flood simulation a case study in the lower limpopo valley, … · 2007-04-25 · flood simulation a...

FLOOD SIMULATION A case study in the Lower Limpopo Valley,

Mozambique using the SOBEK flood model

Tumentsetseg Shaviraachin March 2005

FLOOD SIMULATION A case study in the Lower Limpopo Valley, Mozambique

using the SOBEK flood model

by

Tumentsetseg Shaviraachin Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Environmental Systems Analysis and Management Thesis Assessment Board Dr. Jan de Leeuw, Chairman, ITC Dr. Fred de Boer, External Examiner, Wageningen University Drs. Gabriel Norberto Parodi, Internal Examiner, ITC Dr. Iris van Duren, First Supervivor, ITC Drs. Dinand Alkema, Second Supervisor, ITC

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

I certify that although I have conferred with others in preparing for this assignment, and drawn upon a range of sources cited in this work, the content of this thesis report is my original work. Signed………………………….

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

i

Abstract

Mozambique is highly vulnerable to weather-related natural disaster with both drought and floods affecting the country, especially the Limpopo basin. Mozambique has experienced a number of significant flood events in the last 50 years, and the expected effect of climate change could have more severe and frequent flooding in the future. Due to an increase in flood hazard problem, demand of flood information and digital maps of extent and risk of flooding has been increased for planning and evacuation procedures. To produce these maps, GIS, RS and the flood modelling are very useful. The objective of this thesis is to determine areas at hazard of flooding and simulate impact of flood on the population and land cover in different flood type. The Over Land and Channel Flow module of SOBEK two-dimensional model, developed by WL\DELFT Hydraulics in the Netherlands, was used to define areas at hazard and generate flood map for the study area. This research was based on four major steps. The first step was to prepare input data for the modelling, such as DEM, surface roughness map and hydrological data. The Digital Elevation Model (DEM) was generated in 100, 250 and 500 m pixel sizes, based on more than 13000 elevation points using kriging interpolation. Land cover classification from Landsat ETM+ satellite image was done in order to generate surface roughness map. The second step involved the calibration of the model based on data observed during 2000 flooding in Mozambique including DEM, surface roughness, and cross sections of the Limpopo River. The third and the final steps concern simulation and mapping based on model results respectively. The model results consist of set of maps such as flood starting time, flow velocity, and water depth for Mild, Moderate and Severe flood type. The maps for Severe flood type were classified and overlaid to generate the final flood map was generated.

ii

Acknowledgements

First, I would like to express my deepest gratitude to the Dutch government and ITC for giving me the opportunity to pursue further studies under the Netherlands fellowship program (NFP). Likewise, I would like to thank the Mongolian Academy of Sciences, specially the RS and Informatics Institute for allowing me to follow NRM2. Special thanks to my supervisors, Dr. Iris van Duren and Drs. Alkema Dinand, for their guidance, critical analysis and substantial comments on the approach during the thesis time. Having benefited considerably from the theoretical part of the course, which I applied in the thesis, I am greatly indebted to the ITC professors and staff who in one way or another have handled modules. Thanks are due to their all-out support to my research. Thanks to field supervisors Dr. Bert Toxopeus, Dr. Kees de Bie�and Dr. Liza Groenendijk, for helping us during the field work. I’m grateful to staff at Department of Water Resources Management and Environmental Ministry in Mozambique for providing me with data. I’m especially grateful to Manuela, Fatima and interpreters who are helped to communicate with local residents in Mozambique. My peers/classmates in NRM3 and NRM2 have given me the joy of being a student again. Those classroom and practical days have created a friendly atmosphere one can never forget despite the hassles of frequent exams, group/individual projects and thesis time. At last, I would like to thank my all family members who have been giving me constant support and encouragement during my studies.

iii

Acronyms

1D 1 dimensional 2D 2 dimensional ASCII Raster file format ASI Italian Space Agency Delft FLS Delft flooding system DEM Digital Elevation Model DLR German Space Agency ENSO El Nino Southern Oscillation IPCC Intergovernmental Panel on Climate Change ITCZ Inter-Tropical Convergence Zone NASA National Aeronautics and Space Administration NIMA National Imagery and Mapping Agency RIZA National Dutch Institute of Inland Water Management and Wastewater Treatment SADC Southern African Development Community SOBEK Flood Model SRTM Shuttle Radar Topography Mission UNEP United Nations Environment Programme

iv

Table of contents

ABSTRACT ................................................................................................................................................................. I ACKNOWLEDGEMENTS ............................................................................................................................................. II ACRONYMS ............................................................................................................................................................. III TABLE OF CONTENTS ............................................................................................................................................... IV LIST OF FIGURES ......................................................................................................................................................VI LIST OF TABLES ......................................................................................................................................................VII

1. INTRODUCTION......................................................................................................................................... 1

1.1. BACKGROUND......................................................................................................................................... 1 1.2. FLOOD MODELLING ................................................................................................................................. 2 1.3. RESEARCH OBJECTIVES .......................................................................................................................... 3 1.4. RESEARCH QUESTION ............................................................................................................................. 4 1.5. RESEARCH APPROACH ............................................................................................................................ 5

2. METHODS AND MATERIALS ................................................................................................................. 6

2.1. STUDY AREA........................................................................................................................................... 6 2.2. TOPOGRAPHY.......................................................................................................................................... 7 2.3. CLIMATE ................................................................................................................................................. 7 2.4. LAND USE ............................................................................................................................................... 7 2.5. POPULATION ........................................................................................................................................... 7 2.6. FLOODING ............................................................................................................................................... 8 2.7. DATA SET................................................................................................................................................ 9

2.7.1. Elevation data for creation DEM.................................................................................................. 9 2.7.2. SRTM (Shuttle Radar Topography Mission) DEM........................................................................ 9 2.7.3. Hydrological data ......................................................................................................................... 9 2.7.4. Flood extend of 2000 flooding .................................................................................................... 11 2.7.5. Historical data............................................................................................................................. 11 2.7.6. Ground truthing data for land cover classification..................................................................... 12 2.7.7. Population data........................................................................................................................... 12 2.7.8. Roads and Infrastructure............................................................................................................. 12

2.8. DEM CREATION .................................................................................................................................... 13 2.8.1. DEM creation of the flood plain.................................................................................................. 13 2.8.2. DEM creation of the riverbed...................................................................................................... 15 2.8.3. DEM creation of the main road and railway .............................................................................. 15 2.8.4. Final DEM creation .................................................................................................................... 15 2.8.5. Comparison of SRTM DEM and the created DEM of the floodplain.......................................... 16 2.8.6. Correction of SRTM DEM........................................................................................................... 17

2.9. SURFACE ROUGHNESS MAPPING ............................................................................................................ 17 2.10. HYDROLOGICAL ANALYSIS.................................................................................................................... 19 2.11. FLOOD MAPPING AND ANALYSIS............................................................................................................ 20

3. FLOOD MODELLING.............................................................................................................................. 22

3.1. INTRODUCTION ..................................................................................................................................... 22 3.2. MODEL INPUTS AND CALIBRATION ........................................................................................................ 23

3.2.1. Detailed Digital Elevation Model ............................................................................................... 23 3.2.2. Flow boundary conditions........................................................................................................... 24

v

3.2.3. Cross sections.............................................................................................................................. 24 3.3. FLOOD TYPE SCENARIOUS ..................................................................................................................... 25

3.3.1. Simulation time............................................................................................................................ 25

4. RESULTS .................................................................................................................................................... 26

4.1. RESULT OF MODEL INPUTS .................................................................................................................... 26 4.1.1. DEM accuracy............................................................................................................................. 26 4.1.2. Accuracy of land cover classification.......................................................................................... 27

4.2. MODEL RESULTS ................................................................................................................................... 27 4.2.1. Effect of DEM resolution on flood model extent ......................................................................... 27 4.2.2. Scenarios ..................................................................................................................................... 28 4.2.3. Model sensitivity.......................................................................................................................... 29 4.2.4. Flood extent comparison ............................................................................................................. 29 4.2.5. Flood depth comparison.............................................................................................................. 30 4.2.6. Flood mapping ............................................................................................................................ 31 4.2.7. Analysis of the land cover and population in the flood risk areas .............................................. 32

5. DISSCUSION .............................................................................................................................................. 33

5.1. ACCURACY OF THE DEM...................................................................................................................... 33 5.2. COMPUTATION TIME AND PIXEL SIZE ..................................................................................................... 33 5.3. HYDROLOGICAL DATA........................................................................................................................... 34 5.4. ACCURACY OF THE LAND COVER CLASSIFICATION ................................................................................ 34 5.5. ANALYSIS OF POPULATION AND LAND COVER IN THE FLOOD RISK AREAS .............................................. 34

6. CONCLUSIONS AND RECOMMENDATIONS .................................................................................... 36

7. REFERENCES............................................................................................................................................ 38

8. APPENDICES............................................................................................................................................. 40

