Potential Water Conflicts in Mali
Identification of competing claims for surface water and the impact of climate change in the West Niger Basin
June 2008 Geert Jonathan van Dijk
Department of Water Resources Management Faculty of Civil Engineering and Geo Sciences Delft University of Technology in cooperation with Royal Haskoning
Cover photo © Arthur Morris/Corbis
Acknowledgements This study has been carried out with the Delft University of Technology and Royal Haskoning. To both organisations I owe my gratitude for the support I received. In particular, I want to thank my primer supervisors dr. ir. Trilokya Pradhan (Royal Haskoning) and dr. ir. Maurits Ertsen (Delft University of Technology) for all their comments and suggestions. Additionally I want to thank prof. dr. ir. Nick van de Giesen and dr. ir. Robbert Verhaeghe for taking place in my graduation committee.
Special thanks to my colleagues at Royal Haskoning, the Direction Nationale de l´Hydraulique, Mali and my colleague graduates at the Delft University of Technology for sharing their expertise and having a good time.
Also, I am grateful for the patience of the many Malians willing to share their knowledge with me in French.
Moreover, this research would not have been possible without the support of Deltares, formerly WL Delft Hydraulics. They supplied a license for RIBASIM and a simulation of the West Niger Basin. Consequently, the consent of Deltares is needed for the distribution of the simulation and the results of the RIBASIM model.
Furthermore, I want to thank Het Lamminga Fonds for funding my ticket for the research part of this study in Mali.
Additionally, I want to thank my friends and family, especially my parents, for their support during my study.
Finally, I want to thank the Lord for His creation of all the beauty and complexity of Niger River basin, and for the privilege I have to study it.
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Acronyms ABN Authorité du Bassin de Niger/Niger Basin Authority CSI Controlled Submersion Irrigation DNA Direction Nationale de l’Agriculture DNCN Direction Nationale de la Conservation de la Nature DNGR Direction Nationale Génie Rurale DNH Direction Nationale de l´Hydraulique DNSV Direction Nationale Service Vétérinaire EDMsa Energie du Mali S.A. FAO Food and Agriculture Organisation ID Inner Delta IER Institute Economique Rurale MSL Mean Sea Level ODRS Office du Développement Rural Selingué OMVS Organisation pour la Mise en Valuer du Fleuve Sénégal Organsation ON Office du Niger OPIB Office du Perimètre Irrigué Baguineda ORM Office du Riz Mopti ORS Office du Riz Segou SIGMA Système Informatique de Gestion des resources en eau du Mali
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Summary The West Niger Basin is the topic of many recent studies. The development of a hydropower dam and the extension as well as modernisation of the irrigated agriculture, in a sensitive ecologic system, combined with climatic changes, creates a lot of research potential. This study focuses on the identification of competing claims for surface water resources and the influence of climatic change in the West Niger Basin. Within this framework the comprehension of the hydrology of the basin is important. The Niger rises from the mountains in Guinea, where the climate is tropic and the rains are abundant during the rainy season. Along the river the precipitation volume and the length of the rainy season gradually decreases, resulting in a semi arid, Sahelian climate at the end of the West Niger Basin. Consequently, the Niger River discharge is dominated by the water supply from the tropical zone, causing a flood wave during the rainy season There are several fields of interest that depend on the water and sometimes even on the hydrologic regime of the Niger River. In this study hydropower, irrigation and the Inner Delta are evaluated. Hydropower stations demand enough water to optimally use their reservoirs for high continuous power generation. This is a relatively inexpensive and therefore lucrative way to produce energy. Fully managed irrigation schemes demand water during the dry and rainy season. They are quite insensitive to changes in the water supply system. Consequently, they have good yields and host many farmers. In contrast, controlled submersion irrigation fully depends on a large yearly flood wave for sufficient water supply. This makes this type of irrigation vulnerable to drought. The same holds for less sophisticated types of irrigation, like spate irrigation or flood recession farming. Furthermore, the Inner Delta is a wetlands system in the West Niger Basin. This system contains a number of stakeholders: fishermen, livestock farmers and agricultural farmers. Additionally, it is a complex ecologic system, with flood forests, birds, fish and water mammals. All these interests benefit from a substantial flood wave causing inundation of large areas of the Inner Delta. The yearly maximum inundation is quite steady, irrespective of the hydrologic conditions. Considering these fields of interest, a river simulation model has been developed to indicate the competition between the interests claiming surface water. For every interest the demand is quantified and modelled as a diversion from the river. With a series of scenarios, different interest are given priority and climatic changes are simulated. Based on this model an analysis and evaluation of the power production, water supply for irrigation and the inundation of the Inner Delta is presented. This results in the identification of competition between interests with continuous water demand throughout the year (hydropower and fully managed irrigation) and interests with their water demand during the rainy season (controlled submersion irrigation and the Inner Delta stakes). For the latter, the water supply depends on the size of the Niger River’s flood wave. Additionally, there is a strong inducement to presume that interests depending on the flood wave are in competition with each other. Finally, this study reveals the uncertainty about the power production of the Fomi hydropower dam (which is under consideration), the level of inundation of the Inner Delta and their mutual impact. Several studies use out dated or unreliable discharge data resulting in too extreme benefits and drawbacks.
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Table of Contents 1. Introduction ...... 1 1.1. Research context ...... 1 1.2. Geography ...... 3 1.3. Socio economics ...... 4 1.4. Water interests and allocation problems...... 5 1.5. Research question...... 6 1.6. Methodology ...... 7 1.7. Limitations and Restrictions...... 8 2. Climatic and hydrological specifications ...... 10 2.1. Climate...... 10 2.2. Hydraulics...... 11 2.3. Meteorology ...... 12 3. Fields of Interest ...... 13 3.1. Hydropower ...... 13 3.1.1. Power supply system ...... 13 3.1.2. Selingué dam ...... 14 3.1.3. Sotuba station ...... 15 3.1.4. Fomi dam...... 16 3.2. Irrigated agriculture ...... 17 3.2.1. Types of irrigation practice ...... 18 3.2.2. Present irrigation schemes ...... 20 3.2.3. Valuation of agricultural interest ...... 23 3.3. Inner Delta ...... 25 3.3.1. Livestock...... 25 3.3.2. Fisheries ...... 27 3.3.3. Ecology...... 28 3.3.4. Model data ...... 30 3.4. Other interests ...... 31 4. Simulation Model and Model Calibration...... 32 4.1. Program choice and description ...... 32 4.2. Model descriptions ...... 33 4.3. Model calibration...... 35 4.4. Scenarios...... 39 5. Simulation Evaluation...... 42 5.1. Parameters for evaluation ...... 42 5.2. Model sensitivity ...... 43 5.3. Sensitivity to management and climatic conditions...... 44 5.3.1. Power Generation...... 44 5.3.2. Office du Niger...... 46 5.3.3. Office du Riz Segou ...... 46 5.3.4. Office du Riz Mopti ...... 47 5.3.5. Inundation of the Inner Delta ...... 47 5.3.6. Maximum Reservoir Level Management...... 48 5.4. Competition between Interests ...... 48 6. Conclusions...... 52 6.1. Interests...... 52 6.2. Competition ...... 52 6.3. Discussion...... 54 7. Recommendations ...... 55 7.1. Development of the Simulation Model...... 55 7.2. Analysis Potential...... 55 7.3. Additional Study Areas ...... 55 8. References...... 57 Annexes...... 60
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Figures and Tables figure 1 1 Drought killing livestock (Schwarz, 2006) ...... 1 figure 1 2 Anomaly rainfall for 1900 2100 in percentile deviation from the 1961 1990 average (Hulme, 2000) ...... 2 figure 1 3 Niger River Basin (modified from Ofori Amoah, 2004) ...... 3 figure 1 4 West Niger Basin (modified from BRL, 2007)...... 4 figure 1 5 Schematic overview of the West Niger Basin and its interests for water...6 figure 2 1 Average discharge [m3/s] at the main measurement stations in the Niger River over 1980 2005 (DNH data) ...... 10 figure 2 2 Discharge of the Niger River at Koulikoro for 1950 2004 (DNH data) .... 11 figure 2 3 1961 1990 average metrological profile of Mali (FAOCLIM) ...... 12 figure 3 1 Aerial views Sotuba power station and surroundings (Google Earth) ..... 15 figure 3 2 Fomi reservoir inflow for different time series (Passchier, 2004; Simons, 1984; ABN) ...... 17 figure 3 3 Inlet of a Controlled Irrigation System of Office du Riz Mopti...... 19 figure 3 4 Number of livestock pieces related to the Niger River’s maximum flood level at Akka (Zwarts, 2005)...... 26 figure 3 5 The endangered Manatee (SMFV) ...... 29 figure 3 6 Inner Delta Inundation simulations (Zwarts, 2005; Passchier, 2008)..... 31 figure 4 1 Netter map of the Niger’s River Basin Simulation model ...... 34 figure 4 2 Water level of the Selingué reservoir (ABN, EDMsa, RIBASIM)...... 36 figure 4 3 Discharge downstream of the Selingué dam (ABN, RIBASIM) ...... 37 figure 4 4 Niger River discharge at Kouriomé (ABN, RIBASIM) ...... 38 figure 4 5 Simulation input data for the Bani River at Beney Kengy ...... 39 figure 5 1 Cumulative annual power production of Selingué, Fomi and Sotuba ...... 45 figure 5 2 Percentage of the year a minimum power production is met ...... 45 figure 5 3 Average annual water supply to Office du Niger...... 46 figure 5 4 Average annual water supply to Office du Riz Segou ...... 46 figure 5 5 Average annual water supply to Office du Riz Mopti ...... 47 figure 5 6 Average inundation of the Inner Delta from August to November ...... 48 figure 6 1 Schematic overview of the competition between continuous and periodic demand...... 53
table 2 1 Net Evaporation [mm/day] in the West Niger Basin zones(Schüttrumpf, 2007; Passchier, 2004; FAOCLIM)...... 12 table 3 1 Status of the irrigation [ha] in Mali per 31 December 2005 (DNGR) ..... 18 table 3 2 Extension plans and projects for Office du Niger (Schüttrumpf, 2007) ... 21 table 3 3 Specifications of the zones of Office du Riz Segou (ORS) ...... 22 table 3 4 Specifications of the zones of Office du Riz Mopt (ORM)...... 23 table 3 5 Field employment of the large irrigation offices (ON; ORM; ORS, based on Tamani) ...... 24 table 3 6 Valuation of irrigation systems throughout the West Niger Basin ...... 25 table 4 1 Scenarios for the simulation model ...... 40 table 5 1 Water supply for Office du Riz Segou, all Controlled Submersion Irrigation and Office du Riz Mopti ...... 42 table 5 2 Parameters for the evaluation of the simulation model ...... 43 table 5 3 Impact of climate and water allocation management on the interests for water ...... 50 table 5 4 Absolute value of case 5a minus case 4a for water supply of Office du Riz Segou and inundation of the Inner Delta ...... 51
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1. Introduction This research deals with integrated water resources management in the Niger River basin. Specifically it is about competition for surface water in the West Niger Basin. This subject is introduced by indicating the international research context. Subsequently, a geographic and socio economic description of the basin is given as well as a brief overview of the water interests and water allocation problems in the basin. This leads to the definition of a research question, followed by the research methodology. The last paragraph of this chapter indicates the limitations of this study.
1.1. Research context The Niger River Basin is an area that has a lot of attention from the international water resources and development world. A very logic situation since it is one of the poorest regions of the world (see §1.3 Socio economics) which is largely located in the dry Sahel zone of West Africa; an area where the struggle for water and food is synonymous for the struggle for life. Especially in the end of the 1970s and in the 1980s the region was under large international attention because of the Great Drought, a period with severe water shortages. Many people remember the picture of carcasses laying around in sandy dried out fields, like figure 1 1. Ever since, a lot of development aid (money) is going to West Africa and many research projects are carried out. The latter years integrated basin studies and models have become popular, since the competition for water is growing. Traditional water demands for livestock, figure 1-1 Drought killing livestock fisheries and irrigation are said to be (Schwarz, 2006) affected by modern water demand of large regulated irrigation systems and hydropower dams. For the Niger basin several integrated studies on water resources management have been published recently: The Niger, a lifeline (Le Niger: une Artère vitale, Zwarts, RIZA, 2005): This study aims to indicate the effect of the construction of the Fomi dam on the West Niger Basin, especially on the Inner Delta. Ecologic aspects are elaborately extensively. Finally, from an economic evaluation is concluded that the construction of the Fomi dam is not beneficial. Optimization Development Opportunities in the Niger River Basin (Etude d´optimisation des opportunités de développement dans le Basin du Niger, Royal Haskoning, World Bank, 2007): An economic optimisation study for the development of more hydropower dams within the Niger Basin. It also evaluates the value of the Fomi dam. However it does not adequately elaborate the hydrologic effects.
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The Niger River’s Future (Avenir du fleuve Niger, Marie, IRD, 2007): An evaluation of the present situation of the Niger in Mali and a view on the development towards the future. Elaboration de Plan d´Action de Développement Durable du Bassin du Niger (BRL, ABN, 2007): A study proposing all kind of developments in the Niger Basin. The Fomi dam is one of these developments. Also projects from the Malian government deal with the development of the Niger Basin. Most of them are supported by international organisations. Examples are the GIRENS (integrated water resources management of the Upper Niger) project and the PNIR (national rural infrastructure project containing subjects as irrigation, flood protection, infrastructure and sanitation) Furthermore, all kinds of models have been or are being developed to simulate hydrologic effects. The Niger River Basin Authority has a Mike 11 model of the Niger River. The Direction Nationale de l´Hydraulique in Mali is developing a Mike 11 model for the Bani river. And the IRD is presently busy to develop a detailed hydraulic/GIS model for the West Niger Basin.
A very topical subject that is addressed in many river basin studies is climate change. All over the world impact assessments of climate change on the river basins are carried out, e.g. in the Nile (Alterra Research Center), the Amazon (WWF), the Mississippi ( Iowa State University) , the Yangtze (HSBC Climate Partnership) and the Elbe basin (Glowa Elbe project). Also in West Africa such studies have been carried out. Theories on inter annual, decade on decade and multi decade cyclic patterns are developed (Marie, 2007). And of course global warming gets attention. But the actual climatic development remains very uncertain. For the precipitation figure 1 2 shows the historic variation from 1900 (the black line shows the rainfall with a 20 year Gaussian filter) and the forecasts until the year 2100 for 7 simulation models.
figure 1-2 Anomaly rainfall for 1900-2100 in percentile deviation from the 1961- 1990 average (Hulme, 2000) In this study the strong climatic variations in the past and the uncertainties about the future are combined with the grown potential for conflicts over water resources.
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The resilience to climatic variations of various interests for water is reviewed. And the robustness is of interest to competing water demand is indicated. This means interests for water are analysed as well as existing water supply and demand data. Sometimes the data used in literature are out dated or not properly checked (Annex H Fomi dam and Reservoir Specifications). Accordingly, data should be handled with care. The aim in this study is to use the most recent data from the original source.
1.2. Geography The River Niger is the third biggest river of Africa, after the Nile and the Congo River. It flows through four West African countries, consecutively Guinea, Mali, Niger and Nigeria. Its catchment stretches across the border of six neighbouring countries, Cote d’Ivoire, Burkina Faso, Benin, Algeria, Chad, and Cameroon. The figure 1 3 shows a map of the area. The grey area indicates the catchment of the Niger.
figure 1-3 Niger River Basin (modified from Ofori-Amoah, 2004) The Niger Basin itself covers about 2 274 000 km2. That is nearly 12 times as big as the Rhine Basin. The basin can be divided in multiple sub basins: the Upper Niger, the Inner Delta, the Middle Niger and the Lower Niger. This research focuses on the West Niger Basin, which is defined here as the Upper Niger Basin and the Inner Delta (dark grey in figure 1 3). This area covers about 400 000 km2. The largest part of the West Niger Basin is situated in Mali. But the most significant amounts of water flow from Guinea, where the river originates and many tributaries flow into the Niger River. The Niger River rises from the mountains near the border between Guinea and Côte d´Ivore, west of the Mafou River (see figure 1 4). Near Mopti the Bani River, one of major tributaries converges with the Niger River. The sources of the Bani River mainly rise from Côte d´Ivore. In the Inner Delta the river flow is a complex system with many divergences and confluences, especially in the rainy season. At the end of the West Niger Basin the Inner Delta ends up in the single flow of Niger River. The figure 1 4 shows the West Niger Basin with the Upper Niger Basin in light purple, the Bani Basin in yellow and in blue the Inner Delta.
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figure 1-4 West Niger Basin (modified from BRL, 2007) Within the West Niger Basin there are a number of man made structures that influence the regime of the river. These structures are briefly indicated. The river regime is explained in the §2.1 Climate. Presently there is a hydropower dam with a large reservoir at Selingué in the Sankarani River. There is a rock formation that is transformed into a fixed weir just north of Bamako. This serves the Sotuba power station and the Baguineda irrigation area. Additionally a large regulated weir is located at Markala, to secure water supply for irrigation of Office du Niger. In the Bani River a fixed weir for irrigation has recently been constructed at Talo. Additionally, a new hydropower dam with a large reservoir at Fomi, in the Niandan River, is under discussion. All places are indicated on figure 1 4.
1.3. Socio-economics Mali has a population of about 12 million people; the majority lives in the Upper Niger Basin. Guinea counts about 10 million people, but only a small part lives in the Niger Basin. The same holds true for Côte d´Ivore with about 18 million inhabitants (CIA, 2008). These countries are considered to be among the most underdeveloped countries of the world. Of the 177 countries in the Human Development Index (HDI), Mali ranks 173, Cote d’Ivore 166 and Guinea 160. (Niger Basin countries Burkina
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Faso and Niger are ranked even lower, respectively 176 and 174). Contrastingly, The Netherlands is ranked 9 th (UNDP, 2008). The most important sector of employment in the West Niger Basin is agriculture. Next to cropping, also livestock farming and fisheries play an important role. These activities are strongly bound to locations and ethnic groups. For example: the Bozo people are the fishermen, the Peul people are the livestock farmers and the Touaregs are the desert nomads. These groups mainly live in the North of Mali, from Mopti upwards. In some way or another they all depend on the availability of water. However, the availability varies strongly throughout the year (see §2.1 Climate). Consequently there is a yearly migration of groups throughout especially the northern part of the West Niger Basin. Also other natural resources are scarce. Next to some bauxite mining in the south of Mali, there are little opportunities to use natural resources and develop new industries. The most important energy resource is wood. Meanwhile electric energy demand is rising, due to development of the country and population growth (3% according to CIA, 2008). Further relevant details on socio economic interests are discussed later in this report during the analysis of the fields of interest for water resources in the West Niger Basin, chapter 3 Fields of Interest.
1.4. Water interests and allocation problems In the previous paragraphs a general conception of the West Niger Basin is given. In this section the water related interests and the problems for water are indicated. First a systematic overview of the basin is given; consecutively a few of the present problems are identified. The West Niger Basin can be schematised as shown in figure 1 5. The most important rivers, dams, weirs and hydropower stations, as described in §1.2 Geography, are clearly indicated. Moreover large irrigation systems that depend on the river for their water supply are illustrated. And the Inner Delta, most downstream, is schematised as a reservoir, irrigation system and a place for birds, fish and livestock. These interests depend on the water availability in the Inner Delta. Furthermore the Malian capital Bamako is represented by a factory indicating urban and industrial water supply. And with a boat the navigation during the rainy season is presented. All these interests for water are further elaborated in the chapter 3 Fields of Interest. Presently some problems are occurring or have the potential to evolve. In July 2007 the water supply to the Selingué reservoir had been so small that the reservoir was below its minimum target level. To maintain a small continuous power production the reservoir outflow had to be kept low. But due to the water scarcity there was a strong water demand downstream of the reservoir. Obviously a competing claim for water had evolved. The Malian government has plans to extend the irrigation area of Office du Niger. The higher water demand could be in competition with other/ additional irrigation schemes, the water demand of the Inner Delta or the management of the reservoirs. As previously mentioned, the Guinean and the Malian government are considering the construction of an additional hydropower dam in the West Niger Basin, the Fomi dam. This dam could change the regime of the river and impact considerably all water allocation downstream.
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Office du Niger
Fomi
Office du Riz Segou Inner Delta
Niandan
Niger
Office Perimetre Sankarani Irrigue Baguineda Office Developpement Office du Riz Selingueé Riz Mopti Ban i Selingué
Reservoir Irrigation River Power station Da m/Weir figure 1-5 Schematic overview of the West Niger Basin and its interests for water
A number of irrigation schemes that depend on the seasonal flood level of the Niger River have been designed and constructed during the 1960s. As shown in §2.1 Climate (figure 2 2) the discharge of the river Niger has decreased significantly since this period. Consequently the water supply to these irrigation schemes has currently become a problem. Further changes in the flood level due to human intervention or natural causes can (positively or negatively) affect the functioning of the irrigation system. In summary, the West Niger River serves many stakeholders for water resources. These stakes might be in competition with each other. To avoid conflicts and develop effective management one needs to understand the characteristics of the competing claims for water. This study aims to contribute to this understanding.
1.5. Research question This study attempts to understand, in general, various interests for surface water in the West Niger Basin and especially the way these interests interact with each other presently as well as in the future. This may serve as a base for political, economic, and/or environmental discussions on water allocation and basin management of the West Niger River. And it may also be used as a base for an economic optimisation of water allocation. This objective leads to the following research question:
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What are the potential competing claims over surface water resources in the West Niger Basin? This question can be divided into two parts containing a number of sub questions: 1) What are the (potential) claims for surface water? Which interests for surface water exist? If there is no interest there are no claims nor competition What is the importance of the interests? If the interests do not have any value (economic, social, ecologic or in any other sense), they are no interests. In what way do the interests depend on the surface water resources? To identify competition one needs to quantify the claims with an hydraulic parameter, e.g. water level, discharge or river width How sensitive are the interests for climate? This indicates the resilience of the interests to the natural variations in water availability and to climate change. If an interest is very sensitive one could consider calling it inferior. However this judgement is left for politicians.
2) What competition for surface water will potentially occur? Which interests compete with each other and which ones work together? This is an indication for policy makers of the conflicting interests that have to be dealt with. What is the mutual impact of the competing interests on each others water availability? This indication of the sensitivity of interests to other water demands is very useful for further optimisation studies. How do the climatic/hydrologic circumstances influence the impact of the competition? This gives an indication of the sensitivity of the competition to natural variation in water availability as well as to climate change.
Note that the word “potential” is added to the main question since this study takes the presence of the Fomi dam into account even though it does not exist (yet).
1.6. Methodology The identification of competing claims for surface water in the West Niger Basin is done in several phases using different methods: data collection, analysis of interests, modeling and evaluation. Step by step these are discussed. Data collection The data for this research are collected in Mali as well as in the Netherlands. In Mali data are collected in three ways. First of all representatives of government and non government organisations, considered experts in certain areas of interest, are interviewed. Additionally data and documents on interests for water and on hydrologic basin specifications are collected during these interviews. Furthermore field visits are undertaken to get a better understanding of the framework of water resources in the West Niger Basin. A summary of the activities and collected information in Mali is given in Annex A Overview Mali Mission. Additionally, more documents form different (international) organisations are collected from Holland. Analysis of interests With the collected information an analysis of the different interests for surface water is done. This consists of an explanation of the interests, i.e. their position in the framework of the West Niger basin. Additionally the relevance of the interest is given
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by valuing it in its own contexts. This could be an economic value, but for some interests this is arbitrary. An economic valuation would be subjective and is left for decision makers (politicians) or economists. This can be found in chapter 3 Fields of Interest. Finally, the interests are translated into a hydraulic demand. This could be a volume, inundated area, water level or discharge. For the river basin simulation modelling in RIBASIM, all these values are again translated into a representative discharge demands. The details of this hydraulic quantification is elaborated in the annexes G, H and I. Modelling A Ribasim model is used to simulate the water allocation in the West Niger Basin. This model distributes water according to basin management settings (i.e. priorities). To identify the competition for water among the interests, scenarios that prioritize different interests are used (see §4.4 Scenarios). Moreover the water allocation depends strongly on the water availability. Therefore the scenarios are simulated with dry, average and wet conditions. This allows an evaluation of the sensitivity of the competition to hydrologic conditions next to an evaluation of individual interest to basin management settings. Evaluation The identification of competing claims based on the modelling is done by a sensitivity analysis. This is done in three ways of reviewing: • the impact of management in general on individual interests (compared to general impact of hydrologic conditions) • the impact of water allocation to certain interests on other interests • the impact of hydrologic condition on the impact of management Every interest is represented by an evaluation parameter. For the first sensitivity analysis the changes of the parameter due to management scenarios is reviewed and compared with the changes of the parameter due to hydrologic changes. The influence of different interests on each other is evaluated by analysing the relative sensitivity of parameters to management scenarios. This means that each parameter is compared to its value for the present situation. In this way the value of every parameter can be expressed in a percentage that indicates positive and negative change. For every scenario the impact on all the interests in the basin is given. The scenario represents the prioritisation of one of the interests. So the competition or symbiosis of this interest with other interests is identified. Finally the effect of hydrologic conditions on the competition is evaluated. The extent of the positive or negative impact of management is compared for the three different hydrologic conditions. I.e. do negative effects of a management scenario become more negative if the hydrologic conditions are drier or not? And the same question is asked for the positive effects. With the evaluation based on the sensitivity analysis competing claims for surface water in the West Niger Basin can be identified, conclusions can be drawn on how strong this competition is and as well as on the influence of the hydrologic condition on the competition.
1.7. Limitations and Restrictions A number of restrictions are introduced to delimit the research. Firstly, the research area is limited to the West Niger Basin as described in §1.2 Geography. In this way it is avoided to be a too general analysis. The city of Timbuktu is chosen as the downstream border of the researched system, because the effects on the remaining downstream part of the Niger Basin is minimal (BRL, 2007; Royal Haskoning, 2007)
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For a high level of detail it would be most convenient to take a smaller research area than the West Niger Basin. But it would be ignorant for the effects on the basin scale. And an integrated approach would not have been possible. However the Bani River is left out of the scope of the research. The regime changes due to the water demands in the Bani Basin hardly influence the water supply to interests in the West Niger Basin. The irrigation schemes are generally small and the weir at Talo is fixed, so it does not significantly influence the discharge. Additionally the data on the Bani basin are very limited or difficult to get hold of. The latter – availability of data – problem is also the case for the part of the West Niger Basin in Guinea (and Côte d´Ivoire). And for these countries the Niger River basin does not play a very important role since they have their main activities in their coastal zone. On the contrary, the Niger River is a lifeline for Mali. Therefore the focus of this study is on Mali, which also contains the largest part of the West Niger Basin. Secondly the study only deals with competition for surface water. The main reason is that very little information is available on other water resources. However a short elaboration on the relation between groundwater and surface water is given in Annex F Geohydrology. Thirdly the research does not evaluate urban and industrial water demand. Drinking water is generally extracted from groundwater. Even in the largest city of the West Niger Basin, Bamako, practically everybody has access to a well. Furthermore no data on water extraction from the river for urban or industrial use are available. And according to Malian government officials these extractions are negligible. However in this study an estimated fixed drinking water supply is guaranteed based on estimates of Passchier (2004). Also navigation is not studied in this research. The scale of navigation that is strongly limited by variation in the river level (large non traditional boats) is small. Smaller traditional boats are only limited for a few months of the year. Even though the impact of regime changes on this type of navigation is interesting, it is not further reviewed. The model used during this research in combination with the limited available data on the river slope and depth prevent coming with any reliable results within the timeframe of this research.
Additionally water quality issues are considered beyond the scope of the research. The focus of this study is on water quantity. The economic results of the water allocation are left out of this research as well. It is not the aim of this study to optimise the water allocation, but to identify competition for water. In line with the latter, the calculation of crop yield is left for further research. It is not needed for the identification of competing claims for water to know the yield. Furthermore sufficient information on the agricultural systems to determine the yield is not available.
Resuming this research is limited to quantitative competition for surface water to serve hydropower generation, agriculture, livestock, fisheries or ecology in general within the Malian part of the West Niger Basin, excluding the Bani River.
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2. Climatic and hydrological specifications To understand the need and interest of different stakeholders resulting in competing claims for water, it is useful to understand the hydrological characteristics of the basin. In this chapter an overview is presented of the climatic conditions, the hydraulics (river discharge and water level), and meteorology (precipitation and evaporation). Since this research focuses on surface water, geohydrology is not accounted for in the water balance. However an attempt to indicate the influence of groundwater on surface water is given in Annex F Geohydrology.
2.1. Climate The West Niger River has a strong climatic gradient. In the south, in Guinea, the climate is tropic and annual rainfall can easily exceed the 1500 mm. In the North, near Timbuktu, Mali, the Sahara desert starts and average annual precipitation drops to about 200mm/year on average. The rainfall in this area is further elaborated in §2.3 Meteorology. Consequently the river discharge is dominated by the rainfall in the tropical zone. A strong seasonal variation in rainfall, with the wet season peak from June to September, creates a large peak discharge that gradually reduces to a longer flood wave. The peak discharge is estimated to be at its maximum after the confluence of the Sankarani River, between Banankoro and Koulikoro. In figure 2 1 one can see the development of the flood wave throughout the Niger in Mali. Kouriomé is considered to be the end of the West Niger Basin.
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Banankoro 0 Koulikoro ké macina nov dec Mopti oct sep Akka aug jul Diré jun may Kouriomé apr mar feb jan figure 2-1 Average discharge [m3/s] at the main measurement stations in the Niger River over 1980-2005 (DNH data) From the 1970s another phenomenon has influenced the river regime: severe drought. The first extreme droughts came in the early seventies, but especially the 1980s were very dry. This period is also referred to as the Great Drought. Ever since the rainfall has dropped severely, resulting in a decrease of the annual discharge with 20 to 50% compared with the period from 1940s until the 1960s. This is clear in the annual discharge graph of the Niger River at Koulikoro over 1950 2004 (figure 2 2). Some people say that the first droughts in the seventies and beginning of the eighties changed the hydrology of the West Niger Basin in such that it will not become as wet as in the 1960s anymore (Ozer, 2003; Nicholson, 1983).
