Content area
In Benin, annual water availability per capita far exceeds the critical threshold of about 1,700 m^sup 3^, but during the dry season, water scarcity occurs at the local scale. By modeling the water balance of the Ouémé-Bonou catchment with WEAP (Water Evaluation and Planning System), this study aimed at analyzing Benin's future water situation under different scenarios of socio-economic development and climate change until 2025. The results show that the pressure on Benin's water resources will increase, leading to greater competition for surface water. Furthermore, financial and technological constraints hinder a satisfactory development, and exploration of groundwater and reservoir resources. However, improvements are most needed, especially in rural areas. Decreasing inflows and groundwater recharge due to climate change aggravate this situation. Even though there are uncertainties and constraints concerning the model and input data, this study shows that the WEAP results offer a solid basis to assist planners in developing recommendations for future water resource management by revealing hot spots of action.[PUBLICATION ABSTRACT]
Water Resour Manage (2010) 24:35913613 DOI 10.1007/s11269-010-9622-z
Benin 2025Balancing Future Water Availability and Demand Using the WEAP Water Evaluation and Planning System
Britta Hllermann Simone Giertz Bernd Diekkrger
Received: 28 May 2009 / Accepted: 22 February 2010 / Published online: 24 March 2010 Springer Science+Business Media B.V. 2010
Abstract In Benin, annual water availability per capita far exceeds the critical threshold of about 1,700 m3, but during the dry season, water scarcity occurs at the local scale. By modeling the water balance of the OumBonou catchment with WEAP (Water Evaluation and Planning System), this study aimed at analyzing Benins future water situation under different scenarios of socio-economic development and climate change until 2025. The results show that the pressure on Benins water resources will increase, leading to greater competition for surface water. Furthermore, financial and technological constraints hinder a satisfactory development, and exploration of groundwater and reservoir resources. However, improvements are most needed, especially in rural areas. Decreasing inflows and groundwater recharge due to climate change aggravate this situation. Even though there are uncertainties and constraints concerning the model and input data, this study shows that the WEAP results offer a solid basis to assist planners in developing recommendations for future water resource management by revealing hot spots of action.
Keywords Water resource management WEAP model Water balance
Water demand scenarios West Africa Oum River
1 Introduction
Benin is not a water-scarce country based on its annual precipitation, but due to its location in the wet savanna, seasonal shortages are common (Falkenmark and Rockstrm 2004). Moreover, studies (Schopp 2004; Behle 2005) have revealed that the water supply during the dry season does not meet the actual demand due to
B. Hllermann (B) S. Giertz B. Diekkrger
Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115, Bonn, Germanye-mail: [email protected]
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physical, economic and institutional reasons. Facing the impacts of climate change and the further stress on this resource due to population growth, Benins water supply is at risk (GWP 2007).
Considering the United Nations Millennium Development Goals (MDGs), which aim at significantly increasing access to safe water, many countries such as Benin are challenged in their water management for physical and economic reasons. As a management solution, the Global Water Partnership (GWP) put forward the concept of Integrated Water Resources Management (IWRM). Integrated water management practices must be adapted to handle the impacts of climate change and to secure the current and future water supply (GWP 2007). However, processing hydrological and water-demand data and forecasting the effects of different management strategies under the impacts of climate change are still a challenge. Integrated water resource models try to assist the planner in managing water resources.
WEAP, the Water Evaluation And Planning system is one of many different IWRM models, and is an exemplary application linking supply and demand site requirements. Allowing scenario analysis, changes in supply and demand structures can be simulated in order to discover potential shortages and the effects of different management strategies (Yates et al. 2005). Evaluating scenarios requires validated model results. Therefore, a challenge of many studies in which WEAP was applied is the model validation at different spatial and temporal scales (e.g. Al-Omari et al. 2009; Yates et al. 2009).
The present study is part of the GLOWA IMPETUS-project, analyzing the impact of global change on the water cycle in Benin and Morocco (Christoph et al. 2008). By applying WEAP, this study is aimed at modeling the current and future water balance of the OumBonou catchment in Benin acknowledging the impacts of climate and socio-economic change by applying scenario analysis.
2 Study Area
The Oum Basin is the major river system of Benin. The study area focuses on the Upper and Middle Oum Basin (about 50,000 km2) which enters an inland delta close to the city of Bonou before feeding Lake Nokou and the Lagoon of Porto Novo (Fig. 1). The basin is located in the Sudanian climate zone (1,100 mm/year) and is characterized by bimodal precipitation distribution in the southern part. North of the latitude of Sav, this distribution gradually changes to unimodal. The highest mean annual rainfall amount can be found in the Djougou region (1,309 mm/year; Fink et al. 2008) As a consequence, shortages of water availability occur during the dry season especially from December/January to March/April. No shortages are expected during the short dry season in August/September because enough water is available from the previous rainy season. However, the Oum catchment is particularly vulnerable to water shortages because the annual rainfall is predominantly below average since the 1970s (Vollmert et al. 2003). Even though above-average rainfall occurred in the late 1980s and early 1990s, the declining precipitation trend continues (Fink et al. 2008). Therefore access to deep groundwater is important to meet water demand throughout the year. Due to the fractured character of the crystalline aquifer, groundwater occurs only in preferential fractures. Access is therefore limited (Chilton and Foster 1993; Fa 2004; Barthel et al. 2008, 2009).