8.1. APPENDIX 1........................................................................................................................................... 40 8.2. APPENDIX 2........................................................................................................................................... 41 8.3. APPENDIX 3........................................................................................................................................... 43

vi

List of figures

FIGURE 1CONCEPTUAL MODEL OF FLOOD HAZARD ASSESSMENT............................................................................ 5 FIGURE 2 STUDY AREA MAP...................................................................................................................................... 6 FIGURE 3 2000 FLOODING IN CHOKWE DISTRICT, MOZAMBIQUE............................................................................... 8 FIGURE 4 CROSS SECTION OF THE RIVER MEASUREMENT IN THE FIELD ................................................................... 10 FIGURE 5 FLOOD EXTENT (ETM+ QUICK LOOK OF 1 MARCH 2000) ....................................................................... 11 FIGURE 6 INTERVIEW ABOUT 2000 FLOODING......................................................................................................... 11 FIGURE 7 DISTRIBUTION OF SPOT HEIGHTS.............................................................................................................. 13 FIGURE 8 VARIOGRAM MODEL................................................................................................................................ 14 FIGURE 9 FLOOD PLAIN DEM FOR THE STUDY AREA (IN METERS) .......................................................................... 15 FIGURE 10 FINAL DEM FOR STUDY AREA (IN METERS)........................................................................................... 16 FIGURE 11 HEIGHT DIFFERENCES BETWEEN DEM OF THE FLOODPLAIN AND SRTM IN 500 M PIXEL SIZE ............... 17 FIGURE 12 LAND COVER MAP OF THE STUDY AREA................................................................................................. 18 FIGURE 13 TIME SERIES OF DISCHARGE FOR UPPER BOUNDARY .............................................................................. 19 FIGURE 14 RELATION BETWEEN WATER LEVEL AND DISCHARGE IN SICACATE........................................................ 20 FIGURE 15 RELATION BETWEEN WATER LEVEL AND DISCHARGE IN SICACATE........................................................ 20 FIGURE 16 SETTINGS FOR CHANNEL AND OVERLAND FLOW .................................................................................... 22 FIGURE 17 LOCATIONS OF THE BOUNDARY CONDITIONS AND CROSS SECTIONS ...................................................... 24 FIGURE 18 SIMULATION RESULT FOR SEVERE FLOOD TYPE IN 500 M PIXEL SIZE...................................................... 25 FIGURE 19 ERROR MAP OF KRIGING INTERPOLATION FOR FLOOD PLAIN DEM......................................................... 26 FIGURE 20 SIMULATION FOR 100, 250 AND 500 M PIXEL SIZES (FLOOD EXTENT) .................................................... 27 FIGURE 21 A) FLOOD STARTING TIME (HRS), B) FLOW VELOCITY (M/S) , AND C) WATER DEPTH (M) FOR MILD (1),

MODERATE (2), AND SEVERE FLOODING (3) ................................................................................................... 29 FIGURE 22 EXAMPLE OF SENSITIVITY MODEL FOR DIFFERENT CROSS SECTION (WATER DEPTH IN METERS)............. 29 FIGURE 23 FLOOD EXTEND COMPARISON................................................................................................................ 30 FIGURE 24 SCATTER PLOT FOR FIELD DEPTH (M) AND MODEL PREDICTED DEPTH (M) ............................................. 30 FIGURE 25 A) FLOOD STARTING TIME (HRS), B) FLOW VELOCITY (M/S) AND C) WATER DEPTH (M)......................... 31 FIGURE 26 FLOOD MAP OF THE STUDY AREA .......................................................................................................... 31 FIGURE 27 LAND COVER CLASSES IN FLOOD RISK AREAS ........................................................................................ 32

vii

List of tables

TABLE 1 DISTRICT AREAS IN SQ. KM WITHIN THE STUDY AREA ................................................................................. 7 TABLE 2 RETURN PERIODS OF DISCHARGES IN CHOKWE, SICACATE AND MACARRETANE ...................................... 10 TABLE 3 RELATION BETWEEN RETURN PERIOD, DISCHARGE AND TYPE OF FLOOD................................................... 10 TABLE 4 POPULATION IN THE BASIN....................................................................................................................... 12 TABLE 5 LAND COVER CLASSES AND SURFACE ROUGHNESS COEFFICIENTS ............................................................ 18 TABLE 6 FLOOD TYPE FOR 2000 FLOODING............................................................................................................. 19 TABLE 7 RISK CLASSIFICATION OF THE MODEL RESULT MAPS FOR SEVERE FLOOD TYPE ......................................... 21 TABLE 8 ERRORS OF THE POINT INTERPOLATION (KRIGING).................................................................................... 26 TABLE 9 ACCURACY OF THE LAND COVER CLASSIFICATION.................................................................................... 27 TABLE 10 COMPUTATION TIME FOR DIFFERENT PIXEL SIZES ................................................................................... 28 TABLE 11 FLOOD TYPE AND AREAS INUNDATED BY FLOOD .................................................................................... 28 TABLE 12 FLOOD EXTENT COMPARISON (IN SQ. KM)............................................................................................... 30 TABLE 13 AREAS FOR DISTRICT IN DIFFERENT FLOOD RISK (IN SQ. KM)................................................................... 32 TABLE 14 AREAS FOR LAND COVER TYPES IN DIFFERENT FLOOD RISK (IN SQ. KM).................................................. 32

FLOOD SUMULATION A CASE STUDY IN THE LOWER LIMPOPO VALLEY, MOZAMBIQUE USING THE SOBEK FLOOD MODEL

1

1. Introduction

1.1. Background

Climate change is one of the most important challenges facing the world today.

It is becoming increasingly evident that anthropogenic emissions of greenhouse gases, and other

atmospheric pollutant, are changing global and regional climates (IPCC, 2001). Global temperature

has increased by over 0.5�C since the nineteenth century. Rising global temperatures are expected to

raise sea level, and change precipitation and other local climate conditions (Hulme et al., 1999).

According to Kundzewicz (2003), in recent decades, flood losses have increased worldwide. This can

be linked to socio-economic, hydrological and climatic factors. An increase in the flood risk is also

foreseen for the future. Several land-use changes, such as deforestation and urbanization, reduce the

available water storage capacity and exacerbate the flood hazard (Brouwer and van Ek, 2004).

Demographic pressure causes encroachment of informal settlements into hazardous locations in flood

plains. Changing regional climate could affect vegetation cover, forests, crop yields, water supplies,

many types of ecosystems and flood regime (Prudhomme et al., 2003).

In the region of southern Africa represented by the Southern African Development Community

(SADC) nations, a similar rate of warming – about 0.05�C per decade – has been observed during the

present century. The six warmest years this century in southern Africa have all occurred since 1980

and the warmest decade has been 1986-95 (Hulme, 1996). Rainfall records from the early 1900s show

that Africa's average annual rainfall has decreased since 1968 and natural disasters have increased in

frequency and severity over the past 30 years (UNEP, 2000).

Mozambique is highly vulnerable to weather-related natural disaster with both drought and floods

affecting the country, especially the Limpopo basin. The Basin lies entirely in a low land zone and the

Inter-Tropical Convergence Zone (ITCZ) affects rainfall patterns in the basin. The ITCZ is an area of

low pressure along the earth’s middle or equator where dry hot air and warm humid air meet and

cause rain and thunderstorms. Cold fronts, in particular, bring heavy rains to Mozambique (Intstituto

Nacional De Gestao De Calamidades Mocambique, 2003).


2

Global weather studies show that another factor affecting rainfall is the EL Nino Southern Oscillation

(ENSO) and La Nina. ENSO has been associated with drought and La Nina is associated with

excessive rainfall in Southern Africa (Intstituto Nacional De Gestao De Calamidades Mocambique,

2003).

Mozambique has experienced a number of significant flood events in the last 50 years (Muianga,

2004), and due to climate change more severe and more frequent flooding can be expected in the

future (Hulme, 1996).

1.2. Flood modelling

According to Erich J. Plate (2002) the first step in risk management for floods is the flood hazard

mapping. For planning and evacuation procedures, the demand for flood information and digital maps

of predicted extent and risk of flooding has been increased. To produce these maps, GIS, RS and the

flood modelling is very useful.

Simulation and modelling for flood estimation are a rapidly developing field in hydrology (Boughton

and Droop, 2003). The flood simulation and model results are a good way of providing relevant

information on how the flood is going to behave at the location where people live and how the flood

will affect them.

There are different types of flood models, 1-dimensional (1D) flood models such as the Manning

equation (Chow, 1959), HEC-2 (ESRI, 2004b) and dynamic one-dimensional models such as SOBEK,

MIKE-11 (MIKE-11, 2004; Sobek, 2004) have been used to estimate a possible flood using time

series of river discharge (Pelinovsky et al., 2002). But 1D model has some limitations to include all

details in modelling and it is very difficult to simulate local conditions on a small scale accurately

(Mark et al., 2004).

Two dimensional (2D) modelling based on the raster grids for terrain description, surface roughness

coefficients and hydrological data (water level, discharge and cross section) provide information to

generate flood hazard maps (Willems, 2004).