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Discharge Discharge Koulikoro [m3/s] 2250 average annual discharge 2000 average 30 year discharge
1750
1500
1250
1000
750
500 1950 1960 1970 1980 1990 2000 Year figure 2-2 Discharge of the Niger River at Koulikoro for 1950 -2004 ( DNH data )
2.2. Hydraulics There are several reasons why hydraulic data are essential for this research. First of all a large part of the water supply in West Niger Basin is depending on the Niger river’s discharge (or on one or more of its tributaries). This makes discharges data key information to develop a water balance. Specifically, the supply and demand of water are identified with the support of a river basin simulation model. The discharge data are used as input data for the inflow at the upstream part of the West Niger Basin. Additionally, the more downstream discharge data are used to check the reliability of the simulation scenarios. Furthermore, a combination of the discharge (Q) and water level (h) data is used to generate a Q h relation. In this way the water demand initially specified as water level can be translated into a discharge. This is the case for e.g. irrigation systems that depend on a threshold water level to start irrigation. Also areas, especially the Inner Delta, depend on a certain water level to reach the desired level of inundation. The most important discharge measurement stations are presented in figure 1 4: Banakoro, Koulikoro, Ké Macina, Beney Kengy, Mopti and Kouriomé. The location of all other hydrologic measurement stations (precipitation, piezometric level, river discharge and water level) can be found in the Annex B Hydrologic Measuring Stations. The hydraulic data for Mali are obtained from the Direction National de l´Hydraulique of Mali (DNH) in Bamako. The measurement method used is explained in the Annex C Discharge Measurement Method. The data from Guinea come from the Royal Haskoning (2007), with its source at the Niger Basin Authority (ABN). Most probably the ABN has received this data from the Guinean Hydraulics department. Unfortunately there is very limited information available about the cross sections of the river Niger. Only discharge water level relations are known for a number of measuring locations and in specifically for 1996 (DNH, 2004). Consequently the relation between river width, depth and a discharge often remains arbitrary, making it difficult to model all interests accurately.
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2.3. Meteorology The source of the Niger River and its tributaries is precipitation. Unfortunately it is not easy to obtain precipitation data. The Malian Government sells its data for a very high price. The Global Historical Climatology Network provides average data for each month over the period of the first measurements (between 1897 and 1940 to 1989). The FAO provides figure 2 3 with average monthly data over the period of 1961 1990. The latter gives significantly lower values and is assumed to be a better representation of the present climate. Therefore these data are used to determine the net evaporation (evaporation minus precipitation) of large water surfaces.
figure 2-3 1961 -1990 average metrological profile of Mali (FAOCL IM) The rainfall runoff relation remains ambiguous since precipitation data of individual years lack. Consequently the precipitation is not used as a main input for modeling the availability of water resources. This is done with discharge data. Just like Passchier (2004) the run off due to rainfall is neglected. He states that for example in the Inner Delta 100 m3/s surface runoff is estimated during the rainy season, which is less than 10 % of the river discharge. In fact it is even less then 5%, which is a good enough accuracy for the calculations within this research. To determine the net evaporation data, the plain evaporation data are needed. These are based on information of the FAO (see figure 2 3), Schüttrumpf (2007) and Passchier (2004). Throughout the West Niger Basin there is a significant gradient in precipitation and evaporation. Therefore it is not useful to apply one evaporation value for the whole basin. The figure 2 3 shows this clearly. In this research the basin is divided in three different sections for evaporation and precipitation data: the southern hydropower reservoirs, the Lower Inner Delta and the Upper Inner Delta. In table 2 1 the net evaporation data extracted from different sources is given. In Annex E Meteorological Data the evaporation and precipitation data are given as well as an explanation of how these are developed. Zone Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Southern Reservoirs 6.3 7.1 7.2 5.8 3.5 0.4 4.1 6.3 2.5 2,9 5.5 5.8 Lower ID 7.5 8.8 10.2 9.5 8.4 4.9 3.0 1.6 0.6 5.4 6.7 7.0 Upper ID 6.5 7.2 8.8 9.7 9.9 8.9 7.0 4.9 6.3 7.9 7.6 6.2 table 2-1 Net Evaporation [mm/day] in the West Niger Basin zones(Schüttrumpf, 2007; Passchier, 2004; FAOCLIM )
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3. Fields of Interest In this chapter the interests for the water of the Niger Basin are described. The water interests are divided in four fields: • Hydropower (§3.1) • Irrigated agriculture (§3.2) • Inner Delta (§3.3) • Other interests (§3.4)
The last – other interests includes navigation and urban water demand. For every interest an explanation is given of how it is embedded in the Niger Basin system. For the further assessment of the interest based on this study an indication of the value of the interests is given. This valuation is not necessarily economic. It is done in the context of the interest; for example it could be expressed as a social or ecological value. Further more a hydraulic quantification of the stake is given. This means the dependency on the river translated to a certain, volume, discharge, or water level. With this information the RIBASIM model to identify unmet water demands can be developed. The resulting competing claims are discussed in later chapters.
3.1. Hydropower Hydropower is of great importance for the West Niger Basin. Due to the growth of prosperity and population, electric energy demand is rising. In the meanwhile the most energy in Mali is produced with thermal power station that are fueled by diesel. The production costs are rapidly rising since oil is getting scarce and becomes more expensive by the day. Resulting, hydropower seems a good alternative to prevent the West Niger Basin from energy shortages. It is safe, clean and moreover it is cheap. This is clearly represented in the average power production costs of Energy du Mali s.a. (EDMsa) over 2004 2006: • Hydropower: 9.52 FCFA/kWh • Thermal energy: 132.51 FCFA/kWh Based on these numbers the benefit of replacing thermal energy generation by hydropower would be 123 FCFA/kWh (0.1875 €/kWh), only considering the production costs. This is a reduction of nearly 93%. But there are two sides of the coin. The construction of a hydropower dam demands a substantial investment (e.g. the construction costs of the Fomi dam are roughly estimated around €300 million, see §3.1.4 Fomi dam) and the replacement of many people. Not to mention the impact of the potential changes in the regime of the river Niger influencing ecology and present ways of living in the West Niger Basin.
Presently Mali is the only country in the West Niger Basin providing access to and information on hydropower. Therefore a large part of this analysis is done from the Malian perspective. For a better understanding of the importance and impact of the hydropower dams within the West Niger Basin, a brief overview of the power supply system in Mali will be given. Additionally all dams are briefly described. Further specification on the dams, reservoir and their modelling can be found in Annex G Selingué Dam and Reservoir Specifications and Annex H Fomi dam and Reservoir Specifications.
3.1.1. Power supply system First, we shall have a look at the power supply in the West Niger Basin. Presently there is a large hydropower dam at Selingué, Mali near the Guinean border. And there is a small hydropower station at Sotuba/Bamako, Mali. Additionally there are
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plans for the construction of a new hydropower dam at Fomi in Guinea, near Kankan. (These places are indicated on figure 1 4.)
The two Malian dams are part of an interconnected system for power supply, serving Mali, Senegal and Mauritania. The system reaches from Dakar, Senegal to Markala, Mali. Its power supply comes from a number of stations: two large thermal energy stations near Dakar, called Agrreco 1 and Agrreco 2. Additionally the hydropower station at Manantali in the Senegal River in Mali and the earlier mentioned hydropower stations at Sotuba and Selingué are part of the system. There are also two thermal power plants in Mali connected, Balingué and Darsalam. The three cooperating neighbouring countries have made appointments on the power distribution. Mali and Mauritania can buy energy from Agrecco 1 and 2 stations (245 GWh available for Mali according to EDMsa). For the energy produced at Manantali there is a purchase ratio of 52:33:15 for Mali:Senegal:Mauritania (www.OMVS.org). The production at Manantali for an average hydrologic year this is 547 GWh, according to the Senegal Basin Organisation OMVS (Organisation pour la Mise en Valeur du fleuve Senegal). A part of the energy price of Agrecco and Manantali is fixed and a part is variable. The four Malian production units in the interconnected system are managed by Energie du Mali s.a. (EDMsa). Mali has 100% purchase rights for these stations. The Malian regions outside the range of the interconnected system supply their own electricity with diesel fuelled thermal power station. The main reason for this is said to be that the investment of large infrastructure (long distances energy lines in desolate areas) does not weigh up to the benefit for relative small energy demand. Guinea is not part of the interconnected system, but is expected to join once the Fomi dam is build. Currently, Guinea is said to depend fully on thermal energy, according to an EDMsa employee. Unfortunately it has not been possible to verify this with the Guinean authorities.
3.1.2. Selingué dam The importance of Selingué can be considered substantial. Based on a weekly energy demand of 16 GWh (based the data of week 49/2007 of EDMsa) the yearly energy demand in Mali is about 830 GWh. According to EDMsa Selingué produces 210 GWh in an average hydrologic year. This is about 25% of the yearly demand in Mali. The hydropower generation at Selingué depends on water availability in Lake Selingué. This is an artificial lake before the hydropower dam of Selingué. This lake has a 2135 million m 3 volume (29% of the average inflow) and a surface of 450 km 2 for a water level of 349 m above mean sea level (MSL). The inflow is nearly The production depends on energy head of the lake and the turbinated discharge. In 2007 problems occurred because lack of rain, especially in June. This reduced the water in the reservoir below its aimed minimal level of 340 m MSL, resulting in a reduced discharge below aimed minimum of 100m 3/s. These minima are decided upon by the Commission pour le Gestion de la Reservoir de Selingué, a group that has representatives of different stakeholders. For the energy production this means that only 1 turbine can be used, in stead of 2. This is a reduction of the maximum capacity with 11.7 MW. For the analysis in RIBASIM further detailed specifications of Selingué dam and reservoir are needed. These are the power production – discharge – water level relations , the production efficiency, water availability, the shape of the reservoir. In the Annex G the determination of all this information is presented. Special attention is given to the newly developed inflow data for the Selingué Reservoir.
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3.1.3. Sotuba station Sotuba power station produces about 30 GWh a year (the average over 2004 2006, representative for average years). It doesn’t have a storage reservoir; it depends on the river discharge. During the dry season the discharge depends mainly on the discharge at the Selingué dam. By viewing the satellite pictures (Google Earth) figure 3 1 the hydraulic system can be understood more easily. The upper image shows the river Niger running from west to east (left to right in the picture) during the period of high water level at the eastern part of Bamako (see figure 1 4 for the location of Bamako). The second image (indicated in the first picture with a green frame) shows a large natural rock formation that, together with man made weirs (the vertical white lines in the image), dams up the water before the inlet of the channel to the power station (indicated by the green arrow). The weir sustains sufficient energy head for power production if the river discharge decreases during the dry season. In the third picture (indicated with a red square in the first image) we see the Sotuba power station (indicated by the red arrow) during low river level. It shows the channel towards the station and the rock formation from which the station benefits. There are no extensive specifications and time series data on discharge and power production of the Sotuba power station for this research available. According to EDMsa Sotuba can produce at full power as long as Selingué can run with two turbines. This requires 3 figure 3-1 Aerial views Sotuba power station a discharge of about 100 m /s. The and surroundings (Google Earth) official maximum capacity is 5.7 MW, but in practice the production is never more than 5 MW (3.7 GWh per month). Modelling the Sotuba station is difficult since the available data are insufficient. Therefore the schematisation of Passchier (2004) is used.
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3.1.4. Fomi dam The Fomi dam in Guinea is a project for the future. But it is considered of significant importance for the river basin management that it is taken into account in this study. In this section the status of the plans for the construction of Fomi are discussed. And some general specification and characteristics of the dam are given. An elaboration on the valuation of this, yet be built, dam as well as the technical specifications are given in Annex H.
Presently the plans for the Fomi dam are being assessed. No decision has been taken with respect to the construction of this dam in the Niandan River, a major tributary of the Niger River. Several studies have been done on its potential impact and management. Their conclusions vary: some are positive and others negative. At least some impact can be expected since the active volume of the dam is 68% of the annual inflow volume (used in this study, see Annex H), which is 19% of the total average annual volume of the Niger River passing at Koulikoro. Note that at Koulikoro the discharge in the West Niger Basin is at its maximum. According to some people it is not a matter of whether the dam is going to be build, but rather when it is going to be built. This statement can be understood in the framework of the present international developments in the Niger Basin. All countries through which the Niger River flows want (hydropower) dams. This is supported by the World Bank, stating its objectives to “(i) support the feasibility studies for identified dam sites including Mambilla (4,000MW) and Zunguero (960 MW) in Nigeria and Fomi (250 MW) in Guinea ; (ii) provide complementary studies for the multipurpose dam sites of Taoussa (120MW) in Mali and Kandadji (90 MW) in Niger ” (World Bank, 2006). In December 2007 Reuters reports the funding of the latter two dams and that the construction will start in 2008. Nigeria already has a number of hydropower dams. Consequently, Guinea will remain the only Niger River riparian country without a hydropower dam. Politically this gives them a good position in the international development cooperation community to obtain support to build the next dam in the Niger Basin. One could say this implies (environmental) impact studies do not have any value. However the economic feasibility of the dam is not obvious even if downstream impact is not taken into account. So some proper study could be very useful before taking a decision on the construction of the Fomi dam. Especially because the investment costs are very high, roughly indicated between €200 and €400 million (SNC, 1999; BRL, 2007).
A rough estimate of the benefits of the hydropower production of the Fomi dam is based on a average yearly power production of 374 GWh. Compared to thermal energy it would reduce the production costs with 42 billion FCFA per year (€70 million). However the annual production estimates vary between 312 GWh to 374 GWh (BRL, 2007; ISL, 2006). So some uncertainty is present. As a result of this study these estimate can be doubted some more, since the water supply to the Fomi reservoir cannot be ascertained. Estimates vary between 241 257 m 3/s (Simons 1984 used by Royal Haskoning 2007; Passchier, 2004). The information used in this study (ABN data of 1980 2005) indicates only 172 m 3/s, this results in a decrease of the expected production of nearly 50%. Moreover this means that the planned active volume of the reservoir (3700 million m3) is estimated at 68% of the yearly inflow. In the figure 3 2 the different data sets for the inflow of the Fomi reservoir are presented ( 1980-2001 Passchier for Passchier, 2004; 1960-1979 Simons for Simons, 1984 and 1980-2005 Van Dijk for this study). More details on the valuation, expected inflow and power production is given in Annex H.
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Averages Monthly Time Series Inflow Fomi Reservoir 1000.0 1980-2001 Passchier year average 1960-1979 Simons 800.0 year average 1980-2005 Van Dijk year average 600.0
400.0 Discharge m3/s Discharge
200.0
0.0 jan feb mar apr may jun jul aug sep oct nov dec figure 3-2 Fomi reservoir inflow for different time series (Passchier, 2004; Simons, 1984; ABN) Note that for the Manantali dam wrong production estimates already led to substantial reduction of the benefit of the dam. Initially this dam was designed for 800 GWh/year based on hydrological data from 1950 to 1974, but with new simulations with data from 1974 to 1994 this number was readjusted to 547 GWh, letting the benefit of the dam drop with 5% (OMVS, 2003). The modelling of the West Niger Basin presented in this study should help estimate the energy production reliably.
3.2. Irrigated agriculture Mali’s economy is based on agriculture; its food security is sustained by national production. Millet and sorghum are the most important pluvial crops used for basic consumption in rural areas, followed by maize and fonio (Digitaria exilis). The most important irrigated crop is rice. More and more Malians use rice as their prime nourishment, especially in urban areas. Other cereals are occasionally irrigated, that is generally supplementary irrigation during the rainy season. During the dry season the cash crop gardening is practiced on a small scale, but very beneficial activity (DNGR). Presently, also the commercial farming is increasing with irrigated sugar cane plantations. Due to the geographically strong gradient in water availability (§2.1 Climate) the most southern (upstream) part of the West Niger Basin does not depend on irrigation as much as the northern part. Most agriculture in the regions Sikasso and Koulikoro (southern part) is rainfed cropping, since the rainfall generates enough water. Often millet and sorghum are grown, as well as cotton trees. By the use of flats in combination with small dams of about half a meter rice is grown (in French these systems are called “bas fonds”). In more northern regions sorghum and millet are also grown as rainfed crops, but generally less extensive and often supplementary irrigation is needed. The 100% pluvial agriculture is only done on small scale. Moreover the rains during the time of cropping (the rainy season) are abundant, especially in the
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southern part of the basin. Therefore the influence of the rainfed agriculture on surface water availability is assumed negligible.
In this chapter the irrigated agricultural system is elaborated. There are various types of irrigation in use, at different seasons, in different scheme sizes, for different crops and at different locations. (The table 3 1 shows the variations in the irrigated surface of different crops throughout the regions in the West Niger Basin.) These variables influence or depend on the available water resources. To give a systematic insight into these variables the types of irrigation (§3.2.1) are discussed, followed by the present irrigation schemes (§3.2.2). Additionally a valuation of the interest of irrigated agriculture presented to give an indication of the importance of irrigated agriculture (§3.2.3).
DNGR Malian Regions in West Niger Basin Total surface Speculations KOULIKORO SIKASSO SEGOU MOPTI TOMBOUCTOU [ha] % Rice 11,497 5,364 123,861 35,430 43,625 219,777 80.84% Rice+Other cereales 0 14 0 0 20,285 20,299 7.47% Other cereales 0 15 0 0 12,519 12,534 4.61% Rice+Cash crops 5,179 906 1222 214 654 8,175 3.01% Suger cane 0 0 5,800 0 0 5,800 2.13% Forage 0 0 0 1,916 0 1,916 0.70% Cash crops 69 2 478 652 10 1,211 0.45% Cash crops+Maize 0 897 0 0 0 897 0.33% Other 249 102 35 1 868 1255 0.46% TOTAL (ha) 16,994 7,300 131,396 38,213 77,961 271,864 100.00% table 3-1 Status of the irrigation [ha] in Mali per 31 December 2005 (DNGR)
3.2.1. Types of irrigation practice The most extensive irrigation schemes are used to grow rice. This is done with various types of irrigation: fully managed, controlled submersion, free submersion. Furthermore there is flood recession farming on the river banks and in the Inner Delta. Even though deep well irrigation is used by many traditional rural farmers, it is not taken into account in this research. It assumed that the deep wells are located such far from the river that they do not influence water availability. Based on the SIGMA database of DNH, indicating the location of the deep wells, this seems a reasonable assumption. In the coming paragraphs the irrigation methods will be discussed one by one. Fully managed The fully managed irrigation are schemes with a regulated in (and out)flow for the whole irrigation system and subsystems. The scheme that are evaluated during this study are managed by government offices. In generally the systems are gravitational. Water is often delivered on demand, in this way crops can be grown in optimal conditions. Increasingly popular fully managed irrigation systems are pump supported schemes. Water is pumped from the river into a main canal or directly on the land, depending on the size of the scheme. Pumping irrigation is a very water efficient way of irrigation (a high yield per volumetric unit of water). Additionally pumping irrigation is getting more and more competitive with the earlier described gravitational fully managed irrigation systems. Even while the costs of pumping are high due to the expensive gasoline needed for operation.
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Within the fully managed systems a double season rice production or a combination of rice production and market gardening is practised. Controlled submersion The second largest type of rice irrigation in Mali, after fully managed system, is controlled submersion irrigation (CSI). The system consists of a flat area surrounded by higher grounds, like levees, dams or hills. It is connected to the river by an inlet. If the water in the river is high enough, the water is allowed to inundate the flat plain by opening the inlet. In figure 3 3 Inlet of a Controlled Irrigation System of Office du Riz Moptifigure 3 3 one can see an example of an inlet near Mopti.
figure 3-3 Inlet of a Controlled Irrigation Sys tem of Office du Riz Mopti The filling of the plain starts naturally by the seasonal rains, starting in July. In this period the rice is nursed on small plots. From September onwards, the plain is rapidly filled by opening the inlet. This is needed for once the floating rice is replanted after nursery. Subsequently it is filled more slowly for the non floating rice. At the end of the wet rice season (around December) the river level has decreased below the level of inundation. The water is released back into the river, so the farmers can harvest their rice. Consequently controlled inundation is a way of irrigated agriculture only practiced is the rainy season. Sometimes the edges of the system are also used for non regulated growing of sorghum and millet. In the Annex L Controlled Submersion Irrigation a detailed explanation of this type of irrigation is presented. Spate irrigation A large part of the West Niger Basin is the Inner Delta of the Niger River. This is a vast flat area with a large number of river confluences and divergences. During the rainy season this area is flooded. Some of these naturally submerged areas are used for agriculture, especially wild rice cropping. Comparable irrigation practise is done on the banks of the Niger and its tributaries. Remarkably, according to field observations (by Frerotte, Royal Haskoning), in Guinea this free submersion irrigation is hardly practised. However, there are large river banks getting flooded every year. Based on Iwaco (1996) the irrigable or irrigated land situated south of Bamako can be estimated to be 50000 ha. Concluding, it is very difficult to give a proper estimation of the actual size of spate irrigation in general. Moreover, without cross sections of the river it is difficult to indicate the water levels on which this type of irrigation depends. Since these are not
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present, a proper relation between water level and cultivated uncontrolled inundated areas cannot be determined. Flood recession farming Another way of unregulated cropping that depends on floods is (in French) culture de decrue , agriculture by the receding flood. Once the river level drops and the river banks and plains come to surface the soil is still humid and water is still available. Moreover the soil is enriched with a fresh layer of fertile sediment. During this period many small peasants grow sorghum and cash crops (mainly onions and tomatoes). Especially in the Inner Delta this is popular, since people “can no longer count on rain fed crops for subsistence” (Marie, 2007). The same technique is used on the river banks of the Niger and its tributaries. Like the spate irrigation the relation between the discharge and level in the river, and the surface area and yield of the flood recession farming is not determined. Therefore both irrigation types will not be taken into account in the RIBASIM model. Nevertheless one may assume its practise is closely related with the level of inundation of the Inner Delta, where the largest land parcels are said to be situated. In §3.3 Inner Delta the flooding of this area is elaborated in relation with fisheries, livestock farming and ecology.
3.2.2. Present irrigation schemes For the indication of competing claims for water resources it is important to identify the different irrigation schemes that have substantial influence on the availability of water (in terms of water demand). The following paragraphs explain the large irrigation schemes. The information on the many small and scattered irrigation schemes is too limited and therefore are not presented herein. This should be considered as limitation of this study. A detailed description of the irrigation schemes regarding their water demand and yield is given in Annex J Quantification of the Agricultural Interest. The specification for the modelling of the irrigated agriculture is given in Annex I Irrigated Agriculture Specifications. ODRS and OPIB At the power stations of Selingué and Sotuba there are also two fully regulated schemes, respectively Office du Développement Rural Selingué (ODRS) and Office du Perimètre Irrigué Baguineda (OPIB). Officially they have an exploitation area of about 2500 ha each. In practice ODRS exploits about 1800 ha double season rice fields, for about 250 ha double season market gardens and banana plantations and approximately 50 ha single season maize fields. OPIB is a different story. During the rainy season there is presumably rice cultivation. In practise the cultivation of rice takes place in an area of 4500 ha. During the dry season OPIB has little tenants; only 350 ha are cultivated with rice and 120 ha with market gardens, since the Sotuba power station has priority for water (Schüttrumpf, 2007). Office du Niger The largest irrigation system in the West Niger Basin is Office du Niger . Presently it contains a fully managed irrigated area of about 86000 ha. An additionally 17000 ha benefits from the system of Office du Niger; this is not regulated. During the rainy season 74000 ha of rice is cultivated. In the dry season 11000 ha of rice fields and 8 500 ha of market gardens are irrigated. Additionally another 6000 ha of sugar cane is grown whole year round (Schüttrumpf, 2007). The total number of licensed farmers (i.e. registered peasants, paying a fee for cultivating an irrigated parcel) in 2006 2007 was 417000 according to Office du Niger.
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These data are not very accurate and consistent; Schüttrumpf (2007) already mentions this. Different numbers are mentioned in different reports. Meanwhile data series are not complete for every year; sometimes the data of different years are mixed. Moreover a check on Google Earth (satellite images of the year 2000 or later) gives much lower estimate of the total cultivated area, about 60000 ha. And there is no documentation that implies an expansion of about 30000 ha in the recent years. Nonetheless, the earlier mentioned data of Schüttrumpf 2007 are assumed to represent the present situation. This assumption seems justifiable after comparing these data with the data received form the Malian Direction Nationale Génie Rurale (DNGR). The data do not match exactly, but are close to each other. However, this does not guarantee that the data do not come from the same (questionable) source. But even if these data represent a too large cultivated area, they are useful since rapid expansion of Office du Niger is expected in the near future. In the near future an extension to 120000 ha is expected and plans for an irrigated area of 500000 ha are already under discussion (see table 3 2). Program/ Project Système Superficie Source Culture Calendrier Coût en Mrd. (ha) Financement Envisagé Réalisation FCFA Macina 60000 Pays Membres Riz, maraîchage, CEN SAD 326 Sahel 40000 CEN SAD Agro industrie Costes Ongoïba 10000 Blé, Riz, Agro ALKORAYEF Arabie Saoudite Macina 90000 industrie UEMOA Sahel 11200 UEMOA Riz, Maraîchage 2007 2012
PNIR Phase II Sahel 10000 Banque Mondiale Riz, Maraîchage 2008 2012
Costes Ongoïba 20000 2012 2017 SOSUMAR SOSUMAR (USA) Sucre Macina 15000 2008 2012 185 (incl. usine) Riz, Maraîchage, MCA Sahel 16000 MCA (USA gov.) 2008 2012 160 Agro industrie
SUKALA Sahel 20000 SUKALA (China) Sucre
Projet Libyen Macina 100000 Libye Agro industrie 2007 2012
Quelques Projets Riz, Blè, Sahel et Macina 27310 various 2007 2012 <10000 ha Maraîchage Perspectives 419510 aménagements ON table 3-2 Extension plans and projects for Office du Niger (Schüttru mpf, 2007)
Various Pumping irrigation schemes There is at least 4500 ha of land irrigated with diesel pumps in Northern Mali (BMZ, 2008). In Timbuktu the largest irrigation pump, a 600 l/s Archimedean screw pump, has been installed, according to DNGR. During interviews has been indicated that around Mopti there are many so called Petit Perimètre Irrigué Villageois (PPIV), irrigated plots smaller than 100 ha to use pumps (Office du Riz Mopti and De Bel, Royal Haskoning). And according to Fery (IRD) 200 ha near the Sankarani River and 1500 ha near the Niger River between the Guinea Mali border and Bamako is irrigated with pumps. Moreover in `Avenir du fleuve Niger´ (Marie, 2007) the IRD states that there are many small projects and corporations in the regions Mopti and Timbuktu that promote, share and manage pumping irrigation schemes (e.g. HIPPO, www.hipponet.nl). Unfortunately the vast variety of small plots and projects makes it hard to identify the size, locations, pump type and importance of pumping irrigation, as well as its impact on water demand/availability. For further modelling only a pumping irrigation scheme of 4500 ha at Timbuktu is taken into account. ORS and ORM The largest irrigation systems applying controlled submersion are the Office du Riz Segou and Office du Riz Mopti. Both areas consist of a total cultivated area of about
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35 000 ha. Additionally there is an other 70000 ha of smaller controlled inundated irrigation schemes throughout the West Niger Basin, with 49000 ha are located in the Inner Delta (DNGR, 2001). Controversially BMZ (2008) uses 28000 ha. A reason for this could be that systems with works for retardation of the flood recession are also indicated as “controlled submersion”. A significant difference between controlled submersion and flood retardation is that the latter has no inflow management to the flood plains.
About ORS and ORM some more detailed information (from interviews at ORS and Schüttrumpf, 2007) is presented on the size of the plots and the water level needed to inundate these plots. For ORS there are four zones: Dioro, Farako, Sossé Sibila and Tamani. Dioro (15580 ha) is a zone with high water security since the water level at the inlet is strongly influenced by the backwater curve of the Markala dam. Farako (6670 ha) is a zone that depends on the Niger River’s level. It has one main inlet gate at +283.85 m MSL and the water level for maximum yield is +285.15m MSL. Sossé Sibila (3000 ha) is a zone within the area of Office du Niger, but is managed by ORS. In this way the water supply is guaranteed. The plans are to upgrade this zone in to a completely managed system. And Tamani (8910 ha) is the most complicated zone since it exists of eight sub zones linked with a system of inlet structures. For this research the height of the main inlet and the needed water level for optimal yield have been considered, respectively +283 m and +287.3 m MSL. Note that with a water level of +286m MSL the first 3300 ha can be fully used. The table 3 3 summarises this information. Surface Lowest inlet Max water level River Zone [ha] [m +MSL] [m +MSL] Dioro 15580 Niger Farako 6670 261.26 266.34 Niger Sossé Sibila 3000 Niger Tamani 8910 267.20 270.55 Niger table 3-3 Specifications of the zones of Office du Riz Segou (ORS) In Tamani 7152 tenants are said to practise rice agriculture. Based on this number the total number of tenants with ORS is 25 000. In general there is one tenant per family, the head of the household. The rest of the family helps him.
The accuracy of the cultivated surface area data of ORS is very limited. For example the Tamani scheme is 8910ha and the production of major crops in 2005 is: • Rice: 10403 T • Millet: 22457 T • Sorghum: 18069 T If one calculates the surface area needed to harvest such a yield, the figures seem to be unlikely. Using the high yields from the Agricultural Compendium (Euroconsult, 1989) the need cropping area would be about 15000 ha. Probably the yields are even lower resulting in a larger area needed to meet this production. Ultimately, the yield data of Tamani remain at least questionable. However, this is the best estimate of the cultivated area of Office du Riz Segou, since no other information is available.
