190404 ffews mawa ratuwa - adb

Consultant Report

Project Number: 45206-001 September 2020

Nepal: Water Resources Project Preparatory Facility Flood Forecasting and Early Warning System: Mawa – Ratuwa Basin

This document is being disclosed to the public in accordance with ADB's Access to Information Policy.

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Deliverables\11 FFEWS\Mawa-Ratuwa\1\190404 FFEWS Mawa Ratuwa.docx Mott MacDonald

Mott MacDonald 22 Station Road Cambridge CB1 2JD United Kingdom T +44 (0)1223 463500 F +44 (0)1223 461007 mottmac.com

WRPPF: Preparation of Priority River Basins Flood Risk Management Project, Nepal Flood Forecasting and Early Warning System: Mawa – Ratuwa Basin

4 April 2019

Mott MacDonald Limited. Registered in England and Wales no. 1243967. Registered office: Mott MacDonald House, 8-10 Sydenham Road, Croydon CR0 2EE, United Kingdom

Mott MacDonald | WRPPF: Preparation of Priority River Basins Flood Risk Management Project, Nepal Flood Forecasting and Early Warning System: Mawa – Ratuwa Basin

383877 | REP | 0039 | 4 April 2019 Flood Forecasting and Early Warning System: Mawa – Ratuwa Basin

Issue and Revision Record

Revision Date Originator Checker Approver Description 0 24/11/18 Iqbal

Hassan Cristian Hetmank

Christian Hetmank

1st submission

1 04/04/19 Iqbal Hassan Audrey Despinasse

Peter Ede A Akindiji

Christian Hetmank

Final submission

Document reference: 383877 | REP | 0039 Information class: Standard

This document is issued for the party which commissioned it and for specific purposes connected with the above-captioned project only. It should not be relied upon by any other party or used for any other purpose.

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This Report has been prepared solely for use by the party which commissioned it (the 'Client') in connection with the captioned project. It should not be used for any other purpose. No person other than the Client or any party who has expressly agreed terms of reliance with us (the 'Recipient(s)') may rely on the content, information or any views expressed in the Report. This Report is confidential and contains proprietary intellectual property and we accept no duty of care, responsibility or liability to any other recipient of this Report. No representation, warranty or undertaking, express or implied, is made and no responsibility or liability is accepted by us to any party other than the Client or any Recipient(s), as to the accuracy or completeness of the information contained in this Report. For the avoidance of doubt this Report does not in any way purport to include any legal, insurance or financial advice or opinion.



Contents

Executive summary 3

1 Introduction 81.1 Project background 81.2 Problem statement 81.3 Understanding the need for an FFEWS 91.4 Study area 91.5 River system in the basin 111.6 CDMA and GSM coverage in Nepal 14

2 Hydro-meteorological data 162.1 Introduction 162.2 Hydro-meteorological gauge densities in Nepal and other countries 172.3 Existing hydro-meteorological network Nepal 17

2.3.1 Rainfall 172.3.2 Evaporation 182.3.3 Temperature 18

2.4 Water level stations 182.5 Discharge stations 182.6 Gridded Meteorological data 18

2.6.1 APHRODITE precipitation data 192.6.2 TRMM3B42 Precipitation 192.6.3 MODIS Snow Cover Data 19

2.7 Forecasted Meteorological data 202.8 Summary of availability of data 20

3 DHM and existing flood forecasting models 223.1 DHM’s mandate 223.2 Existing flood forecasting models in Nepal – an overview 233.3 Example of operational flood forecasting models from other countries 233.1 Dissemination of forecast 24

4 Flood forecasting modelling 254.1 Flood forecasting modelling frame work 254.2 Objectives of flood forecasting modelling 264.3 Gauge-to-gauge correlation 274.4 Hydrological modelling 284.5 Routing modelling 284.6 Hydrodynamic modelling 28



4.7 Modelling software 294.8 Modelling software comparative list 304.9 FFEWS cost consideration 30

5 Rain gauge network design 325.1 Introduction 325.2 Auto telemetry rain-gauge 32

5.2.1 Description 325.2.2 Time of observation 335.2.3 Operation and measurement 335.2.4 Data transmission, storage and archive 33

5.3 Radar rain gauge 345.3.1 Description 345.3.2 Specification 34

5.4 Rain gauge network recommended for installation 355.5 Budget for proposed rain gauge network installation 37

6 Hydrometric network design 386.1 Water level gauge network 38

6.1.1 Description 386.1.2 Time of observation 396.1.3 Operation, measurement and maintenance 396.1.4 Data transmission, storage and archive 39

6.2 Discharge measurement stations 396.2.1 Description 396.2.2 Discharge measurement equipment 396.2.3 Cableway flow measurement 406.2.4 Equipment budget for discharge measurement 41

6.3 Hydrometric gauge recommended for installation 426.4 Hydrometric gauging network budget 45

7 Topographic and asset survey 467.1 Topographic survey 467.2 Survey budget 477.3 Satellite imagery 47

8 Flood forecasting model development 488.1 Mathematical modelling 488.2 Rationale for different forecasting approaches 508.3 Gauge-to-gauge correlation 518.4 Hydrological modelling 53

8.4.1 Review of existing data and models 548.4.2 Catchment delineation 54



8.4.3 Hydrological input: Rainfall, temperature and evapotranspiration 558.4.4 Bias correction 558.4.5 Calibration 558.4.6 Validation 56

8.5 Combined rainfall-runoff and gauge-to-gauge correlation 568.6 Pilot pure 2-d modelling 578.7 1-d modelling 59

8.7.1 River network 598.7.2 Calibration and validation 62

8.8 1-d/2-d linked modelling 628.9 Operation of forecasting model 64

8.9.1 Key tasks 648.9.2 Real-time data transmission and maintenance 648.9.3 Existing forecast model operating system within DHM 658.9.4 Delft-FEWS 668.9.5 Dissemination of forecast 678.9.6 Data assimilation 67

8.10 Evaluation of forecast 688.11 Model development schedule 688.12 Model development budget 698.13 Person-months for experts 70

References 72Appendices 74

A. Modelling software comparison 75

B. Comments and responses 78

Mott MacDonald | WRPPF: Preparation of Priority River Basins Flood Risk Management Project, Nepal 1Flood Forecasting and Early Warning System: Mawa – Ratuwa Basin


List of abbreviations

ADB - Asian Development Bank ASCE - American Society of Civil Engineers CBA - Cost Benefit Analysis CBDRM - Community Based Disaster Risk Management CDMC - Community Disaster Management Committee DDC - District Development Committee DDRC - District Disaster Relief Committee DEM - Digital Elevation Model DEOC - District Emergency Operation Centre DHM - Department of Hydrology and Meteorology DMF - Design and Monitoring Framework DoWRI - Department of Water Resources and Irrigation DPR - Detailed Project Report DWIDM - Department of Water Induced Disaster Management EARF - Environmental Assessment Review Framework EIA - Environmental Impact Assessment EIRR - Economic Internal Rate of Return EMP - Environmental Management Plan EPR - Environmental Protection Rule EWS - Early warning system FFEW - Flood forecasting and early warning FHRMP - Flood Hazard Mapping and Risk Management Project FIRR - Financial Internal Rate of Return FMA - Financial Management Assessment GDP - Gross Domestic Product GESI - Gender and social inclusion GFS - Global forecast system GIS - Geographic information system GLOF - Glacier Lake Outburst Flood GoN - Government of Nepal GPS - Global Positioning System ICIMOD - International Centre for Integrated Mountain Development IEE - Initial Environmental Examination IP - Indigenous People IPP - Indigenous People Plan IPPF - Indigenous People Plan Framework IRP - Involuntary Resettlement Plan IRPF - Involuntary Resettlement Plan Framework LDC - Least Developed Countries MoHA - Ministry of Home Affairs MoEWRI - Ministry of Energy, Water Resources and Irrigation MOUD - Ministry of Urban Development NAPA - National Adaptation Programme of Action NEOC - National Emergency Operation Centre NPR - Nepalese Rupiah NPV - Net Present Value OPEC - Organization of the Petroleum Exporting Countries



PAM - Project Administration Manual PCP - ADBs Public Communication Policy PEOC - Provincial Emergency Operation Centre PEP - People’s embankment program PMU - Project management unit PRA - Project Risk Assessment PSA - Poverty and Social Analysis RAH - Resettlement Affected Household RRP - Recommendation Report to the President RUDP - Regional Urban Development Project SDAP - Social Development Action Plan SDG - Sustainable Development Goals SMS - Short Message Service SPRSS - Summary poverty reduction and social strategy SPS - ADB Safeguard Policy Statement TOR - Terms of Reference UK - United Kingdom USD - Unites States Dollar VDC - Village Development Committee WC - Working Committee WECS - Water and Energy Commission Secretariat WRF - Weather research and forecasting WRPPF - Water Resources Project Preparatory Facility



Executive summary

Overview of forecasting tools

It has been proposed to develop a flood forecasting and early warning system (FFEWS) for Mawa-Ratuwa basin. This includes a simple tool for use with the most advanced hydraulic models being employed in countries like Australia and UK. The simple tool could be operational within eight months or as soon as some hydrometric data become available from the new proposed gauge network. Over a period of three years, the advanced hydraulic models will be developed, calibrated, validated and will be made operational as more and more data become available from the new gauging network and new measurements. The following forecasting tools have been proposed:

● Gauge-to-gauge correlation: the simplest and cheapest method, fast to develop, and thus could be operational soon; however, it has a very short lead time and is not appropriate in upper steep slope river reaches. There are also other limitations.

● Combined rainfall-runoff and gauge-to-gauge correlation: due to the addition of a runoff model, the forecast lead time could be up to 72 hours; however, this requires a stage-discharge rating curve at each gauging station. Such rating curve is difficult to develop for out-of-bank flow conditions without a hydraulic model.

● 1-d model: this tool will be developed for the entire river system in the Terai and is appropriate for flood forecasting. The same model type is used in Bangladesh.

● 1-d/2-d linked model: this will be the final delivery around month 24. The pure 2-d model and 1-d model will be transformed into a 1-d/2-d linked model. This is the advanced forecast model used in some areas of Australia, UK and Malaysia.

Rationale for different forecasting approaches

The four approaches described above are inter-linked and essential and/or complementing components to the final deliverable/ flood forecasting and early warning system (FFEWS) model, i.e., the 1-d/2-d linked FFEWS model. The rationale, advantages and disadvantages of each approach are described below:

● Gauge-to-gauge correlation: the simplest and cheapest method. It is an integral part of data analysis; this tool will provide support to the other four components, and thus, could be an option to use as a quick forecasting tool. It can generate new knowledge, to be translated into the final deliverables (1-d model and 1-d/2-d linked model). Advantages will be that CBDRM could be operational earlier and potential areas of uncertainty in flood level forecast could be identified. DHM is using this method in many of their river basins, e.g., in Karnali. This tool and expertise from DHM could readily be used in this basin with some nominal input from international consultant, as the tool has to be customised for new basin, need for minor changes in code and parameters may be required and thus international consultant’s input is considered. Thus, a minimum budget has been proposed for developing this tool. There will be a deployment time, in all of these five basins, for new hydro-meteorological data to become available, and that this work is a good utilisation of the deployment time, as it generates the opportunity for transferring early knowledge to the final product.

● Rainfall runoff model is the main input to all other components: a) gauge-to-gauge correlation, b) 1-d river model, c) pure 2-d model and d) 1-d/2-d linked model. Combining the rainfall model with gauge-to-gauge correlation will increase the lead time (as in the rainfall forecast) up to 24, 48 and 72 hours. However, at the forecasting points, the discharge vs



water level rating curve shall be required so that forecasted runoff can be converted to the water level using the rating curve. The rainfall runoff model provides inflows from the upper catchment and distributed inflows from intermediate catchments to the 1-d, 2-d and 1-d/2-d linked model.

● The 1-d hydraulic model, as a standalone tool, can be applied as a forecasting tool once it is ready. Without the 1-d model, a linked 1-d/2-d model (which is proposed as final deliverable) cannot be developed. Therefore, we have proposed to employ a 1-d model as forecasting tool as soon as it is ready. In any case, for certain reaches of the river, there will only be a 1-d model, as a 1-d/2-d linked model is not feasible to be developed for the entire reach of the river. This tool will also give useful feedback on forecasting performance, which then could be translated into the final deliverable. In summary, 1-d model development is not a duplicating tool; it is an essential pre-requisite. Should DoWRI and ADB decide not to take forward 1-d/2-d linked modelling, then a 1-d model will be the final product. This is the tool which DHM operate in the Bagmati, Koshi and West Rapti basins. The advantage of a 1-d model is that it runs efficiently, which is a key requirement for real time forecasting. However, a 1-d model does not have direct map output for flood risk or hazard. These require separate and customised GIS development, e.g., as practiced by forecast model in Bangladesh (http://ffwc.gov.bd/). Such a GIS tool is under development within DHM. It will need to be developed in this project in the 1-d only model reaches of the river.

● A 1-d/2-d linked model is the final deliverable; such FFEWS models are already in operation in countries like Australia, New Zealand, Malaysia and UK (Syme, 2007; Huxley, 2016). Therefore, we recommend developing this next generation FFEWS tool, otherwise by the time this project is complete (2-3 years from now), it might seem that Nepal uses less advanced tools than other countries. The 1-d/2-d linked model can forecast flood levels with better accuracy (as it is linked to 2-d floodplain model). Flood risk and hazard maps are direct outputs from such modelling. However, run-time is longer than for the 1-d model. As such, it is not feasible to develop it for all reaches of the river. For selected river reaches, where such modelling will be useful, like in the Lower Terai, this tool shall be developed using dense cross-sectional data (proposed for this study), in combination with DEM. To overcome run-time issues for real time forecasting, GPU (graphical processing unit) or HPC (heavily parallelised computing) versions of modelling software shall be used.

In several meetings with DHM, the consultant has proposed the development of a similar FFEWS model, with regards to modelling tools and types of models. We have proposed the same type of advanced 1-d model development for FFEWS, which DHM is presently operating in three different basins (West Rapti, Bagmati and Koshi). The same (or similar) modelling software (e.g. MIKE11 and HEC-RAS), for both hydrological and hydrodynamic modelling, has been recommended (in parallel to other software), thus giving DHM wider options to choose from.

Rain gauge network installation

Four new auto telemetry rain gauge stations have been proposed for installation. Data will be recorded at 15 minutes interval. There is one existing rain gauge station within this basin (source: http://www.hydrology.gov.np,). This will deliver one rain gauge for every 103km2 over the basin, similar to the density found in literature, and not far from England’s density (60km2) where rain gauge density is the highest in Europe. Flood prediction in rural and urban areas requires dense spatial gauge network: one gauge between 10 and 100km2.

Hydrometric gauge network installation

Four new telemetry hydrometric stations have been proposed, one in the Mawa river and three in the Ratuwa river. Data will be recorded at 15 minutes interval. There are no existing



hydrometric stations within this basin. The locations have been chosen carefully to allow good calibration of runoff from the hydrological model and river levels in the hydrodynamic model. In the Ratuwa sub-basin, both water level and discharge will be recorded at three stations. Only water level will be recorded at one station in the Mawa sub-basin. Discharge will be measured using ADCP or propeller type current meter depending on flow conditions (at very low flow, ADCP measurement is not suitable). One of the above discharge stations in the Ratuwa sub-basin will be a cableway station. Stage-discharge rating curve shall be developed at all three discharge stations in the Ratuwa sub-basin. The proposed locations will be finalised through discussion with DHM. DHM’s site selection criteria in other international manual shall be followed.

Hydrometric equipment

ADCP, DGPS and echo-sounder will be purchased for discharge measurement. This set of equipment will be used for discharge measurement; this basin will have one set of equipment as the discharge measurement frequency is fortnightly, and there are three proposed discharge stations within the Ratuwa sub-basin only.

Total three set of equipment has been proposed for six basins (Mohana-khutiya one, Mawa-Ratuwa one, Lakhandei one, and East Rapti, West Rapti and Bakraha none. Bakraha will share the Mawa-Ratuwa one, and East and West Rapti will share from DHM’s existing set of equipment).

Topographic and asset survey

Topographic survey will include river sections sufficiently extended across the adjacent floodplain, any existing structures and a flood embankment profile. The survey will have to be done in the Mawa and in Ratuwa rivers. Along 91km, 239 cross-sections will have to be surveyed. In steep river sections, cross-sections between 200 and 500m intervals are generally essential for accuracy in hydraulic model (HEC-RAS, Users’ Manual, Version 4.1, Figure 8-34). We have proposed cross-sections, on average, at 381m intervals.

For topographic survey, no survey equipment has been proposed for purchase; survey will be done through outsourcing.

Budget

The FF model includes development of models, development of a tool for automation of FF operation and development of a tool for automation of forecast dissemination. This budget (Table 1) will be required over a three-year period. Forecasting will start with the simplest tool from month 8 or 9 of the project using the gauge-to-gauge correlation method. Over the three-year period, advanced sophisticated 1-d, 2-d and 1-d/2-d linked models will be delivered and will remain operational for the years to come.

Table 1: Mawa-Ratuwa basin FFEWS budget Categories Parameter Unit Quantity Development Annual

cost: Operation

Annual cost:

Dissemi-nation

Unit cost ($)

Data: collection, processing, analysis

Per basin No. 1 39,600 - - 39,600

Hydrological Catchment km2 413 96,500 - - 234



Categories Parameter Unit Quantity Development Annual cost:

Operation

Annual cost:

Dissemi-nation

Unit cost ($)

modelling area Gauge-to-gauge correlation

River length

km 51.61 52,000 25,550 24,300 1,973

Pure 2-d modelling

River length

km 31.34 48,500 23,950 23,950 3,076

1-d modelling River length

km 69.29 84,000 37,500 25,750 2,125

1-d/2-d linked modelling

River length

km 40.95 80,500 34,250 23,950 3,387

Modelling software

Suite No. 1 13,000 - - 13,000

Total 414,100 121,250 97,950 Note: Modelling software licence cost is distributed over five basins. Software will have multi user network licence, and cost shown here is per basin. West Rapti is excluded from software cost. Source: Mott MacDonald

The hydro-meteorological data network budget includes establishing auto-rain gauges (Table 2), and auto and manual water level gauges and discharge measurements (Table 3). Discharge measurement is to be carried over a period of three years, while rainfall and water levels are to be collected for three years for this project and also to be maintained beyond the period of this project. Measurement of discharge beyond three years (this project period) will be left to DHM’s choice - whether further occasional discharge measurement would be carried out (or not) by their trained technical staff (who will be trained in this project).

Table 2: Budget for proposed rain gauge network in Mawa-Ratuwa basin Meteorological data network Mawa-Ratuwa budget (US$) No. of

stations No. of

measurements Capital cost/

measurement cost

Unit cost Maintenance cost: 3 years

Total cost

Ground based tipping bucket auto telemetry

4 - 20,000 5000 6,000 26,000

Total 4 20,000 5,000 6,000 26,000 Source: Mott MacDonald

Table 3: Water level and discharge gauge network budget in Mawa-Ratuwa basin Hydrometric data network Mawa-Ratuwa budget (US$) No. of

stations No. of


measurement cost Unit cost Maintenance

cost: 3 years Total cost

Discharge 3 90 390,000 4,333 6,000 396,000 Water level 1 - 7,000 7,000 6,000 13,000 Note: a) Discharge measurement to be carried out fortnightly from mid-May to mid-October; this will be 10 measurements per year, 30 in 3 years at one station and total 90 measurements in 4 stations; b) Operation and maintenance cost is $2,000 per basin for all stations per year; this involves routine site visits, repair and maintenance of the gauge, sediment removal etc., c) Discharge measurement cost is a continuous expenditure, like model development cost (and should be considered similar to capital cost); it includes cost for all skilled human resources and the logistics required

Source: Mott MacDonald



Topographic survey will be outsourced, and thus no procurement of survey equipment is proposed. The budget included (Table 4) here is for surveying cross-sections of the Mawa and Ratuwa rivers. Budget for purchasing high resolution satellite imageries has been included (Table 5) for Mawa and Ratuwa basin for lower catchment only in Terai to provide DEM to 1-d and 2-d model development and flood inundation map preparation.

Table 4: Topographic survey budget for Mawa-Ratuwa basin Topographic cross-section survey

Mawa-Ratuwa survey budget (US$) Length of survey

(km) No. of XS Total cost Unit

cost Ratuwa 58 152 30,400 200 Mawa 33 87 17,400 200 Total 91 239 47,800 -


Table 5: Satellite imagery purchase budget for Mawa-Ratuwa basin High resolution (50cm) satellite imagery

area (km2) Total cost Unit cost (USD for 1

sq.km) Mawa-Ratuwa basin in Terai 281 14,050 50

Total 281 14,050 - Source: Mott MacDonald

Hydrometric equipment purchase (DGPS, Echo-sounder and ADCP) is a capital expenditure. This equipment set will be used for fortnightly discharge measurement. as mentioned earlier and will be used and remain available for discharge measurement over a period of three years and beyond through maintenance of the equipment set. The equipment set also includes the cost for construction of one cableway discharge measurement station on the Ratuwa river (Table 6).

Table 6: Discharge measurement equipment / station budget in Mawa-Ratuwa basin Hydrometric equipment and installation

Mawa-Ratuwa budget (US$) Capital cost Operation and

maintenance (total for 3 years)

Total cost

DGPS 25,000 1,250 26,250 Echo-sounder 25,000 1,000 26,000 ADCP 35,000 1,000 36,000 Cable way discharge station: construction cost

95,000 - 95,000

Total 180,000 3,250 183,250 Source: Mott MacDonald



1 Introduction

1.1 Project background Acknowledging the importance of the Terai region to Nepal, the Government of Nepal (GoN), through the Ministry of energy, Water Resources and Irrigation (MoEWRI), is implementing the ‘Priority River Basins Flood Risk Management Project’ in the Southern Nepal Terai region. The project is the continuation of the pre-feasibility study: Package 3: Flood Hazard Mapping and Risk Management Project (DWIDP, 2016).

During the pre-feasibility study from the 25 basins, 6 priority basins were selected and included in the cost-benefit analysis: i) West Rapti, ii) Mawa –Ratuwa, iii) Lakhandei, iv) Mohana -Khutiya, v) East Rapt, vi) Bakraha. Bakraha was included by replacing Biring basin. Khutiya basin was added to the Mohana basin, and Mawa was added to the Ratuwa basin.

In this study, feasibility level design for developing an FFEWS in the above basins (excluding West Rapti) has been prepared. Note here that FFEWS for the West Rapti basin is currently being developed by the Department of Hydrology and Meteorology in Nepal, funded by the World Bank in the project ‘Building Resilience to Climate Related Hazards (BRCH)’.

This report concerns the development of FFEWS is for the Mawa-Ratuwa basin.

1.2 Problem statement Nepal is considered to be one of the most disaster-prone countries in the world. Alongside other natural hazards, such as earthquakes and landslides, flooding poses risk to large sections of the population. Heavy damage to infrastructure, loss of agricultural production, disruption of livelihoods and loss of lives in Nepal due to floods are frequent occurrences during summer monsoons. It is also expected that economic losses associated with floods is likely to rise with increasing economic and development activities in the flood plains.

