year earthquake return period(nighttime scenario)

Dead

Life Threatening

Hospitalized Injury

Light Injury


38

Figure 2.24 Map of casualties for a 100 year earthquake return period (day and night scenario)

Figure 2.25 District-wide casualties for a 100 year earthquake return period (daytime scenario)

0 100 200 300 400 500 600 700

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

District wide casualties for a 100 year earthquake return period(daytime scenario)

Dead

Life Threatening

Hospitalized Injury

Light Injury


39

Figure 2.26 District-wide casualties for a 100 year earthquake return period (nighttime scenario)


The VRA for the housing sector has been carried out using the aforementioned methodology. Figure 2.27 shows the damage zones for all types of buildings. To simplify, all four damage grades have been considered together and to determine the risk of damage to each particular district. Figure 2.27 shows housing damage risk for a 500 year return period; it can be seen that in the case of a 500 year return earthquake most of the houses will endure a D3 grade of damage. For detailed depiction of the distribution of damage please refer to Figures 2.28 and 2.29.

A 500 year return period earthquake results in the severe collapse of houses in nearly districts. Several districts in mostly western terai zones such as such as Rupandehi, Kapilabastu and Banke sustain no damage. For a 100 year return period earthquake most of the hill and mountain districts suffer a D1 grade of damage. The ‘no damage’ zone is concentrated in the central terai districts. Severe damage grade zones for a 100 year return period earthquake are concentrated in south-east terai zones such as Siraha, Dhanusha and Mahottari.

Kathmandu sustains the highest amount of damaged houses in both 500 and 100 year return period earthquakes, as illustrated in figures 2.28 and 2.29. 14 percent of homes will completely collapse in the case of a 500 year return period, as seen in Figure 2.28. 35 percent houses will receive severe damage, 30 percent will receive moderate damage and remaining will receive minor to no damage. These values can be used for a rapid assessment of damage zones for each district, as long as the earthquake strikes uniformly throughout the country. Over 40 districts have very few ‘no damage’ zones; a ‘no damage’ zone is where damage falls between zero and ten percent as can be seen in the histogram. This figure corresponds to the safe houses left after an earthquake has occurred and is typically used for evacuation purposes. The housing damage assessment reveals that most houses in Nepal are in need of seismic retrofitting to better sustain the impact of earthquakes.

Figure 2.29 shows the housing at risk for a 100 year return period earthquake. The analysis reveals that only one percent of houses will suffer complete collapse while nine percent of houses will experience a severe damage grade. Figure 2.29 also reveals that a large number of houses will fall into the D1 and no damage grades.

0 200 400 600 800 1,000 1,200 1,400 1,600

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

District wide casualties for a 100 year earthquake return period(nighttime scenario)

Dead

Life Threatening

Hospitalized Injury

Light Injury


40

Figure 2.27 Housing risk zones for 500 and 100 year earthquakes return periods

Figure 2.28 Housing risk profile for a 500 year earthquake return period scenario

0 50,000 100,000 150,000 200,000 250,000

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Housing risk profile for a 500 yr earthquake return period

D1

D2

D3

D4

No Damage

0

5

10

15

20

25

30

35

40

45

50

10% 20% 30% 40% 50% 60%

Num

ber o

f Dis

trict

Percentage number of damage to total house per district

No DamageD1D2D3D4

Average PercentageNo Damage 4%

D1 16%D2 30%D3 35%D4 14%


41

Figure 2.29 Housing risk profile for a 100 year earthquake return period scenario


In the education sector, only school data is available in detail. Thus, the analysis focuses on the risk of school buildings for 500 and 100 year return period earthquakes. Building typology for school buildings was not available. Based on information gathered from both field and consultation with national experts it was determined that all school buildings are classified as permanent or semi-permanent buildings.

Figure 2.30 shows the distribution of damage grades to school buildings for a 500 year return period earthquake. The analysis shows that D3 damage is distributed uniformly throughout the country. Figure 2.31 shows that 3.6 percent of school buildings will sustain a D4 grade of damage; roughly 35 percent of schools sustain a D3 grade of damage while 30 percent will sustain a D2 grade of damage.

In the case of a 100 year return period earthquake Figure 2.30 shows that most hill districts school buildings are at low risk and many terai and mountain district schools fall into the ‘no damage’ category. Figure 2.32 shows the distribution of risk throughout these districts. Around 85 percent of schools are at low to no risk, while 13 percent are at moderate damage risk and very few schools are at severe damage risk.

0 50,000 100,000 150,000 200,000 250,000

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Housing risk profilefor a 100 yr earthquake return period

D1

D2

D3

D4

No Damage

0

10

20

30

40

50

60

70

10% 20% 30% 40% 50% 60% 70% 80%

Num

ber o

f Dis

trict

Percentage number of damage to total house per district

No DamageD1D2D3D4

Average PercentageNo Damage 26%

D1 39%D2 25%D3 9%D4 1%


42

Figure 2.30 School damage risk for 500 and 100 year earthquake return periods

Figure 2.31 School damage risk for a 500 year earthquake return period scenario

0 50 100 150 200 250

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Education Infrastructure Damagefor 500 year r.p. Earthquake

D1

D2

D3

D4

No damage

0

10

20

30

40

50

60

70

10% 20% 30% 40% 50% 60% 70% 80%

Num

ber o

f Dis

tric

t

Percentage number of damage to total school per district

No Damage

D1

D2

D3

D4

Average PercentageNo damage 7.1%

D1 24.2%D2 30.3%D3 34.7%D4 3.6%

School damage risk for a 500 earthquake return period


43

Figure 2.32 School damage risk for a 100 year earthquake return period scenario

2.8.4 HEALTH SECTOR

Earthquake disaster will severely impact the health sector of affected areas. The risk analysis has been carried out for health sector infrastructure, namely health posts and hospitals. The condition of health posts and hospitals are a primary concern when carrying out earthquake mitigation and response. The proper spatial distribution of health infrastructure is crucial for relief and recovery as some districts will sustain higher damage than others. The spatial distribution of health post damage risk for a 500 year return period earthquake is illustrated in Figure 2.33, revealing that most districts’ health posts are at risk for a D3 grade of damage. Rupandehi, Kapilbastu and Banke have no risk of damage to school buildings. The analysis and distribution of damage risk to all 75 districts is shown in Figure 2.34. In the case of a 500 year return period earthquake, over 40 districts will have 50 percent of their health post suffer a D3 grade of damage as seen in Figure 2.34. The complete collapse (D4) of health post structures will be less than 10 percent of the total number of health posts the majority of districts. Less than half of the districts in Nepal can be classified as having ‘no damage’ risk for health posts in the case of a 500 year return period earthquake.

An analysis of the data has also been carried out in the case of a 100 year return period earthquake. The analysis reveals that most of the terai districts fall into the ‘no damage’ risk category. Central hill districts have low damage risk. Details of damage risk for a 100 year return period earthquake can be seen in Figure 2.35.

An analysis of hospital infrastructure damage risk is essential for emergency response. For a 500 year return period earthquake scenario more than 45 districts will have severely damaged hospitals. As a central urban area, Kathmandu will have three severely damaged hospitals. All other hospitals in Kathmandu will suffer a lower level of damage. Figure 2.36 shows the distribution of hospital damage risk for a 500 years return period earthquake. An analysis of damage risk has also been calculated for a 100 year return period, revealing that most of hospitals falls into the minor to ‘no damage’ risk categories.

0 50 100 150 200 250

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

School damage risk for a 100 yearearthquake return period

D1

D2

D3

D4

No damage

0

10

20

30

40

50

60

10% 20% 30% 40% 50% 60% 70% 80%

Num

ber o

f Dis

tric

t

Percentage number of damage to total school per district

No Damage

D1

D2

D3

D4

Average PercentageNo damage 42.3%

D1 43.1%D2 13.4%D3 1.2%D4 0.0%


44

Figure 2.33 Health post damage risk distribution for 500 and 100 year earthquake return periods

Figure 2.34 Health post damage for a 500 year earthquake return period scenario

0 10 20 30 40 50

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Health post damage for a 500 yr earthquake return period

D1

D2

D3

D4

No Damage

0

10

20

30

40

50

60

70

10% 20% 30% 40% 50% 60% 70% 80%

Num

ber o

f Dis

tric

t

Percentage number of damage to total healthp post per district

No Damage

D1

D2

D3

D4


45

Figure 2.35 Health post damage for a 100 year earthquake return period scenario

Figure 2.36 Hospital damage for a 500 year earthquake return period scenario

0 10 20 30 40 50

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Health post damage for a 100 year earthquake return period

D1

D2

D3

D4

No Damage

0

10

20

30

40

50

60

10% 20% 30% 40% 50% 60% 70% 80%

Num

ber o

f Dis

tric

t

Percentage number of damage to total health post per district

No Damage

D1

D2

D3

D4

0 1 2 3 4 5 6 7 8

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Hospital damage for a 500 yearearthquake return period

D1

D2

D3

D4

No damage


46

Figure 2.37 Hospital damage for a 100 year earthquake return scenario

2.8.5 TRANSPORTATION SECTOR

A comprehensive network of roads is an important factor in earthquake risk management, particularly in landlocked country like Nepal. The spatial distribution of road and bridge damage risk is needed to assess the extent of impact on sectors that are dependent on a functioning transport system. District-wide transportation damage risk will also assist the government and stakeholders in allocating their rehabilitation and maintenance budgets for transport infrastructure.

In the case of a 500 year return period earthquake the roads in the south, central and east districts have moderate damage risk. The central district around Kathmandu is very developed with a high population density and consequently a high density of roads and bridges. More than 40 districts are at moderate damage risk to their road networks. The analysis reveals that less than 10 percent of roads are at severe damage risk.

Figure 2.38 reveals that in the case of a 100 year return period the road network of terai districts are showing low damage risk, with the exception of Siraha, Dhanusha and Mohatarri. Some central districts around Kathmandu have moderate damage risk. The map reads ‘no data’ in the districts where robust data was not available. Figure 2.40 reveals that 25 districts are at risk of slight damaged. The road networks in most districts are at low damage risk.

Bridge infrastructure is vital to achieving a robust disaster RA and ensuring successful disaster response. Bridges are more vulnerable than roads to earthquakes. For a 500 year return period earthquake scenario more than 25 districts in Nepal are prone to severe damage risk as seen in Figure 2.41. Over 25 districts are at risk of 30% of their bridges completely collapsing. More analysis is necessary to identify bridges at risk of damage as the serve as vital infrastructure during disaster relief; bridges are especially crucial for delivering food and medical supplies after a disaster.

In a 100 year return period earthquake scenario, bridges are at risk of moderate damage as illustrated in Figure 2.41. A lighter mode of ground transport will be needed for relief response as bridges will only be at half of their carrying capacity.

0 1 2 3 4 5 6 7 8

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Hospital damage for a 100 yearearthquake return period

D1

D2

D3

D4

No damage


47

Figure 2.38 Map of the most prominent road damage zones for 500 and 100 year earthquake return periods

Figure 2.39 Road damage for a 500 year earthquake return period scenario

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Road Damage (km) for 500 year earthquake return period

D1 D2

D3 D4

0

5

10

15

20

25

30

35

40

10% 20% 30% 40% 50% 60% 70% 80% 90%

Num

ber o

f Dis

tric

t

Percentage number of damage to total road per district

D1 D2

D3 D4


48

Figure 2.40 Road damage for a 100 year earthquake return period scenario

Figure 2.41 Bridge damage for a 500 and 100 year earthquake return period scenario

0 200 400 600 800 1,000 1,200 1,400 1,600 1,800

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Road Damage (km) for 100 year earthquake return period

D1 D2

D3 D4

0

5

10

15

20

25

10% 20% 30% 40% 50% 60% 70% 80% 90%

Num

ber o

f Dis

tric

t

Percentage number of damage to total road per district

D1 D2

0

5

10

15

20

25

30

35

40

45

50

Panc

htha

rIla

mJh

apa

Mor

ang

Sun

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iR

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P yut

han

Dan

gB

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Dol

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aila

li

Leng

th (k

m)

Bridges Damage (km) for 500 year earthquake

D4 D3 D2 D1

0

5

10

15

20

25

30

10% 20% 30% 40% 50% 60% 70% 80% 90%

Num

ber o

f Dis

tric

t

Percentage number of damage to total bridges per district

D1 D2

D3 D4

0

5

10

15

20

25

30

35

40

45

50

Tapl

ejun

gP

anch

thar

Ilam

Jhap

aM

oran

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riD

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Pyut

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Dan

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Leng

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Bridges Damage (km) for100 year earthquake

D4 D3 D2 D1

0

5

10

15

20

25

30

10% 20% 30% 40% 50% 60% 70% 80% 90%

Num

ber o

f Dis

tric

t

Percentage number of damage to total bridges per district

D1 D2

D3


49

2.8.6 POWER AND ELECTRICITY SECTOR

Electricity is the essential infrastructure of the modern world in terms of overall development. High tension electric lines coupled with electric transformers are the backbone of electricity distribution. Earthquake risk analysis on these two vital facilities has been carried out for Nepal. The spatial distribution of damage risk and its severity is key to successful rehabilitation and emergency response.

Figure 2.42 shows the damage risk of 500 and 100 year return period earthquake scenarios for high tension electric lines. Moderate damage (D2) risk is widespread throughout Nepal in the 500 year return period earthquake scenario. An analysis of the 100 year return period earthquake scenario shows that all districts with high electric lines are at risk of slight damage. A very serious issue that arises with electric line damage is that when just a small portion of the line collapses, the entire system connected to that line is also affected. A more detailed graphical representation of each district will help in assessing the districts most at risk. High tension electric lines are typically designed to be quite resistant to the impact of earthquakes.

In the case of a 500 year earthquake return period D3 grade damages would be experienced in Kathmandu, Tanahu, Kaski as can be seen in figure 2.43. In the case of a 100 year earthquake return period, high electric line risks will experience D1 grade damages. There is a need for a more thorough analysis on earthquake prone areas within the electricity and power sector in Nepal.

Electric transformer damage from earthquakes can disturb electricity and power distribution. For a 500 year return period earthquake most districts are at risk of severe damage as can be seen in Figure 2.45. The Nuwakot district possesses a high concentration of electric transformers; further study of this area is suggested in order to implement successful mitigation measures in the electricity sector. A 100 year return period earthquake results in a lower degree of damage with most districts at moderate damage risk as can be seen in Figure 2.46. Conversely, even in the case of a 100 year return period earthquake transformers in the Nuwakot district is still at severely damage risk. Severe damage to districts housing transformers could have an effect on the entire electricity supply of Nepal.

Figure 2.42 Map of the most prominent high electric line damage zones for 500 and 100 year earthquake return periods


50

Figure 2.43 High electric line damage for a 500 year earthquake return period scenario

Figure 2.44 High electric line damage for a 100 year earthquake return period scenario

0 50 100 150 200 250

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

High tension electric line damage (km)for a 500 yr earthquake return period

D1 D2 D3 D4

0

5

10

15

20

25

30

35

40

45

50

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Num

ber o

f Dis

tric

t

Percentage number of damage to total high electric line per district

D1 D2 D3

0 50 100 150 200 250

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

High tension electric line damage (km)for a 100 yr earthquake return period

D1 D2

D3 D4

0

5

10

15

20

25

30

10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Nu

mb

er o

f D

istr

ict

Percentage number of damage to total high electric line per district

D1 D2


51

Figure 2.45 Electric Transformer damage for a 500 year earthquake return period scenario

Figure 2.46 Electric Transformer damage for a 100 year earthquake return period scenario

0 2 4 6 8 10 12 14 16 18

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Electric transformer damage for a 500 yr earthquake return period

D1 D2

D3 D4

0 2 4 6 8 10 12 14 16

TaplejungPanchthar

IlamJhapa

MorangSunsari

DhankutaTerhathum


SolukhumbuKhotang

OkhaldhungaUdayapur

SaptariSiraha

DhanushaMahottari

SarlahiSindhuli

RamechhapDolakha


LalitpurBhaktapur


MakawanpurRautahat

BaraParsa


KaskiTanahuSyangja

ParbatBaglungMyagdi

MustangPalpa


ArghakhanchiGulmi

RukumSalyanRolpa

PyuthanDang


DolpaJumla

KalikotMugu

HumlaBajhang

BajuraAchham

DotiKailali


BaitadiDarchula

Electric transformer damagefor a 100 yr earthquake return period

D1 D2

D3 D4


52

2.8.7 INDUSTRIAL SECTOR

Earthquakes have longer impact on the industrial sector when critical infrastructure is damaged. The degree of damage to the industrial sector is an important factor when assessing the risk present in an area where industries are operational. Furthermore, the industrial sector itself must be required to follow strict building codes, particularly in areas where a high degree of damage is expected.

Figure 2.47 shows maps of the 500 and 100 year return period earthquake risk on industry infrastructure. In the case of a 500 year return period earthquake the most severe damage risk to industries occurs in the central and eastern districts. Lower damage zones are seen in most of terai area with the exception of the Siraha, Dhanusha and Mahottari districts. In the case of a 100 year return period earthquake most districts prove more resistant to earthquake damage. Central areas such as Kathmandu, as well as several eastern districts, are at slight damaged risk. The damage risk in these areas is thus more prominent than in other districts.

Graphical representation of the damage zone in each district is presented in Figure 2.48. For a 500 year return period earthquake several districts in hill zone such as Kathmandu, Kaski, Bhaktapur and Dhading are at risk of completely collapsed industrial building structures. However, these structures only represent a small fraction of the industrial sector of those districts. Hilly zone districts are also at risk of experiencing a high number of severely damaged structures. Overall, these districts are at greater risk of facing industrial disturbance. In the case of a 100 year return period earthquake, Nepal as a whole predominantly falls into the ‘no damage’ risk category. Slight damage risk is expected in the central hilly area as well as in eastern Nepal. The damage risk expected in the case of a 500 year return period earthquake follows the pattern seen for 100 year return period earthquakes.

Figure 2.47 Map of Industry damage for 500 and 100 year earthquake return period scenarios


53

Figure 2.48 Industry damage for 500 and 100 year earthquake return period scenarios

2.9 SCENARIO BUILDING FOR SIGNIFICANT PAST EARTHQUAKES

Several loss estimation studies have been carried out for the Kathmandu valley. One such project was the Kathmandu valley earthquake risk management project7 (KVERMP) which focused on the impact of earthquakes on building institutions and other infrastructural aspects. The study was administrated by ADPC and implemented by the National Society for Earthquake Technology – Nepal (NSET) and GeoHazards International. Nepalese and international experts were consulted to provide information on earthquake risk in the Kathmandu valley.

The 1934 earthquake (magnitude 8.4) was used as a case study in this project. An iso-seismal map based on the MMI scale was layered over maps depicting the most current infrastructural and demographic data for the area. Impacts were estimated based on the extent of ground-shaking and liquefaction potential. This information was used to help develop a safer Nepalese building code. Through the consideration of the quality of building construction in the Kathmandu valley, a rough estimate of the damages caused to buildings by the 1934 earthquake was conducted. This study was used to estimate potential damages to the Kathmandu valley if a similar scale of earthquake was to occur in the future. The results are depicted below:

• Up to 60 percent of all the buildings in the Kathmandu valley would be damaged heavily, many beyond repair.

• Almost half of the bridges in the valley would become blocked or impassable, and 10 percent of the paved roads would be moderately damaged with deep cracks or subsidence. In addition, many of the narrowest streets in the valley would be blocked by debris from damaged buildings.

• Approximately 95 percent of the water pipes and 50 percent of the other water system components would be seriously damaged. Almost all of the telephone exchange buildings and 60 percent of the telephone lines would be damaged.

• It is expected that there would be more than 40,000 deaths and more than 95,000 injuries reported.

• An additional 600,000 to 900,000 residents in the Kathmandu valley would be left homeless by the earthquake.

Another study in the Kathmandu valley8 was carried out by Japan International Cooperation Agency (JICA). JICA and MOHA, Government of Nepal carried out a “study on earthquake disaster mitigation 7 The Kathmandu Valley, Earthquake Risk Management Action Plan, A product of the Kathmandu Valley Earthquake Risk Management Project implemented by NSET & GHI, USA, 1999

8 The Study on Earthquake Disaster Mitigation in The Kathmandu Valley, Kingdom of Nepal, JICA and MOHA, Government of Nepal, 2002

0

50

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300

350

Tapl

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Num

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f Ind

ustr

y

Number of Industry Damage for 500 year r.p. earthquake D4D3D2D1No Damage

Kathmandu:D1 = 292D2 = 438D3 = 657D4 = 73No Damage = 0

0

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Tapl

ejun

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Num

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f Ind

ustr

y

Number of Industry Damage for 100 year r.p. earthquake D4D3D2D1No Damage

Kathmandu:D1 = 1095D2 = 73D3 = 0D4 = 0No Damage = 292


54

in the Kathmandu valley, Kingdom of Nepal” in 2002. There are a number of related case studies used in this project that consider the historic earthquake catalog and current seismological and tectonic activity in and around Nepal, particularly in the Kathmandu valley; demographic data and infrastructural development was also considered. The case studies include the 1934 Bihar-Nepal earthquake (magnitude 8.4), the mid-Nepal earthquake (magnitude 8.0), the north Bagmati earthquake (magnitude 6.0) and the Kathmandu valley local earthquake (magnitude 5.7). The results of the study are shown below:

• If an earthquake similar to that of the Bihar-Nepal earthquake occurred in the Kathmandu valley, it is estimated that 59,000 (or 23 percent) of the buildings would be heavily damaged. It is estimated that the death toll would be 20,000 people (or 1.4 percent of the total population) in the valley. Around 59,000 people would be seriously injured.

• There are 2,497 schools in the Kathmandu Valley, 689 of which are public schools, 1808 of which are private schools.

• If an earthquake similar to the mid-Nepal earthquake occurred in the Kathmandu valley, it is estimated that 74 percent of the school buildings would be heavily damaged and 30 percent would be partially damaged. It is also estimated that 11 percent of the hospitals would be heavily damaged and 26 percent of the hospitals would be partially damaged.

• If an earthquake similar to the north Bagmati earthquake occurred in the Kathmandu valley, it is estimated that 20 percent of the schools would be heavily damaged and 11 percent would be partially damaged. It is also estimated that 2 percent of the hospitals would be heavily damaged and 9 percent would be partially damaged.

• For every 15 bridges in the area, 13 are likely to collapse in a major earthquake.

• The report also analyzes the impact an earthquake will have on lifeline infrastructure including water supply and sewage pipelines, electric power supply and telecommunication lines.

• For major earthquakes the impact of fire is quantified.

• This report provides a set of recommendations for creating earthquake resilient development strategies, using the mid-Nepal earthquake as a case study. The recommendations include information on how to arrange basic data, set-up a sustainable mechanism for the development of DM strategies, maintain good governance, protect life and property and strengthen socio-economic systems.

The present study has carried out a loss estimation for the 1934 Bihar-Nepal earthquake and the 1833 earthquake. The RA review is provided in the next sub-section.

2.9.1 HAZARD REVIEW

These two case studies were chosen based on their location and magnitude; the MMI scale was estimated by Bilham (1995). As no modern measurement scale was available at the time, the source and location of the 1833 earthquake is less precise than that of the 1934 earthquake. Data for the 1833 earthquake has been collected mainly from witness reports and observations of the damage and displacement in several areas. Bilham (1995) has developed MMI zones for both earthquakes which have been digitalized and presented in Figure 2.49 and Figure 2.50.

In terms of the MMI scale the 1934 earthquake was stronger than the 1833 earthquake. Nevertheless the 1833 earthquake impacted a larger area. The 1934 Bihar earthquake reached X on the MMI scale in the eastern mountain areas of Nepal and stretched up to VIII around central Nepal. The source of the 1833 earthquake was at the Nepal-India border and spread to almost the whole of Nepal.

Based on this MMI scale, a damage and loss estimation analysis for these earthquakes has been carried out.

Figure 2.49. MMI zone for the Bihar 1934 earthquake (Bilham, 1995)


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Figure 2.50. MMI zone for the 1833 earthquake (Bilham, 1995)

2.9.2 RISK ANALYSIS

The RA has been carried out based on earlier discussed EVRA methodology. The estimates of the damage cost for the 1934 and 1833 earthquakes are presented in Table 2.6 and table 2.7.

