Impact of climate change on water resources in a tropical West African catchment using an ensemble of climate simulations

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 COordinated Regional climate Downscaling Experiment (CORDEX-Africa) project. After evaluation of the historical runs of the climate models ensemble, a statistical bias correction (Empirical 15 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 Water flow and balance Simulation Model (WaSiM) to simulate water balance components. 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 twenty-first century under two 20 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 °C for all members of the RCMs-GCMs ensemble; (ii) high uncertainty about how the catchment precipitation will evolve over the period 2021-2050; (iii) individual climate models results lead to opposite discharge change signals; (iv) the RCMs-GCMs ensemble average suggests a +7 % increase in annual discharge under RCP4.5 and a -2 % decrease under RCP8.5; (v) the applied bias 25 correction method only affected the magnitude of climate change signal. Therefore, potential increase and decrease of future discharge has to be considered in climate change adaptation strategies in the catchment. 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. An ecohydrological analysis provides further insight into the behavior of 30 the catchment.


Introduction
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 35 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 on 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 regimes (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 40 (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 fresh water 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 45 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 do not generate the WAM at 50 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, 55 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 are bias corrected, results from hydrological simulations are unrealistic and may lead to incorrect impact assessments (Johnson and Sharma, 2015;Teutschbein and Seibert, 2012;Ahmed et al., 2013).However, correction 60 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 when assessing climate change impacts on water resources.Taking advantage of the results of the COordinated Regional climate Downscaling Experiment (CORDEX-Africa) project, this study evaluates potential climate change impacts on water resources using an ensemble of six RCMs-GCMs in the Dano catchment in Burkina Faso.The catchment experiences seasonally limited water availability, and like other catchments of the region, it has 70 experienced the severe droughts of the 1970s (Kasei et al., 2009) which resulted in a decline of water flow in many West African catchments.
A few studies have already investigated the impacts of projected climate change on water resources in West Africa.
Most of these studies have used an approach based on hydrological models driven by a single RCM or GCM data set (e.g.Mbaye et al., 2015;Cornelissen et al., 2013;Bossa et al., 2014;Bossa et al., 2012).Therefore, uncertainty 75 related to the choice of the climate model was not explicitly evaluated.However, a limited number of studies have used multi-climate model data sets (Kasei, 2009;Ruelland et al., 2012); 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.

Study area 90
The study was carried out in Dano catchment covering a total area of 195 km 2 in the Ioba province of Southwestern Burkina Faso (Fig. 1).The catchment is one of the study areas of the WASCAL project (West African Science Service Center on Climate Change and Adapted Land Use, www.wascal.org);whose main target is to increase resilience of human and environmental systems to climate change.
The major land uses in the catchment include shifting cultivation which accounts for one third of the catchment area; 95 natural vegetation albeit converted into agricultural and fallow lands form part of Sudanian region characterized by wooded, scrubby savannah and abundant annual grasses.Sorghum (Sorghum bicolor), millet (Pennisetum glaucum), cotton (Gossypium hirsutum), maize (Zea mays), cowpeas (Vigna unguiculata) and groundnut (Arachidis hypogaea) are the major crops cultivated in the catchment.
The catchment is characterized by a flat landscape with a mean slope of 2.9 % and mean altitude of 295 m above sea 100 level.According to Schmengler (2011), mean annual temperature of 28.6 0 C was recorded while mean annual rainfall ranged from 800 mm -1200 m for the period of 1951-2005.The catchment receives monsoonal rains with a dry season occurring in the months of November to April while the wet season being experienced in the months of July to September.This kind of rainfall pattern limits water availability especially in the dry season hence communities in the catchment are vulnerable to water scarcity since they heavily rely on surface water.

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Plinthosol characterized by a plinthite subsurface layer in the upper first meter of the soil profile accounts for 73.1 % of the soil types in the catchment, other soil types found within the catchment include gleysol, cambisol, lixisol, leptosol and stagnosol (WRB, 2006).