Holistic management of flood risk requires actions to reduce impact before, during and after extreme events and includes preventive technical measures as well as socioeconomic aspects to reduce vulnerability to hazards. Although flood disaster risk assessment and management processes have been used by the Government agencies in Nepal to help estimate and manage risks associated with floods, these tools are in general not available (other than for isolated flood and erosion control structures) in the five basins under this study and as a result may not serve these basins in an optimal way.

One of the first steps in flood disaster risk reduction is to identify risks. Knowledge of risks raises awareness and allows pre-event planning in contrast to post-event response and recovery. In this context, flood risk management must be coordinated with other development activities in the flood plains, and particularly water resources development in a river basin. To do this, it is necessary to understand better the extent to which the current level of information related to flood disaster risk is adequate for development planning, and societal risk management practice and whether or not this level can be improved. Besides, it requires assessing the degree to which flood risk management has been integrated in other development activities so far and whether or not this integration can be improved by a thorough understanding of flood hazards in river basins, especially in Terai with large flat flood plains.



1.3 Understanding the need for an FFEWS One of the key non-structural measures of reducing flood disaster risks is the provision of a reliable and accurate flood forecasting system including a thorough understanding of flood propagation in rivers and inundation of large flood plains.

Provision of a reliable and accurate flood forecasting system with adequate lead time has been recognised by the Government of Nepal as a key non-structural measure to reduce flood disaster risks. However, due to lack of an integrated hydro-met monitoring network in these six basins and due to lack of real time forecasting technology and tools, the capability to meet the demand of a modern real-time flood forecasting and warning system is limited. An effective flood forecasting and warning system has to be based on hydrological and hydrodynamic models to simulate rainfall-runoff from precipitation and to simulate propagation of floods along the tributaries, main streams and the flood plains. Using real time rainfall data from upper and lower catchments, meteorological forecasts and river gauge data, flood forecasts for up to three days in advance can be developed using the modelling tools. The generated forecasts on flood level and discharge shall be translated easily into understandable warnings including flood inundation maps/risk maps for community-based disaster risk management activities. The forecasts, warnings and risk information shall be disseminated as widely as possible via Internet, mobile phones, public and private media, social media and other means of communication.

Flood risk consists of three key components i) problem of repeated occurrence, ii) exposure of people and assets to flood, and iii) vulnerability. FFEWS will reduce exposure and vulnerability of those exposed.

1.4 Study area The catchment of the Mawa-Ratuwa river basin lies between Northing 2919087 m and 2973609 m (latitude 26°25′ 56.89″–26°49′ 05.14″N), and between Easting 561528 m and 580023 m (longitude 87°36’36.31″E–87°47′24.97″E) in WGS 84, UTM Zone 45 N (see Figure 1). The basin extends from Chure Hills (Siwalik Hills, also known as sub-Himalayan hills, at low altitude) in the north and in Terai (means low flat land) in the south up to Indo-Nepal border. Ratuwa is the main channel, which is joined by the Mawa in the west, and Bidhawa and Chanju Khola in the east. The catchment covers an area of 413km2 in the far west of Nepal (Figure 1). The Mawa-Ratuwa river system shares the district of Ilam and Jhapa in the east and Morang district in the west in Province No. 1. This river system has 366 settlements distributed over rural and urban municipalities with a population of 165,260 and 36,871 households (CBS, 2011). Damak and Urlabari are the two major towns located in this catchment.

The basin has one existing meteorological station which collects rainfall, and there is no hydrometric station (water level and /or discharge) within this basin.

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1.5 River system in the basin A brief summary of the river system in this basin is presented below in Table 7, and its length is shown in Figure 2). Reach-wise detail classifications of these morphologically active rivers have been presented in a separate report in this study (Mott MacDonald, 2018). A brief description, mainly useful for developing hydrological, hydraulic and flood forecasting models, is presented below.

This catchment originating from Chure Hills is relatively small (413km2), much smaller than the size of West Rapti at Bagasoti gauging station. The West Rapti at Bagasoti has a time of concentration of 10hrs (DHM, 2018). Thus, this basin is expected to have much smaller time of concentration. And this will affect both forecast lead time and response time. In steep slope reaches (upper reaches), the flood travel time, in general, is fast. Therefore, gauge to gauge correlation forecasting, which is practised in many basins by DHM in Nepal, will not be generally suitable, particularly in upper reach, as both forecast lead time and response time will become small in this basin. Rivers in Nepal are flashy; as such the lag time (time required to attain peak flow after a rainfall event) is very short. This necessitates that rainfall-runoff and hydraulic models are developed for the well-defined reaches of river and get the benefit of 1 to 3 days of lead time on rainfall forecast from the weather forecast model. The hydraulic model, irrespective of flood lag time, response time and slope of the rivers, will be able to forecast water level and their propagation with same lead time (1 to 3 days) as in weather forecast model. However, depending on the river characteristics, appropriate type of hydraulic model should be developed. In steep slope reaches, flood propagates fast and flooding spreads less in the limited floodplain; so developing 1-d model will be more appropriate in those reaches. In gentle slope reaches, the flood propagation is slow and flood inundates more areas in meandering and braided floodplain; thus, 1d/2d linked model will be more accurate and beneficial to warn people. Considering the characteristic features of the river systems, e.g., hills, river braiding, meandering etc., the types and domains of models, have been identified (see Section 8). However, development of appropriate type of model (1d, 2d or 1d/2d linked) should require several iterations during development stage of these models.

Table 7: Summary of river system in Mawa-Ratuwa basin River Reach ID Reach Characteristics Channel length (km) Slope % Mawa 1 Hill 19.63 8.9

2 Fan 10.24 0.87 3 Peripheral fan 4.08 0.39 4 Flood plain, meander 9.61 0.21

Ratuwa 1 Hill 23.03 6.26 2 Fan 7.44 0.90 3 Peripheral fan 6.58 0.33 4 Flood plain, partially meander 8.04 0.22 5 Flood Plain, partially meander 23.30 0.12


The Mawa river has been divided into four reaches (Table 7). Its total length is 44km. Reaches are mainly straight and between straight and meandering. There is no distinct pattern of meandering reaches. The channel slope is very steep, particularly in the first hilly reach. In the first two reaches, sediments are mainly boulders, gravels and sands, while in the downstream two reaches, sediments are sand and silt (and might have a minor fraction of fine gravel).



The Ratuwa river has been divided into five reaches (Table 7). Its total length is 68km. Reaches are mainly straight and between straight and meandering. There is no distinct pattern of meandering reaches. The channel slope is very steep, particularly in the first hilly reaches. In the first three reaches, sediments are mainly boulders, gravels and sands, while in the downstream two reaches, sediments are sand and silt (and might have a minor fraction of fine gravel).



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1.6 CDMA and GSM coverage in Nepal Real time data acquisition and the issuing and dissemination of flood alerts and warnings from the FFEWS model are the prime objectives. The proposed FFEWS will primarily use the code-division multiple access (CDMA) and Global System for Mobile communication (GSM) technologies of Nepal for dissemination of the flood alerts and warnings. CDMA technology has been used by NTC while the GSM technology is supported by other mobile providers in Nepal. Ncell is one of the companies with the largest GSM networks in Nepal. Both companies are providing services to DHM for real-time data acquisition. However, these service providers have several gaps in their network coverage. In these gap areas, the hydro-meteorological stations cannot transmit data on a real-time basis. Since these technologies are based on line-of-sight communication, some of the hydrometric stations located in deep gorges do not have connection even within the area of their coverage.

The existing CDMA and GSM in Nepal is shown in Figure 3. The Mawa-Ratuwa basin seems to have a good GSM network, which will be very useful for establishing the FFEWS in this basin. However, for installation of any new proposed hydro-meteorological monitoring station, GSM must be checked prior to installation, and if needed, station locations could be shifted through discussion with DHM.



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2 Hydro-meteorological data

2.1 Introduction Certain types of hydro-meteorological data are essential for different types of flood forecast modelling. Meteorological data types are: rainfall/precipitation (P), temperature (T) and potential-evapotranspiration. They are input data for rainfall-runoff (RR) modelling in any hydrological modelling tool. Hydrological data types are: discharge (Q) and water level (H). They are required at boundary conditions as well as at calibration and validation locations of rainfall runoff (Q required) and 1-d and 2-d hydrodynamic models (both Q and H required).

Development of the base model, whether for flood forecasting or for design of flood protection works, would not essentially be very different. In order to be more run efficient, the forecasting model could be simplified in some places, though not at the expense of accuracy. The model has to be calibrated against several past events and the calibration process would be enhanced by testing a greater number of events. The model should be able to replicate any event, for a wide range of return periods (from very low to high exceedance probability). Both long term data and short-term past storm event records can be used for calibration. Long-term data are more appropriate and do not need to be continuous. During the operational phase of the flood forecasting model, continuous data during the monsoon season will be essential to collect as the model will operate at real time daily.

The following data will be required at daily or sub-daily temporal resolution in each phase of FFEWS model development, namely, calibration, validation and operational phase.

● Cumulative rainfall: runoff is the response of total rainfall, rather than a rate of rainfall. As such, rainfall is required as input to the runoff model;

● Mean temperature; ● Cumulative potential-evapotranspiration: for the same reason as rainfall, cumulative

evapotranspiration data is used as input in the runoff model; ● Water level; and, ● Discharge.

Snow cover data may not be required as the altitude of the basin is below 3000m, and thus catchment runoff is not snow-fed (Putkonen, 2004).

For example, in the calibration phase, parameter values within the RR models for each of the sub-catchment will be tuned so that the differences (error) between modelled Q and observed Q are minimal and down to acceptable levels. The level of acceptability needs to be agreed with the client (in this case, DHM) and with due reference to best practice RR modelling (DHI, 2014; HEC-HMS). During the validation phase, the performance of the model is evaluated, without changing any parameters established during the calibration phase, and a similar standard of matching between observed and modelled Q and WL shall be obtained. Otherwise, a recalibration would be needed followed by validation.

In the event that observed meteorological data are insufficient or not available in all the sub-basins, data from other sources shall be explored. This necessitates the consideration of satellite-based data on rainfall, temperature, snow cover, etc. Such data are available as gridded data, are generally in good spatial resolution, and are mainly derived from long records of observed gauge-interpolated data (Section 2.6 for details).



In this chapter, available data from DHM and gridded data from a number of sources have been discussed.

2.2 Hydro-meteorological gauge densities in Nepal and other countries The density of hydro-meteorological stations in Nepal and in other countries is presented in Table 8. The density is mainly dependent on purpose, e.g. the rainfall data will be used for irrigation, flood risk assessment or flood forecasting purposes. Flood prediction in rural and urban areas requires dense spatial gauge network, one gauge between 10 and 100km2 and higher temporal measurement frequency between minutes and hours (Berndtsson and Niemczynowicz, 1988).

Table 8: Rainfall gauge density in Nepal and in some selected countries in the world Country Number

of gauges Average area per gauge

(square km) Nepal - 550

Nepal: Siwalik region - 430

Nepal: Terai region - 370 UK 3,214 76 England 2,169 60 France - 116 Netherlands - 130 Germany - 88 USA - 1,040 India - 790

Source: DoWRI (2016) for Nepal and Allot (2010), Mett Office, England for other countries

2.3 Existing hydro-meteorological network Nepal DHM is the designated government agency for predicting and disseminating weather-based forecasts and warnings. In June 2018, DHM maintained a total of 175 hydrometric stations, 337 rain gauge stations, 68 climatological stations and 15 synoptic stations. These stations include both real-time telemetry stations and non-telemetry stations.

Among the above stations, DHM presently maintains a network of 28 hydrological stations and 88 meteorological stations as real-time telemetry stations. DHM is further upgrading 59 hydrometric stations to real-time telemetry stations. An additional seven stations are also under consideration for upgrading to real-time telemetry. In total, 182 hydro-meteorological stations are scheduled to become operational as real-time telemetry data acquisition systems in the near future.

2.3.1 Rainfall

Existing metrological stations within the Mawa-Ratuwa basin are shown in Section 5 in Figure 6. There is one station within the basin and there are no stations within the 4km buffer zone around this basin. The rainfall station is understood to yield daily data. While it will be useful for calibration and validation of the hydrological model, real time telemetry rainfall at sub-daily frequency, usually 15 minutes resolution, will be required for hindcasting and forecasting and early warning system. As such, new telemetry rain gauges have been proposed (see Section ●).



DHM has planned installation of three radar rain gauges across Nepal. One is under installation at Surkhet near Mohana and one at West Rapti. Installation will be completed in a few months. This C-band long range radar has a 400km diameter range. Temporal resolution from bid document is understood to be of 1-hour as a minimum; however, the temporal resolution is to be agreed with DHM. The spatial resolution, though not mentioned in the bid document, could be of 1km, as obtained by similar long range C-band radar in UK and Germany (Lengfeld et al., undated).

Therefore, the forecasting model development will initially use existing rainfall radar data from DHM (if available), supplemented with gridded rainfall data available from satellite-based sources (see Section 2.6).

2.3.2 Evaporation

The same meteorological stations which monitor rainfall also collect daily PAN evaporation data. However, hydrological modelling software, like NAM, uses monthly potential-evapotranspiration. There is one station within the basin and there are no stations within the 4km buffer zone around this basin. PAN evaporation data from this station will be used in the development of the hydrological model within the FFEWS study.

2.3.3 Temperature

The same meteorological stations, which monitor rainfall, also collect temperature, daily minimum and daily maximum data are available from DHM. There is one station within the basin, but there is no station within the 4km buffer zone around the basin. Temperature data from this station shall be used for the development of the hydrological model with the FFEWS study, in case there is any snow fed runoff in the basin.

2.4 Water level stations Existing hydrometric stations (water level and discharge) within the Mawa-Ratuwa basin are shown in Section 6.3, Figure 8. There is no water level station within this basin. Therefore, for the development of hydrological and hydrodynamic models within the FFEWS study for this basin, water levels must be obtained from the new proposed monitoring stations, which will be real time telemetric data (see Section 6).

2.5 Discharge stations Existing discharge stations within the Mawa-Ratuwa basin are shown in Section 6.3, Figure 8. There is no water level station within this basin. Therefore, for development of hydrological and hydrodynamic models within the FFEWS study for this basin, discharge must be obtained from the new proposed monitoring stations, which will be real time telemetric data. Through conversion of water level to discharge, continuous discharge time series will be generated by using a discharge vs water level rating curve. The discharge vs water level rating curve will be developed in the project (see Section 6).

2.6 Gridded Meteorological data Gridded time series of meteorological data (rainfall/precipitation, surface temperature, evaporation and snow cover), spreading over Nepal and bordering basins in China and India are available from a number of sources. Data are satellite-based, re-analysis based or gauge-interpolated estimates. Gridded time series data will be needed due to non-availability or scarcity of hydro-meteorological observations within these six priority basins. The gridded products available are:



● TRMMv7 precipitation estimates ● APHRODITE precipitation and temperature products ● MODIS snow cover products

Availability and quality of some of the gridded data is briefly discussed below.

During the model development phase, time series of data could be used from the above sources. However, before use, availability and quality of long records shall be examined.

2.6.1 APHRODITE precipitation data

Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) collects and analyses rain gauge observations from thousands of Asian stations. It has about 57 years of daily precipitation (P) datasets available between 1951 and 2007. APHRODITE (APH) precipitation data is gauge-interpolated and takes account of the orographic effect. Temporal resolution of the data is daily; spatial resolution is 0.25° lat/long (approximately 9km cell). APH data also has air temperature data with the same resolution as precipitation data.

APH daily precipitation data is freely available for non-commercial purposes (for Academic Institutions and Research) provided proper acknowledgement is given. No commercial use is allowed. Use of APH data by the Government of Nepal may be considered non-commercial; however, for use of APH data, permission has to be granted. The spatial coverage of APH monsoon dataset extends from 60°E longitude in the west to 150°E longitude in the east and from 15°S latitude in the south to 55°N in the north. This means APH data cover almost all of Asia, including Nepal. It is available for the period 1951-2007: i.e. 57 years of daily precipitation data for each grid. This implies availability of daily data for calibration and validation of runoff models with long historical records. APH data are available in two spatial resolutions –viz. 0.5° lat/long and 0.25° lat/long. Compared to observed data from existing DHM stations, APH-P provides better spatial resolution of precipitation distribution (also over long term periods) over Nepal in this basin and also in the other basins of this study. It is expected that the mean areal precipitation series for each sub-catchment could be obtained from APH-P and will be used for hydrological modelling.

2.6.2 TRMM3B42 Precipitation

Tropical Rainfall Measuring Mission (TRMM) is a joint venture between National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA). TRMM3B42v7. It is a gauge-adjusted version of satellite-based precipitation estimates of TRMM satellites. The products are continuously released with a two-months latency period. Spatial resolution is 0.25° lat/long; temporal resolution is three hours. Data are available from January 1998 to the present time and freely available for all purposes. These data will complement APHRODITE data which end in 2007. Thus, any calibration and validation of hydrological models beyond 2007 will be carried out using these precipitation data.

2.6.3 MODIS Snow Cover Data

The data on snow cover is required to decide upon the portion of a sub-catchment where snow melt generated runoff is dominant. None of the six basins within this study is considered to be under snow cover for significant periods of time. NASA’s MODIS snow cover product can be obtained freely from NASA’s MODIS link: https://modis.gsfc.nasa.gov/data/dataprod/mod10. php.



2.7 Forecasted Meteorological data For operation of FFEWS, Quantitative Precipitation Forecast (QPF) estimates are essential, and Quantitative Temperature Forecast (QTF) estimates are desirable. For PET, flood forecasting models often use average of past monthly records.

The Numerical Weather Prediction (NWP) system of the Indian Meteorological Department (IMD) provides QPF and QTF estimates which could be obtained through FTP access. IMD forecasts have 9km spatial resolution (0.081° lat/long), three hours temporal resolution and 72 hours lead time. IMD-QPF is available at a finer spatial resolution than GFS (details below on GFS) for catchments in Nepal.

Generally, the accuracy of the forecasts from any NWP model deteriorates as lead time increases.

IMD-QTF products have the same spatial and temporal resolutions as the IMD-QPF products, i.e. 0.081° lat/long, three hours temporal resolution and 72 hours lead time.

The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP) of NOAA. The GFS-QPF with 0.25° lat/long spatial resolution, three hours temporal resolution and a lead time of 10 days is freely available. It is understood that DHM already uses GFS and the Weather Research and Forecasting (WRF) model to obtain QPF for forecasting in their existing FFEWS models.

As IMD-QPF has finer spatial and temporal resolution, and GFS-QPF is already in use by DHM, IMD and GFS are considered as the best source of QPF available to Nepal catchments at present. The best approach may be to treat IMD-QPF as the primary QPF should DHM be able to sign a treaty with IMD to obtain IMD-QPF; else GFS-QPF will be used as this is already being used by DHM.

2.8 Summary of availability of data Summary on the availability of existing hydro meteorological data and forecasted rainfall data and their sources are presented in Table 9.

Rainfall data from DHM are available for a considerable number of years at seven stations, and should be usable for calibration and validation of the rainfall runoff model. However, discharge and water level data are not available in this basin. In the rainfall data, the published reports of DHM (downloaded from their web site) shows missing rainfall data. We should note here that data from manual stations have been published in Data Book upto 2016, which indicates that DHM needs about 2 to 3 years to organise, analyse and quality check data before officially publishing.

Satellite based rainfall also available for many years; both APHRODITE and TRMM data could be used for running the hydrological model.

Table 9: Hydro meteorological data – summary of availability

Data type Source of Data Station Collection

method Collection frequency Availability Latency/

Publication Period

available Rainfall & pan evaporation Damak Manual Daily Data Book 2 to 3 years 1964 to 2016

Temperature DHM Damak Manual Daily Data Book 2 to 3 years 2014 to 2016

Water level and discharge ‐ - - - - ‐



Data type Source of Data Station Collection

method Collection frequency Availability Latency/

Publication Period

available

Gridded rainfall

APHRODITE - satellite-based Daily Online - 1951-2007

TRMM satellite-based 3-hourly Online 2 months 1988-present

Forecasted rainfall

IMD satellite-based 3-hourly Online (Near) Real time - NCEP, NOAA

satellite-based 3-hourly Online 7 hours -

Forecasted Temperature IMD satellite-based 3-hourly (Near) Real time -




3 DHM and existing flood forecasting models

3.1 DHM’s mandate The Department of Hydrology and Meteorology (DHM) is the sole organisation in Nepal responsible for flood forecasting. One of the mandates of DHM is to provide flood early warnings to vulnerable communities and to major stakeholders. DHM, so far, have been able to establish flood Early Warning Systems (EWSs) in major rivers, in a few flashy rivers and in areas downstream of two glacial lakes that are considered potentially dangerous (DHM, 2018). The major river basins covered under EWS are: Kamali, Babai, West Rapti, Narayani, Bagmati, Kamala, Koshi, Kankai and Biring. Details of some of the EWS is discussed in Section 3.2. DHM’s aim is to extend flood forecasting services throughout the country.

The re-organised structure of the Government of Nepal implemented on 23 February 2018 has created the Ministry of Energy, Water Resources and Irrigation (MoEWRI). DHM used to operate under the Ministry of Environment and Population but now has been brought under the wing of MoEWRI with a mandate to provide weather and flood forecasts. With a new regulatory setup in place, DHM has been mandated to develop EWS including information dissemination components.

The organisational structure of DHM, which combines hydrology and meteorology, puts DHM in an ideal position to generate flood forecasts and issue forecasts and warnings. DHM has also been working with the Ministry of Home Affairs (MoHA), an organisation involved in the entire disaster management cycle, on disseminating flood warnings through National Emergency Operation Centers (NEOCs) and District Emergency operation Centers (DEOCs). DHM has been supported by WMO since its establishment through collaboration on different meteorological activities and activities related to operational hydrology. As a member of WMO, Nepal has access to global and regional meteorological data required for monitoring and forecasting floods. Furthermore, DHM has received funding for several projects from WMO in the past, including projects on upgrading meteorological observation systems, weather forecasting, agriculture meteorology and hydrological services. In collaboration with the International Center for Integrated Mountain Development (ICIMOD) and countries in the Hindu Kush-Himalayan (HKH) region, WMO has been promoting the World Hydrological Cycle Observation System (WHYCOS) under the name of HKH-HYCOS. DHM has been contributing to this program by sharing real-time data for effective flow forecasting for the rivers originating from the Hindu Kush-Himalayan region.

