2.1. Thornthwaite Model
Before simulating groundwater dynamics using WEAP, groundwater recharge has to be computed, and this is one of the important input for this model. The Thornthwaite model [11
], developed by the U.S. Geological Survey, was applied for this purpose, which uses monthly total precipitation (in millimeters) and temperature (in degrees Celsius) as inputs to estimate groundwater recharge. The Thornthwaite model is based on a monthly water-balance model that is driven by a graphical user interface.
The water-balance model calculates the water amount of the various components of the hydrological cycle (Figure 2
) using a monthly accounting procedure [11
]. Seven input parameters (runoff factor, direct runoff factor, soil-moisture storage capacity, the latitude of the location, rain temperature threshold, snow temperature threshold, and maximum snow-melt rate of the snow storage), incorporated in the model, were adapted during the model calibration process. The model was calibrated to fit the groundwater recharge previously simulated by Toure et al. [6
], using EARTH (Extended model for Aquifer Recharge and soil moisture Transport through the unsaturated Hardrock) model. To calibrate this model, monthly precipitation and temperature from RCP4.5 and RCP8.5, from 2012 to 2013 were used as input data, direct runoff was set to zero because local scale surface runoff infiltrates on the way to the river system. There is no direct runoff contribution to the discharge of the basin. The soil moisture storage capacity was calibrated to be 375 mm by comparing the groundwater recharge of the two models (EARTH and Thornthwaite). All other parameters were unchanged. More detail on this model can be found in [11
]. After calibration, the model was applied to simulate groundwater recharge [6
] from 2006 to 2050, as well as potential evapotranspiration, actual evapotranspiration, soil moisture storage and runoff.
2.2. WEAP (Water Evaluation and Planning System) Model
WEAP is a software tool developed by the Stockholm Environment Institute for integrated water resources planning and as an integrated decision support system (DSS), that is aimed to support water planning system by comparing water supplies generated [12
] from surface water (e.g., streamflow, lake, seepage, spring, etc.) and groundwater (e.g., natural and artificial recharge) of a basin scale, or municipal scale, and “multiple water demands and environmental requirements characterized by spatially and temporally variable allocation priorities, and supply preferences” [13
]. According to Haddad et al. [15
], the DSS for a water resources management system involve three major components (1) stakeholders survey to define key planning issues; (2) database system to facilitate data management; and (3) the WEAP model that simulates and predicts water resources using multiple alternative scenarios. It is straightforward and easy to use, free of charge for organizations and institutions in developing countries and downloadable at: www.weap21.org
The WEAP model can be used to simulate both natural hydrological processes and anthropogenic effects on natural water resources to assess water availability within the basin and the human impact on water use respectively [14
]. The simulations within the WEAP model “are constructed as a set of scenarios using monthly time steps with a time horizon of a single year to more than 100 years” [14
In WEAP, the future behavior of water availability and demand is compared to the current or a baseline year, which is based on a snapshot of actual water demand and supplies, known as Current Accounts. Basically, the baseline year is chosen to determine the recent water availability of a system; and then, to evaluate the impact of different alternative scenarios for the future. Generally, the baseline year is the year where enough information on all input data such as hydrology, precipitation, natural infiltration, temperature, demography, socio-economic, agriculture, etc. is available. The adaptability of the WEAP model to various levels of data availability and its simple graphical user interface make it an appropriate tool to be used under all climatic conditions [17
] including Klela basin case, where data availability is very low. WEAP has been chosen in this study to assess the impact of human actions and the impact of climate change effects on the groundwater resources, by developing multiple alternative scenarios for socio-economic development, population growth, and climate.
The WEAP model is fundamentally based on water balance accounting principle and can be applied to community and agricultural systems, single subbasin or complex transboundary river systems [18
]. It may “address a wide range of issues; for instance, sectoral demand analyses, water conservation, water rights and allocation priorities, groundwater and streamflow simulations” [14
The modeling process within WEAP follows three steps: (i) creation of a geographic representation of the study area on which all the features (supply and demand sites) are represented, (ii) establishment of the current account; this involves choosing a reference year (baseline) in which all or the maximum of data are available. In this part, the reference scenario is created which shows the probable evolution of the system during the simulation period without any modification (business as usual scenario), and (iii) development and evaluating of model scenarios which is the most important part of modeling allowing predicting an eventual change of water resources by answering a wide range of questions. For example, what happens when demographic or economic patterns change? What happens when the mix of agricultural crops changes? What happens when groundwater is more exploited? How does climate change alter demand and supplies? In any case, the general question is how the groundwater resources in the Klela basin will develop under population growth and climate change?
