This study evaluates climate change impacts on water resources using an
ensemble of six regional climate models (RCMs)–global climate models (GCMs)
in the Dano catchment (Burkina Faso). The applied climate datasets were
performed in the framework of the
After evaluation of the historical runs of the climate models' ensemble, a
statistical bias correction (empirical quantile mapping) was applied to daily
precipitation. Temperature and bias corrected precipitation data from the
ensemble of RCMs–GCMs was then used as input for the
The mean hydrological and climate variables for two periods (1971–2000 and
2021–2050) were compared to assess the potential impact of climate change on
water resources up to the middle of the 21st century under two greenhouse gas
concentration scenarios, the Representative Concentration Pathways (RCPs) 4.5
and 8.5. The results indicate (i) a clear signal of temperature increase of
about 0.1 to 2.6
Development of adaptation strategies to deal with potential impacts of climate change on hydrological systems is a considerable challenge for water resources management (Muerth et al., 2013; Piani et al., 2010). Besides being highly exposed to climate change, the West African region presents a low adaptive capacity (IPCC, 2014). Projections for the late 21st century suggest severe consequences of climate change for water resources for the region. This includes an increased risk of water stress and flood (Sylla et al., 2015; Oyerinde et al., 2014) and significant change in river discharge (Aich et al., 2014; Ardoin-Bardin et al., 2009; Mbaye et al., 2015).
Rising temperatures, commonly acknowledged by regional climate models (RCMs) and global climate models (GCMs), are expected to intensify the hydrological cycle due to an increased water holding capacity of the atmosphere, leading to an increased amount of renewable freshwater resources (Piani et al., 2010). Another consequence of temperature increase ascertained by Piani et al. (2010) for some regions is the decrease in precipitation associated with the intensification of the seasonal cycle and the frequency of extreme events. These opposite trends imply that high uncertainties are associated with predicted rising temperatures' impact on the hydrological cycle for some regions (Salack et al., 2015).
Confidence in RCMs and GCMs over West Africa relies on their ability to simulate the West African monsoon (WAM) precipitation (Klein et al., 2015). However, simulating the WAM remains challenging for both RCMs and GCMs (Cook, 2008; Druyan et al., 2009; Paeth et al., 2011; Ruti et al., 2011), as each RCM and GCM produces a version of the WAM, but with some distortion of structure and/or timing. Some GCMs (e.g., CSIRO, GISS_ER, ECHAM5, CCSM) do not generate the WAM at all (Cook and Vizy, 2006). Part of this divergence is related to (i) imperfect characterization of tropical precipitation systems; (ii) uncertain future greenhouse gas forcing; (iii) scarcity of observations over West Africa; and (iv) natural climate variability (Cook and Vizy, 2006; Foley, 2010). The hydrological climate change signal is therefore unclear for the region. Several authors (Kasei, 2009; Paeth et al., 2011; Salack et al., 2015) observed diverging precipitation signals among models. Moreover, several models fail to accurately reproduce the historical rainfall onset, maxima, pattern, and amount of the region (Nikulin et al., 2012; Ardoin-Bardin et al., 2009).
Despite significant advances, outputs of GCMs and RCMs are still characterized by biases that challenge their direct use in climate change impact assessment (Ehret et al., 2012). Indeed, unless the precipitation from climate models is bias corrected, results from hydrological simulations are often reported to be unrealistic and may lead to incorrect impact assessments (Johnson and Sharma, 2015; Teutschbein and Seibert, 2012; Ahmed et al., 2013). However, correction of climate model based simulation results does not ensure physical consistency (Muerth et al., 2013) and may affect the signal of climate change for specific regions as reported by Hagemann et al. (2011). Consequently, simulated hydrological variables using bias corrected data need to be explored in climate change impact assessment.
There is essential consensus on the necessity of performing
multi-(climate)-model assessments to estimate the response of the West
African climate to global change (Sylla et al., 2015). Accordingly, several
studies (e.g., Chen et al., 2013; Zhang et al., 2011) emphasize the
importance of using multiple climate models to account for uncertainty when
assessing climate change impacts on water resources. Taking advantage of the
results of the
A few studies have already investigated the impacts of projected climate change on water resources in West Africa (see Roudier et al., 2014, for a review). Many of these studies have used an approach based on hydrological models driven by a single RCM or GCM dataset (e.g., Mbaye et al., 2015; Cornelissen et al., 2013; Bossa et al., 2012, 2014). Therefore, uncertainty related to the choice of the climate model was not explicitly evaluated. However, other studies have used multi-climate model datasets (Kasei, 2009; Ruelland et al., 2012, Aich et al., 2016); most of these studies have resulted in a diverging projected hydrological change signal. Climate model outputs have often been bias corrected to fit the historical climate variables and then used as input for hydrological models, but few have investigated the necessity of performing such corrections in detecting the signal of future climate change impacts on water resources.
