Final Report - NBCBN · MSc, Civil Eng. Technical Engineer, NBCBN-RE Secretariat Hydraulics...

Final Report

Project Name: Nile Basin Capacity Building Network for River Engineering

Cluster Name: GIS and Modeling Cluster

Group Title: Watershed Group

By Group Members

Coordinated by

Dr. Hoda K. Soussa Associate Professor, Hydraulics & Irrigation Department

Faculty of Engineering – Ain Shams University 1 El Sarayat street – Abassia – Cairo - Egypt

Email: [email protected]

2004

Copyright NBCBN-RE & Croup Members

GROUP MEMBERS

1. Sediment Yield Modeling Subgroup Mr. Ndomba (Tanzania)

Assistant LECTURER/Doctoral STUDENT-UDSM/NTNU Water Resources Management Programme Company: University of Dar es Salaam P.O.Box 35131, Dar es Salaam. Telephone: +255-022-2410029 (office), +255-022-2773512 (residence), Mob: +255-0744-635272 (mobile)

Dr. Mulungu (Tanzania) University of Dar es Salaam Water Resources Engineering Department P.O. Box 35131 Dar es Salaam, Tanzania Email: [email protected] or [email protected] Tel./Fax. +255 22 2410029

Mr. Malisa (Tanzania) Assistant Lecturer/Doctoral Student UDSM/NTNU Dar Es Salaam Institute of Technology P.0.Box 2958,Dar Es Salaam, Tel: +255-22-2410029 Fax: +255-22-2410029 Email: [email protected]

Dr. Neveen Yousif (Egypt)

Associate Professor Ain Shams University, Faculty of Engineering, Irrigation and Hydraulics Department Email: [email protected]

2. Rainfall Runoff Modeling Subgroup

Eng. Mohamed Abdel Atti (Egypt) Manager, Nile Forecast Center, Planning Sector, Ministry of Water Resources and Irrigation, Tel. 0101649823, 5449462 Email: [email protected]

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Eng. Eman Sayed (Egypt) GIS Specialist, GIS Unit-Planning Sector, MWRI

Mobile: 010-5400396 E-mail: [email protected], [email protected]

Eng. Amal Abdel Latief (Egypt) GIS Specialist- Planning Sector - MWRI

Mobile : 010-1649821 E-mail : [email protected], [email protected]

3. Application on Watershed Management Systems (Egypt Case Study)

Eng. Amel Azab (Egypt)

MSc, Civil Eng. Technical Engineer, NBCBN-RE Secretariat Hydraulics Research Institute, Egypt 13621, Delta barrages, Cairo, Egypt Tel: 202-2183450 Fax: 202-2183450 E-mail: [email protected] [email protected] 4. Geodatabase Definition

Eng. Amel Azab (Egypt)

MSc, Civil Eng. Technical Engineer, NBCBN-RE Secretariat Hydraulics Research Institute, Egypt 13621, Delta barrages, Cairo, Egypt Tel: 202-2183450 Fax: 202-2183450 E-mail: [email protected] [email protected]

Dr. Abd El Aal Attia (Egypt) Ph. D. Senior Geologist, Image Processor Geographic Information Systems Unit Egyptian Geologic Surveying and Mining Authority

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Mobile: 0105684724, 6860093 (work), 4107283 (home) Email: [email protected] [email protected]

5. Lake Victoria Water Quality Modeling Eng. Tarek El Sayed Director, GIS Unit-Planning Sector, MWRI

Email: [email protected] [email protected]

Scientific Advisor Dr. Zoltán Vekerdy (the Netherlands) International Institute for Geo-Information Science and Earth Observation (ITC) P.O.Box 6 7500 AA Enschede, the Netherlands Email: [email protected]

Report Compiling by:

Dr. Mohamed Gad (Egypt)

Associate Professor Ain Shams University, Faculty of Engineering, Irrigation and Hydraulics Department Email: [email protected]

Dr. Hoda Soussa (Egypt) Associate Professor Ain Shams University, Faculty of Engineering, Irrigation and Hydraulics Department Email: [email protected]

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TABLE OF CONTENTS

CHAPTER 1 EXECUTIVE SUMMARY 1.1 Introduction 1.2 Overview 1.3 Summary of Progress Evaluation 1.3.1 Objective 1: Sediment yield Modeling 1.3.1.1 Summary 1.3.1.2 Future phase 1.3.2 Objective 2: Rainfall Runoff Modeling and flood hazard mapping 1.3.2.1 Summary 1.3.2.2 Future phase 1.3.3 Objective 3: Water Quality Modeling (Lake Victoria) 1.3.3.1 Summary 1.3.3.2 Future phase 1.3.4 Objective 4: Water Quantity & Quality Management Tools Application on Watershed Management Systems (Case Study: Egypt) 1.3.4.1 Summary 1.3.4.2 Future phase CHAPTER 2 SEDIMENT YIELD MODELING 2.1 Introduction 2.2 Study Area 2.3 Objective & Significance of the Study 2.3.1 Objectives 2.3.2 Significance 2.4 Problem Statement 2.5 Literature Review 2.6 Data Collection and Analysis/Processing 2.6.1 Data collected 2.6.1.1 Spatial Data 2.6.1.2 Hydrologic Data 2.6.1.3 Meteorological Data 2.6.1.4 Channel Geometric Data – X-Sectional Profiles 2.6.1.5 Sediment flow data 2.7 Sediment yield modelling 2.7.1 Methodology 2.7.1.1 Problem schematization 2.7.1.2 Model development 2.7.1.3 Calibration results 2.7.1.4 Model Application 2.8 Results and Discussions

1-1 1-1 1-2 1-2 1-4 1-4 1-4 1-5 1-5 1-5 1-6 1-6 1-6 1-7 1-7 1-7 2-1 2-1 2-1 2-2 2-2 2-2 2-2 2-2 2-5 2-5 2-5 2-7 2-8 2-9 2-10 2-11 2-11 2-12 2-13 2-15 2-16

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2.9 Conclusions and Recommendations CHAPTER 3 ASSESSMENT OF VULNERABILITY OF NON-GAUGED WATERSHEDS TO FLOOD RISKS A CASE STUDY FOR QENA VALLEY CATCHMENT 3.1 Summary 3.2 Introduction 3.3 The Study Area 3.4 Data 3.4 Methodology 3.5 Extracting Watershed Boundaries and Drainage Networks 3.5.1 Geological Characteristics 3.5.2 Morphological Characteristics 3.6 The Hydrologic Model 3.7 Model Calibration 3.8 Risk Analysis 3.9 Conclusions 3.10 Recommendations for Further Work CHAPTER 4 WATER QUALITY MODELLING (LAKE VICTORIA PILOT AREA) 4.1 Summary 4.2 Data Collection 4.3 Lake Victoria Inventory 4.4 Socio-Economic 4.5 Jurisdiction and Political Environment 4.6 Water Management and Shared Water Issues 4.7 Major Threats to the Lake 4.7.1 Lake Biota and Fisheries 4.7.2 Water Hyacinth 4.7.3 Eutrophication 4.7.4 Water Pollution 4.8 Preliminary Findings on Water Quality / Limnology Studies for Lake Victoria (LVEMP) 4.8.1 Implication 4.9 Recommendations from LVEMP 4.10 Lake Victoria Maps Album

2-16 3-1 3-1 3-1 3-1 3-2 3-3 3-5 3-5 3-8 3-10 3-13 3-17 3-21 3-21 4-1 4-1 4-1 4-2 4-2 4-3 4-3 4-4 4-4 4-5 4-6 4-6 4-7 4-7 4-9 4-12

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CHAPTER 5 WATER QUALITY & QUANTITY MANAGEMENT SYSTEMS (EGYPT CASE STUDY ) 5.1 Management System Objectives 5.2 Conceptual design and Computational framework of NWRP DSS 5.3 DSS components and sample data inputs, sample analysis and outputs 5.3.1 ASME 5.3.1.1 Introduction 5.3.1.2 General 5.3.1.3 Regions 5.3.1.4 Data Requirements 5.3.1.5 Outputs 5.3.2 RIBASIM 5.3.2.1 Schematization 5.3.2.2 Model calibration 5.3.3 SIWARE 5.3.3.1 SIWARE Schematization 5.3.3.2 Model Input data 5.3.3.3 Model calibration 5.4 Water Quality Models 5.4.1 WLM 5.4.1.1 Model Schematization 5.4.1.2 Input data 5.4.2 DELWAQ 5.4.2.1 Model output 5.4.3 Preliminary calibration 5.5 Groundwater Models 5.5.1 Nile Valley Model 5.5.1.1 Model design 5.5.1.2 Input data 5.5.1.3 Calibration 5.5.2 Nile Delta Model 5.5.2.1 Model Schematization 5.5.2.2 Input data 5.5.2.3 Calibration 5.6 Exchange of Information between Models 5.7 Developed Database and Sample geo-database 5.7.1 Collected Data 5.7.2 Development of a Database Management System 5.7.3 Possible Future Development of the NWRP-Database 5.7.4 Sample Geo-database 5.8 Extension of some model components to cover the Nile Basin (RIBASIM Component) REFERENCES

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CHAPTER 1 EXECUTIVE SUMMARY

1.1 Introduction

The existence of water in the liquid state makes the Earth unique among the

known planets. The Earth has a great amount of water: over 70% of the surface is covered with water or ice. Water is continually evaporating, condensing, transported, precipitating, and re-transported in a never-ending cycle known as the hydrological cycle. The hydrological cycle involves many hydrometeorological elements such as vapor, precipitation, run-off, infiltrated water, and groundwater. A watershed, defined as the area drained by a river system and usually extending across political boundaries, plays a critical role in the hydrological cycle. This critical role arises due to the fact that watersheds constitute the platform in which many of the different hydrometreological elements of the hydrological cycle interact.