With the implementation of the project ‘Building Resilience to Climate Related Hazards’ (BRCH), the World Bank has supported DHM by upgrading the existing flood forecasting system in Koshi and Rapti. This five-years project started in 2013 and its goal is to upgrade the real-time data acquisition system and establish an end-to-end flood forecasting system. Upgrading of Koshi and Rapti FFEWS are under progress at the moment. Nepal was also able to receive small grants from the Danish and Finnish governments for promoting DHM’s flood forecasting capabilities. Besides collaborating with the international community, Nepal has also been working closely with its neighbour countries on upgrading its hydrological and meteorological monitoring systems. Since all the rivers of Nepal merge into the Ganga-Brahmaputra river system, Nepal has bilateral arrangements with India and Bangladesh that support the sharing of hydro-meteorological data and flood information.



Several INGOs are involved in disaster mitigation in Nepal. Nepal Red Cross Society (NRCS) and UNDP are involved in most of the disaster mitigation activities in Nepal. UNDP has been working with DHM on strengthening Nepal’s hydrological services and promoting community-based flood warning systems. Similarly, small-scale community-based flood warning systems have been implemented by other INGOs either in collaboration with DHM or in collaboration with other government agencies and NGOs. Since flood forecasts and warnings are widely used by communities and with several organisations simultaneously involved in disaster management, there are innumerable stakeholders involved (DHM, 2018).

3.2 Existing flood forecasting models in Nepal – an overview Probabilistic Flood Forecasting Model The model was developed jointly through a research partnership with Lancaster University (UK) and the International NGO Practical Action. The model assimilates rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The model predicts future water level at a site where thresholds for warning and danger levels are known. In this approach each gauged site where warning thresholds are defined requires its own model. The model was piloted in 2002 for the East Rapti River. The pilot model was enhanced and extended, expanding over the next 10 years to cover eight river basins across Nepal (Karnali, West Rapti, Babai, East Rapti, Narayani, Bagmati, Kankai and Koshi basins) (Gautam and Phaiju, 2013). This model was made operational by DHM. Among them, West Rapti, Koshi and Babai FFEWS have been (or being) upgraded to advanced hydrological and hydraulic models (see below).

Advanced hydrological and 1-d hydraulic forecast model Nepal currently has operational flood forecasting models for the Koshi, West Rapti, Bagmati, Karnali and Babai catchments; based on NAM/MIKE11 or HEC-HMS/HEC-RAS software which use rainfall QPF estimates from GFS and WRF. These models are rainfall-runoff and hydrodynamic models which forecast flood flows and flood levels. In addition, Nepal has gauge-to-gauge correlation forecasting covering most of the country, except basins smaller than about 300 to 400km2. This was informed by DHM Forecast Specialist during meetings held between July and October 2018.

The Koshi flood forecasting model uses NAM modelling software for hydrological/rainfall runoff simulation, and MIKE11 for advanced (fully dynamic) 1-d river flow modelling. As the model uses rainfall forecasts from the GFS and WRF model, the lead time is up to 72 hours. The West Rapti flood forecasting model is under development in the same NAM/MIKE11 modelling system and thus has a similar forecasting ability as the Koshi flood forecasting model.

The Bagmati forecasting model is also operational and is developed in the HEC-HMS and HEC-RAS modelling system.

3.3 Example of operational flood forecasting models from other countries Bangladesh has a dedicated flood warning centre in existence for 30 years, which has an FFEWS for the entire country’s river system in one model; the model is referred to as a super model, developed in the advanced hydrological (rainfall-runoff) modelling tool NAM and 1-d hydrodynamic model developed in the modelling tool MIKE11. The Bangladesh FFEW model has a 72-hour lead time. Similarly, advanced FFEW models have been developed in India for the Bagmati river basin in Bihar, the Krishna and Bhima river basins in Maharashtra, and the Brahmaputra river basin in Assam. Unlike those of Bangladesh and India, the UK’s FFEW models are for smaller catchments/basins because of the hilly terrain of the country and require



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4 Flood forecasting modelling

4.1 Flood forecasting modelling frame work Flood forecasting models vary in complexity from simple gauge-to-gauge correlations to highly automated integrated catchment flood forecasting models. The degree of automation and sophistication shall be considered based on the needs for a particular location and a particular community. While the type of forecast system will be typically based on existing hydrometric networks or the enhancement or development of new networks, the system will need to be achievable and affordable. The type of flood forecasting system will therefore depend on:

● Available data ● Basin characteristics/complexity ● Accuracy and reliability required ● Lead time requirements ● Needs of the flood risk communities ● Ability of the operating organisation to routinely operate, maintain and update the models

Catchment aspects that affect the magnitude and timing of floods are wide and varied. These might include:

● Degree of catchment urbanisation ● Presence of reservoirs and flood storage/attenuation ● Quality of the ratings at gauging stations ● Impact of tributary and ungauged catchments ● Impact of backwater effects, confluences and tidal locations ● Seasonality of rainfall ● Upland areas and snowmelt considerations ● Influence of groundwater

An operational flood warning and forecasting system typically uses some form of hydrological and hydraulic modelling to provide sufficient lead time to avoid loss of life and to allow flood defence measures to be operated. Forecast models are at the heart of reliable operational flood warning systems.

The Mawa-Ratuwa basin’s runoff is primarily rain-fed. The upstream catchment in the Chure hills, is below 3,000m amsl, and thus, runoff mainly originates from rainfall with a minor component from groundwater (base flow). Snow-fed runoff normally should not need to be considered for catchments below 3,000m (Putkonen, 2004).

In the mathematical modelling system for flood forecasting in the Mawa-Ratuwa basin, three major approaches could be used:

1. Gauge-to-gauge correlation: this is one of the simplest forecasting tools, based on correlation between gauged water level at two stations: an upstream base station and a downstream target station. This method uses flood levels at the base station when the flood has actually arrived (i.e., it uses the real time observed flood level) to estimate the future water level at the target station. The lead time for forecasts at the target station is small, a



maximum of probably five to six hours in this basin, as the basin is quite small relative to West Rapti (DHM, 2018) where about 8 to 10 hours of lead time is possible.

2. Rainfall-runoff/hydrological modelling: Rainfall-runoff process modelling could be used to improve the forecast accuracy and lead time. The runoff in catchments with elevation below 3,000m AMSL can be modelled using a continuous rainfall-runoff model. Since the rivers are perennial in nature, the base flow component in the rainfall-runoff process is adequately represented. These continuous rainfall-runoff models typically require rainfall and potential-evapotranspiration as input to provide catchment flow as output hydrograph.

3. Channel routing/hydrodynamic modelling: Channel routing is the process which describes the propagation of flood waves along the river. This process can be modelled using hydrologic and/or hydrodynamic modelling. The employment of the model depends upon the need and complexity in the system. 1-d, 2-d and 1-d/2-d linked hydrodynamic models can be developed.

In terms of what types of modelling techniques should be used, the following key factors should be considered:

● Purpose of the study ● Level of complexity for both in-bank flows and out-of-bank flow paths ● Flow controls and structures in the river system ● Flood storages and their representation in the model ● Requirements on the level of model accuracy ● Computational resources availability ● Data availability and accuracy ● Availability of time and budget

It is important to use the most appropriate modelling tool for the project rather than merely the tool that is available. Inappropriate tool selection (such as i) the use of a steady flow model where unsteady flow conditions must be used and where storage is important, and ii) use of 1-d unsteady model, where 1-d and 2-d linked model is most appropriate, like in a dense urban area) can have significant technical and accuracy implications.

4.2 Objectives of flood forecasting modelling The FFEWS shall deliver the following in each basin:

● Implementation of a web-enabled Windows Application on server for routine operation of FFEWS models, dissemination of forecast and routine update of FFEWS models.

● Integration of the knowledge base from hydrological, hydrodynamic modelling and data analysis.

● Seamless connection to temporal and spatial database. ● Dynamic front end module for modifying model inputs, recalibration, data assimilation and

dissemination of model results. ● Processing of flood forecasting results in GIS environment into maps of flood inundation

extent, depth, arrival time, and duration, with other relevant themes in the background. This will particularly be necessary in the river reaches of 1-d models.

● In 1-d/2-d linked model reaches a flood inundation map will be a direct output on the forecasting website and ready for dissemination.

● Design and development of appropriate inundation mapping tools, using appropriate satellite /LiDAR derived from DEM for the critical floodplain to predict inundation.



● Development of a module to generate institutional and community targeted inundation forecasts and alert messages (via SMS) and web-enabled maps.

● Implementation of a standalone user-friendly application, which integrates the flood forecast model with knowledge base of topographic and thematic GIS data and has the capability to run automatically.

● Module for embankment breaching scenarios or structural failure scenarios, in-built in the forecasting tool; magnitude of flooding through breaching can be more devastating than flooding from embankment overtopping.

● Design of appropriate format, content and dissemination protocols to accommodate the current practice within DHM or improvement to the DHM practice for the Flood Alert to flood-affected residents, both designated and broad category to mobile phones in the likely-affected areas.

● Post evaluation of performance of forecast accuracy with respect to level and timing each year.

● Obtain feedback from stakeholders and incorporate suggestions on dissemination of forecasts and warning.

4.3 Gauge-to-gauge correlation Flood forecasting in Nepal is mainly operational based on level-to-level (also called gauge-to-gauge) correlation across the country, except in a few basins where advanced hydrodynamic modelling is being developed and applied.

The level-to-level forecasting tools use observed water level data at upstream locations (base stations) to forecast water level at downstream target locations. This method is simple and most cost effective, as it only requires water level data in real time at base station and at target station.

Flood forecasting from gauge-to-gauge relationships has the limitation of needing to wait till the flood is observed at the base station upstream of the forecasting stations. Therefore, in the process, the possible lead time from the catchment lag (from rainfall to water level response) to the base station is lost. Such lead time can easily be added by introducing a hydrological model that can transform the observed precipitation into a simulated hydrograph at the base station. In case of combined use of runoff model and gauge-to-gauge correlation, the base station must have a stage-discharge rating curve, so that forecast runoff from the hydrological model could be transformed into forecast water level using the rating curve.

The gauge-to-gauge correlation procedure does not incorporate any addition of flow between the base station and the forecasting station, nor does this approach consider any breach or overtopping of the embankments between the stretches. The method also does not provide any other information on the water surface profile, e.g. cumulative effect of many control structures, in the stretch that is very crucial from the embankment safety and out-of-bank flow situation. Another major limitation to the correlation method is the availability of prediction only at selected sites (target stations) and not all along the main river, let alone the tributaries. Flood maps cannot be generated due to the coarseness of the forecast stations as the forecasts will be only at the gauging points along the river which are normally sparse. Thus, in this method, the inundated area and the time, depth and duration of inundation, which are essential for effective flood management, are not provided to relevant agencies and communities.



4.4 Hydrological modelling Hydrological, i.e. rainfall-runoff, modelling can be carried out by various modelling software. One essential element is that the rainfall-runoff model should have a proven record of coupling with a river model (hydrodynamic), so that runoff can be applied as inflows to the river model. A coupled hydrological and hydrodynamic model is essential for designing flood forecasting and warning and will allow much higher lead time in forecasting. Some selected hydrological modelling software are discussed below:

● HEC-HMS (coupled to the 1-d river modelling tool HEC-RAS 1-d and 2-d) ● NAM (coupled to the 1-d river modelling tool MIKE11 1-d and MIKE21FM) ● PDM (coupled to the 1-d river modelling tool Flood Modeller Pro, previous name ISIS and

Infoworks ICM Live)

NAM (DHI, 2016) and HEC-HMS (US Army Corps of Engineers, 2017) are deterministic, lumped, conceptual hydrological models, comprised of a set of linked mathematical statements describing, in a simplified quantitative form, the land phase of the hydrological cycle. They mainly simulate surface and sub-surface runoff, and base flow components. The model parameters require calibration against observed runoff. These parameters remain fixed (constant) over time.

The distributed runoff model, e.g., Probability Distributed Model (PDM) from the Centre of Ecology and Hydrology (CEH Wallingford, 2016) accounts for seasonal variation and spatial and temporal effect on parameters (e.g., soil-moisture deficit).

There are many other hydrological modelling systems (e.g., URBS). However, the coupling of a hydrological model with the hydrodynamic model, the capability of automatic parameter adjustment (auto-calibration) and the long-term continuous simulation ability are important when considering selection of tools.

4.5 Routing modelling Normally, the upper catchment is simulated without any need for inclusion in hydraulic model. If in the upper hilly catchments in Siwalik hills, the travel of flood waves and their volume and attenuation are found to be important, some of the tributaries in the hills could be considered for routing modelling, e.g., by Muskingum-Cunge flood routing units. The topography could be extracted from DEM for such modelling.

4.6 Hydrodynamic modelling Hydrological and hydrodynamic modelling for flood risk/flood inundation are the key components of a FFEWS. Logically, the hydrological and hydrodynamic flood inundation models are developed first, and then transformed into flood forecasting models.

Hydrological and hydrodynamic flood inundation models for flood forecasting purpose are calibrated and validated over a wide range of flood events, ranging from low flows to extreme high flows. During a monsoon, an extreme flood event can occur; as well a low magnitude flood can also occur. As such a FFEWS model should be ready and applicable for any flow condition. In a hydrodynamic model, accuracy of results, convergence of results (oscillation free results) and stability of model are three key controls. A model, which is suitable and developed for low flows, may generate instability at high flows due large depths in the channel and shallow depths in the floodplain. As the flood forecasting models will be run on real-time, they have to be suitable for all flow conditions. The forecast model run must not crush during monsoon period, e.g., due to model instability. Flood inundation models, once calibrated and validated for a wide



range of flows, will then help to establish the locations for flood warning and to set up flood alert and warning threshold trigger levels in rivers for out-of-bank flooding, agricultural land flooding and property flooding.

Flood forecasting models operate in real time, and thus, model run-time has to be efficient. To make the model run time fast, the model will be simplified in reaches away from the area of flood forecasting points. This will be done by increasing the distance between computational nodes maximum permissible limit and by removing other model units, which are less important, e.g. structures which do not create any significant head loss. However, care should be taken as such that the simplified models maintain the same accuracy at all flood forecasting and calibration points. This process will require several trials so that model accuracy is preserved.

4.7 Modelling software Some widely used flood forecasting modelling tools are presented in Table 10. The modelling software presented are mostly popular and widely used. Thus, the list is not exhaustive. A more detailed list is presented in Appendix A.

Table 10: Examples of key modelling software for flood risk and flood forecasting modelling

Model Type Modelling Tools/ Technology Hydrological/rainfall runoff modelling Any lumped conceptual catchment runoff model, e.g,

NAM, HEC-HMS, and PDM Hydrodynamic: flood inundation and flood risk modelling

MIKE11, MIKEFLOOD, MIKEURBAN, MIKE21, MIKE GPU MIKE21FM, HEC-RAS, FLOOD modeler Pro (former name ISIS), TUFLOW Classic, TUFLOW FV, TUFLOW GPU, Info-works ICM

Flood forecasting and warning NAM, and MIKE (11, 21FM, URBAN) and HEC-HMS and HEC-RAS, , and PDM and Flood Modeller Pro and TUFLOW HPC/GPU, and Infoworks ICM Live etc.


Hydrodynamic modelling techniques are at the core of fluvial flood risk assessment and flood forecasting and warning (WMO, 2011). As a common practice which started in previous decades, hydrodynamic models are often developed and used to simulate the flood water in the river system as well as across the floodplain. They are used to predict the flood depth, water level, velocity, flood extent and even flood hazard level. Generally speaking, the river system is represented using 1-d models as the flow travels in the channel direction when it remains in the river channel, whilst the floodplain is represented using 1d, quasi 2d or 2-d models as the flood water spreads in different directions when the water exceeds the river banks. The 1-d river channel and the floodplain models are linked to represent the connection between the river and the floodplain.

It is important to use the most appropriate modelling tool for the project rather than merely the tool that is available. Inappropriate tool selection (such as use of a steady flow model where unsteady flow conditions are prevalent and where storage is important, and use of a 1-d unsteady model, where 1-d and 2-d linked models are most appropriate, such as in a dense urban areas) can have significant technical and accuracy implications for both current and future needs. Several modelling software tools have been tested through bench marking studies (EA/Defra, 2004 and 2013) and are being employed for flood risk mapping studies around the world (Table 11). In general, software, which has not been subject to benchmarking, is not recommended for developing models.



A list of benchmarked hydrodynamic modelling software is presented in Table 11. Software on this list are widely used in flood risk assessment and flood forecasting in UK, Australia, USA, Bangladesh, India, Nepal and other countries. The benchmarking research of the software was conducted by Environment Agency and Defra of UK (EA/Defra, 2013). DoWRI and DHM will have the option to choose from the list. If required, it is recommended to consult recommendations and results of the benchmarking study and choose the suite of software best suited for Nepal.

Table 11: List of benchmarked modelling software Software 1-d 2-d 1-d–2-d Source

MIKE11/21 http://www.dhigroup.com/Software/WaterResources.aspx HEC-RAS 1-d (see note)

http://www.hec.usace.army.mil/software

Flood Modeller (previously ISIS)

1 https://www.floodmodeller.com/about/

SOBEK https://www.deltares.nl/en/software/sobek/ Infoworks ICM https://www.innovyze.com/ JFLOW 2 http://www.jbaconsulting.co.uk

TUFLOW 3 1,4 http://www.tuflow.com

1Available through ISIS-TUFLOW link; 2Not fully hydrodynamic (does not solve momentum); 3Available as ESTRY (provided with TUFLOW); 4Links to ESTRY; HEC-RAS 2-d and 1-d/2-d linked modelling version of software were released in 2016

Source: English Environment Agency (http://evidence.environment-agency.gov.uk/FCERM/en/FluvialDesignGuide/Chapter7.aspx?pagenum=5

4.8 Modelling software comparative list In addition to the list of benchmarked software, a comparative tabular list of widely used other hydrological and hydraulic modelling software is presented in Appendix A. DoWRI and DHM will have the opportunity to choose a suite of modelling tools from this list. In the list, HEC-RAS is a free software, while most of the other widely used software are licensed software. For 1-d/2-d linked modelling and pure 2-d modelling, some software tools such as MIKE FLOOD, MIKE21 and TUFLOW have the edge as they have been used for several decades. HEC-RAS 1-d/2-d linked version is a recent release from 2016.

4.9 FFEWS cost consideration A number of cost elements are required to operate flood forecasting and warning system. The following components may need to be considered as part of a whole life cost appraisal:

● Setting up any new organisational structures, if they do not exist ● Installing, operating and maintaining telemetry hydro-meteorological gauge network,

hydrometric equipment and radar rainfall network if not available ● Maintaining spatial and temporal databases ● Developing, configuring, running and maintaining (and troubleshooting) forecasting models ● Developing, running and maintaining (troubleshooting) systems for generating and

disseminating flood warnings and flood maps ● Buying computer software and hardware to support the above operations



● Obtaining meteorological forecasts from freely available sources, for example GFS, WRF and IMD weather forecast models

● Staff training (continuous) and running flood exercises ● Raising public awareness of flooding and how to respond to flood warnings



5 Rain gauge network design

5.1 Introduction Rainfall is a main input for rainfall runoff and river flow and flooding models. These models all have different types of requirements of rainfall input data.

Two types of rainfall measurement methods have been proposed:

● Point rainfall measurement from ground-based rain gauges ● Areal rainfall measurement from satellite or weather radar, this also forecasts rainfall which

the model needs to read

5.2 Auto telemetry rain-gauge Automatic telemetered rain gauges are proposed for installation as part of this project. Modem (GSM) is proposed as the telemetry data transfer mechanism.

5.2.1 Description

The rain gauge should record rainfall and transmit the data through telemetry to the dedicated servers and hydrological and forecasting experts at DHM and consultants via E-mails at defined time intervals.

Au automated telemetry rain gauge system (Figure 5) should consist of a rain gauge unit, e.g., tipping bucket rain gauge, an in-built data logger, a Modem (GSM) for connecting to internet and transferring data to the server. In addition, it should have the facility to access and download data remotely, and should preferably be solar powered. The tipping bucket rain gauge is to be mounted on a pipe within a stainless-steel enclosure that houses a data logger, modem and battery. Battery charging is to be done via solar panel. The Modem unit must be loaded with a data enabled SIM card purchased from a phone supplier. The user will need to define the time interval of data transfer. At the programmed interval, the modem will initialise a communication with the logger and transmit the rainfall data to the server as well as via e-mails to the flood forecasting experts at DHM and to the flood forecasting consultants of this project.



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programming interface) shall be developed for access via internet to all gauging stations and for transferring data to the central server. Data could also be transferred to the regional office server where DHM has such facilities. During the model development phase, the modelling team should also have access to the API via internet to download data. In a country like the UK, the public can download rainfall data using these API.

5.3 Radar rain gauge

5.3.1 Description

Radar rain gauge will not be implemented in this study; however, write-up here is considered for future reference by DHM.

Measuring rainfall by means of radar is not a new technique. The main advantages are that this provides a better spatially distributed measurement than that obtained from point rain gauge alone. Furthermore, the radar rainfall are grid-based outputs, which are becoming more widely used by rainfall runoff models. However, limitations are measurement accuracy, range, attenuation of signal and calibration, which means that radar measurement does not provide great advantages over ground based rain gauges. Despite these limitations, radar rainfall is useful data which can supplement ground based data if missing from a rain gauge, help correcting suspicious ground based data and still be used for rainfall runoff modelling where suitable. Ground based rain gauge data will be used to ground truth radar rain gauge data. Capital expenditure as well as running costs are high, though there are low-cost short-range radars.

5.3.2 Specification

In mountainous terrain in Nepal, precipitation is highly variable both in space and time because of orographic effects and interactions of mountains with wind fields. Moreover, narrow valleys surrounded by high reliefs cannot be effectively monitored by any of the common long-range weather radars because their beam cannot penetrate deep in the valleys due to the shadow effect.

In mountainous regions, the gauge network needs to be very dense (Volkman et al., 2010) to supplement long range radars.

X-band short range radar is a good alternative to the common long-range C-band radar, for observing precipitation within a valley. It can be installed directly inside the valleys, at lower altitude. Rain gauge networks can be complemented by short range X-band radars. They can provide rainfall estimates with high spatial and temporal resolution and their installation cost is lower than C-band radar, allowing the placement of more sensors in order to gain optimised coverage. Examples of such radar are: CASA radar by the Remote Sensing Group (RSG) of Polytechnic of Turin and Local Area Weather Radar (LAWR) by FURUNO, Japan. X-band radar for rainfall estimates should comprise the following specifications:

● Range of 30 to 70km with radar maps produced at 75 to 150m resolution; ● Fitted with series of anti-clutter filters in order to recover, as far as possible, rain signature

even in presence of clutter; ● Radar maps produced by the X-band mini radar unit are transmitted to the server via in-built

communication network through internet (telemetered); ● Provided with processing software for radar maps of rainfall intensity;



● Un-coherent, pulsed, one polarisation only, non-Doppler, with a fixed elevation of the antenna; exclusively devoted to rain measurements and able to produce one rain map in a few seconds;

● Minimum or maintenance-free and possible to be remotely controlled with software adjustments; routine maintenance will be included in the procurement package during the warranty period;

● All the electronic equipment (antenna, radiofrequency unit, data processing unit, communication unit for data transmission and remote control, power unit) are placed inside a radome;

● All software is required to operate in dedicated applications in open source in order to allow greater reliability and flexibility in the configuration and full control of active processes and packages, as well as low costs. Data will be available in commonly used data format.