Climate data
Observed mean daily temperature and daily precipitation used in the study were collected from the national 110 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.
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 115 (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 the emission scenarios RCP4.5 and RCP8.5 (Moss et al., 2010).The retrieved data (precipitation and temperature) range form 1971-2000 for the historical runs and 2021-2050 for the RCPs.An extent of 20 nodes of the 120 African CORDEX domain, surrounding the catchment, was delineated to simulate the catchment's climate and consider climate variability in the catchment region (Fig. 1 B).
Due to the discrepancy between the RCM-GCM data resolution (0.44°, about 50 * 50 km 2 ) and the hydrological modeling domain (about 18 * 11 km 2 ) the data of each node were separately used as climate input for the hydrological simulation model.Therefore, for each period (historical and projected scenarios) 20 simulations 125 corresponding to the 20 nodes are performed per RCM-GCM.Monthly water balance for each RCM-GCM is then calculated as arithmetic mean.

Bias correction of precipitation data
The RCMs-GCMs ensemble was evaluated to get an estimate of the historical simulated variables for the catchment by comparing RCMs-GCMs based simulations of historical climate variables to the observations provided by the 130 National Meteorological Service (DGM).As presented in section 3.1, whereas temperature simulated by the models ensemble enveloped the observed temperature with moderate deviation, precipitation simulated by individual RCM-GCM exhibited biases such as overestimation of annual precipitation as well as misrepresentation of the timing of Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-387, 2016 Manuscript under review for journal Hydrol.Earth Syst.Sci.Published: 7 September 2016 c Author(s) 2016.CC-BY 3.0 License. the rainy season.A precipitation bias correction was therefore applied to the six RCMs-GCMs following the nonparametric quantile mapping using the empirical quantiles method (Gudmundsson et al.;2012).For each member, a 135 transfer function was derived using observed and modeled precipitation for the period of 1971-2000; afterwards the transfer function was applied to the projected climate scenarios (period 2021-2050).

Hydrological modeling
Observed and RCMs-GCMs based (historical runs and projections) data were used as climate input for version 9.05.04 of the Water flow and balance Simulation Model (WaSiM) (Schulla, 2014).WaSiM is a deterministic and 140 spatially distributed model, which uses mainly physically based approaches to describe hydrological processes.The model configuration as applied in this study is shown in Table 2. Schulla (2014) gives more details of the model structure and processes in the Model Description Manual.
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 145 calibrated and validated using discharge, soil moisture and groundwater depth for the period of 2011-2014.Daily time steps and a regular raster-cell size of 90 m were used.Minimum values of 0.7 for Pearson product-momentcorrelation-coefficient, Nash Sutcliffe Efficiency (Nash and Sutcliffe, 1970) and Kling-Gupta Efficiency (Gupta et al., 2009;Kling et al., 2012) were achieved during the calibration and validation using observed discharge.Soil moisture and groundwater dynamics were also well simulated by the model (R 2 >0.6).Therefore, no further model 150 calibration was done in the current study.
No hydrologic observations (discharge, soil moisture and groundwater level) are available for the reference period  in the catchment.The expected climate change for an RCM-GCM is therefore expressed as the relative difference between simulated hydrological variables under reference period  and future period .

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Nevertheless, discharge simulated with RCM-GCM historical runs (bias corrected and not bias corrected) were compared to the discharge obtained with observed historical climate data.RCM-GCM based simulations able to reproduce the runoff regime of the past were used for climate change impact assessment.These comparison runs (performed with CCLM-ESM) showed that bias correction was necessary for RCMs-GCMs based simulations to reproduce the historical discharge regime.Hydrological variables simulated under historical  and 160 projected (2021-2050) climate conditions were therefore compared with bias corrected RCMs-GCMs data.To integrate the potential effect of bias correction on climate change signal as raised by different authors (e.g.Muerth et al., 2013;Ehret et al., 2012;Hagemann et al., 2011), the hydrological model was also run with not bias corrected future climate for CCLM-ESM (which was randomly selected among the 6 RCMs-GCMs).