Table 2.6 Cost estimates for several factors as a result of the 1934 earthquake

Table 2.7 Cost estimates for several factors as a result of the 1833 earthquake

Sector Type MMI

Total Total / sector VII VIII

Million Rupee

Housing

Permanent 14,839.08 62,568.85 77,407.93

369,719.96 Semi Permanent 7,662.53 80,391.77 88,054.30

Temporary 32,614.66 169,699.32 202,313.99Other 325.84 1,617.90 1,943.74

Education building Permanent 459.23 1,127.59 1,586.81

3,075.39 Semi Permanent 186.23 1,302.36 1,488.58

Health infrastructureHealth post

Permanent 74.25 125.93 200.18

295.54 Semi Permanent 24.75 0.00 24.75

Hospital Permanent 21.00 49.61 70.61

Transportation Infrastructure

Road Permanent 1,402.49 6,800.76 8,203.25100,437.87

Bridges Permanent 17,470.01 74,764.62 92,234.63Industry Factory 3,521.81 3,899.81 7,421.63 7,421.63

Power infrastructure High electric line Permanent 203,100.51 333,606.40 536,706.91 536,877.00 Transformer Permanent 69.79 100.30 170.09

Grand Total 1,017,827.39

Sector Type MMI

Total Total / sector VIII IX X

Million Rupee

Housing

Permanent 5,833.81 41,807.34 98,148.39 145,789.54

513,942.77 Semi Permanent 1,896.03 57,058.56 111,246.54 170,201.13

Temporary 5,894.33 148,875.54 41,238.82 196,008.69Other 69.44 1,340.05 533.91 1,943.41

Education building Permanent 115.54 705.90 4,466.23 5,287.67

7,944.79 Semi Permanent 46.56 810.59 1,799.97 2,657.12

Health infrastructureHealth post

Permanent 13.78 56.95 757.48 828.21

1,325.60 Semi Permanent 4.59 18.98 252.49 276.07

Hospital Permanent 8.40 19.43 193.49 221.31

Transportation Infrastructure

Road Permanent 4,537.08 32,914.85 295.34 37,747.27107,678.63

Bridges Permanent 10,368.36 48,960.00 10,603.00 69,931.36Industry Factory 2,059.59 2,075.63 2,338.03 6,473.25 6,473.25

Power infrastructure High electric line Permanent 101,036.00 316,057.22 48,081.60 465,174.82 465,319.42 Transformer Permanent 37.21 75.89 31.51 144.60

Grand Total 1,102,684.46


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2.9.3 ANALYSIS OF DAMAGE SCENARIO PROFILE

Greater damage overall was caused in the 1934 earthquake than the 1833 earthquake. The damage scenarios are discussed below:

• For the 1934 earthquake:

o The sector that would have been damaged is the power infrastructure, with a total damage of 536,877 million Rupees. Within the power infrastructure, high electric lines accounted for nearly all of the damage incurred.

o Within the housing sector, temporary housing is by far the most severely damaged, followed by semi-permanent and then permanent housing.

o Within the transport sector, the damage is almost entirely due to collapsed bridges.

o Health infrastructure experienced the least amount of damage of all sectors. More health posts than hospitals were damaged.

• For the 1833 earthquake:

o The housing sector experienced the greatest amount of damage, followed by the power infrastructure.

o Although temporary housing suffered the most damage, damage to semi-permanent and permanent housing was also high.

o In the transportation sector, bridges experienced the most damage, however in the 1833 earthquake the impact to roads was far greater than in the 1934 earthquake.

o As in the 1934 earthquake high electric lines comprised nearly all of the damage that occurred within the power infrastructure sector.

o Health infrastructure experienced the least amount of damage, with damage to health posts making up the majority of damage caused.

2.10 CONCLUSION

· The exposure and risk assessment has been carried out for vital sectors in Nepal, namely housing, education, health, industry, power and human life. Due to a lack of data availability and a restricted time frame, the scope of the project was limited. Thus, there are several improvements that may be carried out beyond the project deliverables. Future detailed VRA activities will further support policy-makers, decision-makers and development agencies in achieving safe and sustainable development.

o The Department of Survey categorizes housing into four classes, namely permanent, semi-permanent, temporary and other. However the definition of each class of housing is not entirely clear and fails to reflect the building materials used and their respective earthquake force resilience. Thus, it is difficult to develop fragility curves for such types of houses. In the future, it is necessary to create a system for the categorization of buildings based on their engineering characteristics.

o No database exists that identifies which class of building schools and hospitals are in. Although a database of the spatial distribution of state schools, health posts and hospitals already exists, there is no such database for private schools or health infrastructure. Building a comprehensive database to incorporate these buildings would allow for a comprehensive analysis of the building exposure, vulnerability and risk.

o In the transport sector the spatial distribution of roads has not yet been updated into a GIS format. The available data is based on the 2001 census. However, rapid development has taken place in the last decade in the hill and mountain areas. Due to the unavailability of more recent information in the necessary spatial format, the present analysis has not incorporated updated information on newly built road infrastructure. It is necessary to obtain updated information on road infrastructure from the Department of Roads and translate it into the spatial format needed to develop a realistic and updated EVRA.

o Data on the power and electricity infrastructure in Nepal has not been updated along with new developments. It is necessary to update the database for power and electricity infrastructure. The Department of Electricity Development should develop the updated spatial database to enable an extensive EVRA for the power and electricity sector to be carried out.

o A comprehensive fragility curve must be developed for electricity and power infrastructure. At present the fragility curve and vulnerability characteristics have not yet been developed for the existing power and electricity infrastructure in Nepal. Thus, a realistic fragility curve for such infrastructure must be developed.


57

o Very little information is currently available in terms of the spatial distribution of industrial areas throughout Nepal. A comprehensive database is available but must be translated into a spatial format.

o The EVRA has only been carried out for limited sectors. There remains a need to develop an EVRA for economic sectors such as tourism, trade and real estate.

· The EVRA has been developed at a national scale. The appropriate national level government departments and agencies should take the initiative to develop EVRAs at the district and VDC levels.

· The project has used the fatality ratio developed by Coburn (2002) to estimate the number of casualties in both 500 and 100 year return period earthquakes. The coefficients used have been taken from neighboring countries such as India and Bangladesh, as the necessary information was not available for Nepal.

· There is a need for capacity building within focal development departments for EVRA as will be discussed in the recommendation section of the report. MOHA can take initiatives, and arrange capacity building programs for identified departments and agencies.


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3 FLOOD EXPOSURE, VULNERABILITY AND RISK ASSESSMENT

3.1 OVERVIEW

Nepal is prone to frequent flooding. It is therefore important to analyze the impact of flooding on the population and physical infrastructure that is at risk. There are seven river basins in Nepal that are the most prone to flooding: Bagmati, Kankai, Kamala, Rapti, Tinau, Babai and Narayani; this paper considers these in its analysis. EVRA analyzes important sectors like housing, education, health, agriculture and population. The EVRA approach is presented in the flowchart in Figure 3.1.

3.2 APPLICATION OF FLOOD EXPOSURE ASSESSMENT

• Quantifying the exposure of sectoral assets helps explain the proportion of assets located in flood prone areas. It also helps provide information about the stock of asset which may be vulnerable to flooding. The assessment provides information for policy makers, decision makers and planners about assets which may need mitigation intervention efforts. It does not characterize the performance of assets in varying levels of flood inundation. The EA follows the VRA.

• The impact of flooding on different assets varies depending upon their characteristics. Floods tend to have the greatest impact on the agricultural sector, particularly in low lying areas, followed by the physical infrastructure of an area. This project aims to estimate the agricultural area that is prone to flooding. Housing, education and health infrastructure are also considered, along with population, which is classified into age, gender and dependents.

Figure 3.1 Methodology of Flood Exposure, Vulnerability and Risk Assessment


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3.3 METHODOLOGY FOR FLOOD EXPOSURE ASSESSMENT

• The identification of sectors for EVRA is based on past impacts. Table 3.1 illustrates what effect a flood will have on the various sectors; for the analysis only primary sectors are considered.

Table 3.1 Impact on sectors affected - identified in the Exposure assessment

Type of Hazard Primary affected sectors Secondary affected Sectors Others

Floods

Agriculture, Housing, Industry, Power, Real estate, Education, Hospitals,

Population Tourism, Trade, Financial Institutions

Irrigation Infra

• Data Collection: Data relating to the primary sector is collected from a number of reliable sources. The details of the data may be found in Chapter 2, Part 1 of this project report. The data is structured in GIS format and is created at the district level.

• Application of the GIS tools for EA: Chapter 3, Part 1 comprehensibly discusses the flood hazard maps for seven river basins whilst considering the inundation depth of the areas. GIS tools facilitate overlaying susceptibility flood hazard maps within the identified sectors. The overlapping areas of hazard map and sectoral data allow for the identification of the different elements at risk. This project quantified the area of agriculture (Ha), the number of houses, people’s class, the number of educational buildings, hospitals and health posts and total population falling in the flood prone areas.

• The flood hazard maps are developed for varying return periods: 500 years, 100 years, 50 years, 25 years and 10 years. This report is addressing the EA for frequent (10 year) and extreme return (100 year) periods.

• Analysis of EA: The analysis of the EA provides information about the stock of assets in the flood prone areas. The application of EA has been discussed in brief in paragraph 3.2


GIS tools are used to identify the impacts of flooding on agricultural land, housing, hospitals, educational institutions and the population living in the flood prone areas and respective districts.


• The EA has been carried out for all of the primary sectors mentioned in Table 3.1.

3.5.1 AGRICULTURE SECTOR

• Figure 3.2 illustrates the percentage of agricultural land in the seven major river basins affected by a 10 year flood return period. The graph illustrates that the variation of flood water depth ranges from less than 0.3 meters to over two meters.

o Bardiya and Salyan district are affected by flooding from the Babai River. The percentage of agricultural land affected by flooding varies from 0.034 to 0.683 percent.

o There are seven districts that are affected by flooding in the Bagmati River basin: Latipur, Kathmandu, Bhaktapur, Rautahat, Sarlahi, Makawanpur and Sindhuli. The percentage of agricultural land affected by flooding varies from 0.029 to 5.13 percent.

o In the Kamala River basin Dhanusha, Siraha and Udaypur districts are affected by flooding. The percentage of agricultural land affected by flooding varies from 0.048 to 1.493 percent.

o In the Kankai River basin Ilam and Jhapa districts are affected by flooding. The percentage of agricultural land affected by flooding varies from 0.028 to 1.172 percent.

o In the Narayani River basin, Nawalparasi and Chitawan districts are affected by flooding. The percentage of agricultural land affected by flooding varies from 0.292 to 7.403 percent.

o Banke, Dang and Arghakhanchi districts are affected by flooding from the Rapti River basin. The percentage of agricultural land affected by flooding varies from 0.069 to 3.924 percent.

o Rupandehi district is affected by flooding from the Tinau River. The percentage of agricultural land affected by flooding varies from 0.333 to 1.155 percent.


60

Figure 3.2 Percentage of agricultural land affected by flood in a 10 year return period

• Figure 3.3 illustrates the percentage of agricultural land affected by the seven major river basins in Nepal over 100 year return periods.

o The percentage of agricultural land affected by flooding from the Babai River in the Bardiya district is 0.056 percent. The percentage of agricultural land affected by flooding from the Babai River in the Salyan district is 0.916 percent.

o The percentage of agricultural land affected by flooding from the Bagmati River in Latipur, Kathmandu, Bhaktapur, Rautahat, Sarlahi, Makawanpur and Sindhuli district varies from 0.029 to 8.762 percent.

o The percentage of agricultural land affected by flooding from the Kamala River in Dhanusha, Siraha and Udaypur districts varies from 0.058 to 1.810 percent.

o The percentage of agricultural area affected by flooding from the Kankai River in Ilam and Jhapa district is 0.04 and 1.921 percent respectively.

o The percentage of agricultural area affected by flooding from the Narayani River in Nawalparasi and Chitawan district is 351 and 10.201 percent respectively.

o The percentage of agricultural land affected by flooding from the Rapti River in Banke, Dang and Arghakhanchi district varies from 0.069 to 5.597 percent respectively.

o The percentage of agricultural area affected by flooding from the Tinau River in Rupandehi district varies from 0.188 to 1.651 percent.

Figure 3.3 Percentage of agricultural land affected by a 100 year flood return period


61


Housing is the second worst affected sector after agriculture. Flooding leads to damage and loss of household items, and impacts on the functionality of the household. There are several complex losses associated with the impact of flooding on housing. The EA has been carried out for 10 and 100 year return periods. Figure 3.4 to Figure 3.11 illustrate the profile of flood exposure to all four types of housing that are previously discussed.

• Flood scenario for a 10 year flood return period o Bardiya and Salyan districts are affected by flooding from the Babai River. The analysis

reveals that in both districts up to 1.38 percent of permanent houses, less than 1 percent of semi-permanent and temporary houses and 1.8 percent of other types of houses are exposed to flooding.

o In seven districts housing is affected by flooding from the Bagmati River: Latipur, Kathmandu, Bhaktapur, Rautahat, Sarlahi, Makawanpur and Sindhuli. The results reveal that 12.7 percent of permanent houses, less than 7.5 percent of semi-permanent houses, about 5.8 percent of temporary houses and up to 10 percent of other types of housing are exposed to flooding.

o For Dhanusha, Siraha and Udaypur district less than 1 percent of permanent houses, less than 2 percent of semi-permanent and temporary houses, and less than 3 percent of other types of housing are exposed to flooding from the Kamala River.

o In the case of Ilam and Jhapa district. About 0.5 percent of permanent, less than 1 percent of semi-permanent, 2 percent of temporary and about 1.5 percent of other types of housing are exposed to flooding from the Kankai River.

o In the case of the Narayani River, in Nawalparasi and Chitawan district, 4.5 percent of permanent, 5.6 percent of semi-permanent, 6.5 percent of temporary and 5 percent of other types of housing are exposed to flooding.

o The Rapti River affects housing in three districts: 0.015 – 0.962 percent of permanent, 0.016– 1.328 percent of semi-permanent, 0.020 – 1.795 percent of temporary and 0.247 – 9.622 percent of other types of housing are affected by flooding.

o In the case of Banke, Dang and Arghakhanchi districts, less than 1percent of permanent, 1.5 percent of semi-permanent, 1.8 percent of temporary and 10 percent of other types of housing are exposed to flooding from the Rapti River,

o In the case of the Rupandehi district less than 1.3 percent of permanent, 1.6 percent of semi-permanent, 1.7 percent of temporary and 3 percent of other types of housing are exposed to flooding from the Tinau River,

Similar analysis has been carried out for a 100 year flood return period.

• For a 100 year flood return period:

o Analysis reveals that in districts affected by the Babai River, up to 3.2 percent of permanent houses, 1 percent of semi-permanent houses, 1.2 percent of temporary houses and 2.3 percent of other types of housing are exposed to flooding.

o Seven districts are affected by the Bagmati River. Analysis reveals that up to 28 percent of permanent houses, less than 17 percent of semi-permanent houses, about 10 percent of temporary houses and 17 percent of other types of housing are exposed to flooding.

o In Dhanusha, Siraha and Udaypur district less than 2 percent of permanent houses, less than 2 percent of semi permanent and temporary houses, and less than 3 percent of other types of housing are exposed to flooding from the Kamala River

o In the case of Ilam and Jhapa district about 1 percent of permanent houses, 1 percent of semi-permanent houses, less than 2 .5 percent of temporary houses and about 1.6 percent of other types of housing are exposed to flooding from the Kankai River,

o In the case of Nawalparasi and Chitawan district 10 percent of permanent, semi-permanent, and temporary houses and 8.5 percent of other types of housing are exposed to flooding from the Narayani River,

o In the case of Banke, Dang and Arghakhanchi districts less than 4 percent of permanent houses, 2.5 percent of semi-permanent houses, 2.5 percent of temporary houses and 13 percent of other types of housing are exposed to flooding from the Rapti River,

o In the case of Rupandehi district less than 4 percent of permanent houses, 2.2 percent of semi-permanent houses, 2.3 percent of temporary houses and 3.5 percent of other types of housing are exposed to flooding from the Tinau River,


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Figure 3.4 Percentage of permanent housing exposed to flooding in a 10 year return period

Figure 3.5 Percentage of semi-permanent housing exposed to flooding in a 10 year return period

Figure 3.6 Percentage of temporary housing exposed to flooding in a 10 year return period

Figure 3.7 Percentage of other types of housing exposed to flooding in a 10 year return period

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Figure 3.8 Percentage of permanent housing exposed to flooding in a 100 year return period

Figure 3.9 Percentage of semi-permanent housing exposed to flooding in a 100 year return period

Figure 3.10 Percentage of temporary housing exposed to flooding in a 100 year return period

Figure 3.11 Percentage of other types of housing exposed to flooding in a 100 year return period

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64

3.5.3 EDUCATION

For the education sector, the EA has been carried out for the seven identified rivers basins for 10 and 100 year return periods. Figure 3.12 and Figure 3.13 illustrate the education institutions exposed to flooding in 20 districts.

• 10 years return period o For Bardiya and Salyan districts less than 1 percent of the total number of schools are

exposed to flooding from the Babai River,

o For Latipur, Kathmandu, Bhaktapur, Rautahat, Sarlahi, Makawanpur and Sindhuli, 0.77 to 8.33 percent of the total number of schools in each district are exposed to flooding from the Bagmati River,

o For Dhanusha, Siraha and Udaypur, 0.63 to 0.95 percent of the total number of schools for each district are exposed to flooding from the Kamala River,

o In the case of Ilam and Jhapa 1.04 to 1.29 percent of the total number of schools in each district are exposed to flooding from the Kankai River.

o For Nawalparasi and Chitawan 0.86 to 1.59 percent of the total number of schools in each district are exposed to flooding from the Narayani River.

o In the case of Banke, Dang and Arghakhanchi, 0.52 to 4.15 percent of the total number of schools in each district are exposed to flooding from the Rapti River.

o In the case of the Tinau River, there are no schools affected in this flood return period.

Figure 3.12 Percentage of schools exposed to flooding in a 10 year return period

• 100 year return period

o For Bardiya and Salyan 0.58 to 2.97 percent of the total number of schools in each district are exposed to flooding from the Babai River.

o In the case of Latipur, Kathmandu, Bhaktapur, Rautahat, Sarlahi, Makawanpur and Sindhuli 0.77 to 13.70 percent of the total number of schools in each district are exposed to flooding from the Bagmati River.

o In Dhanusha, Siraha and Udaypur 0.63 to 5.84 percent of the total number of schools in each district are exposed to flooding from the Kamala River.

o In the case of Ilam and Jhapa 1.04 to 2.58 percent of the total number of schools in each district are exposed to flooding from the Kankai River.

o For Nawalparasi and Chitawan 1.09 to 7.61 percent of the total number of schools in each district are exposed to flooding from the Narayani River.

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65

o In the case of Banke, Dang and Arghakhanchi 0.52 to 6.80 percent of the total number of schools in each district are exposed to flooding from the Rapti River.

o Rupandehi is the only district which has been affected by flooding from the Tinau River. The percentage of schools affected by flooding varies from 1.37 to 5.84 percent.

Figure 3.13 Percentage of schools exposed to flooding in a 100 year return period

3.5.4 HEALTH

The EA has also been carried out for health institutions. The EA has been performed for 10 and 100 year return periods; these are presented in Figure 3.14 and Figure 3.15.


o Latipur, Kathmandu, Bhaktapur and Rautahat districts’ health institutions are exposed to flooding from the Bagmati River, The percentage of health posts and hospitals affected by flooding ranges from 12.5 to 100 percent.

o Health institutions of Jhapa district are exposed to flooding from the Kankai River. 2.78 percent of the health posts in this district are affected by flooding. No hospitals in this district are affected by flooding in this return period.

o Health institutions in Nawapalrasi are exposed to flooding from the Narayani River. 8.33 percent of the health posts in this district are affected by flooding. Hospitals are not affected by flooding in this district.

o Health institutions in Banke and Dang are exposed to flooding from the Rapti River. The percentage of health posts exposed to flooding varies from 4.55 to 5 percent. No hospitals are affected in this river basin.

o The health posts and hospitals in Babai, Kamala and Tinau River have hardly been affected by flooding.


o Health institutions in Salyan are exposed to flooding from the Babai River. The percentage of health posts exposed to flooding is 3.85 percent of the total number of health posts in this river basin.

o Health institutions in the 4 districts of Latipur, Kathmandu, Bhaktapur and Rautahat are exposed to flooding from the Bagmati River. The percentage of health posts and hospitals affected by flooding varies from 9.09 to 100 percent.

o Health institutions in Siraha are exposed to flooding from the Kamala River. The percentage of health posts and hospitals affected by flooding varies from 5.26 to 50 percent.

o Health institutions in Jhapa district are exposed to flooding from the Kankai River, The percentage of health posts and hospitals exposed to flooding varies from 2.78 to 4.35 percent.

o Health institutions in Nawapalrasi are exposed to flooding from the Narayani River, 16.67 percent of the health posts in this district are affected by flooding. Hospitals are not affected in this district.

o Health institutions in Banke and Dang are exposed to flooding from the Rapti River. The percentage of health posts exposed to flooding varies from 4.55 to 5 percent of the total number of health posts for this river basin. There are no hospitals affected in this river basin.

o Health institutions in Rupandehi are exposed to flooding from the Tinua River. 20 percent of the health posts in this district are affected by flooding. Hospitals are not affected by flooding in this district.

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Figure 3.14 Percentage of health posts and hospitals exposed to flooding in a 10 year return period

Figure 3.15 Percentage of health posts and hospitals exposed to flooding in a 100 year return period

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Ilam Jhapa Siraha Lalitpur Bhaktapur Kathmandu

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Kankai Kamala Bagmati Narayani Tinau Babai RaptiHealthpost < 0.3 m 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.0

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Healthpost 1 ‐ 2 m 0.0 0.0 0.0 12.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Healthpost >2 m 0.0 2.8 0.0 12.5 0.0 0.0 0.0 8.3 0.0 0.0 4.5 5.0

Hospital < 0.3 m 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Hospital 0.3 ‐1 m 0.0 0.0 0.0 0.0 0.0 14.3 0.0 0.0 0.0 0.0 0.0 0.0

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Ilam Jhapa Siraha Lalitpur Bhaktapur Kathmandu

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Kankai Kamala Bagmati Narayani Tinau Babai RaptiHealthpost < 0.3 m 0.0 0.0 0.0 0.0 0.0 0.0 9.1 0.0 0.0 0.0 0.0 0.0

Healthpost 0.3 ‐1 m 0.0 0.0 5.3 0.0 0.0 0.0 18.2 0.0 0.0 0.0 0.0 10.0

Healthpost 1 ‐ 2 m 0.0 2.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.8 4.5 0.0

Healthpost >2 m 4.3 0.0 0.0 37.5 0.0 0.0 0.0 16.7 20.0 0.0 0.0 5.0

Hospital < 0.3 m 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Hospital 0.3 ‐1 m 0.0 0.0 0.0 0.0 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0

Hospital 1 ‐ 2 m 0.0 0.0 50.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Hospital >2 m 0.0 0.0 0.0 0.0 100.0 14.3 0.0 0.0 0.0 0.0 0.0 0.0

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Percentage of health posts and hospitals exposed to Flooding in a 100 year return period


67

3.5.5 POPULATION

An EA has been carried out to see what affect flooding has had on people living in the area.

o o Figure 3.16 shows male and female exposure to flooding (10 and 100 year return periods).

o Figure 3.17 and Figure 3.18 shows the total distribution of people in the area at risk. The analysis has been carried out for 10 and 100 year return periods.

o Bardiya and Salyan district have been affected by flooding from the Babai River. More than 3,900 males and 3,800 females are exposed to flooding in these districts, the majority of whom are aged between 15 – 59 years old.

o In Latipur, Kathmandu, Bhaktapur, Rautahat, Sarlahi, Makawanpur and Sindhuli more than 270,000 males and 260,000 females are exposed to flooding from the Bagmati River; most of whom are aged between the ages of 15 – 59.

o In Dhanusha, Siraha and Udaypur more than 18,000 males and 17,000 females are affected by flooding from the Kamala River; most of whom are between the ages of 15 – 59.

o In Ilam and Jhapa more than 7,000 males and 7,000 females are affected by flooding from the Kankai River.

o In Nawalparasi and Chitawan more than 33,000 males and 33,000 females are affected by flooding from the Narayani River.

o In Banke, Dang and Arghakhanchi more than 18,000 males and females are affected by flooding from the Rapti River.

o In Rupandehi more than 8,000 males and 7,000 females are affected by flooding from the Tinau River.

Figure 3.16 Population by gender exposed to flooding

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Figure 3.17 Age of population exposed to flooding in a 10 years return period

Figure 3.18 Age of population exposed to flooding in a 100 year return period

3.6 FLOOD VULNERABILITY AND RISK ASSESSMENT

Introduction

The flood VRA primarily defines the characteristics of potentially vulnerable elements, their susceptibility to damage during flood inundation, and their relationship to resilience. There are several ways to carry out a VRA. The assessment is applicable to the social, physical and environmental elements in society. The project scope includes an assessment of physical vulnerability and a direct assessment of sectoral vulnerability. The precision of VRA depends upon the classification of building functions, the materials used in construction, the age and condition of the building, its physical characteristics, and water depth and usages. Conducting VRAs in developing and underdeveloped countries can be difficult for the following reasons:

• Flood VRA of economic sectors at the national level depends on the types of crops being cultivated, the classification of physical assets, and their interaction with flood water. The damage largely depends upon the velocity of flowing flood water, the depth of water and the water detention period. Each asset is impacted differently in these varying circumstances.

• Though the census has compiled data on education and health institutions it is still a challenge to collect detailed characteristics of education, health and industrial buildings.