5.4 Rain gauge network recommended for installation Four new auto telemetry rain gauge stations have been proposed for installation. The four stations will be tipping bucket auto telemetered using GSM. In the Mawa-Ratuwa catchment with an area of 413km2; this will deliver a rain gauge station density (including the existing station by DHM) of one rain gauge per 103km2, similar to the density found in literature, and not far from England where rain gauge density is the highest in Europe (one rain gauge per every 60km2; Allot, 2010).

Gauge network density depends on many factors, particularly spatial and temporal resolution of rainfall over a basin and the purpose of the gauge network (e.g. irrigation management, flood forecast etc.). Flood prediction in rural and urban areas requires a dense spatial gauge network: one gauge between 10 to 100km2 and higher temporal measurement frequency, i.e. between minutes and hours (Berndtsson and Niemczynowicz, 1988). The gauge density may even be higher, one gauge per 20 to 45km2 for mountainous areas of Nepal (Lopez et al., 2015 and Volkman et al., 2010) considering a greater variability of rainfall between the mountains, and the Siwalik and Terai region. However, in view of practicality, management, and with reference to other countries, the implementation of four new stations for this basin is considered to be a practical trade-off between cost and benefit. All proposed rain gauges should be stationed near settlements (for better accessibility and maintenance) and have been distributed considering the main channels and their tributaries. Their positions are shown in Table 12 and Figure 6.

Table 12: Proposed new auto telemetric rain gauge stations in Mawa-Ratuwa basin Catchment/basin name Rain gauge ID Name of nearest

settlement Station coordinates

Longitude, E (deg) Latitude, N (deg) Mawa-Ratuwa basin MR_Rain_01 Urlabari 87.634 26.653

MR_Rain_02 Kohabara 87.655 26.541 MR_Rain_03 Chisapani 87.679 26.877 MR_Rain_04 Mahamai 87.795 26.731




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5.5 Budget for proposed rain gauge network installation The budget estimates for the proposed rain gauge network are considered for ground-based telemetric stations. The budget includes procurement, installation, testing, calibration, monitoring, and operation and maintenance for 3 years. Budgets are shown in Table 13.

Table 13: Budget for proposed rain gauge network in Mawa-Ratuwa basin Meteorological data network Mawa-Ratuwa budget (US$) No. of

stations No. of


measurement cost

Unit cost Maintenance cost: 3 years

Total cost

Ground based tipping bucket auto telemetry

4 - 20,000 5000 6,000 26,000

Total 4 20,000 5,000 6,000 26,000 Source: Mott MacDonald



6 Hydrometric network design

6.1 Water level gauge network

6.1.1 Description

Flood risk assessment and flood forecasting rely heavily on hydrometric data. Key hydrometric data requirements include river levels, river flow and groundwater level.

Any of the following water level monitoring methods are proposed for this project; each approach is telemetric monitoring.

● Measurement by float in a stilling well ● Water Level Radar Sensor ● Water Level Bubbler Sensor

Data will be automatically transferred to the dataset server by GSM telemetry.

Depending on site conditions of the gauging station, one of the monitoring approaches shall be selected. At each telemetric water level station (whether stilling well, radar or bubbler), there will be a manual water level staff gauge. This manual gauge shall be maintained by CBDRM Committee members and can be used by them in the event of a flood alert to communities. This staff gauge should have different distinct colour painting for water levels in flood alert zone, in flood warning zone and in danger level zone (DHM uses such colour level staff gauge).

The water level stations will be included as a forecast point in the FFEWS.

Specification of telemetry kit

The telemetry gauging station should allow:

● Remote monitoring of river levels; ● Transmission of alarms if level rises above user defined thresholds; ● Viewing of historical level data via simple web GUI; ● Transfer of data via API for use in applications and websites; and, ● Preference for solar powered; which will also include a back power system (battery).

The telemetry system should contain:

● Level Sensor having different options to suit depth of river from shallow depth (0.2 to 0.5 m) to several metres of depth (>20m); accuracy 0.5% of range;

● Data logger; ● Solar Powered Telemetry Unit with GSM module and antenna built in; ● Sim Card for the warranty period of 5-years, multi network; and, ● Readings every 5 minutes. ● Transmission of web-based data and alarms by email to designated professionals

(DoWRI/DHM to provide list of emails of designated persons and professionals, and thresholds for high water levels).



6.1.2 Time of observation

Stationarity of record, temporal resolution and the overall accuracy of water level data shall be considered. All water level records will be taken at 15-minute intervals into the data logger and are to be transmitted via telemetry to the central dataset server at DHM and to the hydrological and database experts at DHM.

6.1.3 Operation, measurement and maintenance

Backup and main recorders should be securely mounted and regularly visited and serviced, at least once every month (or more if required). It should be ensured that the pulleys are operating freely, and the float tape or wire sits properly on the drive pulley. Logbooks should be maintained, and calibration of the level sensor should be checked and reset if required.

Sites with known sediment problems shall be carefully checked at each visit, and if there are any indications of a siltation problem, the stilling well must be flushed as soon as possible or proper flow connection to the sensor must be maintained. The Mawa-Ratuwa basin carries highly sediment laden flow, and this routine silt management will be required.

6.1.4 Data transmission, storage and archive

Data will typically be transferred once or twice per day to a central location dataset (server) within DHM for analysis and storage/archive. Such transfer of data usually increases during times of heightened flood risk. In periods of heightened flood risk, even hourly transfer could be required considering the flashy nature of a storm event in the basin. Data could also be transferred from the gauge stations and/or from central dataset server to DHM’s three basin Offices (Karnali Basin Office in Nepalgunj, Narayani Basin Office in Narayanghat and Kosi Basin Office in Biratnagar), and to the regional offices where DHM has data storing facilities. During the model development phase, the modelling team should also have access to the real-time data.

6.2 Discharge measurement stations

6.2.1 Description

Manual discharge measurement stations should be capable of measuring low to moderate flows while the water remains in the channel. For measuring flow beyond certain thresholds, especially when the water level is very high, exceeding the river banks and flowing across the floodplain, the river flows are normally derived from the relationship of stage (level) with discharge, called a stage-discharge relationship or rating curve.

6.2.2 Discharge measurement equipment

Discharge measurement at all stations shall be carried out for a wide range of flows, from low flow to high flows. Discharge measurement is to be carried out fortnightly from mid-May to mid-October at each station. A combination of the following discharge measurement methods can be used:

● Manual measurements using current meter (propeller current meter) during low flows except at the cableway station

● Velocity-depth measurements with ADCP (Acoustic Doppler Current profiler) during medium to high flows except at the cableway station



● Cableway discharge measurements using propeller current meter (ADCP may also be mounted if required) at the uppermost hill station, where velocity measurements at medium to high flows are not advisable without cableway owing to safety reasons.

In manual measurement during low flows, cross-section/depth will be measured using a graduated pole, often referred as manual sounding. When depth is increasing and ADCP (with echo-sounder and DGPS) could be used, all discharge measurements shall be carried out using ADCP. While ADCP will provide velocity scatter, echo-sounder will provide depth and DGPS will provide horizontal positioning. The measurements will be carried out using local engine boat/inflatable boat. The ADCP discharge measurement could be un-manned. The ARC-Boat (http://www.ceehydrosystems.com/products/unmanned-survey-vessels/arc-boat/) is designed to make safe unmanned discharge measurements in rivers and streams using acoustic ADCPs. The hull design minimises air entrainment for optimum ADCP data quality. With a maximum speed of 4.5 m/s (15fps), even high velocity flood stage measurements may be completed. Effects of magnetic interference from the vehicle’s electrical systems are carefully managed to minimise induced compass deflection – critical to obtaining good discharge measurements.

ARC-Boat ADCP measurement at the cableway station could also be considered to replace Cableway depending on the magnitude of velocity and safe operation of measurement.

The budget (unit price) for each discharge measurement at fortnightly intervals is inclusive of all cost elements (2 days input from an equipment engineer including support staff, boats and accessories, travel cost to site, calculation of discharge from raw data, and preparation of report). Discharge measurement equipment (ADCP, DGPS and echosounder) will be provided for this project. Separate budget has been considered for this.

6.2.3 Cableway flow measurement

Slack-line cableways are commonly used for carrying out flow gauging on relatively small rivers and streams. This is particularly useful in rivers in mountainous regions with steep slopes and high velocities. Velocities are measured from a velocity traverse set at a series of fixed depths, and then multiplied with cross-section areas providing total discharge through the river section. Such discharges are useful for developing stage-discharge rating curves. Cableway stations are not suitable where flow goes out of bank and cross-section width is high, e.g., river section in the Terai region.

The components of a slack-line cableway comprise: a static ropeway, suspended between anchor ends – a traveller, a horizontal positioning mechanism, and a lifting mechanism. In operation, the traveller runs on the ropeway and functions as an unmanned ‘cable car’. All operations are carried out from the bank. The horizontal position of the traveller is controlled by means of a manual winch that feeds a line across the span and over a pulley mounted on the far side and back to the traveller. A separate line from a gauging reel feeds out to the traveller, runs over a pulley mounted on the traveller, and connects to a current meter and counterweight, thereby suspending these from the traveller. The gauging reel controls the vertical position of the current meter. Depth will be taken by the sounding reel cable at pre-defined vertical positions by lowering the cable to the river bed; then depth data will be converted to obtain cross-section (river bed level with respect to masl) using the water level gauge reading. As ADCP, DGPS and echosounder will also be available, these set of equipment could also be occasionally mounted to the cableway for depth and velocity measurement, if the Equipment Engineer find it practical.

DHM operates cableway discharge measurement in West Rapti at Kusum. A similar cableway should be established at Satmedi station on the Ratuwa River in reach 2. Standard specifications for establishing cableway could be found in manuals, e.g. NEMS Manual (NEMS,



2016) and iin Figure 7.

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Total 180,000 3,250 183,250 Source: Mott MacDonald

6.3 Hydrometric gauge recommended for installation One hydrometric gauging station in Mawa river and three in Ratuwa river have been proposed (Table 15 and Figure 8) All stations are easily accessible and are located in the neighbourhood of a settlement. All stations have access to GSM (DHM, 2018) as the Terai region has very good network 9DHM, 2018).

The locations have been chosen carefully, which will allow calibration of runoff from the hydrological model and calibration of river levels in the hydrodynamic model. There will be one water level (WL) recording station in the Mawa sub-basin and three water level and discharge (Q) stations in the Ratuwa sub-basin. At all four hydrometric stations, bed material samples shall be collected, only once; and at three discharge stations, suspended sediment sample shall be collected. Bed material shall be collected from mid-channel, and from bed near the banks. Concentration shall be collected from three positions as minimum over the cross-section. If river depth is high (>3m), we recommend collecting concentration over several vertical positions (e.g. 02d, 0.6d and 0.8d; d is total water depth) at each position. If the depth is shallow (<3m), only one sample at 0.5d is recommended

Stage-discharge rating curves will be developed at all three discharge stations in the Ratuwa sub-basin for generating continuous discharge data and for calibration and validation of hydrological and hydrodynamic model.

The three flow stations will allow calibration and validation of runoff from both Mawa and the Ratuwa catchment separately.

The distribution of WL and Q stations will allow a very dense network for calibration of the hydrodynamic model; such a dense network should be considered essential given the relatively steep slope in the upper basin and the mild slope in the lower basin of the terrain (Mott MacDonald, July 2018).

Table 15: Proposed water level and discharge stations in Mawa-Ratuwa basin River name

Water level and discharge gauge ID

Gauge type Name of nearest settlement

Station coordinates Longitude, E

(deg) Latitude, N

(deg)

Mawa

M_G_02 Water Level Tarabari Dipu 87.655 26.685

Ratuwa R_GD_01 Water Level and Discharge

Satmedi 87.643 26.562

R_GD_03 Water Level and Discharge

Damak 87.709 26.666

R_GD_03 Water Level and Discharge

Malaha Toli 87.678 26.439



River Water level d

Gauge type Name of t

Station coordinates

Note: M_G: stands for Gauge only (water level) station in Mawa River; R_GD stands for gauge and discharge in Ratuwa river




Figure 8: ERatuwa bas

Source: Mott



Existing wasin

MacDonald


9 ning System: Maw

ater level an


wa – Ratuwa Basin

nd discharg

d Risk Managemen

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ge station a

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and propossed stationss in Mawa-

444



6.4 Hydrometric gauging network budget The budget for hydrometric gauge network is presented in Table 16. The budget includes procurement, installation, testing, measurement, calibration, monitoring, and operation and maintenance for 3 years.

Discharge measurements will be carried out fortnightly from mid-May to mid-October for a three-year period. The measurements shall also include bed material and sediment concentration; this will be 10 measurements per year, 30 in three years and a total of 90 measurements for the three discharge stations. Operation and maintenance cost is $2000 per basin for all stations per year; this involves routine site visits, repair and maintenance if required. Sediment measurement will have multiple benefits including supporting working design of river training works and also in FFEWS, for example, improving the stage-discharge rating curve; changes in sediment load shall indicate the need for updating the rating curve.

Table 16: Water level and discharge gauge network budget in Mawa-Ratuwa basin Hydro-meteorological data network Mawa-Ratuwa budget (US$) No. of

stations No. of


measurement cost

Unit cost

Operation & Maintenance cost: 3 years

Total cost

Discharge 3 90 390,000 4,333 6,000 396,000 Water level 1 - 7,000 7,000 6,000 13,000 Note: a) Discharge measurement to be carried out fortnightly from mid-May to mid-October (it includes bed material and sediment concentration collection as well); this will be 10 measurements per year, 30 in 3 years and total 90 measurements in 3 stations; b) Operation and maintenance cost is $2000 per basin for all station per year; this involves routine site visits, repair and maintenance, c) Discharge measurement cost is a continuous expenditure, like model development cost (and should be considered similar to capital cost); it includes cost for all skilled human resources and the logistics required




7 Topographic and asset survey

7.1 Topographic survey Current river cross-section and topographic data will be required to develop hydrodynamic (1d, 2d and 1d/2d linked) models. Latest data will be surveyed during the FFEWS model development period. Existing cross-sectional data in this basin were collected in 2014 (during pre-feasibility, Package 3). Thus, this data is considered old for the dynamic rivers in Nepal; there were additional, but limited, cross-section surveys during the feasibility study (this study, Package 7). In UK, where rivers are very stable, the Environment Agency (responsible for flood forecasting), updates their models if topography is more than 5 years old. Thus, fresh cross-sectional survey, in higher spatial resolution (than in Package 3 and 7) should be undertaken. This will improve model calibration and validation, forecast accuracy, and inundation maps.

On steep slopes and in meandering/braided rivers, cross-sections between 200m and 500m intervals are essential (HEC-RAS, Users’ Manual, Version 4.1, Figure 8-34) for accurate model calibration, validation and forecast accuracy and for inundation mapping. Cross-sections, on average at 380m intervals, are proposed. Rivers in Nepal, typically, have very steep slopes. Although the Terai region is referred to as a flat region with mild slopes, the slopes are still many times steeper than those in low-lying plains. Moreover, rivers are braided and meandering in nature due to high sediment load. As a result, cross-sections in the dense intervals proposed will be useful.

Topographic survey will include river section, any existing structures and flood embankment profile. Survey will have to be done in Mawa and Ratuwa Rivers. In 91km, 239 cross-sections will have to be surveyed. For topographic survey, no survey equipment has been proposed for purchase. Survey will be done through outsourcing.

All cross-sections will cover the river, bank to bank and will be extended into the floodplain to sufficiently high ground of highest historic flood water mark. Horizontal projection for survey will be WGS 84 / UTM zone 45N. Ground elevation will be relative to metre above mean sea level (masl) for controlling vertical datum. All cross-sections shall be connected to Nepal National permanent bench mark for horizontal and vertical datum control. Temporary bench marks (TBM) shall also be established for cross-verification of data. All cross-sections, in a basin, shall be surveyed during dry season, prior to or after monsoon so that cross-sections are stable without much morphological change. All cross-section shall be surveyed in one season. This should not be done that half volume of total survey prior to monsoon, and the rest after the monsoon

Ground elevation (vertical position) in cross-section/topographic survey shall be accurate better than 20mm in case of level survey and be better than 50mm in case of echosounder depth survey. Horizontal position accuracy should be better than 1m. There should be enough vertical points to sufficiently represent a cross-section shape, dense points about 0.5 to 1m apart at scoured part of the cross-section, and less points over shallow sand bars, about 1 to 5m interval.

Bank/defences survey crest levels are to be provided at intervals that will adequately describe the river bank (typically every 10m).

The following deliverables are required:

Channel sections, longitudinal sections and structure elevation drawings in AutoCAD DWG and PDF format;



Channel section data in the following formats: o Csv or txt or dat file o AutoCAD; and o Section chainage, X (easting), Y (northing), Z(elevation), distance to next

section Width tables for each bridge opening surveyed Cross section location plan in AutoCAD DWG, Shapefile and PDF format; Bank/defence survey location plan in AutoCAD DWG, ESRI shapefile, spreadsheet .csv

or Excel 2007 format and PDF format Site photographs: at least 3 photographs per cross-section, taken one looking

upstream, one looking downstream and one with another good angle

For topographic survey, no survey equipment has been proposed for purchase; survey will be done through outsourcing.

7.2 Survey budget Budget has been decided based on density (no. of cross-sections) of survey, and per cross-section (see Table 17). Topographic survey shall be outsourced and thus no equipment for such survey has been proposed. Budget for purchasing high resolution satellite imageries has been included (Table 18) for Mawa and Ratuwa basin for lower catchment only in Terai to provide DEM to 1-d and 2-d model development and flood inundation map preparation.

Table 17: Topographic survey budget for Mawa-Ratuwa basin Topographic cross-section survey

Mawa-Ratuwa survey budget (US$) Length of survey

(km) No. of XS Total cost Unit

cost Ratuwa 58 152 30,400 200 Mawa 33 87 17,400 200 Total 91 239 47,800 -


7.3 Satellite imagery Budget for purchasing high resolution satellite imageries has been included (Table 18) for Mawa-Ratuwa basin for lower catchment only in Terai to provide DEM to 1-d and 2-d model development and flood inundation map preparation. High resolution (50cm) Pleiades imageries have been proposed for purchasing. DHM informed that they have already used this imagery in their FFEWS modelling.

Table 18: Satellite imagery purchase budget for Mawa-Ratuwa basin High resolution (50cm) satellite imagery

area (km2) Total cost Unit cost (USD for 1

sq.km) Mawa-Ratuwa basin in Terai 281 14,050 50

Total 281 14,050 - Source: Mott MacDonald



8 Flood forecasting model development

8.1 Mathematical modelling It has been proposed to develop a suite of flood forecasting modelling tools. This includes a simple tool for use in combination with the most advanced hydraulic models being employed in countries like Australia and UK. Hydrological model is essential (input) component for each of the forecasting tools. The simple tool, gauge to gauge correlation, could be operational within eight months or as soon as some hydrometric data become available from the new proposed hydrometric and rain gauge network. Over a period of three years, the advanced hydraulic models will be developed, calibrated, validated and will be made operational as more and more data become available. A conceptual diagram of different components of the models and link between them are presented in Figure 9. The following forecasting tools have been proposed:

● Gauge-to-gauge correlation: the simplest and cheapest method, fast to develop, and thus could be operational within 7 to 8 months from the inception of the project; however, it has a very short lead time (2 to 5 hours) and is not appropriate in upper steep slope river reaches, as flood wave propagates fast in those reaches and correlation of two gauges is weak due to presence of pools and riffles. There are also other limitations.

● Combined rainfall-runoff and gauge-to-gauge correlation: with addition of a runoff model, the forecast lead time could be extended up to 72 hours; however, this requires a stage-discharge rating curve at each gauging station; such rating curve is difficult to develop for out-of-bank flow conditions without a hydraulic model, as soon the hydraulic model will be developed (within 9 to 10 month), such rating curve will be available from the model

● 1-d model: this tool will be developed for the entire river system in the Terai and is appropriate for flood forecasting; the same model type is used in Bangladesh.

● 1-d/2-d linked model: this will be the final delivery around month 24. The pure 2-d model will be developed and transformed into a 1-d/2-d linked model with the linkage to the 1d model.

Mott MacDonaldFlood Forecasti

383877 | REP | Flood Forecasti

Figure 9: Arequireme

Source: Mott

d | WRPPF: Prepaing and Early War

0039 | 4 April 201ing and Early War

A conceptuent of data

t MacDonald

aration of Priority rning System: Maw

19 rning System: Maw

al flow diag

River Basins Floowa – Ratuwa Bas

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in

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ent Project, Nepa

ponents of m

al

models, their links and

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9



8.2 Rationale for different forecasting approaches The four approaches described above are inter-linked and essential and/or complementing components to the final deliverable/ flood forecasting and early warning system (FFEWS) model, i.e., the 1-d/2-d linked FFEWS model. The rationale, advantages and disadvantages of each approach are described below:

● Gauge-to-gauge correlation: the simplest and cheapest method. This could be an option to use as a quick forecasting tool. It can generate new knowledge, to be translated into the final deliverables (1-d model and 1-d/2-d linked model). Advantages will be that flood forecasting components of CBDRM could be operational earlier and potential areas of uncertainty in flood level forecast could be identified. DHM is using this method in many of their river basins, e.g., in Karnali. This tool and expertise from DHM could readily be used in this basin with some nominal input from international consultant; as the tool has to be customised for a new basin, minor changes in code and parameters may be required and thus international consultant’s input is considered. There will be a deployment time in all five basins, for new hydro-meteorological data to become available, and that this work is a good utilisation of the waiting time, as it generates the opportunity for transferring early knowledge to the final product.

● Rainfall runoff model is an important input to all other components: a) gauge-to-gauge correlation, b) 1-d river model, c) pure 2-d model and d) 1-d/2-d linked model. Combining the rainfall model with gauge-to-gauge correlation will increase the lead time (as in the rainfall forecast) up to 24, 48 and 72 hours. However, at the forecasting points, the discharge vs water level rating curve shall be required so that forecasted runoff can be converted to the water level using the rating curve. The rainfall runoff model provides inflows from the upper catchment and distributed inflows from intermediate catchments to the 1-d, 2-d and 1-d/2-d linked model.