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Fig. 1 OumBonou catchment within its regional context
Furthermore, tapping those groundwater reservoirs is expensive and bears the risk of tapping dry holes.
The study area is less populated with about 1.9 million inhabitants, compared to Benins total population of about 6.8 million in 2002. However, higher growth rates, especially due to inland migration are anticipated for the central part of the catchment (Doevenspeck 2004), increasing the competition for land and water resources. The fraction of rural dwellers is 68%, which is slightly higher than the
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national average of 65% (INSAE 2003). Most of the urban population is found in the cities of Djougou, Parakou, and Abomey/Bohicon. The economic activities of the study area focus on agriculture, with a high degree of subsistence farming. Extensive livestock husbandry is another important land use factor, with a dominance of small livestock in the southern part of the Oum catchment and cattle in transhumance in the north, putting further stress on the available water resources (Gruber 2008; Gruber et al. 2009).
3 Current Strategies to Satisfy Water DemandThe Water Supply Sector in Benin
The water supply sector in Benin as well as in many other African countries usually lacks sufficient demand satisfaction (Niemeyer and Thombansen 2000). Even though efforts have been undertaken to improve this situation, the economic and physical conditions aggravate an optimal supply of water, especially potable water.
Benins water supply to meet domestic demand can be distinguished as different water sources. The Socit Nationale des Eaux du Bnin (SONEBNational Water Association) is responsible for the drinking water supply of urban areas, while the Ministre des Mines, de lEnergie et de lHydraulique (MMEHMinistry of Mining, Energy and Water Management) and its Direction General dEau (DG-EauWater Agency) are in charge of Benins water resources management and the rural water supply, including the cities and urban districts not served by SONEB. This responsibility comprises the establishment of rural water supply systems, which are often built with the support of development collaborations (Niemeyer and Thombansen 2000; Behle 2005).
SONEB water is not only consumed by those households with a house connection but by the surrounding, unconnected neighborhood, as it is common practice to resell SONEB water. Other urban households and the rural water demand is served by water towers, wells (protected and unprotected), pumps (hand, feet, solar, or diesel driven), marigts (back waters) and rivers (Schopp 2004). In contrast to the urban water supply, the economic situation of a rural household does not determine the water source used. The preference for a distinct source depends on the availability and immediateness of the access constrained by season and time, as fetching water is very time-consuming (Hadjer et al. 2005; Behle 2005).
In Benin, irrigation is not widespread. Only five larger irrigation sites exist, with a total extent of about 1,600 ha. Furthermore, all-year watering of vegetables in periurban areas, and irrigation in inland valleys occurs during the dry season (Schopp and Kloos 2006; Gruber et al. 2009; Giertz et al. 2007). However, most of the agricultural activity is rainfed. Another important water consumer in agriculture is livestock watering, especially in the northern parts of Benin (Gruber 2008).
Due to its low degree of industrialization, the water demand of industry is not very large and is confined to textile, food, chemical, agro and hotel businesses (Schopp et al. 2007).
While statistically Benins urban population has reasonable access to water for domestic purposes (Schopp et al. 2007), the water situation in rural Benin is not satisfactory. As a result of physical, socio-economic and institutional constraints, the amount of available water for rural households is lower than the minimum required by the WHO of 20 L/day to fulfill basic human needs (Schopp 2004; Hadjer et al. 2005; WHO 2003).
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Socio-economic and institutional factors have been identified as the most limiting factors to a safe and reasonable access to water. Limited financial and technical resources aggravate improvements in this sector (Niemeyer and Thombansen 2000). In addition, a lack of institutional structures at the village level and missing responsibilities do not assure the maintenance of the available water resources (Behle 2005). At the national level, the new water management strategy Gestion Intgre des Ressources en Eau (GIRE = IWRM) puts an emphasis on institutional restructuring
with a distribution and decentralization of responsibilities to regional water offices (MMEH 2005a, c). Even though these offices are intended to act at the catchment level, the GIRE still lacks important issues, e.g. the missing inter-sectoral flow of information, especially between the ministries of agriculture and industry.
4 WEAP Model Description and Application to the OumBonou Catchment
The WEAP system is a demand-, priority-, and preference-driven water planning model (Yates et al. 2005). It aims at closing the gap between water management and catchment hydrology by addressing both bio-physical factors influencing the river and socio-economic factors affecting the level of domestic, agricultural and industrial demand and management of artificial reservoirs. These factors vary over time, for example due to increased gross domestic product, changes in consumptive attitudes, or climate change, and therefore make it difficult to forecast future demand requirements. With its focus on scenario analysis, WEAP supports the planner in forecasting demand and supply structures under various assumptions and management practices and helps in developing resources management policies to meet future demands and solve allocation problems (Sieber and Purkey 2005). WEAP compares future development with a snapshot of actual water demand, pollution loads, resources and supplies for the system which is called current accounts year.