The use of Geographical Information Systems (GIS) was originally limited to the geographical field, in different applications such as the setup of multi dimension information, analysis, utility, and display. Nowadays, it has reached far beyond the limited concept of graphics and providing support for applications. In watershed modeling for example, GIS is extensively implemented as a tool for analyzing the watershed spatial and topographical characteristics. In addition, it is being used in the recent worldwide research as a complete spatiotemporal modeling environment for distributed watershed modeling, rainfall and flood forecasting, groundwater modeling, water resources management, and other environmental and water resources applications.

The main goal of this study is to establish a GIS-based modeling tools and methods for sustainable watershed development and for the integrated management of water resources in the Nile Basin. The research group in this study consists of members from different countries sharing the Nile Basin. Within the main goal of this study, this research project is divided into three basic, relatively concurrent stages:

1- The first stage is the comprehensive literature review. The objective of this stage is to provide a comprehensive summary of the state of the art in the fields of rainfall-runoff modeling, sediment yield estimation techniques, water quality modeling. Sub-groups are established from the members to work in these three fields. By the end of this stage, a document summarizing the literature review outcome will be written and candidate analysis and modeling tools shall be selected. The term "candidate" refers to suitability in terms of the applicability to the Nile Basin regions in the light of the available data, capabilities, and availability.

2- The second stage is the geo-database definition for pilot areas in the Nile Basin. Based on the availability of data, four pilot areas have been selected: Qena Valley catchment area (Egypt), Simiyu River catchments (Tanzania), Sondu River

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basin (Kenya), and Lake Victoria catchments (Kenya and other countries). This stage aims at generating a GIS database and collecting the required hydrometreological data for each pilot watershed.

3- This stage represents the application part. The activity set here consists of five components: flood hazard mapping and mitigation options (Pilot 1), models assessment for sediment yield estimation (Pilot 2), water quality inventory for Lake Victoria watersheds (Pilot 4), definition of an integrated watershed management geo-database, and the conceptual design of the watershed management system. This stage integrates the outputs of the first and second stages.

This report is the final report. It describes the progress and findings in each of the topics this study focuses on at the end of the third stage described above. The following sections present the structure of the report, the working hours reports of the members, the individual technical reports from each of the subgroups, and the conclusions. 1.2 Overview of This Report

This progress report is divided into two main parts. The first part, presented in the following section of this chapter, includes the evaluation and summary of the progress in each of the subgroups of the Watershed Research Group in the GIS and Modelling Research Cluster. This summary is divided into two sections: summary and future phase. This chapter also includes a report on the distribution of funds as prepared by the coordinator and NBCBN secretariat.

The second part of the report (Chapters from 2 to 5) includes progress reports from each of the subgroups: sediment yield estimation techniques (Chapter 2), rainfall-runoff modeling (Chapter 3), water quality modeling (Chapter 4), water quantity and quality managing tools (Chapter 5). Each subgroup plays an essential, but rather different role in the implementation of the project principal objectives. 1.3 Summary of Progress Evaluation Table 1 summarizes the overall progress by tasks and subgroups. Additional details are presented in the individual reports of the project included in the following chapters.

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Table (1-1) Overall Summary of the Progress by Task and Subgroup

Activities Subgroup

Researcher

Country

Data processing

Report writing

Sub-Report editing

Mr. Ndomba Tanzania

Dr. Mulungu Tanzania

Mr. Malisa Tanzania

Sediment Yield Modeling

Dr. Neveen Yousif Egypt

Eng. Eman Sayed Egypt

Eng. Mohamed Abdel Atti

Egypt

Rainfall Runoff

Modeling Eng. Amal

Abdel Latief Egypt

Application on Watershed

Management Systems

Eng. Amel azab Egypt

Water quality modeling Eng. Tarek El-Sayed Egypt

Eng. Amel azab Egypt

Geodatabase Definition

Dr. Abd El Aal Attia Egypt

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1.3.1 Objective 1: Sediment yield Modeling 1.3.1.1 Summary

The Sediment Yield Estimation Subgroup’s Main Supervisor, Mr. Ndomba has completed his analysis and added a sediment yield modeling part to his midterm report. The modeling activities involved data collection/processing and analysis, problem schematization, model development and model application. Results of data collection and analysis phase are also presented. At this stage of research, only a watershed model was used to simulate the sediment yield in the basin. Routing sediment through the channel using CCHE1D model could not be done because of limited data set for the transport capacity model calibration. In the absence of measured sediment loads in the sub-basin, calibration was done based on long time average annual runoff, surface runoff and base flow fluxes. Also, a temporal calibration was carried out in order to simulate the inter-annual variability of fluxes. In order to validate the simulated loads with the scanty measured sediments at the outlet (sampling site), the trained model was then applied to the entire catchment of Simiyu. As a result of this study a GIS-based Sediment yield Computer Model has been developed for the Simiyu-Ndagalu sub-catchment whereby predictions of sediment yield rate due to landuse or landcover dynamics can be assessed. Model application results gave a long-term specific sediment yield of 0.523t/ha/year. Generally, this study has an immediate impact on capacity building to the researchers involved. In particular, the execution of this study highlights some opportunity for applicability of complex models such as SWAT in the developing world and Nile basin in particular.

This report shows a considerable effort to simulate the sediment yield in the lack of continuous sediment flow data at both sub-catchments under consideration. 1.3.1.2 Future phase A number of recommended approaches for model improvement could be proposed:

• The model performance can be improved if adequate sediment calibration dataset is available. In a long-term plan there is a need to install two more gauging stations and sediment sampling stations in the catchment.

• The Simiyu river basin is characterized as complex since in the middle of sub-catchment (i.e. Simiyu-Ndagalu and Simiyu-Sayaga) there are flow regulators such as swamps and dams. Therefore for routing purposes, these hydrologic features should well be presented and modeled.

• Also, for correct model application, researchers in the cluster should conduct a training workshop on advanced model application.

• Documenting the work done to be available for future phases.

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1.3.2 Objective 2: Rainfall Runoff Modeling and flood hazard mapping 1.3.2.1 Summary This report shows the development of a methodology for flood risk assessment and relative vulnerability classification for watershed in arid regions. Geographic Information System (GIS) is used as the main analysis tool for the extraction of the geomorphological and hydrological parameters of Qena area, Egypt. The Swedish HBV model, as distributed by Killingtveit and Saelthun (1995) was selected to simulate the rainfall runoff process. The advantage of the HBV model is its simplicity of usage and its ability to perform continuous hydrograph simulations. The use of the grid cells offers the possibility to turn the HBV modeling concept, which is originally lumped, into a semi-distributed model. However, it must be noted that the HBV model has not been adequately calibrated due to the lack of sufficient runoff data in the catchment area. A statement regarding the applicability of the HBV model in the arid and semi-arid regions can not be drawn. Additional test cases are needed to reach a general statement. In this study, GIS has proved, as expected, to be an easy and efficient tool for watersheds flood risk assessment The research team concluded that the risk classification presented provides general priorities scheme for flood protection programs although the risk map they provided is ambiguous. It should be noted also that the HBV model was not compared to any other commonly used model and a justification for using this model should has been shown. The work presented is a good step, however, it is still incomplete. 1.3.2.2 Future phase Based on the analysis done in this study, the following recommendations are drawn to be implemented in the next phase:

• The calibration and application of the HBV model on gauged areas. • Calibration of other Rainfall/Runoff models like Nash, Sacramento and

NFS models, and comparing their results with HBV model for the purpose of choosing the most suitable model/models for the application in the different regions of the Nile basin.

• Identifying detailed advantages and disadvantages of the HBV model as compared to other models.

• Documenting the work done to be available for any future phases.