Office du Riz Mopti is also divided into 4 zones. But their water source varies among the Niger, Bani and Diaka River (see figure 4 1). Zone Diaka depends on the Diaka River and is situated 75 km west of Mopti. Zone Sofara depends on the Bani River, the inlet is located 50 km south of Mopti. The other two zones Mopti North and Mopti South depend on respectively the Niger and the Bani River. Moreover the zones are,
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just like Tamani, divided in sub zones. Again the zone scale will be used for the RIBASIM model, since more accuracy is not expected to give more reliable results. The characteristics of the zones are presented in . Inline with the dubious reliability of the ORS data one should consider this as official numbers, the single available data. Whether these represent the reality seems worth checking. The number of tenants in ORM is said to be around 11 000 spread over 190 villages. Surface Lowest inlet Max water level River Zone [ha] [m +MSL] [m +MSL] Mopti Sud 10880 262.85 267.05 Bani Mopti Nord 8110 261.26 266.34 Niger Sofara 5460 265.90 276.40 Bani Diaka 6370 267.20 270.55 Diaka table 3-4 Specifications of the zones of Office du Riz Mopt (ORM) There are no detailed data on smaller controlled submersion schemes are available. This means that there are no data for about 30000 and 70000 ha of this type of irrigation. Finally it is worth mentioning an important problem with the schemes of Office du Riz Segou and Office du Riz Mopti . These schemes have been developed in the late 1960s, when the river floods were generally much higher than at present (over one meter). This means that the design flood level is higher than most of the recent floods. Consequently flood plains cannot be fully submerged with the result of a poor or sometimes even no harvest.
3.2.3. Valuation of agricultural interest As mentioned before, the aim of this research is to identify competing claims for water resources. This means no optimisation of the water management based on a common value is done. However the value of an interest is indicated in its own context, e.g. economic values are valued with economic indicators and social values are valued with social indicators. So it is done for irrigated agriculture. Two important values of this irrigated agriculture are characterised: the social and the economic box 1 Prepaid card vendors value. Incentives for the development Incentives for the development of new job of new job opportunities have opportunities have taken place. A good taken place. A good example example comes from the mobile phone comes from the mobile phone industry. Since the use of mobile phones has industry. Since the use of rapidly expanded in Mali. A new job is created: mobile phones has rapidly prepaid card vendors. In Bamako, nowadays expanded in Mali. A new job is one can find more of these vendors than created: prepaid card vendors. corners of the street. This shows that new jobs In Bamako, nowadays one can are scarce and popular. Consequently the find more of these vendors than wages are low. The chances for and perspective corners of the street. This of a farmer is often not better than to continue shows that new jobs are scarce what he has been doing. and popular. Consequently the wages are low. The chances for It is a fact that the value of food is reflected in and perspective of a farmer is the money paid, but it is worth much more. often not better than to Growing food generates employment and it is a continue what he has been need for people to stay alive. Consequently, doing. irrigation schemes have an important social
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value. The following section is based on this premise. First of all, irrigated agriculture generates food security. Water supplied by irrigation, gives peoples the possibility to grow their own food. This is one of the basic needs of someone without another job than farming. This means the possibility for subsistence farming has more than an economic value. It assures people can keep themselves alive. Even if the production costs are high one should consider the value of an individual keeping himself and his family alive. According to ORS referring to the FAO, a person needs 150 kg of millet or 50 kg rice for subsistence on a yearly basis. In practice over 50% of the production of ORS is used for the consumption by the farmers and their families. Often farmers do not see their activities as their job, but as a way of living. Especially among small peasants this is a common thought. In many cases it is something that a family has been doing for generations. A typical verification of this statement is that jobs are often related to ethnic background. Irrigation makes it possible to continue with agriculture even though droughts have come and dams have changed the water availability. Without irrigation, food would be scarce and people would die. Furthermore many people do not have another perspective than farming. It is what the whole society is based upon. Economic development creating new job opportunities is lacking, remaining no other option than to farm box 1 elaborates this with an example. The simplest indicator to get an impression of the social importance of an irrigation scheme is to determine the number of tenants and/or peasants. Unfortunately, with the limited data available it is hard to give an accurate estimate of the social value of the different schemes. Most irrigation systems do not have traceable registers with tenants. For this research only rough data of Office du Niger, ORS and ORM are available, see table 2 1table 3 5. It remains ambiguous whether these numbers are the total number of tenants or the number of farmers. (A tenant often shares his plot with family members, who can all be considered farmers.). Scheme ON ORM ORS Tenants/peasants 417 000 11 000 25 000 table 3-5 Field employment of the large irrigation offices (ON; ORM; ORS, based on Tamani) Moreover it is hard to say anything in general about the number of irrigation farmers. ORM counts about 3 ha per tenant and ON about 5 tenants per ha. Additionally, 270 000 ha of irrigation was registered (DNGR, 2005), but the inclusion of unregistered irrigation could easily double or even triple this surface area as well as the number of peasants in the West Niger Basin. So the number of people depending on agriculture is something to be further researched. Then a better idea of the social impact can be given. The economic value of agriculture can be identified more straight forward. The value of an irrigation system can be determined by multiplying the surface area with the yield and the value of the cultivated crop. The identification of these numbers is less straight forward. This is done in Annex K Economic Value Irrigated Agriculture. Based on that information an idea of the economic value of irrigation agriculture in Mali is indicated. The table 3 6 shows the value of the different irrigation schemes and the value of the water use in the ideal situation of a 100% yield on 100% of the area.
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Scheme million € €/m3 Office du Niger 293.7 0.22 Office Développement Riz Selingué 14.1 0.01 Office Perimètre Irrigué Baguineda 6.8 0.00 Office Riz Segou 13.3 0.02 Office Riz Mopti 13.3 0.02 Irrigation Timbuctu 5.1 0.15 table 3-6 Valuation of irrigation systems throughout the West Niger Basin
3.3. Inner Delta The Inner Delta is a much less specific regarding the interest for water, since it does not represent one kind of stakeholder. In general four important interests can be considered prevailing in the Inner Delta: livestock, fisheries, ecology and spate irrigation. The latter is already discussed in §0 Spate irrigation and §0 Flood recession farming. The one thing all interests have in common is the way they depend on the water. Every year large parts of the Inner Delta inundate. Especially this level of inundation, i.e. the flooded surface area, determines the status of the different interests. Large inundation generally means a positive effect for the interests. In the following paragraphs each Inner Delta interest is elaborated and the inundation of the Inner Delta is described in such way that it can be modelled.
3.3.1. Livestock The Inner Delta plays a key role in the life cycle of a large part of Mali’s livestock. In this paragraph this will be explained and an estimate of the value of the livestock will be given. Interest of livestock related to floods The Inner Delta is the source of water for its surroundings. This holds especially for livestock. During the rainy season rice is planted in the Inner Delta; outside the Inner Delta fresh grass thrives because of the rains. Many herders lead their livestock to these grasslands. But ones the dry season starts and the grass is finished they start to move into the Delta. But they are not allowed to go into the Delta as long as the rice is not harvested (the Dina law). The harvesting moment depends on the date of peak of the flood, which can vary up to more than 4 weeks (Zwarts, 2005). Once the livestock enters the Delta its main food will be bourgou , floating grass that has grown during the flood. According to Zwarts (2005) the prevalence of livestock is strongly related to the availability of bourgou , which depends on the duration and level of the flood or inundated surface. Koné from Wetlands International states in an interview that bourgou needs at least 4 meters of water for a period of 3 months. This seems very high since this has only happened 12 times in the last 30 years (1976 2006). It is assumed that the availability of bourgou gradually decreases with the lack of flooding. Consequently a decrease in livestock numbers is the expected. One should be able to observe this trend in the data on livestock. Zwarts (2005) does an attempt to do so. Other studies on the Inner Delta (NAS, 2006; Marie, 2007) do not go further than mentioning this effect. Zwarts (2005) comes up with a linear relation with a correlations lower than 0,5. Therefore the basic livestock data used by Zwarts and the water level data of DNH are compared. In figure 3 4 one can see the peak level of the Niger River in Akka during the rainy season, compared with the livestock numbers for the following year. The data
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set used contains the available data of 1982 2001. Even though strong fluctuation in water level the cattle numbers remain rather stable. The water level seems to have little influence on the cattle numbers. For the small ruminants, sheep and goats, there are more fluctuations and one may discover a growth trend related to the water level especially in Timbuktu. Marie (2007) mentions a variation in cattle between 0,9 and 1,5 million head for respectively bad and good floods. From the given data this cannot be concludes. In general the relation of livestock and water level remains ambiguous. Even though theoretically it seems a sound explanation, based on the presented data, the relation is clearly not evident. Cattle in Mopti Livestock related to flood level Cattle in Timbuctu 4 Small ruminants in Mopti 3.5 Small ruminants in Timbuctu
3
2.5
2
1.5 millions]
1 Pieces of livestock [in Pieces[in oflivestock 0.5
0 420 440 460 480 500 520 540 560 580 600 620 640 660 water level in Akka [m +MSL] figure 3-4 Number of livestock pieces related to the Niger River’s maximum flood level at Akka (Zwarts, 2005) Disturbing effects of the cattle – inundation relation mentioned in Zwarts (2005) are the Dina law, overgrazing and the replanting of bourgou . With the Dina law, that withholds the livestock to go into to the Delta, the food in the Delta becomes available later. Furthermore Zwarts (2005) mentions that overgrazing has a negative impact over following years on the bourgou production. Finally the contrary effect should be the result of replanting bourgou . This should dampen the effect of overgrazing and dry periods on the flowing years. Evidently more than just the yearly flooding influences the livestock farming. Further research on these influences is recommended. This might result in a clearer relation between livestock numbers and the Inner Delta inundation. Nevertheless it is good to give an indication of the importance and value of livestock, according to Wetlands International Sevaré in an interview. The 5 000 000 small ruminants can be valued 10 000 FCFA each (a total value of 50 billion FCFA, which is €76 million). The 2 000 000 cattle is said to be much more valuable with 150 000 FCFA each (a total value of 300 billion FCFA, which is €457 million). Additionally it is interesting to mention the value of bourgou of which the cattle eats about 6.5 kg per day. Prices range between 15 125 FCFA per kilogram depending on the season and whether the bourgou eaten direct from the field or bought in harvested packages. Assuming an average price of 25 FCFA per kilogram this would mean a daily consumption worth over 300 million FCFA (±€500.000). This seems a considerable business.
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3.3.2. Fisheries A strong field of interest in the West Niger Basin and specifically in the Inner Delta is fisheries. In the following paragraphs this interest will be elaborated in two parts. To identify the dependence of fisheries to the water availability and understand the importance of fisheries, the fishing system (with a focus on the Inner Delta) and the value of fisheries is explained. Fishing system There are various types of fishermen; they can be distinguished by the type of fishing they do and by the intensity of their activities. Traditionally the Bozo tribe is ruling the waters of the Inner Delta. They used to be full time fishermen and the ones allowing other people to fish on “their” fishing grounds. These people were often part time or occasional fishermen. They would work in other sectors, especially agriculture, for half of their time or more. Nowadays this system now nationally coordinated. Fishermen can buy an annual permit for a certain type of fishing: using seines, standing nets or rods. But still the traditional division between full time and part time fishermen can be recognised. Probably this is also related to the availability of fish throughout the season. The fishing season starts with the flood recession in the inner Delta. This is in November/December. In the first months a fisherman’s catch can be as large as 35 kg/day, at the end of the season, in June/July this is reduced to 20%. This implies that at the end of the season nearly all fish have been captured. Resulting fishery in the Inner Delta is at its limits (Kodio, 2002). The concurrence among fishermen is such high that they even start migrating to, for example, Côte d´Ivore. Contrasting consultants (e.g. DHV, 2007) still recommend investing in better fishing management and equipment. But if the system is at is limits more fish cannot be caught. Only fish farming could contribute to a high production and a resulting higher catch. The fish availability strongly depends on the flooding of the Inner Delta. At the start of the flooding the fish spawn in the shallow inundated zones. If these zones are vast, many fish hatch. And the longer the inundation takes the more time the fish have to grow, resulting in larger fish. In general one calculates the fish production (the available biomass for the yearly catch) in a linear with the inundated surface. On the other hand, a relation of a higher power is to be expected, since the surface and the length of the flooding period are both contributing to a higher production. However fish production data presented in Zwarts (2005) do not show this trend. Moreover, Zwarts (2005), Marie (2007) and Royal Haskoning (2007) use estimates of 40 to 60 kg/ha production for the inundated surface area. This is only a rough estimate, as the available data are not accurate. Generally an estimated fish production of 50 kg/ha is used for further study. Note that this can be used for reservoirs in the Niger basin as well (Royal Haskoning, 2007). Moreover Lake Selingué is said to have a very reliable production (Marie, 2007). This adds an extra value to the reservoir initially constructed for hydropower production. Valuation As previously stated the fish production is strongly correlated to the flooding, inherently the interest of fisheries is very sensitive to the flood level. In this paragraph the number of people actually affected and the economic impact is elaborated. The Inner Delta has over 1 million inhabitants (about 10% of the Malian population). According to the Marie (2007) about half of these people directly or indirectly depend on fisheries. His reasoning: There are about 80 000 fishermen. For
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every fisherman there is one job on the shore (e.g. merchants, fish driers and smokers, fish transporters, etc.). And every employed person is said to generate income for two other person (including children and elderly). This results in a population depending on fish for their livelihood of about half a million people. Zwarts (2005) even indicates more than 850 000 local people consuming fish from the Inner Delta. This dependence on fisheries can be divided in subsistence and trade. Zwarts (2005) has worked out a division in six categories: fishermen consumption, local non fishermen consumption and trade (non local). And for these groups he has split the production in dried fish and fresh fish. In general one can say (based on Zwarts´ estimates for 1977 2002) that about 40 % of the fresh fish is used for the auto consumption of fishermen and over 50% for the local market. The total production of fresh fish is about 15 000 tonnes per year. The local and auto consumption of dried fish is about 5000 tonnes per year. This results in a total local and auto consumption of about 20 000 tonnes per year. The surplus over this amount is for the trade outside the Inner Delta. Depending on the flood level this trade varies between 10 000 and 50 000 tonnes per year. This is predominantly dried fish, but if the production is extremely high also the fresh fish are being exported. Although Zwarts shows his production estimates are 17 35 % lower than l´Opération Pêche de Mopti (which is also the source of the FAO data) the ratio between local consumption and trade indicates that fish is primarily an important source of nutrition in the Inner Delta. He also shows that the added value for trade of fish starts to be significant if production exceeds 20 000 tonnes. Raad (2007) and Zwarts (2005) estimate the value of a tonne fish at respectively 250 and 500 FCFA per kilogram. In total the contribution of fisheries to the GDP is significant with an estimated 3 4.2% (Raad 2007; Marie, 2007).
3.3.3. Ecology The Inner Delta is marked as a Ramsar site. This means government of Mali supports the mission of the Ramsar Convention: “the conservation and wise use of all wetlands through local, regional and national actions and international cooperation, as a contribution towards achieving sustainable development throughout the world” (www.ramsar.org). The criteria of the convention this can be summarised as the responsibility to take care of a sensitive and vulnerable ecological and biological system. In this paragraph the most important issues within this framework: soil degradation and bird habitats, are discussed. The main factor of influence is on these subjects is the level of inundation of the Inner Delta. Flood forests and soil degradation The Ramsar Convention is turned into a Plan d´Action National de Gestion des Zones Humides (PAZU) under supervision of Mr. Timbo from the Environment department of the Malian government (Direction Nationale de la Conservation de la Nature). The following information is based on an interview with him. The ecological system of the Inner Delta is losing its balance due to human intervention. This is the result of degradation of the soil, imposing further soil degradation, with strong negative consequences for the natural system. This obviously requires an explanation. Important parts of the biological system in the Inner Delta are the flood forests ( forêt gallery in French). These are forest along the natural canals and gullies in the Inner Delta. For about 6 months a year these forest are flooded. During this period they are important bird (Birds§0) and fish (§3.3.2) habitats. But they are also
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the shelter of large endangered species like the manatee (see figure 3 5) and hippopotamus. In the mean while farmers and fishermen need to access to the water. This leads to competing claims among different interests, especially if the water level does not come sufficiently high. In general this happens more and more. First of all, the flood level has been limited compared to three decades ago (see §2.1 Climate). Secondly agriculture (wild rice harvesting and market gardening) has been intensified. This combination gives increased pressure on the banks, flood plains and channels. Flood forests have been cut so figure 3-5 The endangered Manatee (SMFV ) agriculture could be increased. The counter effect is that flood plains erode due to the agriculture and the channels and gullies silt up due sand deposited by the wind in the dry season, an effect that is already occurring during poor floods. Consequentially the morphology of the area changes in such way that less water can enter the channels and floodplains during the rainy season. The result is desertification of parts of the Inner Delta. This creates even more pressure on the system, since water availability becomes scarcer for ecological and agricultural interests. Resuming, more poor floods and more intensified agriculture push the Inner Delta out of its natural balance. Therefore a choice has to be made to give room to ecologic development to maintain biodiversity, the wild rice production and the prevent desertification. Otherwise the result will be the destruction of the present system. Further cultivation of the Inner Delta with regulated water management systems will remain the only alternative to ensure water availability. In other words: “Do it mother natures way or do it the Dutch way.” Presently projects have started to educate farmers to maintain or even rehabilitate flood forest and channel systems. And agriculture closer than 25 meters to the water is prohibited to prevent erosion. Whether this is sufficient to maintain the balance in the Inner Delta remains the question. How much water is needed to support the Inner Delta is not clear. One can only say that more water is better. But it is difficult to determine the lower boundaries. Nevertheless it should be noted that the limits of the present system of the Inner Delta are close to being reached. Birds Zwarts 2005 spends a significant part of his study to the important ecological value of birds in the Inner Delta. The Delta is the habitat of many migrating birds, predominantly water birds. They can be divided in two types: • Afro tropical birds • Palaearctic birds The first group visits the Inner Delta between April and July with a peak 15000 28000 birds coming from the tropical African regions. The second group arrives in February and leaves in April with peaks of up to 40000 100000 birds (data over 1999 2001 from Zwarts, 2005). Zwarts draws all kind of arbitrary relation between
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the flooding of the Delta and the number of migrant birds observed. However these relations are such ambiguous that the only the theoretical relation is taken over in this study. The peak flood level is strongly related to the time floodplains and lakes remain inundated. In this way the size of the habitat of the birds is determined. If that is small, they will have to compete with other water users. These are mainly humans, like fishermen and farmers, but also bird merchants. This competition could result in a decrease of birds. Even though sufficient data lack to support Zwarts (2005) his theory sounds reasonable and is also supported by Marie (2007). Both writers also state that other animal population decrease as a result of smaller inundation, but without any data. Unfortunately one needs to conclude that it is not possible to directly relate the ecological value to the flooding. However an indirect dependency seems very plausible. In this research it remains something to keep in mind. Especially, since this area is recognised by the Ramsar Convention for its internationally important ecological role. However, the interest will not be further quantified.
3.3.4. Model data In general the interests in the Inner Delta are strongly related to the inundated surface during the yearly flood. Zwarts (2005) did an elaborate study to determine the inundation. This is a complicated issue, since many factors influence the water surface area. The main water source is the discharge of the Bani and Niger River. But also precipitation and evaporation play a role. Evidently the shape of the bed plays a role. Obviously, wide shallow canals can give a much larger inundated surface area than small deep canals. Furthermore the storage capacity of the Inner Delta and the resulting delay time (1 2 months) has influence on the level of inundation. Consequently the water level inundated surface area relation is different for the rising flood than the receding flood (hysteresis). Zwarts (2005) uses remote sensing with water maps, statistical analysis and a simulation model to get a grip on the relation. The simulation model is produced by Passchier (2004) and is used and improved for this study. As a reference herein Zwarts (2005) is used. He indicates a range of 8000 25000 km2 inundated area during the flood (based on the extremes years 1984, 336 cm and 1957, 625 cm water level in Akka). Finally he uses the water level in Akka to estimate the inundation level. His results are in the same range as several other studies he refers to. In the Annex P the relation between the actual water level at Akka and the simulated level at Akka is given, but that is not further applied in this study.
The calibration of the simulation model used by Passchier (2004) based on the satellite images is about 25 % lower than Zwarts´(2005) estimates. And with the additional change made during this study to inflow discharges into the system (presented as Passchier & Van Dijk in figure 3 6) it is even 50% lower than Zwarts (2005). These inundation simulations are presented in figure 3 6. Remarkably, the trend over the years is well comparable.
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Inner Delta Inundation simulations [1000 ha] Zwarts, 2005 1700 Van Dijk, 2008 Passchier, 2004 1500 Passchier & Van Dijk
1300
1100
900
700
500
300 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 figure 3-6 Inner Delta Inundation simulations (Zwarts, 2005; Passchier, 2008) Since Zwarts (2005) refers to other studies that indicate the same range of inundation as he does and that it is worth comparing the estimated effects of the Fomi dam of Zwarts with those of this study, the simulation model results are scaled to the reference of Zwarts (2005). However Passchier states that his estimates are much more reliable and accurate. Actually there is only a scale difference between the two studies. Passchier has calibrated the inundation in such a way that it shows the same fluctuations as Zwarts´ results. Therefore the calculated surface of Passchier is merely scaled with a factor 1.6 to define the inundation simulation of this study. In figure 3 6 one can see the newly scaled estimated indicated as Van Dijk, 2008. In the annex O Peak Inundation Calibration an attempt to explain the difference between the simulation used in this study and Zwarts (2005) is described.
3.4. Other interests There are many (smaller) interests for water to think of in the West Niger Basin. Most of them can be considered insignificant. However two of them are often mentioned in other studies (e.g. BRL, 2007; Royal Haskoning, 2007): navigation and urban water supply. As mentioned before, in the §1.7 Limitations and Restrictions, navigation is neglected and urban water supply is not evaluated. However urban water supply is modelled. It is mainly an issue around the Malian capital Bamako. This city has a population of about 1.2 million people. These people mainly depend on wells for their drinking water supply. However there is a pipe water network in the city that is supplied with (treated) water from the Niger River. Moreover there are said to be small industries that use the water of the Niger. But Marie (2007) only worries about their effect on the water quality, not on the water quantity. Exact data on the urban and industrial water demand are not present. Passchier estimates this demand at a continuous 5 m3/s. This is also used in this study. In the simulations of the model this is always supplied, except for 2 month during an extreme dry year with unfavourable management. However still 2.5 m3/s could be supplied, which is 180 litres per capita per day. So urban water supply is accounted for and guaranteed.
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4. Simulation Model and Model Calibration Now that all interest have been identified and their interest for surface water is quantified it is possible to use a river basin model to simulate the water allocation in the West Niger Basin. In this study a RIBASIM model is used to understand and “proof” competition for water among the interest for surface water. The sensitivity of the water supply for various interests to hydrologic conditions and other interest is used as indication. The sensitivity is tested by simulating various river basin management (water allocation) scenarios and various hydrologic conditions. These scenarios are described in the §4.4 Scenarios. First, the choice for RIBASIM and a description of this modelling program is given. Consecutively the model developed to simulate the West Niger Basin is discussed. Additionally the calibration of the model is shown.
4.1. Program choice and description Why RIBASIM? Before going into depth of the content of the model and the simulations it is useful to explain the choice for RIBASIM. There are many other river basin simulation programs of which WEAP and MikeBasin are the most popular. The most important feature of the model is that it should contain several different types of demand (hydro power, irrigation, etc.) and the option to manage the system with priority to different demands. In general one can say this is possible with most basin simulation models. However the way these models exactly cope with priorities and hydraulic relation differs. But it is very hard to determine which simulation program is most applicable, without really using it. Hydraulic models like Sobek or Mike 11 are less applicable, since they work with water allocation based on hydraulics, but in this study, a river basin model should be based on priorities/management. Furthermore it is practically impossible to develop a representative hydraulic scheme based on physical/hydraulic laws and formulas due to the many lacks of information on hydraulic specifications. The choice for RIBASIM is primarily based on pragmatic reason. Due to close relations of the Delft University of Technology and Deltares it was possible to get a RIBASIM license for free for this research study. Even more important is that Deltares already developed a RIBASIM model for the West Niger Basin in Mali which they would want to share with this study. This model was developed to indicate the impact of the Fomi dam on the inundated surface area of the Inner Delta. This creates a good reference for this study. Furthermore, extending the applicability of the existing model for the identification of competing claims creates a good starting point. In this way the results can be developed faster and more specific.
How does RIBASIM work? RIBASIM is a so called 0 D model. This means that it merely contains information on the water balance. The program does not contain information on water level, slope or any other hydraulic specification. One can define a discharge (Q) water level (h) relation, but this remains node specific. For example, one cannot connect a lateral weir/dam to a river stretch and define a water level in the river for which the weir should start spilling. The water level of the river (a property of the river stretch) is not linked to the level of the weir (a property of the weir). One can only define a ratio of the river discharge that is diverted to the weir. However this does not mean one cannot work with interest that depend on water level, like the controlled submersion irrigation systems or interest that depend on the inundated area, like the Inner Delta stakes. To do so one needs to convert
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water level (h) or surface water area (A) into discharges (Q). The model cannot do this, so one needs to know these relations. In this study the Q h and Q A relations are extracted from existing measurement data and model results. (In Annex I Irrigated Agriculture Specifications this procedure is described for controlled submersion irrigation.) RIBASIM calculates the water balance for the river basin every time step (one month in the cases used in this study). This means that no matter the length of the system, the volume of water available during the time step remains the same. So the inflow of the system in a certain month is used to serve the demand of the same month over the whole basin. There is no delay in the flow unless storage is explicitly added to the system by reservoirs or so called “storage link nodes”. The storage properties of reservoirs are trivial. Storage link nodes are nodes that are used to define a length, and a Q A and Q h relation. In this way a delay can be added to the flow. Water demand can be modelled in two ways. One can define a minimum discharge at a certain place if a certain volume, water level of water surface area is required. And one can define a water volume that is demanded. A percentage of this volume can be defined as a return flow into the river. A typical demand node is an irrigation node. The demand volume is defined by an irrigation requirement in millimeters per day over a specified surface area. Prioritisation of the water demand is discussed in the next section.
4.2. Model descriptions After the general description of the operation procedures of RIBASIM a more specific description of the RIBASIM simulation model developed for this study is presented in this section. Most parts of the model’s specifications concerning water demand of the various interests are discussed in the previous sections or, as referred to, in the annexes. Here the configuration of the water demand in the model is described.
The map of the model (figure 4 1) presents how the model is built up. The purple diamonds represent the inflow points (referred to as inflow nodes in RIBASIM) of the systems. In Annex M the in inflow data for the Niger River, Niandan River, Sankarani River and the Bani River into the study area are given. Subsequently, the blue triangles represent the hydropower dams of Fomi and Selingué. The red sandglass shaped nodes represent diversions of the river based on demand of irrigation schemes, which are represented by the green squares. The orange red sandglass shaped nodes define a continuous diversion into branches of the river. These are used to generate representative flow in the Inner Delta over several river branches. The specifications of these, so called, bifurcations are defined by Passchier (2004). Additionally, there are yellow nodes for urban water demand at Bamako (round) and the power station of Sotuba (rectangle), both located near OPIB. Also the characteristics of these nodes are taken from Passchier (2004). Furthermore there are red nodes: trapezoids for a discharge surface area relation (defining the river width with a certain discharge over a certain river length) and diamonds to define minimal flow. In figure 4 1 one can discover many of the latter type in the lower part of the Inner Delta (around Mopti). These low flow nodes are used if inundation of the Inner Delta has priority. In other case these nodes are not active. Since this study does not deal with the Bani River, only an inflow node is used for the Bani river branch (the western branch). Also the new Talo dam in the Bani River is not taken into account since it has no reservoir function. It merely raises the water level, but does hardly influence the regime of the Bani.
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KKoouurriiioomméé TTIIMMBBUUKKTTUU Macina
IINNNNEERR DDEELLTTAA iiirrrriiiggaatttiiioonn
OORRMM DDiiiaakkaa OORRMM MMoopptttiii NNoorrdd OORRMM MMoopptttiii SSuudd KKéé MMaacciiinnaa OOFFFFIICCEE dduu NNIIGGEERR OORRMM SSoofffaarraa OORRSS TTaammaanniii OORRSS DDiiioorroo BBAANNII KKoouullliiikkoorroo
OOPPIIBB BBaannaakkoorroo
OODDRRSS NNIIGGEERR SSEELLIINNGGUUÈÈ FFOOMMII NNIIAANNDDAANN SSAANNKKAARRAANNII
figure 4-1 Netter map of the Niger’s River Basin Simulation model All water demanding nodes have a source priority list. This list gives the order of the sources used to meet the demand. The standard list is built up from all potential sources upstream of the demand. And the closer the source is to the demand node the higher its priority to supply the demand. In practice this means that the inflow and reservoir nodes are the main water sources. It is important to note that ones a reservoir node is in the priority lists of a demand node, the demand has higher priority than the level management of the reservoir. But if the reservoir produces energy and a production target is set than this has priority over other demand nodes. If water sources, for example the reservoirs of Fomi and Selingué, are not in any source priority list their discharged volume is used from up to downstream to meet shortages in demand. Theoretically this means that if the water availability is low the downstream water user always has a disadvantage.
In the simulations used during this study priorities are set by adding the Fomi and Selingué reservoirs to the source priority lists or leaving it out. In the base scenario, the reservoirs are not present in any source priority list. This means the reservoirs
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will be managed according monthly target level (see Annex G and H) and with a minimum power production target for Selingué. To give priority to a certain demand the reservoirs are added to the source priority list of the demand node. For example to give Office du Niger priority the reservoirs are added to its source list. The same is done for the other scenarios (see paragraph scenario). The minimum power production target is left out in these cases.