Over the past 10 years significant advances have been made in integrating 1D and 2D models

resulting in hydrodynamic model of floodplains and integrated 1D (channel flow) and 2D (overland


3

flow) modelling such as DELFT-FLS (Sobek, 2004). The idea of integrating 1D hydrodynamic

modelling technologies, Digital Elevation Models and GIS systems is to take advantage of the best

combination of 1D hydrodynamic data for rivers together with 2D terrain data, and presenting them in

the GIS as maps (Horritt and Bates, 2002).

The integrated one-dimensional and two-dimensional (1D-2D) model development focuses on the

extension of model capabilities in order to simulate flooding situations more accurately. This includes

improving flood wave propagation over initially dry land, improving the presentation of hydraulic

control (levees and embankment) in the floodplain and integration of one-dimensional hydraulic

elements (pumps, bridges and regulator gates) (Horritt and Bates, 2002). The combined 1D-2D

modelling opens up the possibilities for studying flood control measures, flood forecasting, and

development of flood evacuation plans.

Natural disasters such as Tsunami (USGS, 2004) and inland flooding can not be prevented but

damage can be reduced by proper planning. For this reason the modelling (Koshimura, 2004) is

essential for identifying areas likely to be affected with flood.

Benefit of the integration of RS, GIS and 2D flood modelling is to provide information for users such

as land use planning, evacuation planning and environmental impact assessment.

Previous research studies that have been carried out in Lower Limpopo focused more on hydrological

aspects and had a geomorphologic approach (Muianga, 2004) to assess flood hazard. This research

work is aimed at strategic planning and better management to reduce flood damages in frequently

flooded areas. It will assess also the effect of flooding on different land cover and the population by

simulating different flood level.

The two-dimensional Over Land and Channel Flow module of SOBEK (2004), developed by

WL\DELFT Hydraulics in the Netherlands, was used to compute the overland flow component of the

study.

1.3. Research Objectives

• To determine areas at risk of flooding by simulating impact of flood on the population and

land cover in different flood type

• To give a recommendation for future land use planning to reduce flood risk


4

1.4. Research Question

• What is the spatial extent of flooding?

Specific Objectives Research questions

1. Generate a detailed DEM of the study area

2. Map land cover types

3. Define surface roughness coefficient for land

cover in the study area

4. Define spatial extent, depth, flow velocity

and starting time of flood in different flood type

5. Compare model results with previous flood

water level and extent (2000)

6. Define areas and population at risk in

different discharges

1.1 What is the accuracy of different DEMs?

2.1 What are the land cover types of the study

area?

2.2 What is the accuracy of land cover

classification?

3.1. What are the surface roughness coefficients

for land cover types in the study area?

4.1. What is the spatial extent of inundation area?

4.2. What is the maximum inundation depth?

4.3. What is the maximum flow velocity?

4.4. What is the starting time of flooding?

5.1. How well does the model fit with observed

flood?

6.1. What is the extent of land at risk and

population expected to be affected by flooding in

different discharge value?

6.2. How can the outcome of the flood models be

used in risk reduction?


5

1.5. Research Approach

DEM Hydrological Data

Surface roughness map

Settlements and

infrastructure

Modeling

Spatial extent of

inundation

Time of flooding

Flow velocity

Inundation depth

Overlay and classify

Flood Hazard

Map

Land cover and population likely to

be affected by flooding

overlay

Land cover map

Figure 1Conceptual Model of Flood Hazard Assessment

This thesis is divided into seven chapters. Chapter 1 deals with background information of flooding

and flood modelling, the thesis objectives and research questions. Chapter 2 provides a general

description of the study area and data sets used this research as well as methods used to prepare model

inputs. Chapter 3 presents model input and calibration. Chapter 4 shows the results of the model

inputs and model outputs. The last chapters 5 and 6 present the discussion, conclusions and

recommendations of the thesis.


6

2. Methods and Materials

2.1. Study Area

Lower Limpopo River Basin (Gaza province, Mozambique)

Four countries share the Limpopo river basin: Botswana, Mozambique, South Africa, and Zimbabwe.

Waters descending from the Drakensberg Mountains and the South African highveld mainly feed the

Limpopo River’s main branches. The Limpopo river basin can be divided into three sections: 1)

Upper Limpopo at the South Africa-Botswana-Zimbabwe border; 2) Middle Limpopo South Africa-

Zimbabwe-Mozambique border; 3) The Lower Limpopo, downstream of Pafuri to the mouth of the

river on the Indian Ocean. The Limpopo Basin’s total drainage area is 412000 square kilometres, of

which about 19% actually falls in Mozambique.

Figure 2 Study area map

Source: Atlas for disaster preparedness and Response in Limpopo Basin (Intstituto Nacional De Gestao De

Calamidades Mocambique, 2003)

The study area covers district of Chokwe, Chibuto, Guija, Bilene and Xai-Xai (Table 1).

The study area covers an area of approximately 2456 square km


7

Table 1 District areas in sq. km within the study area

District Areas (km2)

Chokwe 1434.7

Chibuto 579.01

Guija 285.38

Bilene 127.88

Xai-Xai 26.71

2.2. Topography

Mozambique is vulnerable to flooding because large parts of the country have a low elevation. The

rivers headwater start 1000 meter above sea level, but the river drops sharply before entering

Mozambique at only 200 meters above see level. After another sharp descent near Pafuri, the river

flows its final 400 km at elevations of less than 100 meters in the flood plains of Mozambique. The

final 175 km between Chokwe and the river mouth are at elevations of less than seven meters above

sea level (Intstituto Nacional De Gestao De Calamidades Mocambique, 2003).

2.3. Climate

The rainy season occurs from October to March when the Basin also has the highest temperature. This

period is called the wet-hot season or summer. Temperatures are lowest in the dry season from April

to September. This is called the dry-cold season or winter. The mean monthly temperatures vary

between 18,5oC a (in July) and 27oC (in December to February). The wettest months in the basin are

December and March with peak in February and driest months are July and August. The annual

rainfall is between 600-800 mm (Intstituto Nacional De Gestao De Calamidades Mocambique, 2003).

2.4. Land use

The Lower Limpopo is intensively used for agricultural, cattle, and livestock activities. There are

several irrigation schemes and ridge. Due to unreliable climate conditions, rain-fed agriculture is full

of risk. On other hand, because of its location in lowlands and near water bodies, irrigated agriculture

is under risk of flooding during the rainy period.

2.5. Population

Six percent of Mozambique’s population lives in the Limpopo Basin. The national Institute of

Statistics projects the population of the Basin to grow by approximately 2.5% per year. The


8

population of the districts within the Basin is projected to reach 1.9 million by 2010. Most of this

population is expected to be concentrated in the more densely populated districts of Xai-Xai and

Chokwe.

2.6. Flooding

Flooding is one of the major natural hazards to human society. It strongly influences on social and

economic developments in the Lower Limpopo area.

An analysis of 32 years of available data on maximum annual river levels in Chokwe between 1953

and 1994 showed that in half of all years, mild or moderate floods were recorded. Mild floods (with

water level between 4-6 meters above ref. level) hit seven times, moderate floods (with river level 6-8

meters) occurred nine times in 32 years. Severe floods (levels over 8 meters above ref. level)

happened four times in 32 years: 1977, 1975, 1972, and 1955. In 2000, river may have reached over

10.5 meters (Intstituto Nacional De Gestao De Calamidades Mocambique, 2003).

Figure 3 2000 flooding in Chokwe district, Mozambique

According to the USAID report (2002) the provinces the most affected by in 2000 flooding Maputo,

Gaza, Inhambane, Sofala and Manica. The total population affected in these five provinces is roughly

five million people and many people died. Houses, agricultural infrastructure, public buildings,

schools, hospitals, water and energy supply systems, road networks, railways and telecommunications

were severely damaged.

The flooding devastated about 12 percent of the cultivated land and 90 percent of irrigated land in five

provinces. The largest impact was in Gaza Province and 43 percent of the cultivated land was flooded.

Livestock losses were estimated at 20000 cattle, 4000 goats, sheep and pigs, and 180000 chickens.


9

2.7. Data set

Data sets used in this research include elevation data, land cover and surface roughness map, observed

discharge and water level, cross section of the river, population data and roads and infrastructure of

the study area.

2.7.1. Elevation data for creation DEM

Topographic map (Projection – UTM, central meridian – 33, ellipsoid – de Clark, 1866 and scale 1:50

000) for the study area were obtained and scanned during the field work. The elevation points were

digitized from the map.

Spot heights were collected for the irrigation canal from the irrigation office in Chokwe. These points

were measured using levelling equipment. Levelling is the process of determining the difference in

elevation between two or more points. The accuracy of the measurement is 0.01 m.

These elevation points were combined and interpolated to create DEM of the study area.