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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-P ex (1) and energy-E ex (2) existing in the environment is usually observed.Plotting P ex against E ex allows for determining the ecohydrologic status of the catchment.The climate change signal can therefore be detected by the shift of this status.The direction of the shift indicates whether 170 the catchment experienced water stress or increased humidity.The approach was used to test its validity in analyzing the interplay between temperature increase and precipitation change as projected by the RCMs-GCMs ensemble.
Where P is precipitation, ET a and ET p refer to actual and potential evapotranspiration respectively.

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A set of evaluation measures was used to analyze the RCMs-GCMs historical runs, to assess model performance and to estimate the effects of different climate scenarios on hydrological variables: (i) P-Factor, measures the percentage of observed climate data covered by the RCMs-GCMs ensemble historical runs; (ii) the R-factor, indicates for an observation series, how wide the range between minimum RCM-GCM 180 and maximum RCM-GCM for precipitation and temperature is, compared to the observation: Where Var is the climate variable (e.g.precipitation), n refers to the observations data points;  is the standard deviation, obs refers to observation, and   and   are respectively the maximum and minimum values of the RCMs-GCMs ensemble.

Historical runs analysis
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 210 NRMSD (Fig. 2) 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 RCMs-GCMs ensemble mean outscores five members of the RCMs-GCMs ensemble with regard to temperature and precipitation (Fig. 2).

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The RCMs-GCMs ensemble shows a clear deviation from observed precipitation compared to temperature (Fig. 2).
HIRAM-EARTH and CCLM-EARTH present the lowest deviation for temperature and precipitation respectively.
The RCMs-GCMs ensemble mean outscores four out of six RCMs-GCMs for temperature and precipitation with regards to the deviation from observed data.

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The mean annual precipitation pattern is in general well captured by all RCMs-GCMs (Fig. 4).However, the climate models ensemble, when not bias corrected, covers only 50 % of monthly precipitation despite a large dispersion (Fig. 4 UC).After bias correction, the agreement between RCMs-GCMs based precipitation and observation is considerably improved (Fig. 4 BC); and the uncertainty band of the climate model is considerably reduced (R-factor = 0.1).However, a slight positive bias is still presented by the climate models ensemble.
However, a gap of up to -4 °C between some climate models and observations is noted.This translates into an Rfactor reaching 8.2.On average, RACMO-EARTH shows an underestimation of temperatures throughout the year, whereas HIRAM-NorESM indicates an opposite trend.

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The RCMs-GCMs ensemble exhibits a mixed annual precipitation change signal between reference period  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.

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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 an increased annual precipitation, are characterized by an increased rainfall from May to June followed by a decreased rainfall in August.RAMCO-EARTH shows an 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 245 projects mean monthly temperature increase of about 0.1 to 2.3 °C under RCP4.5 and 0.6 to 2.5 °C under RCP8.5 leading to an increase of potential evapotranspiration for the climate models ensemble.

Historical discharge
RCMs-GCMs 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).4).

Discharge change
Projected change in annual discharge for the period of 2021-2050 compared to the reference period is presented in 260   8).The divergence between climate models is reflected through a large amount of uncertainty associated with the projected annual discharge (Fig. 9).

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Under RCP4.5, the discharge change signal for CCLM-ESM is more pronounced with bias corrected precipitation data compared to not bias corrected.Indeed, the projected annual discharge equals -12 % and -5 % with and without bias correction respectively (Table 5).Under RCP8.5, bias correction impact is relatively low.The Wilcoxon (1945) rank

Ecohydrologic status
The Eco-hydrologic status of the catchment for the reference period and future scenarios RCP4.

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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.