4.3. Model calibration The RIBASIM model has been calibrated by Passchier. But for the purpose of this study a number of changes have been made to the model. Passchier´s model is extended with more irrigation schemes and new yearly water demand schedules as well as new reservoir specifications and more. These adjustments will be shortly resumed, before discussion the new calibration: Bani River • In the simulation the Bani River is reduced to an inflow node, because it is assumed that there is no significant water demand that has the potential to create competing claims. Irrigation schemes • All irrigation schemes have revised sizes and water demand schedules (see Annex I). Most eye catching are the reduction of Office du Riz Segou from 60000 ha to 33000 ha and the deletion of the Djenne irrigation system of 85000ha. The latter is only under consideration. • The rivers have discharge threshold, which is derived from the river level, before water can be supplied to the controlled submersion irrigation systems (see table I 3 and table I 6 in Annex I). • The irrigation node of Office du Riz Mopti is split in four parts, since these depend on different rivers and/or threshold levels (see ORM irrigation nodes in figure 4 1) • An irrigation node representing the many small controlled submersion systems is added as well as an irrigation scheme in Timbuktu based on full management by pumps (see figure 4 1). Reservoirs specifications • The shape of the Selingué reservoir is redefined since the dead storage of the reservoir was neglected, see Annex G. (The specifications of the Fomi reservoir are taken over of Passchier after verification, see Annex H.) • For both reservoirs a monthly target level for optimised power production is introduced. (see Annex G and H). Inflow discharge • Both reservoirs, Fomi and Selingué, have new inflow figures for the calibration, based on actual data (see Annex D and G). • The measurement data from Beny Kengy are implemented to represent the inflow of the Bani River (see Annex D and M). Inundation of the Inner Delta • To be able to give inundation in the Inner Delta priority low flow nodes are defined (see §4.2 Model descriptions).
With all these adjustments the model is again calibrated for several parameters. This is done by comparing the simulation over 1985 1999 with the measured data over these years. One by one the parameters are discussed. Note that the calibration with the real data is done in the situation without the Fomi dam, evidently because it does not yet exist.
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Selingué water level The water level of the reservoir of the Selingué dam is depending on three parameters: the inflow based on the historic outflow, water level data and an estimated reservoir shape; the outflow is based on management procedures and the reservoir shape based on constraints given by EDMsa/SNC. An indication of how well this is done is given by a comparing graph, figure 4 2. Water level Selingué reservoir [m + MSL] 350
348
346
344
342
340 Real Target Simulation 338 1985 1987 1989 1991 1993 1995 1997 1999 figure 4-2 Water level of the Selingué reservoir (ABN, EDMsa, RIBASIM) In general it can be said that the simulation give good results. Only the peak levels are somewhat over estimated. This is caused by the fact that in reality the target level is not always followed as rigid as in the simulation. Selingué discharge The level of the Selingué reservoir is regulated by the outflow of the reservoir. The level is managed to meet the target level defined by EDMsa. To maintain a base flow it ha been necessary to set a minimum power production demand of 5 GWh per month. This overrules the target level management. The resulting calibrated discharge is given in figure 4 3. The figure shows that in reality during dry years the peak flow is released a month later. During more wet years the model seems to be able to store more water during the peak months. But in general this is a proper representation considering that it is generated with a rigid management and not influenced by stakeholder as in practice happened. (The Commission pour le Gestion de la Reservoir de Selingué holds monthly meetings with representative of different stakes on the management of the Selingué reservoir.) Furthermore in practice technical malfunctioning, maintenance and repairs cause a deviation from the original management. This could explain why sudden peaks and drops in reality are not followed by the simulation. Unfortunately information to check this hypothesis falls short. If one looks at the total volume that is discharged there is no difference between the simulation and the real situation.
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Discharge Selingué Aval [m3/s] 1200 1100 Real 1000 Ribasim 900 800 700 600 500 400 300 200 100 0 1985 1987 1989 1991 1993 1995 1997 1999 figu re 4-3 Discharge downstream of the Selingué dam (ABN, RIBASIM) Banankoro discharge The discharge of the Niger into Mali is calibrated on the discharge at Banakoro. This means the cumulative discharge of the Niger, Tinkisso, Milo and Mafou rivers in Guinea is estimated by the subtraction of Niandan rivers´ discharge from the Niger rivers discharge at Banakoro. Consequently the simulated discharge at Banankoro is equal to the reality. Kouriomé discharge The outflow point of the West Niger Basin is at Kouriomé. This simulated discharge at this location is the result of the water balance over the whole basin. But especially the Inner Delta has strong impact on the result. It has significant evaporation losses and it creates a delay of the flow. Passchier (2004) has already implemented this delay of approximately 1 2 months. With storage link nodes he has defined the cross section and the length of the river branches in the Inner Delta such that the average discharge is 0.08 m 3/s. Consequently Passchiers´ simulated flow at Kouriomé does give a good delay, but his peak discharge in wet years, like 1994, are over 40% (1700 m 3/s) too high. By the scaling/calibrating of the inundated area of the Inner Delta (see §3.3.4) this peak has decreased significantly. Also the addition of a 50 000 ha of controlled submersion in the Inner Delta contributed to the decrease of the peak. The effect of these calibration actions is a decrease of the overestimation of up to 1000 m 3/s. The result is shown in figure 4 4. The average deviation of the volume that is discharged during a year is 2%. This seems quiet good, especially considering the years 1986 1991 with comparable peak discharges. The simulated wet years 1994 1998 still have a too high peak discharge. This is caused by the lack of smoothening of the peak. This can be seen in figure 4 4, the real peaks are a bit lower and longer than the simulated ones. But also the total volume is about 10% too high (however due to lacking data of many month over 1992 1999 this is not a very reliable estimate.) This could be caused by the lakes in the Inner Delta. According to Marie (2007) the filling of several lakes need a threshold water level that is only caused by high floods. They do not have a return flow into the river, the water just evaporates. This effect is not taken into account in
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the model. First of all it is arbitrary if neglecting this effect really causes the overestimated peaks in the simulation. Secondly and more important it would disturb the calibrated inundation simulation. Thirdly the simulation is based on Passchier who did study the hydraulics of the Inner Delta. During this study no research is done on the hydraulics of the Inner Delta. Changing the hydraulic system set up by Passchier could be considered pure guessing. And finally the results of the simulation have no strong quantitative value, but the qualitative results count. The high discharge peak at Kouriomé does not affect the qualitative results. Discharge Kouriomé [m3/s]
3000.0 Real 2500.0 Ribasim
2000.0
1500.0
1000.0
500.0
0.0 1985 1987 1989 1991 1993 1995 1997 1999 figure 4-4 Niger River discharge at Kouriomé (ABN, RIBASIM) Inundation The inundated area estimates generated by the RIBASIM simulation are scaled to be comparable with the estimates of Zwarts (2005). In the previous description of the calibration of Kouriomé and in the section Model data of § 3.3 Inner Delta, one can read how this is done. Roughly one may say the trend represented by the model is good, whether the exact values are accurate remains arbitrary. But the simulation does give a reliable representation of how the inundation is effected by the river management. Fomi reservoir The Fomi reservoir cannot be calibrated to the reality; it does not exist yet. Its modelling is based on the management of Selingué combined with a monthly water volume balance. This is explained in Annex H Fomi dam and Reservoir Specifications.
The discharge calibration for the measurement stations of Koulikoro, Ké Macina, Mopti in the Niger River and Kara in the Diaka River are given in Annex N Discharge Calibration.
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4.4. Scenarios The simulation used for the calibration of the model represents the current situation. To identify the sensitivity of the different interests for water to management and climatic variations scenarios are used. The results of the simulated scenarios serve to draw conclusions on potential competing claims in the basin (see §6.2 Competition). The calibration on the present situation does not guarantee reliable results of the model for non existing scenarios. A series of scenarios is developed to see how the model responds to changes in the setting of the river basin simulation. In this way one can give an indication of the reliability of the model, based on the sensitivity to different modelling settings. The results are given in chapter 4 Simulation Model and Model Calibration and a detailed elaboration can be found in Annex Q Sensitivity to Model Settings. For both the climatic and management scenarios as well as the scenarios for indicating the reliability of the model a short description in this paragraph The management of the basin is simulated by shifting the priority for water allocation over the different interest. Note that whatever the management/priorities setting are, the water supply always depends on the climatic conditions. Therefore each management setting is simulated for different climatic conditions. For every management scenario a special time series is used, consisting of a biblical 7 years period with extreme dry conditions (Genesis 41:29 30), 7 average years and 7 extreme wet years. Due to the subsequent years with similar conditions the effects of different previous conditions is filtered out. Moreover a cumulative effects might be introduced, showing the effect of long term climatic change. The climatic conditions are based on data from 1977 tot 2007. The extreme years are generated from the monthly extremes over this period. Consequently these are more extreme than the most extreme year of the period 1977 2007. For the dry years this results in a simulation that is drier than ever recorded. But for the wet year it is by far not as wet as in the 1960s (20 40% lower). The variation in the climatic conditions is solely based on the input discharge to the river basin. Variations in the rainfall and evaporation can be assumed negligible compared to the discharge. In figure 4 5 the input data for the Bani River are presented as example of the climatic variations, other input discharge data are presented in Annex M Discharge Data Climatic Scenarios.
Bani River input discharge [m3/s] 1800.0 1600.0 Beney Kengy max Beney Kengy avg 1400.0 Beney Kengy min 1200.0 1000.0 800.0 600.0 400.0 200.0 0.0 jan feb mar apr may jun jul aug sep oct nov dec figure 4-5 Simulation input data for the Bani River at Beney Kengy
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There are 4 main management scenarios used for the evaluation. The first one is the present situation (scenario 0). The second one is the addition of the Fomi dam with management for optimised power production (scenario 1). The other three scenarios also contain the Fomi dam, but give priority to water supply of respectively inundation of the Inner Delta (scenario 4), irrigation of Office du Niger (scenario 2) or controlled submersion irrigation at Segou (ORS) and Mopti (ORM) (scenario 3). For scenarios 2, 3 and 4 there are two versions: one with variable target levels for the reservoirs throughout the year and one with a continuous maximum target level for the reservoirs. In the standard cases the management of the reservoirs aims at a level for optimised power production (based on an average hydrologic year). This means the aimed reservoir level decreases in the dry period to maintain discharging water to turbinate for energy production. But a prioritised interest is more important than meeting the aimed water level. In the second case (the “b” scenarios) the reservoir is used as storage to serve the prioritised interest. So the aim is to fill the reservoir to its maximum (maximum reservoir level management, MRLM), consequently the water availability is maximised for the prioritised interest. Power generation can be considered a secondary concern.
Additionally three scenarios are added to test to sensitivity of modelling settings. Scenario 0 is run with another diversion ratio to the controlled submersion systems. As presented in table I 3 and table I 6 of Annex I, a river discharge – diverted discharge relation is developed to simulate the threshold water level for the controlled submersion systems. However this relation is a rough estimate. Therefore the effect of changing the ratio is examined in the scenarios 0a. In this scenario the river discharge is reduced with 10% while the diverted discharge is maintained. Scenario 4 is run to view the effect of a more extreme inundation target (scenario 4a). Since scenario 4 gives unexpected results in wet years. Scenario 3 is run with target level management and a target baseline power production of 5 GWh per month at Selingué (scenario 3a). In this way one can see whether a small baseline production has any influence on the simulation outcome. Finally a combination of scenario 3 and 4a is developed. A large inundation target together with the priority to the controlled submersion irrigation at Segou (ORS) Is set. In this way the relation between these two can be evaluated. In table 4 1 a summary of the different scenarios is given. Scenarios Priority Target Fomi Reservoir level Dam management 0 Power production 5 GWh/month no variable 0a Power production * 5 GWh/month no variable 1 Power production 5 GWh/month yes variable 2 Office du Niger meet irrgation demand yes variable 2b Office du Niger meet irrgation demand yes maximum 3 Controlled Submersion Systems meet irrgation demand yes variable 3a Controlled Submersion Systems meet irrigation demand, 5 GWh/month yes variable 3b Controlled Submersion Systems meet irrgation demand yes maximum 4 Inner Delta inundation 1 million ha August November yes variable 4a Inner Delta inundation 2 milion ha September November yes variable 4b Inner Delta inundation 1 million ha August November yes maximum 5a ORS + Inner Delta inundation demand ORS + 2 milion ha Sep Nov yes variable * 10% lower river discharge needed for diversion to Controlled Submersion Systems table 4-1 Scenarios for the simulation model
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In table 4 1 is not mentioned that at certain places a minimum discharge is modelled in specific scenarios. At Koulikoro a minimum discharge of 100 m 3/s, which is representative for 60 cm (DNH data 1977 2007), is used to serve Office du Niger in Case 0, 1, 2,2b. In the actual practised management procedure in Mali, the Selingué reservoir is used to do this. At Markala (just past the diversion of the Niger into Office du Niger) a minimum discharge of 40 m 3/s is used for Case 0, 3, 3b, 4, 4a, 4b, 5a to guarantee a base flow in the Inner Delta. The Selingué reservoir is also used to do this in the actual practised management procedure in Mali.
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5. Simulation Evaluation The results of the simulations with the river basin model give insight in the sensitivity of the interests for surface water, to (1) the influence of other interests and (2) the climatic conditions. These results are discussed in this chapter. This is done with a number of parameters, these are defined in the first section of this chapter (§5.1). Then the model sensitivity to its own settings is elaborated to indicate the reliability of the model. Subsequently the sensitivity of the interests for water to climatic conditions and water allocation management is evaluated. Finally the competition between the interests for surface water is identified.
5.1. Parameters for evaluation For each of the fields of interest described in chapter 3 Fields of Interest parameters are defined. Consecutively power generation, irrigated agriculture and the Inner Delta are discussed.
Power Generation Power generation is split into 3 parameters: (1) the yearly power production of the Selingué and Fomi hydropower stations together with the contribution of the Sotuba power station, (2) without the contribution of Sotuba and (3) the percentage of the year a minimum power production is reached at the Selingué, Fomi an Sotuba hydropower stations together. For dry years the minimum monthly production is 11 GWh, for average years 22 GWh and for wet years 33 GWh per month. In the actual present situation the targeted continuous production (for each month of the year) is 22 GWh. This value is not used for a dry year, because it is hardly ever reached. And it is not used for a wet year since it is nearly always reached. In both cases no information would be generated on the level continuity. With the chosen values of 11 GWh and 33 GWh insight is given in the continuity of the power production, taking into account what is reachable with the climatic conditions. This is important since energy demand is continuous throughout the year.
Irrigated Agriculture Irrigated agriculture is evaluated considering the water supply. Deliberately the choice has been made not to use the supply/demand ratio, since this would give a distorted impression. Simulation results give a ratio of 100% if there is no water demand, this seems very good, but Scenario ORS CSI ORM actually nothing happens. This disturbs the evaluation of real demand. 0 6.4 m 3/s 18.8 m 3/s 12.4 m 3/s Additionally one cannot sum up ratios for 1 -8% -10% -11% a number of irrigation schemes since a 2 -8% -10% -11% sense of the actual demanded volume is being lost. Furthermore it tends the 2b 29% 12% 4% reader to think that 100% fulfilled 3 37% 17% 7% demand is the only good outcome. But it 3b 37% 18% 7% is much more interesting to evaluate the improvement (or deterioration) of the 4 -48% -11% 8% water supply due to management 4a -41% -5% 13% scenarios compared to the current 4b -80% -23% 7% situation (scenario 0). Drawback is that table 5-1 Water supply for Office du Riz one has no insight in the fulfilment of the Segou, all Controlled Submersion water demand on which the crop Irrigation and Office du Riz Mopti
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production depends. Consequently, the determination of this production requires more study (see §7.3 Additional Study Areas). To evaluate the irrigated agriculture we look at Office du Niger and the controlled submersion irrigation (CSI) systems ORS and ORM. The latter two cannot be represented in one parameter since this troubles the outcome. The table 5 1 shows this by presenting the relative difference of the water supply for the management scenarios compared to the present situation. Scenario 0 gives the average water supply in an average hydrologic year. For all other scenarios the deviation to this value is presented with a percentage. One can see that ORS can have strong negative values while ORM has positive values. The value of CSI is somewhere in between and does not give useful information.
The evaluation of Office du Niger is considered to be representative of all fully regulated irrigation systems. The results of the simulations with RIBASIM justify this. ODRS and OPIB are not further evaluated.
Inner Delta The Inner Delta evaluation is based on the inundation of the area, since all the interests within the Delta depend on it. For every scenario the cumulative inundation area of 8 sections of the Delta (as Passchier, 2004 has divided it) is compared with the present situation. This is done with two methods: (1) the average inundation over the wet period of August until November and (2) the maximum inundation of the wettest month.
In table 5 2 a resume of all parameters for evaluation is given. Parameters Unit Power production of Selingué, Fomi and Sotuba Total yearly production in GWh Power production of Selingué and Fomi Total yearly production in GWh Continuity of power production Selingué and Fomi * % of time meeting the minimum power production Irrigation Office du Niger Average water supply over the year in m 3/s Irrigation Office du Riz Segou Average water supply over the year in m 3/s Irrigation Contolled submersion systems Average water supply over the year in m 3/s Irrigation Office du Riz Mopti Average water supply over the year in m 3/s Inundation during wet season peak period Average inundated area over August November in ha Inundation during wettest month Inundated area of the most inundated month in ha *this is the only parameter that is not compared with the present situation table 5-2 Parameters for the evaluation of the simulation model
5.2. Model sensitivity In this paragraph the sensitivity of the model to its own setting is discussed. The most important observations for the sensitivity to the discharge threshold for the supply of the controlled inundation systems and the sensitivity of the a small baseline energy demand are presented. Details of these analyses are given in Annex Q Sensitivity to Model Settings.
For the water supply of controlled irrigation systems a threshold value is defined. This value is not very accurate, due to lack of information (see Annex I). Scenario 0a is applied to indicate the impact of changing the thresholds. The discharge of the river needed to supply the SCI system is reduced with 10%. This results in an improved water supply of about 20 40% in an average year for all the CSI systems.
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This indicates that the actual supply of the CSI system cannot be accurately indicated, only the qualitative effect on theses systems can be evaluated. Additionally, the influence of this change on other interests is small. This can be well explained: the threshold river discharge is 10 to 20 times larger than the actual volume of water needed for CSI. Consequently the influence of other interest on the supply to the SCI systems is much larger than vice versa.
For the calibration of the water level and discharge of the Selingue reservoir a small baseline energy demand of 5 GWh is introduced. However in all other scenarios this is not used. Scenario 3a introduces the baseline power production to evaluate the sensitivity of the West Niger Basin model to this setting. The results suggest that this has negligible impact on any of the outcomes of the model (see Annex Q Sensitivity to Model Settings and Annex S Model Results Matrix).
5.3. Sensitivity to management and climatic conditions This section presents the evaluation of the impact of management scenarios and climatic conditions on the individual interests. The most relevant parameters representing the interests are evaluated using bar charts for the most important management scenarios and for different climatic conditions. The charts give a good indication of the relative as well as the absolute changes. However the quantitative values should be considered as an indication of the order of magnitude, not as accurate values. In the bar charts 10 scenarios are presented for 3 climatic conditions ( for dry, for average and for wet conditions). In this study 12 scenarios are presented. However, 0a and 3a are already discussed in the previous paragraph and will not be taken into consideration for further evaluation. The remaining scenarios are: 0: simulation of the present situation 1: priority for power production with the Fomi dam added to the present situation 2: priority for water supply of Office du Niger 2b: scenario 2, with maximum water level management of the hydropower reservoirs 3: priority for water supply of controlled submersion irrigation 3b: scenario 3, with maximum water level management of the hydropower reservoirs 4: priority for water supply for inundation of the Inner Delta 4b: scenario 4, with maximum water level management of the hydropower reservoirs 4a: scenario 4, with a doubled inundation target 5a: priority for water supply for inundation of the Inner Delta and controlled submersion irrigation Detailed explanation of the scenarios can be found in §4.4 Scenarios.
5.3.1. Power Generation Production The figure 5 1 shows the results of the simulation for the total annual power generation. Firstly, the low production of scenario 0 is remarkable. This is caused by the fact that all other scenarios benefit from the presence of the potential Fomi dam. Secondly a strong fluctuation for all climatic conditions can be seen. For all management scenarios the average dry year power production is reduced with 55% compared to an average hydrologic year. In a wet year the power production is increased with about 107%.
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Also a strong fluctuation for all management scenarios is presented. Irrespective of the climatic conditions, the average range of change (the difference between the positive and the negative deviation from the average) due to management is approximately 57% for all climatic conditions. Scenario 4, with a priority for inundation of the Inner Delta, seems a bit strange with its high power production value in a wet year. This is caused by the conservative aim for the inundation. This aim is reached and the second priority, power production, is served. Scenario 4a clearly confirms this theory. The doubled inundation aim of this scenario has the consequence that all water is used to serve this first priority and power production is reduced. Power production [GWh] dry 900.0 avg 800.0 w et 700.0 600.0 500.0 400.0 300.0 200.0 100.0 0.0 0 1 2 2b 3 3b 4 4b 4a 5a figure 5-1 Cumulative annual power production of Selingué, Fomi and Sotuba Continuity The continuity of the power production has a similar trend for the sensitivity to management scenarios as the power production (compare figure 5 1 and figure 5 2). However, it seems much more stable for variation in climatic conditions. But that is predominantly caused by the definition of continuity; this varies for the different climatic conditions between reaching a continuous production of 11 GWh per month for a dry year, to reaching a continuous production of 33 GWh per month for a wet year. If the definition would have been the same for all climatic conditions, the variation in continuity of the power production between the climatic conditions would have been much larger. Power continuity [%] dry P>11GWh 100% avg P>22 GWh w et P>33GWh 80%
60%
40%
20%
0% 0 1 2 2b 3 3b 4 4b 4a 5a figure 5-2 Percentage of the year a minimum power production is met Comparison with other studies Furthermore it is interesting to reflect on the power production in the context of other studies (as mentioned in §3.1.4 Fomi dam). The simulated annual power production of the Fomi hydropower station for an average year is 258 GWh if management is optimised. This is 54 116 GWh less than other studies indicate. This underestimation has been expected due to the smaller inflow discharge into the
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reservoir that is used. But it based on the measurement data of the ABN, so this estimate should be considered seriously. On the other hand the production of the Selingué power station was 18 GWh higher than the normal production of 210 GWh. This overestimation can be the result of continuity of the model. There are no malfunctions, maintenance activities or manual changes distorting the optimised production procedure. And as a matter of fact it is a fine estimate within the accuracy range of 10%.
5.3.2. Office du Niger The most robust interest in the West Niger Basin is Office du Niger. Both climate and management hardly affect its water supply (see figure 5 3). If either Office du Niger or power production has priority, 100% of the water demand is met. The same thing holds true for nearly every scenario during wet years. If years become dry, the effect of management does play a role. Supply Office du Niger [m3/s] dry 100.0 avg w et 80.0
60.0
40.0
20.0
0.0 0 1 2 2b 3 3b 4 4b 4a 5a figure 5-3 Average annual water supply to O ffice du Niger
5.3.3. Office du Riz Segou Segou is the most vulnerable interest in the West Niger Basin. Due to the threshold level of the river needed for the water supply of the system, it is especially sensitive to climatic conditions. In dry years, it does not receive any water at all (see figure 5 4). But in wet years 57 100% of the water demanded is supplied. Even though this is much more than a dry year there is still a large range in the supply. This is caused by the variation in water allocation management. If water availability reduces, e.g. from wet conditions to average hydrologic conditions, the system becomes more sensitive to management. During an average year management causes a deviation ranging aro und the average with 123%, for a wet year this is 39%. Supply Office du Riz Segou [m3/s] dry 20.0 avg w et 15.0
10.0
5.0
0.0 0 1 2 2b 3 3b 4 4b 4a 5a figure 5-4 Average annual water supply to Office du Riz Segou
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5.3.4. Office du Riz Mopti Just like Office du Niger management has small influence on ORM. In contrast, it is highly sensitive to the climatic conditions (see figure 5 5). Dry years cause a reduction of the water supply of 68% compared to an average year; wet years result in a increased supply of 158% compared to an average year. However, with the most beneficial scenario for ORM and wet conditions the water demand is not fully met; the maximum supply is 88% of the demand. Supply Office du Riz Mopti [m3/s] dry 40.0 avg 35.0 w et 30.0 25.0 20.0 15.0 10.0 5.0 0.0 0 1 2 2b 3 3b 4 4b 4a 5a figure 5-5 Average annual water supply to Office du Riz Mopti
5.3.5. Inundation of the Inner Delta For the inundation of the Inner Delta the sensitivity is similar to ORM: small management influence and large climatic impact (see figure 5 6). However the absolute deviation due to management remains large. In a year of drought the deviation in inundation area due to management ranges with 100.000 ha (26% range around the dry year average). In an average and a wet year this rises to respectively 160.000 ha and 170.000 ha (18% and 10% range around the annual average of the specified conditions). These values are comparable with one to two times the cultivated area of Office du Niger. Furthermore, it should be noted that no difference is made between inundation of plains and inundation of lakes. The latter are said to have threshold value to fill up and store water until all is evaporated; this can be several years (Marie, 2007). Consequently, lakes are a water resource of special long term importance in the Inner Delta. With the calibration of the discharge at Kouriomé (§Model calibration4.3) the hypothesis is presented that the fact that lakes are not taken into account in the simulation model results in overestimated discharge peaks. If this is true, then the exceeding of a 2000 m 3/s peak discharge at Kouriomé can be considered to be about the threshold value to fill up the lakes. With this peak discharge the inundation is about 900.000 ha. The figure 5 6 shows that the average climatic condition have values around this threshold. Consequently, the relatively small variations presented in figure 5 6 might be of significant impact on the Inner Delta water resources; they might be the difference between filled or empty lakes. Further research is needed to confirm this theory since the accuracy of the simulation result is too low to draw such conclusions. Additionally it interesting to compare the expected reduction of inundation due to the presence of the Fomi dam of Zwarts (2005) with this study. For this study that difference is indicated by comparing scenario 0 and scenario 1; this is about 100.000 ha. Zwarts indicates the impact between 200.000 and 300.000 ha. And he uses this estimate as the foundation of an elaborated economic impact assessment. The difference can be (partly) explained by the fact that the total discharge of the Niger River is the same, but the discharge into the Fomi reservoir is significantly
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lower in this study. This results in a reduction of 10% of the total discharge that is influenced by a reservoir. This does not change the implication of this study that the conclusions drawn by Zwarts should be handled with caution. Inner Delta Inundation [x 1000 ha] dry 2000 avg w et
1500
1000
500
0 0 1 2 2b 3 3b 4 4b 4a 5a figure 5-6 Average inundation of the Inner Delta from August to November
5.3.6. Maximum Reservoir Level Management From all the bar charts one can observe a more general effect: the impact of maximum reservoir level management (MRLM, see §4.4 Scenarios). The “b” scenarios, representing this type of management, indicate that power production is significantly decreased if the demand of the prioritised interest is mostly fulfilled, e.g. compare scenario 2 and 2b (priority to Office du Niger) or scenario 4 and 4b (priority to Inner Delta inundation) during a wet year. On the contrary if the prioritised interest is by far not fulfilled, like scenario 3 and 3b, the maximum reservoir level management has fairly less influence. The impact of the maximum reservoir level management on irrigation and inundation is small compared to the influence of management scenarios and even more limited compared to the impact of climatic conditions.
More information and data on variations in the results indicating the sensitivity of the interests to management and climate can be found in the Annex R.
5.4. Competition between Interests With the general sensitivity to climatic condition and priority setting for individual interests available, an evaluation of the competition between interests is presented. A large table (table 5 3) is used to give a summary of the simulation results of the most important scenarios and parameters. At the same time it gives an overview of the interaction between the water interest in the basin. An even more elaborate table with all nine parameters and all twelve management scenarios is given in Annex S. In this section first table 5 3 is explained and then an analysis is given. The table 5 3 consists of three parts: a dry year, an average year and a wet year evaluation. For each type of year, nine management scenarios, referred to as “cases”, are evaluated with six parameters. The management scenarios are variations in the water allocation priority for power generation, Office du Niger, controlled submersion irrigation and inundation of the Inner Delta. In §4.4 Scenarios a detailed description is given. Scenarios 0a and 3a are left out since they do not contribute to the analysis of the competing claims.
The parameters used for the evaluation are a selection of the parameters for the power production, irrigated agriculture and the Inner Delta, as presented in §5.1 Parameters for evaluation. This paragraph also explains that the controlled
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submersion systems should not be evaluated collectively. Furthermore, the results of the power production without Sotuba do not give very different results than power production results including Sotuba. The same holds for the inundation of the Inner Delta for the single wettest months of the year; this does not give more information than the result for the average inundation over August to November. Therefore the parameters for joint water supply to the controlled irrigation systems, power production without Sotuba and the Inner Delta inundation for the wettest months are not used for further evaluation of competition between interests.
In table 5 3 scenario 0 represents the current management as used for the calibration (§4.3) under three different climate scenarios. For scenario 0 the absolute values of the yearly power production, the percentage of the year a certain production is met (the power continuity), the yearly average water supply for irrigation schemes ON, ORS and ORM and the average inundation of the Inner Delta over August to November are given. Note that in contrast to all other scenarios, this scenario does not include the Fomi dam. All other scenarios are compared to the values of scenario 0. This is done by giving the percentage of deviation to the value of scenario 0. For example a deviation of +100% means the value is double the value of scenario 0. Power continuity has an exceptional valuation, in all cases it shows the percentage of the year during which a minimum power demand is reached. This minimum demand varies per climatic scenario from 11 to 22 to 33 GWh per month for respectively dry, average and wet hydrologic conditions . Furthermore the values in table 5 3 are coloured to indicate whether the parameter is influenced positively or negatively (see the bottom of the table). The range of 5% to +5% is considered negligible for irrigation and inundation. The power parameters do not have a negligible outcome, since power production deviation is always significant due to the addition of the Fomi dam. The parameters are ordered such that comparable results are next to each other. The management scenarios are ordered in line with the corresponding parameters.