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1.3.3 Objective 3: Water Quality Modeling (Lake Victoria) 1.3.3.1 Summary This chapter reports the activity sets B and C (in the original research proposal) concerning Lake Victoria Pilot Area. Activity B deals with data collection while activity C is concerned with producing water quality inventory for Lake Victoria basin. The data collection for this pilot area was concentrated on two rivers in Kenya and Tanzania. The two rivers are Sondu River in Kenya and Simiyu River in Tanzania. The collected and processed maps include: Rivers and basins, DEM with spatial resolution 90m, contour lines, Landsat satellite images, vegetation index, unsupervised classification, and some hydrological parameters for water quality. The collected maps and data are covering completely Lake Victoria basin. A detailed socio-economic description of Lake Victoria is presented, as the lake basin provides resources for the livelihood of nearly 30 million riparian communities. The report presents the three important and convergent issues relating to management of shared waters within the three main countries recognized by the East African Community (EAC) Partner States. It gathers the main threats to the lake as well as the introduction of new species of fish in the lake over the last thirty years. The main detrimental effects of the spreading mats of water hyacinth are also reported. Water quality in Lake Victoria has declined greatly in the past few decades, owing chiefly to eutrophication arising from increased inflow of nutrients into the lake. A water quality inventory for the Lake is presented. The report specifies the main sources of water pollution affecting the Lake. Through this document, some results of the preliminary findings on Water quality and limnology studies for Lake Victoria (LVEMP) can be found and valuable recommendations to manage water quality problem effectively are underlined, which can be investigated for further contribution by NBCBN project in its second phase. Capacity building is required in the fields of data management, water quality modelling, spatial analysis (GIS), satellite images interpretation, survey methods, statistical analysis, etc. 1.3.3.2 Future phase


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1.3.4 Objective 4: Water Quantity & Quality Management Tools Application on Watershed Management Systems (Case Study: Egypt)

1.3.4.1 Summary The general objective of the developed management system is to describe how Egypt will safeguard its water resources in the future, both with respect to quantity and quality, and how it will use these resources in the best way from a socio-economic and environmental point of view. This report described the Conceptual design and Computational framework of NWRP DSS, which is the main planning tool to assess the impacts of different strategies on the water resources system for a selected combination of demand and supply scenarios. It consists of a set of databases, models and tools for simulation and analysis and presentation of results. It also includes tools to compare the results of alternative strategies. The models include four main areas: ASME model which determines the optimum cropping pattern, RIBASIM and SIWARE models which simulate the water demands and water distribution, WLM and DELWAQ models which simulate the surface water quality and groundwater models which simulate the groundwater behaviour of the Nile aquifer in the Valley and Delta. The different DSS components as well as the different data inputs with a sample are shown in this document. Sample analysis and outputs are described in details as well as the schematization of the different models and its calibration processes, showing some calibration results. Exchange of information between models is underlined. Major exchanges of data are between the ASME model and the DSS-NILE and DSS-DELTA and between the two DSS’s. All data exchanges are off-line through exchange files. There is no exchange of data with the groundwater models. However, relevant information from the groundwater models (groundwater levels, salinity) is incorporated in the SIWARE models. It has been decided to design a DBMS that allows for systematic storage, processing and retrieval of the data. The MS Access system was chosen as a database platform, because it was already available in the project, it is widely used and it is relatively easy to learn. NWRP database interaction with NWRP models is adapted. The report discusses also the possible future development of the NWRP-Database. A sample geo-database is developed for a pilot study area in West Delta (Gharbia Governorate, Egypt). To develop the work on a regional level, the NWRP is jointly carrying out a number of activities with the Lake Nasser Flood and Drought Control Project (LNFDC), from which is:

• Extend the RIBASIM schematization in order to include the whole Nile Basin. • Study the effect of different strategies on the water system of the Nile Basin.

1.3.4.2 Future phase


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CHAPTER 2 SEDIMENT YIELD MODELING

2.1 Introduction

Soil erosion is a worldwide environmental problem that degrades soil productivity and water quality, causes sedimentation to reservoirs and increases the probability of floods (Ouyung, 2001). Soil erosion is defined as a process of detachment and transport of soil particles by erosive agents such as raindrop impact and surface runoff from rainfall (Ellison, 1944).

River basins, such as the Nile River Basin, are subjected to various types of modifications and impacts by human activities and nature. There has been a growing needs to study, understand and quantify the impacts of these modifications on the hydrologic regime; both on water quantity and quality. This is necessary to anticipate and minimize potential environmental damage and to satisfy the water demand requirements in terms of quality and quantity (NBCBN, 2003).

In Lake Victoria basin, in particular in the Simiyu catchment, the impact of erosion and sedimentation has not yet been studied. Besides, there is inadequate data to simulate the sediment transport in the basin. Consequently, no one could successfully work on the entire Nile catchment if small catchments are left uninvestigated.

The objective of this research is therefore to study the erosion processes and ultimately to develop a GIS-based sediment yield model using existing and available software. This paper reports the progress in this research work activities, which include, literature review, data collection and processing and sediment yield modelling 2.2 Study Area

The Simiyu catchment covers an area of 10,659 km2 and is located between 33o 15’ – 35o 00’ E and 2o 30’ – 3o 30’ S as shown on Figure (2-1). The map also shows the administrative boundaries and some infrastructure. The Simiyu River flows from the Serengeti National Park plains to the Lake Victoria. In the downstream region, before it discharges its water to the Lake, the Duma River forms a main tributary to the Simiyu River. The Duma River is a result of the confluence of Ngasamo and Bariadi rivers in the upstream region. In this report therefore, the Simiyu-Duma river system is referred as Simiyu basin. The Road-bridge gauging station (112022) located at the confluence of the two rivers, (Simiyu and Duma) forms the lowest point of this study area. This has been dictated by the data availability.

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2.3 Objective & Significance of the Study

The general goal of the research group is to review the GIS-based modeling tools and methods that use remotely sensed data and field data for integrated water resources management and sustainable watershed development in the Nile Basin. 2.3.1 Objectives Specifically this study focuses on the following objectives:

• Definitions of geo-database (layers/data). • Sediment yield modelling. • Capacity building.

2.3.2 Significance of the Study

• Improved understanding of the impacts caused by human activities in the Nile Watershed.

• Environmentally sound sustainable development of Nile River watersheds.

• Improved collaboration among Nile basin countries in technical and managerial aspects.

2.4 Problem Statement

In recent years, there has been a growing concern on erosion and sedimentation problems in Tanzania. Some sources associated the electrical power rationings in Tanzania during the period of 1990’s with the sedimentation problem of the hydropower reservoirs. Christianson (1981) reported that erosion problems in Tanzania could be dated back as early as 19th century in the era of East African caravan trade. The Uluguru Mountains located in Morogoro region was identified as one of the major stream source areas of Tanzania prone to severe soil erosion and consequent deterioration of water yields (Temple, 1973; Lyamuya et al., 1994; and Little, 1963). Mtalo and Ndomba, (2001), reported alarming high erosion rates in the Pangani river basin, located in the northeastern part of Tanzania. For instance, it was estimated that the average soil loss in the upland reaches 24t/ha/yr. Based on a few examples outlined above, one would understand that there is an urgent need to study the erosion processes and finally to come up with estimates of sediment yield in different parts of the country. Though, there are no reported cases of high erosion rates in the Simiyu basin, the growing human activities and flooding on downstream most in the region suggest potential for high erosion levels. 2.5 Literature Review

Sedimentation problem can be directly related to soil erosion in the upland: lateral erosion, growing gullies, collapsing riverbanks and deepening riverbeds. In the history of erosion and sediment yield modeling, a number of definitions emerged, but this study will consider the following: Soil erosion is defined as a process of detachment and transport of soil particles by erosive agents such as raindrop impact and overland flow (surface runoff) from rainfall (Ellison, 1944).

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Measuring and estimating suspended sediment yields in rivers has long been subject to confusion and uncertainty. Many methods have been developed for collecting data and estimating yields, a fact that suggests the lack of a compelling measurement methodology. The main reason for this situation is the lack of a theoretical framework that defines when discrete samples of suspended sediment should be taken. Obtaining continuous records of concentration, however, is subject to numerous problems (Thomas, 1985). Such measurements are necessarily indirect; turbidity (Walling, 1977a; Truhlar, 1978; Beschta, 1980) and water-sediment density (Skinner and Beverage,1982) are two quantities that can be related to suspended sediment concentration. (Walling and Webb, 1981) investigated a variety of methods for estimating total suspended yield and performed a comparison using Monte Carlo techniques.

It is evident that several methods of sediment transport estimation exist and a thousand of erosion models are in use, but only the popular erosion models and their limitations are discussed below. The models singled out are representing all model categories. The Universal Soil Loss Equation, USLE (Wischmeier & Smith, 1978) is an empirical equation developed for the United States. It is based on large number of Wischmeier plot measurements and predicts average annual erosion for plots or fields but deposition is neglected. The basic equation is a simple multiplication of factors. As shown by Haan et al. (1994), the individual factors can, however, be calculated in complex ways, especially in the Revised version, RUSLE (Renard et at., 1997). The USLE has later been adapted to other areas as well. The model can give potential soil loss as well as the actual soil erosion rates on a site basis. Additionally, the models at the highest possible temporal resolution give the annual soil loss, so, the model is insensitive to seasonal variability.

The Water Erosion Prediction Project, WEPP (Flanagan et al., 1995) is a process-based erosion model that simulates erosion for hill-slope profiles. By combining several profiles with channel segments and impoundments small catchments can also be modeled. WEPP is a continuous simulation model that can also be used for single storms. Since it is a continuous model it needs many parameters that are not needed in event based models. The minimum number of input parameters required running the model is about 100. Infiltration is simulated with the Green-Ampt Model. Overland flow is divided into rill and inter-rill flow. Friction factors are calculated as the sum of partial friction factors. Rill density must be specified beforehand and it is further assumed that all rills have equal discharge. Sediment transport is calculated with a modified Yalin equation. Water routing is in principle done with kinematic wave, but to limit computational time, approximations are used. For erosion calculations a steady state runoff is used, which in practice means that the peak runoff is calculated and used in the erosion calculations. The runoff duration is adapted to a so-called effective duration to ensure that the total runoff volume remains constant. WEPP uses 5 sediment classes: clay, silt, sand, small aggregates and large aggregates. Transport and deposition are calculated separately for these classes. Gullies cannot be modeled.

Soil and Water Integrated Model, SWIM (Krysanova, 2000) is based on two previously developed tools: SWAT (Soil and Water Assessment Tool), (Neitsch et al., (2000)), and MATSALU (Krysanova et al., 2000). SWAT is a continuous-time distributed simulation watershed model. It was developed to predict the effects of

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alternative management decisions on water, sediment; chemical yields with reasonable accuracy of ungauged rural basins. One of its attractive features (SWAT) is that there is a long period of modeling experience behind this model. However, the model is quite complicated, and it cannot be used as a black box. Understanding of the model code is prerequisite for successful applications. Gully erosion is not modeled.