• The extent of expected flood damage must be studied based on historical records of the performance of agriculture and various classes of buildings at varying flood depths, areas and detention periods. However the literature review reveals that there is a distinct lack of damage data as such. For instance there exists little data that can correlate the damaged state of crops to the detention period and velocity of specific floods. Thus it is very difficult to precisely gage expected damage to the agricultural sector and other sectors assets. This lack of historical records and damaged data is seen throughout most other sectors as well.

• The depth-damage (DD) ratio is used to carry out the VRA. However no fragility functions are available for Nepal. It is thus necessary to develop the DD ratio for Nepal.

3.7 METHODOLOGY FOR FLOOD VULNERABILITY AND RISK ASSESSMENT

• Frequent floods have a high damage impact on physical infrastructure and agriculture in Nepal. The

scope of this project addresses flood VRA and includes important sectors like agriculture, housing, education and health sectors. Due to the limitation of data availability for a precise VRA, the following issues must be considered when developing the methodology for a VRA.

• The flood VRA has been carried out for all four categories of buildings. However, only the direct damage to buildings has been considered; this means that in the housing sector only structural

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69

damage has been considered. Intangible impacts such as the loss of household items, functional losses, and loss of livelihood, rental income and the cost of relocation are not considered in a VRA.

• In the education and health sectors, only buildings are considered for physical VRA. Based on field information and expert judgment, only the permanent and semi-permanent categories of buildings are considered for schools and hospital buildings.

• In order to define the vulnerability factors for the aforementioned sectors, there is a need to develop the DD curve based on conditions in the field. However, there is no literature review available about the DD curve for Nepal to determine the performance of various building structures. Previous DD curve models must be modified to suit conditions in Nepal. For this study, the DD curve has thus been appropriately modified. The approach for modification will be discussed in the next paragraph.

The methodology for VRA is as follows:

• Collection of Data: The flood VRA requires flood hazard mapping for various return periods, a database on housing, characteristics of housing types, past data on housing damage due to floods, an EA for housing due to specific flood return period, a cropping calendar, and characteristics of crops at varying stages of growth. This data must be then be correlated with the flood water depth.

• Flood exposure of buildings and agriculture: The flood exposure assessment of buildings and agriculture is carried out using GIS tools. The housing, education and health sectors EA determine the number of buildings (permanent, semi-permanent, temporary and others) located in different depths of flood water within the study area.

• Establishing the DD ratio: There are methodologies established by flood experts and hydrologists based on DD ratios. Dushmantha et.al. (2003), Smith DI et.al., (1993), Kang et.al., (2005), Edward et.al., (1988) have developed a damage depth ratio / curve for floods suitable to diverse urban environments. However, these methods are developed for site specific conditions and cannot be applied on a large regional or country-wide scale. Within the scope of this project, HAZUS (FEMA) guidelines for flood damage estimation could be more appropriately applied. The HAZUS guidelines utilize the DD curve developed by the United States Army Corps of Engineers (USACE) and the USACE Institute for Water Resources. In a VRA HAZUS considers ten classes of buildings. The most commonly seen type of building for this project is a single story building with no basement. The graph used for the DD ratio for such a building has been used as the base VRA curve. Since the graph is largely based on U.S. based data, the DD curve must be corrected to suit Nepal’s conditions. The graph has been corrected and modified based on expert opinion and HAZUS based DD curve. The curve is shown in Figure 3.19. The modification of the DD is based on the past performance of buildings during floods. The characteristics and performance of each type of building in floods has been considered when finalizing the DD ratio curve.

• Calibration of Flood Vulnerability Grade: The VRA results in the identification of a number of houses prone to various grades of damage. The grades are classified as low, moderate high and very high. There are several ways by which the damage ratio can be correlated with the damage grade. HAZUS has defined the depth damage ratio as:

Slight 1-10 percent DD ratio Moderate 11-50 percent DD ratio Significant >= 50 percent

The vulnerability grades are modified as per the country’s situation. In Nepal, the proposed grades are as below: D1 Slight 1-15 percent D2 Moderate 15 – 35 percent D3 High 36-60 percent D4 Very High >=61 percent

Vulnerability Assessment Matrix

The VA matrix presents the distribution of building classes with respect to varying damage grades. The assessment provides the vulnerability profile of housing stock and other sectors. The format of the VA is as below:

Table 3.2 Vulnerability Assessment Matrix

S. No Type of Building / Infrastructure

Number of houses Grade of Damage Remarks

1 Permanent D1 D2 D3 D4

2 Semi-Permanent D1 D2 D3 D4

3 Temporary D1 D2 D3 D4

4 Others D1 D2 D3 D4


70

Figure 3.19 Modified damage depth ratio for buildings (based on HAZUS depth damage ratio)

3.8 ANALYSIS OF FLOOD VULNERABILITY AND RISK ASSESSMENT

• Analysis is carried out for both 100 and 10 year return periods of floods for seven river basins, namely Rapti, Kankai, Kamala, Tinau, Babai, Bagmati and Narayani.

• The VA is carried out for agriculture, housing, education, health sectors. In the case of housing, education and health sectors, the RA quantifies the number of buildings falling in all defined four damage grades. These damage grades are Minor (D1), Moderate (D2), Partial Collapse (D3) and Complete Collapse (D4). The RA is carried out for all building stock at the district level. The profile and distribution of damage grades are tabulated for both 100 and 10 year return periods.


Both extreme and regular floods typically occur during monsoon season. Paddy is the most prominent crop cultivated during monsoon season. VRA is carried out for paddy crops.

Exposure of Inundated Paddy Area

The number of affected VDCs and the inundated areas in VDCs affected by flooding in various return periods are estimated based on secondary data. The paddy areas in particular districts were obtained

from the Ministry of Agriculture and Cooperatives (MOAC) and profiles of the respective districts. The inundated paddy crop area was estimated based on the size of the paddy area in each particular district and the areas within the districts that become inundated during floods (Table 3.3).

Table 3.3 Inundated areas in affected VDC

River basin

Area in Ha in different return periods 10 20 50 100 500

Babai 2,535 2,802 3,025 3,239 3,764 Rapti 8,411 9,453 10,239 11,029 12,295 Kankai 2,376 2,603 2,835 2,945 3,272 Kamala 3,626 4,154 4,674 4,971 5,527 Tinau 1,388 1,948 2,628 3,125 3,914 Narayani 9,400 10,687 11,550 12,783 15,226 Bagmati 21,380 22,972 23,910 24,619 26,110

On the basis of the information available, the inundated paddy area was estimated, as presented in Table 3.4Error! Reference source not found..

Table 3.4 Inundated paddy areas in affected VDCs

River basin

Area in Ha in different return periods 10 20 50 100 500

Babai 1,910 2,112 2,280 2,442 2,837 Rapti 6,555 7,368 7,978 8,592 9,576 Kankai 1,906 2,092 2,289 2,379 2,651 Kamala 2,902 3,324 3,740 3,978 4,423 Tinau 1,041 1,461 1,971 2,344 2,936 Narayani 7,345 8,348 9,016 9,977 11,867 Bagmati 14,074 15,246 15,959 16,487 17,549

• Vulnerability of Paddy Crop due to floods

The effect of floods on the paddy crop was determined through a literature review and expert consultations. The analysis of these effects was conducted by considering inundation of the paddy crop through a two-prong approach. The first aspect of this approach was to consider the number of days of submergence. The second aspect was to consider the stage of growth of the crop at the time of

Modified Depth Damage Ratio for Housing ( Based on HAZUS Depth-Damage Ratio)

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submergence. The survival rates of crop varied with the number of days of submergence and the stage of crop as presented in Figure 3.20.

Figure 3.20 Percentage of paddy crop that survives after being submerged

• Yield of Paddy and Market Price

The yield of paddy in the reference district was obtained from a publication from MOAC9. The price of paddy was calculated from the price of rice in the nearby market in the affected district as published by the Marketing Development Directorate10. A summary of the paddy yield and the price of rice is presented in Figure 3.21. When the paddy crop is converted into rice, only 65 percent of the raw product remains.

9 MOAC. 2009. Statistical Information on Nepalese Agriculture 2008-09. Ministry of Agriculture and Cooperatives. Singhdurbar, Kathmandu 10 MDD, 2009. Agricultural Marketing Information Bulletin. Marketing Development Directorate, Hariharbhawan, Lalitpur.

Figure 3.21 Paddy yield and affected area

• Estimated production loss due to flood

Based on the percentage of the paddy area affected by flooding, coefficients for the percentage lost due to flooding in paddy crop areas at various stages of growth, potential loss was estimated for various return periods as presented in Figure 3.22 to Figure 3.28.

Figure 3.22 Estimated loss of production at various stages of growth (Mt) due to flooding from the Babai River Basin

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Figure 3.23 Estimated loss of production at various stages of growth (Mt) due to flooding from the Bagmati River Basin

Figure 3.24 Estimated loss of production at various stages of growth (Mt) due to flooding from the Rapti River Basin

Figure 3.25 Estimated loss of production at various stages of growth (Mt) due to flooding from the Kankai River Basin

Figure 3.26 Estimated loss of production at various stages of growth (Mt) due to flooding from the Kamala River Basin

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Figure 3.27 Estimated loss of production at various stages of growth (Mt) due to flooding from the Tinau River Basin

Figure 3.28 Estimated loss of production at various stages of growth (Mt) due to flooding from the Narayani River Basin

3.8.2 HOUSING SECTOR • For the Babai River the RA for 10 and 100 year return periods reveals that two districts are expected

to be affected. Most of the building damage estimated falls into the D4 grade. Figure 3.29 shows the distribution of housing at risk for the Babai River basin.

Figure 3.29 Housing sector at risk of flooding from the Babai River Basin

• For the Bagmati River the RA for 10 and 100 year return periods reveals that seven of the districts are expected to be affected by flooding. Most of the building damage estimated falls into the D4 grade. Other types of housing in the seven districts are expected to be much less affected. The Latipur district has the largest amount of permanent housing affected. Conversely the Sindhuli district has the least amount of housing affected. Figure 3.30 shows the distribution of risk in the seven identified districts.

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Figure 3.30 Housing sector at risk of flooding from the Bagmati River Basin

• For the Kamala River the RA for 10 and 100 year return periods shows that a large amount of D4 grade damage is reported for both semi-permanent and temporary housing types. The Udayapur district is the least affected. Figure 3.31 shows the distribution of housing risk for 10 and 100 year return periods.

Figure 3.31 Housing sector at risk of flooding from the Kamala River Basin

• For the Kankai River, the RA for 10 and 100 year return periods reveals that a large numbers of houses are affected with a damage grade of D4. The damage extends to both temporary and semi permanent types of housing. Less impact has been observed on permanent and other types of housing. Figure 3.32 shows the profile of risk in the housing sector.

Figure 3.32 Housing sector at risk of flooding from the Kankai River Basin

• For the Narayani River, the RA for 10 and 100 year return periods reveals that more damage is reported in Nawalparasi than in Chitwan. The risk analysis shows that D4 grade damage is higher than other grades of damage. In both districts other types of housing are least affected. One hundred year return period floods are showing larger damage. Figure 3.33 shows the distribution of damage to buildings.

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Figure 3.33 Housing sector at risk of flooding from the Narayani River Basin

• For the Rapti River, the RA for 10 and 100 year return periods shows that a higher proportion of buildings are affected by D4 grade damage. The analysis shows that the Dang district’s larger stock of buildings is at risk. Temporary buildings are more at risk than both permanent and semi-permanent buildings. The Arghakhanchi district is the least affected. Figure 3.34 shows the distribution of risk in all stated districts.

Figure 3.34 Housing sector at risk of flooding from the Rapti River Basin

• For the Tinau River (Rupandehi district) the RA is carried out for 10 and 100 year return periods. In the case of a 100 year return period flood, large stocks of buildings are affected. A large number of permanent buildings fall into the D2 grade of damage. Semi-permanent and temporary buildings fall into the D4 grade of damage. The analysis further reveals that other types of houses are significantly affected. Figure 3.35 shows the damage distribution for the Tinau River basin.

Figure 3.35 Housing sector at risk of flooding from the Tinau River Basin


The distributions of risk are presented in Figure 3.36 and Figure 3.37.

• For a 10 year return period:

o For the Babai River, Bardiya school buildings are expected to suffer D1 and D2 grades of damage. In Salyan, a D3 grade of damage is expected. For the Bagmati River, six districts are expected to be affected. The most affected districts are Kathmandu, Rautahat, Lalitpur and Bhaktapur. Lalitpur and Kathmandu school buildings are expected to experience a D4 grade of damage.

o For the Kamala River minor damages are expected in all three districts.

o For the Kankai River, two school buildings are expected to receive a D4 grade of damage in both districts.

o For the Narayani River, the Nawalparasi and Chitawan districts are expected to be at risk. Seven schools in Nawalparasi will receive a D4 grade damage. In the case of the Rapti River,

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76

Dang will have larger damage than Banke. Eight school buildings will be affected with a D4 grade of damage.

Figure 3.36 Number of educational institutions at risk of flooding for a 10 year return period


o For the Babai River, Bardiya is more greatly affected than the Salyan district.

o For the Bagmati River, Rautahat and Kathmandu have the highest number of school buildings at risk with D1, D2 and D4 grade respectively. For the Kamala River three districts are expected to be affected. The highest numbers of school buildings are at risk in the Siraha district.

o The Kankai River has two districts which are expected to be affected by floods. The Ilam district has the highest number of schools at risk.

o For the Narayani River, the Nawalparasi and Chitawan districts are expected to be flooded. Seven schools in Nawalparasi will experience D4 grade damage. .

o In the case of the Rapti River, the Dang district is most affected with a large number of educational institutions receiving D2, D3 and D4 grades of damage.

o For the Tinau River, Rupandehi district’s educational buildings will be affected with all grades of damage.

Figure 3.37 Number of educational institutions at risk of flooding for a 100 year return period

3.8.4 HEALTH SECTOR

The RA has been carried out for health sector buildings including health posts and hospitals. The analysis is presented in Figure 3.38 to Figure 3.41.


o The analysis reveals that in case of the Babai River, no health posts are at risk in Salyan.

o In the case of the Bagmati River, two health posts and three hospitals are expected to be affected with varying grades of damage in these districts.

o For the Kamala and the Tinau Rivers, no health posts or hospitals are affected in the river basin.

o For the Kankai River, there is only one health post expected to be affected. The damage grade of the affected health post is shown in Figure 3.38.

o For the Narayani River, there will be only one health post affected in the river basin.

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o For the Rapti River, three health posts are expected to be affected with damage grades of D1 and D4.

Figure 3.38 Number of health posts at risk of flooding for a 10 year return period

Figure 3.39 Number of hospitals at risk of flooding for a 10 year return period


For the Babai River, only one health post is affected with a D3 grade of damage.

o Figure 3.40 and Figure 3.41.

o For the Kamala River, only one health post and one hospital is affected.

o For the Kankai River, there are two health posts affected with D3 and D4 grades of damage.

o For the Narayani River, two health posts are affected with a D4 grade of damage.

o For the Rapti River, four health posts are expected to be affected with damage grades of D2, D3 and D4.

o There is only one health post affected in the Tinau River basin with a D4 grade of damage.

Figure 3.40 Number of health post at risk of flooding for a 10 year return period

Figure 3.41 Number of hospitals at risk of flooding for a 10 year return period

3.9 CONCLUSION

• The EVRA has been carried out for the seven river basins suggested by the national advisory committee. These river basins are the most prone to floods annually. However, there is a need to carry out flood RA for the other river basins as well. It is suggested that focal flood mitigation departments such as the Department of Water Induced Disaster Prevention (DWIDP), the Irrigation Department and the Department of Meteorology and Hydrology be trained to conduct flood risk management for the remaining major river basins. In light of the restricted time and field data available, Hydraulic Engineering Centers River Analysis System (HECRAS) software has been used

0

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post Number of health posts at risk of flooding for a 10 year return period

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ost Number of health post at risk of flooding for a 100 year return period D1

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D3

D4


78

for a flood hazard assessment. It is very important to carry out detailed flood EVR using more detailed and scientific models.

• At present very little research and technical data is available for VRA. The literature review reveals that there is a lack of data and well-established coefficients available for flood VA for various sectors. Research and technical groups should develop DD curves and vulnerability functions for various sectors, including infrastructure and agriculture.

• The outcome of flood RA should be increased awareness of all responsible stakeholders involved in the flood RA. A short term orientation course should be organized by focal DM departments for all associated stakeholders to increase their understanding in RA outcomes and flood risk reduction planning.


79

4 DROUGHT EXPOSURE, VULNERABILITY AND RISK ASSESSMENT

4.1 OVERVIEW

Carrying out a drought VRA helps provide important information which is necessary for appropriate planning to help improve the agricultural sector and thus national food security. It provides information on the geographical areas and development zones that are prone to drought, whilst providing appropriate tools which help decision-makers take the necessary mitigation interventions. The suggested tools could involve: early warning systems (EWS) (for drought monitoring, adaptation to drought resilient cropping, and improving community awareness about the effects of drought.

This chapter explains the methodology and analysis of drought EVRA. The broader methodology is explained in more detail in Figure 4.1.

4.2 APPLICATION OF DROUGHT EXPOSURE ASSESSMENT

• Drought EA aims to identify and quantify sectoral assets which may be affected by severe droughts.

• The assessment provides information for policy makers, decision makers and planners about sectors which may need drought mitigation intervention. Thus EA initiates the process of drought VRA; it does not characterize the performance of assets in varying extremities of drought.

• The impact of drought on different sectors is distinctive. Drought tends to have the greatest effect on the agricultural sector, followed by different social sectors. The EA aims to estimate the amount and extent to which agricultural land exists in drought prone areas.

• Apart from the direct damage to agriculture, drought often causes food insecurity, livelihood, livestock, water and sanitation problems, and a high rate of migration to urban areas. The majority of these impacts will directly affect a country’s national development. Drought may impact on other sectors, but these are comparatively insignificant, and are therefore not discussed in this report.

Figure 4.1 Methodology of Drought Exposure, Vulnerability and Risk Assessment


80

4.3 METHODOLOGY FOR DROUGHT EXPOSURE ASSESSMENT

This project aims to identify the agricultural sector at risk for EA.

• Identification of sectors for drought assessment:

• Data collection: Data relating to the agricultural sector is collected from reliable sources; further details can be found in Chapter 2, Part 1 of this project report. The data is structured in GIS format and is collected at the district level.

• Application of GIS tools for EA: Chapter 3, Part 1 categorizes the drought hazard maps into moderate, severe and extreme drought conditions. The hazard assessment and mapping show average, moderate and extreme drought distribution in the country. The assessment has been carried out for winter, pre-monsoon, during monsoon and post-monsoon seasons. Drought has proved to be more prevalent in the winter season. The details of the assessment can be found in Chapter 3 part 1 of this report. Using GIS tools it is possible to overlay the drought hazard maps over the agricultural land maps. This helps to show the varying susceptibility and vulnerability of the land to drought, and also helps identify crops that are exposed to drought variability. By using these tools it is possible to quantify the area of agricultural land located in drought prone areas.

• The EA estimates the area of crop (Ha) affected by various extremities of drought in various seasons. In the winter and pre-monsoon seasons wheat and barley are affected the most by drought. During the monsoon season paddy and maize are affected the most by drought. Similar trends have been observed for paddy during the post-monsoon season.

• Analysis of EA: The analysis of the EA provides information about the exposure of agricultural land in drought prone areas.


GIS tools are used to find areas of agricultural land in drought prone areas and zones. Using these tools, it is possible to find assets that fall within particular zones.


• The following paragraph provides the analysis from the EA.


• The exposure of the agricultural sector has been analyzed in terms of geographical and development zones. Three geographical zones have been considered: mountain, hills and terai. The development zones include eastern, central, western, mid-western and far-western. Figure 4.2 to Figure 4.8 show the results of the analysis.

• Figure 4.2 illustrates that in the winter season, wheat crops in the terai zone are largely affected by all drought conditions. The hills are the second most exposed zone. The analysis has been carried out for wheat crops in the development regions as well. The analysis reveals that when there is extreme or moderate drought, the western and mid-western regions are the most exposed.

• Figure 4.3 shows the drought exposure on wheat crops during the pre-monsoon season, and illustrates that the terai region is the most exposed to drought. 96 percent of the wheat crop area is exposed to a 5-10 percent probability of extreme drought. In hill areas 81.5 percent of the wheat crop area is exposed to a 5-10 percent probability of drought. For extreme drought conditions 64 percent of the terai wheat field area is exposed to a 10-15 percent probability of drought. The analysis has been carried out for the development regions as well. During extreme drought conditions, more than 58 percent of the area in the eastern zone is exposed to a 10-15 percent probability of drought. Around 96 percent of the area in the central zone is exposed to a 5-10 percent probability of drought. Almost 99.5 percent of the western zone is exposed to a 10-15 percent drought probability.

• Figure 4.4 illustrates the exposure profile of barley crops to drought during the winter season. Barley tends to be produced in mountain and hill zones. Large barley cultivation areas in the mountain zones are the most exposed area to all three drought conditions. Barley crop areas are least exposed in the terai zone. Similar analysis has been carried out for the development zones. The analysis shows that the mid-western zone is the most affected.

• Figure 4.5 illustrates the drought exposure for barley in the pre-monsoon season. The analysis reveals that the mountain area is more exposed than the hill and terai zones. In extreme drought conditions there is less than a 5 percent probability that any of the regions will be affected by drought. The far and mid-western zones are largely exposed to extreme drought conditions with a probability range of 5-10 percent chance of drought.

• Figure 4.6 illustrates the exposure of drought on paddy crops in the monsoon season. Due to a large network of rivers and riverine fertile soil, paddy is more likely to be produced in terai than in mountain or hill zones. The terai zone is the most exposed to drought, followed by the hill zones. The analysis further reveals that the eastern and western zones are the most exposed to extreme drought with a probability range of 5-10 percent.

• Figure 4.7 illustrates the exposure of paddy crops to drought during the post-monsoon season. The exposure trend is similar for paddy crops during the monsoon season. During the post-monsoon season, the terai zone is the most exposed to drought, followed by the hill zones. Extreme drought exposure is identified in the eastern and central zones. Moderate drought during the post-monsoon is most likely in the western and mid-western areas.

• Figure 4.8 illustrates the exposure profile for maize crops during the monsoon season. The hill zones experience the highest drought exposure, as do the central and eastern zones.


81

Figure 4.2 Wheat crop areas exposed to drought during winter A)Geographical zones B) Development zones

Figure 4.3 Wheat crop areas exposed to drought during the pre-monsoon season A) Geographical zones, B) Development zones

MountainHills

Terai

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<5 5-10 10-15 15-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25Extreme drought during WinterSevere drought during Winter

Moderate drought during Winter

Extreme drought during Winter Severe drought during Winter Moderate drought during Winter

<5 5-10 10-15 15-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25Mountain 0 13,696 2,761 0 0 6,991 9,466 0 0 6,460 9,833 164 0 0Hills 5,104 27,165 8,164 84 0 16,284 24,065 169 0 7,521 26,003 6,919 76 0Terai 15,589 50,323 11,749 10,233 3,831 21,580 41,105 21,990 7,050 27,082 41,643 23,000 0 0

Exposure of drought on wheat crops areas(Ha.) during winter-Geographical Zones

Exposed Area (Ha.)

Eastern RegionCentralWesternMid WesternFar Western0

5,00010,00015,00020,00025,00030,00035,00040,000

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Extreme drought during WinterSevere drought during

WinterModerate drought during Winter

Extreme drought during Winter Severe drought during Winter Moderate drought during Winter<5 5-10 10-15 15-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25

Eastern Region 20,116 4,364 0 0 0 1,736 22,744 0 0 0 3,680 20,800 0 0Central 577 11,122 2,355 0 0 12,098 1,957 0 0 7,774 4,449 1,756 76 0Western 0 36,510 0 0 0 20,443 16,067 0 0 139 36,207 164 0 0Mid Western 0 35,573 2,947 0 0 4,198 19,489 14,534 300 25,350 13,170 0 0 0Far Western 0 3,615 17,372 10,317 3,831 6,380 14,380 7,625 6,750 7,799 19,973 7,363 0 0

Exposure of drought on wheat crops areas(Ha.) during winter - Development Zones

Exposed Area (Ha.)

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Probability level (%) at different levels of severity of drought

Extreme drought during premonsoon Severe drought during premonsoon Moderate drought during premonsoon

<5 5-10 10-15 <5 10-15 15-20 5-10 15-20Mountain 15,862 595 1,776 265 0 0 8,525 0Hill 33,159 7,359 2,091 19,767 18 2,350 9,208 0Terai 32,538 59,187 0 36,064 0 23,543 8,777 0

Exposure of drought on wheat crops areas(Ha.) during Pre-monsoon-Geographical Zones

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Probability level (%) at different levels of severity of drought Extreme drought during premonsoon Severe drought during premonsoon Moderate drought during premonsoon

<5 5-10 10-15 <5 10-15 15-20 5-10 15-20 >20Eastern 0 10,336 14,144 0 1,563 0 14,376 3,315 0Central 0 13,557 498 0 1,458 0 1,033 1,663 0Western 0 25 36,485 0 0 0 993 2,777 0Mid Western 0 38,520 0 0 33,144 18 0 11,229 0Far Western 0 19,121 16,014 3,867 19,930 0 9,492 7,527 0

Exposure of drought on wheat crops areas(Ha.) during Pre-monsoon-Development Zones

Exposed Area (Ha.)

