Since the 1970s, the Niger basin has been characterized by hydro-climatic changes which have significant impacts on local populations. These changes are not well documented as a result of a decreasing observation network for hydro-climatic data. Indigenous peoples’ knowledge has increasingly been considered an important component in addressing these data gaps. We evaluated the consistency of indigenous perceptions and adaptive responses with rainfall and river discharge observations in the Niger basin. Socioeconomic data were collected from 239 households in 30 communities across two settlements in the Niger basin. Data on historical rainfall and river discharge from 1950 to 2010 were analyzed and the consistency with local perceptions was assessed. Generally, there was a high agreement between observations and perceptions, but impacts of climate change in the communities were dependent on social and environmental factors that can introduce differences in perception despite identical observations. Indigenous perceptions gave good indication of the most vulnerable sectors as well as communities who also displayed the greatest willingness to combat climate change. These results suggest that integration of indigenous perceptions into climate change science, especially in data scarce regions, is highly valuable.

Climate change will have large impacts on water resources and its predictions are fraught with uncertainties in West Africa. With the current global drive for renewable energy due to climate change, there is a need for understanding the effects of hydro-climatic changes on water resources and hydropower generation. A hydrological model was used to model runoff inflow into the largest hydroelectric dam (Kainji) in the Niger Basin (West Africa) under present and future conditions. Inflow to the reservoir was simulated using hydro-climatic data from a set of dynamically downscaled 8 global climate models (GCM) with two emission scenarios from the CORDEX-Africa regional downscaling experiment, driven with CMIP5 data. Observed records of the Kainji Lake were used to develop a hydroelectricity production model to simulate future energy production for the reservoir. Results indicate an increase in inflow into the reservoir and concurrent increases in hydropower production for the majority of the GCM data under the two scenarios. This analysis helps planning hydropower schemes for sustainable hydropower production.

This study focuses on the ability of the global Land Data Assimilation System, LDAS-Monde, to improve the representation of land surface variables (LSVs) over Burkina-Faso through the joint assimilation of satellite derived surface soil moisture (SSM) and leaf area index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5 as well as ERA-Interim former reanalysis, leading to reanalyses of LSVs at 0.25° × 0.25° and 0.50° × 0.50° spatial resolution, respectively. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated with a simplified extended Kalman filter (SEKF) using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF based on in situ data. Results of four experiments are then compared: Open-loop simulation (i.e., no assimilation) and analysis (i.e., joint assimilation of SSM and LAI) forced by either ERA5 or ERA-Interim. After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below), as reflected by the structure of the SEKF Jacobians. The added value of using ERA5 reanalysis over ERA-Interim when used in LDAS-Monde is highlighted. The assimilation is able to improve the simulation of both SSM and LAI: The analyses add skill to both configurations, indicating the healthy behavior of LDAS-Monde. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina-Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project and sun-induced fluorescence from the Global Ozone Monitoring Experiment-2 (GOME-2) are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.

In the Volta River Basin, flooding has been one of the most damaging natural hazards during the last few decades. Therefore, flood frequency estimates are important for disaster risk management. This study aims at improving knowledge of flood frequencies in the Volta River Basin using regional frequency analysis based on L-moments. Hence, three homogeneous groups have been identified based on cluster analysis and a homogeneity test. By using L-moment diagrams and goodness of fit tests, the generalized extreme value and the generalized Pareto distributions are found suitable to yield accurate flood quantiles in the Volta River Basin. Finally, regression models of the mean annual flood with the size of the drainage area, mean basin slope and mean annual rainfall are proposed to enable flood frequency estimation of ungauged sites within the study area.

Climate simulations in West Africa have been attributed with large uncertainties. Global climate projections are not consistent with changes in observations at the regional or local level of the Niger basin, making management of hydrological projects in the basin uncertain. This study evaluates the potential of using the quantile mapping bias correction to improve the Coupled Model Intercomparison Project (CMIP5) outputs for use in hydrological impact studies. Rainfall and temperature projections from 8 CMIP5 Global Climate Models (GCM) were bias corrected using the quantile mapping approach. Impacts of climate change was evaluated with bias corrected rainfall, temperature and potential evapotranspiration (PET). The IHACRES hydrological model was adapted to the Niger basin and used to simulate impacts of climate change on discharge under present and future conditions. Bias correction with quantile mapping significantly improved the accuracy of rainfall and temperature simulations compared to observations. The mean of six efficiency coefficients used for monthly rainfall comparisons of 8 GCMs to the observed ranged from 0.69 to 0.91 and 0.84 to 0.96 before and after bias correction, respectively. The range of the standard deviations of the efficiency coefficients among the 8 GCMs rainfall data were significantly reduced from 0.05–0.14 (before bias correction) to 0.01–0.03 (after bias correction). Increasing annual rainfall, temperature, PET and river discharge were projected for most of the GCMs used in this study under the RCP4.5 and RCP8.5 scenarios. These results will help improving projections and contribute to the development of sustainable climate change adaptation strategies.