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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 RCMs-GCMs 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 is also 310 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 RCMs-GCMs 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 315 al., 2012;Paeth et al., 2011).However, the climate models ensemble mean should not be considered as 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.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.

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A similar result is achieved by Kunstmann et al. (2008) in the Volta Basin, albeit with a decrease in precipitation at the beginning of rainy season in April.
Precipitation change projected by CCLM-ESM and CCLM-EARTH is consistent with the decrease in 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 20.3 335 % 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 to the RCMs-GCMs ensemble applied in this study, which 340 could suggest that the RCMs inherit the bias from the GCM (Dosio et al., 2015).Consistently with Paeth et al.  (ii) the fact that temperature was not bias corrected.This led to ETp values that vary from one RCM-GCM to another since ETp after Hamon is computed based on temperature values only (Table 2).As a result, a relatively large range of potential evapotranspiration is observed for the climate models as an 360 ensemble (Table 6).
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 (section 2.2) has reasonably bridged the discrepancy between RCMs-GCMs data resolution and hydrological modeling domain.
Therefore, the approach can be considered as eligible for climate change impact assessment for small scale 365 catchments.However, besides regional climate specificities, its reliability might depend on the extent of the RCM-domain used to simulate a given catchment climate, which in the case of this study was set at 0.44°*4 over 0.44°*5 in order to account for climate spatial variability.

Discharge change
A mixed annual discharge change signal is projected by the climate models ensemble for the period of 2021-2050.

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These trends agree with several studies in the region (Table 7), although all were carried out at the mesoscale and macroscale:  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. 10 a); this is partly explained by the fact that the environmental system of the catchment is water limited and not energy limited.

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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°C gaps between observed and simulated temperature were noticed for some months, section 3.1).The annual evaporative demand for the climate models ensemble, including RACMO-EARTH which underestimated observed temperature for the reference period, exceeds (almost doubles) precipitation (Table 6).In such a system, also characterized by extended periods 400 with little to no precipitation (November-May) actual evapotranspiration is strongly controlled by precipitation (Guswa, 2005;Schenk and Jackson, 2002).Therefore, an increase in ETp is not necessarily translated in an increase in ETa as limitation in precipitation (soil moisture) dictates water fluxes (Newman et al., 2006) (e.g.CCLM-EARTH and CCLM-ESM in Table 6).

Ecohydrologic status
405 The E ex -P ex plot (Fig. 11) allows accurately displaying climate change impact on the catchment hydrology, as main water balance components (precipitation, discharge and evapotranspiration) are presented in an integrated manner.
The overall ecohydrologic effect of climate change in the catchment, as shown by the plots, is a trend towards drier environmental conditions due to increased evaporative demand-E ex .This denotes an increase in potential evapotranspiration higher than the increase in actual evapotranspiration.By contrast, change in the proportion of 410 precipitation converted to discharge-P ex appears specific to each climate model, with a marginal trend towards discharge increase for the models ensemble under RCP4.5 and discharge decrease under RCP8.5.
The climate models ensemble mean projects a precipitation increase of about 1.5 % under RCP8.5 with a resulting discharge decrease of 2 %.This indicates that the catchment ecosystem is able to optimize the use of water and energy available in the environment, thus reducing unused water (P ex ) with temperature increase (Caylor et al., 415 2009).Such an optimization, although not investigated in this study, may lead plants to change the allocation of fixed carbon to various tissues and organs (Collins and Bras, 2007;Milne et al., 2002).The suitability of the catchment area for the current plant species could also be affected (McClean et al., 2005) by the projected climate change.
In a previous study (Yira et al., 2016), land use in the catchment was found to be characterized by conversion from