The principle algorithm of WEAP is a spatially-resolved water balance calculated on a monthly basis by balancing water supply and demand at each node and link in the system. Nodes represent demand or supply points, while links connect them. This node and link structure allows aggregating and disaggregating the components of the water balance if necessary, depending on the research question or available input data, and is applicable to all scales.
WEAP offers different methods for projecting the hydrology for the study period. The hydrological processes can either be calculated internally considering driving forces like precipitation and evapotranspiration or pre-calculated results of other hydrological models are read in (Yates et al. 2005). As the later option was used in this study, model validation was not required as it has been carried out before applying WEAP. Hydrology as used in this study is described in Section 5.1.
The hydrological processes of the Oum river basin vary throughout the catchment due to differences in climate and land cover. Accounting for these differences requires subdividing the catchment into smaller sub-basins. Thus, 27 sub-basins with an average area of about 2,000 km2 were derived from the Digital Elevation Model provided by the Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007). To analyze the water demand and supply on the catchment level, it was necessary to disaggregate the information available on the municipality level in order to account for different development paths and aggregate the information at the village level in order to achieve a manageable amount of data. The aggregation took into account
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Fig. 2 WEAP structure of the OumBonou catchment, including a close-up of one sub-basin and its disaggregated demand and supply structure
the affiliation to a municipality and to a rural or urban district. This approach recognizes the recommendations of Wallgren (2006), who suggests applying water demand models on a more local, e.g. municipality level. This disaggregation allows a detailed analysis of the hydrology in the catchment as well as the extent of water demand per catchment, per municipality and their urban and rural villages. As a result of this procedure, Fig. 2 visualizes the WEAP structure with more than 32 river segments, four reservoirs, 28 groundwater aquifers, and 188 demand sites. In contrast, other studies applying WEAP in Africa are less detailed (Andah et al. 2003 [basin = 400,000 km2, seven sub-catchments, three demand sites each]); McCartney
and Arranz 2007 [catchment = 54,475 km2, eight sub-catchments, five demand sites
each]); SEI 2006 [basin = 50,000 km2, 13 sub-catchments, three demand sites each,
three major cities]) and therefore allow only limited analysis.
5 Current and Future Water Availability and Demand in the Oum CatchmentInput Data and Data Sources
This study relies solely on secondary data derived from the IMPETUS database or most recent publications. As model results are only as good as the input variables, careful attention has been paid to choosing and processing the secondary data for the current accounts year representing the system as it currently exists (2002) and the scenario analysis (20032025).
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Table 1 Data sources; citations in italics have been used to develop own scenarios
Data types Current accounts Scenarios (20032025) year (2002)
Hydrology Climate Giertz et al. (2010a) Giertz et al. (2010a)Land use Thamm and Judex (2008)
Discharge Giertz et al. (2010a) Giertz et al. (2010b) Water demand Domestic Schopp (2004), SONEB (2007, personal
(urban and rural) Schopp et al. (2007), communication),INSAE (2003) WHO (2003),
Gleick (1996),
GTZ (2007, personal communication)
Irrigation Schopp and Kloos (2006), MMEH (2000),Gruber et al. (2009), FAO (1986)
Giertz et al. (2007)
Livestock Gruber (2008) Gruber (2008) Industry Schopp et al. (2007) Shiklomanov (1999),
Rosegrant et al. (2002)
Demography INSAE (2003) Heldmann andDoevenspeck (2008)
IMPETUS applies two model-based climate scenarios and three socio-economic scenarios (Christoph et al. 2008).
The climate scenarios were developed using the land use scenarios by the FAO and two IPCC-Emission scenarios (A1B; B1) as driving forces for the regional climate model REMO, which is nested into the global climate model ECHAM (Paeth et al. 2005).
The three socio-economic scenarios are E1 economic growth, E2 economic stagnation, and E3 business as usual. Scenario E1 describes economic growth, especially regarding the industrial sector and the consolidation of decentralization. E2 assumes economic stagnation and institutional instability, leading to a downward spiral. In E3, IMPETUS assumes only weak economic development regarding the industrial and tertiary sectors, while the agricultural sector keeps its dominant role (Christoph et al. 2008). The current degree of decentralization remains stable. All scenarios assume a deceleration of population growth, whichs rate is lowest in scenario E2 and highest in E1 (Heldmann and Doevenspeck 2008).
The socio-economic scenarios drawn by IMPETUS were enhanced in this study concerning future water demands based on information provided by interview partners and the literature (Table 1).