Hydrological Simulation Program—FORTRAN (HSPF)(ems-I, 2002) is an analytical tool designed to allow simulation of hydrology and water quality in natural and man-made systems. HSPF is used to apply mathematical models to simulate the movement of water, sediment and other constituents through watersheds. This analysis helps predict possible environmental problems in the watershed. With the growing need to care for and monitor the effects of man on the environment, it became apparent that a method for rapid analysis of those effects was needed. The HSPF model under WMS interface creates a way for the user to input a multitude of parameters into the input file and then to run HSPF. The input parameters are linked to all the tools within WMS including automated basin delineation and parameter definition using a graphical user interface. Nevertheless, the model uses many parameters; as such data requirement is definitely intense.

CCHE1D (Center for Computational Hydroscience and Engineering, One-Dimensional Channel Network Model) integrates the flow model and the sediment transport model with the landscape analysis tool called Topographic Parameter Zonation (TOPAZ) (Garbrecht and Martz, 1995) and the watershed models Agricultural Non-Point Source Pollution Model (AGNPS) (Young et al., 1987; Bosch et al., 1998) and SWAT (Arnold et al., 1993) through a highly automated Graphical User Interface (GUI). The CCHE1D flow model simulates unsteady flow in channel networks using either the diffusive wave model or the dynamic wave model. The CCHE1D sediment transport model calculates non-uniform sediment transport in rivers and streams using the non-equilibrium transport model, and simulates bank erosion and channel widening process. In addition, multiple options for sediment-related parameters have been implemented.

Surface Water Modelling System (SMS) (ems-I, 2002) is a pre-and post-processor for surface water modeling and analysis. It includes two- and three-dimensional finite element and finite difference models, and one-dimensional step backwater modeling tools. Computer Interfaces specially designed to facilitate the utilization of several numerical models comprise the modules of SMS. Each numerical model is designed to address a specific class of problem. Some calculate hydrodynamic data such as water surface elevations and flow velocities. Others compute wave mechanics such as wave height and direction. Still others track contaminant migration or suspended sediment concentrations. Some of the models support both steady state and dynamic analyses, while others support only steady-state analysis. Some support supercritical flow, while others support only subcritical. More models could be discussed, but the common observation suggests that there is no ideal model yet developed for all hydrologic conditions . Also, it should be learnt that each model has specific limitations, for instance most models in use do not adequately route the sediment from upland all the way to the outlet. Other models such as WEPP are promising and may suite the purpose of this research but they are

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disqualified because of their data requirement.The Simiyu basin is ungauged and the high-resolution data required to run the Green-Ampt equation in WEPP is not available.

This research will use a couple of models to simulate the hydrology and sediment transport capacity in the catchment. Literature survey tells that no comprehensive model exists that can simulate sediment transport from catchment through river network to a basin outlet. It is therefore envisaged to use different models such as SWAT for watershed modeling and CCHE1D and/or SMS for channel network modeling.

The selection criterions for the models (i.e. SWAT and CCHE1D/SMS) are availability (some of them are already available to other research clusters such as FRIEND/Nile Initiatives). The models are distributed, physically based and support long-term simulations. Also, the models are GIS based, which make use of spatial data and therefore easily represent distributed phenomena.

Due to the complexity of the variables involved in soil erosion study, and since they interact in a wide spatial domain, simplicity of data management and the ability to transfer from data-rich to data-poor areas become very important. Thus the use of GIS techniques and erosion models is one way of achieving this. Models such as SWAT and, CCHE1D under Geographical Information System (Arc View 3.2a) environment will be used. Since, the study will use a seven days period of sediment flow data, thus the physically based, distributed models suggested are crucial. 2.6 Data Collection and Analysis/Processing 2.6.1 Data collected 2.6.1.1 Spatial Data The spatially distributed data was obtained from Lake Victoria Environmental Management Project (LVEMP) and the United States Geological Survey (USGS) data for Africa. Land cover coverage in Universal Transverse Mercator (UTM) projection was sourced from LVEMP database, whereas Digital elevation Models (DEM) and Soil texture coverages were obtained from USGS website with a resolution of 30 arc second (approx. 1 km) in either Geographic or Lambert Equal-Area Azimuth projections. The followings are some spatial data coverages.

2-5

Figure (2-1) A location map of the Simiyu river basin

Figure (2-2) Digital Elevation Model (DEM) of Simiyu River Basin

2-6

Figure (2-3) Land cover map Simiyu-Duma River Basin

2.6.1.2 Hydrological data

Data on river flows has been collected for three (3) gauging stations (i.e.

112012, 112022, and 112032). Data description is presented in Table (2-1) below. Since the gauging station with code 112022 is located at the basin outlet (near Road bridge) thus this study will use its data set to calibrate the hydrodynamic models. Figure (2-4) below depicts the long-term hydrological flow regime at the catchment outfall.

Table (2-1) Description of river flow data for the Simiyu basin

FLOW DATA Record period

Station code Catchment Subcatchment Area (km2) Lat Long FROM TO % Miss

112012 Simiyu Simiyu - Ndagalu

5918

-2.639 33.568 Jan-50 Jan-01 47 112022 Simyu Simyu at Rd Bridge 10659 -2.575 33.462 Jan-69 Dec-00 22

112032 Simiyu Duma at Sayaga

5320 -2.586 33.497 Jan-70 Dec-78 0

2-7

Simyu

-100.0

0.0

100.0

200.0

300.0

400.0

500.0

600.0

Apr-49

Oct-54

Mar-60

Sep-65

Mar-71

Aug-76

Feb-82

Aug-87

Jan-93

Jul-98

DATE

DIS

CH

AR

GE

M3^

SSimyu

Figure (2-4) River flow regime at the Simiyu River Basin outlet

2.6.1.3 Meteorological Data

A good length of time series data has been collected and it is summarized in Table (2-2). The data set includes mean rainfall and pan evaporation in daily time step. The areal mean rainfall was obtained by the mean of rainfall data series at five (5) stations (Figure 2-1 & Table 2-2): 09233005, 09233031, 09234005, 09333005 and 09333015. The time series data availability has been described in the Nile Friend report (Bauwens et al., 2003).

Table (2-2) A list of rainfall stations and its data descriptions

RainfalL data Hydromet

ID Station Name Longitude Latitude Altitude (masl) Period

Missing % File

09233005 NGUDU 33.350 -2.950 1219 29-95 6 1 09233031 NYANGUGE 33.200 -2.550 70-90 15 1 09234005 SERONERA 34.92 -2.33 1540 61-95 17 1 09235009 LAKE NDUTU LODGE 35.217 -2.583 79-94 32 1 09333005 MASWA HYDROMET 33.767 -3.167 1341 28-93 9 1 09333015 MALYA STOCK FARM 33.483 -3.000 1251 50-95 5 1 09333040 KIMIZA PRIMARY 33.483 -3.017 72-94 11 1 09333051 MASELA 33.633 -3.333 73-93 2 1 09333053 ISANGA 33.633 -3.250 73-93 12 1

2-8

Table (2-3) A list of climatic stations and its data descriptions

Climate data Hydromet

ID Station Name Latitude LongitudeAltitude (masl) Period

Missing data Data File

9232009 Mwanza -2.467 32.917 1140 70-74

1133

1134

1135

1136

1137

1138

1139

1140

1141

0 20 40 60 80 100

Figure (2-7) Upstream station no. 3 2.6.1.5 Sediment flow data

A short record of sediment flow data (7 days data set, between 7/12/2000 and 13/12/2000) for the river catchment has been collected (Figure 2-8) and Table (2-4). The data was obtained as secondary data from Water Resources Engineering department, University of Dar es Salaam, database. Raw data on sediment concentrations and the information about sampling techniques is missing. Also, the method of analysis of Total Suspended Sediment (TSS) could not be revealed as well. Nevertheless, this research work will use this set of data to simulate the sediment transport in the Simiyu river basin system.