The schematic view is a spatial physical feature of the water supply and demand system. It permits to connect all supply and demands by using nodes and transmission links. The schematic of the Klela basin is shown in Figure 3
. Red points in this figure represent water demands in the study area. These water demands include irrigation, industry, livestock, rural and urban, which explain the quantity of water use in each sector; urban and rural indicate domestic water use. The green square is groundwater source, which furnishes water to other sectors via a transmission link, in the green arrow.
2.2.1. Development of Scenarios
According to the last census in Mali [20
], the average population growth rate was estimated to be 3.6%, and it is increasing from one census to another. Moreover, Traore and Sissoko [21
] argued that the population of Mali could be doubled in 2050 but the projection made by DAES, cited in [21
], suggested that the growth rate will decrease from 2010 and will reach 1.43% between 2045 and 2050. Besides population growth, the increasing trend of urbanization rate is also noticeable in Mali. In 1998, urbanization rate was approximatively 27%, and it was estimated to exceed 40% in 2015 (PNUD cited in [22
]). All these (population growth and urbanization evolution) increase the water demand for the population.
What are the implications for the groundwater resources facing this pressure of population growth? The question comprises various variables including the closeness and accessibility of groundwater resources, groundwater system vulnerability, the consistency and comprehensiveness of existing governance regimes, type of current stresses, and climate change impacts.
The same growth rate (3.6% for the whole Mali) was considered in the Klela basin. The population of the Klela basin is estimated to be 462,544 in 2009, calculated to be 532,834 in 2013 (baseline for the model), and will reach 1,971,982 in 2050 assuming a constant growth rate.
In this study, climate data series (past and future) were taken from the General Circulation Model (GCM) ECHAM downscaled to a 0.44° (approximatively 50 km) resolution by the Swedish Meteorological and Hydrological Institute (Sveriges Meteorologiska och Hydrologiska Institute, SMHI), and furnished by the CORDEX (Coordinated Regional Climate Downscaling Experiment) initiative. These data are used to assess the impacts of climate change on groundwater resources in the Klela basin. CORDEX is a program funded by World Climate Research Programme (WCRP) of which the main aim is to develop a framework allowing the use of downscaled global climate projections and assessing regional climate downscaling techniques and finally using them into impact and adaptation studies within the IPCC Fifth Assessment Report (AR5) timeline [23
]. CORDEX domains cover most of the land surfaces of the world. Based on regional focus and improved resolution, “it is anticipated that the CORDEX dataset will provide a link to the impacts and adaptation community” [24
]. CORDEX is focused on the GCM experiments applying emission scenarios that are based on Representative Concentration Pathways (RCPs). The RCPs “are four greenhouse gas concentration trajectories adopted by IPCC for its fifth assessment report in 2014”, which replace the Special Report Emission Scenarios projections in 2000 [25
]. The RCPs describe an emission trajectory and concentration by the year 2100, unlike SRES that starts by socio-economic from which emission trajectories and climate impacts are projected [25
]. A set of four pathways were created based on radiative forcing degrees of 8.5, 6, 4.5 and 2.6 W/m2
corresponding to RCP8.5, RCP6, RCP4.5 and RCP2.6, respectively, by the end of 2100. Only the two scenarios (RCP4.5 and RCP8.5) were taken into account in this case study and are described below. The RCPs dataset cover the period 1950–2100 [26
the scenario RCP4.5 is an intermediate pathway that is around the stabilization level of approximatively 4.5 W/m2
], supposing that all the world countries undertake emission mitigation policies [28
]. Comparing RCP4.5 with the GCAM (General Circulation Atmospheric Model) reference scenario, it has been demonstrated in [28
] that the population and income drivers are the same, but they are different from the policy applied to “greenhouse gas emissions to stabilize atmospheric radiative forcing”. The main anthropogenic gas emission for RCP4.5 is carbon dioxide (CO2
) and comprises the widest contribution to total radiative forcing followed by methane (CH4
) and others [28
]. In order to decrease greenhouse gas emissions in the atmosphere and stabilize radiative forcing by 2100, the RCP4.5 scenario is projected to inform research on the atmospheric consequences [28
]. Refer to [28
] for more details.