A

B


82

Figure 4.4 Barley crop areas exposed to drought during winter A) Geographical zones, B) Development zones

Figure 4.5 Barley crop areas exposed to drought during the pre-monsoon season A) Geographical zones, B) Development zones

Mou

ntai

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Extreme drought during Winter

Severe drought during Winter


Probability level (%) at dif ferent levels of severity of drought


Extreme drought during Winter Severe drought during Winter Moderate drought during Winter

<5 5-10 10-15 15-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25Mountain 0 4,550 2,430 0 0 5,314 1,666 0 0 5,173 1,721 85 0 0Hill 39 1,577 239 2 0 797 1,055 5 0 333 1,466 57 0 0Terai 0 293 70 5 2 287 67 12 3 227 139 3 0 0

Exposure of drought on barley crop areas(Ha.) during winter-Geographical ZonesExposed Area (Ha.)

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Probability level (%) at different levels of severity of drought Extreme drought during Winter Severe drought during Winter Moderate drought during Winter

<5 5-10 10-15 15-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25Eastern Affected area (Ha) 36 202 0 0 0 33 205 0 0 0 200 38 0 0Central Affected area (Ha) 3 503 65 0 0 260 311 0 0 216 342 13 0 0Western Affected area (Ha) 0 915 0 0 0 859 56 0 0 0 833 82 0 0Mid Western Affected area (Ha) 0 4,156 1,899 0 0 4,045 2,001 9 0 5,173 882 0 0 0Far Western Affected area (Ha) 0 645 774 7 2 1,202 214 8 3 344 1,070 13 0 0

Exposure of drought on barley crop areas(Ha.) during winter-Development Zones

Exposed Area (Ha.)

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Hill Affected area (Ha)

Terai Affected area (Ha)

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<5 5-10 >10 <5 10-15 >15 5-10 15-20 >20Mountain Af fected area (Ha) 0 2,785 317 357 14 0 0 1,269 0Hill Af fected area (Ha) 0 1,371 486 58 1,132 1 25 329 0Terai Af fected area (Ha) 0 249 121 0 19 0 8 5 0

Exposure of drought on barley crop areas(Ha.) during Pre-Monsoon-Geographical Zones

Eastern Af fected area (Ha)Central Af fected area (Ha)Western Af fected area (Ha)Mid Western Af fected area (Ha)Far Western Total area (Ha)Far Western Af fected area (Ha)

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<5 5-10 >10 <5 10-15 >15 5-10 15-20 >20Eastern Af fected area (Ha) 0 238 0 0 25 0 6 180 0Central Af fected area (Ha) 0 557 14 0 7 0 5 101 0Western Af fected area (Ha) 0 13 902 0 0 0 3 186 0Mid Western Af fected area (Ha) 0 2,177 0 0 1,103 1 0 568 0Far Western Total area (Ha)Far Western Af fected area (Ha) 0 1,420 8 415 31 0 18 568 0

Exposure of drought on barley crop areas(Ha.) during Pre-Monsoon-Development Zones

A

B


83

Figure 4.6 Paddy crop areas exposed to drought during the monsoon season A) Geographical zones, B) Development zones

Figure 4.7 Paddy crop areas exposed to drought during the post-monsoon season A) Geographical zones, B) Development zones

Mountain Af fected area (Ha)

Hill Af fected area (Ha)Terai Af fected area (Ha)

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Extreme drought during Monsoon Severe drought during

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Extreme drought during Monsoon Severe drought during Monsoon Moderate drought during Monsoon

<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Mountain Af fected area (Ha) 0 6,906 2,214 49 5,663 3,408 0 2,436 5,371 1,313 0Hill Af fected area (Ha) 95 54,511 0 0 17,122 37,484 0 14,053 26,932 13,117 504Terai Af fected area (Ha) 0 258,620 0 0 115,994 142,626 0 87,150 100,628 44,070 26,772

Exposure of drought on paddy crop areas(Ha.) during Monsoon - Geographical Zones

0100002000030000400005000060000700008000090000

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Extreme drought during Monsoon Severe drought

during MonsoonModerate drought during

Monsoon



Extreme drought during Monsoon Severe drought during Monsoon Moderate drought during Monsoon

<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Eastern Af fected area (Ha) 0 85,794 726 0 11,914 74,606 0 76,220 8,330 1,970 0Central Af fected area (Ha) 95 45,360 1,325 49 40,792 5,939 0 0 2,126 17,378 27,276Western Af fected area (Ha) 0 78,695 0 0 0 78,695 0 18,043 60,652 0 0Mid Western Af fected area (Ha) 0 53,362 163 0 37,468 16,057 0 9,376 37,610 6,540 0Far Western Af fected area (Ha) 0 56,826 0 0 48,606 8,220 0 0 24,214 32,612 0

Exposure of drought on paddy crop areas(Ha.) during Monsoon - Development Zones

A

B

Mountain Affected area (Ha)Hill Af fected area (Ha)Terai Af fected area (Ha)

020000400006000080000

100000120000140000160000180000

<5 5-10 >10 <5 5-10 10-15 >15 <5 5-10 10-15 15-20 >20

Extreme drought during post-Monsoon Severe drought during post-

MonsoonModerate drought during post-Monsoon



Extreme drought during post-Monsoon Severe drought during post-Monsoon Moderate drought during post-Monsoon

<5 5-10 >10 <5 5-10 10-15 >15 <5 5-10 10-15 15-20 >20Mountain Af fected area (Ha) 457 8,663 0 0 3,083 6,037 0 0 2,225 4,346 2,549 0Hill Af fected area (Ha) 15,201 39,405 0 7,858 33,359 13,389 0 0 4,119 22,292 21,787 6,408Terai Af fected area (Ha) 164,281 94,339 0 31,035 129,786 97,799 0 28,867 24,655 109,196 92,210 3,692

Drought Exposure on Paddy crops during Post-Monsoon-Geographical Zones

Eastern Af fected area (Ha)Central Af fected area (Ha)Western Af fected area (Ha)Mid Western Af fected area (Ha)Far Western Af fected area (Ha)0

100002000030000400005000060000700008000090000

<5 5-10 >10 <5 5-10 10-15 >15 <5 5-10 10-15 15-20 >20

Extreme drought during post-Monsoon Severe drought during post-

MonsoonModerate drought during post-

MonsoonProbability level (%) at dif ferent levels of severity of drought


Extreme drought during post-Monsoon Severe drought during post-Monsoon Moderate drought during post-Monsoon

<5 5-10 >10 <5 5-10 10-15 >15 <5 5-10 10-15 15-20 >20Eastern Affected area (Ha) 0 86,520 0 0 83,415 3,105 0 0 0 7,603 78,917 0Central Af fected area (Ha) 12,177 34,603 0 369 19,311 27,099 0 0 0 12,499 24,180 10,101Western Affected area (Ha) 76,755 1,940 0 0 0 78,695 0 0 0 78,692 3 0Mid Western Af fected area (Ha) 40,899 12,626 0 7,607 44,592 1,326 0 0 10,452 29,697 13,376 0Far Western Af fected area (Ha) 50,109 6,717 0 30,916 18,911 6,999 0 28,867 20,547 7,343 70 0

Drought Exposure on Paddy crops during Post-Monsoon-development Zones

A

B


84

Figure 4.8 Maize crop areas exposed to drought during the monsoon season A) Geographical zones, B) Development zones

4.6 DROUGHT VULNERABILITY AND RISK ASSESSMENT

Introduction

The drought VRA primarily defines drought risk as insufficient rainfall in the study area. There are several ways to carry out a drought VA. The scope of this project focuses on agricultural VRA. The precision of the VA depends upon the classification of crops cultivated and the particular characteristics of the region studied. It is challenging to estimate the precise VRA for drought due both to the unavailability of studies on drought impact and the difficulty of defining the vulnerability of various regions and types of crops in Nepal. Based on data available for particular regions and crop characteristics, the vulnerability functions of various identified crops can be identified. Based on vulnerability functions the risk of agricultural losses has been calculated in this study.

4.7 METHODOLOGY FOR DROUGHT VULNERABILITY AND RISK ASSESSMENT

Estimation of hazard and exposure: Drought hazard assessment has been carried out for four main seasons, namely winter, pre-monsoon, monsoon and post-monsoon. The Table 4.1 shows the details for each season and their corresponding months. The exposure of agricultural areas has been calculated for various drought scenarios. The details of exposure for each area studied, in each season, have been described in the EA section of this chapter.

Table 4.1 Season used for drought indices analysis

Season Corresponding months Winter December, January, February Pre-monsoon March, April, May Monsoon June, July, August, September Post-monsoon October, November

Crops considered for further analysis: There are several types of crops being grown during these seasons in different parts of the country. Covering all crops was beyond the scope of this study. Hence only the major agricultural crops that contribute to food security during these seasons were identified for further impact analysis. The details are presented in Table 4.2.

Table 4.2 Major agricultural crops considered for drought impact analysis

Season Corresponding crops considered for further analysis Winter Wheat in Mid-hill and Terai, and wheat and barley in the High hills Pre-monsoon Wheat in Mid-hill and Terai, and wheat and barley in the High hills Monsoon Paddy in Terai, and Paddy and Maize in Mid-hills Post-monsoon Paddy in Terai

Mountain Affected area (Ha)Hill Affected area (Ha)Terai Affected area (Ha)

020000400006000080000

100000120000140000160000180000

<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Extreme drought during

Monsoon Severe drought during Monsoon

Moderate drought during Monsoon


Probability level (%) at different levels of severity of drought Extreme drought during

MonsoonSevere drought during

Monsoon Moderate drought during Monsoon

<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Mountain Affected area (Ha) 0 30,579 18,620 820 25,165 23,214 0 30,447 13,536 5,215 0Hill Affected area (Ha) 332 165,137 0 0 55,673 109,796 0 51,123 69,442 43,136 1,768Terai Affected area (Ha) 0 93,926 0 0 81,417 12,509 0 8,172 16,010 20,982 48,762

Drought Exposure on Maize crops during Monsoon-Geographical Zones

Eastern Affected area (Ha)Central Affected area (Ha)Western Affected area (Ha)Mid Western Affected area (Ha)Far Western Affected area (Ha)0

20000400006000080000

100000120000

<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Extreme drought during Monsoon Severe drought


Monsoon





<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Eastern Affected area (Ha) 0 59,120 13,045 0 17,112 55,053 0 44,646 22,224 5,295 0Central Affected area (Ha) 332 103,848 4,500 168 88,478 20,035 0 0 7,452 50,698 50,530Western Affected area (Ha) 0 39,324 795 652 143 39,324 0 36,238 3,125 756 0Mid Western Affected area (Ha) 0 58,753 280 0 39,723 19,311 0 8,859 45,807 4,368 0Far Western Affected area (Ha) 0 28,596 0 0 16,800 11,796 0 0 20,380 8,216 0

Drought Exposure on Maize crops during Monsoon-Development Zones

A

B


85

Information of which areas cultivate the selected crops was obtained from a MOAC publication11. The areas impacted by drought for a particular crop were estimated by comparing data revealing the total area in each district affected by drought combined with data of the total area being used for the cultivation of each crop.

Intensity of drought: The severity of drought was classified in three categories namely moderate drought, severe drought and extreme drought. The percentage of area affected under each category of drought combined with the area being cultivated in each particular season allowed the total area affected to be estimated.

Probability of the occurrence of drought: The probability of the occurrence of drought (as described in Part 1 of the report) was taken in the range of five percentage points such as “<5”, “5-10”, “10-15”, “15-20”, and so forth.

Vulnerability Coefficients for drought impact on crops

Cropping calendar: The cropping calendar for various geographical regions of Nepal for major food crops was obtained through an agricultural literature review on Nepalese agriculture and agricultural bulletins.

Vulnerability coefficients: Through a literature review and expert consultations, the effect of drought on major agricultural crops was analyzed while considering the various intensities of drought. Impact on crops was experienced as a result of delayed planting and a lack of sufficient moisture during crop growth. The vulnerability coefficients for major crops due to various intensities of drought are presented in Figure 4.9

Figure 4.9 Drought vulnerability coefficient for different stages of crop growth 11 MOAC. 2009. Statistical Information on Nepalese Agriculture 2008-09. Ministry of Agriculture and Cooperatives. Singhdurbar, Kathmandu

Crop Yield

The yield of selected crops in the reference district was obtained from a publication provided by MOAC12. A summary of crop yield is shown in Table 4.3.

Table 4.3 Crop Yield

Crop and region Yield mt/ha High hill Mid hill Terai

Paddy Eastern Region 1.81 2.46 2.88 Central Region 2.51 3.20 3.15 Western Region 0.00 2.71 3.23 Mid Western Region 1.82 2.61 3.18 Far Western Region 1.72 2.28 2.67 Maize Eastern Region 2.10 2.11 2.39 Central Region 2.25 2.22 2.55 Western Region 1.65 2.46 2.55 Mid Western Region 1.65 1.86 2.03 Far Western Region 1.79 1.77 1.94 Wheat Eastern Region 1.15 1.59 2.26 Central Region 1.30 1.86 2.41 Western Region 1.62 1.46 2.54 Mid Western Region 0.64 1.27 2.34 Far Western Region 0.81 0.90 2.06 Barley Eastern Region 1.10 0.96 1.00 Central Region 1.08 1.00 1.10 Western Region 1.25 1.05 0.95 Mid Western Region 0.82 0.92 0.91 Far Western Region 0.71 0.79 0.96

12 MOAC. 2009. Statistical Information on Nepalese Agriculture 2008-09. Ministry of Agriculture and Cooperatives. Singhdurbar, Kathmandu


86

4.8 ANALYSIS OF DROUGHT VULNERABILITY AND RISK ASSESSMENT


• The drought VRA illustrates the risk of wheat, barley, paddy and maize crop prevention loss in various seasons and geographic and development zones of Nepal.

• Figure 4.10 illustrates the risk of production losses of wheat during winter. During winter the risk of wheat production losses is more evident. When comparing the three classes of drought, more production losses are estimated in the far western zone, followed by the western zone in the case of extreme drought. In moderate drought conditions the risk of production losses is comparatively less in all five development zones. The analysis further reveals that the terai zones are at the highest risk and accrue the most losses to wheat crops. A similar trend has been observed in severe drought conditions.

• The drought risk analysis has been carried out for wheat crops in pre-monsoon conditions. Figure 4.11 shows the risk of wheat production losses during the pre-monsoon season. The western and mid-western zones are largely affected in extreme and severe drought conditions. In addition terai and hill zones are more at risk during extreme and severe drought.

• Figure 4.12 shows the risk of barley production losses in barley crops. In the winter mountain and hill zones are largely affected. The risk of production losses is higher in extreme and severe drought conditions. The drought risk is higher in the western, mid-western and far-western zones.

• Figure 4.13 shows the risk of production losses for barley crops as analyzed in pre-monsoon season. The analysis reveals that mountain and hill zones are largely affected and large risk of production loss is expected in extreme and severe drought conditions. In addition the eastern and far-western zones have a greater risk of barely production losses. The probability of drought conditions and their respective estimated crop losses are presented in the graphs.

• Figure 4.14 and Figure 4.15 show the risk profile of paddy crops during the monsoon and post-monsoon seasons. Paddy is one the most important crops in Nepal. The analysis reveals that paddy production is most affected in the terai zone, followed by the hill zone. The most damaging impacts are seen in extreme and severe drought conditions. In addition the western and eastern zones are largely affected in extreme and severe drought conditions.

• Paddy cultivation is further analyzed during post-monsoon season. It is observed that the terai region is largely affected in extreme and severe drought conditions. Paddy cropping is largely affected in western and eastern zones during extreme and severe drought conditions.

• Maize is one of the important crops grown in the hill and mountain regions. However, it is the hill and mountain regions that are also largely affected in extreme and severe drought conditions. The

central, western and far-western regions risk very high production losses during extreme drought conditions as shown in Figure 4.16.

Figure 4.10 Drought risk on wheat production losses in winter A) Geographical zones, B) Development zones

Eastern Zone (Mt.)Central Zone (Mt.)Western Zone (Mt.)Mid Western Zone (Mt.)Far Western Zone(Mt.)0

5,00010,00015,00020,00025,00030,00035,00040,00045,000

<5 5-1010-1515-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25Extreme drought during

Winter Severe drought during Winter Moderate drought during

WinterProbability level (%) at different levels of severity of drought

Prod

uctio

n Lo

sses

(Mt)


<5 5-10 10-15 15-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25Eastern Zone (Mt.) 21,924 3,692 0 0 0 1,080 14,934 0 0 0 1,271 5,141 0 0Central Zone (Mt.) 645 12,834 2,837 0 0 8,817 1,374 0 0 1,872 1,510 654 28 0Western Zone (Mt.) 0 23,855 3,036 0 0 4,795 1,458 10,181 210 3,933 2,387 78 0 0Mid Western Zone (Mt.) 0 43,750 3,477 0 0 2,137 9,306 10,181 210 5,762 3,351 0 0 0Far Western Zone(Mt.) 0 2,242 12,795 10,588 3,947 2,665 6,910 4,670 4,173 1,536 4,219 1,508 0 0

Risk of wheat production losses in winter drought-Development Zones

A

B

Mountain …Hill Production loss …

Terai Production …0

10000

20000

30000

40000

50000

<5 5-10 10-15 15-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25Extreme drought during WinterSevere drought during

Winter Moderate drought during Winter


Prod

uctio

n Lo

ss (M

t)


Extreme drought during Winter Severe drought during Winter Moderate drought during Winter<5 5-10 10-15 15-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25

Mountain Production loss (Mt) 0 13,071 1,575 0 0 3,167 4,143 0 0 1,297 3,011 79 0 0Hill Production loss (Mt) 4,968 30,991 4,662 45 0 9,788 11,757 61 0 1,589 6,939 2,246 28 0Terai Production loss (Mt) 17,601 42,309 15,908 10,543 3,947 6,538 18,082 24,972 4,593 10,218 2,788 5,056 0 0

Risk of wheat production losses in winter drought -Geographical Zones


87

Figure 4.11 Drought risk on wheat losses in pre-monsoon drought A) Geographical zones, B) Development zones

Figure 4.12 Drought risk on barley production losses in winter A) Geographical zones, B) Development zones

Mountain Production loss (Mt)Hill Production loss (Mt)Terai Production loss (Mt)

020000400006000080000

100000

<5 5-10 10-15 <5 10-15 15-20 5-10 15-20 >20Extreme drought during

premonsoon Severe drought during premonsoon Moderate drought during

premonsoonProbability level (%) at different levels of severity of drought

Prod

uctio

n L

oss

(Mt.)



<5 5-10 10-15 <5 10-15 15-20 5-10 15-20 >20Mountain Production loss (Mt) 0 8,875 628 796 168 0 0 2,695 0Hill Production loss (Mt) 0 28,125 6,984 1,035 14,379 13 1,285 3,916 0Terai Production loss (Mt) 0 48,762 90,166 0 43,425 0 18,085 7,139 0

Risk of Wheat production losses in Pre-Monsoon drought -Geographical Zones

Eastern Production loss (Mt)Central Production loss (Mt)Western Production loss (Mt)Western Production loss (Mt)

Far Western Production loss (Mt)

0100002000030000400005000060000

<5 5-10 10-15 <5 10-15 15-20 5-10 15-20 >20Extreme drought

during premonsoon Severe drought during premonsoon Moderate drought

during premonsoon


Prod

uctio

n L

oss

(Mt)


premonsoon Severe drought during premonsoon Moderate drought during premonsoon

<5 5-10 10-15 <5 10-15 15-20 5-10 15-20 >20Eastern Production loss (Mt) 0 10,444 20,762 0 1,304 0 11,173 1,336 0Central Production loss (Mt) 0 19,128 779 0 1,494 0 673 1,032 0Western Production loss (Mt) 0 26 54,804 0 0 0 884 1,421 0Western Production loss (Mt) 0 41,345 0 0 33,086 13 0 7,510 0Far Western Production loss (Mt) 0 14,819 21,434 1,831 22,087 0 6,640 2,453 0

Risk of Wheat production losses in pre-monsoon drought -Development Zones

A

B

A

B


0200400600800

100012001400160018002000

<5 5-10 10-1515-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25Extreme drought during

Winter Severe drought during Winter Moderate drought during

WinterProbability level (%) at different levels of severity of drought

Prod

uctio

n Lo

ss(M

t)


<5 5-10 10-15 15-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25Mountain Production loss (Mt) 0 1,920 265 0 0 503 933 0 0 404 325 133 0 0Hill Production loss (Mt) 22 991 117 1 0 451 693 0 0 89 474 9 0 0Terai Production loss (Mt) 0 160 38 2 1 94 22 3 1 27 15 0 0 0

Risk of barley production losses in winter drought -Geographical Zones

Eastern Production loss (Mt)Central Production loss (Mt)Western Production loss (Mt)Mid Western Production loss (Mt)Far Western Production loss (Mt)0

200400600800

10001200140016001800

<5

5-10

10-1

5

15-2

0

>20

5-10

10-1

5

15-2

0

>20

5-10

10-1

5

15-2

0

20-2

5

>25

Extreme drought during Winter Severe drought during

Winter Moderate drought during Winter


Prod

uctio

n Lo

ss (M

t.)


<5 5-10 10-15 15-20 >20 5-10 10-15 15-20 >20 5-10 10-15 15-20 20-25 >25Eastern Production loss (Mt) 21 155 0 0 0 16 108 0 0 0 66 7 0 0Central Production loss (Mt) 2 339 36 0 0 87 166 0 0 24 102 3 0 0Western Production loss (Mt) 0 649 0 0 0 425 18 0 0 0 206 31 0 0Mid Western Production loss (Mt) 0 1,619 15 0 0 118 1,013 2 0 451 185 0 0 0Far Western Production loss (Mt) 0 308 369 3 1 403 341 1 1 45 255 101 0 0

Risk of barley Production losses in winter drought -Development Zones


88

Figure 4.13 Drought risk on barley production losses in the pre-monsoon season A) Geographical zones, B) Development zones

Figure 4.14 Drought risk on paddy production losses in the monsoon season A) Geographical zones, B) Development zones

A

B


0200400600800

1000120014001600

<5 5-10 >10 <5 10-15 >15 5-10 15-20 >20Extreme drought during premonsoon Severe drought during



Prod

uctio

n Lo

ss(M

t.)