2.7.2. SRTM (Shuttle Radar Topography Mission) DEM

SRTM DEM data products result from a collaborative mission by the National Aeronautics and Space

Administration (NASA), the National Imagery and Mapping Agency (NIMA), the German space

agency (DLR) and Italian space agency (ASI), to generate a near-global digital elevation model

(DEM) of the Earth using radar interferometry (Jet Propulsion Laboratory, 2004). 3 Arc Second (90

meter) DEMs for global coverage were developed from the SRTM C-band radar observations. The

data was expressed in geographic coordinates (latitude/longitude) and was horizontally and vertically

referenced to the WGS84 Geoid. The Shuttle Radar Topography Mission (SRTM) DEM of the study

area was downloaded from the internet.

SRTM DEM was used to compare with the created DEM for the flood plain of the study area.

2.7.3. Hydrological data

For the model calibrating discharge, water level (since 1979), and the result of the flood frequency

analysis were collected at three stations (Chokwe, Changane and Sicacate) during the field work.

The above result of analysis for flood level frequency was used for determining the relation between

discharge and water level in study area.

The return period for the above stations was calculated using Log-Person and Pearson III (Chow,

1959) and the results are shown in table 2.


10

Table 2 Return periods of discharges in Chokwe, Sicacate and Macarretane

Return period

(years)

Chokwe

(m3/s)

Sicacate

(m3/s)

Return periods

(years)

Macarretane

(m3/s)

2 1 902 1 496 10 6260

5 3 639 3 169 20 8000

10 4 741 4 270 50 10200

25 6 072 5 627 100 12000

50 7 019 6 607 500 15800

100 7 930 7 559 1000 17500

(Source: DNA, 1996)

Originally the plan was to collect cross section data (for the modelling) of the river Limpopo from the

office, but unfortunately this data does not exist. Therefore, an attempt was made by measuring only

one cross section at an accessible location (figure 4). At that point river was 100 m wide and water

depth was measured for every 10 meter. The largest depth was 1.60m.

Figure 4 Cross section of the river measurement in the field

The following table was used as input in the modelling for the scenarios study.

Table 3 Relation between return period, discharge and type of flood

Return period (years) Discharges (m3/s) Type of flood

<10 <4700 Normal

10-50 4700-7000 Moderate

>50 >7000 Severe

(Source: Msc thesis (Muianga, 2004)


11

2.7.4. Flood extend of 2000 flooding

Landsat ETM+ Quick look of 1 March 2000 was used to compare predicted flood extent.

Figure 5 Flood extent (ETM+ Quick look of 1 March 2000)

2.7.5. Historical data

Historical data for 2000 flood was collected by interviewing people at about 30 different places

(Figure 6). It included locations, started date, time, and highest water level of flooding and duration of

staying water. This information is used for the validation of the model results.

Figure 6 Interview about 2000 flooding

Water depth 4.4 m

Water depth about 5 m


12

2.7.6. Ground truthing data for land cover classification

Landsat ETM+ image of 30 October 2001 was used. Landsat ETM+ has 8 bands. Spatial resolutions

of the Landsat ETM+ image are 30 m in bands 1-5, 7, 15 m in PAN and 60 m in band 6. This image

was used to prepare land cover map in order to determine surface roughness coefficients of the study

area for the flood modelling.

In this study it is done by acquiring land cover classes prepared by collecting ground truth from the

field. Total of more than 60 points were collected using Garmen Etrex GPS according to mapping

units of the visual interpretation of the satellite image.

2.7.7. Population data

In order to determine how many people in flood risk area, it was expected that demographic

information or population data at village level could be collected. Unfortunately, this type of data was

not available.

Table below shows population of each district in the study area and percent of the districts population

that lives within basin.

Table 4 Population in the Basin

District Population within basin

(1997)

Total population

(1997)

Percent within basin

(%)

Chibuto 160.067 164.791 97

Chokwe 169.270 173.277 98

Guija 57.217 57.217 100

Bilene 77.238 133.173 58

Xai-Xai 140.177 165.569 58

Source: Atlas for disaster preparedness and Response in Limpopo Basin (Intstituto Nacional De Gestao De

Calamidades Mocambique, 2003)

2.7.8. Roads and Infrastructure

Road and river networks, railway, district boundaries, location of the villages and irrigation canal

features were digitised from the 1:50000 topographic map. These were later overlaid with the flood

risk map to visually identify the effect of the flood on them.


13

2.8. DEM creation

2.8.1. DEM creation of the flood plain

A Digital Elevation Model is computerized presentation of the Earth’s terrain.

A DEM is useful for many analyses such as topographic feature extraction, flood modelling, and

landscape analysis. Such analyses require a high accurate DEM. The accuracy of a DEM is usually

represented by the spatial resolution and height accuracy.

In this research two different spot heights are used: digitized spot heights from the topographic map

(1:50000) and spot heights acquired from for the irrigation canal.

In total 1127 spot heights and all contour lines were digitized from the topographic maps at a scale

1:50 000, with spot height intervals of 0.1 m and contour lines of 20 m. Then two point maps

(digitized spot heights indicated as red dots in the figure, spot heights for irrigation scheme - yellow)

(figure 7) are combined into one file. The data sets consisted of 13492 points.

Figure 7 Distribution of spot heights

Landsat ETM+ RGB-543 UTM-WGS-84 Zone-36 South


14

The density of digitized points was not good enough to directly create a DEM. Though the spot

heights for the irrigation canal were dense, they did not cover the whole study area. Therefore, the

gaps between the known points had to be filled up with estimated values to create the DEM. The

DEM was created using point interpolation method of the geostatistical tool (ITC-ILWIS, 2001).

Before starting the interpolation, the spatial correlation of the data set was established. The omni

directional method was used to investigate spatial correlation of input data. The lag interval was

specified at 200m. The omni directional method determines semi-variogram values in all directions.

After that, it was tested which model and which model parameters fitted best to the variogram model.

The power model fitted best. The parameters (figure 8) were:

Nugget = 1.2

Slope = 0.0006

Power= 1

AvgLag x SemiVar

1000 2000 3000 4000 5000

AvgLag1

2

3

4

5

Sem

iVar

AvgLag x SemiVarPower Model

Figure 8 Variogram model

Points were interpolated with 100m, 250m, and 500m pixel sizes using ordinary kriging interpolation

method specifying a limiting distance (5000m), maximum (100) and minimum (4) number of points

(figure 9). These cell sizes were considered to have a suitable balance between computational time

and data processing during further modelling for flood prediction.


15

Figure 9 Flood plain DEM for the study area (in meters)

2.8.2. DEM creation of the riverbed

The river was digitized as a polygon from the 1:50 000 topographic maps. An attribute table and maps

were created with average depths (1.6m) of the river bed and the main channel.

2.8.3. DEM creation of the main road and railway

The railway, main road (segment) and their heights (point) were digitized from the 1:50 000

topographic maps in a separate layer. Segment maps were converted into raster format. The point map

with the heights was interpolated using Thiessen interpolation and masked with segment raster maps

of the road network.

2.8.4. Final DEM creation

The DEM of the flood plain, the DEM of the riverbed and road network DEM were combined using

ILWIS software (ITC-ILWIS, 2001).


16

Figure 10 Final DEM for study area (in meters)

2.8.5. Comparison of SRTM DEM and the created DEM of the floodplain

Some pixels of the original SRTM DEM had a “0” value. After removal of all incorrect “0” values,

the Minimum height was 1.0 m and the Maximum was 49.0 m. The SRTM DEM was clipped with the

boundary of the study area and resampled to pixel sizes of 100, 250 and 500 m.

The DEMs of the floodplain in different pixel sizes (100, 250 and 500m) were subtracted from the

SRTM DEMs in order to determine differences between them.

From the result map (figure 11), a positive difference (pixel value of SRTM DEM > pixel value of the

floodplain DEM) were found in woodland, grassland and build up areas and a negative differences

(pixel value of SRTM DEM < pixel value of the floodplain DEM) were found near to water body and

wetlands.


17

Figure 11 Height differences (in meters) between DEM of the floodplain and SRTM in 500 m pixel size

2.8.6. Correction of SRTM DEM

The mean value of the height difference map (figure 11) was calculated and resulted mean value was

2.97. The SRTM DEM was converted to the point map and the mean value (2.97) was subtracted.

After than the point map from the STRM DEM was combined with the digitised and irrigation scheme

spot heights. The spatial correlation and variogram model were created for kriging using the combined

data sets which resulted in more than 300 000 points that was unable to run the point interpolation due

to computer memory and long processing time of the computer.

2.9. Surface roughness mapping

A roughness coefficient represents the effect of the channel bank and bed particles as well as form

losses attributed to dynamic alluvial bed forms and vegetation of various types (grass, shrubs, field

crops, brush, and trees) located along the banks and floodplain (Maidment, 1992).

Band combinations 4, 5, 3 of Landsat ETM+ satellite image with 30 m resolutions of October 2001

was used for the land cover classification in preparation of the surface roughness map. Land use

categories can be often distinguished quite well by assigning a combination of bands 5-4-3 or 4-5-3 to

RGB (Bakker et al., 2001).


18

Based on visual interpretation, a preliminary map was created. After collecting ground truth data in

the field, supervised classification was applied using Maximum likelihood classification. The

classification of the land cover for the study area consists of 6 classes (water, wetland, woodland,

farmland, bare land and build up area). The following map (figure 12) shows land cover classes and

areas of them.