A few conclusions can be drawn from table 5 3. First of all, a “green diagonal” can be discovered in the tables for all climatic conditions. In general this means that once an interest is given priority, it benefits. More interesting is to look away from the diagonal, to what happens to the other interests. Do the model results imply symbiosis, competition or is there no relation at all between the different interests? One can see that power production and water supply to Office du Niger have similar favourable management. Only if a continuous maximum reservoir level target (MRLM) is used the continuity of power production decreases for average and wet hydrologic conditions. This favours the CSI schemes as well as inundation. The result for Office du Niger does not change. This can be interpreted as competition between the CSI systems and inundation of the Inner Delta with power production. In general one can conclude from the table that management prioritising power production or Office du Niger has a negative impact on the water supply to CSI systems and inundation. In the same way, management supporting the peak flow during the rainy season (scenarios 4 and 3 giving priority to CSI and Inundation) draws down the power production and the water supply to Office du Niger. Only if this peak is already sufficient, as we can see for case 4 in a wet year, the competition ceases.
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Case Prioritised interest Power Time % Supply Supply Supply Inundation F+Se+So power ON ORS ORM aug-nov [GWh] >11 GWh [m3/s] [m3/s] [m3/s] [ha] 0 Calibration 69.3 0% 74.4 (72%) 0.1 (0.05%) 4.3 (11%) 429607 1 +Fomi 200% 83% 17% 100% 17% 18% 2 ON priority 200% 67% 17% 100% 17% 18% 2b ON MRLM 235% 83% 17% 69% 13% 9% 3 CSI priority 100% 25% 17% 70% 5% 5% Dry year 3b CSI MRLM 100% 25% 17% 70% 5% 5% 4 ID inundation 96% 33% 26% 100% 3% 6% 4b ID inundation MRLM 96% 33% 26% 100% 3% 6% 4a ID inundation double 106% 33% 26% 100% 8% 6% 5a ID double + ORS 102% 25% 19% 70% 5% 5%
Case Prioritised interest Power Time % Supply Supply Supply Inundation F+Se+So power ON ORS ORM aug-nov [GWh] >22 GWh [m3/s] * [m3/s] [m3/s] [ha] 0 Calibration 261.1 25% 86.8 (100%) 6.4 (28%) 12.4 (31%) 875030 1 +Fomi 99% 100% 0% 8% 11% 8% 2 ON priority 99% 100% 0% 8% 11% 8% 2b ON MRLM 99% 50% 0% 29% 4% 5% 3 CSI priority 1% 42% 19% 37% 7% 8% 3b CSI MRLM 5% 42% 21% 37% 7% 8% Average year 4 ID inundation 13% 33% 13% 48% 8% 8% 4b ID inundation MRLM 6% 33% 14% 80% 7% 10% 4a ID inundation double 8% 33% 13% 41% 13% 10% 5a ID double + ORS 0% 42% 13% 37% 7% 8%
Case Prioritised interest Power Time % Supply Supply Supply Inundation F+Se+So power ON ORS ORM aug-nov [GWh] >33 GWh [m3/s] * [m3/s] [m3/s] [ha] 0 Calibration 345.5 25% 86.8 (100%) 14.3 (64%) 30.0 (76%) 1677613 1 +Fomi 144% 100% 0% 0% 0% 5% 2 ON priority 144% 100% 0% 0% 0% 5% 2b ON MRLM 118% 58% 0% 4% 4% 3% 3 CSI priority 59% 50% 4% 31% 14% 5% Wet yearWet 3b CSI MRLM 46% 50% 10% 31% 16% 4% 4 ID inundation 144% 100% 0% 0% 0% 5% 4b ID inundation MRLM 77% 50% 0% 3% 3% 4% 4a ID inundation double 46% 50% 4% 10% 3% 5% 5a ID double + ORS 29% 42% 4% 15% 5% 4%
Good >141% 76 100% >11% >11% >11% >11% Moderate 91 140% 51 75% 6 10% 6 10% 6 10% 6 10% Neutral 5 5% 5 5% 5 5% 5 5% Insuffucient 41 90% 26 50% 5 11% 5 11% 5 11% 5 11% Bad <40% 0 25% < 11% < 11% < 11% < 11% * For average and wet years 0% at Office du Niger is dark green since the full demand is met (100%) Supply/demand ratio for scenario 0 CSI Controlled Submersion Irrigation: Office du Riz Segou and Office du Riz Mopti ID Inner Delta MRLM Maximum Reservoir Level Management ON Office du Niger ORS Office du Riz Segou table 5-3 Impact of climate and water allocation management on the interests for water June 2008 50 G.J. van Dijk
The competition is considered to be caused by the significant influence of the reservoirs on the distribution of the river flows. The total volume of the reservoir is 20% of the annual volume passing through the river at Koulikoro, the point with the largest annual discharge in the West Niger Basin. Note that not even 20% of the annual discharge passes the river in the eight month period of December to July Next to the competition between the continuous water demand, for power production and ON, and the seasonal water demand, of CSI systems and Inner Delta inundation another competition, seems present. A comparison of case 4 with case 4a for an average and especially a wet year indicates a competition between ORS and inundation. During a wet year the supply to ORS decrease with 10% and the inundation rises with 10%. Even ORS and ORM seem to compete for water. Case 3 and 3b entices one not to think so, but that is only because this scenario gives them both priority. In contrast, a symbiosis between ORM and inundation can be discovered. The symbiosis between ORM and inundation as well as the competition between ORM/inundation and ORS can be explained rather simply. The scheme of ORM is located in the lower part of the Inner Delta. Large inundation caused by high water levels has the effect that the threshold water level for irrigation is met for ORM. ORS is located south of the Inner Delta and south of Office du Niger. So once ORM or inundation have priority ORS is not allowed to take water. With the model this is straightforwardly simulated, but in practice it is easier said than done. The principle is also applicable the other way around, which is more expectable since the upstream user, ORS, takes the water first. Scenario 5a (with priority for large inundation of the Inner Delta and for water supply to ORS) confirms that higher supply for ORS has negative impact on the inundation. However the effects seem marginal or even hardly noticeable in table 5 3. Therefore the absolute differences between case 4a and 5a are given for water supply to ORS and the inundation of the Inner Delta in table 5 4. The increasing supply with 5 m 3/s and the complementary decrease of inundation of over 20000 ha seems a strong relation. But the competition is arbitrary, since also the power production is changed in case 5a compared with 4a. This could imply that the extra supply to ORS comes from the reservoirs. Furthermore, the margins indicating the competition of ORS with ORM and inundation are within the margins of accuracy of this study. So more elaborate research is needed to draw appropriate conclusions. Climate Supply ORS [m3/s] Inundation aug-nov [ha] dry 0.2 6037 average 5.0 22273 wet 3.6 17682 table 5-4 Absolute value of case 5a minus case 4a for water supply of Office du Riz Segou and inundation of the Inner Delta
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6. Conclusions The identification of competing claims for surface water resources in the West Niger Basin is done by analysing and evaluating fields of interests for these resources. To draw conclusions on the competition first an overview with the most important characteristics of the individual interests is given. Additionally the competition between the interests is indicated. Finally the questionability of the results and conclusions are discussed.
6.1. Interests A substantial part of the Malian electricity supply is generated relatively low cost with hydropower. For a continuous power production during the dry season large amount of water will be stored during the rainy season, especially if the Fomi dam is built (a total of 5835 million m 3). An adequate wet year is needed to keep the production up. Additionally, water allocation management favouring other interests can reduce the power production. The year round irrigated agriculture of fully managed irrigation schemes is not only responsible for an important part of Mali’s rice production; also very profitable market gardening is practiced during the dry season. Consequently, these systems support the food security as well as income generation. Additionally, the production is pretty secure, since the sensitivity to the climate or water allocation management is not very high. Even though the largest two controlled submersion irrigation systems are responsible for a potential 70.000 ha irrigated agriculture, their contribution is less significant than the controlled irrigation systems. First, they employ five times less farm labour per hectare, the yield is lower and the water use is higher. Secondly the systems are very sensitive to yearly maximum river level. Only very wet years allow a maximum yield. The Inner Delta interest is a combination of the interests of many different stakeholders in this wetlands system. These constitute agricultural farmers, livestock farmers and fishermen. In addition, it is a vulnerable ecological system that is habitat for many animals, especially birds. All these interests depend on the natural flooding of this area during the rainy season. How sensitive the interests are to the flooded area is rather arbitrary. But it can be concluded that the level of inundation is quite sensitive to natural variation of the river’s yearly peak level. Basin management has relatively small impact on the level of inundation.
In general, it can be said that hydrologic conditions have significantly more influence on the water availability for each individual interest, than the water allocation management in the West Niger Basin. Consequently climate change, more dry or more wet, is a major factor to take into account for basin planning and management.
6.2. Competition The aim of this study is to indicate the potential competition between the previously discussed fields of interest. The general conclusion is that competition for surface water is predominantly between continuous water demand throughout the year and periodic water demand in the wet season. The hydropower generation and the fully managed irrigation schemes have a year round water demand. These interests compete with the controlled submersion irrigation schemes around Segou and Mopti as well as with the inundation of the Inner Delta, which all depend on a large rainy season discharge peak of the Niger River.
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At same time this competition can be indicated as a spatial tension between the southern and the northern part of the West Niger Basin. This can be roughly indicated as North or South of Markala. Power production and fully managed irrigation are both situated in the South. And they compete with Office du Riz Mopti and inundation of the Inner Delta in the North. The controlled submersion irrigation around Segou is situated in the South, but generally competes with the power generation and fully managed irrigation. However, it is also indicated that it competes with the northern interests, see §6.3 Discussion. The competition is mainly caused by the reservoirs of the hydropower stations Fomi and Selingué. The reservoirs store water during the rainy season reducing the peak discharge of the river to support the water supply in the dry season. In other words, the same water is demanded during a different period of the year, causing competition. In figure 6 1 (a simplification and modification of figure 1 5) the competition is schematically presented with indicative water demand charts for seasonal and continuous demand.
N
Inner Delta
Office du Riz Segou
Office du Niger
Office du Markala Riz Mopti
Fomi Selingué
ov n
figure 6-1 Schematic overview of the competition between continuous and periodic demand
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The relative impact of the competition decreases if a year is wet. However, this is not always the case for the absolute impact. In general the West Niger Basin will benefit from a climatic change turning the situation into that of the wet 1960s. A change to a more dry system will increase the water stress and have a devastating effect on controlled submersion irrigation and the Inner Delta, but also on the availability of energy. Consequently, competition for water will increase and might even result in conflict.
6.3. Discussion Another type of competition, due to simultaneous water demand, should be considered. This competition is caused by water demand of different stakeholders at the same moment, while there is not enough water for all of them. This study gives reason to believe that this is the case for interests depending on the wet season discharge peak. However, two uncertainties have to be overcome for a well founded conclusion. First of all, the sensitivity of the water supply for controlled submersion irrigation to the modelling settings is considerable. Small changes in the threshold values of the river discharge defining the supply to the CSI systems result in considerable changes of the water supply. Additionally the controlled submersion irrigation is modelled with a water demand spread over 5 months, but in ideal circumstances 3½ months should do. Reducing the period of water demand could influence the presented results, especially quantitatively. This effect should still be analysed. Secondly, the accuracy and reliability of the results of the simulated inundation of the Inner Delta should be improved. The qualitative trend is comparable with other studies, but the absolute quantification is uncertain. The discrepancy between the absolute inundation estimates of Passchier (2004), Zwarts (2005) and this study, which has results in between the other two studies, confirms this. Consequently, absolute quantitative results for inundation have limited reliability. However, the conclusions of this study are predominantly qualitative, so the significance of this uncertainty for the conclusions are considered to be of minor influence.
A different result of this study is the indication of the consequence of the Fomi dam and reservoir. This differs considerably from other studies. The annual power production is estimated at 258 GWh. This is 20 30% (i.e. 54 116 GWh) lower than in other studies (SNC, 1999; BRL, 2007; Zwarts, 2005). Consequently the profitability of the dam has become questionable. The deviation of this study to other estimates is caused by the definition of the inflow of the Fomi reservoir. The annual discharge is taken about 30% lower than in other studies. This is based on the ABN discharge data over 1980 2005 of the Niandan River, that flows into the reservoir. This can be considered a credible argumentation. Moreover, it would not be the first time that overestimated inflow and production figures are used for the presentation of a potential hydropower dam. The effect of the Fomi dam on the Inner Delta inundation is also different than presented in an other study. Zwarts (2005) presents a reduction of the inundation that is 2 to 3 times higher than this study estimates. This is caused by the lower estimated inflow of the Fomi reservoir, but it is uncertain whether this is the sole cause. Consequently, the need for well founded and verified data on the inflow of the Fomi reservoir, to specify the consequences of the Fomi dam and reservoir, has become more clear than ever.
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7. Recommendations From the previous Discussion (§6.3) it becomes clear additional information is needed to draw more definite conclusions. This counts especially for the information on the discharge of the Niandan River for inflow of the Fomi reservoir and the inundation of the Inner Delta. What is more interesting to mention is the way this study can be improved, what additional analyses can be done to get more insight in the competition for water in the West Niger Basin and what extended studies can be done for an integrated understanding of the importance of the water resources in the basin. These subjects are successively discussed in this chapter.
7.1. Development of the Simulation Model An important upgrade of the simulation model used to identify the competition for water in the West Niger Basin will be a more accurate definition of the discharge water level relation at the places of controlled submersion irrigation. As presented in §5.2 Model sensitivity, this relation has significant impact on the simulated water supply to this type of irrigation. A better representation of the discharge water level relation will lead to a more accurate and reliable evaluation of the interest of the controlled submersion irrigation, especially for Office du Riz Segou. Another enhancement of the simulation will be adding lakes to the Inner Delta. In this way, the discharge of Kouriomé can be better calibrated. It would also give a better insight of effects of climate and management on the inundation of the Inner Delta over consecutive years.
7.2. Analysis Potential With the present model some additional analyses can be done to expand the understanding of the sensitivity of the West Niger Basin to management and climatic changes. Firstly, the water demand period for rice irrigation may be reduced to less than the present 5 months. It is interesting to determine whether this will influence the results for water supply to irrigation systems or to other interests. Secondly, it is very interesting to get insight into the effect of the potential extension of Office du Niger. Simulating an increased water demand for this irrigation system will indicate whether it will become more vulnerable for water allocation management in the basin. It will also indicate whether the competition for water will increase. Thirdly, a more difficult analysis of the impact of climate and management on navigation in the West Niger Basin can enhance this study. However the discharge water level relation at a number of places and the obstruction in the river (e.g. rock formations) have to be identified. Then an appropriate estimate of the sensitivity of navigation to climate and management can be done. The studies of Royal Haskoning (2007) and BRL (2007) might contribute to the execution of this analysis.
7.3. Additional Study Areas Several study areas are closely related to the subject of competing claims for surface water. And some subjects mentioned in this study are of a scale different than this study. For both type of subject this research can be considered a starting point. Additional studies may help to develop an integrated comprehension of the competition between, and benefit of, the water resources in the West Niger Basin. Several additional studies are proposed in this section.
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Competition of simultaneous claims The §5.4 Competition between Interests and §6.3 Discussion show that there could be competition between the interests that have a simultaneous water demand during the rainy season. More specifically, the claims for water of Office du Riz Segou and Office du Riz Mopti might be competing. To draw a definite conclusion on this matter, a more detailed study is needed. The level of detail of this study is too low to do so. Crop yield This study only indicates the water supply to irrigation systems and the relative change to the average situation. The relation of the water availability to the actual yield has been left for further research. To determine the yield, more than the water supply should be known, since that is not the only factor of influence. Another factor is the strategy of farmers for water shortage. For example with the same amount of water a small plot can be well irrigated or a large plot can have an irrigation deficit. Farmers have to set out their strategy before they know how much water will be available. Consequently, the complexity requires a special study to define the yield. RIBASIM extensions AGWAT and WADIS or CropWat (developed by the Land and Water Development Division of the FAO ) might contribute to this study. Optimisation The simulation of water allocation and an indication of the value of the interests for water, as presented in this study, can be used as a first step to an economic optimisation. To do so the value of all interests has to be economically quantified. Additionally scenarios should be used or an optimisation algorithm has to be developed. However the economic valuation is rather subjective and manipulative. Therefore a satisfying optimisation based on a dialogue between the stakeholder having an interest for water is preferred. This study can be used as a support tool to define satisfying water allocation taking into account the competing claims for water. Interest of small farmers The main economic activity in the West Niger Basin is agriculture. In this study the interest of the large irrigation scheme is quantified. It is only mentioned that there are many small irrigation schemes scattered throughout the basin. For a more realistic view on the interest for surface water of farmers, it will be helpful to indicate the extend of the small scale irrigation. This might lead to the conclusion that the competition for water between hydropower and irrigated agriculture is larger than indicated in this study. Additionally, it can identify competing claims for water between groups of farmers. Groundwater The influence of groundwater on competition for water is not elaborated in this study. However an analysis in Annex F Geohydrology gives reason to think that there is an influence of surface water on groundwater or vice versa. It is very interesting to do a more elaborate study on the surface water groundwater relation, especially if this can be linked to the impact on the population that depend on wells close to the river. Water quality and health The evaluation of water quality is considered beyond the scope of this research. But it is an important issue especially in relation with health. For example, schistosomiasis prevalence approaches 50% along the Niger river and over 60% in Office du Niger (UN Water, 2006). But also urban waste water causes water quality problems, especially if people depend on the Niger River for drinking water. A study on water quality and health could also indicate more competition for water.
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8. References (In case of web articles the date of the last visit is indicated between brackets.)
Agence Malienne de Presse et de Publicité, (AMAP) ; October 2005 ; Sucre : les prix refluent; l'Essor n°15549 du 2005 10 03; http://www.essor.gov.ml/cgi bin/view_article.pl?id=10880 (09 06 2008)
BRL Ingénerie, DHV, CIRA, ISADES; July 2007 ; Elaboration du Plan d’Action de Développement Durable du Bassin du Niger Phase II : Schéma directeur d’aménagement et de gestion, Rapport définitif; Autorité du Bassin du Niger (ABN); Niger
Bundesminister für Wirtschaftliche Zusammenarbeit und Entwicklung (BMZ), GTZ, KFW, PAM, ECHO, UNHCR; 1995 2008; Programm Mali Nord; http://www.Mali Nord.de (01 04 2008)
Central Intelligence Agency (CIA); May 2008; The World Factbook; https://www.cia.gov/library/publications/the world factbook (31 05 2008)
DHV, BRL Ingénerie; October 2007; Study on the Preparation of the Investment Plan and Formulation of Investment Projects Necessary for Implementing the Shared Vision, draft report; Autorité du Bassin du Niger (ABN); Niger
Direction Nationale Génie Rurale (DNGR); 2001; Revue du sous secteur de l'irrigation; Projet d'appui institutionnel; Bamako, Mali
Direction Nationale Génie Rurale (DNGR); 2005; Etat irrigation au 31 Décembre 2005; Bamako, Mali
DNH; August 2004; Annuaire Hydrologique 1996; République du Mali, Ministère des Mines, de l’’Energie et de l’Eau, Direction Nationale de l’Hydraulique; Bamako, Mali
Euroconsult; 1989, Agricultural Compendium for Rural Development in the Tropics and Subtropics; The Netherlands Ministry of Agriculture and Fisheries, The Hague; Elsevier Science Publishers B.V., Amsterdam; ISBN 0 444 42905 5
Food and Agriculture Organization of the United Nations (FAO); FAOSTAT; http://faostat.fao.org/ (01 05 2008)
Google Earth Beta (v4.0.24XX); October, 2006; Google software
HIPPO Foundation;1997 2008; www.hipponet.nl (01 06 2008).
Hulme, M. et al.; April 2000; African Climate Change: 1900 2100; Climate Research; Climate Research Unit, School of Environmental Science, University of East Anglia; Norwich, UK
ISL, SNC Lavalin; August 2006 ; Etude d’actualisation et d’impact hydraulique du projet d’aménagement du barrage de Fomi sur le Haut Niger; Beaucouzé, France
June 2008 57 G.J. van Dijk
Iwaco, WL Delft Hydraulics; August 1996; Carte de Vulnerabilité du Fleuve Niger; Republique du Mali, Republique de Guinee, DGIS; Rotterdam, The Netherlands
Jolly, L.; 2001; The Commercial Viability of Fuel Ethanol from Sugarcane; International Sugar Organization; London; United Kingdom
Keita, M.; August, 2004; Etude sur la Competitivite des Filieres Agricoles au Mali, Rapport final ; Bamako, Mali
Kodio, A; Morand, P.; Diénépo, K.; Laë, R.; 2002; Dynamique de la Pêcherie du Delta Intérieur du Niger revisité à la lumièredes données récentes; Gestion Intégrée des Ressouces Naturelles en Zones Inondable Tropicale; IRD; Paris, France
Mahé, G.; Olivry, J.; Dessouassi, R.; Didier Orange, D; Bamba, F.; Servat, E.; 2000 ; Relations eaux de surface–eaux souterraines d’une rivière tropicale au Mali; IRD; Sciences de la Terre et des planètes; Académie des sciences; Paris, France.
Marie, J., Morand, P., N’Djim H.; 2007; The Niger River’s Future (Avenir du fleuve Niger), IRD; Paris, France ; ISBN 978 2 7099 1632 5
National Academy of Sciences (NAS); 2006; Lost Crops of Africa: Volume I: Grains; UN Sudano Sahelian Office; National Academic Press; Washington, USA; http://www.nap.edu/openbook.php?record_id=2305&page=251 (09 06 2008)
Nicholson, S.E.; 1983; Sub Saharan Rainfall in the Years 1976 80: Evidence of Continued Drought; Monthly Weather Review, Volume 111, p. 1646 1654; Clark University, Graduate School of Geography; Worcester, Massachusetts, USA
Ofori Amoah, A.; Spring 2004; Water Wars and International Conflict; University of Wisconsin Eau Claire, USA ; http://academic.evergreen.edu/g/grossmaz/OFORIAA/ (02 10 2007)
Organisation pour la Mise en Valuer du fleuve Sénégal (OMVS); Le volet énergie; http://www.omvs.org/fr/realisations/energie.php (31 05 2008)
Organisation pour la Mise en Valeur du fleuve Sénégal (OMVS); 2003; Senegal River Basin (Guinea, Mali, Mauritania, Senegal); UN World Water Development Report; http://www.unesco.org/water/wwap/case_studies/senegal_river/senegal_river.pdf (05 06 08)
Ozer, P., Erpicum, M., Demarée, G., Vandiepebeeck, M.; June 2003; Discussion of “Analysis of a Sahelian annual rainfall index from 1896 to 2000; the drought continues”; Hydrological Sciences–Journal–des Sciences Hydrologiques, 48(3), p. 489 492; Fondation Universitaire Luxembourgeoise, Arlon, Belgium
Passchier, R., Maaten, R., Meijer, K.; October 2004; Integrated water resources modelling of the Upper Niger River (Mali); WL Delft Hydraulics; Delft, The Netherlands.
Reuters; December 2007; Niger, Mali secure Islamic funding for new dams; http://www.reuters.com/article/latestCrisis/idUSL03339422 (22 01 2008)
June 2008 58 G.J. van Dijk
Royal Haskoning; January 2007; Optimization Development Opportunities in the Niger River Basin (Etude d´optimisation des opportunités de développement dans le Basin du Niger); World Bank; Nijmegen, The Netherlands
Save Mayport Fishing Village (SMFV); Cruise ship poses threat to environment and Marine life; http://www.savemayportvillage.net/sitebuildercontent/sitebuilderpictures/manatee1.jpg (11 06 2008)
Schaffer Global Group; 2007; Cane Variety Trials, Mali; Louisiana USA; http://schafferglobalgroup.com/Cane%20Variety.htm (09 06 2008)
Schüttrumpf, dr. R., Bökkers, T.; July 2007; Analyse du Potentiel d´Irrigation lors de la Saison Seche dans la Zone de l´Office du Niger; Office du Niger, Minstère de l´Agriculture, Republique du Mali; Segou, Mali
Schwarz, T. ; September 2006; Kenya Drought; http://picasaweb.google.nl/SchwarzAfrika/ (27 05 2008)
Simons, Li & Associates, Louis Berger International; April 1984; Analyse Geomorphique du Bassin du Fleuve Niger /Geomorphic Analysis of the Niger River Basin; California, USA
SNC Lavalin; March 1999; Etudes de réactualisation du dosier de faisabilité du barrage de Fomi, République de Guinée; International; Agence Canadienne pour le Développement International, Montréal, Canada
Temoust ; 10 janvier 2008; Mali : Prix des hydrocarbures : La hausse inévitable; Temoust Survie Touarègue; Lyon, France; http://www.temoust.org/spip.php?article4219 (09 06 2008)
United Nation Development Program (UNDP); 2008; 2007/2008 Human Development Index rankings; http://hdr.undp.org/en/statistics (31 05 2008)
UN Water; 2006; Rapport national sur la mise en valeur des ressources en eau; Republique du Mali; Une étude de cas du WWAP préparée pour le 2ème Rapport mondial des Nations Unies sur la mise en valeur des ressources en eau, L’eau, une responsabilité partagée; Mali
Wikipedia; Brazil Ethanol Project; http://en.wikipedia.org (15 03 2008)
World Bank; March 2006; Integrated Safeguard Data Sheet, concept; Niger Basin Water Resources Development and Ecosystems Management project; http://www wds.worldbank.org/external/default/WDSContentServer/IW3P/IB/2006/03/20/00010 4615_20060321125954/Rendered/INDEX/Integrated0Saf1heet010Concept0Stage.txt (31 05 2008)
Zwarts, L., Beukering, P. van, Kone, B., Wymega, E.; 2005; The Niger, a lifeline (Le Niger: une Artère vitale); RIZA, Wetlands International, IVM, A&W; Mali/The Netherland; ISBN 90 807150 7 7
June 2008 59 G.J. van Dijk
Annexes A. Overview Mali Mission ...... i B. Hydrologic Measuring Stations...... ix C. Discharge Measurement Method ...... x D. Discharge Data 1985 1999 ...... xi E. Meteorological Data ...... xiii F. Geohydrology...... xiv G. Selingué Dam and Reservoir Specifications ...... xvi H. Fomi dam and Reservoir Specifications ...... xxii I. Irrigated Agriculture Specifications...... xxvii J. Quantification of the Agricultural Interest...... xxxi K. Economic Value Irrigated Agriculture...... xxxiii L. Controlled Submersion Irrigation ...... xxxiv M. Discharge Data Climatic Scenarios ...... xxxv N. Discharge Calibration ...... xxxviii O. Peak Inundation Calibration ...... xli P. Estimate of the Akka Water Level ...... xlii Q. Sensitivity to Model Settings ...... xliii R. Sensitivity to Management and Climatic Conditions ...... xlvii S. Model Results Matrix ...... xlix
June 2008 60 G.J. van Dijk
A. Overview Mali Mission This annex will give a brief overview the mission to Mali that, I, Geertjo van Dijk undertook from 13 November till 23 December 2007. Information and data collected during this mission serve as a source and reference for this study. I will shortly describe the activities undertaken. Subsequently, I will specify the interviews I did, the documents and data I received and the observation I did.
Activities The mission had 3 objectives. First of all, I aimed to apprehend more of the Niger Basin in general. Second of all, the mission was meant to collect information on the different stakes for the water of the rivers in the Upper Niger Basin and the Inner Delta of the Niger river. Part of this second aim was to quantify the stakes in hydraulic demands (water level, discharge or volume). Thirdly, the mission was meant to collect hydrologic and hydraulic data as input for a river basin simulation model.