5.1 Hydrology
The input data for river discharge, runoff and groundwater recharge entered into WEAP are derived from the UHP-HRU model (Giertz and Diekkrger 2006) applied by IMPETUS. The UHP-HRU model is a spatially differentiated version of the UHP model which was especially developed for the application in Benin (Bormann and Diekkrger 2004). It simulates evapotranspiration, infiltration, runoff, interflow, and groundwater recharge. The model is applicable at the local and regional scales and can adequately simulate the hydrological processes under climate (Giertz and
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Diekkrger 2006) and land use change scenarios (Giertz et al. 2006, 2010a, b; Christoph et al. 2008). The model is based on climate data derived from REMO consortial runs according to the two different climate scenarios (A1B, B1) (see before). The REMO data were derived by downscaling the REMO grid data from a 55 km grid to virtual station data (Paeth et al. 2005, 2009). As no explicit spatially distributed land use scenarios are available for the whole catchment, this study concentrates on the effects of climate change. As described before, UHP-HRU simulations were used as input. Therefore, no explicit hydrological modeling was performed in WEAP.
Reservoirs present another source of surface water to satisfy water demand. In this study, only the largest reservoirs were taken into account. These comprise the reservoirs in Djougou, Parakou, Savalou, and Sav (for the location of the reservoirs cf. Fig. 1).
The WEAP integrated groundwater model considers only alluvial aquifers (Yates et al. 2005). Hence, the applicability of this method for the groundwater aquifers of the study area is limited, as it mainly consists of fractured crystalline basement (El Fahem 2008; Fa 2004; Chilton and Foster 1993; Barthel et al. 2008, 2009). Because groundwater presents an important source to satisfy water demand in Benin, it has to be considered in the analysis. Therefore, this study includes groundwater modeling by applying a simple storage approach which is in contrast to other regional studies (Lvite et al. 2003; SEI 2006). For each sub-basin own aquifer properties are required. The maximum storage capacity was calculated using the mean well depth derived from the national borehole database BDI (Banque de donnes intgres) of the water agency DG-Eau and Niemeyer and Thombansen (2000), medium porosity values (Fa 2004; MMEH 2005b; Biscaldi 1967; Engalenc 1978) and a groundwater relevant area. The groundwater storages are not hydraulically interconnected as shown by Fa (2004), El Fahem (2008) and Barthel et al. (2009). The groundwater relevant area is confined to the populated areas of the sub-basins by drawing buffers with a 2 km radius around each village, a distance that corresponds to the maximum distance women travel to fetch water. Furthermore, this is twice the distance which is regarded as appropriate according to the WHO/UNICEF (2000). The radius increases to 5 km for urban settlements to consider their greater spatial impact. This approach acknowledges that huge parts of the catchment are not populated. Therefore the potentially available groundwater resources are not accessible for the different users in the area.
5.2 Water Demand
In Benin, four types of water users are identified: households, industry, irrigation and livestock. Their water demands are characterized at the catchment/municipality level, allowing an analysis down to these local scales.
The domestic water demand depends strongly on the water source being used. The shorter the distance from a source to a demand site and the more immediate the access, the higher the satisfied demand (Hadjer et al. 2005; Schopp 2004). Three distinct sources are distinguished: water supplied by SONEB, water from a well or water tower, and water from rivers. The 2002 census data (INSAE 2003) provide a valuable source of information concerning the population of each village and the frequency per household using these specific water
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sources. According to studies by Schopp (2004) and Schopp et al. (2007), the amount of water consumed from rivers is 14 L/person/day, while water consumption increases to 19 L/person/day if water from wells is used. The urban water demand supplied by SONEB ranges from 57 to 80 L/person/day. Population growth and economic development are major determinants for future water demand concerning the activity level and the water use rate, which depends on type of access. By using the population scenarios of Heldmann and Doevenspeck (2008) on the municipality level, national plans concerning the extension of the water infrastructure (SONEB, DG-Eau, and Development Collaborations), and minimum water requirements set by WHO (2003), and Gleick (1996), specific water use scenarios have been developed. For example, the Millennium Development Goals concerning access to water are expected to be attained for scenario E3 in 2015.
Benins industrial sector is only poorly developed, with most businesses found in the south, and in contrast to other sectors, with low water demand. A comprehensive survey by Schopp et al. (2007) captured all industrial and service businesses and analyzed the water demand of 170 of them using SONEB water bills. The survey and regional forecast studies by Shiklomanov (1999) and Rosegrant et al. (2002) provided a basis for the scenario assumptions. In scenario E3, the industrial water use rate increases annually by 3.375%. While there is no increase in E2, the economic growth in E1 also leads to new industry entries in 2015.
The agricultural water demand includes irrigation demand and livestock watering. We distinguish the irrigation areas into three types: inland valley irrigation, urban and periurban horticulture, and large scale irrigation. Studies by Schopp and Kloos (2006), Gruber et al. (2009), Giertz et al. (2007) and DPP/MAEP (2003) provide information on the size and crop mixtures of these areas. Crops are irrigated usually only during the dry season. Thus, the irrigation water demand was set equal to the crop water demand of specific crops (FAO 1986). National extension plans (MMEH 2000), average population growth rates and expert interviews by Schopp and Kloos (2006) and Gruber et al. (2009) build the basis of the different scenarios, where scenario E1 shows the highest increase in irrigated area and E2 remains roughly at the status quo. The water requirements for livestock watering are derived from Gruber (2008) and depend strongly on the amount of livestock. This in turn depends on surface restrictions, pricequantity elasticity, and the development of markets, and therefore shows no sensitivity to the climate scenarios. All socio-economic scenarios assume extensive livestock husbandry. In addition, intensive livestock husbandry is expected in scenario E1.