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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 the use of an ensemble of climate simulations and an echohydrological 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 2021-85 2050 compared to the reference period of 1971-2000; (iii) assess the impacts of climate change on the hydrology of the catchment by the middle of the 21 st century; (iv) evaluate the uncertainty related to the projected hydrological change signal; and (v) investigate the effect and necessity of bias correction on the detected signal.
. Earth Syst.Sci.Discuss., doi:10.5194/hess-2016-387,2016 Manuscript under review for journal Hydrol.Earth Syst.Sci.Published: 7 September 2016 c Author(s) 2016.CC-BY 3.0 License.assess the RCM-GCM based discharge simulations ability to reproduce discharge computed using observed climate data; (v) change signal (∆) in climate and hydrological variables (precipitation, temperature and discharge) expresses the difference between projected and historical values (4 is the evaluated variable (e.g.discharge), Proj = the projected period (2021-2050 under RCP4.5 and RCP8.5) and Ref = Reference or historical period (1971-2000).(vi) the Wilcoxon (1945) rank-sum test was used to compare discharge change signal with bias corrected and not bias corrected precipitation data (for CCLM-ESM) following Muerth et al. (2013).The test 200 evaluated the null hypothesis: "discharge change signal under bias corrected CCLM-ESM data equals discharge change signal under not bias corrected CCLM-ESM 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 not bias correction yield the same result, and thus bias correction do not alter the climate change signal on 205 projected discharge.

Fig. 3
Fig.3shows a trend towards an overestimation of annual precipitation throughout the reference period for the 250Accordingly, performances (R 2 , NSE and KGE) achieved by the climate models are presented in Table4.Fig.7 (a)    shows good agreement between (bias corrected) climate models based discharge and observation based discharge, with a trend towards discharge overestimation for some climate models (RACMO-EARTH, CCLM-EARTH and Hydrol.Earth Syst.Sci.Discuss., doi:10.5194/hess-2016-387,2016 Manuscript under review for journal Hydrol.Earth Syst.Sci.Published: 7 September 2016 c Author(s) 2016.CC-BY 3.0 License.HIRAM-EARTH).All climate models show good statistical quality measures after bias correction.Bias correction impact on simulated historical discharge is shown in Fig. 7 (b) for CCLM-ESM.As an example, simulated discharge 255 for CCLM-ESM with not bias corrected data leads to a misrepresentation of the discharge regime, as peak flow is shifted from August to September and discharge is highly overestimated.Moreover, poor quality measures are achieved by CCLM-ESM with not bias corrected data (Table precipitation and a consistent increase in potential evapotranspiration for CCLM-ESM, CCLM-EARTH and HIRHAM-EARTH (RCP8.5);(ii) about 5 % decreased in annual discharge for the RCMs-GCMs ensemble mean under RCP8.5 which is the consequence of a slight increase in precipitation counterbalanced by a high increase in 265 potential evapotranspiration; (iii) 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, HIRHAM-EARTH (RCP4.5)and the RCMs ensemble mean (RCP4.5).The intra-annual change in discharge appears strongly determined by the precipitation change signal (Fig. -sum, testing the significance of the difference between bias corrected and not bias corrected discharge change signal, indicates that the two signals are not different at p-level equals 0.05.A p-value of the Wilcoxon rank-sum test 275 equals 0.51 and 0.7 is required under RCP4.5 and RCP8.5 respectively to reject the null hypothesis (H 0 : discharge change with bias corrected CCLM-ESM data = discharge change with not bias corrected CCLM-ESM data).Hence, the bias correction impact on discharge change signal alteration can be considered negligible.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 280 change.The result shows that change in total discharge cannot be strongly related to change in potential evapotranspiration (Fig. 10 a).However, a high sensitivity of river discharge to precipitation change (Fig. 10 b) isobserved.Under scenario RCP4.5, an increase of +5 % in precipitation leads to an increase of discharge of about +12.5 %, whereas a decreased precipitation of the same order leads to a decrease of discharge of -13 %.The same simulations under RCP8.5 yield in a +8.3 % discharge increase and a -14.7 % discharge decrease.Interestingly, 285 under RCP8.5 and assuming comparable precipitation between reference and future periods, a discharge decrease of about -3.2 % should be expected (Fig.10 b).
5 and RCP8.5 is shown in Fig. 11 to illustrate the use of energy and water by the catchment while undergoing temperature increase 290 and precipitation change.Moving left to right along "Excess water-P ex " axis indicates that the environmental conditions in the catchment lead to an increase in discharge (CCLM-CNRM, RAMCO-EARTH and HIRHAM-NorESM).Reduction of discharge is experienced when moving the other way round (CCLM-ESM and CCLM-EARTH).Moving upwards along "Excess evaporative demand-E ex " implies drier environmental conditions due to an increase 295 in evaporative demand and soil water deficit.Except for HIRAM-EARTH, all the climate models project drier conditions (increase in Excess evaporative demand) under RCP4.5 as a result of an increased temperature not compensated by the amount and/or timing of precipitation.Increased evaporative demand, with marginally aggravated drier conditions, is shown by CCLM-ESM, HIRAM-NorESM, CCLM-EARTH and RCMs-GCMs ensemble mean under RCP8.5.
Hydrol.Earth Syst.Sci.Discuss., doi:10.5194/hess-2016-387,2016   Manuscript under review for journal Hydrol.Earth Syst.Sci.Published: 7 September 2016 c Author(s) 2016.CC-BY 3.0 License.period of 1971-2000, a clear temperature increase signal is projected for 2021-2050 by the six members of the RCMs-GCMs ensemble in the catchment.This feature is common to all multi-model ensemble studies performed in the region (IPCC, 2014).However, the climate models ensemble does not agree on the projected precipitation change signal as wetter (RAMCO-EARTH), drier (CCLM-ESM and CCLM-EARTH) as well as mixed (CCLM-CNRM, HIRHAM-NorESM and HIRAM-EARTH) trends are shown by the individual 325 model.It is worth noting that the Dano catchment is located in a region where the "Coupled Model Intercomparison Project Phase 5 (CMIP5)" models showed divergent precipitation change for the mid-21 st century (IPCC, 2014).