During the mission I was based at the Direction Nationale de l’Hydraulique (DNH). There I could collect most of the hydrologic and hydraulic data. And from the DNH I could get introduced to other government agencies representing stakeholders in the Niger Basin. These agencies are: • Direction Nationale de l’Hydraulique DNH) • Direction Nationale de l’Agriculture • Direction Nationale de la Conservation de la Nature (DNCN) • Direction Nationale Génie Rurale (DNGR) • Direction Nationale Service Vétérinaire (DNSV) • Institute Economique Rurale, Bamako • Energie du Mali SA (EDMsa) • Office du Riz Mopti • Office du Riz Segou • Office du Niger
At these offices I had interviews with various persons. Furthermore I collected information at some international institutes and other non Mali governmental organizations and people. • Groupement d’Ingenieurs conseils pour le Developpement(GID) • Monsieur Bogotigui Bagayoko, Social Economist, Consultant • Local boatman in Mopti • Ambassade Royaume des Pay Bas, Bamako • Institute de Recherche pour le Developpement (IRD) • Protos Gestion Integré des Ressources en Eaux de Delta Interieur du fleuve Niger (GIREDIN) • Union for the Conservation of Nature and Natural Resources (IUCN), Institute de Recherche pour le Développement (IRD) • Wetlands International Mali
Additionally I did field observation for comprehension of the Niger basin system at a number of places: • Bamako • Mopti • Markala • Selingué
June 2008 i G.J. van Dijk
Interviews Next an overview of the interviews will be given. The interviews will be grouped per organization/institute. The summary of the interview will be given with the following structure: Date – Name – Background/Position Subject of interview
Direction Nationale de l’Hydraulique, Bamako 19/11/2007 – M. Tamba Kanouté – Hydrologist Data collection for integrated approach and hydrologic data collection based on level
19/11/2007 M. Seidou Maiga – Geo Hydrologist, chef de Section Inventaire des Ressources en Eaux Souterraines Groundwater/River level interaction, collection of groundwater data
21/11/2007 M. Navon Cissé – Hydrologist, Chef de Gestion Intégrée des Ressources en Eaux Niger Superieur (GIRENS) au Mali Methodology of (hydrologic) data collection and availability and reliability of data
27/11/2007 – M. Ali Thiam – Hydrologist Transformation water level measurements (H) to discharge data (Q), measuring methods and determination of cross sections
28/11//2007 – M. Sékou Haïdara – IWRM Expert and Hydropower General discussion on the Niger basin system and stakes
10/12/2007 – M. Soumana Touré – Expert Gestion Eaux Urbaine Significance of urban water use
11/12/2007 Réunion Commission Gestion des Eaux de la Retenue Sélingue Meeting of stake holding government representatives in the Niger Basin in Mali on water shortage and dam stability
Direction Nationale de la Conservation de la Nature (DNCN), Bamako 05/12/2007 Mme Coulibaly – aménagiste DNCN Ecologic stakes and conflicts in the Malien Niger Basin
05/12/2007 – M. Soumana Timbo – Ingénieur Forestier, Coordinateur du Plan d’Action de gestion des Zones Humides (PAZU) System, importance and vulnerability of the Inner Delta of the Niger
06/12/2007 – M. Diabate – expert forestier Importance of conservation of river bank flood forests
Direction Nationale Génie Rurale (DNGR), Bamako 06/12/2007 Abdourahamane Toure – Directeur Adjoinct Direction National Génie Rural (DNGR) – DNGR, Bamako Agriculture in the Niger Basin of Mali
06/12/2007 – M. Soumayla Samaké – Directeur Direction National Génie Rural – DNGR, Bamako Stakes of agriculture in water resources management in the Niger Basin of Mali
June 2008 ii G.J. van Dijk
Direction Nationale Service Veterinaire, Bamako 18/12/2007 Dr. Idriditto Sissoko – Chef Division Suivi et Evaluation Explanation of different types of livestock farming in Mali
Institute Economique Rurale, Bamako 3/12/2007 Dr. Bino Teme – Agricultural economist, Directeur Institute Economique Rurale Large agriculture systems of major economique importance
04/12/2007 Dr. Mamadou Kabirou N’Diaye – Soil scientist Soil degradation
10/12/2007 Kodio Amaga – Fisheries expert System, importance and vulnerability of fisheries in the Inner Delta of the Niger Basin
Energie du Mali SA (EDMsa), Bamako 21/112007 (and 04/12/2007) M. Oumar Sidibe Barrage expert Direction National de l’Energie – DNH, Bamako Barrage specifications of Fomi and Selingue
07/12/2007 – M. Sisogo expert power distribution System, infrastructure and management of power distribution in Mali
07/12/2007 – M. Ladio Sogoba Ing. MBA– Directeur Direction de la Production Electricité, Réseau Interconnecté Power production cost and value
Bamako commercial 16/11/2007 – M. Diély Moussa Kouyaté President Directeur Géneral Groupement d’Ingenieurs conseils pour le Developpement(GID), Bamako
17/11/2007 – M. Bogotigui Bagayoko – Social Economist, consultant – Hotel Fouta, Bamako Broad discussion on the Niger Basin system and stakes
Mopti 14/12/2007 Beni Traore – Forester, Chef de Section Genie Rurale Office du Riz Mopti (ORM) Explanation of the rice irrigation system of ORM and data on constraining water level
15/12/2007 – Local boatsman Mopti Prices river transport of small boats
15/12/2007 – Local boatsman Mopti Transport period over the year of big boats
Segou 17/12/2007 Daouda Thiero –Chef Division Infrastructure Rurale, Office du Riz Segou (ORS) Specification on infrastructure of Office du Riz Segou
June 2008 iii G.J. van Dijk
17/12/2007 Bakary Thiero – Chef Cellule de planification et statistiques (CPS) Office du Riz Segou Data on production, consumption and revenue in ORS
17/12/2007 – Assistant operator service section de l’Eau, Office du Niger, Dafina/Markala Operational management Office du Niger
18/12/2007 Soulamana Sidibe – Chef service d’aménagement, Office du Niger Planning and management Office du Niger
18/12/2007 Brehima Doumbia – Agronomist, Chef Vulgarisation Recherche et Développement, Office du Niger Production and revenue Office du Niger
Ambassade Royaume des Pay-Bas, Bamako 03/12/2007 Mme. Nana Danté – Ecologist, GIRENS project consultant General discussion on potential conflict in the Upper Niger Basin
19/12/2007 Jacob Mebius – Premier Secrétaire Ambassade Pays Bas Decision making in planning of Office du Niger
International Organisations 07/12/2007 M. Aliou Faye – Country Representative International Union for the Conservation of Nature and Natural Resources (IUCN), Bamako. Causes and effects of problem in the Inner Delta of the Niger river
12/12/07 – M. Luc Ferry – Hydrologist, director of research Institute de Recherche pour le Développement, Bamako Knowledge sharing on modelling water conflicts in the Niger Basin
14/12/07 Bakari Koné – Ecologist, Coordinator Wetlands International Mali, Sevare System, importance and vulnerability of the Inner Delta of the Niger
14/12/2007 Huub Mustenge Coordinator Protos project Gestion Integré des Ressources en Eaux de Delta Interieur du fleuve Niger (GIREDIN), Sevaré Discussion on GIREDIN, Office du Niger and generally on the Niger Basin
Documents Next, the documents collected during the mission will be mentioned. The documents are grouped per organisation. Of every document there is a short description of its content.
Documents received from the Direction Nationale de l’Hydraulique (DNH)
Digital water level and discharge data from the Hydracces database of DNH; Times series of water level and discharge at a range of locations in the Niger Basin in Mali in various periods between 1906 and 2007
Digital piezometric data from the SIGMA database of DNH; Time series of piezometric level at a range of locations in Mali in the various periods between 1981 and 1995
June 2008 iv G.J. van Dijk
“Annuaire Hydrologique 1996”, République du Mali, Ministère des Mines, de l’’Energie et de l’Eau, Direction Nationale de l’Hydraulique, August 2004 ; Hardcopy of the daily level and discharge data in 1996 and details on the measurement stations
“Bulletin Hydrologique Numéro : 50/2007 DNH” Hardcopy, week 50’s water level and discharge data compared with the year before and the long term average.
“Débits Moyens Journaliers de la Station – Capteur Koulikoro – J1, Année 2007” “Débits Moyens Journaliers de la Station – Capteur Kirango Aval (Diamarabougou) – J1, Année 2005,2006, 2007” “Cotes Moyennes Journalières de la Station – Capteur Koulikoro – J1, Année 2005,2006, 2007” “Cotes Moyennes Journalières de la Station – Capteur Kirango Aval (Diamarabougou) – J1, Année 2005,2006, 2007” “Cotes Moyennes Journalières de la Station – Capteur Bamako – J1, Année 2005,2006, 2007” Hydraulic journals over 2005 2007 of water level and discharge of a three stations alang the Niger.
“Note Technique sur le suivi de la Stabilité du Barrage de Sélingué”, Commission Gestion des Eaux de la Retenue Sélingue, 2007; Hardcopy, description and state of the Selingué dam.
“Sites des tout les PETs GIRENS” Digital spreadsheet of river cross section in the Basin of the Upper Niger
Documents used from the library of the Direction National de l’Agriculture (DNA)
“Plan de Campagne Agricole 2004/2005, Ministere de l’Agriculture, Direction National de l’Agriculture, Republique du Mali” Planning and evaluation of the agriculture in Mali
“Plan de Campagne Agricole 2005/2006 Ministere de l’Agriculture, Direction National de l’Agriculture, Republique du Mali” Planning and evaluation of the agriculture in Mali
“Schéma Directeur du secteur du développement Rural (SDDR); actualisation 2000, volume I, II,III, Ministère du Développement Rural, République du Mali, Décembre 2001” Description of the Agricultural sector with statistics and analysis
Documents received from Direction National de la Conservation de la Nature (DNCN)
“Politique Nationale et Plan s’Action de Gestion des Zones Humide” Leaflet for the wetland management plan
Documents received from the Direction Nationale du Génie Rural (DNGR)
“Etat irrigation au 31 Décembre 2005”; Digital spreadsheet, crop variation per Region of Mali
June 2008 v G.J. van Dijk
“Revue du sous secteur de l'irrigation, Projet d'appui institutionnel à la D.N.G.R au Mali”; Digital spreadsheet, detailed statistics of irrigation in Mali (received via PROTOS)
Received from the Direction de la Production Réseau Interconnecté, Energie du Mali SA (EDM SA)
“Centrales du Réseau Interconnecté”; Digital text file and hardcopy description of the main power plants serving Mali’s energy demand
“CHARGES PRODUCTION RI 2004 2005 2006 2”; Digital spreadsheet energy production costs over 2004, 2005 and 2006
“Commentaire sur la Gestion de la Retenue de Selingue du 01 Mai au 07 Juin 2007”, 08 june 2007; Hardcopy, Data on the level, inflow and outflow of the Selingué reservoir January 2005 June 2007
“Commentaire sur la Gestion de la Retenue de Selingue du 01 Octobre au 10 D Décembre 2007”, 11 december 2007; Hardcopy, data on the level, inflow and outflow of the Selingué reservoir January 2005 – December 2007
“Courbe de Charge prévisionnelle semaine 49 2007 et Programmation du Parc de Production”; Digital spreadsheet simulation quantity and source of energy production for a particular week
“Etude d’actualisation et d’impact hydraulique du projet d’aménagement du barrage de FOMI sur le Haut Niger” page 111 112, ISL and SNC Lavalin, August 2006; Hardcopy of operational management plans of the Fomi dam
“Invitation de Reunion de la Comission Gestion des Eaux de la Retenue Sélingue” Hardcopy, agenda of the meeting of the Management committee of the Selingué reservoir and list of members of the committee
“Project Hydroélectrique de Fomi, Sommaire des Principal Caractéristiques”, SNC ; Hardcopy, details on the planned Fomi dam
“Suivi journalier de la disponibilité des groupes du réseau interconnecté, Journée du 05/12/2007”; Digital spreadsheet file and hardcopy energy production statistics one specific day
“Suivi optimal de la cote de Sélingué 2007”; Digital spreadsheet statistics and analysis of optimised management of reservoir at the Selingué Hydropower station
June 2008 vi G.J. van Dijk
Information received from the Direction Office du Riz de Mopti (ORM)
Detailed data and explanation of the physics of the irrigated rice schemes managed with the system of controlled submersion, verbally transferred
Documents received from the Direction Office du Riz de Segou (ORS)
Detailed data on the water levels needed for irrigated rice schemes managed with the system of controlled submersion, hardcopy
Digital drawing with the system and statistics of the Tamani zone of ORS
“Carte de Localisation des Actions – PDIS” Digital map of the ORS
“Schéma du complexe hydraulique de Tamani (8910ha)” Specifications of the ORS Tamani zone
Documents received from Direction de Office du Niger (ON)
“Analyse du Potentiel d’Irriagtion , lors de la Saison Seche, dans la Zone de l’Office du Niger, Rapport Minute, Dr. R. Schüttrumpf et T. Bökkers, A. Sangare, Republique du Mali, Minstère de l’Agriculture, Office du Niger, Juillet 2007” Digital text and spreadsheet files on dry season potential of Office du Niger.
“Compte d’exploitation selon le type d’amenagement (Niono) ˝ Hardcopy on production quantities, costs and values of the Niono zone of Office du Niger
Documents received from Wetlands International Sevaré
“PREM Policy Brief No. 3 April 2005”, DGIS; Hardcopy, special on impact of river management on poverty and the environment in the niger River Basin in Mali, by the Poverty Reduction and Environmental Management (PREM) Program.
Field observations Next an overview of the field observations will be given. This will be done in the order of the date of the observations. The structure of the description will be: date, place: observation.
14/11/2007 Sotuba Pont, Bamako: Rock formation in the river bed preventing navigation
20/11/2007 & 21/12/2007 Pont des Martyrs, Bamako: rapid decrease of the water level
16/12/2007 Office du Riz Mopti, Mopti: Irrigation with the system of “Submersion Controllée”
16/12/2007 Bani river tributary, Mopti: Local activities in the dry river bed
June 2008 vii G.J. van Dijk
16/12/2007 Harbour, Mopti: Commercial activities, like navigation, fisheries and tourism
18/12/2007 Barrage de Markala: Large dam securing water supply Office du Niger
18/12/2007 Office du Niger: Irrigation system with over 70.000 ha and a potential of 980.000 ha.
22/12/2007 Barrage de Selingue: Important hydropower dam with a lot of influence on the regime of the river Niger and a large irrigation scheme “Office du Développement Rural Selingue”
June 2008 viii G.J. van Dijk
B. Hydrologic Measuring Stations In figure B 1 one can see the measuring stations throughout the West Niger Basin for river discharge and/or water level (blue diamond of flow), rainfall (purple square of precipitation) and groundwater head (yellow triangle of Piezometric). A selection of these stations is used to support this study. However the quality and length of the measured time series vary. Nevertheless they are used, but obviously in a responsible scientific way. flow precipitation Piezometric 17.5
16.5
15.5
14.5
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12.5
11.5
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9.5
8.5 -11.7 -10.7 -9.7 -8.7 -7.7 -6.7 -5.7 -4.7 -3.7 -2.7 -1.7 figure B-1 Hydrological measuring stations in the West Niger Basin
June 2008 ix G.J. van Dijk
C. Discharge Measurement Method In Mali all hydraulic measuring station produce water level data are collected. For some station this is translated into a discharge (Q), by using a programmed transformation formula in a Hydracces database. This formula is based on a the cross section (A) of the river, which is measured with an Acoustic Doppler Current Profiler (ADCP). With this device a cross section is determined by echoes. Furthermore, the velocity (v) and water level(h) are measured during a certain period. And with the collected data on A, v and h the Q h v relation is determined. The ADCP units are only used for this determination. Presently the discharge is automatically calculated based on hourly water level and discharge measurements by respectively gauges and mills. The accuracy of these data is ambiguous. They seem to be very exact: water level in cm and discharge in 0,1 m3/s. Whether this is really measured as exact and more important, really as accurate is not clear. The profiles determined with the ADCP are up to 15 years old. So if the Q h relation is representative is not known. Additionally, during interviews at DNH it has not become clear whether the velocity measurement are taken as regularly as the water level measurements. And if they are taken they seem to be taken at one place in the river. If this is a representative velocity and an accurate measurement, is again not clear. (This information is based on explanations of DNH employees. Since my understanding of their French has been limited part of the uncertainty can be ascribe to this.)
June 2008 x G.J. van Dijk
D. Discharge Data 1985-1999 The data used for the calibration of the River Basin Simulation Model are based on measurements of DNH Mali and the ABN over 1985 1999. Lacking data are filled up with the data from Passchier (2004) or nearby measuring stations. The discharge of the Fomi dam is represented by the measuring station at Baro (Guinea) in the Niandan River (table D 1). The data of the Niger River and its tributaries (except the Niandan River) before Malian border are represented by the Banakoro (Mali) measuring station minus the data for Baro (table D 2). The Bani River is represented by the measuring station of Beney Kengy, lacking data are filled up with data from Djenne Aval (table D 3). The discharge of the Sankarani River before Selingué is discussed in Annex G Selingué Dam and Reservoir Specifications. Niandan jan feb mar apr may jun jul aug sep oct nov dec 1985 7.2 2.1 0.8 0.3 1.7 8.6 132.0 389.0 611.0 327.0 103.0 39.9 1986 10.0 5.5 1.5 0.9 0.6 23.8 83.2 380.0 680.0 422.5 150.7 43.6 1987 14.7 5.1 1.5 0.7 1.2 164.0 106.5 331.9 506.1 487.5 117.0 48.6 1988 15.0 3.7 1.6 0.6 0.7 7.1 114.0 529.0 590.0 223.0 103.0 31.3 1989 8.3 3.0 1.3 0.9 0.9 45.6 53.9 332.0 529.0 458.0 143.0 58.9 1990 17 1.8 0.6 0.4 1.1 14.5 89.6 467.0 400.0 362.1 132.8 49.2 1991 18.5 4.4 1.6 1.5 2.0 16.6 129.0 351.0 378.0 399.0 154.0 53.4 1992 15.3 4.5 0.9 0.1 1.1 54.4 171.0 329.0 338.0 364.0 193.0 62.4 1993 22.2 5.1 2.7 0.7 0.6 17.5 69.4 357.5 450.3 357.5 189.9 75.3 1994 25.1 6.5 1.8 1.5 7.7 51.7 168.3 452.7 949.4 879.7 531.6 128.1 1995 52.0 23.5 11.2 10.4 14.7 32.5 99.6 552.5 1012.1 826.4 290.1 94.5 1996 42.7 26.0 10.9 7.3 14.3 39.7 119.5 431.7 826.4 705.7 248.4 76.4 1997 30.6 13.9 5.8 16.2 40.1 312.0 540.0 609.0 985.0 543.2 208.0 77.8 1998 32.7 14.8 3.0 1.2 4.5 72.4 235.0 637.0 654.0 516.0 190.0 69.8 1999 28.3 11.0 29.1 11.9 2.9 10.2 84.7 320.3 819.4 712.6 129.7 80.0 table D-1 Dis charge Niandan River at Baro 1985 -1999 Niger jan feb mar apr may jun jul aug sep oct nov dec 1985 56.6 23.9 8.9 5.0 4.8 9.7 315.7 1446.0 2114.0 1475.0 403.0 129.8 1986 49.0 15.4 4.3 2.5 6.6 3.8 143.9 812.0 1900.0 1186.3 423.0 147.4 1987 56.3 24.6 5.6 2.0 3.3 20.9 299.2 932.1 1420.9 1368.8 582.5 174.8 1988 63.7 25.7 6.7 3.3 2.2 19.7 216.6 972.0 2057.0 930.0 324.7 103.6 1989 37.0 13.6 5.2 3.3 3.7 5.8 151.2 703.0 1427.0 1125.0 382.5 151.6 1990 52.6 19.5 5.3 2.2 20.6 40.7 251.6 900.0 1647.0 1016.8 372.8 138.2 1991 52.0 16.7 5.4 4.3 5.6 46.5 288.8 984.0 1521.0 1187.0 541.2 178.2 1992 71.5 29.4 8.6 3.0 4.4 51.9 374.9 930.0 1703.0 991.0 387.3 141.7 1993 51.3 21.2 10.8 6.7 13.2 49.0 194.9 1003.8 1264.5 1003.8 533.2 218.8 1994 79.0 30.7 15.1 5.5 2.0 145.4 472.6 1271.0 2665.9 2470.4 1492.6 359.8 1995 146.0 65.8 31.5 29.1 41.3 91.3 279.6 1551.3 2841.9 2320.4 814.8 265.3 1996 119.9 73.0 30.7 20.5 40.3 111.5 335.7 1212.4 2320.4 1981.5 697.4 214.4 1997 110.1 50.4 16.2 10.0 26.0 115.4 146.8 872.0 1800.0 1525.2 735.8 261.6 1998 103.3 43.4 25.3 8.9 16.5 119.4 352.7 1485.0 2398.0 2239.0 677.0 213.5 1999 79.5 31.0 81.5 33.5 8.0 28.5 237.9 899.5 2300.9 2001.1 364.4 240.0 table D-2 Discharge Niger River and confluents (excl. Niandan) at Banankoro 1985-1999
June 2008 xi G.J. van Dijk
Bani jan feb mar apr may jun jul aug sep oct nov dec 1985 13.5 6.3 1.7 0.1 0.0 8.0 73.0 284.8 600.7 434.6 119.2 42.1 1986 17.3 8.6 2.8 0.4 0.3 14.9 46.7 177.8 552.2 344.1 104.2 38.9 1987 16.8 8.9 3.1 0.3 0.0 8.3 20.1 130.5 305.3 303.3 110.8 32.3 1988 15.6 7.6 2.2 0.2 0.0 6.3 143.8 475.7 1019.0 671.9 178.1 60.3 1989 26.5 15.8 9.1 5.2 1.1 0.4 23.1 334.5 866.4 475.7 124.5 43.3 1990 22.4 15.2 8.5 2.5 0.1 7.7 92.6 549.5 480.5 329.7 112.0 42.5 1991 21.8 13.5 6.5 1.6 0.1 28.4 58.2 487.6 751.0 424.0 196.3 68.9 1992 30.1 19.2 11.2 5.2 1.4 23.6 40.0 178.4 619.6 362.9 129.4 49.9 1993 24.7 15.9 9.2 3.5 0.5 0.2 86.2 188.4 594.5 344.9 128.5 47.6 1994 25.5 16.5 9.6 3.3 0.5 19.9 83.5 768.2 1207.0 1343.0 930.4 272.8 1995 86.4 43.4 25.7 17.9 11.8 27.4 34.6 377.1 760.4 676.9 283.3 96.5 1996 41.2 22.4 12.6 7.6 5.3 19.4 37.9 420.3 716.3 555.6 200.4 71.9 1997 30.5 16.8 9.2 5.0 4.8 24.3 65.6 418.6 815.9 438.1 173.6 65.6 1998 29.2 15.9 8.1 3.7 3.6 11.8 60.6 677.5 1228.0 1226.0 377.2 106.7 1999 47.2 25.1 13.4 7.9 4.2 4.6 96.8 1005.0 1652.0 1334.0 564.5 177.3 table D-3 Discharge Bani River at Beney Kengy 1985 -1999
June 2008 xii G.J. van Dijk
E. Meteorological Data Precipitation and (Net)Evaporation[mm/day] Zone Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Evaporation Southern Reservoirs 6.3 7.8 7.2 6.6 5.3 4.1 3.3 3.4 4.2 5.1 5.9 5.8 Lower ID 7.5 8.8 10.2 9.5 8.6 7.2 5.1 4.8 5.0 5.8 6.7 7.0 Upper ID 6.5 7.2 8.8 9.7 9.9 9.2 8.4 6.7 7.2 7.9 7.6 6.2 Precipitation Southern Reservoirs 0.0 0.0 0.1 0.7 1.8 4.5 7.4 9.6 6.8 2.1 0.2 0.0 Lower ID 0.0 0.0 0.0 0.0 0.2 2.3 2.1 3.2 4.4 0.4 0.0 0.0 Upper ID 0.0 0.0 0.0 0.0 0.0 0.4 1.4 1.8 0.9 0.0 0.0 0.0 Net-Evaporation Southern Resvoirs 6.3 7.8 7.1 5.9 3.5 0.4 4.1 6.2 2.6 3.0 5.7 5.8 Lower ID 7.5 8.8 10.2 9.5 8.4 4.9 3.0 1.6 0.6 5.4 6.7 7.0 Upper ID 6.5 7.2 8.8 9.7 9.9 8.9 7.0 4.9 6.3 7.9 7.6 6.2 table E-1 Evaporation and precipitation in the West Niger Basin (Schüttrumpf 2007, Passchier 2004, FAOCLIM)
From FAOCLIM (figure 2 3) only rough meteorological data can be estimated. The exact data are not freely available. So these are extracted form other documentation. The primer source is Passchier (2004), his report explains the RIBASIM model used as basis for this research. In general he uses the evaporation and precipitation data of the meteorological measurement station at Macina. These give a comparable result with the FAOCLIM measurement of Bougouni and are used for the lower reservoirs, like Fomi and Selingué. The data Passchier (2004) uses for the Inner Delta seem arbitrary since they date from 1986. Therefore Schüttrumpf (2007) is used for the evaporation and precipitation data form measurement station Sahel. These data can be compared with the graph of Mopti from FAOCLIM. Passchier (2004) also indicates that the northern part of the Inner Delta has significantly more evaporation and less precipitation. Therefore he uses the evaporation data from 1986 and neglects precipitation. In this research the data from the FAOCLIM figure of Timbuktu are used to estimate the net evaporation in the very north of the study area. The data used in this study are presented in table E 1. It is hard to say something about the reliability of the data since measuring methods are not known. But comparing the sources gives the impression a decent estimate is taken.
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F. Geohydrology Within this research on water availability conflicts, geohydrology needs to get attention. Since a large part of the drinking and sanitary water supply comes from boreholes and wells. In the scope of this research it is interesting to know the sensitivity of groundwater to changes in the Niger River’s regime. This could identify whether surface water interests interfere with groundwater interests. Data There is not much known of Mali’s geohydrology. During the 80s and early 90s some research has been done, resulting in the SIGMA 2 (Système Informatique de Gestion des resources en eau du Mali, version 2) database. This data base contains time series of piezometric level data at various locations in Mali. Since 1995 all measuring stations have been abandoned due to lack of funding. At the governmental hydraulics department (DNH), one presently discusses the rehabilitation of the measuring stations. The piezometric data series have a lot of gaps. And there are only few piezometric measuring stations that are close to water level measuring stations in the river. This makes it difficult to determine any significant relation between groundwater and surface water. Only the stations at Bougouni (a discharge measurement station about 40 km east of Selingué in the Baloue River, part of the Bani Basin) and Kollasokoro (a piezometric station 5.5 km north of Bougouni) are suited for an attempt to discover a relation between surface water and groundwater. Unfortunately only the discharge and not the water level data of Bougouni are available; the river level is expected to influence the groundwater, not the discharge. However the trend is not much different. The analysis of the relation between groundwater and surface water is done by comparing the data and developing an empirical relation. The next figures give the discharge and the average discharge over the years 1983 1990 at Bougouni (figure F 1) and the actual piezometic level and its empirical approach at Kollasokoro (figure F 2). One can recognize the year trend (long term) as well as the short term trend giving the peaks during the rainy season in the discharge and the piezometric graph. As one should expect for the groundwater the peak is slightly later and is has a more gradual decrease. These effects are incorporated in an empiric formula transforming the river discharge in a piezometric level. The formula is based on the average discharge over the past 2, 4, 6, 9, 12, 18 and 24 months, with a double weight for 12 and 24 months. The result is the “derived piezometric level” in figure F 2. The visual correlation seems quite high. This would indicate a dependency of the groundwater table on the river level, as specially over long periods (a year or more). But one has to be careful here. First of all the precipitation graph is expected to have a shape comparable to the discharge graph. Unfortunately this graph is not available. But assuming this is the case, then the precipitation could be the major factor influencing the groundwater. And at the same time the precipitation could determine the discharge. Mahe (2000) does this comparison of precipitation and groundwater level. He concludes there is a dependence. Remarkably (but not surprising) he turns the causal relation between surface water and groundwater around. With a sufficient groundwater level the base flow of the river is supported by the recharge from the groundwater. So the groundwater level is determined by the hydrology of the previous years. But Mahe (2000) does not give more than a qualitative analysis for this relation.
June 2008 xiv G.J. van Dijk
Furthermore the derivation performed in this study is purely empiric. Taking into account water level, as well as soil type, conductivity, transmissivity, porosity, etc. one could determine the water level based on geohydrologic science, using formulas like Hooghoudt or Hellinga De Zeeuw. Unfortunately all these specification are not known at the groundwater department of DNH. Since the previous analysis gives reason to think there is an influence of surface water on groundwater, it is advisable to do a more elaborate study. Additionally it would be interesting to identify the impact on the population depending on the groundwater that is influenced by the river level. But this exceeds the scope of this study.
Discharge Bougouni [m3/s] 300 Discharge 250 Yearly average discharge
200
150
100
50
0 Jan 83 Jul 84 Jan 86 Jul 87 Jan 89 Jul 90 figure F-1 Baloue River discharge at Bougouni 1983 -1990
Piezometric level Kollasokoro [m +MSL] 362 Piezometric level 361 Derived piezometric level 360 359 358 357 356 355 354 353 Jan 83 Jul 84 Jan 86 Jul 87 Jan 89 Jul 90 figure F-2 Piezometric level Kollasokoro 1983 -1990
June 2008 xv G.J. van Dijk
G. Selingué Dam and Reservoir Specifications To analyse the dependency of the Selingue dam on the water of the West Niger Basin a RIBASIM model will be used. For this model three relations are needed. First of all, the Power – Turbinated discharge –Energy head relation is needed. Second of all, the production efficiency should be clear. Finally the Volume Water level relation and the inflow of the reservoir have to be known. Relation Power – Turbinated discharge –Energy head The hydro energy production depends on the water level of lake Selingue and the turbinated discharge. The general formula for this relation is:
P= α * g * ρ * Q * H (formula 1)
P= power production [W] α= efficiency factor for the power production g= gravitational acceleration of 9.81 m/s 2 ρ= volumetric weight of water, 1000 kg/m 3 Q= turbinated discharge [m 3/s] H= energy head of the water, no flow is assumed due to the large reservoir, so this is equal to the water level [m]
If the H is constant this would be a simple linear function. But H varies due to the reservoir outflow (turbinated flow and spillway flow), but also because of the incoming flow (natural inflow). This gives a yearly fluctuation of about 10m which is significant with a 23 meter high dam and a maximum head of 19 meter. Production efficiency Like any other energy production process, also hydropower generation has no 100% efficiency. This has a number of reasons. First of all the power is generated with a system of 4 turbines (11,7 MW each, monthly maximum of 8.4GWh). The turbines transform hydraulic head to electric energy. Their rotation resistance causes energy dissipation. Secondly, the shear resistance, in and outflow of the tube result in energy dissipation. This is related to the velocity of the water. The general formula for energy head loss due to the velocity of water is:
2 Hdiss = β*v /2g (formula 2)
β= ratio number [ ]
Hdiss = dissipating energy head [m] v= velocity of the water [m/s] g= gravitational acceleration of 9.81 m/s 2
Thirdly the downstream level influences the production. In first instance the turbine level is assumed to be positioned at 330.0m. Passchier (2004) assumes this is 331 m, but this would mean that the efficiency would be more than 100% in many cases. But if one has a closer look at October 2007 December 2007 the downstream water level varies between 330.0m and 333.5m (EDMsa). This has impact on the power production. This is made visible in figure G 1. The efficiency with a constant downstream level of 330m MSL is shown (yellow triangles). This seems strongly related to the water level downstream of Selingue. The average downstream water level over 1982 2002 is also plotted in the graph (blue diamonds). Now we
June 2008 xvi G.J. van Dijk
compensate the energy head for this downstream water level over August, September, October and November. The levels for these months are raised to respectively 331.4, 333.7, 332.9 and 330.9 m MSL. The resulting efficiency is given (purple squares). One may conclude that there is no strong variation in the production efficiency, but it is the energy head causing a fluctuation in the electricity production. In general on can say that the efficiency relatively constant at 89% with a 6% deviation. Water level and Efficiency 334 120% 333 115% 332 110% 331 105% 330 100% 329 95%
328 90% Efficiency 327 85% Water Level [m MSL] [m Level Water 326 80% 325 75% 324 70% 1 2 3 4 5 6 7 8 9101112 Month water level selingue aval compensate efficency plain efficiency figure G-1 Selingue water level, production and efficiency calibration For Selingué the conclusion can be drawn that formula 1 with compensated data for the downstream water level and an efficiency of 89 % seems to be good representation of reality. In figures below this is clearly shown. The figure G 2 shows the relation between turbinated discharge (horizontal axis) and the monthly power production (vertical axis) depending on the water level in meters above MSL (color). The dots represent the real values from 1982 2002 and the x points represent the calculated values with a fixed water level of 338.5 m MSL (blue x) and 349.45 m MSL (red x). The figure G 3 shows the relation between the water level (horizontal axis) and the monthly power production (vertical axis) depending on the turbinated discharge in m 3/s (color). The dots represent the real values (1982 2002) and the x points represent the calculated values with a fixed turbinated discharge 34 m 3/s (blue x) and 289 m 3/s (red x).
