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.

In the context of climate change in West Africa characterized by a reduction of 11 precipitation, this study was conducted to evaluate the impact of climate change on water resources 12 from now to the end of the 21st century in the transboundary watershed of the Sassandra River 13 shared by Guinea and Côte d'Ivoire. Historical and future climate (Representative Concentration 14 Pathways or RCPs 4.5 and 8.5 scenarios) data were projected with the model. The Abdus Salam 15 ICTP RegCM4 was used. The hydrological modeling of the river basin was carried out with the 16 conceptual hydrological model, GR2M. This model is a monthly time steps model that allows the 17 assessment of the discharge of the Sassandra River for each climate scenario according to the 2030 18

Abstract:The objective of this study was to assess the performance and predictive uncertainty of the Soil and Water Assessment Tool (SWAT) model on the Bani River Basin, at catchment and subcatchment levels. The SWAT model was calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE) approach. Potential Evapotranspiration (PET) and biomass were considered in the verification of model outputs accuracy. Global Sensitivity Analysis (GSA) was used for identifying important model parameters. Results indicated a good performance of the global model at daily as well as monthly time steps with adequate predictive uncertainty. PET was found to be overestimated but biomass was better predicted in agricultural land and forest. Surface runoff represents the dominant process on streamflow generation in that region. Individual calibration at subcatchment scale yielded better performance than when the global parameter sets were applied. These results are very useful and provide a support to further studies on regionalization to make prediction in ungauged basins.

The aim of this study was to quantify climate change impact on future blue water (BW) and green water (GW) resources as well as the associated uncertainties for 4 subbasins of the Beninese part of the Niger River Basin. The outputs of 3 regional climate models (HIRHAM5, RCSM, and RCA4) under 2 emission scenarios (RCP4.5 and RCP8.5) were downscaled for the historical period (1976–2005) and for the future (2021–2050) using the Statistical DownScaling Model (SDSM). Comparison of climate variables between these 2 periods suggests that rainfall will increase (1.7% to 23.4%) for HIRHAM5 and RCSM under both RCPs but shows mixed trends (−8.5% to 17.3%) for RCA4. Mean temperature will also increase up to 0.48 °C for HIRHAM5 and RCSM but decrease for RCA4 up to −0.37 °C. Driven by the downscaled climate data, future BW and GW were evaluated with hydrological models validated with streamflow and soil moisture, respectively. The results indicate that GW will increase in all the 4 investigated subbasins, whereas BW will only increase in one subbasin. The overall uncertainty associated with the evaluation of the future BW and GW was quantified through the computation of the interquartile range of the total number of model realizations (combinations of regional climate models and selected hydrological models) for each subbasin. The results show larger uncertainty for the quantification of BW than GW. To cope with the projected decrease in BW that could adversely impact the livelihoods and food security of the local population, recommendations for the development of adequate adaptation strategies are briefly discussed.