● The 1-d model, as a standalone tool, can be applied as a forecasting tool once it is ready. Without the 1-d model, a linked 1-d/2-d model (which is proposed as final deliverable) cannot be developed. Therefore, we have proposed to develop a 1-d model as forecasting tool as soon topography has been surveyed. In any case, for certain reaches of the river, there will only be a 1-d model, as a 1-d/2-d linked model is not feasible for the entire reach of the river due to higher model run time, and instability in 1d/2d model in steeper reaches. This tool will also give useful feedback on forecasting performance, which then could be translated into the final deliverable. In summary, 1-d model development is not a duplicating tool; it is an essential pre-requisite. Should DoWRI and ADB decide not to take forward 1-d/2-d linked modelling, then a 1-d model will be the final product. This is the tool which DHM operate in the Bagmati, Koshi and West Rapti basins. The advantage of a 1-d model is that it runs efficiently, which is a key requirement for real time forecasting. However, a 1-d model does not have direct map output for flood risk or hazard and these require separate and customised GIS development, e.g., as practiced by forecast model in Bangladesh (http://ffwc.gov.bd/). Such a GIS tool is under development within DHM. It will need to be developed in this project in the 1-d only model reaches of the river

● A 1-d/2-d linked model is the final deliverable; such FFEWS models are already in operation in countries like Australia, New Zealand, Malaysia and UK (Syme, 2007; Huxley, 2016). Therefore, we recommend developing this next generation FFEWS tool, otherwise by the time this project is complete (2-3 years from now), it might seem that Nepal uses less advanced tools than other countries. The 1-d/2-d linked model can forecast flood levels with better accuracy (as it is linked to 2-d floodplain model). Further, flood risk and hazard maps



are direct outputs from such modelling. However, run-time is longer than for the 1-d model; it requires more accurate DEM, and therefore it is not feasible to develop it for all reaches of the river. For selected river reaches, where such modelling will be useful, like in the Lower Terai, this tool shall be developed. To overcome run-time issues for real time forecasting, GPU (graphical processing unit) or HPC (heavily parallelised computing) versions of modelling software shall be used.

In several meetings with DHM, the consultant has proposed the development of a similar FFEWS model, with regard to modelling tools and types of models. We have proposed the same type of advanced 1-d model development for FFEWS, which DHM is presently operating in three different basins (West Rapti, Bagmati and Koshi). The same (or similar) modelling software (e.g. MIKE11 and HEC-RAS), for both hydrological and hydrodynamic modelling, has been recommended (in parallel with other software), thus giving DHM wider options to choose from.

8.3 Gauge-to-gauge correlation The development of a forecasting tool using gauge-to-gauge correlation has been proposed for the Ratuwa and Mawa rivers. In Ratuwa, 38km river length, and in Mawa, 13km river length, have been considered for gauge to gauge correlation (Figure 10 and Table 19). The furthest upstream and downstream water level gauges in both the rivers will be used for correlation.

River reaches with relatively mild slope have been considered. The above proposed reach lengths could be changed (decreased or increased) during the development phase after analysing physical data (water level, DEM and cross-sections) will become available. Reaches with steep slopes have not been considered as in steep slope rivers, a downstream gauge has minimum influence to an upstream gauge as the river’s flow regime is mainly flow dominated from the upstream (due to the high Froude1 number, i.e. velocity is relatively high, see Mott MacDonald, 2018b). Further, the benefit of gauge-to-gauge correlation forecasting is very limited in steep slope reaches.

There are existing gauge-to-gauge correlation tools within DHM which are operational in many basins (e.g. Karnali). We propose to make use of the existing tools. Thus, the new tool can be developed fast and with minimum cost; it will mainly involve analysis and feeding in of the new hydrometric data.

This tool shall be maintained in parallel to advanced 1-d and 1-d/2-d linked models.

Table 19: Proposed river reaches for development of gauge-to-gauge correlation flood forecasting model in Mawa-Ratuwa basin

River Reach ID Reach Characteristics Channel length (km)

Slope % FF Model type

Mawa 1 Hill 19.63 8.9 - 2 Fan 10.24 0.87 - 3 Peripheral fan 4.08 0.39 Gauge-to-gauge 4 Flood plain, meander 9.61 0.21 Gauge-to-gauge

Ratuwa 1 Hill 23.03 6.26 - 2 Fan 7.44 0.90 - 3 Peripheral fan 6.58 0.33 Gauge-to-gauge 4 Flood plain, partially 8.04 0.22 Gauge-to-gauge

1 Froude number (Fr) = u/(gh)0.5 where u is flow velocity, h is water depth and g is acceleration due to gravity





meander 5 Flood plain, partially

meander 23.30 0.12 Gauge-to-gauge




Figure 10: flood forec

Source: Mott

8.4 HydThis chapteinvolves var



Proposed rcasting mod

MacDonald

drological mer describes rious system


9 ning System: Maw

river reacheel in Mawa-

modelling developmen

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533



● Selection of the appropriate precipitation-runoff module; ● Identification of initial estimates of the model parameters ● Selection of the calibration and validation period covering full range of wet and dry events ● Calibration of the model ● Validation of the model ● Sensitivity analysis of the model parameters required to upgrade the model in future.

The runoff model domain shall cover the entire basin area (413km2) from Chure Hills to the lower Terai area up to the Indian border in the south.

8.4.1 Review of existing data and models

● Use the existing hydrological model (Feasibility study model, Package 7) to improve sub-catchment delineation, parameter (calibration) improvement; model parameterisation using local/donor data; probable modelling tools include HEC-HMS and NAM; DHM is experienced with both tools

● Review available rainfall data from DHM and other secondary sources ● Cross-check tipping bucket rain gauge with storage gauge data, including double-mass

analysis and / or cumulative-mass time series plots ● Review data against general meteorological records, in particular to identify periods where

there may have been snow / snow melt ● Comparison of rainfall radar totals with rain gauge information, investigate spatial and

temporal distribution of rainfall for selected calibration and validation events ● Provide a commentary on the suitability of weather radar information to supplement gauge

rainfall for rainfall-runoff model development ● Assess the availability of data, and the uncertainties in the accuracy of the data and what

effect this could have on the reliability and accuracy of model outputs ● Selection of calibration period using long records of meteorological data, minimum of three to

five years. A long period of calibration data is essential due to sensitivity of the runoff model to the initial condition

● Selection of validation period model using long records of meteorological data (minimum of three to five years). A long period of validation data is essential due to sensitivity of runoff model to the initial condition.

8.4.2 Catchment delineation

The basin shall be divided into smaller hydrologic sub-catchments to define catchment topology according to geomorphologic homogeneity. The following will be considered while delineating the sub-catchments:

● Topography, DEM based on satellite data and their resolution, e.g. SRTM 30m, Cartosat-1 or high resolution Pléiades imageries

● Drainage network based on satellite imageries ● Changes in sub-catchment response, key tributaries/confluences, flood storage reservoirs

etc. ● Catchment delineation shall be verified including use of surface water sewer data in

urbanised sub-catchments ● Permanent snowline and snow cover ● Soil/sediment and land use data



● Urbanisation extents: land use in urban areas ● Embankment layout and location of flood control sluices and structures ● Location of hydrological monitoring sites ● Average annual precipitation over the basin with reasonably good resolution

8.4.3 Hydrological input: Rainfall, temperature and evapotranspiration

Key input data for hydrological modelling are:

● Rainfall ● Temperature ● Evapotranspiration

Initially the model development shall start with data available at DHM. If quality data within the basin is unavailable, the nearest met station shall be used. Gridded rainfall data could be used from satellite-based sources (IMD, APH etc.) where available.

As soon as data from new proposed gauging stations become available (should be available after the first monsoon during the development phase of the project), they shall be used for improving both calibration and validation of the hydrological model.

8.4.4 Bias correction

Gridded rainfall data cannot be directly used for runoff modelling. Bias correction on historical precipitation series shall be developed for using such data in the FFEWS model.

TRMM-P has the highest possible temporal resolution (three hours) of all gridded precipitation data sources; it is freely available, there is a long record of historical archives, and it is probably the most accurate Satellite-based Precipitation Estimate (SPE) available globally. However, being SPE, it requires bias correction. Region-specific bias in TRMM-P exists and the bias increases with smaller spatial scales, higher temporal resolution and higher magnitude of precipitation values. It has also been observed that capacity of TRMM-P in resolving orographic precipitation in Himalaya is limited. Thereby, raw TRMM-P has to be bias corrected (eQM) before it can be used for rainfall-runoff modelling.

8.4.5 Calibration

The calibration period shall cover hydrological data of at least three hydrological years, but preferably five or more. Availability of data, particularly rainfall, from different sources has been shown in Section 2.8, Table 9. The years shall be selected judiciously so that observed rainfall and discharge are available at most observation stations, if not at all stations. Missing, inconsistent and erroneous data, and non-availability of data are generally an issue in data collected from existing sources and secondary sources; examples of such data are point and gridded rainfall, temperature, PET, observed discharges. Therefore, these factors shall be considered while selecting the calibration period. In the hydrological model, as the initial condition being sensitive, so, first 3 to 6 months of simulation period shall be considered as initialisation time, and thus shall be ignored while using the model runoff to hydrological model; one key factor of considering longer simulation period for validation (or calibration) is due to this initial condition effect; other factors are to cover wide range of flow condition, which will allow low to high range of flows to the hydrodynamic model. Calibration of runoff model against log record helps finalising catchment parameters; we need to emphasise that most hydrological modelling tools, e.g., HEC-HMS, NAM, are conceptual model, and thus, finalising catchment



parameters from model runs of short hydrological events (like in scale of week to months) can generate mis-leading catchment parameters in Hydrological model.

To assess calibration performance between the modelled and observed discharges, scattered plot shall be constructed at all observed gauging stations on matching of peak magnitude and time to peak, also the overall shape of modelled and observed hydrographs. In addition to visual comparison from these graphs, statistical methods shall also be used to measure the model’s performance. Example of such statistical methods are:

● Nash–Sutcliffe efficiency ● Coefficient of determination (R2) ● Volumetric error

8.4.6 Validation

Model validation is a process of testing the model’s ability to simulate observed data for a different set of rainfall events than those used in calibration, within accuracy agreed with the client. In model validation, calibrated model parameters shall not be changed; the same set of parameter values used in calibration shall be used during validation. The validation period shall cover hydrological data of at least three hydrological years but preferably five (please see preceding section on criteria and issues on selecting period of validation).

To assess validation performance, the same procedure as for the calibration shall be followed; performance shall be checked by comparing the graphics of the peak magnitude and time to peak, as well as comparing the overall shape of modelled and observed hydrographs. The statistical parameters listed above under calibration shall also be checked in validation.

8.5 Combined rainfall-runoff and gauge-to-gauge correlation Combined rainfall runoff and gauge-to-gauge correlation will also cover the same river reaches as were covered in the gauge-to-gauge correlation: in Ratuwa, 38km river length, and in Mawa, 13km river length (Table 20).

Once the hydrological models are ready, they could be combined with gauge-to-gauge correlation. A ready hydrological model means a calibrated and validated model; if there is need for re-delineation off catchment than in the Package 7 model, then satellite imageries and in-built GIS tool will be used to redefine the watershed boundary; calibration will be carried out using new discharge data proposed for measurement in this study. This will increase lead time to 72 hours (as in the rainfall forecast). However, to utlilise the benefit of increased lead time, the stage-discharge rating curve will be required to convert runoff from the hydrological model into the river level at the upstream base station in gauge-to-gauge correlation.

Table 20: Proposed river reaches for development of combined rainfall-runoff and gauge-to-gauge correlation flood forecasting model in Mawa-Ratuwa basin


Slope %

FF Model type

Mawa 1 Hill 19.63 8.9 RR 2 Fan 10.24 0.87 RR 3 Peripheral fan 4.08 0.39 RR+Gauge-to-

gauge 4 Flood plain, meander 9.61 0.21 RR+Gauge-to-

gauge




Slope %

FF Model type

Ratuwa 1 Hill 23.03 6.26 RR 2 Fan 7.44 0.90 RR 3 Peripheral fan 6.58 0.33 RR+Gauge-to-

gauge 4 Flood plain, partially

meander 8.04 0.22 RR+Gauge-to-

gauge 5 Flood Plain, partially

meander 23.30 0.12 RR+Gauge-to-

gauge Source: Mott MacDonald

8.6 Pilot pure 2-d modelling Pure 2-d modelling has been proposed only in this basin and in the Mohana-Khutiya basin. In other basins, 2-d model will be developed through 1d/2d linked modelling. The 2-d model domain shall only be used in the very flat region in the Terai where flood water spreads very easily, i.e. for 31km reach in the Ratuwa river only (Table 21 and Figure 11).

DHM, up to the present time, has not applied any 2d or 1d/2d linked model for flood forecasting. Thus, on-the-job training will be provided on 2-d modelling to DHM Forecasting Experts through this 2-d model development. This 2-d model, afterwards, will be transformed into a 1-d/2-d linked model.

The model shall be calibrated and validated for the same hydrological events as mentioned for hydrological modelling (Section 8.4) and also see data availability in Table 9.

Table 21: Proposed river reaches for development of combined rainfall-runoff and 1-d modelling for flood forecasting model


Slope %

FF Model type

Mawa 1 Hill 19.63 8.9 - 2 Fan 10.24 0.87 -

3 Peripheral fan 4.08 0.39 -

4 Flood plain, meander

9.61 0.21 -

Ratuwa 1 Hill 23.03 6.26 -

2 Fan 7.44 0.90 -

3 Peripheral fan 6.58 0.33 -

4 Flood plain, partially meander

8.04 0.22 Pure 2-d model

5 Flood Plain, partially meander

23.30 0.12 Pure 2-d model




Figure 11:proposed r

Source: Mott



: Two-dimereaches with

MacDonald


9 ning System: Maw

ensional (2hin 2d doma


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2d) model ain

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n Mawa-Ratuwa basinn showing

588



8.7 1-d modelling All reaches of the Mawa and the Ratuwa rivers have been proposed for 1-d modelling. A total of 69 km reach has been proposed: 45km for Ratuwa and 24km for Mawa (Table 22 and Figure 12).

The model shall be calibrated and validated for the same hydrological events as mentioned for hydrological modelling (Section 8.4) and also see data availability in Table 9.

8.7.1 River network

A forecasting tool in the 1-d modelling approach has been proposed for the Mawa and Ratuwa rivers.

The river reaches in the Terai as shown in Figure 1 and Table 7 shall be considered in 1-d river modelling. Runoff from the catchment in the Siwalik Hills shall be routed to the 1-d river model by hydrological modelling. Details of reaches and catchments considered in rainfall runoff (RR) and 1-d river modelling are presented in 8.7.1. If during the development phase it will be deemed appropriate, depending on topography and channel density in the Siwaliks, flood routing, e.g. based on Muskingum-Cunge, will also be considered. Such hydrological routing may improve modelling of flood attenuation, flood volume and flood travel time to the downstream 1-d model in the Terai. The above proposed reach lengths in the 1-d model shall be fine-tuned during the development phase depending on the field conditions as more physical data (water level, discharge, DEM and cross-sections) become available. A single fluvial model shall be built considering all main rivers and their tributaries included in the same model set-up. Models with all branches interconnected deliver better results. None of the tributaries/branches shall be built as separate 1-d models.

Modelling approach shall be submitted for acceptance by the client (i.e., DHM) before model build commences.

Key characteristics of the model shall include:

● ● Distributed inflows to reflect the key hydrological characteristics of the catchment ● All structures which influence flood flows/levels between 50% (generally bankfull discharge)

and 0.1% AEP plus climate change allowance ● All flood defence work ● All model nodes and units are to be geo-referenced, to true geo-graphic co-ordinates (i.e.,

schematic set-up of model units shall not be accepted) ● Channel (1-d), bank to bank.

Shall include floodplain, as extended section in 1-d, as flood cells connected to river (1-d) or floodplain as separate channel and connected to main channel

Table 22: Proposed river reaches for development of combined rainfall-runoff and 1-d modelling for flood forecasting model in Mawa-Ratuwa basin

River Reach ID

Reach Characteristics Channel length (km)


Mawa 1 Hill 19.63 8.9 RR 2 Fan 10.24 0.87 RR+1-d model 3 Peripheral fan 4.08 0.39 RR+1-d model



River Reach ID



4 Flood plain, meander 9.61 0.21 RR+1-d model Ratuwa 1 Hill 23.03 6.26 RR

2 Fan 7.44 0.90 RR+1-d model 3 Peripheral fan 6.58 0.33 RR+1-d model 4 Flood plain, partially

meander 8.04 0.22 RR+1-d model

5 Flood plain, partially meander

23.30 0.12 RR+1-d model




Figure 12:proposed r

Source: Mott



: One-dimereaches with

MacDonald


9 ning System: Maw

ensional (1hin 1d doma


wa – Ratuwa Basin

-d) model ain

d Risk Managemen

n

domain in

ent Project, Nepal

n Mawa-Ratuwa basinn showing

611



8.7.2 Calibration and validation

The model shall be calibrated and validated for the same hydrological events as mentioned for hydrological modelling (Section 8.4). To assess calibration performance between the modelled and observed discharges, the same statistical methods as mentioned in Section 8.4 shall be applied.

8.8 1-d/2-d linked modelling Flat reaches in the Terai shall be transformed to the 2-d model and then linked with the 1-d model. Total length for 1-d/2-d linked model shall be 41km (Table 23 and Figure 13). The advantage of pure 2-d and 1-d/2-d linked modelling will be the generation of flood outlines as direct output from the model results, whereas in 1-d modelling, flood outlines have to be generated separately using 1-d model results and floodplain DEM.

Table 23: Proposed river reaches for development of combined rainfall-runoff and 1-d/2-d linked modelling for flood forecasting model in Mawa-Ratuwa basin

River Reach ID


Slope %

FF Model type

Mawa 1 Hill 19.63 8.9 RR 2 Fan 10.24 0.87 RR+1-d model 3 Peripheral fan 4.08 0.39 RR+1-d model 4 Flood plain, meander 9.61 0.21 RR+1-d/2-d linked

model Ratuwa 1 Hill 23.03 6.26 RR

2 Fan 7.44 0.90 RR+1-d model 3 Peripheral fan 6.58 0.33 RR+1-d model 4 Flood plain, partially

meander 8.04 0.22 RR+1-d/2-d linked

model 5 Flood plain, partially

meander 23.30 0.12 RR+1-d/2-d linked

model Source: Mott MacDonald



Figure 13: showing pr

Source: Mott



One-dimenroposed rea

MacDonald


9 ning System: Maw

sional (1d) aches of mo


wa – Ratuwa Basin

and 1d/2d lodel domain

d Risk Managemen

n

linked mode

ent Project, Nepal

el domain in Mawa-Rattuwa basin

633



8.9 Operation of forecasting model

8.9.1 Key tasks

Operating the forecast model on continuous basis will involve automisation. The main activities would probably include:

● Writing scripts/program which will automatically download forecasted rainfall from WSF/WRF/IMD; the frequency of downloading shall be discussed and agreed with DHM; if forecast is issued three times during the day, the downloading will be at the same frequency

● Writing scripts/program which will convert forecasted rainfall into format which is compatible as input to runoff modelling tool, e.g., in case of NAM, it should be a dfs0 file while in HEC-HMS, it should be a HEC-HMS-DSS output file

● Writing scripts/program which will download real time discharge and water level from DHM’s central server, convert the data into HEC-RAS or MIKE11 compatible format; this data will be used for the forecast; forecast run is usually for 7 days duration – 4 days of hindcast whose performance is verified using real time water level and discharge and 3 days (72 hours) of forecast run.

● Writing a scripts, which will identify missing and erroneous data, particularly for rainfall data, which is used as input to hydrological model for generating run-off, and which are then input to the hydrodynamic model; both erroneous and missing rainfall record shall be replaced with data from other sources (e.g., from neighbouring station/grid)

● Writing script/program which will trigger automatic run of hydrological and the hydrodynamic models, for the same number of times during a day as agreed with DHM

● Writing script/program which will extract output (water level and discharge, and flood inundation map) in graphical formats at all forecasting points.

8.9.2 Real-time data transmission and maintenance

Maintaining a central database server for telemetric data and also for near real time data is essential. This study will uitlise the existing real time data management system (Figure 14) within DHM for data transmission to the central server, analysis and preparation of input for the model run. Input by International and National Experts have been kept for integration of telemetric data from the proposed new telemetric gauge network to DHM’s system.

Key elements for real time data transmission and management involves:

Operation of telecommunication system: this is outsourced and supervised by DHM Processing of data received from telemetered gauges by Flood Forecasting Centre,

DHM Processing of data received from manual gauges by Flood Forecasting Centre, DHM

During forecast run, the hydrological model and hydrodynamic models use following data:

Forecasted rainfall from weather forecast model Real time rainfall, water level and discharge data from the telemetric gauging network

During each forecast run, once daily (or more), the model will run for a 4-day hindcast period and a 3-day forecast period. For the hindcast part of the simulation, input data (rainfall, water level and discharge) should be real-time data, which may also be supplemented by TRMM



gridded rainseason is ca The 7-day rprocessing,expected totype of com

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Source: DHM

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system, shhe operation

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the system assimilation urements in r uncertainty

n this basin of flow and accuracy of

677



Data assimilation needs to be applied on the following forecast model on discharge and water level where real time telemetry data are available:

● Combined rainfall-runoff and gauge-to-gauge correlation model ● 1-d hydrodynamic model ● 1-d and 2-d linked hydrodynamic model

8.10 Evaluation of forecast The forecasts issued during the first, second and third years shall be evaluated. Based on the evaluation, follow-up (continuous) model update shall be recommended and implemented within the duration of this project, while the knowledge transfer to DHM shall be ensured for future update, operation and maintenance of the following forecast models:

● Gauge-to-gauge correlation ● Combined rainfall-runoff and gauge-to-gauge correlation model, within duration of this

project ● 1-d hydrodynamic model, within duration of this project ● 1-d and 2-d linked hydrodynamic model, within duration of this project.

Forecast evaluation should be carried out using the Skill Scores as per the criteria described in WMO’s Manual on Flood Forecasting and Warning (WMO, 2011).

8.11 Model development schedule The suite of forecasting models shall be developed and made operational over a period of three years (Figure 17 represented by sub-programmes to be completed in nine periods (PR1 to PR9, one period is 4 month); this 36 months period is deemed essential as the model will use new data from the proposed gauging network and topography including DEM from high resolution (50cm) satellite imagery.

● Gauge-to-gauge correlation can start fairly early as soon as some water level data are available from the new proposed water level gauges; it is noted again that there is no existing hydrometric network in this basin.

● Hydrological modelling will also be started from PR2 by using third party and existing rainfall data; as soon as rainfall data from proposed new rainfall gauges are available. The model will be updated, calibrated, validated and improved with new rainfall data and discharge data.

● Forecast issuing will immediately be started using the gauge-to-gauge correlation approach (which has limited lead time). In parallel, as soon as the RR model is ready, the RR model will be combined with gauge-to-gauge correlation forecasting; combining with the RR model will give power to gauge-to-gauge correlation to forecast with much higher lead time (up to 72 hours).

● Parallel to the above modelling, 1-d, -2-d and 1-d/2-d linked model development will continue. This advanced modelling is more data dependent (topographic and hydro-meteorological data).

● 2-d modelling will be carried out as a pilot exercise and for capability development. This experience will be used in 1-d/2-d linked model development. Forecasts, however, will be issued using the 2-d model for the domain where the model is developed. The 1-d forecast model will be operational from PR5 5 and the 1-d/2-d linked model will be operational from the middle of PR 7.