The current study aims to investigate the future climate change impacts on the hydrology of the Dano catchment in Burkina Faso, thus contributing to the management of water resources in the region. Besides the small scale of the catchment that implies addressing scale issues, the novelty of the study includes a water-energy budget analysis. Specifically, it has the following objectives: (i) evaluate the historical runs of six RCMs–GCMs at the catchment scale; (ii) analyze the climate change signal for the future period of 2021–2050 compared to the reference period of 1971–2000; (iii) evaluate the ability of the climate models to reproduce the historical discharge; (iv) assess the impacts of climate change on the hydrology of the catchment by the middle of the 21st century; and (v) perform an ecohydrological analysis of the catchment under climate change.
The study was carried out in the Dano catchment covering a total area of
195 km
Location map:
The major land uses in the catchment include shifting cultivation, which
accounts for one-third of the catchment area; natural vegetation albeit
converted into agricultural and fallow lands forms part of the Sudanian
region characterized by wooded, scrubby savannah and abundant annual grasses.
Sorghum (
The catchment is characterized by a flat landscape with a mean slope of
2.9 % and mean altitude of 295 m a.s.l. (above sea level). According to
Schmengler (2011), a mean annual temperature of 28.6
Observed mean daily temperature and daily precipitation used in the study were collected from the national meteorological service of Burkina Faso (DGM). The dataset covers the reference period of 1971–2000. Although the national observation network includes several rainfall gauges and synoptic stations, solely the data of the Dano station were used as it is located in the study area.
RCM–GCM products and the corresponding label used in the study.
An ensemble of six RCM–GCM datasets is exploited in the study (Table 1). The RCM–GCM simulations were performed in the framework of the CORDEX-Africa project. The datasets were produced by three RCM groups (CCLM: Climate Limited-area Modelling Community, Germany; RACMO22: Royal Netherlands Meteorological Institute, Netherlands; HIRHAM5: Alfred Wegener Institute, Germany) using the boundary conditions of four GCMs (CNRM-CM5, EC-EARTH, ESM-LR, NorESM-M). Each dataset consists of historical runs and projections based on emission scenarios RCP4.5 and RCP8.5 (Moss et al., 2010). The retrieved data (precipitation and temperature) range from 1971 to 2000 for the historical runs and from 2021 to 2050 for the RCPs.
An extent of 9 nodes (3
Absolute precipitation bias (corrected and not corrected) for the model ensemble compared to the observed data for the period of 1991–2000. The transfer functions were calibrated for the period 1971–1990.
Due to the discrepancy between the RCM–GCM data resolution (0.44
The RCM–GCM ensemble was evaluated to get an estimate of the historical simulated variables for the catchment by comparing RCM–GCM based simulations of historical climate variables to the observations provided by the National Meteorological Service (DGM). As presented in Sect. 3.1, whereas temperature simulated by the models' ensemble encompassed the observed temperature with moderate deviation, precipitation simulated by individual RCMs–GCMs exhibited biases such as overestimation of annual precipitation as well as misrepresentation of the timing of the rainy season. A precipitation bias correction was therefore applied to the six RCMs–GCMs following the non-parametric quantile mapping using the empirical quantiles method (Gudmundsson et al., 2012). For each member, transfer functions (TFs) were derived using observed and modeled precipitation for the period of 1971–2000; afterwards the transfer functions were applied to the projected climate scenarios (period 2021–2050).