Daily variations of TSS with River Discharge -Simiyu

02000400060008000

1000012000

7.12.0

0

8.12.0

0

9.12.0

0

10.12

.00

11.12

.00

12.12

.00

13.12

.00

Days

TSS

Load

in

tonn

es/d

ay

-40

20

80

140

200

Dis

char

ge in

m

3 /sec

TSS LoadRiver Discharge

Figure (2-8). A Plot of Daily variation of Total Suspended Solids with River

Discharge at the main bridge

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Table (2-4) Secondary data on Total Suspended Solids (TSS) at the Simiyu River basin, near the main bridge

Discharge, Q

Total suspended Solids (TSS)

Date (m3/s) (tonnes/day)

Remarks

07/12/2000 152.71 5,673.48 Secondary data 08/12/2000 122.775 3,871.83 Secondary data 09/12/2000 71.23 1,323.17 Secondary data 10/12/2000 97.04 3,605.23 Secondary data 11/12/2000 134.52 10,750.84 Secondary data 12/12/2000 139.4 11,020.41 Secondary data 13/12/2000 91.02 1,966.03 Secondary data

2.7 Sediment yield modelling 2.7.1 Methodology The modeling activities involved data collection/processing and analysis, problem schematization, model development and model application. Results of data collection and analysis phase are as presented in foregoing section 2.6. Considering the quality of data and time constraints for carrying out modeling exercise did problem schematization. In figure (2-9) a conceptual Model for entire Simiyu catchment is presented. At this stage of research, only a watershed model was used to simulate the sediment yield in the basin. Routing sediment through the channel using CCHE1D model could not be done because of limited data set for the transport capacity model calibration. In developing the model a SWAT model under GIS environment (i.e. Arc View 3.1 software) was calibrated at one of the sub-catchment called Simiyu-Ndagalu (Figure 2-9). In the absence of measured sediment loads in the latter sub-basin, calibration was done based on long time average annual runoff, surface runoff and base flow fluxes as shown in Table (2-6). Also, a temporal calibration was carried out in order to simulate the inter-annual variability of fluxes (Figure 2-11). In order to validate the simulated loads with the scanty measured sediments at the outlet (sampling site), the trained model was then applied to the entire catchment of Simiyu (Table 2-7). 2.7.1.1 Problem schematization A sediment routing conceptual model (Figure 2-9) of the Simiyu catchment is designed in such a way that simulated hydrological fluxes from the sub-catchments are routed through the river network down to the outlet. A SWAT model simulates sediment yield and water yield from sub-catchments, whereas the hydrodynamic and sediment transport capacities modules for the river network are built under CCHE1D model. Routed fluxes through river channels are aggregated at the main bridge outlet for validation purposes. At this stage therefore only Simiyu-Ndagalu sub-catchment dataset has been used to calibrate the SWAT model.

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CCHE1D -FLOW -SEDIMENT

CCHE1D -FLOW -SEDIMENT

OUTLET AT MAIN BRIDGE -FLOW -SEDIMENT

Sayaga Basin (Duma)

SWAT (SYLD

SWAT (WYL

SWAT (SYLD

SWAT (WYL

Ndagalu basin (Simiyu)

Figure (2-9) A conceptual model for Sediment Yield model for Simiyu River basin 2.7.1.2 Model development Model development entailed activities such as database preparations (i.e. rainfall, temperature, radiation, wind speed, relative humidity and weather generator model), watershed model development (DEM processing), development of hydrological response units based on landuse and soil type coverages, parameterization or scenario generation and model simulation. Weather generator model is developed using monthly average data summarized over a number of years. It therefore estimates the watershed hydrology and fills in values whenever a missing dataset is encountered. Although, the spatial dataset used is in raster format the analysis in SWAT model is done under similar hydrological response units (HRU). SWAT model is one of the complex models and thus it is built-up of several parameters. Therefore, parameter setting or scenario generation is an involving task whereby sensitive parameters are altered basing on the hydrologic conditions of the study area. In this study, eight parameter were calibrated, that include Curve number (CN2), Channel transmission losses (CH_K2), Threshold depth of water in shallow aquifer for “revap” to occur (REVAPMN), Groundwater “revap” coefficient (GW_REVAP), Baseflow Alpha factor (ALPHA_BF), Soil Available Water Capacity (SOL_AWC), Cropping management factor (USLE_C), and Soil erodibilty factor (USLE_K).A SWAT model

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is one of the comprehensive models that exist in the market with a number of alternative functionalities built in. During simulations exercise and depending on the type of data and purpose of the modeling, a range of options are there to facilitate simulation. Because, this study used daily data to simulate hydrology in the watershed, the following options and methods were selected (Table 2-5). Table (2-5) A list of options and methods selected during SWAT model simulation No. Option/Method Purpose Preference 1 Starting date and Ending

date To set a period of simulation

2 Daily rain/CN/Daily Rainfall/Runoff/Routing Works on a daily values dataset

3 Skewed normal Rainfall distribution 4 Penman-Monteith method Potential ET method Performs well in

tropic regions 5 Active Crack flow 6 Muskingum Channel water routing

method Routing simulated fluxes through main channel of the sub-catchment

7 Active Channel dimensions 8 Active Stream water Quality

processes Simulate sediment yield

9 Daily/Yearly Printout frequency Daily was used to simulate inter-annual fluxes variability whereas yearly option was used for long-time simulation (simulate average annual volumes)

2.7.1.3 Calibration results Results for model calibration for Simiyu-Ndagalu sub-catchment are presented in table (2-6), Figures (2-10) and (2-11) below.

Total Water Yield Surface Flow BaseflowWYLD (mm) SURF (mm) GWQ (mm)

OBSERVED 84.3 58.4 25.9SIMULATED 77.64 52.41 25.15Table (2-6) Long time average annual volumes calibration results Generally, from Table (2-6) one could learn that simulated and observed annual volumes are comparable, however surface runoff (SURF) and groundwater or

2-13

baseflow (GWQ) components are well simulated than the total water yield (WYLD) does.

Long-time Simulation Results

05

101520253035

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

Time [Year]

Ave

rage

Ann

ual F

low

s [m

3/s] Observed

Simulated

Figure (2-10) Long time simulation results for Simiyu-Ndagalu Sub-catchment A general observation from the Figure (2-10) above suggests that observed and simulated annual flows are comparable with means of 12.93m3/s and 12.67m3/s respectively. Temporal calibration between May 1,1970 and April 31,1971

R^2=58%

-50

0

50

100

150

200

250

300

350

400

5/17/1970

7/16/1970

9/14/1970

11/13/1970

1/12/1971

3/13/1971

5/12/1971

7/11/1971

Time [daily]

Flow

[m3/

s]

ObservedSimulated

Figure (2-11) Temporal calibration results (inter-annual flow variability) between May 1, 1970 and April 30, 1971

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From figure (2-11), one could deduce that there is a general agreement between observed and simulated daily flows (R2 =58%). However, some runoff peaks on 22nd January 1971 and 9th February 1971 are not captured. Besides, low flows are well estimated. 2.7.1.4 Model Application In order to validate the model with scanty sediment loads measured at the main bridge outlet the calibrated model was applied to the entire catchment. Optimum parameter values as determined in the model training exercise were used to develop the Simiyu sediment yield model and later on simulating flows and sediment fluxes at the outlet. In Table (2-7), a relative comparison between observed sediments loads as sampled during the start of rains season of 7th to 13th December 2000 and simulated fluxes is presented. Table (2-7) Model validation results at Simiyu catchment main bridge outlet

OBSERVED SIMULATEDDATE FLOW SEDIMENT LOADS DATE FLOW SEDIMENT LOADS REMARKS

[d/m /yyyy] [m 3/s] [tonnes/day] [d/m /yyyy] [m 3/s] [tonnes/day]7/12/2000 152.7 5673.5 9/3/1972 154.6 3482.3 Rain season

2/4/1975 151.2 4444.323/4/1977 155.0 3526.3

8/12/2000 122.8 3871.8 3/4/1975 122.9 2924.8 Rain season22/2/1989 121.9 3350.0

9/12/2000 71.2 1323.2 15/4/1974 71.6 1853.226/4/1977 70.8 1856.5

10/12/2000 97.0 3605.2 26/12/1982 99.8 3658.2 Rain season29/12/1982 97.7 1966.829/1/1985 97.1 3762.824/1/1987 98.4 3084.6

26/12/1989 94.6 2528.011/12/2000 134.5 10750.8 20/1/1987 132.2 7117.0 Rain season

16/3/1978 131.7 6114.312/12/2000 139.1 11020.4 7/4/1978 133.6 11815.0

10/1/1979 142.7 9189.030/12/1979 135.8 9844.4

9/3/1980 146.8 7798.83/1/1981 144.1 8024.05/1/1981 137.6 5489.0

13/12/2000 91.0 1966.0 6/1/1980 89.2 1590.4 Rain season15/4/1978 91.4 1953.9

30/12/1982 90.0 1401.231/12/1988 89.5 1595.1

1/1/1989 92.1 1196.9

A lack of continuous sediment flow data at both sub-catchments and catchment levels brought about the idea of validating the available scanty data so as to give an insight on the performance of the developed model. The results in Table (2-7) look reasonably matching, but however the researchers in this study did not expect them to equal because of the fact that no concurrent data was used. For instance, sediments transport or yields in the catchment during the period of 1970’s were expected to differ form those of year 2000’s, simply because the landuse or landcover has changed significantly and hence catchment hydrology and response has no doubt changed. As a result a comparison on the seasons of the year was based, because literature suggests that, sediments yields from catchment during the start of rain or rainy seasons are comparable.