6 WEAP Modeling Results
6.1 Hydrology
The water balance for the OumBonou catchment has been calculated for the period of 2002 to 2025, with 2002 as the basis year (Giertz et al. 2010a, b). The modeled surface water and groundwater inflows into the OumBonou catchment show a distinct variation throughout the year, representing the influence of the equatorial transition regime. Furthermore, a distinct decrease in inflow from 2015
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Table 2 Pentadal climate scenario comparison of annual groundwater recharge and outflow at OumBonou as computed by the UHP-HRU model (Giertz et al. 2010a)
Pentades Annual groundwater recharge Annual outflowof accessible aquifer in billions at OumBonou in billionsof cubic meters of cubic metersClimate Climate Climate Climate scenario A1B scenario B1 scenario A1B scenario B1
20022005 1.2 1.3 6.9 7.4 20062010 1.1 1.1 6.6 7.1 20112015 1.2 0.9 7.9 5.5 20162020 0.8 0.8 4.8 5.3 20212025 0.8 0.9 5.1 5.7
The first period comprises only 4 years simulation because it starts 2002
to 2025 as a consequence of the declining precipitation in climate scenario A1B is projected. In contrast, the mitigated decrease in climate scenario B1 results in higher inflows from 2015 to 2025 (compare Table 2). Hence, the groundwater recharge in scenario A1B has a decreasing trend from 2015 on, while the initially decreasing trend of B1 stabilizes in 2015. This difference has an important effect on the development of the groundwater storage (Fig. 3). The groundwater aquifers tend to fill up completely during the rainy season until 2014 as the recharge rate from 2015 on is not high enough due to climate change. Therefore a decrease in storage is observed. However, the stabilizing groundwater recharge trend in scenario B1 mitigates a further decrease in storage compared to A1B. As shown in Fig. 4, these changes have a significant effect on the groundwater level for the catchments of the crystalline basement. In the period 20152025, the groundwater level decreases during the rainy season as well. However, the absolute values presented should be
Fig. 3 Dynamic of accessible groundwater storage in billions of cubic meters under different climate scenarios
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Fig. 4 Change in groundwater level of selected sub-basins in m below the surface
treated with caution, as the applied groundwater modeling allows only qualitative statements (compare Section 7).
The inflow to the reservoirs in Djougou, Parakou, and Savalou varies during the year with no or hardly any inflow from November to May. This typical annual dynamic decreases the storage to a minimum in the months of February to June. From July to September/October, the storage reaches its maximum. This annual dynamic is retained in both climate scenarios but the amplitude of the annual change in storage increases constantly from 2002 to 2025. This increase is more significant in climate scenario A1B under socio-economic scenario E1, presenting the highest water extraction from reservoirs. While the domestic demands of the different users are constant throughout the year, the urban and periurban irrigation in Parakou double their demand during the dry season, aggravating the intra annual variability in reservoir storage. For the reservoirs in Parakou and Djougou, the decrease in volume during the months from January/February to April/May is as high as the maximum storage capacity when applied to water demand scenarios E1 and E3, while the reservoirs do not run dry under scenario E2. In Savalou, the reservoir does not run dry, but in scenario E1 a decrease in storage can be observed. A particularity is observed for the reservoir in Parakou for the years 2017, 2019, and 2020, where the inflows from upstream are reduced significantly and cannot refill the reservoir (Fig. 5).
Under climate scenario B1, the presented effects on the decrease in storage are less pronounced which is especially important for the last decade of the study period.
Thus, the water balance results show that the different climate scenarios A1B and B1 have strong impacts on water availability.
6.2 Water Demand
In 2002, the total water demand reached about 47 million m3 which could not be satisfied as discussed in Section 6.3 Due to the socio-economic scenario assumptions,
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Fig. 5 Dynamic of the reservoir storage in a Parakou and b Savalou under climate scenario A1B and water demand scenario E1 economic growth
scenario E1 shows the most rapid increase in total water demand, reaching about 133 million m3 in 2025, while scenario E2 barely doubles its demand by 2025, and the demand in scenario E3 increases by a factor of 2.2.
Due to the variation in agricultural demand, the water demand is not constant year-round but shows a seasonal variation. Demand is highest from December to March during the dry season and decreases with the start of the rainy season.
Examining the development of each demand site type, Fig. 6 reveals that the domestic water demand and the demand of periurban irrigation and livestock watering account for most of the current and future demand, while industrial water demand and the demand of the large scale and inland valley irrigation only play a minor role.
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Fig. 6 Scenario comparison of water demand per user type in millions of cubic meters
6.3 Linking Water Demand and SupplyUnmet Demands
Unmet demand is found in all socio-economic and climate scenarios. Figure 7a shows the unmet demands of all socio-economic scenarios under climate scenario A1B. The figure shows that the amount of unmet demand corresponds to the amount of demanded water. Therefore, scenario E1 with its highest demand also has the highest unmet demand.