<5 5-10 >10 <5 10-15 >15 5-10 15-20 >20Mountain Production loss (Mt) 0 1,239 628 0 9 0 0 367 0Hill Production loss (Mt) 0 1,529 330 0 1,269 1 4 70 0Terai Production loss (Mt) 0 176 86 0 10 0 3 2 0

Risk of barley production losses in Pre-Monsoon drought -Geographical Zones

Eastern Production loss (Mt)Central Production loss (Mt)Western Production loss (Mt)Mid Western Production loss (Mt)Far Western Production loss (Mt)

0200400600800

10001200140016001800

<5 5-10 >10 <5 5-10 >15 5-10 15-20 >20Extreme drought during premonsoon Severe drought during



Prod

uctio

n Lo

ss(M

t)


premonsoonSevere drought during

premonsoonModerate drought during

premonsoon<5 5-10 >10 <5 5-10 >15 5-10 15-20 >20

Eastern Production loss (Mt) 0 167 0 0 14 0 2 69 0Central Production loss (Mt) 0 393 10 0 4 0 2 38 0Western Production loss (Mt) 0 11 660 0 0 0 1 68 0Mid Western Production loss (Mt) 0 1,626 0 0 635 1 0 249 0Far Western Production loss (Mt) 0 747 374 0 635 1 1 13 0

Risk of barley production losses in pre-monsoon drought -Development Zones

A

B

Mountain Production loss (Mt)Hill Production loss (Mt)

Terai Production loss (Mt)0

50000100000150000200000250000300000350000400000

<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Extreme drought during Monsoon Severe drought during



Prod

uctio

n Lo

ss(M

t)




<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Mountain Production loss (Mt) 0 8,505 3,452 80 7,131 3,892 0 1,324 2,813 988 0Hill Production loss (Mt) 181 87,775 0 0 29,212 58,745 0 7,380 13,675 7,941 322Terai Production loss (Mt) 0 389,929 0 0 170,630 219,299 0 51,317 63,096 24,705 16,854

Risk of paddy production losses in monsoon drought -Geographical Zones


20000400006000080000

100000120000140000


during MonsoonModerate drought during Monsoon


Prod

uctio

n Lo

ss(M

t)




<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Eastern Production loss (Mt) 0 123,955 920 0 17,041 107,687 0 43,552 4,098 968 0Central Production loss (Mt) 181 75,723 2,325 80 66,755 11,228 0 0 1,360 11,198 17,176Western Production loss (Mt) 0 127,022 0 0 0 127,022 0 10,745 39,119 0 0Mid Western Production loss (Mt) 0 83,980 207 0 58,731 25,254 0 5,724 22,606 4,088 0Far Western Production loss (Mt) 0 75,529 0 0 64,446 10,744 0 0 12,401 17,379 0

Risk of paddy production losses in monsoon drought -Development Zones


89

Figure 4.15 Drought risk on paddy production losses in the post-monsoon season A) Geographical zones, B) Development zones

Figure 4.16 Drought risk on maize production losses in the monsoon season A) Geographical zones, B) Development zones

A

B


050000

100000150000200000250000300000350000

<5 5-10 >10 <5 5-10 10-15 >15 <5 5-10 10-15 15-20 >20Extreme drought

during post-Monsoon

Severe drought during post-Monsoon

Moderate drought during post-Monsoon


Prod

uctio

n Lo

ss(M

t


post-MonsoonSevere drought during post-

Monsoon Moderate drought during post-Monsoon

<5 5-10 >10 <5 5-10 10-15 >15 <5 5-10 10-15 15-20 >20Mountain Production loss (Mt) 581 11,376 0 0 3,910 6,339 0 0 1,213 2,326 1,585 0Hill Production loss (Mt) 27,569 75,047 0 10,372 45,660 17,266 0 0 1,969 11,383 11,871 4,096Terai Production loss (Mt) 326,014 180,893 0 33,161 153,447 125,335 0 15,411 13,869 70,037 54,330 2,325

Risk of paddy production losses in post-monsoon drought -Geographical Zones


20000400006000080000

100000120000140000160000

<5 5-10 >10 <5 5-10 10-15 >15 <5 5-10 10-15 15-20 >20Extreme drought

during post-Monsoon

Severe drought during post-Monsoon

Moderate drought during post-Monsoon


Prod

uctio

n Lo

ss(M

t


post-MonsoonSevere drought during post-

Monsoon Moderate drought during post-Monsoon

<5 5-10 >10 <5 5-10 10-15 >15 <5 5-10 10-15 15-20 >20Eastern Production loss (Mt) 0 158,941 0 0 97,115 3,597 0 0 0 3,740 44,878 0Central Production loss (Mt) 24,975 72,780 0 590 28,818 34,121 0 0 0 7,915 15,399 6,421Western Production loss (Mt) 159,558 3,679 0 0 0 102,090 0 0 0 49,862 2 0Mid Western Production loss (Mt) 83,169 22,750 0 9,932 56,663 1,585 0 0 6,297 18,651 7,471 0Far Western Production loss (Mt) 86,463 9,166 0 33,011 20,421 7,546 0 15,411 10,755 3,578 36 0

Risk of paddy production losses in post-monsoon drought-Development Zones

A

B


050000

100000150000200000250000300000

<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Extreme drought during Monsoon Severe drought during

MonsoonModerate drought during Monsoon


Prod

uctio

n Lo

ss(M

t




<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Mountain Production loss (Mt) 0 48,705 27,541 945 31,713 38,141 0 21,395 7,893 3,389 0Hill Production loss (Mt) 441 279,672 0 0 116,117 163,997 0 26,747 45,856 19,985 784Terai Production loss (Mt) 0 119,524 0 0 103,896 15,627 0 4,712 10,707 11,298 24,899

Risk of maize production losses in monsoon drought -Geographical Zones

Eastern Production loss (Mt)

Western Production loss (Mt)

Far Western Production loss (Mt)020000400006000080000

100000120000140000



Monsoon


Prod

uctio

n Lo

ss(M

t




<5 5-10 >10 <5 5-10 >10 <5 5-10 10-15 15-20 >20Eastern Production loss (Mt) 0 77,910 19,199 0 22,859 71,191 0 25,199 9,413 2,239 0Central Production loss (Mt) 441 134,989 7,099 246 115,150 26,627 0 0 3,317 24,263 25,683Western Production loss (Mt) 0 57,452 918 699 153 57,452 0 17,840 1,586 374 0Mid Western Production loss (Mt) 0 82,633 324 0 55,319 27,321 0 5,113 25,263 2,773 0Far Western Production loss (Mt) 0 94,917 0 0 58,245 35,174 0 4,701 24,877 5,022 0

Risk of maize production losses in monsoon drought -Development Zones


90

4.9 CONCLUSION

The drought EVRA determined the estimated agricultural losses caused by various categories of drought severity and probability in Nepal. The analysis has been carried out for all development and geographical zones. Based on the analysis the study proposes the following:

• At present, drought hazard assessment has been carried out in pilot study areas by various agencies, in the country. However there remains a need to study the gross impact of drought for the country in its entirety. There is no report on drought impact on various major crops such as wheat, paddy, barely and maize. It is necessary for national agriculture research organizations to carry out a detailed VA study for drought. It is important to study growth patterns and expected damages resulting from various categories of drought severity.

• The drought assessment here has been carried out for defined geographic and development zones. However it is necessary to study the impact of drought at the district and VDC levels as well.

• This analysis pinpoints drought hotspots throughout the country. It is recommended that the most severely affected zones be scaled down for study at the district and VDC levels.

• The EVRA will provide the necessary guidance to stakeholders engaged in agriculture and natural resource development for drought risk reduction. The suggested mitigation strategies include:

o Setting up a drought early warning system for the country

o Modification of the cropping pattern in high drought risk zones

o Adequate water resource management

o Livelihood management during the drought period for agricultural areas

o Adequate supply and maintenance of food and crops during drought conditions

o Health surveillance during drought conditions, linking drought risk to the health status of children, women and the elderly


91

5 ASSESSING THE ECONOMIC IMPACTS OF DISASTERS IN NEPAL

The economic and developmental impacts of disasters in Nepal have been reported to be significant (see Upreti, 2006), although there is very little quantitative evidence and no economic modeling involving disaster risk has been done on Nepal to our knowledge. To this effect, based on the modeling of hazard, exposure, vulnerability and risk as described elsewhere in this report, IIASA’s (International Institute of Applied System Analysis) part in the project focuses on modeling the indirect risk of disasters in terms of potential macroeconomic impacts. While assessing direct risks relates to assessing the impacts of extreme events on people, the environment and assets (stocks in economic terminology), the modeling of indirect, economic effects involves assessing the follow on effects on aggregate or individual income, taxes and unemployment, to name a few variables, which can generally be subsumed under the heading of flows. In this report, we focus on the macroeconomic level and discuss aggregate as well sectoral consequences of disasters risk and impacts.

This element of the project discusses (i) the quantification and modeling of economic disaster risk for Nepal in an objective fashion; (ii) incorporating DRM into fiscal and development planning; (iii) assessing the economic consequences of large scale events. Generally, this work is part of a risk model for Nepal that may help to inform decisions and the development of effective risk management strategies.

The chapter is organized as follows: We first discuss the burdens imposed by disasters on Nepal in 5.1, then introduce key aspects relevant for assessing and modelling economic disaster risk including our approach based on the IIASA CATSIM model in 5.2. Section 5.3 deals with disaster risk management options, which may be informed by economic modeling of disaster risks, before we turn to discussing key features of CATSIM in 5.4, which is followed by a discussion of results in 5.5 and a few concluding remarks.

5.1 THE BURDEN OF DISASTERS IN NEPAL

As demonstrated by many observed events, natural hazards in Nepal carry along large burdens in terms of direct and indirect economic and developmental effects. While the direct impacts can be readily observed, many indirect effects go along unnoticed and generally few data are available. For both direct and indirect impacts, we discuss the evidence in terms of observed impacts.

5.1.1 THE DIRECT BURDEN OF NATURAL DISASTERS

Nepal can be hit by different types of hazards, including earthquakes, floods, droughts, landslides and epidemics. Looking back over the last 40 years to determine the top 10 natural disasters in terms of people killed and affected as well as economic damages earthquakes and flooding can be said to represent the largest threats to human life and assets, and will be the hazards to be examined further below(see Table 5.1)

Table 5.1 Top 10 natural disasters in terms of killed, number affected and direct damages. (CRED, 2010)

Top 10 Natural Disasters - number killed:

Disaster Date No Killed

Earthquake 1934 9040

Epidemic 1991 1334

Flood 1993 1048

Epidemic 1950 1000

Flood 1996 768

Earthquake 1988 709

Flood 1981 650

Epidemic 1992 640

Mass mov. 2002 472

Flood 1970 350

Top 10 Natural Disasters - number affected:

Disaster Date No Total Affected

Drought 1979 3500000

Drought 1972 900000

Flood 2004 800015

Flood 2007 640706

Flood 1993 553268

Flood 1987 351000


Mass mov. 2002 265865


Flood 1983 200050

Top 10 Natural Disasters - economic damage:

Disaster Date Damage (000)

Flood 1987 727500


Flood 1993 200000


Flood 2009 60000

Flood 1998 22000

Drought 1972 10000

Flood 1983 10000

Flood 2000 6300

Wildfire 1992 6200

Note: Economic damages in this table means direct asset damages, which is not the same as the indirect, economic losses discussed further below.

Disasters are not evenly spread out over Nepal, and it is the Eastern part that has reported the highest impacts over this period, such as in terms of damages (Figure 5.1).


92

Figure 5.1 Regional distribution of damages (Source: Upreti, 2006)

Before discussing the developmental aspects of disaster risk, we summarize key characteristics of socioeconomic development in Nepal.

5.1.2 SOCIOECONOMIC CHARACTERISTICS

Nepal is classified as a low income country with a high prevalence of poverty. Population under the poverty line recently exceeded 30%. Income per capita (in 2008, about 250 USD in constant 2000) is low compared even to the low-income group countries as defined by World Bank (Figure 5.2).

Figure 5.2 Comparison of key development and economic indicators for Nepal with the low-income group (Source: World Bank, 2009)

Key socio-economic indicators and data for Nepal are summarized in Table 5.2.

Table 5.2 Socio-economic indicators for Nepal (year 2008)

Social Population 28 millionSurface area 147180 km2Population density/km2 200Population growth 1.83%Life expectancy at birth 66Infant mortality (per 1,000 live births) 40Poverty (% of population below national poverty line) 31

Economic GDP (Constant USD 2000) 7.3 billionGDP per capita (Constant USD 2000) 253GDP growth 5.3%

Fiscal Revenue : excluding grant(including grant)

12.2% of GDP(15.8% of GDP)

General government final consumption expenditure (current USD)

1.2 billion(9.9% of GDP)

Net official development assistance and official aid received (constant 2007 US$)

686 million

Inflation, consumer prices (annual %) 10.9%

Present value of External debt (Current USD)

2.2 billion

Source: World Bank, 2009

As one important indicator, the present value of external debt has been over 240% of revenue in 2008. This means that the amount of debt which the government can additionally borrow from abroad is quite limited, given that 250% is often considered a threshold for debt sustainability. While the World Bank classifies Nepal as a less indebted country, the level of external debt is high compared to the public budget. The total debt service burden was 47% of current revenue in the fiscal year 2001 (Alamgir and Ra, 2005).

Almost all external borrowing is done by the central government. Figure 5.3 illustrates the composition of debt in 2008. Most of the external debt is multilateral and is extended by the World and Asian Development Banks. According to the World Development Indicators, external debt stock in the private sector was zero in 2008, and the private sector does not have access to international financial markets. Also, domestic savings are low compared to the country low-income group.


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Figure 5.3 Composition of 2008 debt (USD milion) (Source: World Bank, 2009)

5.1.3 INDIRECT EFFECTS AND DEVELOPMENTAL IMPLICATIONS

Disaster impacts on development in Nepal have been significant. However, there is little reported quantitative evidence (Upreti, 2006) and next to nothing is known about the macroeconomic implications. As a resource-constrained and least developed country (LDC), Nepal has been suffering disaster damages in the several million rupees annually over the last few years.

Figure 5.4 Foreign assistance for disaster relief spending(Source: Upreti, 2006)

Also, the government of Nepal is already highly dependent on foreign assistance and lending, and about a third of country income is owed to foreign aid with 64% of development spending financed by multilaterals (Bhattarai and Chhetri, 2001). It comes as no surprise that foreign assistance for disasters has been large and rising as documented by Figure 5.4. Given a fragile economy exposed to substantial disaster risk, the Government of Nepal and funders are faced with developing far-reaching strategies, programmes and projects in order to ensure that key development and poverty alleviation objectives are achieved. It thus seems important to inform such planning by estimates of economic risk and losses as a consequence of disasters.

5.2 ASSESSING AND MODELLING ECONOMIC DISASTER RISK

5.2.1 CLASSIFYING ECONOMIC IMPACTS The standard approach for estimating natural disaster risk and potential impacts is to understand natural disaster risk as a function of hazard, exposure and vulnerability leading to a variety of effects which are usually classified into social, economic, and environmental impacts as well as according to whether they are triggered directly by the event or occur over time as indirect or macroeconomic effects (fig. 5.5).

Figure 5.5 Natural disaster risk and categories of potential disaster impacts

1507

77

1680

360

57

IDA

IMF

Other multilateral

Bilateral

Short‐term


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Economic effects are usually grouped into three categories: direct, indirect, and macroeconomic effects (Burby, 1991). These effects fall into stock and flow effects: In our setting here direct damages only describe the physical and –once monetized- financial impacts on private and public sector assets. They are roughly equal to stock impacts. Resulting from these direct stock damages are impacts on the flow of goods and services: Indirect losses occur as a consequence of physical destruction on firms and households, e.g. business interruption and wages lost. Macroeconomic impacts comprise the aggregate impacts on economic variables like GDP, consumption and inflation due to the effects of disasters, as well as due to the reallocation of government resources to relief and reconstruction efforts. As the macroeconomic effects reflect indirect effects as well as the relief and restoration effort, these effects cannot simply be added up without causing duplication (Otero and Marti, 1995). Box: Terminology related to distinguishing damages and losses used in the report • Damages/direct risk: impacts on stock, including physical and human capital. • Losses/indirect risk: reduction of flows. • First-order effects: economic losses due to due to asset damage in a given sector. • Higher-order effects: system-wide impact on flows caused by the first-order losses through interindustry

relationships. • Total Impacts: total of flow impacts, adding first-order and higher-order effects.

5.2.2 OBSERVING ECONOMIC IMPACTS OF DISASTERS Disaster risk has the potential to cause significant economic and developmental consequences. Over the last years, a growing literature has emerged finding important adverse macroeconomic and developmental impacts of natural disasters in a developmental context (see e.g. Otero and Marti, 1995; Benson and Clay, 2004; Mechler, 2004; Noy, 2009). Key adverse macroeconomic impacts experienced include reduced direct and indirect tax revenue, dampened investment and reduced long term economic growth through negative effects on a country’s credit rating and an increase in interest rates for external borrowing. This body of evidence generally finds that natural disasters can be a setback for development in the short- to medium-term. In turn, poor development status of communities and countries increases the sensitivity to disasters. The example in the box below discussing the case of Grenada may be insightful.

Box: Case of economic impacts of disasters in Grenada

As a typical example of a county experiencing severe economic impacts in the aftermath of a disaster, the case of Grenada is interesting. Grenada is a Caribbean country composed of three islands with a total land area of 350 sq. km only and classified as a middle income country with a population of around 100,000. Grenada lies within the Caribbean hurricane belt and its economic performance has been negatively affected by hurricanes in the past, such as in the 2004 and 2005 hurricanes. In 2004, Hurricane Ivan caused the largest historical damages ever, exceeding over USD 890 million (more than 200% of GDP), killing 39 people and affecting more than 80,000 people (90% of the population). This was followed by Hurricane Emily in 2005, which caused about USD 50 million in economic losses. Given the scale of the damages Grenada’s economic performance was negatively affected by the 2004 and 2005 hurricanes. The figure shows an illustration of the economic growth path for Grenada after Hurricane Ivan in comparison to an estimated growth path had the hurricane not occurred.

Figure 5.6 Fiscal and economics effects of hurricanes on Grenada. (Source: OECS, 2004, 2005)

Without the event, positive growth was expected, yet actually growth became negative, as the event knocked out a large part of export cash crops. Also fiscal effects were noted as shown on the right panel estimating effects on the budget with and without the hurricane.

Grenada’s economic performance can thus heavily be affected by extreme events and hence this risk should be considered in economic and fiscal planning. There are a number of countries like Grenada for whom disaster risk is clearly important. Disasters can exert significant costs to national governments due to the role they need to assume in dealing with disaster losses and risks. Generally, governments assume responsibility for replacing damaged infrastructure, providing relief post-event and ensuring rapid recovery of the economy overall. From an economic perspective, governments are exposed to natural disaster risk and potential damages due to their two main functions: the allocation of goods and services (security, education, clean environment and the distribution of income as shown on. According to Stern (2007), in many cases market forces are unlikely to generate an adequate adaptation to disaster risks, broadly because of the following three reasons: 1) uncertainty and imperfect information and 2) missing and misaligned markets and 3) financial constraints. In case of a disaster event, consequently, there may be substantial contingent liabilities.

5.2.3 MODELING ECONOMIC DISASTER RISK AND IMPACTS There is a substantial, if not very integrated body of modelling research on the economic impacts of disaster (see Rose, 1997; Mechler, 2004; Okuyama, 2009). Existing approaches utilize a plethora of models such as Input-Output (I-O), Computable General Equilibrium (CGE), economic growth frameworks and simultaneous-equation econometric models. Okuyama (2009) discusses the pros and cons of these models as summarized in table 5.3. In this report, we will make use of growth modeling using the IIASA CATSIM model and I-O analysis in the form of a Social Accounting Matrix (SAM) (indicated by the shaded rows). The growth modeling framework has been chosen as it allows to study the longer term effect of a loss of capital assets (stocks) on economic variables (flows). As well, we use I-O analysis to gauge the intersectoral linkages and ripple effects of disasters. CGE models are not employed, as these models have a have heavy emphasis on the market price mechanism in order to

Fiscal Effects of Emily

-250

-200

-150

-100

-50

01998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Year

Ove

rall

Bal

ance

EC

$m

With EmilyWithout Emily


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coordinate the allocation of goods across markets, which for disasters heavily affecting markets seems less relevant. One specific issue worth mentioning for this line of research is that models often are of deterministic nature. In essence, disaster risks often are represented as averages (expected annual damages), or singular events in the past are remodeled. This does not lend itself to a forward-looking and more comprehensive analysis of risk and may lead to a serious underestimation of the potential consequences of natural disasters, which by “nature” are low-probability-high impact events. Also, the modelling has mostly been developed to address more developed countries’ issues and interest on sectoral and distributional impacts of disasters. In contrast, CATSIM is a risk-based modelling framework which can be used to address planning issues for less developed countries and governments.

Table 5.3 Economics models for assessing the effects of disaster risk

Approach Time horizon Issue addressed Pros Cons

Growth models - CATSIM

Medium to longer term (3-5 years)

Longer term trajectories of macroeconomic variables

Longer term trajectory independent of short term price volatility help with development planning

Simple economic framework, focus on produced capital

Input-Output

(I-O) and Social accounting Matrix (SAM)

Short term (1 year)

Interdependencies within an economy, higher-order effects across different socio-economic agents

Distributional impacts of a disaster help to evaluate equity considerations for public policies, based on actual data, simplicity

Rigid structure with respect to input and import substitutions, a lack of explicit resource constraints, and a lack of responses to price changes

Computable General Equilibrium (CGE)

Short to longer term

Sectoral and price effects within an economy

Non-linear framework, can respond to price changes, can incorporate input and import substitutions,

and handle supply constraints

Often used for rather long-run equilibria, may lead to underestimation of economic impacts due to its optimizing behaviour features

Source: extended based on Okuyama, 2009

Our analysis in the following will focus on two issues that may be informed by the modeling:

• Incorporating DRM into fiscal and development planning, in order to determine risk acceptance and management needs using CATSIM.

• Assessing the growth and intersectoral consequences of losses.

In terms of hazards and risks addressed, we focus on earthquakes and floods, which are the most serious risk for the fiscal position and economic growth. We do not examine drought with our macroeconomic models, as this hazard is considered a more regional issue affecting the terai area. Before we discuss the modeling approach, we first focus more closely on decisions that may be informed by our analysis.

5.3 POLICY OPTIONS FOR MANAGING DISASTER RISK

5.3.1 OVERVIEW Many options for reducing and managing disaster risk are available, and they are usually grouped into options for assessing risk, reducing risk (prevention and preparedness), sharing and financing risk financing, and finally responding to an event in terms of managing reconstruction and rehabilitation. As well, measures may be taken by households, business and the public sector: this discussion predominantly focuses on the public sector and presents key options to be taken by national governments as well as international institutions. While after the fact, ex post efforts such as providing relief and reconstruction are ever important and still the dominant approach, there is need for upgrading planned, ex ante efforts, which can be subsumed under the headings of risk assessment, risk prevention, preparedness and risk financing (table 5.4). Which options can/should be informed by economic modelling? In the table those measures that bear a close relationship to economic approaches are shaded in grey.

Table 5.4 Overview of disaster risk management measures

Type Ex ante risk management Ex post disaster management

Risk assessment and planning

Prevention Preparedness Risk sharing and financing Response

Reconstruction and rehabilitating

Effect Assessing risk Reduces risk addressing underlying factors

Reduces risk in the onset of an event

Transfers risk (reduces variability and longer term consequences)

Responding to an event

Rebuilding and rehabilitating post event

Key options

Hazard assessment and monitoring (frequency, magnitude and location, including climate change)

Physical and structural risk reduction works (e.g. irrigation, embankments)

Early warning systems, communication systems

Risk transfer (by means of (re-) insurance) for public infra-structure and private assets, microinsurance

Humanitarian assistance

Rehabilitation/ reconstruction of damaged critical infrastructure

Vulnerability Land-use planning and

Emergency response

Alternative risk transfer Clean-up, Revitalization for


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Type Ex ante risk management Ex post disaster management

assessment (population and assets exposed)

building codes temporary repairs and restoration of services

affected sectors (tourism, agriculture, exports etc.)

Risk assessment as a function of hazard, exposure and vulnerability

Economic incentives for proactive risk management

Networks of emergency

responders

(local/national)

National and local reserve funds Damage

assessments Macroeconomic and budget management (stabilization, protection of social expenditures)

Mainstreaming risk into development planning

Education, training and awareness raising about risks and prevention

Shelter facilities and evacuation plans

Calamity Funds (national or local level)

Mobilization of recovery resources (public/ multilateral/insurance)

Incorporation of disaster mitigation components in reconstruction activities

In terms of ex ante approaches involving an estimate of risk, impact/risk assessment and planning should also be informed using economics; importantly, if risk is considered to be important, it should be mainstreamed into development. For risk prevention economic incentives play a role, which can be informed by estimates of the developmental cost of disasters as well as the benefits of reducing those; education and awareness raising is another important category for ensuring risk prevention. Importantly, risk sharing and financing options need to be based on economic analysis in order to well identify which risks to keep, finance or transfer. In terms of ex post approaches, involving an (deterministic) analysis of impacts, response options can be informed by economic loss assessments, consequently leading to a mobilization of financial and other recovery resources from sources such as public sector, multilaterals or the insurance sector. For matters of reconstruction and rehabilitation, economic modelling can be helpful for designing options for revitalizing affected sectors such as tourism, agriculture, exports etc., as well as sound macroeconomic and budget management in order to stabilize and protect social expenditures.

5.3.2 FURTHER CATEGORIZING OPTIONS

The available options can as well be categorized according to whether they are market-based, done as public investment, information-based, and conducted by means of international cooperation. As well, options may be distinguished whether they are of shorter term (<10 a), and/or longer term (>10 a) focus, which is a distinction that to some extent overlaps with differentiating between DRM and climate adaptation.

Clearly, choosing among these manifold options should also involve a number of decision criteria, such as the efficiency of options, considerations of equity, understanding and acceptance of risk preferences (such as risk aversion) as well as acceptability by the public at large.

Table 5.5 Further categorizing policy options for dealing with extreme events

Category Time horizon of options

Policy options Market-based

Public investment

Information-based

Int’l cooperation

Short term (<10 a)

Longer term (>10 a)

Hazard assessment X X X

Vulnerability assessment X X Risk assessment X X R

isk

asse

ssm

ent

Mainstreaming risk into development planning

X X X

Physical and structural risk reduction works

X X X X

Land-use planning and building codes

X X X

Economic incentives for proactive risk management

X X X X X

Risk

Red

uctio

n

Education, training and awareness raising

X X X X

Early warning systems, communication systems

X X X X

Contingency planning X X X X

Networks of emergency Responders

X X X

Prep

ared

ness

Shelter facilities and evacuation plans

X X X X

Risk transfer X X X X X X

Alternative risk transfer X X X X X

National and local reserve funds

X X X X

Risk

fina

ncin

g

Calamity Funds X X X Humanitarian assistance X X X

Clean-up, temporary repairs and restoration of services

X X X

Damage assessment X X X Res

pons

e

Mobilization of recovery resources

X X X

Rehabilitation/ reconstruction of damaged critical infrastructure

X X X

Revitalization for affected sectors

X X X

Macroeconomic and budget management

X X X X R

econ

struc

tion

and

reha

bilit

atin

g

Incorporation of disaster mitigation components in reconstruction activities

X X X X


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5.3.3 ASSESSING, PLANNING AND FINANCING ECONOMIC RISK

As the CATSIM model underlying this analysis focuses on these options, we now will discuss options related to planning, mainstreaming and financing in some more detail.

Mainstreaming risk into development planning Disaster risk can be planned for and mainstreamed into different policy levels as shown on figure below.