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2.8 Results and Discussions A long-time simulation of the Simiyu-Ndagalu Sub-catchment sediment model gives an average specific yield of 0.523t/ha/year (i.e. 710t/day) and sediment transport capacity of 683t/day. Using the derived specific yield for entire catchment, the average daily sediment yield at the main bridge outlet is estimated to be 1439t/day. The study has found that the rate of sediment yield in Simiyu-Ndagalu sub-catchment is mainly affected by surface runoff, landuse/cover and soil type as illustrated in Table (2-8). Table (2-8) Factors affecting sediment yield as derived from sensitivity analysis. No. Sensitive Model

Parameter Data type used Impact on

1. Curve number, CN2 Landcover or landuse maps

Runoff estimation and thus affect estimates of sediment yield

2. Soil available water capacity, SOL_AWC

Soil type maps Runoff estimation and thus affect estimates of sediment yield

3. Cropping management factor, USLE_C

Landuse or landcover maps

Sediment yield estimation

4. Soil erodibility factor, USLE_K

Soil type maps Sediment yield estimation

Moreover, it should be noted that the results presented in this progress report assume a number of factors: The predictions by SWAT so far can be viewed as “natural” flow without the influence of human factor (e.g. irrigation). The modeling exercise assumes that landuse does not change since only static landuse coverage created in the middle of 1990’s is used to develop the computation units (HRU). Also, the dominant source of sediment is from rill and inter-rill (sheet erosion), thus other forms of erosion such as gullying have not been considered. Based on the hypothesis that the sediment yield is mainly influenced by runoff, thus accurate calibration of the water balance equation gives closer estimates of the sediment loads in the catchment. In this work, therefore the lack of adequate and continuous sediment flow data did not inhibit the executions of the research. Nevertheless, validation of model results to scanty sediment loads at the Main Bridge site, the Simiyu outlet was considered necessary. 2.9 Conclusions and Recommendations A set of hydro-climatic and geo-spatial database has been collected, processed and analyzed. For instance, Simiyu river system is categorized as ephemeral stream whereby the river goes dry and wet such that some percentage of water yield from sub-catchment is disappeared in the main channel as a result of transmission losses and in-stream evaporation. As a result of this study a GIS-based Sediment yield Computer Model has been developed for the Simiyu-Ndagalu sub-catchment whereby predictions of sediment

2-16

yield rate due to landuse or landcover dynamics can be assessed. Model application results gave a long-term specific sediment yield of 0.523t/ha/year. Generally, this study has an immediate impact on capacity building to the researchers involved. In particular, the execution of this study highlights some opportunity for applicability of complex models such as SWAT in the developing world and Nile basin in particular. A number of recommendable approaches for model improvement could be proposed: The model performance could be improved once adequate sediment calibration dataset is available. In a long-term plan there is a need to install two more gauging stations and sediment sampling stations in the catchment. The Simiyu river basin is characterized as complex since in the middle of sub-catchment (i.e. Simiyu-Ndagalu and Simiyu-Sayaga) there exist flow regulators such as swamps and dams. Therefore for routing purposes, these hydrologic features should well be presented and modeled. Also, for correct model application, researchers in the cluster should conduct a training workshop on advanced model application.

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3-1

CHAPTER 3 ASSESSMENT OF VULNERABILITY

OF NON-GAUGED WATERSHEDS TO FLOOD RISKS A CASE STUDY FOR QENA VALLEY CATCHMENT

3.1 Summary Qena area located at the western side of the Red Sea Hills. The area is embedded within a network of active watersheds, which are subjected to recurrent flash flooding. This report is directed towards developing a methodology for flood risk assessment and relative vulnerability classification for watersheds associated with wadi systems in arid regions. Geographic Information System (GIS) is used as the main analysis tool for modeling Qena area. Watersheds in the study area have been identified, and geomorphology parameters and watershed characteristics have been estimated. Different parameters, which mostly affect the runoff, have been calculated. The HBV model has been calibrated and used to define the flash flood vulnerable areas at the catchment. The results of flood risk classification compared well with the estimated runoff peak discharge. GIS has proved, as expected, to be an easy and efficient toll for watersheds flood risk assessment 3.2 Introduction More than ninety percent of the Egyptian territories are classified as arid and hyper arid desert regions. In many locations the desert is characterized by the presence of an intense wadi system, which is subjected to harsh climatic conditions, and extreme water scarcity. Nevertheless, many of such wadies experience extreme precipitation events in the form of flash floods, where a considerable amount of rainfall occurs, suddenly, for a short duration, and with a long period of recurrence. Efforts are therefore directed to serve two objectives: (1) Making use of the available water during rare precipitation events, and (2) Protection against potential damage associated with flash floods. A methodology for flood predictions, risk assessment, and vulnerability estimation is seen to be in evitable. In the last few years, a considerable amount of attention has been devoted to the development of the Eastern Desert of Egypt, especially Wadi Qena area as shown in Figure (3-1). Wadi Qena is embedded within a network of active watersheds, which are subjected to recurrent flash flooding. In some cases these flash floods cause damage to roads and infrastructure. On the other hand the area is classified as arid zone which, in general, is limited in water resources. To ensure the sustainable development in this area, an assessment for flash flooding potential, and vulnerability of the watersheds located in this area should be carried out. Limited watershed data are available from digital elevation model (DEM) and rainfall records. Therefore a suitable approach that depends on such limited data should be applied to study those watersheds. 3.3 The Study Area Wadi Qena is located at the western side of the Red Sea Hills and joins the Nile with right angle north of Qena and collects the water of many effluents which join it all along its 270 km course from the east and south east, It is a unique geomorphic

3-2

feature in the Egyptian Eastern Desert . It extends in a nearly north-south direction (parallel to the Nile River) and joins the Nile Valley at Qena Town. The Wadi Qena basin covers approximately 18.000 sq. km and is bounded by Latitudes 26° 00' 00''- 28° 15' N and Longitudes 32° 30'- 33° 00'E. 3.4 Data The Digital Elevation Model (using contour lines) have been developed and Land use/cover maps (vegetation, domestic, etc.) Are generated using Satellite Image (Landsat 7 band), in addition to Soil classification maps and Location of climatic stations around Qena Valley. The Hydrological parameters (drainage network, rainfall, evaporation and evapo-transpiration, temperature, infrastructure locations, etc.) And Geological classification and ground water aquifers are calculated using GIS. Figure (3-1) shows the general layout of Qena Wadi area.

Figure (3-1) General Layout for Wadi Qena Area

Two meteorological stations representing the observed rainfall data for Wadi-Qena are used in this analysis. The location of the raingauge stations is represented in Figure (3-7). The main storms events are used together with daily-observed data are used for calibrating the hydrologic model.

Wadi Qena area

3-3

3.4 Methodology A methodology for flood risk assessment and relative vulnerability classification for watersheds associated with wadi systems in arid regions is herein proposed and verified for Wadi Qena study area. The flood risk assessment methodology comprises ranking the studied watershed according to its flood risk using morphological parameters as in Table (3-1). The study uses the advantages of modern technologies including remote sensing and Geographic Information System, as recommended by several researchers; DeVantire et al (1993), Garbrecht et al (1995), Maidment (1993) (1994), and Elbadawy (2003). Figure (3-2) shows the methodology flowchart, while Figure (3-3) illustrates the GIS analysis flowchart .

Figure (3-2) GIS Analysis Flowchart

INPUT

OUTPUT

Contour

Soil

RainfallRecord

GIS

WatershedParameter

WatershedCharacteristic

Risk Calculation

HBV Model

Statistical Analysis

Runoff Hydrographs

Risk map

Flood Risk Assessment Flowchart

3-4

Figure (3-3) GIS Analysis Flowchart

3-5

3.5 Extracting Watershed Boundaries and Drainage Networks The geomorphologic characterize of Wadi Qena basin have been extracted using the available data and modern GIS tools. It could be classified into the following main landforms: 3.5.1 Geological Characteristics 1. Limestone Plateau

This plateau is dissected and consists mainly of hard, jointed and fractured limestone beds.These beds are horizontal to slightly dipping to the north. Dendritic and sub-parallel drainage patterns are dominant at Gabal Aras and Gabal Ras El-Jisr. On Landsat images, they show rough drainage texture. A flat-topped surface at Gabal Aras (523 m. a.s.l.) represents a hard, massive, structurally-controlled landform and provides a suitable catchment area. 2.Nubia Sandstone Plateau

This plateau is composed mainly of hard, massive sandstone beds forming dissected patches. These patches comprise some beds of iron oxides and clays, that highly affect the ground water conditions and quality. The plateau is characterized by a dendritic drainage pattern and fine texture as in the cases of Gabal Abu Had and Assuray, whereas at Gebel Qurayaah the drainage texture appears coarse on the Landsat images. This sandstone plateau is cut by a few main faults. 3. Tors

This geomorphologic unit is represented by a small part at the northeast corner of the investigated area and represents exposures of Precambrian basement rocks. The rocks are hard, massive and form dissected isolated hills of medium to high topographic relief. Also, they are characterized by high weathering and represent a part of the groundwater aquifers catchment’s areas. 4. Fault Scarps

The area is affected by structural disturbances that created major fault scarps with steep slopes (38° - 75°), which are well developed in the sandstone terrain. These scarps moderate to trend NW-SE and N-S. 5. Alluvial Fans

Alluvial fans are dispersed in the investigated area due to the presence of fault scarps inducing topographic difference between the plateaus and the wadis. These fans are composed mainly of sands, clay and gravels. Most of them are adjacent to Wadi Qena. 6. Flood Plain

The flood plain surrounds the River Nile and is composed mainly of mud, silt and clay with some sands. It belongs to the Pre-Nile and is of Quatemary age (Said, 1981). This flood plain is nearly flat and completely cultivated. All Geomorphologic Units of Wadi Qena shown in Figure(3-4).

3-6

Figure (3-4) Geomorphologic Units of Wadi Qena The boundaries of the watersheds and the corresponding drainage networks have to be determined. The DEM with 100 m cell size is created from the elevation of contour lines. A DEM represents a surface using an array of equally spaced sample points that are referenced to common origin and have a constant sampling distance in the x and y directions. Each mesh point contains the z value at that location, which is referenced to a common base z value, such as sea level in this case. GIS tools are used for extracting the watersheds and drainage network following the procedure indicated below. Figure (3-5) and Figure (3-6) show the watershed boundary and the streams networks.

