Contrasting changes in hydrological processes of the Volta River basin under global warming

Abstract

Abstract. A comprehensive evaluation of the impacts of climate change on water resources of the West Africa Volta River basin is conducted in this study, as the region is expected to be hardest hit by global warming. A large ensemble of 12 general circulation models (GCMs) from the fifth Coupled Model Intercomparison Project (CMIP5) that are dynamically downscaled by five regional climate models (RCMs) from the Coordinated Regional-climate Downscaling Experiment (CORDEX)-Africa is used. In total, 43 RCM–GCM combinations are considered under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5). The reliability of each of the climate datasets is first evaluated with satellite and reanalysis reference datasets. Subsequently, the Rank Resampling for Distributions and Dependences (R2D2) multivariate bias correction method is applied to the climate datasets. The bias-corrected climate projections are then used as input to the mesoscale Hydrologic Model (mHM) for hydrological projections over the 21st century (1991–2100). Results reveal contrasting dynamics in the seasonality of rainfall, depending on the selected greenhouse gas emission scenarios and the future projection periods. Although air temperature and potential evaporation increase under all RCPs, an increase in the magnitude of all hydrological variables (actual evaporation, total runoff, groundwater recharge, soil moisture, and terrestrial water storage) is only projected under RCP8.5. High- and low-flow analysis suggests an increased flood risk under RCP8.5, particularly in the Black Volta, while hydrological droughts would be recurrent under RCP2.6 and RCP4.5, particularly in the White Volta. The evolutions of streamflow indicate a future delay in the date of occurrence of low flows up to 11 d under RCP8.5, while high flows could occur 6 d earlier (RCP2.6) or 5 d later (RCP8.5), as compared to the historical period. Disparities are observed in the spatial patterns of hydroclimatic variables across climatic zones, with higher warming in the Sahelian zone. Therefore, climate change would have severe implications for future water availability with concerns for rain-fed agriculture, thereby weakening the water–energy–food security nexus and amplifying the vulnerability of the local population. The variability between climate models highlights uncertainties in the projections and indicates a need to better represent complex climate features in regional models. These findings could serve as a guideline for both the scientific community to improve climate change projections and for decision-makers to elaborate adaptation and mitigation strategies to cope with the consequences of climate change and strengthen regional socioeconomic development.

Unravelling the Impacts of Climate Variability on Surface Runoff in the Mouhoun River Catchment (West Africa)

“…In this regard, it should be outlined that the use of the MERRA-2 reanalysis data helped in representing spatial patterns in the rainfall over the catchment, which was certainly critical in attaining optimal model calibration. Therefore, the potential of using reanalysis datasets in hydrological modeling should be further explored as a potential and viable pathway to improve hydrological modeling efforts in data-scarce, poorly gauged, or even ungauged catchments, which are quite common in the West African Sahel [19,42].…”

“…This vulnerability arises from the heavy reliance on water resources to sustain vital sectors such as agriculture and livestock husbandry and the indispensable provision of potable water to local populations [2,3,16,17]. Within the Mouhoun River Catchment (MRC), located in the south-western region of the country, 14,900 km 2 in size, the situation is further compounded by the escalating impact of climate change on water resources [18,19]. The MRC is of specific importance in Burkina Faso for several reasons: in terms of water supply, the Mouhoun River is the largest in Burkina Faso, and the catchment provides drinking water (to over 10 million people), irrigation (over 200,000 ha), and livestock.…”

“…The MRC is already grappling with pronounced climatic fluctuations, including drought spells and episodes of extreme rainfall, affecting water provision to local populations [21][22][23][24]. Yet, the context is also depicted by the lack of long-term and consistent climate and hydrology data records, rarely gap-free, since gauging equipment is seldom and poorly maintained [15,19,23,25]. This further hinders the understanding of the rainfallrunoff relationship, the assessment of the available water resource, and the development of well-informed and adapted water management policies.…”

Future climate or land use? Attribution of changes in surface runoff in a typical Sahelian landscape

“…To the best of our knowledge, few modelling studies in the Sahel have been dedicated to this exercise still [Aich et al, 2015, Akoko et al, 2021, Angelina et al, 2015, Dembélé et al, 2022, Karambiri et al, 2011, Séguis et al, 2004. Among the reasons often put forward, some authors mention the difficulty in representing accurately Sahelian hydrological processes in most of the available models [Cornelissen et al, 2013, Karambiri et al, 2003, or the data scarcity [Mahé and Paturel, 2009].…”

Parameter transferability of a distributed hydrological model to droughts

“…Distributed process-based hydrological models allow spatial estimates of hydrological fluxes and states (Fatichi et al, 2016), even at large scales and hyper-resolutions (< 1 km, Bierkens et al (2015)). Such models are increasingly being used for assessments of climate change impacts on droughts (Van Huijgevoort et al, 2014;Cammalleri et al, 2020b;Dembélé et al, 2022), drought monitoring (Cammalleri et al, 2017(Cammalleri et al, , 2020aSaha et al, 2021), forecasting (Trambauer et al, 2015;Van Hateren et al, 2019;Sutanto et al, 2020), and drought studies in general (Mastrotheodoros et al, 2020;Yang et al, 2021;Rakovec et al, 2022). However, some studies revealed reduced model performances when simulating streamflow droughts (Kumar et al, 2022) and their generating processes (Van Loon et al, 2012;Avanzi et al, 2020).…”