June 2008 xvii G.J. van Dijk
figure G-2 Water level – power produ ction relation for Selingué
figure G-3 Turbinated discharge – power production relation for Selingué
Reservoir specifications For the water availability throughout the year it is important to know the relation between the water level of the reservoir and the volume of the reservoir. To be able to take the evaporation into account one needs to know the water surface area of reservoir in relation to its water level . Passchier (2004) uses a relation for the active storage (the volume actually available for turbination). In this way he assumes there is no surface area and no volume when there is still 11,5 meter of water in the lake. For his way of working this is justified, because actually he does not use the reservoir. He uses the same inflow data for the reservoir as the outflow, probably because there are no data on the inflow discharge. Concluding his data are not suitable for further use if one wants to model the Selingué reservoir properly. Therefore a new Volume – Surface Area Water Level relation is determined. More over a new dataset for the inflow discharge is deduced from the new estimated reservoir specification and measured outflow discharge data. The reservoir relations are based on a combination of two triangular pyramids (see figure G 4. The constraints for the reservoir level are (EDMsa): Lowest level: 327 m MSL Highest level: 350 m MSL Surface area at 349 m MSL: 450 km 2 Utilised volume 338,5 349 m MSL: 2135 hm 3
June 2008 xviii G.J. van Dijk
h
L
B figure G-4 Schematisation of Selingué reservoir with two joint triangular pyramids The length of the reservoir is estimated at 100km with a maximum width of 9km. The exact specification of the reservoir shape used for this study are presented in table G 1. Level [+m MSL] Surface [ha] Volume [hm3] 327 0 0.00 330 237 2.37 333 948 18.96 336 2133 63.98 338.5 3482 133.48 339.5 5416 177.62 340.5 7775 243.22 341.5 10559 334.54 342.5 13769 455.83 343.5 17404 611.34 344.5 21465 805.33 345.5 25950 1042.05 347.5 36198 1660.70 348.5 41960 2051.14 349.5 48147 2501.31 table G-1 Relation of water level with the water surface area and the volume of the Selingué reservoir The newly estimated dimensions of the Selingué reservoir give the opportunity to estimate the basin inflow as a result of the water balance of the reservoir. The time series of water level data of Lake Selingué are used to estimate the monthly volume (and surface) variation in the lake. These variations are compensated with the monthly discharge at the hydropower dam. This results in an a yearly average inflow of 224 m 3/s, while the outflow is 211 m 3/s (Calculated over 1982,1985 1999, 2006). This means there is a 13 m 3/s surplus on the discharge balance. A 10 m 3/s surplus is expected, based on the estimated average net evaporation (precipitation minus evaporation) of 2.6 mm per day (Passchier, 2004). The other 3 m3/s can be subscribed to water use directly from the reservoir and groundwater recharge as well as to an accuracy error. This seems an acceptable estimate for further calculation and modeling. In figure G 5 one can see the result of the estimated inflow discharge with the assumed geometry of the reservoir based on the average water level and out flow discharge of the Selingué reservoir. Additionally the exact inflow data used for simulation of 1985 1999 is presented (table G 2).
June 2008 xix G.J. van Dijk
Selingue Reservoir Hydro specification
800.0 349.0
700.0 348.0
600.0 347.0
346.0 500.0 345.0 400.0 344.0 300.0
343.0 Water Level [m] Discharge [m3/s] 200.0 342.0
100.0 341.0
0.0 340.0 jan feb mar apr may jun jul aug sep oct nov dec inflow outflow Lake level
figure G-5 Average hydraulic relation for the Selingue reservoir throughout the year year jan feb mar apr may jun jul aug sep oct nov dec 1985 31.6 10.9 19.7 25.1 37.0 107.7 360.4 556.8 1049.5 572.0 137.2 40.9 1986 24.6 14.5 27.7 33.2 48.4 66.4 196.2 526.4 655.6 372.1 144.5 42.4 1987 33.6 17.9 17.6 15.0 101.7 69.8 117.2 499.4 412.9 320.3 73.9 26.7 1988 16.6 18.4 12.3 6.9 47.4 50.3 191.0 485.2 444.8 225.0 50.0 12.5 1989 17.5 32.6 13.5 21.6 93.8 63.2 173.0 518.0 386.1 269.7 71.9 43.3 1990 10.4 0.0 0.0 43.6 5.1 109.3 369.4 560.5 430.5 358.7 84.9 51.2 1991 23.4 8.7 36.8 15.2 44.2 104.4 386.1 604.5 638.6 499.9 190.4 62.3 1992 41.5 0.8 9.8 15.6 46.6 112.5 319.9 653.5 858.8 450.3 147.1 79.4 1993 9.1 0.0 37.6 28.4 44.9 69.2 250.5 635.7 588.5 475.1 148.2 75.3 1994 28.8 0.0 17.1 19.2 16.7 107.9 307.9 506.4 733.7 1155.8 697.4 154.7 1995 44.4 11.1 28.9 42.1 2.7 91.0 219.9 642.8 967.3 795.9 184.6 80.9 1996 51.2 6.8 5.6 17.5 70.2 90.6 120.5 806.1 407.3 661.8 166.8 31.5 1997 37.6 0.0 0.0 10.5 4.3 56.8 396.6 688.9 1034.6 555.1 170.2 73.6 1998 0.0 0.0 0.0 47.2 83.2 101.4 378.3 642.2 1071.3 984.5 244.4 109.3 1999 0.0 13.2 2.7 6.2 77.3 19.4 196.8 646.6 1220.9 782.6 300.1 114.2 table G-2 Discharge into the Selingué reservoir from 1985 -1999 Subsequently an estimation of the effects of the discharge on the downstream level as previously discussed is given based on historical measurements. The effect is called the tail raise and is showed in figure G 6.
June 2008 xx G.J. van Dijk
Tail raise Selingue 336
335
334 1982 333 1985 1992 332 1994 1995 331 2000 330 2005
Downstream water level water [+mDownstream MSL] tail raise 329 0 200 400 600 800 1000 1200 Dam discharge (turbined + spilled) [m3/s] figure G-6 Discharge –tail raise relation downstream of the Selingué dam Finally a target level for the Selingue reservoir is given. This is based on the present management practice of EDMsa, see figure G 7.
Target level Selingué reservoir [m +MSL] 349.0
347.0
345.0
343.0
341.0
339.0 jan feb mar apr may jun jul aug sep oct nov dec level 348.2 347.6 346.5 345.1 343.5 341.5 340.2 344.5 348.0 349.0 349.0 349.0 figure G-7 Monthly t arget water level for the Selingué reservoir (EDMsa)
June 2008 xxi G.J. van Dijk
H. Fomi dam and Reservoir Specifications Valuation of the Fomi dam For valuation of the Fomi dam in the context of hydropower a short analysis will be given from the Malian perspective. Mali’s yearly energy demand is estimated at 830 GWh (EDM week 49/2007). Part of this demand is met by three hydropower stations. According to the data of OMSV Mali receives 284 GWh per year (52% of 547 GWh § 3.1.1). The Selingué station produces 210 GWh per year according to EDMsa. And the Sotuba station’s production is estimated at 30 GWh per year, based on EDMsa records. This means the total hydropower production is estimated at an average of 524 GWh. The deficit of 306 GWh is bought from Senegal or produced by diesel generated thermal power stations. If we assume that the cost of the bought energy is the same as the self produced thermal energy the benefit would be 123 FCFA/KWh (§3.1). And if we assume Fomi could supply this deficit to Mali, it would reduce the production costs with 37.6 billion FCFA per year (€57.4 million).
The real situation is a little bit more complex, since dam is in Guinea and the capacity is limited. According to Malien officials at EDMsa about 50% of the energy produced at Fomi is going to Mali and 50% to Guinea. The average yearly energy production is 374 GWh (ISL, 2006). If we assume the 123 FCFA/KWh production cost reduction also hold for Guinea that would result in a yearly cut in the expenses of 42 billion FCFA (€70 million), excluding the investment costs. Controversially, Zwarts et al. (2005, p233) uses an added value for hydropower of 75 FCFA/kWh and an annual benefit of about € 35 million once the Fomi dam is in full operation. It does not become clear if and how he takes the investment costs into account. An interesting note is that, according to the same document (ISL, 2006), the average production is less than 50% of the maximum planned capacity (788GWh) and 272 GWh (about a third of the maximum capacity) can be 90% guaranteed. Moreover 251 GWh can be guaranteed for a 100% guarantee. These numbers are based on historic data of 1947 1997. One needs to realise that ever since the Great Droughts of the early 70s and especially 80s the water availability has structurally decreased (see figure 2 2). Remarkably, SNC had already reduced its previously mentioned production forecast with 17%. It used to estimate a 90% guarantee of 328.5GWh. This is good sign that might give reason to regain some confidence in their present estimates. But the fact that the Zwarts (2005) takes a 312 GWh yearly production average at Fomi and BRL (2007) uses 320 GWh/year shows the uncertainty. The World Bank (2006) mentions even mentions a capacity of 250 MW, which is nearly three times mote than the design plans of the last decade of 90 MW.
Next to the (uncertain) direct benefits of low cost hydro electricity production, there is the investment that needs to be done to construct the Fomi dam. According to BRL (2007) the investment including mitigating measures is 288 259 million FCFA (€440 million). Zwarts et al. (2004) estimates an investment 288 million US$ based on data from Agence Canadienne pour le Développement International 1999. With a present €/US$ rate of €0,685 per US$ (23/01/2008) this is €197 million, but with the rate of 1999 (about €1.05 per US$) it would be about €300 million. Resuming the exact investment costs can only be roughly indicated between 200 and 400 million Euros. A negative effect of the dam is the resettlement of about 16000 people (BRL, 2007). These people need to build up a new live in a new place. This social impact needs t be valued. BRL (2007) does this by the development of a resettlement plan of €30 million.
June 2008 xxii G.J. van Dijk
Besides this effect and the interest of energy there are more benefits and drawbacks in the fields of ecology, economy and social issues. These are discussed in chapter 3 Fields of Interest. But since there is said to be considerable impact on the Inner Delta (Zwarts et al., 2005) it is worth mentioning that according to the EDMsa there are plans to manage the Fomi dam in a way its negative consequences for the Inner Delta are minimised. This means that it cannot be operated at full potential. It will have to be managed in such a way that the harm to the natural regime, especially the peak discharge, is minimized. In the scenarios used to simulate the West Niger Basin this is taken into account. However a comment needs to be made on heavy impact of the Fomi dam estimated by Zwarts (2005). His approach to identify the impact of the Fomi dam in Guinea on the Inner Delta in Mali is rough. He states that since the reservoir of Fomi is 2.9 times larger than Selingue the consequential reduction of the discharge is 2.9 times larger. In the most impacting case the effect on the volume may be approached this way. But since the Fomi dam is build in the Niandan river (a tributary of the Niger) other tributaries, the Tinkisso, Milo and Mafou river as well as the main upstream Niger are not effected. Accordingly this is an exaggerate effect. From the model one can see that the impact of the Fomi dam on the Niger river flow does not even reach half of the impact of the Selingue dam on the Niger rivers flow during the peak of an average hydrologic year. Fomi Model Data Obviously there are no historic data on the performance of the Fomi dam since it is not built. In general we know that the production discharge – energy head relation is P= α * g * ρ * Q * H (see Annex G). The production is limited by the capacity of the 3 Kaplan turbines of each 30 MW, resulting in a maximum production of 66 GWh per month. The Fomi dam is 42 meters high with the crest at 393 m MSL. Its minimal energy head for turbination is 17 meter at 380 m MSL and the maximum head 29 m at 392 m MSL. The aimed level maximum level is 390.5m MSL. The production efficiency is assumed to be comparable to the 89% of Selingue. 3 hm Fomi Level- Volume 8000
7000
6000 5000
4000
3000
2000 1000
0 348 353 358 363 368 373 378 383 388 m Passchier 2004 ISL/SNC 2006 figure H-1 Water level – volume relation Fomi reservoir
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ha Fomi Level- Area 60000
50000
40000
30000
20000
10000
0 348 353 358 363 368 373 378 383 388 m Passchier 2004 ISL/SNC 2006 figure H-2 Water level – water surface area relation Fomi reservoir The water availability depends on the storage volume (V) of the Fomi Lake and the evaporation losses related to the lake’s surface area (A). Both are related to the water level (H) of the lake. However the H A V relation is not evident. Passchier (2004) and ISL (2006) use different relations (see figure H 1 and figure H 2). The water surface area estimated by the two sources varies up to 20300 ha. The significance of this difference can be indicated by assuming 3 mm/day evaporation. The additional evaporation is 7 m3/s. Compared with an average discharge of about 172 m 3/s, this is about 4%. That is assumed to be within the range of the estimated accuracy. The average yearly inflow to the reservoir seems to be overestimated in most studies. Just like the overestimation of the production in Manantali (as mentioned in §3.1.4) old data are used, resulting in generally higher discharges than presently. In this study the available monthly data of 1980 2005 (178 of the 300 months) of the Baro measuring station from the Autorité du Basin du Niger (ABN) are used, giving a yearly average of 172 m 3/s. Other studies have much higher yearly average discharges: • WL Delft Hydraulics (Passchier 2004) bases the time series for 1980 2001 to run a simulation on measurement between 1999 2002 resulting in an average yearly discharge of 257 m3/s. • “Analyse Geomorphique du Bassin du Fleuve Niger” by Simons (1984) uses data from 1960 – 1979 and estimates an average yearly discharge of 241 m3/s (Royal Haskoning, 2007). Simons (1984) estimate is comparable with that of an old SNC study. But in a newer study of the SNC in 2006 on Fomi at least 7.5% less power production is calculated. Most probably this is the effect of newer, lower, discharge data. This adjustment of SNC confirms the need to use a lower estimated inflow of the Fomi reservoir. How ever it does not confirm the rigorous reduction of this study. Still the most recent data, based on the same source as used for the other studies (the ABN), indicates this lower inflow. Moreover (and dangerous to say) it is advantageous for engineers to use older data, because they make the construction of the dam more likely than by using real data. In this way they create their own potential work. To give an impression of the impact an example calculation will be given. Let us assume an average water level of 386 m MSL (23m energy head) and an efficiency of 89%. In table H 1 the resulting power production with the various
June 2008 xxiv G.J. van Dijk
discharge time series is given. The difference can be up to 50%. This is a significant difference that might lead to the conclusion that the construction of the Fomi dam is not profitable even when one only considers power generation. In this study the lowest discharge will be used. Note that the calculated estimated annual production in table H 1 is much higher than the estimates mentioned previously in this Annex. This could be caused by lower efficiency estimates or a lower estimated average energy head. Nonetheless the qualitative implications for the power production remain the same. Dataset ABN Simons Passchier Time series 1980 2005 1960 1979 1980 2001 Average yearly discharge (m 3/s) 172 241 257 Annual production (GWh) 303 424 452 table H-1 Comparison of discharge and power production of different studies The tail raise is estimate to be about 6 meter with a maximum discharge of 3300 m3/s the same curve as Selingué is applied to interpolate the tail raise (see table H 2)
Discharge [m3/s] Tail level [m + MSL]
0 363.4 100 363.6 200 363.9 300 364.1 660 364.8 1000 365.5 1700 366.8 2500 368.0 3300 369.0 table H-2 Water level – discharge relation downstream of the Fomi dam For the management of the Fomi reservoir a target level per months is determined to generate a maximum of energy continuous over the year. This maximum is based on the water availability of an average hydrologic year. The total volume available for power generation is the inflow into the reservoir (V_in) minus the net evaporation from the reservoir (V_evap). The net evaporation is estimated with Ribasim by simulation the Fomi dam managed with a target level based on the target level of Selingué. Based on these numbers the total volume available over the year is estimated (4957 hm 3) resulting in an average monthly water availability estimated (413 hm 3). The monthly water demand from the reservoir is determined by calculating the difference between the estimated monthly availability (V_in V_evap) and the average monthly availability. In dry months this results in a deficit and in wet month this results in a surplus of water. The reservoir supplies the deficiency and uses the surplus to fill up again. With an estimate of the volume level (V h) relation the theoretical optimal level of the reservoir is determined. Additionally some scaling is done to generate a smooth power production. This results in the target level described in figure H 3 Monthly target water level of the Fomi reservoirfigure H 3
June 2008 xxv G.J. van Dijk
Target level Fomi reservoir [m +MSL] 390.0
388.0
386.0
384.0
382.0
380.0 jan feb mar apr may jun jul aug sep oct nov dec level 388.2 387.1 385.8 384.5 383.3 382.8 383.2 385.3 388.0 389.4 389.7 389.1 figure H-3 Monthly target wat er level of the Fomi reservoir
June 2008 xxvi G.J. van Dijk
I. Irrigated Agriculture Specifications The RIBASIM model consists of nodes representing the irrigation schemes. Based on the previous elaboration an overview of the properties of these nodes is given. Note that RIBASIM is a 0 D model, so it does not contain any information on water level or hydraulic flow relations. It merely copes with volumes. For the controlled submersions systems, depending on water level, some smart steps are taken to model the water demand and allocation. For the fully managed systems modeling is more straightforward. By defining the irrigated surface with a certain water demand (in mm/day) the desired volume per time step is calculated. The uncontrolled irrigation systems are not dealt with in the model, since sufficient data lack. Water demand has been accounted for in m 3/ha/season (Annex J Quantification of the Agricultural Interest). But as model input one needs to define monthly water demand in mm/day. To translate from seasonal demand to daily demand the length of the irrigation cycle should be known. In reality, rice has a 120 days irrigation cycle. However a 150 days irrigation cycle is modeled, since not all farmers start cropping at the same time. Moreover the peak of the rainy season, on which the irrigation in the wet season depends, varies from year to year. The extra 30 days create the needed flexibility. For market gardening also a 150 days irrigation cycle is used. The irrigation nodes reflect the total water demand of an irrigation scheme. This means different crops are discounted in one representative water demand. For the resulting water allocation this makes no difference. The properties of the nodes of the irrigation schemes are dealt with from upstream to downstream. Office du Développement Rurale Selingue: 1 node ODRS jan feb mar apr may jun jul aug sep oct nov dec Total area [ha] 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 2100 water demand [mm/day] 5.83 10.52 10.52 10.52 10.52 5.83 4.01 6.87 6.87 6.87 6.87 4.01 table I-1 Monthly irrigated area and water demand of ODRS
Office du Perimetre Irrigue Baguineda: 1 node OPIB jan feb mar apr may jun jul aug sep oct nov dec Total area [ha] 500 500 500 500 500 500 4500 4500 4500 4500 4500 4500 water demand [mm/day] 4.30 8.60 8.60 8.60 8.60 4.30 4.17 8.33 8.33 8.33 8.33 4.17 table I-2 Monthly irrigated area and water demand of OPIB
Office du Riz Segou: 2 nodes Two zones of the four zones of ORS are not depended on water level: Tamani and Farako. As mentioned earlier the water level is not represented within the model. It is possible to assign a Q h relation to a section of the river (“connection” in RIBASIM), but these do not relate to other sections or nodes. So it is not possible to define the diversion of the river to an irrigation scheme depending of the water level in the river. But it is possible to do this depending on the discharge of the river. A general explanation of this procedure is given. For the modeling of the inlet flow to the water level depending irrigation zone the discharge ratio between river and the irrigation inlet is determined. This is done by matching the water demand (in m 3/s) of the irrigation scheme at certain water levels, to the river discharge at the same specified water levels.
June 2008 xxvii G.J. van Dijk
Water level unsufficient to submerge any of the sub zones: Q_diverted = 0
Water level is sufficient to submerge one sub zone: Q_diverted = Q_demand1
Water level is sufficient to submerge two sub zones: Q_diverted = Q_demand1 + Q_demand2
Water level is sufficient to submerge three sub zones: Q_diverted = Q_demand1 +Q_demand2 + Q_demand3
Water level is sufficient to submerge four sub zones: Q_diverted = Q_demand1 + Q_demand2 + Q_demand3 + Q_demand4
River Irrigation canal Inlet Submerged field Dry field figure I-1 Schematic explanation of the dependence of water supply to controlled submersion irrigation on the discharge of the river Resulting, at the inlet a Q h relation of the river is created based on the daily water level and discharge data from DNH (2004). (Since the Q h relation differs for the ascending and the descending flood, the linear trend with data throughout the year is used.) This is linked to the water demand of the irrigation scheme. The determination of the water demand depends on the area that can be submerged at a certain water level. This is related to the sub zones in which the scheme is divided. Each sub zone needs a specific water level to be submerged. The discharge diverted to the irrigation scheme at this water level is equal to the water demand of the sub zone. From the rivers’ Q h relation the river discharge at the same water level is determined. In this way a Q_river/Q_diverted ratio is established for one water level. The same thing is done for the second sub zone. Only now the Q_diverted is the cumulative water demand of sub zone 1 and 2. The figure I 1 gives a schematic representation of this explanation up to 4 sub zones. So far the general explanation, to determine the discharge diversion ratio of Tamani, the water level data of DNH (2004) of the Tamani measuring station are used. For the discharge the average of the data from Koulikoro and Ke Macina are used. These are the closest up and downstream discharge measuring stations. In the model the Farako zone is merged into the Tamani zone, because these zones are close to each other, without other diversions or confluences in between. This means that Farako, the more downstream zone is modelled as a sub zone of Tamani. Moreover Farako has no measurement station. So the water level is estimated from the station at Segou and Tamani with a 1:3 ratio and the discharge is assumed the same in Tamani. To compensate for the downstream position of Farako the real inlet level of Farako is lifted with 95 cm. The discharge diversion data for ORS zones Tamani and Farako are presented in table I 3.
June 2008 xxviii G.J. van Dijk
Q_river [m3/s] 1609 1993 2057 2210 2674 3165 3445 3652 3722 Q_diverted [m3/s] 1.8 4.3 10.2 28.6 37.7 38.1 39.5 41.4 44.5 table I-3 Water supply to ORS based on the discharge of the Niger River Remarkable is that these irrigation scheme do not meet water shortages because of lack of water, but due to lack head to get the water into the controlled submersion planes For the ORS non level dependent zones of Dioro and Sosse Sibila one node is used in the model with the cumulative irrigated surface area of 18580 ha rice fields with a water demand of 20.000 m 3/ha. The resulting water demand for the ORS nodes are: ORS jan feb mar apr may jun jul aug sep oct nov dec Tamani, Farako [ha] 14910 14910 14910 14910 14910 14910 Dioro, Sossé Sibila [ha] 18580 18580 18580 18580 18580 18580 water demand [mm/day] 6.7 13.3 13.3 13.3 13.3 6.7 table I-4 Monthly irrigated area and water demand of ORS
Office du Niger: 1 node The node is modelled as a combination of four cropping patterns: • wet season rice, 78 000 ha • dry season rice, 11 000 ha • dry season market gardening, 8 500 ha • double season sugar cane, 6 000 ha In contrast to rice and market gardening, sugar cane production has a 360 days irrigation cycle. Based on this information and the earlier defined seasonal water demand the characteristics of ON are defined, see table I 5 ON jan feb mar apr may jun jul aug sep oct nov dec Total area [ha] 25500 25500 25500 25500 25500 25500 80000 80000 80000 80000 80000 80000 water demand [mm/day] 5.2 8.4 8.4 8.4 8.4 5.2 4.5 8.3 8.3 8.3 8.3 4.5 table I-5 Monthly irrigated area and water demand of Office du Niger Office du RIz Mopti: 4 nodes The modelling of the ORM nodes is done exactly the same way as for ORS. For all zones there is a measurement station with Q and h data (DNH, 2004), except for Mopti Nord, which lacks discharge data. Since Mopti is located more or less in the Inner Delta it is hard to determine the exact discharge from stations further up or downstream due to the complex of branches and flood planes. Therefore the Q h relation is constructed with the assumption that the discharge at Mopti Nord is 1.5x the Bani discharge at Mopti Sud. The measuring stations supplying the Q and h data used for the modelling of the ORM zones: • Kara (Diaka river), Diak zone • Sofara (Bani river), Sofara zone • Mopti (Bani river), Mopti Sud zone • Nanataka (Niger river) only for water level, Mopti Nord zone The diversion used for the zones is defined as presented in the table I 6.
Diaka Mopti Nord Mopti Sud Sofara Q_river [m3/s] 346 525 1660 1937 2019 2255 1500 1869 2004 2039 561 793 1220 Q_diverted [m3/s] 14.2 19.6 3.3 5.2 22.2 26.0 14.0 20.9 22.3 33.5 2.1 9.2 16.8 table I-6 Water supply to ORM based on the river discharge
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The irrigated area and the water demand as modelled are shown in the table I 7 . ORM jan feb mar apr may jun jul aug sep oct nov dec Diaka [ha] 6370 6370 6370 6370 6370 6370 Sofara [ha] 5460 5460 5460 5460 5460 5460 Mopti Sud [ha] 10880 10880 10880 10880 10880 10880 Mopti Nord [ha] 8110 8111 8112 8113 8114 8115 water demand [mm/day] 6.7 13.3 13.3 13.3 13.3 6.7 table I-7 Monthly irrigated area and water demand of Office du Riz Mopti SCS-ID: 1 nodes Small Controlled Submersion scheme in the Inner Delta cannot be modelled. As with the previous controlled submersion systems it is matter of water level rather than matter of discharge to be able to irrigate. Unfortunately Q h relations for the Inner Delta are unknown because of the complexity of the system of branches combined with the flood planes. Moreover the controlled submersion schemes in the Inner Delta are small and not centrally managed by an administration like ORS or ORM. This means there are no data available on inlet levels or their locations. However an estimate is given with an irrigation node representing 50.000 ha of rice cropping (DNGR). The inlet level is chosen arbitrarily. Since these values are such inaccurate the node is not evaluated, but contributes to a better representation of the hydrology of the Inner Delta. Pumping Timbuktu: 1 node At Timbuktu an irrigation node with 4500 ha of irrigation and a water demand of 7500m3/ha per season spread over 150 days is modelled (see table I 8). TIMBUKTU jan feb mar apr may jun jul aug sep oct nov dec Total area [ha] 4500 4500 4500 4500 4500 4500 4500 4500 4500 4500 4500 4500 water demand [mm/day] 4.2 8.3 8.3 8.3 8.3 4.2 table I-8 Monthly irrigated area and water demand of Timbuktu pumping irrigation
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J. Quantification of the Agricultural Interest This annex elaborates on the yield of different types of irrigation schemes. In this way the importance of the schemes can be indicated by its potential. As mentioned earlier a concrete valuation is not the aim of this research. Further study can use this information as a starting point to identify the economic value of water. Within this research mainly the indication of water demand is used for modelling the water availability in the West Niger Basin. In the next and last paragraph of this chapter on irrigated agriculture the water demand is elaborated and translated to the data needed for the irrigation nodes in the RIBASIM model of the West Niger Basin. First a brief overview of the rice production under various types of irrigation is given by giving the expected yield and the water use. Subsequently this is done for sugar cane cropping and market gardening.
Fully managed rice systems 3100-9000 kg/ha, 7500-1500m 3/ha According to Marie (2007) pumping irrigation for rice has a yield of 6000 9000 kg/ha and gravitational systems have a yield of 4500 6000 kg/ha. Office du Niger indicates a lower yield for gravitational systems: 3100 4700 kg/ha. The water demand for pump irrigated rice is estimated at 7500 m 3/ha by the Director of DNGR. For gravitational systems he estimates 10000 15000 m 3/ha for rice. Schüttrumpf (2007) is in the same range with 12500 15000 m 3/ha, but according to Wetlands International, Mali the water supply is often double this demand.
Controlled submerged rice 1000-3000kg/ha, 14000-25000 m3/ha ORS estimates the rice yields around 2000 3000 kg/ha. Euroconsult (1989) is more conservative with 1000 2500 kg/ha. DNGR estimates a water use of 20000 25000 m3/ha which is considerably more than the ORS estimates of 14000 m 3/ha.
Free submerged rice 900 kg/ha Marie (2007) estimates the unregulated rice production yielding about 900 kg/ha. The water demand is hard to determine since there is no control over the water, but the systems can be estimated less efficient than regulated schemes.
Other crops 29000-80000 kg/ha, 10000-29000 m3/ha Sugar cane in fully managed irrigation areas demands 29000 m 3/ha (Schüttrumpf, 2007). The annual yield has been between 41 80 tonnes/ha over the last ten years (FAOSTAT). Fully managed market gardens demand less water, about 10000 m 3/ha (Schüttrumpf, 2007). Their yield can be roughly estimated on 29 tonnes/ha per annum for all crop on average, based on the yields in Office du Niger over 2006/2007, see table J 1. For less advanced irrigation systems the water demand is higher and the yield lower.