Figure 17:

Source: Mott

8.12 ModThe modelliand catchmIreland (Env

In UK, hydrconsidered sub-basins cost during

Table 24: FCategories

Data: collection, processing, analysis Hydrological modelling Gauge-to-gauge correlation Pure 2-d modelling 1-d modelling



Flood forec

MacDonald

del developing budget is

ment size, unvironment Ag

rological modhere is relat(from pre-feFFEWS.

Forecasting Parame

Per bas

Catchmarea River length

River length

g River length


9 ning System: Maw

casting mod

pment buds presented init cost has gency, 2015)

del developmively low bec

easibility leve

model deveeter Unit

sin No.

ment km2

km

km

km


wa – Ratuwa Basin

el developm

get n Table 24. been derive

).

ment cost is cause the hyel and feasib

elopment buQuantity

1

413

51.61

31.34

69.29

d Risk Managemen

n

ment progra

Based on rived. Similar u

$500 to $1,ydrological mbility). This a

udget for MaDevelopmen

39,600

96,500

52,000

48,500

84,000

ent Project, Nepal

mme for Ma

ver length (counit costs are

500 per km2

model is alreaadvantage w

awa-Ratuwat Annual

cost: Operatio

-

-

25,550

23,950

37,500

awa-Ratuwa

onsidered fore applicable

2 of catchmeady existing iwill reduce d

a basin

on

Annual cost:

Dissemination

-

-

24,300

23,950

25,750

a basin

r modelling) e in UK and

ent; the cost in these five evelopment

i-

Unit cost ($)

39,600

234

1,973

3,076

2,125

69

9



Categories Parameter Unit Quantity Development Annual cost:

Operation

Annual cost:

Dissemi-nation

Unit cost ($)

1-d/2-d linked modelling

River length

km 40.95 80,500 34,250 23,950 3,387

Modelling software

Suite No. 1 13,000 - - 13,000

Total 414,100 121,250 97,950

Note: Modelling software licence cost is distributed over five basins. Software will have multi user network licence, and cost shown here is per basin. West Rapti is excluded from software cost. Source: Mott MacDonald

8.13 Person-months for experts The suite of FFEWS tools shall be developed for this basin following development of each tool in the Mohana-Khutiya basin, and thus, expert input in this basin shall be less.

To consider person-months for experts, one key assumption is that the Mohana-Khutiya basin FFEWS model development will start first, and thus, will require higher expert inputs. The next basins shall be Mawa-Ratuwa. Thus, the other basins will benefit from the experience gained from these two basins. As a result, the other three basins will require fewer person-months. Catchment size and river lengths were also key factors in deciding person-months. With experience, the input from international experts will decrease in the basins modelled after Mohana-Khutiya and Mawa-Ratuwa.

In developing FFEWS in five basins, the inputs of three international experts and four national experts have been considered over a period of three years; one GIS cum data analysis expert (National) has also been considered to support the team. The discipline of both international and national experts shall be:

International

Senior/Principal Hydraulic & flood forecasting modelling expert Hydraulic & flood forecasting modelling expert Hydrologist & flood forecasting modelling expert

National

Hydraulic & flood forecasting modelling expert-I and expert-II Hydrologist & flood forecasting modelling expert-I and expert-II GIS cum data analysis expert

Following activities will be first carried out in this basin. As these activities could easily be applied/customised to other basins, so the cost for these activities will be relatively low in the other basins than this basin.

● Detail model development conceptualisation document (according to feasibility document) shall be developed first in the Mohana-Khutiya basin and shall be copied to this basin

● Training and capacity building for 1-d, 2-d and 1-d/2-d linked modelling will be offered to national experts and DHM professionals during tool development in Mohana-Khutiya basins, and thus the Mawa-Ratuwa basin will have input from more experienced experts

● Script for automisation of FF model runs in real time shall be developed first for Mohana-Khutiya basin, and can be adopted to this basin with very minimum input



● Script for automisation of forecast dissemination in real time shall be developed first for Mohana-Khutiya basin, and can be adopted to this basin with minimum input

Considering the above activities, the person-months for Mawa-Ratuwa have been calculated as below (Table 25).

Table 25: Experts’ person-months for Mawa-Ratuwa basin Models: Mawa-Ratuwa

Development phase

Operational phase

Dissemination

Inter-national

National Inter-national

National Inter-national

National

Data: collection, processing and analysis

0.7 1.6

Hydrological 2.5 5

Gauge-to-gauge correlation

1 3 0.25 0.8 0.2 0.8

1-d modelling 2 5 0.7 1 0.3 0.5

Pure 2-d model 1 2 0.2 0.7 0.2 0.7

1-d/2-d Linked Modelling

2 4 0.5 1.5 0.2 0.7

Total 9.2 20.6 1.65 4 0.9 2.7 Source: Mott MacDonald



References

[1] Allot, T (2010), The British Rainfall Network in 2010, https://www.rmets.org/sites/default/files/pdf/presentation/20100417-allott.pdf

[2] Berndtsson, R and Niemczynowicz, J (1988), Spatial And Temporal Scales in Rainfall Analysis Some Aspects And Future Perspectives, Journal of Hydrology, 100 (1988) 293-313

[3] DHM (2018), Standard Operating Procedure for Flood Early Warning System in Nepal

[4] DoWRI (2016), Package 3: Flood Hazard Mapping and Preliminary Preparation of Flood Risk Management Projects, Final Report – VOLUME 1, Prepared by Lahmeyer International in association with Total Management Services

[5] EA/Defra (2013), Benchmarking the latest generation of 2D hydraulic modelling packages, Report – SC120002

[6] EA/Defra (2004), Benchmarking of hydraulic river modelling software packages, Project Overview, R&D Technical Report: W5-105/TR0, URL for this research is below:

(https://consult.environment-agency.gov.uk/engagement/bostonbarriertwao/results/appendix-6---neelz--s.---pender--g.--2013--benchmarking-the-latest-generation-of-2-d-hydraulic-modelling-packages.-bristol_environment-agency.pdf)

[7] Environment Agency (2015), Cost estimation for flood warning and forecasting – summary of evidence, Report –SC080039/R13

[8] Huxley C (2016), GPU – Next Generation Modelling for catchment Floodplain Management, BMT-WBM, ASFPM Conference

[9] Lopez M/G. et al, (2015). Location and Density of Rain Gauges for the Estimation of Spatial Varying Precipitation. Geografiska Annaler: Ser. A Physical Geography. 97, (1) 167-179

[10] Lengfeld et al. (undated), Pattern: Advantages of High Resolution Weather Radar Network, American Meteorological Society 36th Conference on Weather Radar Networks

[11] Mott MacDonald (2018a), Morphology Assessment: Mohana – Khutiya basin, WRPPF: Preparation of Priority River Basins Flood Risk Management Project, Nepal

[12] Mott MacDonald (2018b), River Hydrology Assessment: Mohana – Khutiya basin, WRPPF: Preparation of Priority River Basins Flood Risk Management Project, Nepal

[13] NIWA (2014), Climate Manual, National Institute of Water & Atmospheric Research Ltd



[14] Schaake, J., 2004. Application of prism climatologies for hydrologic modeling and forecasting in the western U.S. In Proceedings of 18th Conference on Hydrology. Seattle, Washington, 2004. American Meteorological Society

[15] Smith, P.J., Brown, S and Dugar, S (2017), Community-based early warning systems for flood risk mitigation in Nepal, Nat. Hazards Earth Syst. Sci., 17, 423–437

[16] Syme, B (2007), 2-d and 1-d/2-d modelling, BMT WBM

[17] Volkman T. H.M., Lyon, S. W., Gupta, H. V. and Troch, P. A. (2010), Multicriteria design of rain gauge networks for flash flood prediction in semiarid catchments with complex terrain. Water Resources Research 46, W11554, doi:10.1029/2010WR009145, 16pp

[18] Werner et al. (2012), The Delft-FEWS Flow Forecasting System, Environmental Modelling and Software, 40 (2013), 65-77

[19] WMO (2011), Manual on Flood Forecasting and Warning, WMO-No. 1072



Appendices

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of

para

met

ers

x x

x x

x x

x x

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

77

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

FEAT

UR

E D

ESC

RIP

TIO

N

MIK

E Fl

ood

Mod

elle

r Pr

o S

OB

EK

H

EC

TUFL

OW

W

EAP

MO

DSI

M

RIB

ASIM

In

fow

orks

IC

M

Sedi

men

t tr

ansp

ort

(opt

iona

l) Se

dim

ent t

rans

port

Wat

er

qual

ity

(opt

iona

l) Tr

ansp

ort

and

deca

y of

su

bsta

nces

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

78

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

B. C

omm

ents

and

resp

onse

s

Tabl

e B

.1: C

omm

ents

on

FFEW

S R

epor

ts fr

om A

DB

C

omm

ents

fro

m A

DB

wer

e ge

neric

for

the

rep

orts

fiv

e ba

sins

: M

ohan

a-Kh

utiy

a, M

awa-

Rat

uwa,

Lak

hand

ei,

Bakr

aha

and

East

Rap

ti an

d

Lakh

ande

i.

C

omm

ents

wer

e re

ceiv

ed in

a M

S W

ord

file

whi

ch a

re p

rese

nted

in T

able

bel

ow

Com

men

ts w

ere

also

rece

ived

on

the

hard

cop

y of

the

Moh

ana-

Khut

iya

(M-K

) Rep

ort o

n ea

ch c

hapt

er (c

hapt

er 0

to 8

). Th

ose

com

men

ts,

thou

gh m

ade

on M

-K re

port,

are

mos

tly g

ener

ic a

nd a

pplic

able

for a

ll th

e ot

her f

our b

asin

s. A

ll co

mm

ents

mad

e on

the

hard

cop

y ha

ve

been

add

ress

ed in

all

five

basi

n re

ports

and

repo

rts h

ave

been

upd

ated

acc

ordi

ngly

. The

cha

nges

mad

e ar

e av

aila

ble

on tr

ack

chan

ges

mod

e. F

or s

ome

com

men

ts, t

here

was

nee

d fo

r a re

spon

se fo

r the

AD

B re

view

er; t

hose

resp

onse

s ar

e m

ade

on th

e pd

f ver

sion

of e

ach

chap

ter.

Res

pons

es fo

r the

se c

omm

ents

are

pre

sent

ed b

elow

.

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

1 La

ngua

ge:

En

glis

h ne

eds

to

be

impr

oved

. R

epet

ition

of

text

is o

bser

ved

at m

any

plac

es..

Sugg

estio

ns h

ave

been

pro

vide

d on

a m

arke

d-up

cop

y of

the

Moh

ana-

Khut

iya

FFEW

S re

port.

All s

ugge

stio

ns m

ade

on th

e m

arke

d up

cop

y of

M-K

Rep

ort h

ave

been

add

ress

ed;

rem

oved

repe

titio

n at

pla

ces

and

have

als

o im

prov

ed E

nglis

h.

2 Ex

istin

g hy

drom

et d

ata

: Pro

vide

an

over

view

ta

ble

of th

e ex

istin

g an

d fo

reca

st h

ydro

met

dat

a (ra

infa

ll, w

ater

-leve

ls,

flow

s, t

empe

ratu

re a

nd

evap

otra

nspi

ratio

n), i

nclu

ding

; o

Ty

pe &

sou

rce

We

have

pro

vide

d an

ove

rvie

w in

Tab

le 9

in c

hapt

er 2

.

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

79

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

o

Lo

catio

n o

Pe

riod

avai

labl

e o

C

olle

ctio

n m

etho

d o

C

olle

ctio

n fre

quen

cy

o

Tran

smis

sion

met

hod

o

Late

ncy

o

Whe

re it

is s

tore

d, e

tc.

3 Pr

ovis

ion

of X

-ban

d R

adar

s: It

has

bee

n fo

und

that

D

HM

(u

nder

th

e W

orld

Ba

nk’s

PP

CR

pr

ojec

t) ha

s in

stal

led

a C

-ban

d ra

dar a

t Sur

khet

, w

hich

cov

ers

an a

rea

with

a r

adiu

s of

250

km

. Si

mila

rly,

DH

M is

in t

he p

roce

ss o

f in

stal

ling

2 m

ore

C-b

and

rada

rs a

t C

entra

l an

d Ea

ster

n N

epal

, an

d se

vera

l x-b

and

rada

rs.

The

C-b

and

rada

rs w

ill co

ver t

he e

ntire

Ter

ai a

nd th

e X-

band

ra

dars

will

cove

r inn

er v

alle

ys.

Ther

efor

e, th

ere

is n

o ne

ed o

f pro

curin

g th

e 5

X-ba

d ra

dars

.

Agre

ed;

We

have

dro

pped

this

item

from

bud

get;

how

ever

, we

have

kep

t the

writ

e-up

in S

ectio

n 5.

3, if

D

HM

wis

hes

to c

onsi

der t

hem

in n

ear f

utur

e. W

ithin

the

text

s, w

e ha

ve m

ade

this

cle

ar th

at X

-ba

nd ra

dar w

ill no

t be

cons

ider

ed in

this

pro

ject

, and

thus

, no

budg

et h

as b

een

incl

uded

.

How

ever

, we

wan

t to

men

tion

that

the

C-B

and

long

rang

e ra

dar,

whi

ch D

HM

is in

the

proc

ess

of

inst

alla

tion

at th

ree

loca

tions

, may

not

be

oper

atio

nal o

r dat

a m

ay n

ot b

e av

aila

ble

durin

g ne

xt 2

to

3 y

ears

, by

whi

ch ti

me

this

pro

ject

may

be

com

plet

ed.

We

also

wan

t to

men

tion

that

tota

l 3 n

os. o

f C-b

and

rada

r acr

oss

Nep

al w

ill pr

ovid

e a

dens

ity o

f on

e ra

dar p

er 4

9,06

0 km

2 in N

epal

, whi

le s

uch

dens

ity, f

or e

xam

ple

in U

K, is

one

C-b

and

rada

r pe

r 14,

264

km

2 .

Ther

efor

e, D

HM

/AD

B, if

wis

hes

in fu

ture

, can

als

o co

nsid

er s

hort

rang

e ra

dar i

nsta

llatio

n.

4 Pr

ovis

ion

of

ADC

Ps:

DH

Ms

has

alre

ady

awar

ded

the

proc

urem

ent

of 5

AD

CPs

(un

der

the

Wor

ld B

ank’

s PP

CR

pro

ject

). O

ut o

f the

se 5

AD

CPS

, DH

M p

lans

to p

rovi

de o

ne e

ach

to it

s ba

sins

offi

ces

at B

iratn

agar

, Po

khar

a, B

haira

wa

and

Koha

lpur

, so

that

thes

e ba

sin

offic

es w

ill be

re

spon

sibl

e fo

r m

easu

ring

disc

harg

e in

all

the

river

s of

Nep

al. T

o su

pple

men

t DH

M’s

AD

CPS

,

We

have

mod

ified

the

disc

harg

e m

easu

rem

ent e

quip

men

t lis

t, ho

wev

er, a

bit

diffe

rent

th

an A

DB’

s su

gges

tion.

We

have

pro

pose

d th

ree

set o

f equ

ipm

ent;

plea

se s

ee o

ur c

onsi

dera

tions

:

M

ohan

a-Kh

utiy

a: o

ne s

et o

f equ

ipm

ent f

or th

is b

asin

alo

ne

(Thi

s ba

sin,

in fa

r wes

t Nep

al, i

s fa

r aw

ay fr

om th

e ot

her f

ive

basi

ns; t

his

basi

n ha

s 12

0dis

char

ge m

easu

rem

ents

dur

ing

thre

e ye

ars;

thus

it w

ill be

ver

y di

fficu

lt fo

r thi

s ba

sin

to s

hare

its

equi

pmen

t with

ano

ther

bas

in. S

imila

rly, i

t will

also

be

diffi

cult

to

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

80

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

it

mig

ht b

e us

eful

to

prov

ide

two

mor

e AD

CPs

fo

r tra

inin

g an

d as

sp

ares

fo

r m

easu

ring

disc

harg

e in

the

prio

rity

basi

ns.

Ther

efor

e, it

is

sugg

este

d th

at th

e co

nsul

tant

mod

ify th

e lis

t of

equi

pmen

t.

borro

w D

HM

’s e

quip

men

t for

120

mea

sure

men

ts;

M

awa-

Rat

uwa

and

Bakr

aha:

one

set

of e

quip

men

t for

thes

e tw

o ba

sins

M

awa-

Rat

uwa

and

Bakr

aha

will

have

30

mea

sure

men

ts p

er y

ear

for

each

ba

sin

(tota

l 60

mea

sure

men

ts in

two

basi

ns),

and

thes

e tw

o ba

sins

are

sid

e by

sid

e, a

nd th

us s

harin

g on

e eq

uipm

ent s

et in

this

bas

in is

pos

sibl

e. T

hus,

w

e ha

ve p

ropo

sed

one

equi

pmen

t set

for t

hese

two

basi

ns

Wes

t Rap

ti: w

e ar

e no

t pro

posi

ng a

ny e

quip

men

t set

. DH

M’s

equ

ipm

ent w

ill be

use

d in

this

bas

in

Ea

st R

apti

and

Lakh

ande

i: w

e ar

e pr

opos

ing

one

equi

pmen

t set

for t

hese

two

basi

ns; t

his

set w

ill be

sha

red

betw

een

the

two

basi

ns; h

owev

er, o

ccas

iona

lly,

ther

e m

ay b

e ne

ed to

bor

row

DH

M’s

equ

ipm

ent i

n cr

isis

man

agem

ent.

Ther

e w

ill ha

ve 3

0 m

easu

rem

ents

per

yea

r for

eac

h ba

sin

5 D

isch

arge

m

easu

rem

ent

: Th

e co

nsul

tant

is

su

gges

ted

to d

escr

ibe

the

disc

harg

e m

easu

ring

appr

oach

in t

he s

ix b

asin

s (in

clud

ing

the

Wes

t R

apti

Riv

er)

usin

g th

e AD

CPs

. Th

e ap

proa

ch

shou

ld

incl

ude

frequ

ency

, us

e of

bo

ats

or

cabl

eway

s an

d de

velo

pmen

t of

rat

ing

curv

es.

Also

, co

nsul

tant

sh

ould

de

scrib

e th

e in

volv

emen

t of D

HM

’s b

asin

offi

ces

with

a fo

cus

on c

apac

ity b

uild

ing.

We

have

fur

ther

des

crib

ed t

he a

ppro

ach

in S

ectio

n 6.

2.2

for

ADC

P m

easu

rem

ents

, Se

ctio

n 6.

2.3

on c

able

way

dis

char

ge m

easu

rem

ents

, and

Sec

tion

6.3

in m

easu

rem

ent

frequ

ency

. Rat

ing

curv

es in

Sec

tion

6.3,

par

a 3.

Invo

lvem

ent o

f DH

I bas

in o

ffice

:

Agre

ed;

in a

ll di

scha

rge

mea

sure

men

t, t

echn

ical

pro

fess

iona

l fro

m b

asin

/regi

onal

of

fice

of D

HM

will

be in

volv

ed; w

e ha

ve u

pdat

ed te

xts

acco

rdin

gly

in S

ectio

n 6.

2.2,

in

the

para

bel

ow th

e bu

llet p

oint

s

6 D

isch

arge

sta

tions

: S

tream

flow

gau

ging

and

ra

ting

curv

es a

re p

ropo

sed

for o

nly

som

e w

ater

-le

vel

stat

ions

. Pr

ovid

e ex

plan

atio

n fo

r w

hy t

he

wat

er-le

vel-o

nly

stat

ions

will

not b

e ga

uged

.

We

have

incl

uded

suf

ficie

nt n

umbe

r of s

tream

flow

and

ratin

g cu

rve

stat

ions

. Her

e, w

e w

ill ha

ve th

ee d

isch

arge

sta

tions

(on

e ex

istin

g an

d tw

o ne

w)

in a

ppro

xim

atel

y 59

km

reac

h of

the

Moh

ana.

In

Bang

lade

sh a

nd In

dia,

dis

char

ge m

easu

rem

ent s

tatio

ns a

re

at fa

r dis

tanc

es th

an th

is.

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

81

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

If no

new

trib

utar

y is

join

ing

the

mai

n riv

er o

r if t

he in

term

edia

te c

atch

men

t is

not l

arge

en

ough

, the

n th

ere

is n

o ne

ed to

add

new

dis

char

ge s

tatio

n; a

ll w

ater

leve

l sta

tions

do

not n

eed

to h

ave

flow

gau

ging

as

wel

l; th

is is

the

prac

tice

arou

nd th

e co

untri

es, l

ike

I In

dia,

Ban

glad

esh,

UK

and

in m

any

othe

r cou

ntrie

s.

7 C

alib

ratio

n/va

lidat

ion

data

: T

he c

alib

ratio

n &

valid

atio

n se

ctio

ns o

f th

e re

ports

men

tion

the

need

for c

alib

ratio

n/va

lidat

ion

data

, but

for m

any

basi

ns s

uch

data

are

una

vaila

ble.

Lin

ked

with

th

e ab

ove

bulle

t poi

nt, p

rovi

de a

sum

mar

y of

the

exis

ting

data

ava

ilabl

e fo

r ca

libra

tion/

valid

atio

n.

If no

dat

a ar

e av

aila

ble,

then

the

basi

n’s

wor

ks

prog

ram

me

shou

ld

refle

ct

the

need

to

st

art

colle

ctin

g da

ta

early

fo

r su

bseq

uent

us

e in

ca

libra

tion/

valid

atio

n.

We

have

pro

vide

d an

ove

rvie

w o

f da

ta a

vaila

bilit

y in

Tab

le 9

in S

ectio

n 2.

And

all

hydr

omet

net

wor

k ha

s be

en p

ropo

sed

for

inst

alla

tion

with

in f

irst

six

mon

ths,

so

that

pr

opos

ed m

odel

ling

can

use

the

data

fro

m t

he f

irst

mon

soon

for

cal

ibra

tion

and

valid

atio

n.

8 To

po s

urve

y : E

xpla

in w

hy a

dditi

onal

x/s

sur

vey

is re

quire

d. Is

this

requ

ired

for;

o Ac

cura

cy o

f lev

el fo

reca

st

o Ac

cura

cy o

f inu

ndat

ed a

rea

fore

cast

o R

atin

g cu

rve

exte

nsio

n (b

y hy

drau

lic

mod

el)

We

have

exp

lain

ed th

is in

the

repo

rt in

Cha

pter

7. T

his

surv

ey is

requ

ired

for a

ll th

ree

bulle

t po

ints

as

men

tione

d, a

nd a

s w

ell w

e sh

ould

rep

lace

all

cros

s-se

ctio

ns w

hich

w

ere

surv

eyed

in 2

014;

thes

e w

ill be

mor

e th

an 5

yea

rs o

ld. I

n U

K, w

here

riv

ers

are

very

sta

ble,

the

Envi

ronm

ent A

genc

y (re

spon

sibl

e fo

r flo

od fo

reca

stin

g) u

pdat

es th

eir

mod

el if

topo

grap

hy is

mor

e th

an 6

yea

rs o

ld. H

ere

in N

epal

, we

need

suc

h up

date

ea

rlier

as

the

river

s ar

e m

orph

olog

ical

ly d

ynam

ic. W

e al

so n

eed

mor

e cr

oss-

sect

ions

in

Tea

ri to

dev

elop

1d/

2d li

nked

mod

el a

nd 2

d m

odel

.