Furthermore Fig. 7bd reveal the unmet demands for each user type under the different socio-economic scenarios. While the water demand of industry, inland valleys and large scale irrigation are fully met, the domestic water demand and that of livestock and periurban agriculture show shortages. In scenario E2, the shortages for
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Fig. 7 Unmet water demand under climate scenario A1B in millions of cubic meters. a Scenario comparison, b water demand scenario E1 economic growth, c water demand scenario E2 economic stagnation, and d water demand scenario E3 business as usual per user type
urban water demand are very small compared to the unmet water demand of rural areas. The shortages experienced in periurban irrigation remain stable throughout the study period. In contrast, only livestock water demand is increasingly unmet. Until 2014, the unmet demands develop equally in scenarios E1 and E3 under climate scenario A1B. From 2015 on, the unmet demand in scenario E1 significantly exceeds the shortages experienced in scenario E3. Compared to scenario E2, the unmet urban water demand is about four times higher than the unmet rural water demand. Livestock water demand and periurban irrigation experience the highest water shortages of all user types and in all scenarios.
The unmet demands of climate scenario B1 are smaller than those of climate scenario A1B. Especially from 2015 on, climate scenario A1B experiences higher unmet demands compared to climate scenario B1. At the end of the study period, the moister climate of B1 reduces the unmet demand by about 11% compared to climate A1B.
The simulated annual changes in water availability and water demand have an effect on the distribution of unmet demand throughout the year as well. From August to November, no shortages in water supply are experienced. While the monthly variations of unmet demand in scenario E1 and E3 are comparable, the unmet demand in scenario E2 decreases more quickly, showing only small shortages in June and July (Fig. 8a). A comparison of monthly unmet demand between the period of 20022014 and 20152025 reveals that the unmet demand of the second period continues to rise until February, while in the first period the unmet demand is already decreasing by January (Fig. 8b). In addition, the number of months without any
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Fig. 8 Monthly unmet water demand under climate scenario A1B in millions of cubic meters a per scenario averaged over the period 20022025, b water demand scenario E1 economic growth averaged over the periods 20022014 and 20152025
shortages decreases with time and the total unmet demand increases. The differences are highest in the dry season and converge in the rainy season.
The results show that unmet demand is solely experienced by demand sites relying on surface water. For example, the cities of Djougou and Parakou, which rely to a considerable extent on surface water, suffer from unmet demand, in contrast to the cities of Abomey and Bohicon, with no unmet demand. The water demands of these cities are solely satisfied by groundwater, which is sufficiently available according to the model results. Therefore, one could argue that the fraction of surface water usage
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correlates with the risk of unmet demand. As a consequence, the WEAP results state that rural areas mostly relying on groundwater do not suffer from unmet demand. However, this finding differs partly from reality (cf. Section 7).
7 Uncertainties and Constraints
WEAP is intended to support the skilled planner in decisions concerning water resources management, but does not replace him or her (Yates et al. 2005). As WEAP is a modeling tool that relies on a large number of input data and is restricted to a simple water balance algorithm, it faces several uncertainties and constraints. It is therefore important to provide stakeholders and policy-makers with information about the uncertainties and constraints of the model and input data to ensure a correct interpretation of the results, as decision makers frequently deduct recommendations for future water resources management from the results provided. A real challenge is to communicate uncertainties to decision makers and to consider how uncertainties are framed in the water management practice (Isendahl et al. 2010). In this study, the focus is laid on the discussion of uncertainties related to model structure and data.
7.1 WEAP Model Uncertainties and Constraints
Modeling the groundwater aquifers bears some uncertainties and constraints, especially for the aquifers of the crystalline basement. The geology of the basement aquifer with its various properties of the shallow and deep aquifers (El Fahem 2008; Chilton and Foster 1993) could not be modeled as two separate aquifers, but had been treated as one, assuming an average of the aquifer properties. This aggregation of the shallow and deep aquifers does not acknowledge the fact that the shallow aquifer often runs dry during the dry season (Behle 2005) and, hence, that large parts of the groundwater are not available for use. As a consequence, the modeled satisfaction of water demand by groundwater should be handled with caution because the applied approach might result in an overestimation of the coverage rate.
The assumption that each sub-catchment has its own groundwater aquifer also bears a risk as it neglects the connectivity of the groundwater aquifers and therefore the interregional groundwater flows. Nevertheless, studies have shown that the regional interconnectivity is of minor importance (El Fahem 2008; Barthel et al. 2009).
7.2 Data Input Uncertainties and Constraints
7.2.1 Hydrological and Water Supply Data
The hydrological input data into WEAP are based on regional downscaled climate data (Paeth et al. 2009) and are validated using gauging stations (calibrated at gauge TerouIgbomakoro, catchment size 2,323 km2, simulation period 20032004, R2 = 0.92 and NashSutcliff model efficiency (ME) = 0.91; inter alia validated at
gauge Bonou, catchment size 49,285 km2, simulation period 19802002, R2 = 0.83
and ME = 0.78). This process and the measured data already bear uncertainties.