3-7

B5

B4

B3

B2

B1

Figure (3-5) The Isochrones for Study Area Table (3-1): Characteristics of Different Sub basins Within Qena Valley Cathcement Area

Volume Elevation Basin1 353 0-125 Basin2 1923 125-250 Basin3 5366 250-375 Basin4 9813 375-525 Basin5 15900 525-1550

3-8

Figure (3-6) Stream Networks for the Study Area

3.5.2 Morphological Characteristics The morphological parameters as described in Table (3-2) for the watersheds in the study area are computed using the generated DTM for the study area using the GIS

3-9

tools. Table (3-2) Morphological Parameters for the Watersheds in the Study Area

Parameter Value Description Area (A) 16000000000 Area of watershed

Perimeter (P) 730000 Perimeter of watershed

Basin length (LB) 215542 The maximum distance from the outlet point to the watershed divide. Valley length (VL) 351374 The distance measured along the main channel from the watershed outlet to the basin divide

Stream frequency (F) 1.28275E-06 A

SNu = F

Drainage density (D) 0.001185457 A

SLu =D

Length of overland flow (Lo)

421.7784012 D

Lo 21

=

Stream highest order (U) 16 Strahler order Sum of stream number (SNu)

20524 Sum of stream numbers

Sum of stream length (SLu)

18967306 Sum of stream lengthes

Bifurcation ratio (RB) 3.7

N

N = R 1Bω

ω−

Length ratio (RL) 1.354120593 L

L = R1

L−ω

ω

Shape index (Ish) 0.038130981

227.1 PAI sh =

Parameter Value Description

Circularity ratio (Rc) 0.038212866 AA =R

0c

Elongation ratio (Re) 0.662147873

⎟⎠⎞

⎜⎝⎛πA

L2 = R

0.5

Be

Relief ( R) 1508 the elevation difference between watershed outlet and the highest point on the watershed perimeter.

Internal relief (E) 327 the difference between elevation of 10% of the watershed length from the source and 15% of watershed length from the outlet.

Relief ratio (Rr) 0.001517106 B

r LRR =

Sonuosity (Si) 1.630188084 B

li L

VS =

Slope index (Sl) 0.001240843 VL

ES l 75.0=

Ruggedness number (Rn) 0.387644316 DRR n .=

Texture ratio (Rt) 0.028115068 P

NR

n

t

∑=

==

ω

ωω

1

3-10

A tm osphere

evaporation ra infa ll

in filtration

F ree w aterreservo ir

snow m elt

refreezing D ry S now

Snow pack

S oil seepagedirec t runoff

snow fall

U pper zone

perco lation

quick runoff

L ow er zone runoffslow runoff

in terflow

R u no ff respon se

As mentioned before, the nearest climatic stations around Qena Valley that shown in Figure (3-7) are used for representing the Qena Wadi area. These data are used for calibrating the hydrologic model.

Figure (3-7) The Location of climatic stations around the Qena Vally

3.6 The Hydrologic Model The catchment is divided into a number of grid cells. For each of the cells individually, daily runoff is computed through the application of the standard version of the HBV model, as distributed by Killingtveit and Saelthun (1995). The use of the grid cells offers the possibility to turn the HBV modeling concept, which is originally lumped, into a distributed model. Figure (3-8) shows a schematic view of the HBV hydrologic model concept. The land-phase of the hydrological cycle is represented by three different components: a snow routine (neglected) a soil routine and a runoff response routine.

Figure (3-8): Schematic view of the relevant components of the HBV model

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The Soil Routine The incoming water from the rainfall (snow routine), Sin, is available for infiltration in the soil routine. The soil layer has a limited capacity, Fc, to hold soil water, which means if Fc is exceeded the abundant water cannot infiltrate and, consequently, becomes directly available for runoff.

( ){ }S SM S Fdr in c= + −max ;0 (2)

Where Sdr is the abundant soil water (also referred to as direct runoff) and SM is the soil moisture content. Consequently, the net amount of water that infiltrates into the soil, Inet, equals:

drinnet SSI −= (3)

Part of the infiltrating water, Inet, wills runoff through the soil layer (seepage). This runoff volume, SP, is related to the soil moisture content, SM, through the following power relation:

SP SMF

Ic

net=⎛⎝⎜

⎞⎠⎟β

(4)

where β is an empirically based parameter. Application of equation (4) implies that the amount of seepage water increases with increasing soil moisture content. The fraction of the infiltrating water which doesn’t runoff, Inet - SP, is added to the available amount of soil moisture, SM.

Fc

Direct runoff

SM

Seepage

Infiltration Evaporation

Soil water

Deficit

Figure (3-9): Schematic view of the soil moisture routine

A percentage of the soil moisture will evaporate. This percentage is related to the measured potential evaporation and the available amount of soil moisture:

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E SMT

E SM T

E E SM T

am

p m

a p m

= <

= ≥

;

;

(5)

where Ea is the actual evaporation, Ep is the potential evaporation and Tm (≤ Fc) is a user defined threshold, above which the actual evaporation equals the potential evaporation.

The runoff response routine The volume of water which becomes available for runoff, Sdr + SP, is transferred to the runoff response routine. In this routine the runoff delay is simulated through the use of a number of linear reservoirs. Three types of runoff are distinguished:

1. Quick runoff 2. Interflow 3. Slow runoff (baseflow)

Two linear reservoirs are defined to simulate these three different processes: the upper zone (generating quick runoff and interflow) and the lower zone (generating slow runoff). The available runoff water from the soil routine (i.e. direct runoff, Sdr, and seepage, SP) in principle ends up in the lower zone, unless the percolation threshold, Pm, is exceeded, in which case the redundant water ends up in the upper zone:

( ){ }

( ){ }

∆

∆

V P S SP

V S SP P

LZ m dr

UZ dr m

= +

= + −

min ;

max ;0

(6)

where VUZ is the content of the upper zone, VLZ is the content of the lower zone and ∆ means “increase of”. The lower zone is a linear reservoir, which means the rate of slow runoff, QLZ, which leaves this zone during one time step equals:

Q K VLZ LZ LZ= * (7)

where KLZ is the reservoir constant. The upper zone is also a linear reservoir, but it is slightly more complicated than the lower zone because it is divided into two zones: A lower part in which interflow is generated and an upper part in which quick flow is generated.

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UZ1

quickflow

interflow

Figure (3-10): Schematic view of the Upper zone

If the total water content of the upper zone, VUZ, is lower than a threshold UZ1, the upper zone only generates interflow. On the other hand, if VUZ exceeds UZ1, part of the upper zone water wills runoff as quick flow:

{ }

( ){ }

Q K UZ V

Q K V UZ

i i UZ

q q UZ

=

= −

* min ;

* max ;

1

1 0

(8)

Where Qi is the amount of generated interflow in one time step, Qq is the amount of generated quick flow in one time step and Ki and Kq are reservoir constants for interflow and quick flow respectively. The total runoff rate, Q, is equal to the sum of the three different runoff components:

Q Q Q QLZ i q= + + (9)

The runoff behaviour in the runoff response routine is controlled by two threshold values Pm and UZ1 in combination with three reservoir parameters, KLZ, Ki and Kq. In order to represent the differences in delay times between the three runoff components, the reservoir constants have to meet the following requirement:

K K KLZ i q< < (10)

3.7 Model Calibration The Model have been calibrated using the single and multi zone HBV Using the geographical and morphological characteristics of different basins that have been estimated and delineated using the GIS. These data are presented in tables 3-1,2. The model sensitivity to the main parameter like Beta, Fc, and Tm is checked. It is found that the model is more sensitive to Beta and Fc parameters more than the Tm parameter. The estimated model parameters are presented in Table (3-3), while the

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sensitivity of the model to the main parameters and the corresponding Runoff (mm\day) with different storms are presented in Figures 3-11,12 and 13.

Table (3-3) Model Parameters as Estimated for Qena Valley

Tx 0 *C Critical temperature below snowfall occurs Ts 0 *C Critical temperature above snow melt starts TL 0.6 *C/hm Temperature lapse per 100m Cx 4 mm**C-1 Melt constant in temperature-index Cr 0.005 - Refreezing efficiency constant of free water CP 0.1 - Fraction of Snow volume that can store water soil routine Beta 3 - Exponent in soil runoff generation

Tm 0.7 - Fraction of Field capacity above which Evaporation equals potential evaporation

Fc 20 mm Soil moisture capacity runoff routine Pm 0.2 mm/day maximum percolation from Upper to Lowerzone Kq 0.1 day-1 recession constant upperzone very quickflow KI 0.1 day-1 recession constant upperzone quickflow KLZ 0.005 day-1 Recession constant base flow UZ1 20 mm threshold for very quick flow

Figure (3-11) Sensitivity Of The Model To Beta Coefficient

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Figure (3-12) Sensitivity of the model to Fc coefficient.

Figure (3-13) Sensitivity of the model to Tm coefficient Single and mutli-zone HBV model results are presented in figures 3-14,15and 16 that show the precipitation and corresponding Runoff (mm/day) at Wadi Qena Catchments that resulted from running the model for each rainfall event. These runoff values are used together with the DTM and catchement characteristics to generate and define the vulnerable areas and the risk zones.

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Figure (3-14) Precipitation and Runoff (mm/day) at Wadi Qena

Figure (3-15) Precipitation and Runoff (mm/day) at Wadi Qena.