However, a consistent application of bias correction is subject to numerous hypotheses that need to be fulfilled, at the risk of altering the climate change signal (Muerth et al., 2013; Ehret et al., 2012; Hagemann et al., 2011). This includes the hypotheses of reliability, effectiveness, time invariance or stationarity, completeness, etc. (a complete discussion of these hypotheses is provided by Ehret et al. (2012)). Precipitation in the Dano region is characterized by a strong decadal variability and a non-stationary annual behavior (Oyerinde et al., 2014; Karambiri et al., 2011; Waongo, 2015), which implies that a TF derived from a short period (e.g., a decade) does not fulfill the time invariant hypothesis. Similarly, a TF derived from a short period precludes the hypothesis of completeness and is likely not to be suitable for application to a period that does not overlap the derivation period as TFs are likely to change from one period to another (Piani et al., 2010).
Selected submodels and algorithms of WaSiM.
A cross-validation approach (e.g., Lafon et al., 2013; Teutschbein and
Seibert, 2013) using the periods of 1971–1990 and 1991–2000 for calibration
and verification, respectively, showed that for the climate model ensemble
biases in precipitation are in general reduced by the bias correction method
(Fig. 2). However, due to the mentioned decadal signal that characterizes
precipitation variability in the region, considerable deviations between bias
corrected and observed precipitation (up to 40 mm month
Observed and RCM–GCM based (historical runs and projections) data were used
as climate input for the Richards equation based version 9.05.04 of the
A previous study confirmed the suitability of WaSiM to model the hydrology of
the Dano catchment. Details of the model setup and parameterization are
available in that study (Yira et al., 2016). Briefly summarized, the model
was calibrated and validated using observed discharge for the period
of 2011–2014, daily time steps, and a regular raster-cell size of 90 m.
Latin hypercube sampling (LHS) was used to identify and optimize sensitive
parameters (drainage density, storage coefficient for surface runoff, and
storage coefficient for interflow) with the sum of the squared error set as
an objective function. Following the LHS, several model parameterizations
lead to equally good model quality measures. Out of these good parameter
sets, the one scoring the highest sum of the Pearson product-moment
correlation coefficient (
In the absence of long-term observation discharge for the catchment, the reliability of the model parameters in time could not be assessed in a classical way. Therefore, a soft validation approach was adopted. The approach consisted in determining, based on the Standardized Precipitation Index, whether the calibration/validation years represented normal years in the catchment (considering the historical period of 1990 to 2014). This evaluation showed that both calibration and validation periods are normal and reflect the annual rainfall pattern in the catchment for the period 1990–2014 (Fig. 1_supplementary materials in Yira et al., 2016, shows this evaluation). Therefore, the model parameters for the catchment are expected to be reliable for a long period.
In addition to the validation using the discharge, the model was further
validated against soil moisture under the dominating soil type and
groundwater level recorded by four piezometers. Minimum values of 0.7 for
NSE, KGE, and
Discharge simulated with RCM–GCM historical runs (bias corrected and non bias corrected) was compared to the discharge obtained with observed historical climate data. These comparison runs showed that bias correction was necessary for RCM–GCM based simulations to reproduce the historical discharge regime. To integrate the potential effect of bias correction on climate change signal as discussed in Sect. 2.3 and raised by different authors (e.g., Muerth et al., 2013; Ehret et al., 2012; Hagemann et al., 2011), the hydrological model was run with both bias corrected and non bias corrected climate inputs for the climate model ensemble.
No hydrologic observations (discharge, soil moisture, and groundwater level) are available for the reference period (1971–2000) in the catchment. The expected climate change for an RCM–GCM is therefore expressed as the relative difference between simulated hydrological variables under the reference period (1971–2000) and future period (2021–2050).
A concept of water-energy budget (Tomer and Schilling, 2009; Milne et al.,
2002) was applied to estimate the effectiveness of water and energy use by
the catchment as it undergoes climate change. While experiencing climate
change, a trend towards the optimization of total unused
water-
A set of evaluation measures was used to analyze the RCM–GCM historical
runs, to assess model performance and to estimate the effects of different
climate scenarios on hydrological variables.
The The normalized root-mean-square deviation (NRMSD): expresses the deviation
of each RCM–GCM based precipitation and temperature from the observations. The Pearson product-moment correlation coefficient ( The change signal ( The Wilcoxon (1945) rank-sum test was used to compare the discharge
change signal with bias corrected and non bias corrected precipitation data
following Muerth et al. (2013). The test evaluated the null hypothesis
“discharge change signal under bias corrected data equals discharge change
signal under non bias corrected data”. The rejection of the test
at 5 % implies that future discharge change under bias correction and no
bias correction are significantly different. If the test is not rejected,
both discharge change under bias correction and change under non bias
correction yield the same result, and thus bias correction does not alter the
climate change signal on projected discharge.