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However, the results of the UHP-HRU model are expected to adequately simulate the hydrological processes under different climate and land use scenarios (Giertz et al. 2010a, b; Christoph et al. 2008).
The determination of the maximum aquifer capacity per catchment was based on porosity values derived from the literature. Even though careful attention was paid in choosing the porosity values of the different aquifers, data was very limited anddue to the fractured appearance of the deep aquiferaverage porosity values may under- or overestimate the real porosity. Furthermore, the assumed depths of both aquifers only represent average values and therefore neglect the fact that the shallow saprolite aquifer, which stores the most water, is missing in some parts of the catchment.
The groundwater relevant area was constrained to the settled areas (cf. Section5.1). In order to determine the groundwater volume per sub-basin, which is available for human use, the average borehole depths of the sub-basin were multiplied with the groundwater relevant area. These limitations were undertaken in order to acknowledge that not all groundwater in the research area is available for human use or extraction.
In sum, these constraints and limitations result in uncertainties in the actual amount of groundwater available for use. However, while the results of the absolute groundwater volume may not adequately simulate reality, the model does show realistic trends of the groundwater dynamic.
7.2.2 Water Demand Data
The domestic water demand of the OumBonou catchment is based on a broad array of empirical data and a careful review of available literature, and represents a reliable demand. In contrast, irrigation and industrial water demands show higher levels of uncertainty, as the empirical basis for these subjects was limited.
Finally, a lack of access to groundwater for many people in rural areas due to insufficient infrastructure such as broken pumps could not be considered. This is important when interpreting the water supply coverage results of rural areas.
Despite these constraints, all modeled data are still based on solid figures and are detailed enough to show trends for different development options and to give valuable information on the potentially available amount of water.
8 Water supply security of the OumBonou catchment
8.1 Interpretation and Discussion of the WEAP Modeling Results
The water balance results of WEAP imply that the water supply security depends strongly on the water sources used. While the available amount of groundwater is potentially high enough to satisfy demand, users relying on surface water from rivers and reservoirs experience shortages.
As a result, the different user types relying on surface water are competing with each other. Especially in the northern parts of the study area, a distinctive competition between domestic water users and livestock watering can be found. This has also described by Gruber (2008). However, the unmet demand for livestock may be overrated by WEAP as many small reservoirs that exist to water livestock
3608 B. Hllermann et al.
(DG-Eau/MMEH n.d. b) could not be considered in the study. This overestimation of unmet demand does not imply that no problems exist. Especially at the end of the dry season, the small reservoirs tend to dry up. Hence, pressure on water resources increases causing a higher competition for water. In future, shortages will aggravate due to decreasing reservoir inflows.
Other competitors for surface water are the periurban irrigation sites, which double their demand during the dry season. The reservoirs in Djougou, Parakou, Sav, and Savalou are intended to mitigate the effects of the dry season and release the pressure on the water resources. However, only the reservoirs in Sav and Savalou hold enough water to bridge the water gap between the seasons, while the capacities of the other reservoirs cannot meet the growing water demands.
There are two reasons for the increase in unmet demand in the last decade of the study period, especially in scenarios E1 and E3. One reason is the increased demand due to higher per capita use rates and population growth, while the other is the declining inflow to the catchment due to climate change. The sensitivity to climate change is observed for example for the reservoir in Parakou in 2014 (Fig. 5a). The rainy season in 2014 starts as early as March, with inflows to the reservoir high enough to mitigate a further decrease forced by continued demand. In the following year, inflows occur as late as July, and consequently the reservoir runs dry, increasing unmet demand significantly. Therefore, water scarcity results not only from increased pressure on the water resources due to increased per capita use rates and a growth of users, but is also aggravated by climate variability.
Water security of users relying on surface water is not assured for the whole study period. Furthermore, the situation worsens with the observed changes in climate, because the months with no unmet demand decrease from 4 months (20022014) to 2 months (20152025).
In contrast, the WEAP results imply that the water security for users relying on groundwater is high. However, these results must be treated with caution. First, the easy availability of groundwater for inland valley irrigation is unlikely. Usually, this irrigation uses shallow groundwater, which is accessed by digging holes, while accessing deep groundwater via motor pumps is less common. However, shallow groundwater aquifer may not store water throughout the year (Giertz et al. 2007). Therefore, the results of readily and unlimited available groundwater for inland valley irrigation do not reflect reality, and shortages can be expected. Secondly, the projected good coverage for rural areas is doubtful, as the model does not take into account the socio-economic and institutional factors preventing an optimal exploration of groundwater, hence reducing water security.
The uncertainties concerning the total amount of groundwater are quite high. Therefore no quantitative statements are possible. However, the observed negative trend in aquifer storage allows qualitative projections about future groundwater availability. Firstly, the pressure on groundwater increases to mitigate shortages due to the declining surface water supply. In turn, this demand accelerates the negative effects on groundwater storage. Secondly, the reduction in aquifer storage results in a decrease of the groundwater level during the dry and rainy seasons, increasing the risk of shallow wells running dry.