Figure 5.7 Planning for disaster risks. (Source: Bettencourt et al., 2006)

The associated planning problem in this subset of the economics of DRM and adaptation is one of contingency liability planning, with fiscal disaster risk emanating from explicit and implicit contingent public sector liabilities as classified in the table below. The explicit liability consists of rebuilding damaged or lost infrastructure, which is due to the public sector’s allocative role in providing public goods. Implicit liabilities are related to the commitment of providing relief due to the distributive function in reallocating wealth and providing support to the needy (table 5.6).

Table 5.6 Government liabilities and disaster risk

Source: Modified after Schick and Polackova Brixi, 2004

There are two problems to be noted: One is that it is standard practice for (central) governments to plan and take appropriate measures for direct liabilities, but little is generally done to systematically tackle contingent liabilities. Also, in reality, governments recognize their normative roles to varying degrees due to an implicit or explicit assumption of risk neutrality, i.e. the ability to pool and share disaster losses after they have occurred. In case of an event, governments of developing countries typically finance their post-disaster expenses by diverting from their budgets or from already disbursed development loans, as well as by relying on new loans and donations from the international community. In the past, these post-disaster sources of finance have often proven woefully inadequate to assure timely relief and reconstruction in developing countries. What is more, post-disaster assistance is not only often inadequate, but it can discourage governments and individuals from taking advantage of the high returns of preventive actions (Gurenko, 2004).

Risk planning and financing: the case of risk aversion

When should governments plan and engage in DM, particularly the financing for those liabilities? According to an early theorem by Arrow and Lind (1970) governments should not insure if they are not averse to risks, i.e. if financial risks faced by the government can be absorbed without major difficulty. According to Arrow and Lind (1970) a government may Pool risks as it possesses a large number of independent assets and infrastructure so that aggregate

risk is negligible, or Spread risk over population base, so that per-capita risk to risk-averse household is negligible.

In theory, thus, governments are not advised to incur the extra costs of transferring their disaster risks if they carry a large portfolio of independent assets and/or they can spread the damages of the disaster over a large population. Because of their ability to spread and diversify risks, Priest (1996) refers to governments as "the most effective insurance instrument of society." Furthermore, the extra costs of insurance can be significant; for example Froot (2001) reports cost of up to seven times greater than the expected damage, due to high transaction costs, uncertainties inherent in risk assessment, the limited size of risk transfer markets and the large volatility of damages. Consequently, according to Arrow and Lind governments should behave risk-neutrally and evaluate their investments only through the expected net present (social) value. The Arrow and Lind theorem serves as the basis for government strategies for dealing with risk. In practice, most governments neglect catastrophic risks in decision making), thus implicitly or explicitly they behave risk-neutrally. The case against sovereign insurance, however, may not hold for highly exposed developing country governments, especially those that are not sufficiently diversified or cannot spread damages over the tax-paying public. Already in 1991, the Organization of American States' (OAS) primer on natural disasters states that the risk neutral proposition is valid only up to certain point and that the reality in developing countries suggests that some governments cannot afford to be risk-neutral:

The reality of developing countries suggests otherwise. Government decisions should be based on the opportunity costs to society of the resources invested in the project and on the damage to economic assets,

Liabilities Direct: obligation in any event Contingent: obligation if a particular event occurs

Explicit Government liability recognized by law or contract

Foreign and domestic sovereign borrowing, Expenditures by budget law and budget expenditures

State guarantees for nonsovereign borrowing and public and private sector entities, reconstruction of public infrastructure

Implicit A "moral" obligation of the government

Future recurrent costs of public investment projects, pension and health care expenditure

Default of subnational government and public or private entities, disaster relief


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functions and products. In view of the responsibility vested in the public sector for the administration of scarce resources, and considering issues such as fiscal debt, trade balances, income distribution, and a wide range of other economic and social, and political concerns, governments should not act risk-neutral (OAS, 1991).

In these cases governments may justifiably act as risk-averse agents. This means that the Arrow-Lind theorem may not apply to governments of countries that have: • high natural hazard exposure; • economic activity clustered in a limited number of areas with key public infrastructure exposed to

natural hazards; and • constraints on tax revenue and domestic savings, shallow financial markets, and high indebtedness

with little access to external finance (see Mechler, 2004). These conditions are fundamental to determining the fiscal vulnerability of a state. Governments are fiscally vulnerable to disasters if they cannot access sufficient funding after a disaster to cover their liabilities with regard to reconstructing public infrastructure and providing assistance to households and businesses. Such a fiscal gap is a useful measure of sovereign financial vulnerability. The repercussions of a fiscal gap can be substantial. The inability of a government to repair infrastructure in a timely manner and provide adequate support to low-income households can result in adverse long-term socio-economic impacts. As a case in point Honduras experienced extreme difficulties in repairing public infrastructure and assisting the recovery of the private sector following Hurricane Mitch in 1998. Five years after Mitch’s devastation the GDP of Honduras was 6% below pre-disaster projections. In considering whether Honduras and other highly exposed countries should protect themselves against fiscal gaps and associated long-term negative consequences, it is important to keep in mind that risk management measures have associated opportunity costs, which means that they can reduce GDP by diverting financial resources from other public sector objectives, such as undertaking social or infrastructure investments. Over the last few years, many disaster exposed countries and regions have recognized the need to planning for disaster events and taken steps to improve their assessment and management procedures using novel risk sharing mechanisms (for more information, see Linnerooth-Bayer and Mechler, 2007; Cummins and Mahul, 2009).

5.3.4 THE RELEVANCE OF RISK FOR ASSESSING OPTIONS

How much should be invested in the prevention of disaster damages, and how much in risk financing? This is a complex question, which ultimately depends on the wider costs and benefits of both types of activities, on their interaction (financial instruments, through incentives, influence prevention activities) and their acceptability. Cost and benefits, in turn, depend on the nature of the hazard and risk. One way to think about prevention and risk financing is illustrated by the layering approach shown in Figure 5.8. For the low- to medium damage events that happen relatively frequently, prevention is likely to be cost effective in reducing burdens. The reason is that the costs of prevention often increase disproportionately with the severity of the consequences. Moreover, individuals and governments are generally better able

to finance lower consequence events (disasters) from their own means, for instance, savings or calamity reserve funds, and including international assistance. The opposite is generally the case for risk-financing instruments, including reserve funds, catastrophe bonds and contingent credit arrangements. For this reason, it is generally advisable to use those instruments mainly for lower probability hazards that have debilitating consequences (catastrophes). Finally, as shown in the uppermost layer of Figure 5.8, individuals and governments will generally find it too costly to use risk financing instruments against very extreme risks occurring less frequently than, say, every 500 years.

Figure 5.8 The layering approach for risk reduction and risk financing

By taking a probabilistic approach, CATSIM tries to inform the full spectrum of options across the risk continuum.

5.4 ASSESSING AND PLANNING FOR ECONOMIC RISK: THE CATSIM MODEL

We now turn to introducing key features of our modeling approach, which is based on CATSIM. Incorporating DRM into fiscal and development planning requires the integration of disaster risk with a country’s risk profile, development plans, as well as the government’s fiscal, budget and debt situation. The CATSIM allows policy makers to interactively assess and view their country or region’s exposure to direct asset risks and (indirect) fiscal, fiscal and economic impacts of disaster scenarios. The policy outcomes for reducing disaster risk are assessed by the model and expressed with indicators of interest to policy makers, such as the budget stance, debt, and economic growth. Based on an assessment of their country or region’s vulnerability and risk, the main purpose of the tool is to assess policy options related to fiscal risk management, including planning for and investing in risk-financing instruments (reserve funds, insurance and catastrophe bonds).


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The model has a graphical user interface and is interactive, that is, users can and should change the model parameters given different preferences and parameter uncertainty. For example, the user can adjust the amount of risk and debt the country is willing to take on, and the model will display how this changes the countries vulnerability to disasters and how it affects different policy paths.

5.4.1 METHODOLOGY

CATSIM probabilistically assesses the economic impacts of natural disasters within a risk-based economic framework accounting for the macroeconomic impacts due to natural disasters as well as allowing to study the costs and benefits of measures for reducing those impacts. The model is organized around a Solow-type growth model, considered one of the workhorses of economic growth research (see Barro and Sala-i- Martin, 2004). CATSIM’s focus is on the potential for medium to longer term growth and development of aggregate economic variables given the explicit consideration of disaster risks. As one key application, CATSIM can be used to assess risks, economic resilience and fiscal vulnerability of governments to extreme events, and finally assist policy makers in developing public financing strategies for disaster risk.

The model estimates the fiscal and economic consequences of disasters that occur as stochastic events (see Mechler et al., 2006; Hochrainer, 2006). The model shows how monetary disaster risks may be absorbed by the economy and assesses a government’s contingent disaster obligations and the potential shortfalls for financing (fiscal vulnerability), as well as the costs and benefits of vulnerability-reducing options. CATSIM incorporates rare disasters explicitly as probabilistic events. Decisions on adaptation are thereby based on the whole range of possible future scenarios.

CATSIM approaches the modeling and decision problem in five steps (figure 5.9) : (1) The risk of (direct) asset damages expressed in terms of their probability of occurrence and destruction in monetary terms is modeled as a function of hazard (frequency and intensity), the elements exposed and their physical vulnerability; (2) Economic resilience of the public and private sectors generally depending on prevalent economic conditions (such as determined by economic structure, unemployment, fiscal position) is factored in; (3) Economic and fiscal vulnerability, measured in terms of the potential fiscal gap, is gauged by combining risks to the public sector and fiscal resilience; (4) consequences on macroeconomic outcome variables are considered, such as on economic growth or a country’s external debt situation; (5) Strategies are developed and illustrated that reduce risk or build fiscal resilience. Overall, the development of risk management options, including risk financing strategies, has to be understood as an adaptive process, where measures are revised after their impact on fiscal vulnerability and risk has been assessed within the modeling framework.

Figure 5.9 CATSIM framework for assessing fiscal vulnerability and the management of extreme event risk

5.4.2 CATSIM STEPS Step 1: Assessing asset risk In the first step the risk of direct losses is assessed in terms of the probability of asset losses in the relevant country or region. Consistent with general practice, risk is modeled as a function of hazard (frequency and intensity), the elements exposed to those hazards and their physical sensitivity. Much of this has been done in other parts of the project and we thus do not discuss this further, yet note that this step in the methodology of CATSIM involves devising loss-frequency distributions, which relate probabilities to damages of assets. Step 2: Assessing economic and fiscal resilience Another key aspect is the operationalization of economic resilience. The focus is on a country or region’s sector availability of internal and external savings to spread risks so as to minimize those and refinance losses as well as increased post-disaster expenditure, e.g. for supporting the private sector with relief and recovery assistance. As discussed above for Nepal, the ability of the private sector (households and business) is often limited when it comes to dealing with disaster risks and events, and our analysis focuses to a larger extent on public sector responses. Based on the information on direct risks to the government portfolio, fiscal resilience can be evaluated by assessing the government’s ability to finance its obligations for the specified disaster scenarios. Fiscal resilience is directly affected by the general conditions prevailing in an economy, i.e., changes in tax revenue have important implications on a country’s financial capacity to deal with disaster losses. Governments can raise funds ex-post or after a disaster by accessing diverting funds from other budget items, imposing or raising taxes, taking a credit from the Central Bank (which either prints money or depletes its foreign currency reserves), borrowing by issuing domestic bonds, borrowing from international institutions, issuing bonds on the international markets, and finally asking for outside assistance (Benson and Clay, 2004; Fisher and Easterly, 1990).


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In addition to accessing ex post sources, a government can arrange for financing before a disaster occurs (ex ante). Ex ante financing options include reserve funds, credit lines, traditional insurance instruments (public or private), alternative insurance instruments, such as catastrophe bonds. The government can create a reserve fund, which accumulates in years without catastrophes and in case of an even use the accumulated funds to finance reconstruction and relief. These ex-ante options can involve substantial annual payments and opportunity costs; statistically, the purchasing government will pay more with a hedging instrument than if it absorbs the damage directly. Each of these financing sources can be characterized by costs to the government as well as factors that constrain its availability, which are assessed by this CATSIM module. Sources not considered feasible are not included in the module. As an example, disaster taxes are expensive to administer and generally not part of the public sector financing portfolio. Borrowing to finance deficits in the budget is heavily constrained by existing deficits and debt, and in accordance with the EU Maastricht criteria we use a limit of 3% of GDP as the maximum deficit permissible. Concerning debt, we employ a debt threshold for the present value of debt of 150% of GDP (see HIPC, 2001). Step 3: Measuring financial vulnerability by the “fiscal gap”

Using the information on direct risks to the government portfolio and financial resilience, financial vulnerability can be evaluated. Financial vulnerability is thus defined as the lack of access of a government to domestic and foreign savings for financing reconstruction investment and relief post-disaster. The shortfall in financing is measured by the term fiscal gap. In this report, the fiscal gap is understood as the lack of financial resources to restore assets lost due to natural disasters and continue with development as planned (Figure 5.10).

Figure 5.10 Estimating fiscal vulnerability

Step 4: Mainstreaming disaster risk into development planning

Ultimately the implications of disaster risk on economic development and other “flow variables” is of major interest when mainstreaming disaster risks into development planning and macroeconomic management. For that matter, fiscal risk, fiscal vulnerability and the prevalent economic conditions in Nepal are combined in order to derive an estimate of potential fiscal and macroeconomic impacts, such as on GDP.

Aggregate impacts

For the aggregate impacts, a production function approach is utilized for assessing GDP losses. Here, GDP loss can be calculated by using production function and data of capital as well as labour. A Cobb-Douglas type production function is employed as follows.

βα LAKY = ,

where K is capital, L is labour as well as Y represents GDP.

GDP loss can be calculated by the drop in GDP caused by decrease in capital stock, which is formulated as follows

βαβα LKKALAKY )( Δ−−=Δ .

The decrease in capital stock in each event scenario can be obtained from the damage distribution curve used in CATSIM.

Sectoral impacts

As well, sectoral impacts can be estimated using an I-O model. I-O models are statistically estimated representations of the interlinkages between economic sectors illustrating the interconnectedness within an economy. An IO table displays the flows of transaction within an economy. An SAM is an extension of I-O models and additionally summarizes the distribution of income across certain types of households (see Stone, 1961; Pyatt and Thorbecke, 1976; Pyatt and Roe, 1977). IO and SAM analysis have regularly been employed for economic loss estimation of natural disasters (see, e.g., Okuyama, 2009).

A disruption of one industrial sector can affect other economic agents through interdependencies within an economy. This is called a higher order effect. Disruption of one factory, for example, would reduce its orders for the components. It would cause its suppliers to decrease their production and reduce their orders for inputs. The suppliers of the suppliers could follow the same way. Also, the shutdown of a factory could decrease its demand for labour. This would decrease the income of households, which would in turn reduce the final consumption of products. A multiplier effect can thus be observed.

Figure 10 illustrates the logic of the SAM approach. It starts from setting a primary loss for each industry at first, which is a loss in output caused by a damage to capital stock. Due to the limitation of the data, primary loss is simply given by using capital-output ratio. Capital-output ratio was calculated based on the SAM data and exposure assessment result.


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Based on the information of the primary loss, higher-order effects are calculated using the SAM multiplier matrix. Because a SAM is a demand-driven model, the input data is in terms of change in demand. Primary loss, therefore, needs to be converted to a change in demand. In this paper, a change in demand is calculated by dividing primary loss by diagonal elements of the multiplier matrix (see Okuyama, 2009). Economic loss considering multiplier effects can be calculated by multiplying the demand change to the multiplier matrix. It can thus be observed how much the loss can be enlarged by the multiplier effect.

Figure 5.11 Outline of the Social Accounting Matrix approach

Step 5: Reducing risk and building resilience Vulnerability and resilience must be understood as dynamic. Economic and social systems can be adapted and managed. There are two types of policy interventions for reducing public sector financial vulnerability: those that reduce the risks of disasters by reducing exposure and sensitivity and those that build resilience of the responding agencies. Based on an assessment of the fiscal gap and potential economic consequences, CATSIM can illustrate the pros and cons of strategies for building economic and fiscal resilience using ex-ante financial instruments. As this is beyond the scope of this report, we do not further go into detail here.

5.5 RESULTS

5.5.1 STEP 1: ASSESSMENT OF DIRECT, ASSET RISKS

To estimate the damage potential of hazards different techniques can be used such as stochastic or engineering approaches within catastrophe models or using extreme value theory on past losses or a combination of both. Catastrophe risk models usually consist of three components which have to be detailed enough so that risk management strategies can be sufficiently assessed: (i) the hazard component includes estimation of frequency and intensity of future events, (ii) the elements at risk component examines the assets exposed to each of possible event scenarios, (iii) and the physical vulnerability component then combines the information so that damages can be estimated. This approach was applied in the previous chapters and the cost estimates of direct damages based on the calculations in the Chapters 2, 3 and 4 are used here. This means the total cost estimates and corresponding return periods of the 1833 and 1934 earthquake events as well as the flood damage estimates for the 10, 50 and 100 year events are utilized.

For the economic risk assessment all possible future risk scenarios had to be incorporated (not only the discrete events). Thus an extreme value distribution had to be fitted to the point estimates shown in Table 5.7 and 5.8.

Table 5.7 Return periods and damages due to flooding

Return Period Probability Damages (Million Rupee) Damages (million USD) 10 0.9 6464.4 92.4 50 0.98 7580.5 108.3

100 0.99 8132.9 116.2

Table 5.8 Return periods and damages for earthquake risk

Return Period Probability Damages (Million Rupee) Damages (million USD) 100 0.99 1017,827.4 14,540.4 500 0.998 1,102,685.0 15,752.6

The basic extreme value distribution examined here is the Generalized Extreme Value (GEV) distribution, defined to be:

⎪⎩

⎪⎨⎧

=−−≠−+−=

0 if )exp(exp(0 if /1)1(1exp(

)(ξξξξ

ξxx

xH

Primary loss for each industrial sector

Change in final demand

Dividing the changes in output by the diagonal term of the multiplier matrix

Loss for each sector(Higher order effect)

Multiplied by Multiplier matrix

Income impact for households


102

where 01 >+ xξ . However, also the Generalized Pareto (GPD) distribution, as well as the Weibull distribution, are used to compare the different fits (the estimations are based on a gradient optimization algorithm). To account for uncertainty within the estimation procedure uncertainty bounds in the form of central estimate (most probable case) as well as minimum and maximum cases are looked at too. Statistics based on data from observed extreme events can also serve well to estimate catastrophe risk. Due to the small number of damage return periods for the estimation of the extreme value distribution past damage observations are used, too, involving minimum-distance optimization procedures as well as maximum likelihood techniques.

To provide more detail, observed damages from various sources including the Emergency Events Database (EM-DAT) databases (CRED, 2010) were recalculated to 2000 prices and the assumption was taken that physical vulnerability remained constant over time. This is a rather strong assumption. Figure 5.12 shows the damages due to flooding and earthquake in constant 2000 USD for the last 40 years.

Figure 5.12 Damages in constant 2000 USD for past earthquake and flood events. (source: CRED, 2010)

For the two sudden onset hazards, earthquake and flooding, the parameters are estimated but with different methods due to the lack of data: while for flooding the maximum likelihood method is applied, for the earthquake data a minimum distance optimization is used. As it can be assumed that earthquakes and floods will occur independently a convolution is performed to get a total damage distribution for Nepal. Convoluting two independent distributions with densities “f” and “g” is defined to be:

∫ −= dR dyyxgyfxgf )()())(*(

This serves as an approximation of the total risk of damages due to the two hazards earthquake and flooding. We now further discuss the procedure to finally estimate disaster risk in terms of potential damages to public and private assets.

Government exposure: Contingent public liabilities

If disaster strikes, the government of Nepal will need to take responsibility for the following:

• Reconstruction of public assets: roads, bridges, schools, hospitals, etc; • Support to private households and businesses for relief and reconstruction; • Provision of relief to the poor; As shown in table 5.9, the values at risk for which the government is liable (contingent liabilities) are approximated at USD 37.5 billion. The calculation is made as follows: because little information is available on public sector capital stock in Nepal, it is assumed that approximately 30% of the total capital stock calculated is public (this is in line with global averages). Since one third of the population of Nepal is poor, the government will absorb a large extra burden in the case of a disaster. Consistent with average figures (see Freeman et al., 2002) it is further assumed that the government will have to spend an amount equivalent to another 20% of the total stock damages to provide relief. For an estimated total capital stock of USD 75.3 billion,13 thus the contingent liabilities of the government of Nepal amount to USD 37.5 billion.

Table 5.9 Elements exposed to risk

Type USD billion Private capital 52.8 Public capital 22.5 Total capital 75.3 Government contingent liabilities (public assets and assistance to private sector and households )

37.5

Calculating direct asset risk in probabilistic terms

Based on the above information, probabilistic asset damages for earthquake and flood risks, as well for a combined distribution are estimated. Figure 5.13 summarizes the results of this analytical step in terms of a cumulative damage frequency curve showing the cumulative probability (y-axis) of potential damages as a percent of GDP (x-axis).

Figure 5.13 Damage-frequency distribution for EQ, flood and joint damage distribution

13 Given the lack of data on assets, a rough approximative value total capital stock in 2008 was estimated using the production function approach which we estimate below.

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An important summary measure of this probability distribution is the annual expected damages, or the damages to be expected on average every year. The annual expected damage is the sum of all damages weighted by the probability of occurrence. Graphically, the expected damages are represented by the area above the cumulative distribution curve. It has to be kept in mind that disasters are not average events, but are rather characterized by low probability, high impact occurring very rarely. Over a longer time period, however, such as 100 or 500 years, catastrophe damages actually suffered will be close to the sum of annual expected damages. In the following, we discuss the calculation of these estimates of risk.

Asset Risk due to Floods

Using the numbers in Table 5.7 for flood damages an estimated GEV distribution would show a slight thin tail with 018.0−=ξ and this fit does quite well (see Figure 5.14)

Figure 5.14 Fitted GEV for the given return periods

All other distributions give similar estimates. Based on the fitted GEV new quantiles were calculated needed for the economic risk analysis. Table 5.10 shows the corresponding numbers.

Table 5.10 Potential damages due to flood risk

Central estimate million USD

Low estimate million USD

High estimate million USD

20-year event damage 124.3 101.3 134.2 50-year event damage 157.9 108.3 345.8 100-year event damage 182.6 116.1 1,659.0 250-year event damage 214.7 123.1 13,269.7 500 year event damage 238.6 124.2 14,000.8

As one can see, a comparison with Table 5.7 reveals that the fitted curve overestimates the damages computed by the catastrophe risk models. However, as Table 7 indicates the 50 year event and 100 year event damages are very close and therefore estimating higher quantiles with extreme value distributions is difficult (leading to the same damages associated, e.g., with 100 and 250 year events) and therefore the estimates in Table 5.10 are used. However, to account for this uncertainty, we calculate a low estimate case as well using a polynomial of degree 4. Furthermore, as past damages due to flood were exceptionally high as compared with those from the catastrophe model, a high scenario is also used here using past observations based on a minimum distance optimization algorithm.

Asset Risk due to Earthquakes

Earthquake asset risk is much higher than flood risk and we set the probability for a first damage due to an earthquake to the 10 year return period, so that it is possible to estimate a GEV with reasonable estimates. Again, the damages for the two return periods in Table 5.11 are so close that sensitivity tests had to be performed to see what outcomes are more reasonable. For the earthquake damages a GEV distribution would give a thick tail with 2.0=ξ and the following quantiles.

Table 5.11 Potential damages due to earthquake risk (central estimate case)

Central estimate billion USD

Low estimate billion USD

20-year event damage 7.1 5.1 50-year event damage 9.3 6.6 100-year event damage 11.2 7.9 250-year event damage 14.2 10.1 500 year event damage 16.8 11.9

As before, comparisons with the damages from the cat models show an underestimation of damages for the 100 year event scenario and an overestimation for the 500 year scenario. Again, we use a minimum estimate here as well to account for uncertainty.14

Combined Flood and Earthquake Asset Risks

As it can be assumed that earthquakes and floods occur independently, a convolution is performed to get a total damage distribution for Nepal. Convoluting two independent distributions with densities “f” and “g” is defined to be:

∫ −= dR dyyxgyfxgf )()())(*(

This serves as a total risk estimate of damages due to the two hazards, earthquake and flooding. We use both the central estimate as well as minimum and maximum cases to get to total damage estimates (table 5.12).

14 High estimate damages based on observations would lead to lower estimates than the central estimate and therefore such estimates are not included.