The last table of this annex, table J 2, shows a summary of the previous overview of yield and water demand. A representative estimate based on the previous elaboration is given. Note the water demand is per cropping season. The remarkable high water demand of sugar cane is due to the fact that it takes about a year before it can be harvested, while rice has a 120 day cropping cycle.
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Crop Surace Yield Production Water Water [ha] [kg/ha] [tonnes/y] demand demand * [m3/ha/y] [hm3/y] Onion 4910 34000 166940 10000 49 Tomato 699 24000 16776 10000 7 Sweet patatoe 561 30000 16830 10000 6 Garlic 197 17000 3349 10000 2 Pepper 252 7000 1764 10000 3 mais 356 4000 1424 10000 4 Patatoe 168 23000 3864 10000 2 gombo 249 12000 2988 10000 2 Cash crop 7392 28941 213935 10000 74 table J-1 Cashcrop specification s Office du Niger 2006/2007 (ON )
Type Crop Yield Water demand [kg/ha] [m3/ha] Fully managed (gravity) Rice (rainy season) 4500 12500 Fully managed (gravity) Rice (dry season) 4500 15000 Fully managed (gravity) Cash crop 29000 10000 Fully managed (gravity) Sugar cane (raw) 60000 29000 Fully managed (pump) Rice 6000 7500 Controlled Submersion Rice 2000 20000 Free Submersion Rice 900 *25000 Free Submersion Cash crop *15000 *20000 * author's estimate table J-2 Summary of yield and water demand per crop and type of irrigation
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K. Economic Value Irrigated Agriculture To indicate the economic value of irrigation the sales prices of the crops are presented. The pricing is not fixed but varies throughout the season. Moreover different sources mention different prices. Therefore overview of the price ranges of various crops is given in this annex. Together with the Annex J Quantification of the Agricultural Interest, the relation between economic value, yield and water demand can be drawn. Rice 100-200 FCFA/kg According to Wetlands International the value of paddy rice is 200 FCFA/kg. Office du Niger mentions a price of 125 FCFA/kg. The planning and control sevice of Office du Riz Segou mentions paddy rice prices of 100 140 FCFA/kg Sugar (cane) 375-397 FCFA/kg In 2004 the sugar price was 375 FCFA/kg (AMAP, 2005). Note that only about 10% (Euroconsult, 1989 and Schaffer, 2007) of the cane mass is turned into sugar. Moreover there are plans to produce ethanol from the sugar cane. This is largely made from the leftovers of the sugar production. Ethanol prices are high, because of its potential to substitute gasoline. According to Jolly (2001): “In a free ethanol market – a market without ethanol subsidies – the true value of ethanol would be equal to the price of unleaded gasoline.” In January 2008 the gasoline price in Mali was 545 FCFA/litre (Temoust, 2008). Inline with the specification of the Brazil ethanol project over 2003/2004 every tonnes of raw sugar cane gives 40 litres of ethanol (Wikipedia). This results in an added value of 22 cent FCFA Cash Crops 80-1000 FCFA/kg According to FAOSTAT the producer price of a selection of the most popular market gardening crops is for: • Tomatoes between 300 and 450 FCFA/kg over 2000 2005 • Onion/shallots between 375 and 550 FCFA/kg over 2004 2005 • Sweet potatoes between 700 and 1000 FCFA/kg over 2000 2005 Keita (2004) mentions a wider range for tomatoes and onions, respectively 350 750 FCFA/kg and 80 800 FCFA/kg. Probably this difference is caused by the fact that the FAO uses yearly average prices and Keita takes seasonal differences into account. Other cash crops are assumed to be in the same range of pricing. Millet and Sorghum 55-135 FCFA/kg According to FAOSTAT the producer price of millet is between 60 and 135 FCFA/kg over 2000 2005, for Sorghum 55 to 123 FCFA/kg over the same period. Note that these are subsistence crops, predominantly used for food security (since they are drought resistant and have a short growing period). They are hardly used as trading goods.
For further indication of the economic value average values are used as stated in the following table E 1. Crop Price [FCFA/kg]
Rice 125
Sugar Cane (raw) 60
Cash crops 500 Millet & Sorghum 90 table E-1 Sales value per crop in Mali
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L. Controlled Submersion Irrigation The figure L 1 is used to support the explanation of this irrigation type. The system consists of a flat area surrounded by higher grounds, like levees, dams or hills (red dashed). The inundated area exists of main and secondary canals to distribute the water over the entire plane. If the water has fully filled the plane one can identify four different areas. The deep part of the plain (dark blue), where the water level is too high for rice cropping, over 1,5 meter. In the area of 90 150 cm (dark green) floating rice is grown. The area of 45 90 cm (light green) is meant for non floating or standing rice ( riz dressée in French). The area of 0 45 cm is used as buffer for a decrease in water level up to a maximum of 30 cm. Sometimes this edge is also used for non regulated growing of sorghum and millet.
River
Main canal
Secondary canal
Inlet
Levee
Deep plain
Floating rice
Non floating rice
Shallow plain
figure L-1 Schematic Controlled Submersion Irrigation System
June 2008 xxxiv G.J. van Dijk
M. Discharge Data Climatic Scenarios jan feb mar apr may jun jul aug sep oct nov dec avg 125.5 57.9 26.8 17.6 33.1 144.2 516.9 1642.9 2661.3 2073.7 887.9 340.2 Banankoro max 318.6 152.2 69.8 79.1 129.0 435.6 1411.0 3138.0 3908.0 3848.0 2666.0 1320.0 min 45.2 16.6 5.9 2.6 2.8 18.3 117.8 1035.0 1485.0 1070.0 427.7 134.9 avg 26.4 12.1 4.7 6.1 17.6 98.7 217.9 452.7 571.8 420.1 173.9 62.8 Baro max 57.7 35.4 16.8 27.7 70.7 312.0 540.0 708.0 985.0 707.0 272.0 123.0 min 7.2 1.8 0.6 0.1 0.6 8.6 83.2 329.0 338.0 223.0 103.0 31.3 avg 99.1 45.7 22.1 11.6 15.5 45.5 298.9 1190.2 2089.5 1653.6 714.0 277.4 Niger max 260.9 116.8 53.0 51.4 58.3 123.6 871.0 2430.0 2923.0 3141.0 2394.0 1197.0 min 38.0 14.7 5.3 2.5 2.2 9.7 34.6 706.0 1147.0 847.0 324.7 103.6 avg 43.4 31.2 31.9 31.9 54.2 95.6 255.6 584.8 790.6 578.3 192.9 73.6 Selingue max 118.5 93.0 96.9 108.0 112.0 204.2 396.6 824.5 1447.9 1155.8 697.4 154.7 min 1.1 0.8 2.7 6.2 2.7 19.4 57.5 113.0 174.0 225.0 50.0 12.5 avg 38.7 20.3 10.9 6.3 7.0 17.5 66.3 393.5 768.3 578.9 245.5 86.6 Beney max 93.0 43.4 25.7 17.9 34.1 36.1 160.2 1005.0 1652.0 1343.0 930.4 272.8 Kengy min 12.5 5.3 1.3 0.25 0.456 0.406 20.1 68.8 228.0 214.1 62.9 24.7 table M-1 Inflow discharge data for climatic simulation scenarios
This annex presents the data used for the simulation of three climatic scenarios used to identify the sensitivity of the interest for water in the West Niger Basin to climatic changes. The table M 1 give the data, the figure J 1 to J 4 are a representation of these data.
1600.0 Selingue avg 1400.0 Selingue max 1200.0 Selingue min
1000.0
800.0
600.0
400.0
200.0
0.0 jan feb mar apr may jun jul aug sep oct nov dec
figure J-1 Sankarani River discharge into Selingué reservoir (DNH data)
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1000.0 Baro avg Baro max 800.0 Baro min
600.0
400.0
200.0
0.0 jan feb mar apr may jun jul aug sep oct nov dec figure J-2 Niandan River discharge into Fomi Reservoir (ABN data)
3500.0 Niger max 3000.0 Niger avg Niger min 2500.0
2000.0
1500.0
1000.0
500.0
0.0 jan feb mar apr may jun jul aug sep oct nov dec figure J-3 Niger River discharge into Mali excluding Niandan discharge (ABN and DNH data)
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1800.0 Beney Kengy max 1600.0 Beney Kengy avg 1400.0 Beney Kengy min 1200.0
1000.0
800.0
600.0
400.0
200.0
0.0 jan feb mar apr may jun jul aug sep oct nov dec
figure J-4 Bani River discharge at Beney Kengy
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N. Discharge Calibration Koulikoro and Ké Macina The simulated discharge in middle of the West Niger Basin is difficult to calibrate since it is just a result of the simulated water demand and supply throughout the basin. Of course it is possible to add some extra inflow points to supply at locations and on moments the water is short. But this does not make sense if this does not represent any physical effect. Especially if one wants to simulate none existing scenarios with the model, such tricks do not gain confidence in the output. Even though no real calibration is done with the discharge throughout the basin, it is good to have a look at the accuracy. If we look at Koulikoro (downstream of Baguineda, OPIB) and Ké Macina (downstream of Office du Niger) we see that the discharge peaks are generally 10 20% too low compared with reality. And they are too short. One way to explain these differences is the manner RIBASIM calculates. The simulation is based on the volume balance not on physical flow properties. However a long wave is influenced by diffusion causing smoothening of the peak flow (making it lower and longer). Ribasim does not account for this effect. Additionally the total yearly volume has an average deficit of 6% and 11% for respectively Kouriomé and Ké Macina. For a basin simulation this is not too bad, especially if one looks at the graphs, figure N 1 and figure N 2.
Discharge Koulikoro [m3/s] 5000
4500 Real 4000 Ribasim 3500
3000
2500
2000
1500
1000
500
0 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 figure N-1 Discharge calibration of the Niger River at Koulikoro
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Discharge Ké Macina [m3/s] 4500 Real 4000 Ribasim
3500
3000
2500
2000
1500
1000
500
0 1985 1987 1989 1991 1993 1995 1997 1999 figure N-2 Discharge calibration of the niger River at Ké Macina
Inner Delta bifurcate Based on Passchier’s modelling the bifurcation and confluences of the Inner Delta are used. However giving a sense of accuracy is difficult since time series over several years are only available for Mopti (figure N 3) and Diré. But the latter is not simulated as in reality; a number of branches are modeled as one branch so it cannot be compared. Discharge Mopti [m3/s] 3500
Real 3000 Ribasim Nord
2500
2000
1500
1000
500
0 1985 1987 1989 1991 1993 1995 1997 1999 figure N-3 Discharge calibration of the Niger River at Mopti
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For the Diaka river only the data over 9 month over 1996 are available. In figure N 4 the comparison with the simulated data is given. Some correspondence of the simulation with the reality can be observed from both figures, but it is hard to validate the simulation because it is a simplification of a complex of many branches represented by only 3 branches. Discharge Diaka [m3/s] 1200
1000 Real Ribasim 800
600
400
200
0 Jan 96 Mar 96 May 96 Jul 96 Sep 96 Nov 96 figure N-4 Discharge calibration of the Diaka River at Kara
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O. Peak Inundation Calibration Since the results of the simulation of inundation of the Inner Delta do not exactly fit with Zwarts, the hypothesis is checked whether this is caused by the fact that Zwarts uses the peak day and RIBASIM uses the peak month. By adding the average of the absolute difference between the peak month and respectively the preceding and the successive month a day peak is estimated. Peak inundation [1000 ha] 1800 1700 Zwarts 1600 Ribasim 1500 Peak estimate 1400 1300 1200 1100 1000 900 800 700 600 1985 1987 1989 1991 1993 1995 1997 1999 figure O-1 Monthly simulated and daily estimated peak inundation of the Inner Delta compared with Zwarts’ (2005) data From figure O 1 it can be concluded that sometimes the plain RIBASIM result corresponds better with Zwarts (on average 7% too low); sometime the peak estimate does (on average 5% to high). Which estimate is better is arbitrary.
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P. Estimate of the Akka Water Level However water level is not a used as to represent reality in Ribasim, a remarkable proper relation can be found. In Ribasim the water level in the Inner Delta is used to calibrated the inundation and the discharge delay. This results in a simulated water level that is way too low. But the sum of the water level at Akka of 3 subsequent month times a factor 2.5 is a good estimate of the real water level, see figure P 1. Akka water level [+cm reference level] 550 real level Ribasim 2,5 x sum of last 3 month 450
350
250
150
50
50 1985 1987 1989 1991 1993 1995 1997 1999 figure P-1 Empiric water level estima te based on simulated water level
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Q. Sensitivity to Model Settings In the §4.3 Model calibration, the correspondence of the model with the reality is discussed. The emphasis of the calibration was based on hydrologic parameters. However the interests are hardly calibrated since there is no information on their actual water supply available. Therefore the “a” scenarios have been developed as described in the §4.4 Scenarios. One by one these will be discussed to test the reliability of the model and the relevance of the scenarios. Sensitivity to discharge threshold for the submerged inundation systems Scenarios 0 and 0a have one difference, for the latter the threshold river discharge to supply controlled inundation 3 systems is 10% lower. In this way Average yearly supply [m /s] scenario 0 one can see how sensitive the Climate Total SMC ORM ORS system is to this diversion relation. dry 115.1 4.4 4.3 0.1 For the power production, the average 180.5 18.8 12.4 6.4 supply of Office du Niger and the wet 224.8 44.3 30.0 14.3 inundation of the Inner Delta this Deviation of scenario 0a has no or negligible influence (less Climate Total SMC ORM ORS than 0,3% change). But the effect dry 1% 11% 9% 85% on the supply of Office du Riz Segou average 3% 25% 20% 37% (ORS) and Office du Riz Mopti (ORM) wet 2% 8% 9% 7% is significant. In table Q 1 the relative rise table Q-1 Controlled submersion irrigation in water supply is shown for compared for scenario 0 and 0a respectively all irrigation areas, the cumulative and the individual supply of the Selingue + Sotuba controlled submerged irrigation (CSI) systems power production in GWh ORM and ORS. One may conclude that the scenario 3 3a deviation* 10% change in the discharge diversion dry 48.3 48.1 0% relation has significant impact on the water average 89.5 87.9 -2% supply for the controlled submerged irrigation. wet 227.6 226.4 -1% Therefore one cannot translate the model period with power continuity of 3 GWh outcomes to crop production. The model can scenario 3 3a deviation* only be used to monitor a qualitative effect of dry 50% 50% 0% climate and basin management on the CSI water supply. Note that even though the average 58% 75% 29% relative rise of the supply is large this does wet 75% 100% 33% not always mean a significant absolute rise of period with power continuity of 11 GWh the water supply, e.g. ORS in the dry season. scenario 3 3a deviation* dry 25% 25% 0% Sensitivity to small baseline production of average 50% 42% -17% energy wet 67% 58% -13% Furthermore it is known that a small baseline Irrigation supply Office du Niger [m 3/s] energy demand, like in scenario 0, is needed to keep producing in the dry season if no scenario 3 3a deviation* other interest than energy has priority (§4.3 dry 61.9 60.5 -2% Model calibration). And it is known that a large average 70.7 75.0 6% baseline demand will disturb every other wet 83.3 86.8 4% prioritised interest (since it is always *deviation = relative difference 3 and 3a overruling according to the characteristic of table Q-2 Comparison of scenario RIBASIM). Still it is not clear whether a small 3 and 3a baseline energy demand has any influences
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on the water allocation of the simulation. Therefore scenario 3a is developed and compared with scenario 3. Both scenarios have priority for the controlled submersion. Additionally scenario 3a has a baseline power demand of 5 GWh at Selingué. If one looks at the yearly power production at Selingué and Sotuba (table Q 2) hardly any change can be seen ( 2% to 0%). If we look at the time a baseline production of 3 GWh is met an improvement in scenario 3a can be seen. But if one evaluates with a base production of 11 GWh the effect is turned around and for higher base productions this effect is even more amplified. The production of Office du Niger has slightly improved but that can be considered within the margins of inaccuracy. So one can conclude that the addition of the small baseline power demand is insignificant for further evaluation. Sensitivity to water level target in reservoirs As explained in paragraph scenarios a “b” case has been developed for scenarios 2, 3 and 4. These case have different target level management of the reservoirs, aiming at continuous full storage (maximum reservoir level) . The sensitivity to this management scenario is evaluated for all three scenarios for different climatic conditions. During a dry year the reservoir level management does not Average hydrologic year give different results for scenario 4 Scenario Power production Supply ORS 3 and 4b. (These scenarios aim for a 4 [GWh] [m /s] months 1.000.000 ha inundation.) 4 294 3.3 But in an average hydrologic year 4b 278 1.3 power production and the irrigation deviation -6% -61% at ORS are disadvantaged by the scenario 4b compared to 4. However table Q-3 Comparing scenario 4 and 4b for the absolute changes are small, see power and water supply to ORS table Q 3. This change is caused by the management setting. The first priority is to meet the inundation target, the second is to bring the reservoir to maximum level and after that other interest receive water. The difference between the two case is caused during the month August and September. In August both reservoirs, Fomi and Selingué are totally emptied to meet the inundation target. In September the river base flow together with the Fomi dam can supply the Inner Delta. The Selingué reservoir is not needed to support inundation. Therefore it starts to fill up to the aimed water level and does not discharge. However the aimed water level of scenario 4 is lower than that of scenario 4a. The water availability was more than enough reach the target of scenario 4, so the extra volume was used to generate energy and supply ORS. But the water availability was just not enough to meet the target of the “b” scenario, so no water was discharged from Selingué during the months. For a wet year we see that the 4b scenario has negative effect on the power production compare to scenario 4. This is logic since there is no demand during the dry season the reservoir is kept full and does not serve the power production. In this way the total Wet hydrologic year production and the Scenario Power production Power continuity Inundation time a minimum [GWh] [time % >33 GWh] aug-nov [ha] power production of 4 844 100% 1599576 33 GWh is met is 4b 612 50% 1751774 significantly reduced. On the contrary in deviation -28% -50% 10% scenario 4b the table Q-4 Comparing scenario 4 and 4b for power and reservoir can serve inundation of the Inner Delta
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the inundation of the Inner Delta in August since the reservoir is full, which is not the case for scenario 4. The variation of the parameters is shown in table Q 4. For scenario 2 the effect of the change from variable reservoir level management to maximum level management (“b” scenario) is different. Since the demand of the Office du Niger is always met (the aim of this scenario). So the consequences of the level management are for the other interests. Considering power production one can see a positive effect during the dry year. This effect is not caused by the volume that is discharged, that is practically the same. But the moment, i.e. the water level, of discharging makes the difference in power generation. With variable level management more water is turbinated during low reservoir level in the dry season and due to the lack of a good peak flow the reservoir low remains during the rainy season. The result is a low power production. The maximum water level management also has a dry season base flow due to the demand of Office du Niger. But all the surplus over the demand of Office du Niger is discharged with maximum water level, resulting in higher power production. During a wet year we see the contrary effect of the aim for maximum reservoir level. The reservoirs do not need to support the supply of Office du Niger as much as during a dry year since the Niger river has a discharge in the dry season if a wet year. This means that there is a much smaller turbinated discharge during this dry period of the year. And since the reservoir is already full during the start of the rainy season, no extra water is stored for power generation at the end of the rainy season. During an average year most effects of a dry and a wet year balance out. However the power production is less stable with a maximum reservoir level target. Other interest than power production are not effected or benefit from the increase of the peak flow, comparing scenario 2 and scenario 2b. The benefits, of ORS and inundation of the Inner Delta can be clearly seen in table Q 5. Note that the 40% extra supply in an average year is only 2.4 m 3/s. Scenario 3 and 3b do not differ except power production in GWh for the power production in a wet year. Then the same effect as for scenario 4 and 4b as scenario 2 2b deviation well as 2 and 2b can be discovered. The power dry 208 232 11% production is lower during the dry season average 519 520 0% since there is no discharge because is it not wet 844 753 -11% demanded for the controlled submersion power continuity of 11/22/33 GWh irrigation. And during the rainy season the scenario 2 2a deviation reservoirs are already full so no benefit can be dry 67% 83% 25% gained from storage of abundant inflow of average 100% 50% -50% water into the reservoirs. wet 100% 58% -42% In the general on can say that the 3 Irrigation supply ORS [m /s] system is sensitive to the reservoir scenario 2 2b deviation management. And especially for power production is has a negative effect during wet dry 0.0 0.0 0% years. The benefit of the power production in average 5.9 8.3 40% case 2b during a dry year should not be wet 14.3 14.9 4% considered as a pure result of the maximum Inundation aug-nov [ha] reservoir level management. Actually a scenario 2 2b deviation turbinated base flow is created by the supply dry 351705 389715 11% for Office du Niger. In this way an alternative average 804811 919572 14% variable water level management is created wet 1599576 1720987 8% that is more suited for a dry year than the table Q-5 Comparing scenario 2 and defined variable management of scenario 2. 2b for power, ORS and inundation
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From the evaluation of ORS in scenario 2(b) (table Q 5) and 4(b) (table Q 3) can be concluded that the model is sensitive to the definition of the demand period. If different interests have simultaneous demand during a period of water abundance the priority is set to one of them, the other benefits (average year, case 2b). A slight difference in the period could result in competition (dry year, case 4b). Further study to this effect is recommended. Concluding, the outcome of the scenarios can be strongly affected by the reservoir level management, however these effects are not always obvious (scenario 2 and 2b). And in some cases there is hardly any effect (scenario 3 and 3b).
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R. Sensitivity to Management and Climatic Conditions For the identification of competing claims for water two types of sensitivity are important to analyse: the sensitivity of the interests to the climatic conditions and the sensitivity of the different interests to each other. Details of both evaluations are discussed in this Annex. Sensitivity to climatic conditions First of all we compare the sensitivity to the climate (hydrologic conditions). box 2 Calculation averages This is done by identifying the range For the calculation of averages every for which the dry season and wet main scenario is take into account season results differ for all the equally. Otherwise the results of the parameters. So for every scenario the sensitivity analysis for climatic deviation of the extreme hydrologic conditions could be disturbed by one years as a relative difference to the management policy. Consequently the average hydrologic year is calculated. averages (for the dry year / average For every parameter the average year / wet year) are calculated from 4 (according to special procedure, see values, those of scenario 1, the box 2) of this deviation is taken as a minimum / average / maximum of 2, value that represents the sensitivity. 2a and 2b, the minimum / average / The range of difference between the maximum of 3 and 3b, and the dry and the wet year can be minimum / average / maximum of 4, considered as a value that indicates 4a and 4b. the sensitivity of a parameter to the climate, see table R 1. For the reader an indication of the average value is given. Note that the deviation are not relative this average, since these are the average of deviations and not the deviation of the average. From the range in table R 1 one can conclude that the controlled irrigation systems are very sensitive to the hydrologic conditions. The sensitivity of Office du Riz Segou is extreme, because the volumes in the simulation are very small. This results in a high relative deviation with small absolute deviation. Inundation seems a bit less sensitive. But this should be contributed to opposite effect of the same theory. The large surface area makes that rather large absolute deviations are relatively small.
Parameter Average Average of the Average of the Range value deviation of deviation of dry year wet year Power F+Se+So [GWh] 426 55% 107% 162% Power F+Se [GWh] 397 58% 116% 174% Time power>11/22/33Gwh 64% 23% 72% 94% Supply ON [m3/s] 79.4 12% 9% 20% Supply ORS [m3/s] 7.2 99% 362% 462% Supply CSI [m3/s] 23.2 79% 169% 248% Supply ORM [m3/s] 15.9 68% 158% 226% Inundation aug-nov [ha] 980406 55% 88% 142% peak inundation [ha] 1185488 54% 79% 133% table R-1 Deviation from the average value of the characteristic parameters due to climatic extremes
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For the power it is especially interesting to see that the total production is much more sensitive than the continuity over the year. In the following elaboration one can read that this is opposite for the sensitivity to management, see table R 2. Furthermore, Office du Niger has a really stable water supply, since it does not depend on the flood level of the river. Sensitivity to management For the analysis of the influence of basin management (i.e. prioritising different interests) a comparable deviation range is constructed as for the climatic sensitivity. But now only the range is presented. For all three climatic conditions, the average values of all management scenarios for the parameters are taken (according to the method of box 2). Subsequently deviation of the minimum and the maximum value to the average value for each parameter is calculated. The difference between the deviations is used to identify the sensitivity of the parameters to the basin management. In table R 2 the results are given for every climatic condition as well as for the average for all climatic conditions. Range of deviation due to management Parameter Average value dry average wet total Power F+Se+So [GWh] 426 55% 69% 48% 57% Power F+Se [GWh] 397 56% 72% 50% 59% Time power>11/22/33Gwh 64% 108% 107% 68% 94% Supply ON [m3/s] 79.4 44% 23% 10% 26% Supply ORS [m3/s] 7.2 367% 123% 39% 176% Supply CSI [m3/s] 23.2 28% 41% 21% 30% Supply ORM [m3/s] 15.9 27% 24% 15% 22% Inundation aug-nov [ha] 980406 26% 18% 10% 18% peak inundation [ha] 1185488 31% 24% 16% 24% table R-2 Deviation from the averag e value of the characteristic parameters due to water allocation management First of all one can see that in general the sensitivity to the management, i.e. other interest, rises if water is more scarce. Remarkably, the power production is most sensitive to the river management in an average year. This is caused by the fact that during a dry year the water availability is such low that storage in the reservoir has a minor effect, since the reservoir is often empty. During a wet year the water availability is such high that storage in the reservoir has a minor effect, since the reservoir full most of the time. Second of all one can see that the sensitivity to other interests is less than to extreme climatic conditions. However this is a somewhat arbitrary conclusion, since this strongly depends on the extremity of the simulated climatic conditions. As already mentioned, for the continuity of the power production over time is much more sensitive to the basin management than the total power production. The irrigation systems as well as the inundation of the Inner Delta are again less sensitive than the power production. However one should still realise that the Inner Delta is very large system so small relative deviation are absolutely already large.
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S. Model Results Matrix Power Power % of peak Dry year Prioritised interest F+Se+So F+Se year ON ORS CSI ORM Inundation inundation Power average peak GWh GWh >11Gwh m3/s supply Aug-Nov month 0 Calibration 69.3 46.1 0% 74.4 0.1 4.4 4.3 429607 546607 0a -10% demand CSI 0% 0% 0% 0% 85% 11% 9% 0% 0% 1 +Fomi 200% 281% 83% 17% -100% -19% -17% -18% -19% 2 ON priority 200% 280% 67% 17% -100% -19% -17% -18% -19% 2b ON -MRLt 235% 340% 83% 17% -69% -15% -13% -9% -4% 3 SMC priority 100% 162% 25% -17% 70% 7% 5% 5% 6% 3a SMC + 5GWh 99% 162% 25% -19% 70% 7% 5% 5% 6% 3b SMC - MRL 100% 162% 25% -17% 70% 7% 5% 5% 6% 4 ID priority 96% 158% 33% -26% -100% -6% -3% 6% 7% 4a ID priority high peak 106% 172% 33% -26% -100% 6% 8% 6% 10% 4b ID - MRL 96% 158% 33% -26% -100% -6% -3% 6% 7% 5a ID high peak + ORS 102% 25% 19% 70% 5% 5% Average Power Power Power peak year Prioritised interest F+Se+So F+Se >22Gwh ON* ORS CSI ORM Inundation inundation 0 Calibration 261.1 228.3 25% 86.8 6.4 18.8 12.4 875030 1074112 0a -10% demand CSI 0% 0% 25% 0% 37% 25% 20% 0% 0% 1 +Fomi 99% 113% 100% 0% -8% -10% -11% -8% -8% 2 ON priority 99% 113% 100% 0% -8% -10% -11% -8% -8% 2b ON -MRLt 99% 114% 50% 0% 29% 12% 4% 5% 6% 3 SMC priority -1% 2% 42% -19% 37% 17% 7% 8% 12% 3a SMC + 5GWh -3% -1% 42% -14% 37% 17% 7% 8% 11% 3b SMC - MRL -5% -1% 42% -21% 37% 18% 7% 8% 12% 4 ID priority 13% 17% 33% -13% -48% -11% 8% 8% 2% 4a ID priority high peak 8% 12% 33% -13% -41% -5% 13% 10% 18% 4b ID - MRL 6% 10% 33% -14% -80% -23% 7% 10% 2% 5a ID high peak + ORS 0% 42% 13% 37% 7% 8% Power Power Power peak Wet year Prioritised interest F+Se+So F+Se >33Gwh ON* ORS CSI ORM Inundation inundation 0 Calibration 345.5 312.6 25% 86.8 14.3 44.3 30.0 1677613 2018102 0a -10% demand CSI 0% 0% 25% 0% 7% 8% 9% 0% 0% 1 +Fomi 144% 160% 100% 0% 0% 0% 0% -5% -6% 2 ON priority 144% 160% 100% 0% 0% 0% 0% -5% -6% 2b ON -MRLt 118% 130% 58% 0% 4% 4% 4% 3% 1% 3 SMC priority 59% 66% 50% -4% 31% 19% 14% -5% -8% 3a SMC + 5GWh 58% 65% 50% 0% 31% 19% 14% -5% -8% 3b SMC - MRL 46% 52% 50% -10% 31% 21% 16% -4% -9% 4 ID priority 144% 160% 100% 0% 0% 0% 0% -5% -6% 4a ID priority high peak 46% 51% 50% -4% -10% -1% 3% 5% 6% 4b ID - MRL 77% 85% 50% 0% 3% 3% 3% 4% -5% 5a ID high peak + ORS 29% 42% 4% 15% 5% 4% * for average and wet years 0% at Office du Niger is dark green since the full demand is met figure S-1 Results of the river basin simulation model of the West Niger Basin
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