WEAP has already been applied to the OumBonou catchment by the River-twin project. However, due to the coarse scale of the input data and lack of ground-water estimation, the results of the Rivertwin study are limited (Wallgren 2006; SEI
Benin 2025Balancing Future Water Availability and Demand 3609
2006). Only the detailed nodes and links structure in this study and the applied groundwater approach allow a substantiated representation of the water demand and supply structure of the OumBonou catchment at the local scale.
Therefore, despite all uncertainties and constraints, the results and their discussion offer a solid basis to assist planners in developing recommendations for future water resources management. The model has revealed hot spots of action and raised awareness for the groundwater problem.
8.2 Recommendations to Assure Water Supply Security
The WEAP results show that theoretically enough water is available despite shortages during the dry season. A stronger development of surface water availability during the dry season includes measures such as increasing the rate of small reservoirs to improve water security for livestock in the northern areas or increasing reservoir capacities to meet growing domestic demand and demand for periurban irrigation. However, the effects of a larger reservoir for downstream users should be analyzed beforehand.
While the water supply situation of rural areas is not alarming from the perspective of the WEAP results, studies by Behle (2005) and Schopp (2004) show that the water supply in rural areas is more than critical because wells already run dry on a regular basis, broken pumps prevent access to water, and a lack of institutional organization on the local level makes effective management of the water supply points difficult. This is further aggravated when water quality aspects are considered (Barthel et al. 2009). Due to lack of data, this aspect was not considered in this study. WEAP does not consider these economic, technological, and institutional aspects, but estimates the potentially available water. We find that there is enough water potential to satisfy rural demands. Therefore, instead of only increasing the surface water efficiency, the access to groundwater should be improved. According to Behle (2005), improvements should include enhancement of the infrastructure with a distribution of clear responsibilities, establishment of a local water committee, increased involvement of women as main actors in the rural water supply, perpetuation of control through governmental authorities, and further development cooperation concerning the rural quantitative and qualitative drinking water supply.
In conclusion, increase in surface water efficiency as well as technical improvements of the rural infrastructure, e.g. reliable pumps and sufficiently deep wells, can mitigate the current and future shortages in water supply and therefore increase water supply security. However, an improved infrastructure to explore groundwater might also further deplete the groundwater.
9 Evaluation of the Applicability of WEAP for Water Resource Management
Studies that have already applied WEAP in other contexts and river basins show highly satisfactory performance and usability (Andah et al. 2003; Juzo and Lidn 2008; Lvite et al. 2003; McCartney and Arranz 2007; SEI 2006; Van Loon and Droogers 2006). This software is regarded as a valuable tool for integrated water resources planning. In general, this study supports this judgment, as the model results help reveal hot spots of action and visualize conflicts with water resources.
3610 B. Hllermann et al.
However, the limited integrated groundwater modeling option within WEAP is one important constraint. As Benin relies heavily on groundwater, a more detailed view of groundwater is necessary. By linking WEAP to MODFLOW, groundwater modeling can be improved. However, applying MODFLOW would reduce the user friendliness of the model, as MODFLOW is a complex groundwater model that requires detailed input data, which are rarely available in developing countries, as well as skilled and trained labor. In contrast, the WEAP model is comprehensible and can be easily learned within a short time and without special training due to the comprehensive tutorial (Sieber and Purkey 2005).
Summarising, the limitations mainly result from the insufficient groundwater modeling options; however, the strength of WEAP to quantitatively assess surface water resources (McCartney and Arranz 2007), to maximize water allocation in times of scarcity through the demandpriority driven approach (Juzo and Lidn 2008) and to gradually extend the comprehensive framework when more detailed information is available (Van Loon and Droogers 2006), is obvious in this study. Another advantage is that WEAP offers integrated modeling tools to simulate inflows if no input data of hydrological models such as the UHP-HRU model are available. Furthermore, the WEAP model is free of charge for institutions and organizations in developing countries. With these additional advantages, this study concludes that the WEAP model is recommended for use in integrated water resources management, even though the link to MODFLOW proves necessary to quantitatively assess groundwater. A calibrated and validated MODLFOW application is currently not available for the whole basin.
The decentralization process in Benin also comprises the water sector, with the establishment of local water agencies responsible for managing river basins on the sub-catchment scale. To improve and support the management of these agencies, two software solutions (WEAP and Mike Basin) are currently reviewed by the national water resources agency. As shown in this study, WEAP is a user-friendly, scalable and cost-efficient tool, and would be a suitable application to support Benins local authorities in integrated water resources management.
Acknowledgements The authors would like to thank the Federal German Ministry of Education and Research (BMBF, Grant No. 01 LW 06001B) as well as the Ministry of Innovation, Science, Research and Technology (MIWFT) of the federal state of North Rhine-Westfalia (Grant No. 313-21200200) for the funding of the IMPETUS project in the framework of the GLOWA program. Many thanks to our partners in Benin and all colleagues of the IMPETUS project, who provided data and assistance. The authors thank the editor and the anonymous referees for their helpful comments.
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