0.7 0.8 0.9 1 1.1 1.2 1.3 1.40.82

0.84

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Table 5.12 Potential total damages due to combined flood and earthquake risk

Central estimate billion USD

Low estimate billion USD

High estimate million USD

20-year event damage 5.3 4.5 8.0 50-year event damage 9.7 7.0 10.5 100-year event damage 12.2 8.7 13.9 250-year event damage 16.8 12.0 17.6 500 year event damage 17.0 12.1 30.8

5.5.2 STEP 2: ESTIMATION OF THE FISCAL RESILIENCE OF THE PUBLIC SECTOR An understanding of the sources for financing a disaster in Nepal, including the costs and constraints, is crucial for planning a DRM strategy. Concerning ex-post sources, Nepal is constrained by its fiscal inflexibility and low revenue base. Diversion from the budget is considered highly constrained, and we assume 10% of the budget can be diverted. In line with empirical estimates across a sample of events, international assistance is assumed to be up to 10.4% of the total damages (see Freeman et al, 2002). Also, Nepal has limited access to international capital market. It is assumed that Nepal can borrow only from multilateral sources at concessional rates and cannot issue any bond in international capital market after a disaster. The present value of external debt is over 240% of revenue in 2008. This means that the amount of debt which government can additionally borrow from abroad is quite limited if a value of debt of 250% of GDP is considered a binding threshold for debt sustainability.

Table 5.13 Sources for financing of disaster damages

Source Parameter value used International donor assistance 10.4% Diversion from budget 10% Domestic bonds and credit 0 Multilateral borrowing: IDA limited to 26 million USD Reserve fund 0 International borrowing 0

5.5.3 STEP 3: FISCAL VULNERABILITY AND THE “FISCAL GAP” Summarizing all potential sources, the IIASA CATSIM model can provide an estimate of the government’s fiscal vulnerability. Given the assumptions and data as described above, for flood risk fiscal vulnerability for Nepal’s government is shown in the figures below.

Figure 5.15 Fiscal vulnerability and fiscal gap for flood risk (central estimate)

Figure 5.16 Fiscal vulnerability and fiscal gap: flood (minimum and maximum cases)


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Note that Nepal will not face difficulties in raising sufficient funding for central estimate and minimum cases. However, for high risk assumptions it may experience difficulties in raising sufficient funding close to a 100-year flood event.

Figure 5.17 Fiscal vulnerability and fiscal gap: earthquake (central estimate)

For the massive earthquake risk, the situation is very different and fiscal vulnerability considerable. Even for a 20 year event the public authorities in Nepal would face difficulties raising sufficient funding, and the fiscal gap could amount to more than 2 billion USD given our analysis, for which we estimate that aid inflows could amount to as much as 850 million USD, and 50 million USD may be diverted from the budget, then another 24 million USD could actually only be borrowed even on highly concessional terms, such as offered by the World Bank through the International Development Bank (IDB). Keeping data limitations and restrictive assumptions in mind, this analysis shows that the government of Nepal has insufficient financing available even using international assistance as well as budget diversion. It is observed that the extent covered by external borrowing is relatively limited. While individual risks and vulnerabilities may be examined, it is most meaningful to assess the fiscal and economic consequences of exposure to both hazards jointly, as those are independent and thus may coincide. Doing so, leads to the following results and chart.

Figure 5.18 Fiscal vulnerability and fiscal gap for the joint risk distribution (central estimate)

Given that earthquake risk is at least a magnitude higher in Nepal, flooding overall does not factor in as prominently, and the results derived by the analysis of fiscal vulnerability to earthquake hazards apply here as well: Nepal would experience difficulties in raising sufficient funding for even a combined 20-year flood and earthquake event, and fiscal gaps are very large.


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5.5.4 STEP 4: MAINSTREAMING DISASTER RISK INTO MACROECONOMIC AND DEVELOPMENT PLANNING

As a next step, risk and fiscal vulnerability can be integrated with a snapshot of the economic system in order to assess aggregate fiscal and economic effects.

Aggregate analysis

In order to mainstream and assess risk, a production function needs to be established relating assets to flows. For Nepal, parameters of a production function are estimated by the data of GDP, capital and total labour force from 1980 to 2004. Table 5.14 is the result of the estimation of parameters.

Table 5.14 Parameters of the production function

Coefficient S.E t value P-value Constant 4.64 2.02 2.295 0.0316 Capital 0.46 0.06 7.149 0.001 Labour 0.60 0.20 3.005 0.007

R square: 0.996

Now, after mainstreaming disaster risk into fiscal and macroeconomic projections and simulating the system many times using Monte Carlo simulation, figure 5.19 and 5.20 show a selection of trajectories for fiscal and macroeconomic impacts for Nepal. 15 In figure 18, potential trajectories for the revenue subtracted by repayment for debt are outlined. This may be useful as it represents budget flexibility after mainstreaming disaster damages and government relief requirements. The graph shows that in the cases without disasters, budget flexibility would increase; yet in many instance, there is a potential for disasters seriously affecting budget flexibility. This is further investigated in detail using an indicator representing the risk. The present value of budget for development is calculated for each scenario. The budget for development represents revenue subtracted by repayment for debt as well as fixed budget. A decrease of the indicator due to disasters can be calculated by the difference from the scenario where no disaster happens.

15 In the calculation, it is assumed that the private sector invests a certain ratio of GDP to capital if no disaster happens. If a disaster happens, private sector does not get any external fund for recovery so the damaged capital cannot be restored immediately.

Figure 5.19 Potential fiscal impacts due to the joint risk of flood and earthquake.

Many development trajectories are possible, and disaster risk seems important in terms of introducing an important downside risk, which has the ability to lead to deteriorations of macroeconomic variables, in this case the fiscal position. The trajectories can best be evaluated by examining key indicators at the end of the model time horizon, which here is 10 years. Table 5.15 identifies a number of key indicators.

Table 5.15 Indicators for investigation of disaster risk for government (in year 10 of the modeling time horizon)

Key variable Decrease in present value of budget finances (mean)

Decrease in PV of budget (standard deviation)

Probability of fiscal gap

Joint earthquake and flood

(central estimate) 29.7% 36.2% 57.8%

Over this time horizon, on average the present value of budgetary resources would now decrease by about 30% when factoring disaster risk in explicitly with a standard deviation of about 36%. The probability of a fiscal gap is close to 60%, which means that over the 10 years it seems quite likely that an event occurs that deteriorates public finances and causes longer term adverse macroeconomic impacts. Figure 5.20 identifies aggregate impacts on GDP based on severe risk, very limited ability of the private sector and limited ability of public authorities to respond to a large event. The GDP indicator show that given the fiscal resilience of the government and private sector’s financial vulnerability, disaster events may put the economy on a lower trajectory. The occurrence of such trajectories is stochastic and depends on the probability distribution of the damages. About 10,000 trajectories are calculated in this chart. These trajectories do not have equal probability: the cases with economic growth

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proceeding as planned (the trajectories in the upper part) have a higher probability than the catastrophic cases at the bottom. Overall, such an assessment is meant to illustrate the worst outcomes compared to the planned business-as-usual case of economic development.

Figure 5.20 Potential GDP impacts due to joint risk of flood and earthquake

Note: the time period considered is 2011-2020 Intersectoral linkages

We now turn to assessing the intersectoral distribution of losses using a social accounting matrix. We employ an SAM as calibrated by Acharya (2007) for Nepal. Based on the damage distributions estimated with CATSIM and the aggregate GDP estimates presented above, we calculate sector specific loss and income impacts for household groups taking into account higher-order effects. The characteristics of the matrix approach are as follows:

o Four industrial sectors are considered in the employed matrix, o The factors of production are capital, low-skilled labour and high-skilled labour, o There are four population groups: urban households, large rural households, small rural

households, and landless rural households.

Due to computational problems, using a SAM approach cannot generally be reconciled with a risk analytical methodology, and we focus on a scenario earthquake event with a 100 year return period, roughly equal in intensity to the devastating event of 1934.

In the case of such an event estimated above to lead to asset losses of about 14.5 billion USD, primary affected sectors are housing, education, health, transportation, industry (manufacturing), and

power infrastructure. This study focuses on the ripple effect due to the primary losses. Table 5.16 summarizes the primary losses and calculated higher order loss as well as income impact of households for this scenario earthquake as one example. It can be observed that the primary GDP loss (732 million USD) is doubled (1,435 million USD) by the multiplier effect through the involved economic interdependencies considered as linkages reduce demand for agricultural goods as well as commercial and public services. The total value of the higher order loss would amount to as much as approximately 19.1 % of today’s GDP, which seems reasonable for such a catastrophic event destroying a fifth of the total assets.

Table 5.16 Primary and higher order losses of a scenario earthquake of the severity of the 1934 scenario earthquake current (million USD)

Sector Primary GDP loss Higher order GDP loss Income loss

Agriculture 0 353.9 -

Industry 264.8 398.3 -

Commercial service

337.2 478.3 -

Public service 129.4 204.5 -

Urban household - 220.8

Large rural household

- 155.9

Small Rural household

- - 200.8

Landless rural household

- - 108.7

Total 731.5 1435.1 686.3

% GDP 9.8% 19.1%

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Figure 5.21 Primary and higher order losses for a 1934 scenario earthquake

Also, income losses may spread from urban areas to the countryside due to reduced demand for agricultural commodities by those directly affected.

Figure 5.22 Income effects for an earthquake of the size of the 1934 event

It has to be noted here, that this analysis is of short term nature (1 year) and in the medium term response mechanisms facilitating a recovery would need to be considered. As a next step of the CATSIM

methodology, risk and DM options could be identified and tested in this modelling framework, which, however, had to be left as a potential consideration for further model applications.

5.6 DISCUSSION AND CONCLUSIONS

Disasters are considered a serious and regular threat to lives and property in Nepal, and the burden imposed by disasters is considered heavy, yet little is known in terms of economic impacts and losses. This is where the macroeconomic risk assessment based on the CATSIM model undertaken here becomes important. We assessed the fiscal and economic effects of earthquakes and flood risk over Nepal, which were considered the key hazards leading to macroeconomic impacts. Droughts were considered to be more localized and not further considered. Earthquake risk exhibited the biggest effect and is thus considered of major importance.

The analysis shows that the economic and fiscal risks posed by natural disasters are large for Nepal, and there is a clear case for specifically considering these impacts in economic and fiscal planning. Particular earthquake risk, for which an event of the size of the 1934 event may mean losses exceeding 15 billion USD, can lead to large fiscal and economic impacts. In terms of fiscal vulnerability, events as (in)frequent as a 20 year events may lead to a fiscal gap, an inability to service key relief and reconstruction requirements post disaster. Also, when factoring in disaster risk and considering a 10 year planning horizon, budgetary resources may be by about 30% lower compared to a case without consideration of disaster risk.

As well, when using an SAM approach to derive intersectoral linkages we find large events, such as of the size of the 1934 earthquake, to lead to substantial (20%) reductions in GDP due to cross-sectional linkages across primarily unaffected sectors such as agriculture.

As key applications, the economic modelling may inform the mainstreaming of disaster risk into development planning as is discussed in more detail in section 6.7. As well, and more specifically, another application is contingency liability planning for public and private sector agents in disaster exposed and vulnerable countries involving setting aside reserves or seeking risk financing mechanisms. As another application, such modelling may inform relief and reconstruction efforts post event. The analysis demonstrates that disasters like earthquakes and floods may ripple through an economy and indirectly affect sectors that were not hit directly by the disaster event. Thus, in any strategy considering the management of the affected economy such cross-sector linkages should be considered as well.

Overall, we conclude that in the face of massive disaster risk, the government position should be one of risk aversion and risks should be explicitly accounted for before actual events materialize (ex ante approach). The need for taking a risk averse position can also be supported by looking at the empirical indicators related to disaster spending discussed in Section 2. Even without the occurrence of major disasters, the government of Nepal is already highly dependent on foreign assistance and support with about a third of country income owed to foreign aid, and 64% of development spending financed by

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multilaterals. As disasters and the necessary spending on relief and reconstruction often lead to a significant loss of these scarce resources, implementing options for limiting such drain are important.

To end, a word of caution has to be expressed regarding model calibration as well as risk estimates. Those are necessarily associated with considerable uncertainties, which to some extent were captured by us, where possible, using sensitivity analysis. These large uncertainties need to be factored in and before attempting to derive very specific policy recommendations in terms of implementing risk management options would need more addressing and discussion with key experts and stakeholders.


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6 NATIONAL STRATEGY FOR DISASTER RISK REDUCTION IN NEPAL

6.1 OVERVIEW

Part 1 of this project report provides information on the major hazards exist in Nepal, detailing the zoning of the different natural hazards including earthquakes, floods, landslides and drought and epidemics. The initial chapters of this part of report provide the vulnerability and risk profile of the country at the district level. Based on the results from this report a set of recommendations have been developed for setting up a national strategy for DRR. These recommendations have been categorized into the following components:

• Policy, institutional mandates and institutional development • Hazard, vulnerability and risk assessment • Multi-hazard EWS • Preparedness and response planning • Integration of DRR into development planning • Community-based disaster risk management (CBDRM) • Public awareness, education and training

Within each component of the recommendations, geographical area of project, associated activities, expected outputs, focal or lead departments and cooperating agencies are provided.

6.2 POLICY, INSTITUTIONAL MANDATES AND INSTITUTIONAL DEVELOPMENT

The objective of this chapter is to establish a culture of safety within the DM field through policy support and the strengthening of institutional mandates and capacities. In order to ensure a coordinated approach to DRR and disaster preparedness, individual institutions have to work in accordance with their current mandates whilst also taking on additional responsibilities. This might require formulating national disaster management acts (NDMA) and legislations, as well as developing institutional capacities across various levels, particularly in high risk areas. To achieve this framework, the component will need to enhance capacities through policy support, the institutional development of MOHA, promoting durable solutions for peace, and greater involvement of the public in shaping national policies and legislations and strengthening human rights, particularly of women, children, the elderly, and other disadvantaged groups.

6.3 NATIONAL DISASTER MANAGEMENT ACT

Nepal has already developed a national strategy for disaster risk management (DRM), in close consultation with national and local stakeholders and with the support of UNDP and the European Union. There is a need to develop an NDMA which will define the roles and responsibilities of various national government and non-governmental organizations (NGO), departments and offices. This will also help provide the structure of DRM at the zonal, district and VDC levels. The NDMA will further provide specific executive power to various departments and agencies, which will help the system to be more proactive than reactive.

Geographical Area: Whole country

Activities: • Develop a NDMA through a consultative process • Develop an expert group that represents various DM practitioners, professionals, academics, NGOs,

development agencies and so on. • Study the recommendations provided in this report • Obtain approval from the Central Disaster Relief Committee (CDRC) • Prepare DM strategies at the national, ministry and agency levels as required

Output • NDMA prepared • Advisory committees established • National Disaster Management Plan (NDMP) for Nepal prepared • DM strategy for ministries, line agencies and corporations prepared

Contact ministry / department / agency / institute • MOHA, Government of Nepal • Other line ministries and departments related to DM

6.3.1 REVIEW AND FORMALIZE INSTITUTIONAL MANDATES FOR LINE AGENCIES TO PERFORM DISASTER RELATED ACTIVITIES

DRM requires the support and cooperation of various agencies. There are several institutions which are responsible for disaster related activities; it is important to review their roles and responsibilities and formalize the institutional mandates that detail what their contributions should be.

Geographical Area: Entire Country

Activities: • Identify relevant functions that are not currently assigned to DM institutions • Identify institutions without appropriate mandates for DRM • Address the gaps in the institutional functions, by looking at the provision of mandates, issued by the

appropriate gazette notifications • Introduce measures which assign responsibility and accountability for different DM functions

Outputs: • Institutional mandates established or clarified for relevant line agencies • New regulations implemented as required



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6.3.2 DEVELOPING INSTITUTIONAL MANDATES AND CAPACITIES

There is a concern that the capacity of various DM institutions and their resources are not currently adequate. As a result of this, the MOHA need to enhance the resources available in order to strengthen the present capacity of these institutions.

Geographical Area: Entire Country

Activities: • The development of an institutional framework that encompasses all the new DRM mandates and

strategies for ministries, departments, agencies, and national and International NGOs will be provided to provincial councils, local government agencies and DM line agencies

• Identification of the needs and gaps in human resources, equipment and offices relating to DRM • Training and capacity-building of different divisions within organizations that function as a DM line

agency • Institutionalized cooperation and coordination structures and facilitated as needed • Enhancing the capacity of MOHA. This can be done by building an emergency operations centre

(EOC) within the head office, enhancing communications and other necessary facilities, and by training personnel

Outputs: • Institutional capacities that correspond to the new mandates will be developed and enhanced • EOC will be set up within the headquarters of MOHA, with the appropriate facilities provided


6.3.3 FORMULATION OF CBDRM POLICY

A community’s first line of defense to the threat of a disaster, is preparedness; this is key to reducing a community’s vulnerability and increasing their disaster resilience. Having a well resourced and sustainable program for community-based disaster risk management (CBDRM) is therefore a key strategy for Nepal. The activity recognizes CBDRM as a tool for DRR and capacity-building at the local level, enabling the community to play an active role in DRR.


Activities: • Recognize and provide legal basis for CBDRM groups at the local level • Clear linkages established and roles identified for CBDRM and NGOs within different DM strategies

at the VDC, district and divisional levels • Create national mechanisms for the coordination of the National EOC and NGOs in promoting

CBDRM • Development of common approaches and methodologies for CBDRM • Registration system for disaster response volunteers created

Outputs: CBDRM recognized as a tool by line agencies and ministries for DRR at the local level


6.3.4 ENFORCEMENT OF POLICIES

DRM is not currently always considered within national projects or strategies, which is posing a challenge for mainstreaming DRR efforts. Nevertheless due to rapid development within the country, it is essential that DRM is mainstreamed in order to ensure that the development does not create new hazards or environmental problems. Integrating DRM into national planning policies will make it mandatory for agencies to include DRM within their projects.


Activities • Review existing ordinances, acts and regulations • Identify the gaps and causes of the current inadequate enforcement • Clarify and resolve areas of overlap or contradiction • Develop capacities or resources needed for effective enforcement

Outputs: • Implement and optimize existing DRR policies • Enforcement of relevant existing ordinances, acts and regulations



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6.4 HAZARD, VULNERABILITY AND RISK ASSESSMENT

Information on the HVRA has been provided in Part 1 and previous chapters of this report. The project has developed HVRA at the national scale; nevertheless there is a need to develop HVRA at the district and most hazard prone VDC levels. By considering the results and outcomes of this project, it will be possible to identify the issues and gaps in DM, which will need to be addressed in order to achieve safer and sustainable development. These could include:

• Natural hazards within the HVRA • Database management systems for HVRA • HVRA capacity-building for focal agencies • Science and technology in HVRA

6.4.1 NATURAL HAZARD, VULNERABILITY AND RISK ASSESSMENT

Flood Risk assessment

Geographical Area: Flood Prone areas, major flood prone river basins (please also refer back to the Flood hazard zoning information found in part 1 of this project report)

Activities: • Development of digital elevation models (DEM) including data collection • Rainfall data analysis • Development of appropriate simulation models (as in the past modeling has predominantly been

one dimensional) • Enhance prediction and early warning (EW) capacity • Collection of field data on historical events such as the 2008 Koshi flood • Digitization of topographic data into DEM • Development of GIS mapping for the most vulnerable urban areas • Assess the performance of existing flood protection works in order to establish what

improvements could be made • Develop scientific and field validated vulnerability curves for buildings and other physical

infrastructures • Assess the characteristics of various crops grown within different seasons that are vulnerable to

flooding

Outputs: • Flood hazard mapping in flood plain areas (digitally and hard copies)

Contact ministry / department / agency / institute • Department of Meteorology and Hydrology (DMH), Government of Nepal • DWIDP, Department of Irrigation, Department of Agriculture (DoA), MOHA and other DM line

ministries and departments

Earthquake Risk assessment

Geographical Area: Whole country (please refer back to the earthquake hazard zoning information found in part 1 of this project report)

Activities: • Identification and mapping of active faults • Development of extensive macro and micro level earthquake catalogues (available at reasonable

cost for research and development) • Development of geological maps at lower scale for seismic risk assessment • Large scale seismic-tectonic mapping for the country • Development of scientific and field validated vulnerability curves for buildings and other

physical infrastructures with respect to the earthquake severity

Outputs • Detailed seismic zoning maps for the very high seismic prone areas • Micro-zoning for Kathmandu, Pokhra and other important urban areas

Contact ministry / department / agency / institute • Department of Mines and Geology, Government of Nepal • Ministry of Construction and Urban Development, DMH, MOHA and other DM line ministries

and departments

Landslide Risk Assessment

Geographical Area: Whole country (please refer back to the landslide hazard zoning information found in part 1 of this project report)

Activities: • Field mapping and collection of field data • Acquisition of satellite data development from GIS mapping for the delineation of vulnerable

areas • Upgrading of computer hardware and software used for GIS mapping • Development of GIS databases and the delineation of vulnerable areas • Dissemination of map information • Geological and geo-technical investigation for the collection of necessary field data • Data analysis and delineation of associated risks • Development of risk maps and disaster response maps • Dissemination of maps and proposed mitigation measures

Outputs • Provide landslide hazard maps for the urban areas and other highly populated areas as mentioned

in the landslide hazard zoning chapter found in part 1 of this report.

Contact ministry / department / agency / institute • Department of Mines and Geology, Government of Nepal • Department of Roads, DWIDP, Ministry of Construction and Urban Development, DMH,

MOHA and other DM line ministries and departments


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Drought Risk Assessment

Geographical Area: Whole country (please refer back to the drought hazard zoning information found in part 1 of this project report)

Activities: • Based on the information on the drought hazard assessment provided in part 1 of this report, a

detailed study should be carried out for the most drought prone areas • Development of a drought catalogue that considers, at the minimum, the past 30 years

Development of a better drought modeling and ground validation system • Development of a robust rainfall database management system • Defining a rainfall threshold for the identification of drought conditions • Identifying the drought vulnerability characteristics of various existing crops

Output • Comprehensive meteorological and climatic database available for drought hazard and risk

assessment • Precise methodology for drought risk assessment defined

Contact ministry / department / agency / institute • DMH, Government of Nepal • DWIDP, DoA, Department of Irrigation, MOHA and other line ministries and departments

related to DM

6.4.2 DATABASE MANAGEMENT SYSTEMS


Activities • Housing categories identified, based on the type of building materials and load paths used, in

order to achieve a more precise vulnerability and risk assessment • Development of a database identifying the class of educational buildings and health or essential

institutions • Development of GIS based mapping of various industries, including trade and mining • Update databases so that they provide current information and status of road and power lines • Develop GIS based mapping of the arable and temporal changes to cropping • Develop a database providing information on the real estate in major cities, such as Kathmandu

and Pokhara

Output • Database management system: GIS based mapping for all major infrastructures, which will then

be compatible for sectoral risk assessment

Contact ministry / department / agency / institute • Department of Survey, Government of Nepal • Bureau of Statistics, MOHA and other line ministries and departments related to DM

6.4.3 HVRA CAPACITY BUILDING FOR FOCAL AGENCIES

Geographical area: Whole country

Activities: • Identification of focal agencies for HVRA, taking into consideration natural and human-induced

hazards • Encouraging technical and scientific institutions to build their capacity to carry out hazard

assessments using up-to-date techniques and tools • Establish links between scientific institutions and departments with user agencies • The development of precise site specific drought models which can be used for crop production

management by the agricultural and irrigation departments • Establish a system to support access to the departmental data collected through VRA • Regular and consistent training and capacity building on HVRA for stakeholders and the

appropriate allocation of resources for such activities

Output • Strategy paper on capacity building for HVRA • Identifying focal agencies and targets for HVRA training • Allocation of funds from respective departments and for above stated activities

Contact ministry / department / agency / institute • Department of MOHA, Government of Nepal • Line ministries and departments related to DM

6.4.4 SCIENCE AND TECHNOLOGY IN HVRA


Activity • Identification of Institutions and departments that will be able to help and develop a center of

excellence for HVRA • Provision of additional funds for developing center of excellence • Collaboration of national and international institutions to help to develop a center of excellence

for HVRA

Output • Strategy paper for creating center of excellence • Identification of Institutions suitable for the center of excellence • Defining the research and development mandate for HVRA by the Government of Nepal • Regular allocation of funds for the center of excellence

Contact ministry / department / agency / institute • Department of MOHA, Government of Nepal • Department of Mines and Geology, DWIDP, DMH, and other line ministries and departments

related to DM


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6.5 ESTABLISHMENT OF NATIONAL EARLY WARNING CENTER OF NEPAL

As a result of the various hydro-meteorological and geological hazards that occur in the country, it is necessary to establish national EWS in Nepal. In order to do so, meteorological observation and prediction capacities needs to be improved, the hydrometric network for enhancing flood monitoring and forecasting capability needs to be improved, landslide prediction and EW capabilities needs to be refined and long and short-term drought forecasting and an effective monitoring system for agriculture and associated sectors needs to be established.