The worst case scenario RCP8.5 is a reference scenario and representing the highest RCP scenario regarding GHG emissions without any explicit climate policy. “RCP8.5 is a rising radiative forcing pathway leading to 8.5 W/m2
in 2100” [29
]. In RCP8.5, increasing global population (approximatively 12 billion by 2100) and economy associated with a lower rate of technology development lead to increasing primary energy demand [30
]. An increasing global population in RCP8.5 is mostly due to increasing use of cropland and grasslands [26
]. It is mentioned in Riahi et al. [30
] that in RCP8.5 the greenhouse gas emissions continue rising due to mainly the high intensity of fossil energy as well as growing population and also high demand for food [30
]. Most of the GHG emissions rising are due to those of CO2
from energy sector; but from agriculture sector, it is principally attributed ”to increasing use of fertilizers and intensification of agricultural production, giving rise to the main source of nitrogen dioxide (N2
O) emissions” [30
]. Besides the principal gases responsible for radiative forcing such as CO2
, etc., there are some others additional tropospheric ozone in RCP8.5, which are “expected to increase the radiative forcing by an additional 0.2 W/m2
by 2100” [31
In this study, the scenarios have been developed for the time interval 2013–2050, with the year 2013, the current account (baseline). The baseline (2013) was chosen based on the availability of consistent and reliable data. Six scenarios were developed such as:
Reference scenario: it refers to the current account scenario in which the socio-economic is used (business as usual). Climate (precipitation) data is based on current account year (2013). Therefore, the recharge was constant over time from 2013–2050.
High population growth scenario: the present growth rate (3.6%) will increase by 2% to become 5.6% in 2050. Other parameters are used as reference scenario.
Socio-economic scenario E1: all water demand data is moderately increasing, except livestock which is the same as in reference scenario, and population growth decreases (based on DAES projection, see before) slightly compared to the reference scenario.
Socio-economic scenario E2: high water demand scenario with slight decrease of population growth, but greater than in scenario E1. All socio-economic data are increased to cover the possible future water demand.
Climate change scenario using RCP4.5: only the climate data from the RCP4.5 scenario was used. The population and other demands were not changed.
Climate change scenario using RCP8.5: only the climate from the RCP8.5 scenario was used. The other parameters were used as in the reference scenario.
The raw climate data from CORDEX were used directly to the model without any bias correction because its historical mean datasets of precipitation and temperature were nearly fitted to the observed dataset measured from the field (Figure 4
and Figure 5
). Figure 4
compares the mean monthly precipitation from measurements to that from CORDEX, and the correlation coefficient is greater than 0.5, which reveals an acceptable relation between both. In addition, the correlation coefficient of 0.802 for temperature shows a good correlation between observed measurements and simulated from CORDEX (Figure 5
2.2.2. Water Supply Resources
The main source of water in the Klela basin is groundwater, which is accessed through wells and boreholes. These groundwater sources are principally replenished by local precipitation through the infiltration. This infiltration has been estimated by Toure et al. [6
] using the EARTH model to be approximatively 13.9% of mean annual precipitation. The precipitation data from DNM for the current account (the year 2013) and from SMHI-ECHAM for the scenarios (2014–2050) were used in this study.
Other water sources which are seasonally important are stream flows. These resources are notably used for irrigation and livestock.
2.2.3. Water Demands
Four types of water users are known in the Klela catchment: households, irrigation, industry, and livestock. Water demand per person in Mali depends strongly on the state of urbanization, which is in turn based on the number of inhabitants per region. For example, in the village (with less than 2000 inhabitants) the water demand is 20 liter per capita per day (lpcpd) [32
] while it is 31 lpcpd in semi-urban areas (between 2000–10,000 inhabitants) and in urban areas (greater than 10,000 inhabitants) it is 45 lpcpd [32
]. Since most of the population in the Klela basin is living in the villages, 20 lpcpd has been used as their water demand. The remainder of the population living in town receives water via SOMAGEP (Société Malienne de Gestion de l’Eau Potable). During the rainy season, the rivers contribute to satisfying water demand in rice irrigation and livestock water need.
The population data was used to estimate the domestic annual water use. The data for irrigation was provided by DRGR Sikasso (Direction Régionale du Génie Rural de Sikasso) and PCDA (Projet pour la Competitivite et Diversion Agricole). In fact, two major types of crops (rice and potato) that consume a huge quantity of water were used to compute irrigation water demand. There are other types of crops, but data are not available and the quantity of water used is small compared to rice and potato. Rice cultivation is carried out during the rainy season from June to October. Potato is irrigated in the dry season from November to February. Groundwater is the principal source exploited for this need [35
]. This source is extracted from wells manually or through motor pumps depending on the extension of the irrigated surface. The industry is not well developed in this rural study area, as there are only some few small factories that withdraw water from groundwater (source DRI: Direction Regionale d’Industrie). The statistical details on the current situation in 2013 are given in the Table 1
. The details concerning the water demands are shown in Table 2