9 To

po s

urve

y :

Will

the

x/s

surv

ey i

nclu

de t

he

flood

plai

n, if

so

then

sta

te.

Yes,

cro

ss-s

ectio

ns w

ill be

ext

ende

d to

floo

dpla

in. I

t has

alre

ady

been

men

tione

d in

th

e Ex

ecut

ive

sum

mar

y; w

e ha

ve n

ow a

lso

men

tione

d th

is in

cha

pter

7 a

s w

ell.

10

2D m

odel

ling

– D

TM :

The

pro

pose

d hy

drau

lic

mod

el m

etho

dolo

gy i

nclu

des

2D m

odel

ling

for

som

e ar

eas.

Pr

evio

us

wor

k ha

s sh

own

Than

ks fo

r thi

s co

mm

ent a

nd c

omin

g up

with

you

r sup

port

to in

clud

e so

me

purc

hase

of

bet

ter D

TM b

y di

verti

ng th

e fu

nd o

f X-b

and

Rad

ar.

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

82

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

in

com

patib

ility

betw

een

the

avai

labl

e SR

TM-

deriv

ed D

TM a

nd t

he s

urve

yed

cros

s-se

ctio

ns.

The

repo

rt do

es

not

outli

ne

how

th

is

inco

mpa

tibilit

y w

ill be

re

solv

ed

eith

er

by

(i)

obta

inin

g a

new

DTM

fro

m a

ltern

ativ

e sa

tellit

e so

urce

s,

or

(ii)

surv

eyin

g th

e flo

odpl

ain,

by

Li

DAR

or

tradi

tiona

l met

hods

. If i

t’s is

pro

pose

d to

use

the

exi

stin

g SR

TM d

eriv

ed D

TM t

hen

cons

ider

atio

n sh

ould

be

give

n to

whe

ther

the

in

accu

raci

es in

the

DTM

are

com

men

sura

te w

ith

the

impr

oved

acc

urac

y of

the

2D a

ppro

ach.

The

co

nsul

tant

may

rec

omm

end

to c

arry

out

top

o su

rvey

s of

the

flood

affe

cted

are

a us

ing

LiD

AR

or a

noth

er m

oder

n m

etho

d. E

xpla

natio

n m

ay

also

be

incl

uded

on

the

use

of a

ccur

ate

DTM

S fo

r Irr

igat

ion

infra

stru

ctur

e pl

anni

ng

by

the

Gov

ernm

ent

of N

epal

. Ad

ditio

nal

cost

of

topo

su

rvey

s m

ay b

e co

vere

d fro

m th

e co

st a

lloca

ted

to X

-ban

d ra

dars

and

AD

CPS

.

We

have

incl

uded

now

the

purc

hase

of P

LEIA

DES

sat

ellit

e im

ager

y, w

hich

DH

M h

as

alre

ady

used

(inf

orm

ed b

y D

HM

Flo

od fo

reca

ster

); th

is im

ager

y is

ava

ilabl

e up

to 5

0cm

re

solu

tion.

The

prop

osed

top

o an

d cr

oss-

sect

ion

surv

ey i

n ea

ch b

asin

will

have

mor

e cr

oss-

sect

ions

in th

e fla

t Ter

ai re

gion

. Thi

s w

as p

artic

ular

ly p

lann

ed w

here

2-d

mod

el w

ill be

bu

ilt a

nd 1

-d/2

-d m

odel

will

be li

nked

. Thi

s w

as a

lread

y m

entio

ned

in th

e re

port.

The

purc

hase

of t

he n

ew D

TM (P

LEIA

DES

) will

help

dev

elop

ing

a be

tter a

nd a

ccur

ate

2-d

mod

el in

com

bina

tion

with

cro

ss-s

ectio

nal d

ata

11

2D m

odel

ling

– co

mpu

tatio

nal t

ime

of r

eal t

ime

inun

datio

n fo

reca

stin

g: M

any

of t

he b

asin

s ar

e fa

st re

spon

ding

(12-

24 h

ours

) and

fore

cast

s w

ill ne

ed t

o be

iss

ued

with

out

unne

cess

ary

dela

y.

The

repo

rt do

es

not

outli

ne

the

estim

ated

in

crea

se

in

com

puta

tiona

l tim

e re

quire

d to

un

derta

ke t

he 2

D m

odel

ling

and

whe

ther

the

tim

e in

crea

se

is

prac

tical

fo

r th

ese

fast

re

spon

ding

cat

chm

ents

.

Sum

mar

y: t

otal

tim

e fo

r is

suin

g fo

reca

st w

ill be

abo

ut 7

0 to

75

min

utes

for

a b

asin

. Fo

reca

st m

odel

ope

ratio

ns a

re d

escr

ibed

in d

etai

ls in

Sec

tion

8.9.

30 m

inut

es o

f da

ta p

roce

ssin

g an

d an

alys

is,

30 m

inut

es o

f m

odel

run

tim

e an

d 15

m

inut

es fo

r dis

sem

inat

ing

the

fore

cast

. Ope

ratio

nal p

hase

will

be re

lativ

ely

easi

er, f

or

whi

ch D

HM

has

alre

ady

exis

ting

syst

em (

sim

ilar

to D

elft-

FEW

S);

so h

opef

ully

the

re

will

not m

uch

issu

es a

t ope

ratio

nal a

nd d

isse

min

atio

n ph

ase.

The

re s

houl

d ha

ve 2

to 3

op

erat

ors

(tech

nici

ans)

to

do t

his

rout

ine

proc

ess

each

day

dur

ing

fore

cast

sea

son

(mon

soon

). An

d co

nsul

tant

(at

lea

st o

ne i

nter

natio

nal

and

one

natio

nal)

from

thi

s pr

ojec

t will

rem

ain

avai

labl

e fu

ll tim

e fo

r thr

ee y

ears

.

The

core

wor

k of

thi

s pr

ojec

t is

the

dev

elop

men

t of

the

mod

els

(runo

ff, 1

d, 2

d an

d

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

83

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

1d/2

d lin

ked

mod

els)

.

In th

is n

ote,

we

wan

t to

men

tion

that

ther

e ar

e 2d

mod

ellin

g, fo

r flo

od fo

reca

stin

g, in

us

e in

Ind

ia (

Bagm

ati

Riv

er i

n Bi

har)

and

Aust

ralia

, w

here

run

-tim

e in

rea

l-tim

e is

ar

ound

30

min

utes

(we

have

refe

rred

the

liter

atur

e, h

ave

men

tione

d it

at a

num

ber o

f pl

aces

with

in th

e re

port

(Hux

ley,

201

6).

Onc

e th

e fo

reca

sted

rai

nfal

l will

be r

ecei

ved

on a

day

and

rea

l tim

e w

ater

leve

l and

di

scha

rge

data

will

be r

ecei

ved,

afte

r pr

oces

sing

the

dat

a (w

hich

will

also

be

auto

mis

ed li

ke in

Del

ft-FE

WS)

for m

odel

run

(runo

ff m

odel

and

hyd

rody

nam

ic m

odel

), th

e m

odel

s to

com

plet

e ru

n w

ill ta

ke a

bout

30

to 4

0 m

inut

es (

for

all

five

basi

ns,

runn

ing

from

a b

atch

file

).

12

Floo

d fo

reca

stin

g sy

stem

:

The

repo

rts

(esp

ecia

lly A

pp A

) don

’t m

ake

a cl

ear d

istin

ctio

n be

twee

n a

hydr

olog

ical

/hyd

raul

ic

mod

ellin

g sy

stem

and

a f

lood

for

ecas

ting

syst

em.

The

flood

fo

reca

stin

g sy

stem

is

th

e to

ol

whi

ch

inte

grat

es r

eal t

ime

data

, co

nduc

ts m

odel

run

s an

d

crea

tes

flood

fo

reca

sts

and

war

ning

in

clud

ing

Web

pub

licat

ions

and

SM

S al

erts

. A

Fore

cast

ing

syst

em g

ener

ally

nee

d to

car

ry o

ut

the

follo

win

g ac

tiviti

es;

o R

ead

obse

rved

hyd

rom

et a

nd r

ainf

all

fore

cast

.

o Q

ualit

y as

sura

nce

on

obse

rved

an

d fo

reca

st in

put d

ata

o D

eter

min

e ho

w to

inte

rpre

t poo

r qu

ality

or

mis

sing

dat

a (ie

rain

fall

hier

arch

y)

Dis

tinct

ion

betw

een

Hyd

rolo

gica

l and

hyd

raul

ic m

odel

:

Yes,

thi

s is

cor

rect

, in

App

endi

x A,

we

mai

nly

wan

ted

to l

ist

the

hydr

odyn

amic

m

odel

ling

softw

are,

and

then

hav

e ad

ded

in a

ny o

f the

hyd

rody

nam

ic s

oftw

are,

ther

e is

a c

oupl

ed h

ydro

logi

cal s

oftw

are.

In th

e lis

t of m

odel

ling

softw

are

(see

Tab

le 1

1), w

e w

ante

d to

men

tion

key

and

benc

hmar

ked

hydr

odyn

amic

mod

ellin

g so

ftwar

e on

ly, a

nd

wan

ted

to in

clud

e th

ose

whi

ch D

HM

use

s at

pre

sent

.

Ther

e is

no

benc

h m

arki

ng r

esea

rch

(to m

y kn

owle

dge)

for

hyd

rolo

gica

l m

odel

ling

softw

are.

How

ever

, w

e ha

ve d

escr

ibed

thr

ee k

ey h

ydro

logi

cal

mod

ellin

g so

ftwar

e:

NAM

, H

EC-H

MS

and

PDM

am

ong

whi

ch N

AM a

nd H

EC-H

MS

are

bein

g us

ed b

y D

HM

. W

e di

d no

t ai

m t

o de

scrib

e al

l hy

drol

ogic

al a

nd h

ydro

dyna

mic

mod

ellin

g so

ftwar

e av

aila

ble

arou

nd th

e w

orld

. We

are

afra

id, w

e w

ill st

rugg

le w

ith o

ur a

lloca

ted

inpu

t.

Floo

d fo

reca

stin

g sy

stem

:

We

have

now

add

ed o

n Fo

reca

stin

g sy

stem

/Too

l in

Sect

ion

8.9.

2 to

8.9

.4 a

nd D

elft-

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

84

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

o Pr

epar

e m

odel

in

put

files

, in

clud

ing

boun

dary

con

ditio

n an

d ho

tsta

rt fil

es

o Sc

hedu

le, d

istri

bute

am

ongs

t com

putin

g re

sour

ces

and

laun

ch s

imul

atio

ns

o C

arry

out

dat

a as

sim

ilatio

n

o Ex

tract

rele

vant

sim

ulat

ion

resu

lts

o D

eter

min

e st

atus

dur

ing

fore

cast

per

iod

o Pr

epar

e an

d is

sue

war

ning

s

o D

isse

min

ate

war

ning

s to

W

eb

and

crea

te S

MS

aler

ts.

o Ar

chiv

e re

sults

FEW

S in

Sec

tion

8.9.

4

Bulle

ted

item

s m

entio

ned

here

, lik

e : R

ead

obse

rved

hyd

rom

et a

nd r

ainf

all f

orec

ast,

Q

ualit

y as

sura

nce

on

obse

rved

an

d fo

reca

st

inpu

t da

ta,

data

as

sim

ilatio

n ar

e m

entio

ned

in S

ectio

n 8.

9.

Now

, ple

ase

see

belo

w b

ulle

t wis

e re

spon

se:

R

ead

obse

rved

hyd

rom

et a

nd ra

infa

ll fo

reca

st.

Plea

se s

ee b

ulle

t 1, 2

and

3 in

Sec

tion

8.9.

1

Q

ualit

y as

sura

nce

on o

bser

ved

and

fore

cast

inpu

t dat

a

Plea

se s

ee b

ulle

t 4 in

Sec

tion

8.9.

1

Q

ualit

y as

sura

nce

on o

bser

ved

and

fore

cast

inpu

t dat

a

Plea

se s

ee b

ulle

t 4 in

Sec

tion

8.9.

1

Pr

epar

e m

odel

inpu

t file

s, in

clud

ing

boun

dary

con

ditio

n an

d ho

tsta

rt fil

es

Plea

se s

ee b

ulle

t 2 to

5 in

Sec

tion

8.9.

1

C

arry

out

dat

a as

sim

ilatio

n

Plea

se s

ee S

ectio

n 8.

9.6

Ex

tract

rele

vant

sim

ulat

ion

resu

lts

Plea

se s

ee b

ulle

t 6 in

Sec

tion

8.9.

1

D

eter

min

e st

atus

dur

ing

fore

cast

per

iod

Not

und

erst

ood,

wha

t is

mea

nt b

y th

is c

omm

ent

Pr

epar

e an

d is

sue

war

ning

s

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

85

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

Plea

se s

ee b

ulle

t 1 in

Sec

tion

8.9.

5

D

isse

min

ate

war

ning

s to

Web

and

cre

ate

SMS

aler

ts.

Plea

se s

ee b

ulle

t 1 in

Sec

tion

8.9.

5

13

Floo

d fo

reca

stin

g sy

stem

:

Out

line

the

diffe

renc

e be

twee

n an

ope

n so

urce

sof

twar

e w

hich

req

uire

s co

ding

the

se f

eatu

res,

and

a

prop

rieta

ry s

oftw

are

whi

ch in

clud

es m

ost o

f the

in

-bui

lt fu

nctio

nalit

y fo

r the

se fe

atur

es

We

have

now

men

tione

d th

e fu

nctio

nalit

y of

Del

ft-FE

WS

and

use

of t

he e

xist

ing

fore

cast

mod

el o

pera

tion

syst

em w

ithin

DH

M (

see

sect

ion

8.9.

3 an

d 8.

9.4)

14

Floo

d fo

reca

stin

g sy

stem

: Pr

ovid

e a

shor

t ov

ervi

ew o

f Del

ft FE

WS

and

the

fact

that

it c

an

inco

rpor

ate

man

y di

ffere

nt

hydr

olog

ical

an

d hy

drau

lic m

odel

s, in

clud

ing

HEC

and

MIK

E.

Prov

ided

, see

Sec

tion

8.9.

4

Also

pro

vide

d be

low

for r

eady

refe

renc

e

Del

ft-FE

WS

prov

ides

an

open

she

ll sy

stem

for

man

agin

g fo

reca

stin

g pr

oces

ses

and/

or

hand

ling

time

serie

s da

ta

(http

s://p

ublic

wik

i.del

tare

s.nl

/dis

play

/FEW

SDO

C/H

ome)

. Th

e fo

reca

stin

g sy

stem

was

es

sent

ially

bui

lt as

a s

hell

arou

nd th

e hy

drol

ogic

al a

nd h

ydra

ulic

mod

els

used

(Wer

ner

et a

l., 2

012)

. Th

e sy

stem

con

tain

s no

mod

ellin

g ca

pabi

litie

s (ra

infa

ll-ru

noff

and

hydr

odyn

amic

mod

ellin

g) w

ithin

its

code

bas

e. In

stea

d, it

ent

irely

rel

ies

on th

ird p

arty

m

odel

ling

com

pone

nts

for r

ainf

all-r

unof

f and

hyd

rody

nam

ic m

odel

ling.

The

stru

ctur

e of

th

e D

elft-

FEW

S in

clud

es a

dat

a st

orag

e la

yer,

a da

ta a

cces

s la

yer,

as w

ell a

s se

vera

l co

mpo

nent

s fo

r im

porti

ng, m

anip

ulat

ing,

vie

win

g an

d ex

porti

ng d

ata.

The

stru

ctur

e of

D

elft-

FEW

S is

sho

wn

in F

igur

e 16

.

Cur

rent

ly D

elft-

FEW

S is

use

d in

ove

r 40

cou

ntrie

s ov

er t

he w

orld

. D

elft-

FEW

S ca

n ei

ther

be

depl

oyed

in

a st

and-

alon

e, m

anua

lly d

riven

env

ironm

ent,

or i

n a

fully

au

tom

ated

dis

tribu

ted

clie

nt-s

erve

r env

ironm

ent.

DH

M, i

n th

eir e

xist

ing

FFEW

S, (e

.g.,

in K

osi

and

Bagm

ati

by h

ydro

logi

cal

and

hydr

aulic

mod

ellin

g, a

nd i

n Ka

rnal

i an

d N

aray

ani b

y pr

obab

ilistic

mod

ellin

g) u

ses

thei

r ow

n op

erat

iona

l sys

tem

(D

HM

, 201

8).

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

86

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

Sim

ilarly

, Ban

glad

esh

and

Indi

a (B

ihar

), in

thei

r flo

od fo

reca

stin

g m

odel

s de

velo

ped

in

NAM

and

MIK

E11

and

HEC

-HM

S an

d H

EC-R

AS,

also

use

the

ir ow

n op

erat

iona

l sy

stem

.

15

Ope

ratio

nal

FFEW

S :

Prov

ide

a su

mm

ary

over

view

of

how

eac

h FF

EWS

will

oper

ate,

in

term

s of

;

o C

over

age

(hyd

rolo

gica

l, 1D

hyd

raul

ic,

linke

d 1D

-2D

hyd

raul

ic)

o In

put

data

(ob

serv

ed)

and

topo

(x/

s &

2D).

Wha

t w

ill th

e gr

idde

d m

eteo

rolo

gica

l dat

a be

use

d fo

r ?

o Q

uant

itativ

e pr

ecip

itatio

n fo

reca

st

(QPF

), al

so

cons

ider

ing

DH

M’s

W

RF

mod

el

resu

lts

o Fr

eque

ncy

of fo

reca

st,

o H

indc

ast a

nd fo

reca

st h

oriz

on,

o Fo

reca

st p

oint

s,

o Fo

reca

st d

eliv

erab

les

o Ti

min

g,

incl

udin

g la

tenc

y,

pre-

proc

essi

ng,

runt

ime,

pos

t-pro

cess

ing,

for

ecas

t pr

epar

atio

n,

fore

cast

ap

prov

al,

fore

cast

is

suan

ce (n

otin

g th

at y

ou w

ill ne

ed to

coo

rdin

ate

five

FFEW

S sy

stem

s).

We

have

inco

rpor

ated

this

com

men

t in

chap

ter 8

in d

etai

ls.

C

over

age

(hyd

rolo

gica

l, 1D

hyd

raul

ic, l

inke

d 1D

-2D

hyd

raul

ic)

Plea

se s

ee F

igur

e 10

to 1

3

All w

ater

leve

l and

wat

er a

nd d

isch

arge

gau

ges

are

fore

cast

poi

nts;

thes

e w

ill be

the

fore

cast

poi

nts

at g

auge

d lo

catio

ns. A

t un-

gaug

ed lo

catio

ns, e

ach

com

puta

tiona

l nod

e w

ill be

a fo

reca

st p

oint

, app

roxi

mat

ely

300

to 4

00m

apa

rt al

ong

the

river

In

put d

ata

(obs

erve

d) a

nd to

po (x

/s &

2D

). W

hat w

ill th

e gr

idde

d m

eteo

rolo

gica

l dat

a be

use

d fo

r ?

Grid

ded

data

will

be u

sed

from

APH

RO

DIT

E, T

RM

M a

nd IM

D (

in c

ase

of IM

D, D

HM

w

ill re

quire

a tr

eaty

with

Indi

a fo

r usi

ng th

eir d

ata)

Fr

eque

ncy

of fo

reca

st

Dai

ly o

nce

or m

ore

durin

g hi

gh o

r mul

tiple

pea

k, p

leas

e bu

llet 5

in S

ectio

n 8.

9.1

H

indc

ast a

nd fo

reca

st h

oriz

on

Seve

n da

ys: 4

day

s fo

r hin

dcas

t and

thre

e da

ys fo

r for

ecas

t. Pl

ease

bul

let 3

in S

ectio

n 8.

9.1

Fore

cast

del

iver

able

s

Wat

er le

vel,

disc

harg

e at

all

fore

cast

poi

nts

and

floo

d in

unda

tion

map

(Ple

ase

bulle

t 6

in S

ectio

n 8.

9..1

), an

d ye

arly

eva

luat

ion

repo

rt on

for

ecas

ting

perfo

rman

ce (

Plea

se

see

Sect

ion

8.1)

Ti

min

g, in

clud

ing

late

ncy,

pre

-pro

cess

ing,

runt

ime,

pos

t-pro

cess

ing,

fore

cast

pr

epar

atio

n, fo

reca

st a

ppro

val,

fore

cast

issu

ance

(not

ing

that

you

will

need

to

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

87

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

co

ordi

nate

five

FFE

WS

syst

ems)

.

Thes

e ar

e co

vere

d be

twee

n bu

llet 1

to b

ulle

t 6 in

Sec

tion

8.9.

1 an

d al

so s

ee 8

.9.2

last

pa

ra

16

Cos

t of

m

odel

ling

softw

are

: Th

e co

st

of

mod

ellin

g so

ftwar

e is

list

ed a

s U

SD13

,000

per

ba

sin,

as

sum

ing

the

sam

e so

ftwar

e ca

n be

us

ed f

or t

he f

ive

basi

ns,

tota

ling

USD

65,0

00.

Wha

t abo

ut a

nnua

l mai

nten

ance

cos

ts?

Shou

ld

freew

are

softw

are

be u

sed

then

this

pric

e w

ould

be

ni

l, bu

t th

ere

may

be

ex

tens

ive

codi

ng

requ

irem

ents

. Th

e w

ay t

he c

ost

of m

odel

ling

softw

are

is

pres

ente

d do

es

not

refle

ct

this

. Be

caus

e of

thi

s, p

erha

ps it

sho

uld

be in

clud

ed

as a

sep

arat

e ite

m.

If H

EC-H

MS

and

HEC

-RAS

are

use

d, th

ey w

ill be

free

If M

IKE

is u

sed,

DH

M h

as a

lread

y ha

ve L

icen

ses;

the

y w

ill ne

ed s

ome

addi

tiona

l bu

dget

to

get

mul

tiple

Lic

ense

and

get

the

late

st r

elea

se o

f M

IKE

(rele

ase

2016

or

late

r if a

lread

y av

aila

ble)

In c

ase

of T

UFL

OW

, Flo

od M

odel

ler p

ro o

r Inf

owor

ks IC

M o

r SO

BEK,

the

softw

are

will

have

to

be p

urch

ased

, an

d th

us U

SD 6

5,00

0 ha

ve b

een

kept

; w

e ha

ve t

aken

thi

s pr

ice

from

the

WBM

hom

e pa

ge

17

Cos

ts:

The

exec

utiv

e su

mm

ary

shou

ld i

nclu

de

all c

ost

com

pone

nts,

ie t

opo

surv

ey,

hydr

omet

an

d ra

infa

ll pr

ocur

emen

t an

d O

&M,

mod

ellin

g so

ftwar

e pr

ocur

emen

t and

cod

ing

Incl

uded

in E

S, p

leas

e se

e Ta

ble

1 to

6

18

Res

pons

e tim

e :

Sect

ion

1 sh

ould

out

line

the

resp

onse

tim

es

(eg

time

of

conc

entra

tion,

la

gtim

e) a

t var

ious

loca

tions

in th

e ba

sin.