6.5.1 ESTABLISHMENT OF NATIONAL EW CENTER OF NEPAL


Activity • Establish a multi-hazard EW division of MOHA at DMH premises • Institutionalize inter-agency arrangements for national EW with the relevant lead agencies • Establish a coordinating mechanism that incorporates an effective communication system for the

dissemination of EW • Formalized dissemination arrangements through parallel local radio communication

Outputs: • Establishment of the EW centre

Contact ministry / department / agency / institute • DMH, Government of Nepal • Department of Mines and Geology, DWIDP, and other line ministries and departments related to

DM

6.5.2 IMPROVEMENT OF METEOROLOGICAL OBSERVATIONS AND PREDICTION CAPABILITIES


Activities: • Develop new automatic weather observation stations with a data processing and display system at

DMH, Kathmandu linked with a real-time communication system • Enhance the prediction capabilities for floods

Outputs: • Improved meteorological observation and prediction capabilities


DM

Improve the hydrometric network for enhancing flood monitoring and forecasting capabilities


Activity • Establish new gauging stations • Upgrade existing gauging stations • Procurement of new measuring and communication equipment • Establish an effective communication network to help communicate data from the field • Upgrade the data processing units • Make improvements to the data processing system and develop a well organized database system • Improve the existing flood forecasting system or develop a suitable flood forecasting model • Train engineer and operational staff on the modern hydrological applications and instruments that are

used

Outputs Real time forecasting system established to mitigate flood hazards comprising of

o Well-established hydrometric networks for the country o Well-organized database systems o Well-established data analysis and forecasting systems

Contact ministry / department / agency / institute • DMH, Government of Nepal • Department of Mines and Geology, DWIDP, Department of Irrigation and other line ministries and

departments related to DM

6.5.3 IMPROVEMENTS IN LANDSLIDE PREDICTION AND EW CAPABILITIES


Activities • Updating and up-scaling of the already prepared landslide hazard forecasting system • Collection of data and soil parameters to be added as an additional layer to hazard maps • Identification and installation of automatic rain gauge stations in landslide prone areas • Installation of suitable instruments to measure precipitation rates • Analysis of historical data on rainfall and the occurrence of landslides in order to obtain threshold

limits for the initiation of landslides • Develop a proper data exchange network system among relevant institutions and communities at risk • Develop an EWS and disseminate information down to the grass-root level • Enhance training and capacity-building

Outputs Properly established EWS to protect at-risk communities and their property from landslides

Contact ministry / department / agency / institute • DMH, Department of Roads, DWIDP, and other line ministries and departments related to DM • Department of Mines and Geology, Government of Nepal


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6.5.4 DEVELOP LONG AND SHORT-TERM DROUGHT FORECASTING AND MONITORING SYSTEMS FOR AGRICULTURAL AND ASSOCIATED SECTORS


Activities • Upgrading of agro-meteorological observation networks in the country

o Procurement of new equipment o Provision of communication links to each agro-meteorological stations with email facilities

• Upgrading of the agro-meteorology division of the DoA o Procurement of high-capacity computers o Dedicated communication links leased to the internet lines

• Establishment of a crop-forecasting unit at the DoA headquarters o Building construction o Procurement of computers, office and furniture o Establish a database on weather and crop information o Formulate a crop-weather watch group from relevant agencies that meet for a bi-monthly

meeting o Publish a seasonal news bulletin o Issue a yield forecast of the major food crops one month before the end of each growing

season

Outputs • Establish an end-to-end early drought forecasting network • The Agro-meteorological observation network • Real-time database on agro-meteorology covering the entire country • Database on crops and other related data with seasonal updating • Seasonal newsletter • Yield forecast of major crops in each growing season with a sufficient lead time

Contact ministry / department / agency / institute • DMH, Government of Nepal • DoA, DWIDP, Department of Irrigation and other line ministries and departments related to DM

6.6 PREPAREDNESS AND RESPONSE PLAN

The objective of a disaster preparedness and response plan is to minimize the adverse effects of a hazard through effective pre-cautionary actions whilst preparing adequate responses to ensure the timely and coordinated delivery of relief and assistance following a disaster. The recommendations include hazard specific response plans, national rapid response teams, EOCs, hazard specific contingency plans, emergency service networks, knowledge-management systems, health sector preparedness and response mechanisms, private sector preparedness for disaster response, capacity-building of the local government, provision of storage facilities for emergency reserves and resources needed, construction of multi-purpose shelters , establish a nation-wide emergency communication system and so on.

6.6.1 HAZARD SPECIFIC RESPONSE PLANS


Activities • Set up technical advisory committee and standard operating procedure (SOP) for each type of hazard • Facilitate meetings to develop the risk-profile and risk management approaches or strategies to be

used • Share guidelines for the development of a draft response plan at the different district, divisional and

local authority levels • Develop a draft plan, linking response plans at the various levels (VDCs, district, divisional and local

authorities) and link with the working group developing the SOPs • Field test and finalize the plans

Outputs • Hazard specific response plans are in place at the national level, with appropriate links to the

provincial, district, divisional and village level • Community-level plans for earthquakes, floods, landslides, droughts, lightning and

thunderstorms


6.6.2 NATIONAL RAPID RESPONSE TEAM


Activities: • Identification of specialized skill-sets required and establish a team that hold a legal mandate • Training and capacity-building of team members • Standard command and control procedures developed Which are then linked with the

contingency plans • Public dissemination of the availability of these teams • Develop a telephone directory for these team members


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Outputs: • Specialized national rapid -response teams operational for deployment during a disaster


DM

6.6.3 EMERGENCY OPERATION CENTER


Activities • Specific role for the EOC and the incident command system (ICS)to be defined and adopted • Infrastructure to be provided • SOPs and institutional tools to be made available to the center • Field-testing and regular drills to be established • Provision of financial resources to activate any response or contingency plans, followed by

continuous monitoring • Hiring of consultants and subject experts for the SOPs • Establish EOCs at MOHA and 75 districts • Office equipment for EOC and other districts provided • Communication equipment for the EOC provided • Study tours and exposure visits to EOCs in the region • Identify agencies with adequate material and human resources for emergency response • Design and operationalize the resource networks for emergency response

Outputs • Setting up of ICS – an institutional framework for a response operation. This will provide unified

command and structure of disaster response through existing ministries / departments / agencies. • EOCs at the district level form a decentralized mechanism for response operations, whilst

maintaining a standard approach to emergency operations. This will emphasize the development of standing orders, whilst providing legal status to concerned agencies to carry out tasks whilst working under EOCs.


6.6.4 HAZARD SPECIFIC CONTINGENCY PLANNING


Activities

The capacity of people and the risk exposure to the hazard is mapped by each ministry, department and agency, whilst:

• Developing institutional safety plans to protect or limit the damage to its own infrastructure and facilities

• Developing contingency plans to maintain its organizational preparedness, so as to be in a position to offer services as and when required

• Developing public warning and awareness systems for each type of contingency plan • Providing a legal-base detailing their own self-compliance to the contingency plans • Establishing and strengthening monitoring capacities of the responsible governments or private

sector entities • Improve social protection or a safety-net through insurance

Outputs • Hazard specific contingency plans are put in place by various line ministries, departments and

government agencies, as required o National Industrial and chemical hazard management permits o Forest-fire management plans o Dam-related hazard management plans o Biological-hazard management plans o Urban-fire suppression and management plans o Road traffic accident management plans o Epidemic management plans o Explosion and bomb blasts management plans


6.6.5 EMERGENCY SERVICE NETWORKING


Activities • Set up emergency services, in order of priority, in all areas not currently covered • Upgrade existing services (skills, equipment and human resources) to meet the risks faced • Set up a technical advisory committee to identify the scale and type of needs of the community at

risk under different emergency scenarios whilst also considering the focal departments or bodies that will help carry out or provide services under these sectors

• Identify team leaders for each sectoral service agency or department at the national and district level

• Establish operational links and mechanisms of the EOC roles under the response and contingency planning

• Disseminate information about the existence of such focal bodies, respective mandates and operational mechanisms

Outputs • Emergency service networks (warning, evacuation, mass care, disaster victim identification

(DVI), tracing and family re-union, health and medical care, public warning and so on) in place at provincial, district, municipal and urban council levels


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6.6.6 KNOWLEDGE MANAGEMENT SYSTEMS


Activities • Organize a multi-stakeholder national-level workshop to:

o Develop an institutional mechanism to carry out a workshop on the lessons-learned after every disaster response operation, based on a standard methodology

o Develop a mechanism to record, interpret and transfer such knowledge into disaster response and mitigation planning in the country

o Review and renegotiation the institutional framework as required • Organize district-wide meetings that enable different stakeholders to communicate and establish

local mechanisms to operationalize such measures • Publish and disseminate lessons-learned, suggest improvements for mitigation measures and

decide on changes to the institutional framework after every disaster or every year, depending on what occurs first.

Outputs • Knowledge management systems established to update the national response plans and

incorporate the lessons learned • Institutional framework established to record, analyze, interpret and act upon the knowledge

generated through lessons-learned after every disaster • Lead agency identified to carry out this task at the district and national level, depending upon

scale of disaster • Communication protocol established to ensure the incorporation of the knowledge management

system into the DM framework at all levels

Contact ministry / department / agency / institute • MOHA, Government of Nepal • Department of Survey, other line ministries and departments related to DM

6.6.7 HEALTH SECTOR PREPAREDNESS AND RESPONSE MECHANISM


Activities • Establish health sector DM committees and action groups • Review and update laws, regulations and by-laws relating to the health sector preparedness

strategies • Improve the resilience and response capacity of health institutions • Train health staff • Compile an inventory of resources for the health sector DM • Network with other relevant agencies

• Establish an emergency operation room at all levels • Strengthen the risk analysis process relating to the health sector preparedness and response

strategies • Prepare DM plans at all sub-national levels • Prepare disaster or emergency preparedness plans and response plans at the institutional level • Conduct community-based awareness programs • Conduct research relating to the health sector disaster preparedness and response plans and

activities

Disaster response • Preparation of SOPs for emergency response and relief • Establish special teams for rapid deployment • Preparation of plans for mass-casualty management at the hospital level • Network among relevant agencies on disaster response and relief

Outputs • Health sector DM committees and action groups at all levels established • Laws, regulations and by-laws on the health sector DM complied and regularly updated • Capacity of health sector institutions and staff at all levels improved for disaster preparedness

and response • Inventory of resources for health sector DM compiled and updated • Emergency operation rooms at the national and district-level established • Disaster preparedness plans and monitoring and evaluation mechanisms prepared • Community-level action groups help assist health services

Contact ministry / department / agency / institute • Department of Health Services, Government of Nepal • Other line ministries and departments related to DM

6.6.8 PRIVATE SECTOR PREPAREDNESS FOR DISASTER RESPONSE


Activities • Identify and establish contacts within the existing national and local level trade or business

organizations • Where they do not exist, form new relationships between business communities at the local and

national levels • Organize meetings and workshops to improve awareness and provide guidance and assistance to

help business units establish their own plans for responding to disasters and for rapid recovery after a disaster

• Provide assistance to businesses to help them set up and provide a forum for exchanging information exchange which will help enhance emergency preparedness and contingency planning within the business community

• Monitor disaster preparedness whilst updating the plans so that businesses remain prepared for any future hazards that may occur


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Outputs • New associations formed among business communities at the local and national level • Existing associations among businesses identified • Guidance and assistance provided to businesses so that they have their own plans for responding

to disasters and for rapid recovery after a disaster • Assistance provided to businesses so that they are able to organize themselves to provide a forum

for information exchange to enhance emergency preparedness


6.6.9 CAPACITY-BUILDING OF LOCAL AUTHORITIES FOR EMERGENCY RESPONSE


Activities • Assessment of the response requirements • Recruitment and training of staff by relevant municipal councils (MCs) and major urban

councils (UCs)

Outputs • Equipment provided for selected local authorities • Recruitment of staff and training provided


6.6.10 PROVISION OF FACILITIES FOR STORAGE OF EMERGENCY RESERVES AND RESOURCE NEEDS

Geographical area: Whole country,

Activities • Selection of locations • Identification of type of storage materials used • Design of facilities of 2000 M2 each, in selected locations

Outputs • Storehouse for emergency resources needed in the selected locations


6.6.11 ESTABLISH A NATION-WIDE EMERGENCY COMMUNICATION SYSTEM


Activities • Identify exact requirements of the regional, district, and local authorities, with MOHA and EOC

as the main focal points • Supply the required equipment, in order of priority

Outputs • A nation-wide radio communication system in place

Contact ministry / department / agency / institute • Ministry of Tele-communication, Government of Nepal • Other line ministries and departments related to DM


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6.7 INTEGRATION OF DRR INTO DEVELOPMENT PLANNING

The National Planning Commission (NPC) is the focal point for national development planning for the country. NPC has published a base document for three years, providing information on the development plan for the country with effect from 2010 to 2013 (for more details please go to their website: http://www.npc.gov.np/). The base document describes background, objectives, strategy, implementation planning and expected outcomes. The planning phase solely comprises elements of DRR. In contrast there are several other areas where the DRR concept can be incorporated; including: revenue, foreign support system, agricultural-rural and small credit schemes, cooperative systems, poverty alleviation, labor and livelihood sectors, transportation systems, agricultural and food security sectors, forest and land conservation, industry, trade, tourism and civil aviation, culture, local development, population, NGOs, education, health and nutrition, hygiene and sanitation, rural infrastructure, water resources, water-induced disaster prevention, roads and other communication systems, building and construction, science and technology and climate and environment change. The following table shows the incorporation of the DRR component within each sector.

Table 6.1 DRR component within specified sectors

No. Sectors described in base document for the three year

development plan:

Strategy for integrating DRR into sectoral plan

1. Revenue • Create a reserve for the disaster response and recovery process though the Prime Minister Relief Fund and so on

• Introduction of Catastrophe bonds (CATBONDS) for risk transfer

2. Foreign support system • Collaboration and cooperation of DRR between different technical and scientific institutions

• Adopting good practices for DRR • Long term partnerships with leading DRR institutions

and organisations to help develop country specific systems for DRR

3. Agricultural-rural and small credit schemes

• Introduction of risk transfer systems for marginalized farmers and rural citizens at risk of hydro-meteorological hazards

4. Poverty alleviation • DRR should be incorporated into poverty alleviation projects as a cross-cutting theme

• Community-based DRR and preparedness will reduce disaster-led poverty

5. Labor and livelihoods • Formal and non-formal training for industrial and occupational safety

• Cataloguing accidents relating to occupation and developing a national strategy for personnel safety

• Establish a project to ensure livelihood security,


development plan:


particularly to people affected during disasters, with a specific focus on marginal farmers and laborers

6. Transportation system This sector has already addressed certain DRR measures, including: • Reducing accidents • Enhancing safety standards • Community awareness for safe transportation • Pollution control

In the future:

• The safety auditing of the transportation system should be introduced

• The transportation of hazardous chemical substances should be strictly monitored

7. Agricultural sector and food security sector

The sector has incorporated some measures to sustain the agricultural sector and food security already. However it is necessary to include certain additional measures, such as: • Incorporating poverty alleviation within projects on

agriculture and food security • Establishing EWS for hydro-meteorological disasters

including floods, droughts and hailstorms • Community awareness about crop pattern changes with

respect to EW provided by the meteorological department

• Counter support by the associated ministries and agencies for flood and drought mitigation

• Introduce surveillance for identifying disease and epidemics among animals and whether there is the possibility of transmission to human beings

• Introduction of information systems and consultation for pest control to ensure food security

8. Forest and land conservation • This sector has already taken considerable measures for DRR

• 40 percent of the forest has been made into a protected reserve in order to help maintain the ecosystem and to help prevent disasters

• There is a strategy for reducing the impact of climate change including community-based livelihood based forestry and vulnerability assessments on the effect of climate change on vulnerable communities


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development plan:


9. Industry • The industry sector needs thorough inclusion of industrial safety norms. These include:

• Detailed exposure, vulnerability and risk assessment of the industrial sector in order to implement appropriate mitigation strategies

• Introduction of industrial safety acts • Establishment of national safety councils that can help

national authorities facilitate industrial safety and mitigation

• Laws and policies for transportation and handling of chemical substances

• Enforcement of on-site and off-site emergency management plans for hazardous industrial zones

• Community awareness about industrial safety

10. Trade and supply • The sector is committed to provide all necessary civic requirements to high altitude and poor access areas. However it does not discuss the issue of providing food supply and everyday requirements to these and other areas during a disaster. It is necessary to introduce such a plan because in the case of a heavy landslide in the central districts or following flooding in the terai areas, the supply could be continued.

• Chapter 5 explains the implications of a disaster and the adverse impacts it is likely to have on an economy. In view of this scenario, it is necessary to include the following issues within the trade sector:

o Business continuity plans in addition to creating a fund reserve for disasters

o Community awareness among traders for DRR

11. Tourism, culture, and civil aviation

• This sector needs to include a DRR component in order to ensure continuity of sectoral functions. These measures could include:

o Exposure, vulnerability and risk assessments of important touristic centers

o Contingency planning for disasters in tourist areas

o Special safety measures in the mountain tracking areas

o Special attention and contingency planning for all airports

12. Local governance and development

• This sector needs to include several measures for DRR. Few of these measures include:


development plan:


o Defining the roles and responsibilities of the local government for DRR strategies

o Local government should develop a NDMA o The Building Permission Act needs to be

enforced o Land use mapping needs to be included within

the master plan to consider local disaster risk o Community awareness about existing hazards

and mitigation measures o DRR should be considered as a cross cutting

theme in urban development 13. Population and human resources • The population and human resource sector is important

and can include various aspects of DRR in regular development.

• Several issues are already included such as education, health, sustainable development, the de-centralization of urban hubs, safety of vulnerable groups including women, children and elderly, and food security.

14. Social protection and security • The social protection and security sector is extremely important for Nepal. The country is committed to take all necessary measures for its community’s protection. This must include the identification of vulnerable groups, hazard and risk assessments, better education, health, water and sanitation, ensuring food and safe drinking water, emergency social services, structuring policy and a strategic plan for disaster affected areas

15. Education • The education sector needs to include DRR within future educational development. Some of these measures should include:

• Exposure, vulnerability and risk assessments of educational institutions

• Contingency planning for continuation of education during disasters

• Inclusion of DRR in the education curriculum • Training of volunteers for disaster preparedness and

response 16. Health and nutrition • This sector includes certain DRR components within the

health infrastructural development sector. However more issues should be incorporated:

• Exposure, vulnerability and risk assessments of health institutions

• Contingency planning for hospitals • Surveillance of epidemics and diseases • Mapping for epidemics and diseases • Comprehensive database of epidemics and diseases


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development plan:


• Training new health volunteers for disaster response

17. Hygiene and sanitation • This sector is key for reducing health related hazards. It includes several measures including: advanced system for water purification, local and environment hygiene control, reducing open defecation and toilet provision, sever treatment systems

18. Rural infrastructure development • The rural infrastructure development sector broadly covers DRR components including measures for water induced disasters, safe and strong housing, food, water, basic infrastructure, a housing scheme for temporary and other types of rural housing, and poverty alleviation. However the following measures will further enforce safe and sustainable rural development:

• Introducing a policy for safe rural housing construction • Development of a database for housing and other

infrastructure that can be used for a vulnerability and risk assessment

• Community-based DRR project implementation • Integration of DRR in all rural development projects

19. Water resources and water induced disaster prevention

• The sector includes DRR components in several aspects of rural and urban development including livelihood, poverty alleviation, agriculture, irrigation, water conservation, flood control and community participation

20. Roads and other communication systems

• The DRR components in the road development sector needs to be introduced. A few suggestions are offered below:

• Hazard and risk assessments during the planning stages for specific sites that will have road construction

• Community-based landslide risk mitigation • Contingency planning for road blockage management as

a result of disasters • Resource management for DRR in the road development

sector • Stocktaking of roads at risk in landslide hazard prone

areas

21. Building, housing and urban development

• This sector has already made the linkages between its different components and DRR. Several mitigation measures have already been taken by the focal ministry


development plan:


including the enforcement of building codes and bye-laws and the decentralization of cities and infrastructure. Nevertheless several additional measures should be taken for safe and sustainable development:

o Strict enforcement of building bye-laws in secondary cities

o Promotion of disaster resistant building construction through awareness and education

o Training of professional engineers and architects for disaster resistant construction

22. Science and technology • The current science and technology policies consider the DM and climate change adaptation measures used within the national development plan for the country. It is necessary to provide necessary support to universities and institutions for future research on DRR

23. Climate and environment change • The climate change and environment sector of Nepal considers all important aspects of DRR. There is a need to incorporate information and projects on climate change and disasters into all other development sectors


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6.8 COMMUNITY BASED DISASTER MANAGEMENT

Communities are affected by the primary impacts of disasters, and are consequently the first to respond. Preparing communities for disasters is the first step and first line of defense against different hazards and is therefore key to reducing vulnerability and increasing disaster resilience. Having a well resourced and sustainable program will help communities better prepare and manage disasters; CBDRM is therefore a key strategy for helping to achieve a safer Nepal. Nevertheless so far there has been insufficient coordination between the efforts of the government at the district and divisional level and action planning and the interventions of NGOs at the community level. After the devastating tsunami, there has been significantly greater acceptance within the country (and among international donors) of the country's relatively high vulnerability to disasters. Consequently there is a greater willingness to invest resources in pre-disaster preparedness and mitigation, especially at the community level; this provides a significant opportunity for the government to help initiate a national program on CBDRM. Under the CBDRM theme the following activities are recommended:

a. Community DRM teams in all VDC and district levels: This sub-component seeks to establish a community team for DRM in all at-risk communities in the country. These community DRM teams will be responsible for EW, preparedness, response and mitigation. The sub-component will support the establishment of these teams, provide initial orientation and training, support the undertaking of village-level hazards, vulnerability and capacity assessment, preparation of response and evacuation plans, and the identification of micro-projects on mitigation. The teams will provide periodic training and will be expected to conduct periodic drills and rehearsals.

b. An effective national network of local volunteers for DRM: There is a need to establish a national scheme for community-based disaster response volunteers. These volunteers will support the dissemination of EW, assist in evacuation, search and rescue, provide first aid and medical response, and support the running of camps. They will be active members of the CBDRM groups. The components will record the registration of volunteers in a database and this will be made available at the divisional, district and VDC levels. A scheme for providing recognition of volunteers needs to be established in the form of certificates and identify cards. A standard curriculum and large scale training for these volunteers will be provided and is to be undertaken by all who want to volunteer.

c. Establishing a CBDRM resource center in each district: This component will establish a CBDRM resource centers in each district. Members of staff in the resource centers will act as facilitators for the CBDRM teams and activities in the district, provide technical support, support the district commissioners with regards to the maintenance of the database, as well as undertake documentation and dissemination of CBDRM experiences. The aim is to identify agencies at the district level that are willing to take on this role and then to support them in strengthening their capacity to serve this function.

d. Small grant program: There is a plan to create a CBDRM Small Grants Program to support the implementation of priority community preparedness and mitigation projects by community DRM teams for EW, preparedness, response and mitigation projects.

e. Applied research grant schemes for government agencies to implement community level risk management programs: The aim is to establish a fund in the EOC to award annual applied

research grants to implement mitigation and risk management programs at the community-level, in partnership with at-risk communities; this fund will be made available to government institutions, universities, academic institutions and professional bodies.

f. Develop a micro-finance scheme to reduce community vulnerability at the household level and to help promote alternative livelihood options: This sub-component will work with a number of Nepal micro-finance institutions to support the establishment of schemes that help enable vulnerable communities, in hazard prone locations, to identify and take up alternative and additional livelihood options, as well as invest in mitigation measures to improve the disaster resilience of household and livelihood assets.

g. Training of key state and non-state actors at the local level: This sub-component will help strengthen the capacity of key actors at the local level such as grama seveaks (the volunteers from specified villages) and the local citizens’ committee to help in the area of conflict resolution and peace keeping through specific training.

h. Promoting mechanisms for communities to seek accountability and express grievances: An independent and effective redress mechanism for complaints and grievances in relation to disasters will be created in close collaboration with the UN human rights commission.


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6.9 PUBLIC AWARENESS, EDUCATION AND TRAINING

An important aspect of any DRM program is to anticipate the requirements for disaster related public awareness, education and training. The planning process will only be effective if those who are the ultimate beneficiaries know how to mitigate disasters, respond in times of disasters and develop capacities to cope in their aftermath. For this reason, an essential part of DRM planning is the education of those who may be at risk of potential disaster events. At present, many government organisations, NGOs and community based organizations (CBOs) conduct DRM related training. Nevertheless efforts are often being duplicated or training is being conducted without the appropriate resources.

The main programs identified under the theme of public awareness, training and education are: • Promote public awareness at the national level: This can be done though effective implementation of

a national public awareness program for disaster preparedness • Promote awareness within schools: This can be achieved through the introduction of DM related

subjects in the school curriculum, and through awareness campaigns and publications produced in Nepali.

• Promote awareness and train university students by incorporating DRR into the university curriculum: The integration of information on DM within different subjects at university, such as urban planning, civil engineering and architecture would help promote awareness and will help with DRR planning in the future. This has already been implemented in some universities, but needs to be addressed on a larger scale.

• Increased awareness of DRM within the school curriculum: DM would not be a new subject area in the curriculum, but instead should be integrated into existing subjects.

• Promote awareness among professional groups and key decision makers through training: This would involve training on disaster preparedness and reduction at a number of different levels, including entry level, refresher training and in service training of government staff at various levels.

• Increase capacity among key institutions through the training of officials and the provision of training tools and resources.

• Integrate DRM training within development and educational initiatives: Professionals who are involved in planning, implementation, financial management and so on need to understand the implications of DRM and the positive impact it will have on sustaining development efforts.

• National awareness campaign on public safety in disasters.


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