Statistics of RCM–GCM based precipitation and temperature compared to observations (Obs) for the reference period (1971–2000). Climate model data are not bias corrected. Statistics are computed based on average monthly values.
Historical mean annual
The comparison between RCM–GCM historical runs and observations for temperature and precipitation is done for the reference period of 1971–2000 for average monthly values. The correlation coefficient is plotted against the NRMSD (Fig. 3) for a cross-comparison between RCMs–GCMs in order to assess the relative ability of each RCM–GCM to represent historical climate conditions in the catchment. The correlation coefficient for the RCM–GCM ensemble is in general higher than 0.7 for both precipitation and temperature. The highest coefficients (0.96) are scored by CCLM-ESM for temperature and HIRAM-NorESM for precipitation. The RCM–GCM ensemble mean outscores five members of the RCM–GCM ensemble with regard to temperature and precipitation (Fig. 3).
The RCM–GCM ensemble shows a clear deviation from observed precipitation compared to temperature (Fig. 3). HIRAM-EARTH and CCLM-EARTH present the lowest deviation for temperature and precipitation, respectively. The RCM–GCM ensemble mean outscores four out of six RCMs–GCMs for temperature and precipitation with regard to the deviation from observed data.
Figure 4a and b show a trend towards an overestimation of annual
precipitation throughout the reference period for the RCM–GCM ensemble when
precipitation data are not bias corrected (UC). Although the RCM–GCM
ensemble presents a large dispersion (
Monthly air temperature derived from climate models and observations
for the reference period (1971–2000). Data are not bias corrected.
Climate change signal of precipitation, air temperature, and evapotranspiration between the reference (1971–2000) and future (2021–20150) periods under emission scenarios RCP4.5 and RCP8.5. BC is bias corrected and UC refers to non bias corrected.
The mean annual precipitation pattern is in general well captured by all
RCMs–GCMs (Fig. 4c and d). However, the climate models' ensemble, when not
bias corrected, covers only 50 % of monthly precipitation despite a large
dispersion (Fig. 4c). After bias correction, the agreement between RCM–GCM
based precipitation and observation is considerably improved (Fig. 4d), and
the uncertainty band of the climate model is considerably reduced
(
Figure 5 shows that the RCM–GCM ensemble fully captures the annual
temperature pattern (
The RCM–GCM ensemble exhibits a mixed annual precipitation change signal between the reference period (1971–2000) and future period (2021–2050) (Table 3). CCLM-CNRM, RAMCO-EARTH, and HIRHAM-NorESM project a precipitation increase of about 2.5 to 21 %, whereas CCLM-ESM and CCLM-EARTH indicate a decrease of 3 to 11 %. Bias correction has a minor impact on these signals, as the magnitude of projected precipitation increase ranges from 1 to 18 % and the decrease is around 5–13 % after bias correction.
A much more complex intra-annual precipitation change signal is projected by
the climate models' ensemble (Fig. 6). CCLM-CNRM and HIRHAM-NorESM, which
projected increased annual precipitation, are characterized by increased
rainfall from May to June followed by decreased rainfall in August.
RAMCO-EARTH shows increased rainfall throughout the season except in July.
The decrease in annual precipitation projected by CCLM-ESM and CCLM-EARTH is
consistent throughout the entire season. The climate model ensemble
consistently projects a mean monthly temperature increase of about 0.1 to
2.3
Projected rainfall change between the reference (1971–2000) and future (2021–2050) periods with bias corrected and non bias corrected RCM–GCM based simulations.
Performance of RCM–GCM based discharge compared to observation based discharge. Performance is calculated using mean monthly discharges for the period 1971–2000.
Historical RCM–GCM based discharge simulations and observation
based discharge:
RCM–GCM ensemble based discharges are compared to discharge simulated using
observed climate data to evaluate the climate models' ability to reproduce
the historical discharge regime over the reference period (Fig. 7).