Thi

s is

im

porta

nt

to

appr

ecia

te

the

need

fo

r ra

pid

fore

cast

s.

Hav

e in

clud

ed it

with

refe

renc

e in

Sec

tion

1.5,

par

a 2.

19

Dat

a flo

w :

A d

iagr

am o

utlin

ing

the

data

flo

ws

(from

ob

serv

ed

to

harv

este

d by

FF

EWS

to

diss

emin

atio

n) w

ould

be

help

ful.

Prov

ided

, see

Fig

ure

14 a

nd s

uppo

rting

text

s in

Sec

tion

8.9.

2, b

ulle

t 1, 2

and

3

20

Wat

er-le

vel g

auge

: D

HM

will

take

ow

ners

hip

of

We

have

pro

pose

d th

ree

type

s of

gau

ges;

dep

endi

ng o

n th

e si

te c

ondi

tion

and

DH

M’s

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

88

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

th

e ga

uges

. Li

aise

with

DH

M o

n w

hat

type

of

gaug

e th

ey w

ill ac

cept

, si

nce

they

mig

ht o

nly

wan

t do

wnw

ard-

look

ing

rada

r co

mpa

tible

to

th

eir r

ecen

tly in

stal

led

real

tim

e sy

stem

in m

any

basi

ns.

pref

eren

ce,

gaug

e ty

pe w

ill be

sel

ecte

d at

eac

h si

te.

The

thre

e ty

pes

of g

auge

co

nsid

erat

ion

is a

lso

base

d on

spe

cific

atio

ns g

iven

by

RTS

, th

e Ka

thm

andu

bas

ed

hydr

omet

equ

ipm

ent

prov

ider

. Bo

th A

DB

and

DH

M a

dvis

ed t

o co

ntac

t R

TS;

DH

M

how

ever

men

tione

d us

to ta

ke n

ote

that

RTS

is o

nly

thei

r ven

dor

21

O&

M c

osts

: O

utlin

e w

heth

er th

is is

per

yea

r or

over

a n

umbe

r of y

ears

(how

man

y?).

Hav

e m

ade

them

cle

ar in

eve

ry b

asin

rep

ort,

see

Tabl

e 1

to 6

and

als

o pl

ease

see

Fe

asib

ility

Rep

ort,

Cha

pter

6 a

nd A

ppen

dix

E

22

Num

ber

of h

ydro

met

gau

ges

: En

sure

rep

orts

in

dica

te

inte

rnal

ly-c

onsi

sten

t nu

mbe

rs

of

hydr

omet

gau

ges.

The

Moh

ana-

Khut

iya

repo

rt is

inco

nsis

tent

.

Hav

e co

rrect

ed t

hem

in

each

bas

in r

epor

t. So

rry t

hat

we

mad

e so

me

mis

take

s in

nu

mbe

rs, m

ainl

y du

e to

cop

y an

d pa

stin

g

23

Wat

er-le

vel

and

disc

harg

e bu

dget

:

mea

sure

men

t co

st

and

an

O&M

co

st

are

sepa

rate

, w

hat’s

th

e di

ffere

nce

betw

een

mea

sure

men

t and

ope

ratio

n ?

Hav

e cl

arifi

ed it

, Men

tione

d in

not

e (c

) in

Tabl

e 3

and

Tabl

e 16

.

24

Rea

l tim

e da

taba

se (

SCAD

A): I

nclu

de th

e ne

ed

to

deve

lop

a re

al

time

data

ba

se

syst

em

inte

grat

ed

to

DH

M’s

ex

istin

g da

taba

se.

Upd

atin

g an

d m

aint

enan

ce o

f th

e da

taba

se b

y D

HM

sho

uld

also

des

crib

ed

We

will

use

DH

M e

xist

ing

real

tim

e da

taba

se s

yste

m.

We

have

add

ed a

sec

tion

( see

sec

tion

8.9.

2) t

hat d

ata

from

new

tele

met

ric g

auge

s w

ill be

tra

nsm

itted

to

DH

M’s

ser

ver;

how

ever

, co

nsul

tant

for

thi

s pr

ojec

t w

ill do

the

ch

ecki

ng, a

naly

sis

and

qual

ity a

ssur

ance

for

thes

e ne

w g

auge

s; w

e ha

ve c

onsi

dere

d bu

dget

for t

his

25

Floo

d fo

reca

stin

g ap

proa

ches

: T

he t

abul

ated

flo

od f

orec

astin

g ap

proa

ches

(Ta

bles

12,

13,

14

, 15

& 1

6) a

re n

ot c

lear

. Pl

an/s

chem

atic

di

agra

ms

wou

ld m

ake

this

cle

arer

.

Plan

map

add

ed, p

leas

e se

e Fi

gure

s (m

aps)

10

to 1

3

26

Dis

sem

inat

ion

: pr

ovid

e a

sect

ion

on f

orec

ast

diss

emin

atio

n, ie

wha

t, w

hen,

how

, etc

Ad

ded,

ple

ase

see

Sect

ion

8.9,

bul

let 1

to 5

and

als

o Se

ctio

n 8.

9.5

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

89

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

27

FFEW

S pr

ogra

mm

e : D

o no

t rep

lace

gau

ge-to

-ga

uge

fore

cast

ing,

kee

p it

as a

n al

tern

ativ

e (b

acku

p)

Com

plie

d; g

auge

to g

auge

cor

rela

tion

will

be m

aint

aine

d in

par

alle

l to

othe

r too

l

28

FFEW

S pr

ogra

mm

e : t

o Fi

gure

8 a

dd/c

larif

y;

o G

auge

inst

alla

tion

perio

d

o D

efin

e w

hat Q

1 to

Q8

are

o R

ainf

all-r

unof

f is

a st

anda

lone

act

ivity

o En

sure

cal

ibra

tion

data

are

ava

ilabl

e fo

r ca

libra

tion

activ

ity.

Ther

e m

ay

need

to

be

an

othe

r ca

libra

tion/

valid

atio

n ac

tivity

nea

r th

e en

d w

hen

mor

e da

ta is

ava

ilabl

e.

o En

sure

al

l fiv

e pr

ogra

mm

es

are

not

coin

cide

nt, t

here

sho

uld

be s

ome

stag

ger i

n th

e pr

ogra

mm

es

Cla

rifie

d,

Reg

ardi

ng m

odel

dev

elop

men

t for

five

bas

ins,

we

have

con

side

red

suffi

cien

t res

ourc

e in

put (

3 in

tern

atio

nal a

nd fi

ve n

atio

nal e

xper

ts. T

hus,

the

stag

gerin

g of

act

iviti

es w

ill re

mai

n up

to h

e co

nsul

tant

. The

aim

in th

e pr

ogra

mm

e th

at m

odel

for

each

bas

in w

ill go

in p

aral

lel a

nd s

houl

d be

cal

ibra

ted

and

valid

ated

for

all

thre

e ye

ars,

and

eac

h ba

sin

mod

el w

ill be

han

ded

over

to D

HM

at t

he e

nd o

f 36th

mon

th

29

FFEW

S m

aint

enan

ce

: pr

ogra

mm

e sh

ould

in

clud

e at

lea

st a

3-y

ear

mai

nten

ance

per

iod.

M

aybe

ev

en

a 5-

year

pe

riod

mai

nten

ance

pe

riod.

O

utlin

e w

hat

is

requ

ired

in

the

mai

nten

ance

per

iod;

o Pr

e-se

ason

set

up (2

wee

ks)

o Ea

rly s

easo

n as

sist

ance

(2 w

eeks

)

o O

n-ca

ll tro

uble

-sho

otin

g du

ring

flood

se

ason

o Po

st-s

easo

n re

view

(2 w

eeks

)

Dur

ing

the

perio

d of

dev

elop

men

t of

thi

s pr

ojec

t, w

hich

is t

hree

yea

rs,

ther

e w

ill be

op

erat

ion

and

mai

nten

ance

wor

k an

d co

nsul

tant

will

rem

ain

avai

labl

e fu

ll tim

e fo

r any

su

ppor

t inc

ludi

ng tr

oubl

e sh

ootin

g

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

90

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Ref

eren

ce

Com

men

ts fr

om A

DB

R

eply

con

sulta

nt

30

Coo

rdin

atio

n of

fiv

e FF

EWS

syst

ems

: ex

plai

n ho

w

the

over

all

syst

em

will

coor

dina

te

five

indi

vidu

al F

FEW

S sy

stem

s.

Thes

e ar

e fiv

e di

ffere

nt b

asin

s an

d fiv

e di

ffere

nt m

odel

s. In

our

sch

edul

e, a

ll fiv

e ba

sin

mod

el w

ill be

dev

elop

ed in

par

alle

l. W

e ha

ve in

clud

ed s

uffic

ient

and

logi

cal h

uman

re

sour

ce (i

nput

) for

this

(see

Sec

tion

8.13

).

For o

pera

tion

and

diss

emin

atio

n, a

s th

ese

will

be a

utom

ated

sys

tem

(exi

stin

g sy

stem

of

DH

M –

the

new

mod

els

will

be c

usto

mis

ed t

o th

e sy

stem

), th

e op

erat

ion

and

diss

emin

atio

n tim

e is

ver

y m

inim

al,

for

five

mod

els

(or

10 m

odel

s),

the

run

will

be

mad

e th

roug

h a

batc

h fil

e. F

or t

he f

irst

thre

e ye

ars,

all

inte

rnat

iona

l an

d na

tiona

l ex

perts

to

geth

er

with

DH

M

expe

rts

will

rem

ain

avai

labl

e. A

fter

3rd

year

, D

HM

co

ntin

ues.

The

y ha

ve s

peci

alis

ts w

ho w

ork

on s

hifts

(DH

M, 2

018)

31

CBR

DM

: Inc

lude

CBR

DM

act

iviti

es in

the

Wes

t R

apti

basi

n ,

as

this

as

pect

w

as

not

fully

co

vere

d in

th

e W

orld

Ba

nk

PPC

R

proj

ect.

CBR

DM

sho

uld

also

foc

us o

n th

e ro

le o

f lo

cal

mun

icip

aliti

es.

Incl

uded

. It i

s in

a s

epar

ate

repo

rt on

CBD

RM

for a

ll si

x ba

sins

32

Floo

d Sh

elte

rs:

Incl

ude

Floo

d Sh

elte

rs in

Wes

t R

apti

Basi

n.

Incl

uded

, ple

ase

see

CBD

RM

Rep

ort,

it is

a s

epar

ate

repo

rt

33

Evac

uatio

n ro

ute:

Inc

lude

the

nee

d to

pre

pare

ev

acua

tion

rout

es in

all

the

basi

ns b

ased

on

2D

mod

el re

sults

and

road

net

wor

k.

Incl

uded

, ple

ase

see

CBD

RM

Rep

ort;

it is

a s

epar

ate

repo

rt

34

Floo

d R

isk:

Inc

lude

the

pro

visi

on o

f flo

od r

isk

asso

ciat

ed w

ith a

n flo

od fo

reca

st e

vent

, so

that

co

mm

uniti

es

and

Gov

ernm

ent

agen

cies

ar

e aw

are

of th

e ris

ks.

Yes,

this

is in

-bui

lt de

liver

able

; flo

od r

isk

map

s w

ill be

issu

ed d

aily

, irr

espe

ctiv

e of

a

maj

or e

vent

or

regu

lar

flow

; Pl

ease

see

bul

let

6 in

Sec

tion

8.9.

1 w

here

we

have

m

entio

ned

flood

map

as

deliv

erab

le in

eac

h fo

reca

st ru

n

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

91

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Tabl

e B

.2: C

omm

ents

on

FFEW

S R

epor

ts fr

om W

RPPF

(Dat

e of

rece

ipt:

13/0

1/20

19)

Com

men

ts fr

om W

RPP

F w

ere

gene

ric fo

r the

repo

rts o

n fiv

e ba

sins

: Moh

ana-

Khut

iya,

Maw

a-R

atuw

a, L

akha

dei,

Bakr

aha

and

East

Rap

ti

Ref

eren

ce

Com

men

ts

from

: W

RPP

F;

Rec

eive

d on

: 13

/01/

2019

R

eply

con

sulta

nt

Bul

let 1

Th

e la

ngua

ge

of

the

repo

rt ne

eds

to

be

impr

oved

ed

ited

in

stan

dard

fo

rmat

as

te

xt

writ

ing

in a

dditi

on t

o gr

amm

atic

ally

cor

rect

. Al

l th

e te

xt is

to b

e th

orou

ghly

che

cked

.

We

have

gon

e th

roug

h th

e re

port

thor

ough

ly,

have

im

prov

ed t

exts

and

cor

rect

ed

gram

mat

ical

erro

r

Reg

ardi

ng s

tand

ard

form

at a

s te

xt w

ritin

g, t

he r

epor

t ha

s be

en w

ritte

n in

Mot

t M

acD

onal

d’s

stan

dard

tem

plat

e; s

o w

e ho

pe th

e fo

rmat

is a

lrigh

t

Bul

let 2

Ab

brev

iate

d w

ords

are

to b

e st

anda

rdis

ed

We

have

sta

ndar

dise

d ab

brev

iate

d w

ords

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

92

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

Bul

let 3

Th

e re

ports

are

nic

e bu

t I

foun

d so

me

typi

ng

erro

rs a

nd s

ome

tech

nica

l iss

ues

like

inst

alla

tion

of l

ot o

f ra

in g

auge

inc

ludi

ng X

-ban

d ra

dar

inst

alla

tion

and

wat

er

leve

l se

nsor

st

atio

ns

sim

ilar

to t

hat

of U

K an

d Au

stra

lian

stan

dard

. Te

chni

cal

disc

ussi

ons

are

requ

ired

abou

t pr

opos

ed lo

catio

ns a

nd n

umbe

r of s

tatio

ns.

Mai

n is

sue

is s

usta

inab

ility

of t

he s

tatio

ns a

nd

syst

em fo

r ope

ratio

n an

d m

aint

enan

ce a

fter

the

proj

ect.

Prop

osed

wat

er l

evel

sen

sors

lik

e flo

atin

g in

st

illing

wel

l and

pre

ssur

e ai

r bub

ble

sens

ors

are

not o

pera

ble

in h

igh

sedi

men

t loa

d riv

er.

The

hydr

o-m

etric

st

atio

ns

may

ne

ed

spec

ial

stru

ctur

es li

ke fl

ood

pilla

r in

river

cha

nnel

hav

ing

high

fluc

tuat

ions

of w

ater

leve

ls a

nd c

hang

e of

riv

er c

hann

els

as w

ell.

The

deta

il de

sign

of

cabl

eway

is

not

incl

uded

w

hich

var

y w

ith p

ropo

sed

site

to

site

of

the

stat

ions

. est

imat

ed c

ost o

f cab

lew

ay is

sam

e fo

r al

l ba

sins

al

thou

gh

ther

e ar

e va

riatio

n in

pr

opos

ed

num

ber

of

stat

ions

w

ith

disc

harg

e m

easu

rem

ent

Than

ks fo

r you

r app

reci

atio

n ab

out t

he re

port,

we

have

rem

oved

typi

ng e

rrors

.

Num

ber

of g

roun

d ba

sed

rain

gau

ge s

tatio

ns h

ave

been

dec

ided

bas

ed o

n re

sear

ch

reco

mm

enda

tions

and

bas

ed o

n pr

actic

e, e

.g.,

in E

urop

e (p

leas

e se

e Se

ctio

n 2.

2 of

th

e re

port

for r

efer

ence

). Ke

epin

g in

min

d th

e fu

ture

mai

nten

ance

, the

tota

l num

ber o

f st

atio

ns h

ave

been

cho

sen

on th

e hi

gher

end

of t

he re

com

men

ded

spat

ial d

istri

butio

n.

For f

lood

fore

cast

ing

purp

ose,

one

sta

tion

with

in 1

0 to

100

km

2 is re

com

men

ded.

Our

pr

opos

ed d

istri

butio

n is

one

in

arou

nd 1

00 k

m2 .

The

area

is

even

hig

her

per

one

gaug

e in

big

ger

basi

n lik

e Ea

st a

nd W

est

Rap

ti. B

ased

on

the

perfo

rman

ce o

f th

e FF

EWS

mod

els,

whi

ch w

ill be

dev

elop

ed in

this

stu

dy, w

e ho

pe th

at D

HM

, in

futu

re,

can

add

few

mor

e st

atio

ns b

ringi

ng th

e st

atio

n de

nsity

like

in th

e U

K.

We

have

app

rised

the

num

bers

of g

auge

s an

d th

eir l

ocat

ions

to D

HM

thro

ugh

seve

ral

mee

tings

; w

e al

so

sent

th

ese

docu

men

ts

(Cha

pter

5

and

6)

to

DH

M;

they

re

com

men

ded

to d

ecid

e on

the

num

bers

bas

ed o

n hy

dro-

met

eoro

logi

cal c

limat

e in

N

epal

, w

hich

we

have

com

plie

d th

roug

h lit

erat

ure

and

rese

arch

rev

iew

s. F

urth

er t

o th

is,

we

also

pub

lishe

d tw

o ba

sins

rep

orts

(FS

Rep

ort)

for

M-K

and

M-R

bas

ins

in

adva

nce

in J

uly

2018

sho

win

g th

is s

patia

l dis

tribu

tion

of r

ain

gaug

es,

and

rece

ived

fe

edba

cks

from

AD

B an

d D

OI

and

impl

emen

ted

thos

e in

fin

alis

ing

rain

gau

ge

num

bers

in a

ll si

x ba

sins

.

Reg

ardi

ng X

-Rad

ar r

ain

gaug

e, w

e ha

ve n

ow d

ropp

ed t

his

item

fol

low

ing

ADB;

s ad

vice

(see

AD

B’s

com

men

t 3 in

Tab

le B

.1)

As o

f the

pro

pose

d ne

w s

tatio

ns a

re a

utom

ated

tele

met

ric s

tatio

ns, t

hey

have

min

imal

op

erat

ion

cost

. Th

e m

aint

enan

ce c

ost

is a

lso

min

imal

, w

hich

we

belie

ve N

epal

G

over

nmen

t/DH

M w

ill co

ntin

ue fr

om th

eir a

nnua

l bud

get a

fter t

his

proj

ect i

s co

mpl

eted

We

have

pro

pose

d th

ree

type

s: s

enso

r in

stil

ling

wel

l, ai

r bu

bble

s se

nsor

s an

d ra

dar

sens

or;

deep

enin

g on

site

con

ditio

n an

d D

HM

’s p

refe

renc

e, t

he g

auge

typ

e w

ill be

se

lect

ed d

urin

g pr

ocur

emen

t and

inst

alla

tion.

The

budg

et in

clud

es a

ll in

frast

ruct

ures

for i

nsta

llatio

n of

a g

auge

, rai

n ga

uge

or w

ater

le

vel g

auge

. We

have

men

tione

d th

is in

the

repo

rt (p

leas

e se

e Se

ctio

n 5.

5, p

ara

1) a

s fo

llow

s: th

e bu

dget

incl

udes

pro

cure

men

t, in

stal

latio

n, te

stin

g, c

alib

ratio

n, m

onito

ring,

an

d op

erat

ion

and

mai

nten

ance

for 3

yea

rs

We

have

pro

vide

d al

l inf

orm

atio

n ne

eded

for

inst

alla

tion

of th

e ca

blew

ay (

see

Figu

re

7); t

he e

quip

men

t tec

hnic

ian

wou

ld h

ave

to d

eliv

er th

is; m

ore-

over

, DH

M h

as a

lread

y ca

blew

ay

in M

ohan

a an

d W

est R

apti

basi

n; s

o a

sim

ilar

one

shou

ld b

e co

nstru

cted

.

Mot

t Mac

Don

ald

| WR

PPF:

Pre

para

tion

of P

riorit

y R

iver

Bas

ins

Floo

d R

isk

Man

agem

ent P

roje

ct, N

epal

93

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

3838

77 |

REP

| 00

39 |

4 Ap

ril 2

019

Floo

d Fo

reca

stin

g an

d Ea

rly W

arni

ng S

yste

m: M

awa

– R

atuw

a Ba

sin

4 R

egar

ding

hy

drol

ogic

al

mod

ellin

g an

d flo

od

early

w

arni

ng

syst

em

deve

lopm

ent,

list

of

mod

els

are

prov

ided

bu

t no

t re

com

men

ded

final

ly a

lthou

gh t

hey

have

est

imat

ed c

ost

for

softw

are

purc

hase

. The

dev

elop

men

t cos

t of t

he

hydr

olog

ic a

nd h

ydra

ulic

mod

el s

hall

redu

ce

sign

ifica

ntly

onc

e it

is b

uilt

for

a ba

sin

and

just

re

plic

ate

with

oth

er w

ith s

mal

l ch

ange

s ba

sed

on d

ata

avai

labi

lity.

The

cost

of

land

acq

uisi

tion

and

fenc

ing

and

civi

l wor

ks e

tc. n

ot m

entio

ned.

Cha

pter

8, i

n Ta

ble

19 to

23,

spe

cific

reac

h-w

ise

mod

els

have

bee

n pr

opos

ed..

Mod

ellin

g co

st:

The

hydr

olog

ical

and

hyd

raul

ic m

odel

s fo

r ea

ch b

asin

are

sep

arat

e m

odel

; i.e

., se

para

te m

odel

set

-up

prep

arat

ion,

sep

arat

e ca

libra

tion

and

valid

atio

n; th

us, t

he c

ost

is a

lmos

t si

mila

r am

ong

the

basi

ns;

how

ever

, as

exp

ert

will

beco

me

expe

rienc

ed

thro

ugh

wor

k in

one

bas

in, t

he c

ost w

ill sl

ight

ly r

educ

e in

oth

er b

asin

s, a

nd w

e ha

ve

cons

ider

ed th

is fa

ctor

, ple

ase

see

text

s ex

plai

ning

this

in S

ectio

n 8.

13

All c

ivil

wor

ks a

nd fe

ncin

g ar

e in

clus

ive

in th

e co

st; p

leas

e se

e Se

ctio

n 5.

5, p

ara

1. W

e as

sum

e th

at a

ll ra

in g

auge

s w

ill be

ins

talle

d at

Gov

ernm

ent/P

ublic

pre

mis

es,

e.g.

, D

HM

O

ffice

pr

emis

e,

DW

IDM

of

fice

prem

ise,

Lo

cal

mun

icip

ality

pr

emis

e,

Adm

inis

tratio

n O

ffice

pre

mis

es, G

ram

Pan

chay

at e

tc.



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