Figure 12 Land cover map of the study area

The end land cover map was reclassified to hydrological roughness values according to Manning

Equation for Coefficients of Roughness for flood plains (Chow, 1959).

These coefficients were specified for five different land cover classes and the values ranged from 0.03

to 0.1 (table 5).

Table 5 Land cover classes and surface roughness coefficients

Land cover classes Surface roughness coefficients Bare land (sand) 0.03

Build up (buildings, houses and roads) 0.1

Farmland (crop and pasture) 0.05

Water (water courses and water bodies) 0.03

Wetland (marshes) 0.04

Woodland (trees, bushes and shrubs) 0.07


19

2.10. Hydrological analysis

Water discharges of Chokwe, Changane and Sicacate stations from the long term dataset were

selected for upper and lower boundaries of the model.

• Time series of the discharge of Chokwe hydrological station (Upper boundary) during 2000

flooding (1/21/2000 – 4/29/2000) (figure 13) was used from the long term data sets and

classified into 3 classes of flooding (table 6).

•

100.0

5100.0

10100.0

15100.0

20100.0

25100.0

1/7/

2000

1/21

/200

0

2/4/

2000

2/18

/200

0

3/3/

2000

3/17

/200

0

3/31

/200

0

4/14

/200

0

4/28

/200

0

5/12

/200

0

5/26

/200

0

Date

Dis

char

ge (m

3/s)

Figure 13 Time series of discharge for upper boundary

Table 6 Flood type for 2000 flooding

Type of flood Min and Max Discharges (m3/s)

Starting time (date) Ending time (date)

Mild 968-3526 22 January 2000 10 February 2000

Moderate 4785-5742 11 February 2000 26 February 2000

Severe 9500-20000 27 February 2000 02 March 2000

Moderate 5064-5882 03 March 2000 05 March 2000

Mild 1805-4456 06 March 2000 23 March 2000

Moderate 4787-4978 24 March 2000 25 March 2000

Mild 1061-4288 26 March 2000 29 April 2000

• Water discharges in Sicacate were estimated using relation between water level and

discharges (H-Q relation curve) for different water levels period of 1970-2004 (Figure 14).

These were set as a first boundary condition (appendix 2).


20

0

20

40

60

80

100

120

140

0 1 2 3 4 5 6 7 8 9

Water level (m)

Dis

char

ge (m

3/s)

Figure 14 Relation between water level and discharge in Sicacate

• Water discharges in Changane were estimated using the H-Q relation curve for different water

levels in period of 1941-1967 (figure 15). These were set as a second lower boundary

condition (appendix 3).

0

500

1000

1500

2000

2500

1 3 5 7 9 11 13

Water Level (m)

Dis

char

ges

(m3/

c)

Figure 15 Relation between water level and discharge in Sicacate

2.11. Flood mapping and analysis

The maximum flood starting time, flow velocity, and water depth maps (figure 26) in severe flood

type which resulted from the modelling were imported and classified using ILWIS software in order

to produce the final flood risk map (figure 27).

Flood starting time, flow velocity and water depth maps were classified into 3 risks (table 7) such as

low, medium, and high.

Based on the risk classification, the final flood map (figure 27) was created using crossing and slicing

operation in ILWIS software.


21

Table 7 Risk classification of the model result maps for severe flood type

Maps

Risk

Flood starting time

(hours)

Flow velocity

(m/s)

Water depth

(m)

Low 48< 0.5> 0.2>

Medium 24-48 0.5-1.5 0.2-0.5

High <24 1.5< 0.5<

The final flood map was overlaid with district boundary, village and settlements locations, railway,

road network, and irrigation canal maps. Areas of the districts and land cover types in the study area

that likely to be affected by flood were estimated (table 14) in different flood types.


22

3. Flood Modelling

3.1. Introduction

SOBEK (Delft-Hydraulics, 2004) has nine modules such as Rainfall-runoff, Channel flow, Sewer

flow, river flow, Real-time control, Water quality, Emission, Overland flow and Groundwater (figure

16).

Figure 16 Settings for channel and overland flow

The Overland and Channel Flow module of SOBEK Rural which is designed to calculate two-

dimensional flooding scenarios is used in this research.

SOBEK has been developed by WL | Delft Hydraulics in partnership with the National Dutch Institute

of Inland Water Management and Wastewater Treatment (RIZA), and the major Dutch consulting

companies. SOBEK a valuable instrument for flood forecasting, navigation, optimising drainage

systems, controlling irrigation systems, reservoir operation, sewer overflow design, ground water level

control, river morphology regulation, and water quality control. SOBEK can combine river systems,

urban systems and rural systems for a total water management solution. The program has been

enhanced with facilities to import GIS data into the model and to export computational results to GIS

systems for presentation and evaluation. It simulates very well the influence of the existing/planned

infrastructure on flooding processes. Advantages of this model are:


23

• Flow computation on initially dry land, without using any special drying or wetting

procedures.

• Accurate and stable flow computation on very steep slopes, such as dike walls and other man-

made structures.

• Especially suitable for short event predictions (hours and days).

• Realistic flood predictions of dike break due to heavy rainfall or other natural hazards.

• Pre- and post-processing within a GIS environment.

Model data input

• Detailed Digital Elevation Model of the study area

• The relation between discharge and water level

• Cross section of the river

• A surface roughness map

Model output

The model output is a set of indicator maps and a video show to visualize the flooding. Flood

indicator maps (Alkema, 2003) are:

• Water level – Maximum inundation depth

• Flow velocity – Maximum flow velocity

• Impulse – water level x flow velocity = Quantity of moving water

• Rising – maximum speed of rising of the water level

• Duration – Estimated duration of the inundation

• Warning time – Time between dike failure and the arrival of the floodwaters

3.2. Model inputs and calibration

The Overland and Channel Flow module in SOBEK that was set for the study area included a

topography grid, a surface roughness grid, observed discharge data and cross sections of the river.

Details of each input data are described in chapter 2 above.

3.2.1. Detailed Digital Elevation Model

The model covers the area of 2434 km2. The grid sizes used were 100, 250 and 500 m (figure 10) and

were exported from ILWIS software to SOBEK model in ASCII format.


24

3.2.2. Flow boundary conditions

Three boundary conditions that were considered for modelling were the extent, depth, and velocity, as

well as warning time.

Upper flow boundary

Macarretane barrage

Location: latitude - 24o 24’, longitude - 32o52’ and altitude – 31 m

Lower flow boundary (1)

Changane hydrological station in Chibuto

Location: latitude - 24o 39’, longitude - 30o31’ and altitude - 11m

20 m3/s discharge value was selected as a flow boundary condition.

Lower flow boundary (2)

Sicacate hydrological station in Chibuto

Location: latitude - 24o 44’40’’, longitude - 33o32’ and altitude - 7m

H-Q relation was used for the flow boundary condition.

3.2.3. Cross sections

The model requires at least two cross sections. However, only one cross section was measured during

the field work. The other cross sections that were used for modelling were estimated by measuring the

channel width and bed elevations of the river from the satellite image and with the help of DEM. The

sensitivity analysis of the model was done by changing cross section parameters (channel bed, bed

elevations and location of the cross sections) for simulation of the mild flooding time.

The figure 17 shows the location of the boundary conditions including cross sections on river

Limpopo and Changane.

Figure 17 Locations of the boundary conditions and cross sections

Macarretane

Sicacate

Changane


25

3.3. Flood type scenarious

Based on the above criteria (Table 3), different flood type scenarios such as Mild, Moderate and

Severe were developed in order to determine the flood extents, flood starting time, flow velocity and

water depth (figure 22) at different time using 500 m pixel sizes.

Figure 18 Simulation result for severe flood type in 500 m pixel size

3.3.1. Simulation time

The 2000 Mozambique flood data sets were used for the modelling (appendix 1). Using the criteria for

classification of flood type in Chokwe station shown in the table 2 (Muianga, 2004), a flood type class

was developed as shown in table 7.

The model was run for four different periods. First, the simulation time was chosen for 99 days from

22/01/2000 until 29/04/2000. Discharges for these days were more than 1000 m3/s. Second, mild flood

was simulated by selecting a flood period of 20 days from 22/01/2000 until 11/02/2000. Next, a flood

period of 14 days simulated a moderate flooding from 11/02/2000 till 25/02/2000. And finally severe

flood was simulated for a period of 8 days from 25/02/2000 till 04/03/2000.


26

4. Results

4.1. Result of model inputs

4.1.1. DEM accuracy

The optimal pixel size of the interpolation is the one with smallest error (ESRI, 2004a), which can be

computed as:

ERROR = Residual sum of squares (RSS)/Total sum of squares (TSS), where,

TSS = Square of (Actual values – Mean of actual values)

RSS = Square of (Actual values – Predicted values)

Table 8 Errors of the point interpolation (kriging)

Maps RSS TSS ERROR

Spot height x DEM 100 12352.65 319806.6 0.386

Spot height x DEM 250 8668.04 200854.9 0.342

Spot height x DEM 500 6713.94 109035.9 0.062

The result show that, DEM in 100, 250 and 500m have an error values of 0386, 0,342 and 0,062

respectively (table 8).