Accordingly, performances (
Projected change in annual discharge for the period of 2021–2050 compared to the reference period is presented in Table 5. As for precipitation, a mixed annual discharge change signal is projected by the climate model ensemble. With bias corrected climate data, the following is projected: (i) a more than 15 % decrease in annual discharge, which is a consequence of relative decrease in precipitation and a consistent increase in potential evapotranspiration for CCLM-ESM, CCLM-EARTH, and HIRHAM-EARTH (RCP8.5); and (ii) a low to very high (3 to 50 %) increase in total discharge due to increased precipitation not counterbalanced by the evapotranspiration for CCLM-CNRM, RAMCO-EARTH, HIRHAM-NorESM, and HIRHAM-EARTH (RCP4.5). This divergence between climate models is reflected through a large amount of uncertainty associated with the projected annual discharge (Fig. 8). The projected intra-annual change in discharge (Fig. 9) is very similar to the precipitation change signal shown in Fig. 6. The discharge changes with non bias corrected climate data are similar in trend (with however differences in magnitude) compared to the changes observed with bias corrected data, which is consistent with changes in the climate signal induced by the bias correction.
Projected annual discharge for the climate models' ensemble. Simulations are performed with bias corrected precipitation data.
The Wilcoxon (1945) rank-sum test, testing the significance of the difference
between bias corrected and non bias corrected discharge change signals for
the climate model ensemble, indicates that the signals are not different at a
The sensitivity of the catchment discharge to precipitation and temperature
change is tested by plotting, for each member of the climate models'
ensemble, predicted precipitation, and temperature change against predicted
discharge change. The result shows that change in total discharge cannot be
strongly related to change in potential evapotranspiration (Fig. 10a).
However, a high sensitivity of river discharge to precipitation change
(Fig. 10b) is observed. Under scenario RCP4.5, an increase of
The ecohydrologic status of the catchment for the reference period and future
scenarios RCP4.5 and RCP8.5 is shown in Fig. 11 to illustrate the use of
energy and water by the catchment while undergoing temperature increase and
precipitation change. Moving left to right along the “Excess
water
Mean annual discharge change projected by the RCM–GCM ensemble for the period 2021–2050 compared to the reference period 1971–2000.
Moving upwards along “Excess evaporative demand
The ecohydrologic status of the catchment, irrespective of climate model and emission scenario, projects a shift for the period of 2021–2050 compared to the reference period. Therefore, differences in climate conditions between the two periods influence the hydrology (discharge, evapotranspiration, precipitation) of the catchment.
Monthly discharge change between the reference period (1971–2000) and the future period (2021–2050) under emission scenarios RCP4.5 and RCP8.5. BC and UC refer to bias corrected and non bias corrected, respectively.
Change in the annual discharge as a response to potential
evapotranspiration
Plot of excess precipitation (
All GCMs and RCMs applied in this study have proved in previous works to fairly reproduce the climatology of West Africa (Cook and Vizy, 2006; Dosio et al., 2015; Gbobaniyi et al., 2014; Paeth et al., 2011). The RCM–GCM ensemble reasonably captures the annual cycle of temperatures, and following several authors (e.g., Buontempo et al., 2014; Waongo et al., 2015) no bias correction was performed for this climate variable. The systematic positive bias and large deviation from observed precipitation exhibited by the climate models' ensemble in this study are also reported by several authors (Nikulin et al., 2012; Paeth et al., 2011) for the southern Sahel Zone. This deviation motivated the bias correction of precipitation. After correction, the positive bias is significantly reduced for all individual climate models and the improvement is clearly visible.
In general, the RCM–GCM ensemble mean outperforms individual climate models for both temperature and precipitation. This is due to the fact that individual model errors of opposite sign cancel each other out (Nikulin et al., 2012; Paeth et al., 2011). However, the climate models' ensemble mean should not be considered an expected outcome (Nikulin et al., 2012). Rather, considering a large ensemble of climate models should be seen as necessary to properly perform future climate impact studies in the catchment (Gbobaniyi et al., 2014) and to assess the range of potential future hydrological status required for adaptation and management strategies.
Compared to the period of 1971–2000, a clear temperature increase signal is
projected for 2021–2050 by the six members of the RCM–GCM ensemble in the
catchment. This feature is common to all multi-model ensemble studies
performed in the region (IPCC, 2014). It is further in line with the
historical temperature change observed in the region as reported by
Waongo (2015), who used the same observation dataset applied in the current
study. He reported an average
The precipitation change projected by CCLM-CNRM and HIRHAM-NorESM, wetter conditions associated with drought during specific months, is consistent with the change reported by Patricola and Cook (2009) for the West African region. They highlighted an increase in precipitation in general, but also noted drier June and July months. A similar result is achieved by Kunstmann et al. (2008) in the Volta Basin, albeit with a decrease in precipitation at the beginning of the rainy season in April.