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Figure (3-16) Runoff (mm/day) at Wadi Qena Catchment Area Zones

3.8 Risk Analysis The geomorphological parameters that estimated earlier are used to define the risk zone that is more vulnerable to flooding risks. Some parameters were selected to represent all categories. Figure (3-17) Shows the longitudinal cross section along the 5 basin in the direction of moving the water after coming the flush flood .The longest cross section shows that basin 1 has minimum elevation and consider the first risk area. The other cross sections show the relation between the distance and the elevation in 2 different basins. Figure 3-17 ,18 ,19 ,20 ,21 show the different cross section along the 5 basins.

Figure (3-17) Location of cross sections along the 5 basin

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Figure (3-18) Longitudinal cross section (D-D) along the 5 basins

Figure (3-19): cross section (A-A) along the 5 basins

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Figure (3-20) Cross section (B-B) along the 5 basins

Figure (3-21) cross section (C-C) along the 5 basins

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Figure (3-22) shows the risk map for the study area after analyses the results of HBV model and apply it in the GIS layers .We found there are many cities will effected by the flash flood and it should be take a protect policy for this zone to safe people, buildings, roads, cultivated land …. Etc. the pink circle consider the most dangerous zone all the development plan should avoid this buffer.

Figure (3-22) Risk Map for the Study Area

Nile Roads

Railway

Risk Circle Cultivated land

Cities

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3.9 Conclusions

The following major conclusions could be drawn from this study:

• The different characteristics of watersheds are estimated using Geographic Information System (GIS) techniques. This means that GIS is a useful tool for delineating the characteristics of water sheds.

• The proposed flood risk assessment and relative vulnerability methodology is proved to be suitable for small wadi systems especially when detailed data is lacking.

• The selected geomorphological parameters resulted in risk assessment, which is well matched, with the results from estimated runoff hydrograph when both peak discharge are considered

• The risk classification presented provides a general prioritisation scheme for flood control and flood protection programmes.

• Assessing the risk potential on the sub-catchment level is crucial to eliminate mis-interpretation of results based on full-scale watershed analysis.

• The study presents an integrated approach for flood risk assessment for Wadi Qena Valley area.

• The methodology used represents an appropriate way for evaluating the vulnerability to risks and can be applied in different catchment areas.

3.10 Recommendations for Further Work Based on this study and the drawn conclusions, the following work is recommended to be implemented in the next phase:

• Calibration of other Rainfall/Runoff models like Nash, Sacramento and NFS models, and compare their results with HBV model with the purpose of choosing the best appropriate, applicable model that is suitable for arid, semi arid and wet watersheds.

• Use a better resolution DTM that is released recently (30x30 m) to get better representation of the study area.

• Converting this methodology into a computer friendly rainfall runoff model that is able to deal with different models and can be easily used.

• Implement a training manual for other NBCBN who are willing to apply this methodology in other areas.

• To verify this methodology, it is recommended to apply it in another area with different characteristics.

CHAPTER 4 LAKE VICTORIA PILOT AREA

4.1 Summary This chapter reports the activity sets B and C (in the original research proposal) concerning pilot no. 4 (Lake Victoria Pilot Area). Activity B is concerned with data collection while activity C is concerned with producing water quality inventory for Lake Victoria basin. The data collection for this pilot area was concentrated on two rivers in Kenya and Tanzania. The two rivers are Sondu River in Kenya and Simiyu River in Tanzania. General data for Lake Victoria basin had to be collected by other members in the group. Unfortunately, data couldn’t be collected by the distinguished members participating in this group in full coverage. Therefore, only the available data and the information will be reported in the following. 4.2 Data Collection The following are list of the collected data in GIS and tabular format. This data were collected from previous projects, studies, and some international organizations. Most of the data are published and available on the internet. Some data had been processed from satellite images. The process was made in the GIS unit at the planning Sector of Ministry of Water Resources and Irrigation, Egypt. The collected and processed maps are: 1. Rivers and basins 2. Digital Elevation Model with spatial resolution 90m 3. Contour Lines 4. Land sat satellite images 5. Vegetation index 6. Unsupervised classification 7. Some hydrological parameters The collected maps/data are covering completely Lake Victoria basin. Some data were defined in the research proposal but could not be collected. This data and maps are; soil classification, land use/cover maps, geology and ground water aquifers, water demand data, some hydrological parameters. Print out of the collected maps exists in the attached album.

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4.3 Lake Victoria Inventory Lake Victoria, the second largest fresh water lake in the world, is also the largest lake in Africa, with a surface area of some 68,000 km2. However, unlike other Great Lakes of East Africa, it is comparatively shallow. The lake’s main physical parameters, as summarized by Balirwa (1998), are given in the following table. Table (4-1) Lake Victoria Basic Characteristics

Characteristics Measure Latitude 00o 20’ N to 03o 00’ S Longitude 31o 39’ E to 34o 53’ E Altitude [m above mean sea level] 1134 Catchment area [km2] 184,000 Lake Surface Area [km2] 68,800 Lake area as % of catchment 37 Shoreline [km] 3,440 Maximum length, North–South [km] 400 Maximum width, East–West [km] 240 Mean width [km] 172 Maximum depth [m] 84 Mean depth [m] 40 Volume [km3] 2,760 Inflow [km3yr-1] 20 Outflow [km3yr-1] 20 Precipitation [km3yr-1] 114 Annual fluctuations in level [m] 0.4–1.5 Flushing time [yrs] 138 Residence time [yrs] 21

4.4 Socio-Economic The lake basin provides resources for the livelihood of nearly 30 million riparian communities. The basin is used as a source of food, energy, drinking and irrigation water, shelter, transport, and as a repository for human, agricultural and industrial waste. The resources also provide facilities for cultural activities as well as leisure for the people. This constitutes about one third of the population of Kenya, Tanzania and Uganda estimated to be 90 million. Over 70% of the population of the three countries is engaged in agricultural production mostly as small scale farmers such as sugar, tea, coffee, maize, cotton, livestock keeping, and horticulture within the lake catchment. The fishery of the Lake Victoria is major source of income to the fishing communities and government tax revenue. Lake wide fish production is estimated at between 400 – 500 metric tons with Tanzania landing 40%, Kenya 35% and Uganda 25%. Up to 500,000 tones of Nile perch are caught annually in the lake. The fishery, together with associated industries, provides

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an annual fish export earnings of US$ 600 million, of which US$ 240-460 million is paid directly to fishers (Ntiba et al., 2001). This provides a per capita income in the range of US$90-270 p.a (World Bank. 1996). The population of the riparian municipalities of the three countries is growing at above 6% per annum that is among the highest in the world. The population of the area is concentrated in and around the municipal centres with the population growth is 3.6 and 7.6 for the districts and municipalities. Plants from the riparian areas and associated wetlands provide fiber, fodder and fuel wood. The lake is the source of the White Nile and thus, is an important asset for all countries within the Nile Basin. The waters originating from the lake provide hydropower through its only outlet, the Nile River, at Owen Falls in Uganda and other power plants lower down the river. The power from the two plants at Owen Falls provide 260 MW, part of which is exported to Kenya. These waters also support extensive irrigated agriculture schemes in Egypt, ecological values in the Sudan and other wetlands, an important tourism industry on the Nile River, and navigation and transport over large distances in the lower river. 4.5 Jurisdiction and Political Environment In terms of surface area, Kenya, Tanzania and Uganda, now partner states in the East African Community (EAC), respectively have control over 6%, 49% and 45% of the total lake area. Overwhelmingly, the politics of management and ownership of the lake fall into the larger context of the establishment and development of the East African Community (EAC, 2000). Within the Community, two institutions on Lake Victoria have been established. These are the Lake Victoria Fisheries Organization (which is specific for fisheries) and the Lake Victoria Development Program (covering general development matters of the Basin). The EAC Partner States recognize three important and convergent issues relating to management of shared waters. These are, firstly, that they share an interest in the well being of the lake and its living resources and in the rational management and sustainability of these resources. Secondly, they recognize the need to develop Lake Victoria region as an Economic Growth Zone. Thirdly, they agree that management decisions relating to any portion of the lake, within the territorial limits of any one of the Partner States, will affect the others, and hence there is the concomitant necessity that management decisions take such into account. 4.6 Water Management and Shared Water Issues Whereas geographic sovereignty and political ownership of land is clear, the ownership and management jurisdiction of the highly mobile aquatic resources is less so. The Lake Victoria basin is not just a water body shared among the three East African Community states, but its geographical location has international legal implications, especially with Sudan and Egypt, within the River Nile Basin, and with Rwanda and Burundi, due to its connection with the Kagera River Basin. The mechanisms governing the utilization of water and living aquatic resources shared by two or more states raises many concerns, partly because of the value of the resource for national policy and partly because states often invoke legislative or diplomatic interests (Okedi, 1980). Given the

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immense value of the export-oriented fishery and its nutritional importance domestically, it is understandable that protection of national interests may sometimes take precedence over the spirit of the EAC Treaty and LVFO Convention (LVFO, 2001). Principally, three categories of issues arise concerning the shared fishery resource of the lake. The first of these is traditional practice, where artisanal fishers encroach on the jurisdiction of others, given that the target species can be highly migratory. The second relates to the rights of fishers to land fish in another country where price/tariff structures and access to commodities may be more conducive to business. The last issue concerns trade, especially the purchasing of fish by commercial processors in one country and their processing and export from another. 4.7 Major Threats to the Lake The lake basin is used as a source of food, energy, drinking and irrigation water, she

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