The highest error is in places where there is little or no data (Figure 20). Near the irrigation canal,

accuracy is good, but in the North East parts of the area, the accuracy is in relatively low.

Figure 19 Error map of kriging interpolation for flood plain DEM


27

4.1.2. Accuracy of land cover classification

Table 9 shows the error matrix for calculating the accuracy of classification and land cover classes

and areas of the study area. The overall accuracy of classification is 59.38%, while the average

producer accuracy (PA) 68.33% and the average user accuracy (UA) is 71.31 %.

Table 9 Accuracy of the land cover classification

Classes Bare land Build up Farmland Water Wetland Woodland Total UA(%)

Bare land 2 0 0 0 0 0 2 100

Build up 0 3 1 0 0 0 4 75

Farmland 0 7 28 0 1 2 38 74

Water 0 0 0 1 0 0 1 100

Wetland 0 1 0 0 2 0 3 67

Woodland 0 6 8 0 0 2 16 13

Total 2 17 37 1 3 4 64

PA (%) 100 18 76 100 67 50 OA 59.38

4.2. Model results

4.2.1. Effect of DEM resolution on flood model extent

The purpose of the modelling was to compare the results of the flood prediction using different pixel

sizes. It was aimed to run 100, 250 and 500 m pixel sizes. But the 100 and 250 m pixel size resolution

DEMs caused a crash after 2 hours calibration time in the modelling exercise. The comparison of

using different pixel sizes could only be made based on the result of the first 2 hours of flooding.

Due to these technical problems that could not be solved and it was decided to continue further

simulations only based on the 500 m resolution DEM.

This is presented in figure 21. Pixel size of 250 m gives close extent with 2000 flooding (figure 5).

Figure 20 Simulation for 100, 250 and 500 m pixel sizes (flood extent)

Table 10 shows the flooded areas and computation time for different pixel sizes.


28

Table 10 Computation time for different pixel sizes

Pixel Size (mxm) Simulation Period (days) Computation Time

100x100 2 days 3 hours 8 hours 30 min

250x250 2 days 23 hours 20 min

500x500 3 5 min

4.2.2. Scenarios

To compare the effect of Mild, Moderate and Severe flood types, mild flood have a discharge less

than 4700 m3/s, moderate floods have a discharge of 4700-7000 m3/s and severe floods more than

7000 m3/s with area coverage of 657.5, 929.2 and 1962.2 sq. km respectively (table 11).

Table 11 Flood type and areas inundated by flood

Flood Type Simulation time

(days)

The Total Area Inundated by Flood

(km2)

Mild 20 657.5

Moderate 14 929.2

Severe 8 1962.2

The following maps (figure 22) show the flood extent, flood starting time, flow velocity and water

depth of study site and the simulation movies were recorded for each flood type.

1. Mild flooding

2. Moderate flooding

a)

b)

c)

a)

b)

c)


29

3. Severe flooding

Figure 21 a) flood starting time (hrs), b) flow velocity (m/s) , and c) water depth (m) for mild (1), moderate

(2), and Severe flooding (3)

4.2.3. Model sensitivity

When cross section parameters and locations are changed, flood extent was changed also. It appeared

that when a cross section was put near to a meandering, it was resulting in large changes in the flood

extent and water depth.

Figure 23 shows one example of the result of the sensitivity analysis using a mild flood type. The

most obvious changes are shown in circle. In this case flood extent changed form total area coverage

of 658 km2 to 701km2 and water depth changed from of 4.9 m to 5.3 m.

Figure 22 Example of sensitivity model for different cross section (water depth in meters)

4.2.4. Flood extent comparison

Flood extent 2000 flooding digitised by the Water Department in Maputo was used to compare the

model result. The following table and figure show comparison of areas and spatial extent for flooded

and not flooded areas.

a)

b)

c)


30

Table 12 Flood extent comparison (in sq. km)

Flooded area Not flooded area

Model result 1969 489

2000 flood extent 1861 597

Figure 23 Flood extend comparison

4.2.5. Flood depth comparison

The water depth was collected in more than 30 places during the field work interviewing the people

and 7 of them was measured based on building line.

The observed flood depth and predicted flood extend were plotted in the graph in order to see how

well model predicted the flood extent. The following graph (figure 25) shows relation between them.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00Field depth

Mo

del

dep

th

Figure 24 Scatter plot for field depth (m) and model predicted depth (m)


31

4.2.6. Flood mapping

The following maps (figure 26) show the classification of the flood starting time, flow velocity and

water depth maps for Low, Medium and High risk types.

Figure 25 a) Flood starting time (hrs), b) Flow velocity (m/s) and c) Water depth (m)

The flood map (figure 27) shows areas at severe flood risk in severe flood type. Red colour shows the

areas of high flood risk while the brown colour shows areas of (294.2) medium flood risk. Yellow

colour shows areas that have the least flood risk.

Figure 26 Flood map of the study area

a) b) c)

UTM-WGS-84 Zone 36 South


32

4.2.7. Analysis of the land cover and population in the flood risk areas

The study site covers a total area of 2452.93 km2, of which 12 percent of land has a high risk of

flooding, while 63 % of the land has a medium flood risk, and 5 percent has a low or no risk.

There are 5 districts in the study area within identified flood risk areas. In order to analyse which land

cover types were at what type of flood risk, an overlay of the flood maps and district maps were made.

This resulted in the tables show below.

Table 13 Areas for district in different flood risk (in sq. km)

Flood risk District

High Medium Low Total Flooded area

Chokwe 166.5 955.5 100.8 1222.8 Chibuto 27.5 57.5 368.8 453.8 Guija 55.8 21.5 126.0 203.3 Bilene - 67.7 6.75 74.45 Xai-Xai - 12.0 0.75 12.75

The following table 14 and figure 28 show each land cover area with different flood risks levels in the

study area. The farm land is the most affected (figure 28).

Table 14 Areas for land cover types in different flood risk (in sq. km)

Flood risk Land cover

High

Medium

Low

Total

Built-up 66.93 223.15 22.39 312.47

Farm land 116.32 840.42 109.97 1066.71

Water 7.98 10.9 1.04 19.92

Wetland 3.68 40.71 2.49 46.88

Bare land 11.82 2.4 0.21 14.43

Wood land 45.55 421.19 45.22 511.96

Total 252.28 1538.77 181.32 1972.37

0

200

400

600

800

1000

1200

Built-up Farm land Water Wetland Bare land Wood land

Land cover classes

Are

as (k

m2)

Figure 27 Land cover classes in flood risk areas


33

5. Disscusion

5.1. Accuracy of the DEM

DEM is the most important input of the hydrological modelling. In this study, digitised spot heights

from 1:50000 topographic map were used to generate DEM of 100, 250 and 500 m pixel sizes using

kriging interpolation. Generally, the result of kriging interpolation shows that, if pixel size increases

the error decreases and the interpolation with 500 m pixel size had the smallest error (table 7). Most

of the errors occurred in the areas where spot heights were not dense (figure 20).

However, kriging interpolation has given good result; DEM has given error close to the Changane

river (Figure 20) due to inaccuracy of topographic map (1:50 000). So, this error has influence on the

model results.

When the comparing the predicted flood extent and depth and the observed 2000 flood, it appeared

different (section 4.2.4 and 4.2.5). This can be explained by in accuracy of topographic map (1:50

000). Since quality of the model results depends on the quality of DEM (Mark et al., 2004).

Therefore, for more accurate results, much attention should be given to improvement of the DEM.

5.2. Computation time and pixel size

There was also limitation for computation time. The model was prepared to run in 100m, 250m and

500m pixel sizes. But the simulation for 100m and 250m pixel sizes crushed without finishing the

computation time. This software problem could not be solved in a short time. Therefore we could

only compare the results of the first 2 hours computation time. It seems that using smaller pixel sizes

result in more accurate predictions. But more test need to be done on this issue.

Comparing the flood extent for 3 different pixel sizes after 2 days simulation, 250 m pixel size

simulation gave quite close extent of flood to Landsat ETM+ 1 quick look of 1 march 2000 (Figure 5).

Perhaps the area size was too large for the model to use pixel sizes much smaller than 500 m

(2434km2).

Similar study (Alkema, 2003), used SOBEK model gave good result for small area (compare to area I

have chosen) that have dense hydrological network.


34

Werner (2001), has tested the impact of grid size in GIS based flood extent mapping and proved that if

pixel size increases the computation time decreases and smaller pixel sizes can give reliable results.

5.3. Hydrological data

Another input that could cause inaccuracy was the cross section. In this research all cross sections

were estimated using DEM and satellite image for the modelling, except one cross section that was

measured during the field work. Sensitivity analysis was done changing parameters and locations of

the cross sections. The result has shown that the changes in the cross section parameters also changed

flood extent and water depth. Therefore, in future modelling attempts additional information should

be collected for cross sections through the river bed.