Precipitation change projected by CCLM-ESM and CCLM-EARTH is consistent with the decrease in the June–July–August season noted by Buontempo et al. (2014). A reduction in precipitation during the rainy season is also achieved with RegCM3, driven by ECHAM5 in the Niger River Basin (Oguntunde and Abiodun, 2012). Up to 20.3 % reduction of precipitation in some months is projected, but an increased precipitation during the dry season is also expected.
A critical analysis of CCLM (by Dosio et al., 2015) showed that the model is significantly influenced by the driving GCM (including EC-Earth, ESM-LR, and CNRM-CM). Such an analysis was not found for RACMO and HIRAM. Overestimation of precipitation is a common feature of the RCM–GCM ensemble applied in this study, which could suggest that the RCMs inherit the bias from the GCM (Dosio et al., 2015). Consistently with Paeth et al. (2011), the relation between RCM trend and driving GCM cannot be observed in the current study as CCLM-EARTH and RACMO-EARTH clearly show opposite trends although both are driven by EC-EARTH. Differences in projected trends are also highlighted by individual RCMs driven by different GCMs (e.g., CCLM-EARTH and CCLM-CNRM).
Compared to the observation based simulation, non bias corrected RCM–GCM
based discharge is characterized by an overestimation of annual discharge.
This misrepresentation results from the positive precipitation bias presented
by the climate models' ensemble. The bias correction significantly improves
the ability of all members of the climate models' ensemble to reproduce the
historical discharge regime. By comparing simulated discharge with bias
corrected and non bias corrected precipitation data, it clearly appears that
the bias correction methodology is effective with regard to both discharge
regime and total discharge; thus, it increases the quality (correspondence
between projection and observation) of the model (Murphy, 1993). However, a
trend towards discharge overestimation was noticed after bias correction of
precipitation. This could be related to
the relatively long period used for the bias correction (1971–2000). As
noticed by Piani et al. (2010), fragmenting the correction period to decade
and deriving several transfer functions can improve the bias correction
result and further contribute to capturing the decadal rainfall change that
characterizes the West African climate; and the fact that temperature was not bias corrected. This led to ET
In view of the general good simulation of historical discharge for the
climate models' ensemble, it is worth noting that running the hydrological
model with simulated climate data of one node at a time (Sect. 2.2) has
reasonably bridged the discrepancy between RCM–GCM data resolution and the
hydrological modeling domain (see Fig. S1 of the Supplement for the
hydrological spread of the 9-node approach and Fig. S2 for the difference in
precipitation between the 9-node approach and the standard
3
A mixed annual discharge change signal is projected by the climate models'
ensemble for the period of 2021–2050. These trends agree with several
studies in the region (Table 7), although all were carried out at the
mesoscale and macroscale.
Negative trend (CCLM-ESM and CCLM-EARTH): a discharge decrease of 30 to 46 % is reported by
Ruelland et al. (2012) using MadCM3 and MPI-M in the Bani catchment. A
similar trend, resulting from a combination of temperature increase and
precipitation decrease, was reached by Mbaye et al. (2015) using the REMO
climate model in the Upper Senegal
Basin, as did Cornelissen et al. (2013) and Bossa et al. (2014) in the
Térou and Ouémé catchments in Benin, respectively. Positive trend (CCLM-CNRM, RAMCO-EARTH, and HIRHAM-NorESM): an increase of 38 % in annual discharge
in the region is reported by Ardoin-Bardin et al. (2009) for the Sassandra
catchment (south of the Dano catchment) using climate projections of
HadCM3-A2. This results from a 11 % increase in precipitation not
counterbalanced by the 4.5 % increase in potential evapotranspiration. Mixed trend (HIRHAM-EARTH and RCM–GCM ensemble): a mixed
discharge change signal for the future period is the common signal projected
by multi-climate model studies performed in the West African region. In the
Niger basin, Aich et al. (2014) simulated change in annual discharge ranging
from an increase of up to 50 % to a decrease of up to 50 % using an
ensemble of five climate models. Similar signals are reported by
Kasei (2009), who applied two climate models (MM5 and REMO) in the Volta
basin.