(
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, not bias corrected RCMs-GCMs 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 for CCLM-ESM 350 with bias corrected and not bias corrected precipitation data, it clearly appears that the bias correction methodology is effective with regards to both discharge regime and total discharge.However, a trend towards discharge overestimation was noticed after bias correction of precipitation.This could be related to:Hydrol.Earth Syst.Sci.Discuss., doi:10.5194/hess-2016-387,2016   Manuscript under review for journal Hydrol.Earth Syst.Sci.Published: 7 September 2016 c Author(s) 2016.CC-BY 3.0 License.(i)therelative long period used for the bias correction.As noticed byPiani et al. (2010), fragmenting the correction period to decade and deriving several transfer functions can improve the 355 bias correction result and further contribute to capture the decadal rainfall change that characterizes the West African climate; and 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 375 the climate model REMO in the Upper Senegal Basin; as did Cornelissen et al. (2013) and Bossa et al. (2014) in the Térou and the Ouémé catchment 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 380 precipitation not counterbalanced by the 4.5 % increase of potential evapotranspiration. Mixed trend (HIRHAM-EARTH and RCMs-GCMs ensemble).A mixed discharge change signal for the future period is the common signal projected by multi-climate models 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 385 signals are reported by Kasei (2009) who applied two climate models (MM5 and REMO) in the Volta basin.Hydrol.Earth Syst.Sci.Discuss., doi:10.5194/hess-2016-387,2016 Manuscript under review for journal Hydrol.Earth Syst.Sci.Published: 7 September 2016 c Author(s) 2016.CC-BY 3.0 License.This mixed hydrological change signal is the result of high uncertainties associated to 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 390

Table 5 .
Alike for precipitation, a mixed annual discharge change signal is projected by the climate model ensemble.
It projects: (i) more than 15 % decrease in annual discharge, which is a consequence of relative decrease in

Table 7
Selected studies of climate impact on water resources in the West African Region