Prediction of probable maximum flood inundation through field measurements or surveys needs an

estimation of the river cross-section. The cross section data is needed for any flood model

(Jothityangkoon and Sivapalan, 2003).

In addition, the flood forecasting and warning demand close cooperation between meteorological and

hydrological communities to make real scientific progress (Moore et al., 2005). Therefore, the model

should include meteorological data especially rainfall data.

5.4. Accuracy of the land cover classification

Based on the land cover classification, the surface roughness coefficients are defined for different

land cover types. The image classification for ground truth was done on September 2004 using

Landsat ETM+ image of October 2001. The overall accuracy was 59.38 percent. According to

Congalton (1991), the best accuracy should be more than 85 percent. However, the result was less

than 85 percent implying that the accuracy level is low. This could be due to differences in timing

data collection and image acquisition.

5.5. Analysis of population and land cover in the flood risk areas

From the result of the analysis on the estimating land covers in flood risk areas, farmland (pasture and

agricultural area) covers an area of 1,066.7 square km which is the highest land cover affected (figure

28). According to the projection of the National Institute of Statistics in Mozambique, the population


35

of the basin will grow by approximately 2.5% per year. Most of this population is expected to be

concentrated in the more densely populated district of Chokwe and Xai-Xai. The main economic

activity in the basin is agriculture (Intstituto Nacional De Gestao De Calamidades Mocambique,

2003). This means there will be always a risk of flooding. The distribution of people should be

mapped more accurately and this should have priority in development and evacuation planning.

On other hand, many studies, for example (De Roo et al., 2003) have proved that the population

growth and the major changes in land use affects hydrology.

There are many studies (Boughton and Droop, 2003; Horritt and Bates, 2002) that have compared the

flood models and proved that to forecast flood extent using the flood model is essential to use the

results of the model for planning and evacuation processes.

Therefore, it is very important to prepare for the flood risks by minimizing its impact through better

early warning system and good management plan. It includes a reassessment of the existing risks and

an evaluation of the hazards depending on the newest information available (Plate, 2002).


36

6. Conclusions and Recommendations

In this research through the integration of RS, GIS and 2D flood modelling, the flood maps and

simulations were prepared for purposes of local planning.

The Digital Elevation Model (DEM) was generated in 100, 250 and 500 m pixel sizes, based on more

than 13000 elevation points using kriging interpolation.

Land cover classification from Landsat ETM+ satellite image was done in order to generate surface

roughness map.

The calibration and simulation of the model based on data observed during 2000 flooding in

Mozambique including DEM, surface roughness, and cross sections of the Limpopo River.

Scenarios such as Mild, Moderate and Severe flood type were done. The results consist of set of maps

such as flood starting time, flow velocity, and water depth. The maps for Severe flood type are

classified and overlaid and final flood map is generated.

Although, the model couldn’t give a promising result due to inaccurate input data, I conclude and

recommend that,

• The flood simulation and model result maps, such as flood starting time, flow velocity, flood

extent and water depth, are a good way of providing relevant information on how the flood is

going to behave at the location where people live and how the flood will affect them.

• It is very essential to improve quality of DEM using small scale topographic map or aerial

photo and add number of hydrometric stations and measure cross sections in many places

especially in meandering area.

• To produce thematic maps such as population density, land use, soil and vegetation in small

scale at least 1:10000.

• According to the National Institute of Statistics in Mozambique, population of this region are

growing very fast over the last few years. So, rapid population increase can influence natural


37

resources and environmental management at study area. Therefore, it is important to map the

distribution of the population in the flood risk area accurately.

• Flood plain zoning should be integrated to land use planning policies. Land use planning can

play very important role to reduce the adverse effect of flooding. So, land use and

construction plans should be reviewed.

• To improve and provide flood map based on flood model results (flood starting time, water

depth and flood extent) to the decision makers, environmental planners, and local community.

These kinds of maps will help the authorities for quick assessment pre and post disaster

situation and formulate their development strategies according to the available risk to the area.

• An effective and early warning and forecasting system should be supported by meteorological

information, especially precipitation. Reliable forecasting and the development of flood early

warning system need to be established in a regional level using computerised GIS database

and flood model.

• Early flood warning, flood information and forecasts are extremely important to be able to

recognize the dangerous situation in time and it can be used to prevent and to reduce damage.


38

7. References

Alkema, D., 2003. Flood risk assessment for EIA: an example of a motorway near Trento, Italy. Studi Trentini di Scienze Naturali-Acta geologica, 78: 147-153.

Bakker, W.H. et al. (Editors), 2001. Principles of remote Sensing. ITC, Enschede, The Netherlands.

Boughton, W. and Droop, O., 2003. Continuous simulation for design flood estimation--a review. Environmental Modelling & Software, 18(4): 309-318.

Brouwer, R. and van Ek, R., 2004. Integrated ecological, economic and social impact assessment of alternative flood control policies in the Netherlands. Ecological Economics, 50(1-2): 1-21.

Chow, V.T., 1959. Open Channel Hydraulics. McGraw-Hill, New York.

Congalton, R.G., 1991. A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing of Environment, 37(1): 35-46.

De Roo, A., Schmuck, G., Perdigao, V. and Thielen, J., 2003. The influence of historic land use changes and future planned land use scenarios on floods in the Oder catchment. Physics and Chemistry of the Earth, Parts A/B/C, 28(33-36): 1291-1300.

Delft-Hydraulics, 2004. WL, Delft.

ESRI, 2004a. ArcGIS Geostatistical Analyst Literature.

ESRI, 2004b. Dynamic Hydrological Modeling Using ArcView GIS.

Horritt, M.S. and Bates, P.D., 2002. Evaluation of 1D and 2D numerical models for predicting river flood inundation. Journal of Hydrology, 268(1-4): 87-99.

Hulme, M. (Editor), 1996. Climate Change and Southern Africa: an exploration of potential impacts and implication in the SADC region, Norwich, UK.

Hulme, M. et al., 1999. Climate change scenarios for global impacts studies. Global Environmental Change, 9(Supplement 1): S3-S19.

Intstituto Nacional De Gestao De Calamidades Mocambique, 2003. Atlas for Disaster preparedness and Response in the Limpopo Basin. Creda Communications (Pty) Ltd., Capetown, South Africa.

IPCC, 2001. IPCC, Climate change 2001, Cambridge.

ITC-ILWIS, 2001. ILWIS Academic. International Institute for Aerospace Survey and earth Sciences, Enschede.


39

Jet Propulsion Laboratory, 2004. Shuttle Radar Topography Mission.

Jothityangkoon, C. and Sivapalan, M., 2003. Towards estimation of extreme floods: examination of the roles of runoff process changes and floodplain flows. Journal of Hydrology, 281(3): 206-229.

Koshimura, S., 2004. The December 26, 2004 Andaman-Nicobar Islands Earthquake Tsunami.

Kundzewicz, Z., 2003. Extreme precipitation and floods in the changing world. IAHS-AISH Publication(281): 32-39.

Maidment, D.R. (Editor), 1992. Handbook of Hydrology. R.R.Donnelley & Sons Company.

Mark, O., Weesakul, S., Apirumanekul, C., Aroonnet, S.B. and Djordjevic, S., 2004. Potential and limitations of 1D modelling of urban flooding. Journal of Hydrology, 299(3-4): 284-299.

MIKE-11, 2004. MIKE 11 Website.

Moore, R.J., Bell, V.A. and Jones, D.A., 2005. Forecasting for flood warning. Comptes Rendus Geosciences, 337(1-2): 203-217.

Muianga, G., 2004. Flood Hazard Assessment and Zonation in the lower Limpopo, Mozambique. Msc Thesis, ITC, Enschede, the Netherlands, 85 pp.

Pelinovsky, E., Kozlov, S. and Vinogradov, S., 2002. Numerical Simulation of spring Floods in Nizhny Novgorod Region (RUSSIA).

Plate, E.J., 2002. Flood risk and flood management. Journal of Hydrology, 267(1-2): 2-11.

Prudhomme, C., Jakob, D. and Svensson, C., 2003. Uncertainty and climate change impact on the flood regime of small UK catchments. Journal of Hydrology, 277(1-2): 1-23.

Sobek, 2004. Sobek: managing your flow, Delft, Netherlands.

UNEP, 2000. Africa Environment Outlook.

USAID, 2002. Mozambique Flood Resettlement Grant Activity.

USGS, 2004. Tsunami and Earthquakes at the USGS.

Werner, M.G.F., 2001. Impact of grid size in GIS based flood extent mapping using a 1D flow model. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(7-8): 517-522.

Willems, P., 2004. River Flood Modelling.