This mixed hydrological change signal is the result of high uncertainties
associated with the precipitation change projected by climate models for the
catchment (IPCC, 2014). The Wilcoxon rank-sum test further indicated that
bias correction did not significantly alter these discharge change signals.
Due to the high sensitivity and nonlinear response of the catchment discharge
to precipitation, any change in precipitation will have a strong impact on
the discharge; the impact will further be pronounced under RCP8.5 compared to
RCP4.5. Irrespective of emission scenario, change in potential
evapotranspiration alone failed to strongly explain change in annual
discharge (Fig. 10a); this is partly explained by the fact that the
environmental system of the catchment is water limited and not energy
limited.
The water limited environment of the catchment might also explain the
performance of the hydrological model for the climate models' ensemble
despite the non bias correction of temperature data (up to 4
Mean annual water balance components per RCM–GCM for the historical (1971–2000) and projected (2021–2050) periods. Precipitation data are bias corrected.
Selected studies of climate impact on water resources in the West African region.
The
All the climate models that project a precipitation increase result in an ETa
increase due to the warmer climate. For some of the climate scenarios the
projected increase in ETa outperforms the increase in precipitation,
resulting in a decrease in river discharge (unused water). This indicates
that the catchment ecosystem (defined as the vegetation within the catchment
and provided by the land use and land cover map of the catchment) is able to
optimize the use of water and energy available in the environment, thus
reducing unused water (
In a previous study (Yira et al., 2016), land use in the catchment was found
to be characterized by conversion from savannah to cropland, implying the
reduction of the vegetation-covered fraction, root depth, leaf area index,
etc. Such a land use and land cover change strongly affects the ecohydrologic
status of a catchment. Tomer and Schilling (2009) highlighted that removal of
perennial vegetation leads to an increase in both Excess
Water-
An ensemble of six RCM–GCM data, all produced in the framework of the CORDEX-Africa project, were used as input to a hydrological simulation model to investigate climate change impact on water resources in the Dano catchment by the mid-21st century. The ability of the RCM–GCM ensemble to simulate historical climate and discharge was evaluated prior to future climate change impact assessment.
The six climate models fairly reproduce the observed temperature. By contrast, bias correction was necessary for all climate models to accurately reproduce observed precipitation and historical discharge. The applied bias correction method further proved not to alter the discharge change signal. However, projected discharge change signals with and without bias corrected data were tested very comparably. This result indicates that (i) it is safe to perform bias correction; (ii) bias correction improves the quality of climate models' outputs; and (iii) it is not necessary to perform bias correction in order to detect a future discharge change signal in the catchment when relative changes in climate variables are used as reported by several authors (e.g., Muerth et al., 2013; Hagemann et al., 2011).
A temperature increase is consistently projected by the models' ensemble. This reinforces the commonly acknowledged warming signal for the region. However, the lack of agreement among models with regard to the projected precipitation change signal creates considerable uncertainty about how the catchment discharge will evolve by 2050. As discharge in the catchment is strongly determined by precipitation, no clear trend in future development of water resources can be concluded due to the high variability of the different climate models and scenarios. Therefore, potential increase and decrease in future discharge have to be considered in climate change adaptation strategies in the region.
The ecohydrological concept as applied in this study proved to fully capture
climate change impact on the hydrological conditions within the catchment as
both discharge change signal, precipitation, and actual/potential
evapotranspiration change signal are consistently displayed by the
The results further underline on the one hand the need for a larger ensemble of projections to properly estimate the impacts of climate change on water resources in the catchment and on the other hand the high uncertainty associated with climate projections for the West African region. Therefore, assessing future climate change impact on water resources for the region needs to be continuously updated with the improvement of climate projections.
The CORDEX-Africa data applied in this study are publicly
accessible at
The authors declare that they have no conflict of interest.
The authors are grateful for the financial support provided by the German Federal Ministry of Education and Research (BMBF) (grant no. 01LG1202E) under the auspices of the West African Science Service Centre for Climate Change and Adapted Land Use (WASCAL) project. They thank J. Schulla for providing support for the application of WaSiM. Thanks go to the CORDEX project and partner institutions for making climate data available and to D. Wisser for providing a R-code for bias correction. T. Jütten and F. Op de Hipt are acknowledged for their comments on the manuscript. We thank the editor and the anonymous HESS reviewers for their constructive comments. Edited by: E. Zehe Reviewed by: two anonymous referees