40

8. Appendices

8.1. Appendix 1

Discharges of Limpopo River during 2000 flooding Date Discharge Flood Type Date Discharge Flood Type 22/01/2000 1475.9 mild 04/03/2000 5525.8 moderate 23/01/2000 3153.2 mild 05/03/2000 5064.4 moderate 24/01/2000 3526.4 mild 06/03/2000 4587.4 mild 25/01/2000 2739.1 mild 07/03/2000 4120.9 mild 26/01/2000 1976.8 mild 08/03/2000 3698.3 mild 27/01/2000 1500.6 mild 09/03/2000 3401.0 mild 28/01/2000 1222.0 mild 10/03/2000 3147.0 mild 29/01/2000 1130.5 mild 11/03/2000 2742.8 mild 30/01/2000 1074.0 mild 12/03/2000 2251.0 mild 31/01/2000 1006.0 mild 13/03/2000 2194.0 mild 01/02/2000 968.1 mild 14/03/2000 2144.1 mild 02/02/2000 1006.4 mild 15/03/2000 1976.6 mild 03/02/2000 1169.6 mild 16/03/2000 2045.4 mild 04/02/2000 1288.1 mild 17/03/2000 2105.8 mild 05/02/2000 1244.7 mild 18/03/2000 2042.5 mild 06/02/2000 1110.3 mild 19/03/2000 1924.8 mild 07/02/2000 1036.2 mild 20/03/2000 1805.9 mild 08/02/2000 1046.5 mild 21/03/2000 2118.5 mild 09/02/2000 1490.8 mild 22/03/2000 3292.5 mild 10/02/2000 3374.7 mild 23/03/2000 4456.8 mild 11/02/2000 4785.6 moderate 24/03/2000 4978.3 moderate 12/02/2000 5444.3 moderate 25/03/2000 4787.4 moderate 13/02/2000 5742.2 moderate 26/03/2000 4288.4 mild 14/02/2000 5509.0 moderate 27/03/2000 3775.0 mild 15/02/2000 5321.3 moderate 28/03/2000 3312.7 mild 16/02/2000 5370.9 moderate 29/03/2000 3109.0 mild 17/02/2000 5434.8 moderate 30/03/2000 3079.8 mild 18/02/2000 5555.7 moderate 31/03/2000 2848.0 mild 19/02/2000 5610.2 moderate 01/04/2000 2683.9 mild 20/02/2000 5599.6 moderate 02/04/2000 2577.8 mild 21/02/2000 5466.4 moderate 03/04/2000 2520.4 mild 22/02/2000 5460.4 moderate 04/04/2000 2536.8 mild 23/02/2000 5382.6 moderate 05/04/2000 2453.6 mild 24/02/2000 5079.6 moderate 06/04/2000 2152.5 mild 25/02/2000 7200.0 severe 07/04/2000 1707.1 mild 26/02/2000 10000.0 severe 08/04/2000 1622.1 mild 27/02/2000 20000.0 severe 09/04/2000 1633.9 mild 28/02/2000 17000.0 severe 10/04/2000 1585.9 mild 29/02/2000 12000.0 severe 11/04/2000 1607.7 mild 01/03/2000 9500.0 severe 12/04/2000 1582.8 mild 02/03/2000 8500.0 severe 13/04/2000 1617.8 mild 03/03/2000 7500.0 severe 14/04/2000 1561.4 mild


41

15/04/2000 1493.2 mild 23/04/2000 1207.1 mild 16/04/2000 1450.4 mild 24/04/2000 1206.7 mild 17/04/2000 1404.1 mild 25/04/2000 1191.7 mild 18/04/2000 1345.3 mild 26/04/2000 1169.0 mild 19/04/2000 1289.5 mild 27/04/2000 1141.8 mild 20/04/2000 1250.6 mild 28/04/2000 1115.1 mild 21/04/2000 1198.7 mild 29/04/2000 1061.7 mild 22/04/2000 1180.3 mild

8.2. Appendix 2

Discharges and water level of Sicacate (1970-2004)

Water level (m)

Discharge (m3/s)

Water level (m)

Discharge (m3/s)

Water level (m)

Discharge (m3/s)

1.71 3.37 2.25 1.63 2.72 20.38 1.76 4.02 2.25 13.42 2.77 35.5 1.8 4.44 2.25 1.64 2.78 29.89

1.94 0.1 2.25 13.48 2.8 33.36 1.95 0.42 2.27 7.85 2.83 24.61 1.96 4.98 2.28 16.75 2.84 29.17 2.05 8.34 2.29 4.97 2.84 59.25 2.05 1.94 2.29 6.23 2.87 34.75 2.06 8.63 2.29 5.12 2.88 31.15 2.08 8.9 2.31 10.18 2.94 39.39 2.09 2.73 2.32 6.45 2.95 35.51 2.09 1.22 2.32 3.54 2.96 36.43 2.09 2.72 2.32 5.83 2.96 42.62 2.11 0.23 2.34 6.52 2.96 35.72 2.12 3.42 2.34 7.9 2.98 44.69 2.12 1.34 2.34 6 3 56.46 2.13 9.93 2.34 10.11 3 20.08 2.13 1.59 2.35 3.81 3.03 43.74 2.14 0.04 2.35 9.57 3.09 52.19 2.15 1.97 2.39 18.54 3.1 53.5 2.17 0.97 2.39 12.02 3.12 51.07 2.17 0.6 2.4 13.35 3.12 51.63 2.18 2.52 2.43 5.08 3.16 58.89 2.18 1.02 2.44 5.26 3.18 51.16 2.18 1.01 2.45 5.92 3.2 48.77 2.18 5.28 2.46 18.62 3.21 58.6 2.18 1.05 2.47 8.24 3.21 63.07 2.19 2.66 2.47 17.21 3.23 70.71 2.2 7.09 2.48 9.91 3.23 44.61

2.21 5.89 2.49 6.11 3.25 59.91 2.21 1.13 2.52 6.86 3.25 66.88 2.21 5.93 2.53 17.74 3.25 68.89 2.22 2.28 2.54 15.56 3.27 68.35 2.22 11.91 2.56 21.48 3.32 40.71 2.22 2.37 2.57 7.88 3.35 76.24


42

2.22 3.38 2.58 19.94 3.36 3.93 2.23 3.38 2.59 20.62 3.41 78.21 2.23 2.33 2.6 16.75 3.42 78.93 2.23 3.51 2.66 22.02 3.42 82.41 2.24 3.73 2.66 23.99 3.43 77.95 2.24 2.58 2.69 23.88 3.47 84.44 2.24 2.87 2.7 11.92 3.48 82.94 2.24 6.95 2.7 20.89 3.48 94.89 2.25 3.96 2.7 28.58 3.51 52.26 2.25 14.28 2.71 26.56 3.52 92.55

Water level (m)

Discharge (m3/s)

Water level (m)

Discharge (m3/s)

3.53 94.89 5.33 327.85 3.54 83.07 5.46 346.71 3.55 81.84 5.53 448.23 3.56 71.64 5.61 460.55 3.59 100.43 5.61 303.72 3.65 98.86 5.75 308.02 3.66 10.06 5.89 423.96 3.67 80.7 6.01 467.2 3.71 97.86 6.48 531.6 3.73 83.11 6.57 565.44 3.77 113.98 6.6 553.96 3.8 125.07 6.73 593.63

3.84 90.7 6.79 629.61 3.89 133.5 6.80 764.4 3.90 138.21 6.91 620.82 3.9 126.05 7.06 661.37

3.91 130.87 7.07 677.09 3.92 136.79 7.12 679.82 3.94 140.1 7.29 718.75 3.95 130.27 7.41 655.42 4.03 139.35 7.51 777.98 4.05 157.82 7.73 835.12 4.08 125.08 7.95 746.3 4.08 169 8.1 933.09 4.1 158.73 8.35 607.47

4.13 163.24 8.5 1111.28 4.16 118.4 8.85 1204.97 4.17 157.57 9.62 1394.89 4.19 238.18 9.63 1181.11 4.24 166.52 9.9 1351.27 4.24 167.97 10.28 1535.79 4.27 139 10.55 1702.73 4.28 182.19 11.67 2131.81

4.34 176.4 4.79 252.23 4.5 215.6 4.88 190.05

4.59 229.48 4.9 254.62 4.62 216.8 4.95 220.99 4.65 182.2 4.99 242.34 4.65 166.74 5.03 304.79 4.68 322.71 5.03 338.32


43

8.3. Appendix 3

Discharges and water level of Changane (1941-1967) Water level (m)

Discharge (m3/s)

1.19 0.13 1.41 0.34 1.48 0.22 1.55 0.34 1.63 0.67 1.68 0.06 1.7 0.3

1.75 0.76 1.79 0.24 1.82 5.94 1.91 0.63 1.92 1.27 2.17 1.03 2.2 2.36

2.22 1.14 2.36 1.56 2.41 3.56 2.46 4.14 2.49 2.03 2.56 2.31 2.6 2.49

2.62 2.59 2.66 2.74 2.7 2.95

2.73 6.47 2.76 3.22 2.86 8.58 3.31 11.35 3.33 14.15 3.87 33.45 4.05 27.62 4.27 30.57 4.73 43.71 6.96 98.2 7.74 120.92 8.01 129.58

flood simulation a case study in the lower limpopo valley, … · 2007-04-25 · flood simulation a...

Documents