This paper assessed the current and mid-century trends in rainfall and temperature over the Mono River watershed. It considered observation data for the period 1981–2010 and projection data from the regional climate model (RCM), REMO, for the period 2018–2050 under emission scenarios RCP4.5 and RCP8.5. Rainfall data were interpolated using ordinary kriging. Mann-Kendall, Pettitt and Standardized Normal Homogeneity (SNH) tests were used for trends and break-points detection. Rainfall interannual variability analysis was based on standardized precipitation index (SPI), whereas anomalies indices were considered for temperature. Results revealed that on an annual scale and all over the watershed, temperature and rainfall showed an increasing trend during the observation period. By 2050, both scenarios projected an increase in temperature compared to the baseline period 1981–2010, whereas annual rainfall will be characterized by high variabilities. Rainfall seasonal cycle is expected to change in the watershed: In the south, the second rainfall peak, which usually occurs in September, will be extended to October with a higher value. In the central and northern parts, rainfall regime is projected to be characterized by late onsets, a peak in September and lower precipitation until June and higher thereafter. The highest increase and decrease in monthly precipitation are expected in the northern part of the watershed. Therefore, identifying relevant adaptation strategies is recommended.

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.

The impacts of climate change on photovoltaic (PV) output in the fifteen countries of the Economic Community of West African States (ECOWAS) was analyzed in this paper. Using a set of eight climate models, the trends of solar radiation and temperature between 2006–2100 were examined. Assuming a lifetime of 40 years, the future changes of photovoltaic energy output for the tilted plane receptor compared to 2006–2015 were computed for the whole region. The results show that the trends of solar irradiation are negative except for the Irish Centre for High-End Computing model which predicts a positive trend with a maximum value of 0.17 W/m²/year for Cape Verde and the minimum of −0.06 W/m²/year for Liberia. The minimum of the negative trend is −0.18 W/m²/year predicted by the Model for Interdisciplinary Research on Climate (MIROC), developed at the University of Tokyo Center for Climate System Research for Cape Verde. Furthermore, temperature trends are positive with a maximum of 0.08 K/year predicted by MIROC for Niger and minimum of 0.03 K/year predicted by Nature Conservancy of Canada (NCC), Max Planck Institute (MPI) for Climate Meteorology at Hamburg, French National Meteorological Research Center (CNRM) and Canadian Centre for Climate Modelling and Analysis (CCCMA) for Cape Verde. Photovolataic energy output changes show increasing trends in Sierra Leone with 0.013%/year as the maximum. Climate change will lead to a decreasing trend of PV output in the rest of the countries with a minimum of 0.032%/year in Niger.

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.

Precipitation projections from regional climate models in West Africa are attributed with significant biases with respect to the observed. This study aims at evaluating of six methods of precipitation bias correction on four RCM (CCLM, CRCM, RACMO and REMO) outputs in the Ouémé basin. The bias correction methods used are classified into three namely: the Delta approach, the Linear Scaling method and the quantile approaches. Corrected and uncorrected RCM precipitation data were compared with the observed using Mean Absolute Error (MAE) and Root Mean Square error (RMSE). The findings showed that raw outputs of regional climate models (RCMs) are characterized with several biases. In general, the models overestimate precipitation. For daily precipitation correction, the quantile approaches assuming a gamma distribution for daily precipitation were not able to reduce the biases of precipitation. The empirical quantile mapping and the adjusted quantile mapping are the most effective in correcting the biases of daily precipitation. Thus the adjusted quantile mapping can be used to correct biases of precipitation projections for modeling the future availability of water resources.

This study analyzed trends in extreme precipitation based on daily rainfall data provided by Bénin Méteo Agency for the Upper Ouémé valley in Benin over the period 1951–2014. Eleven indices divided into two groups were considered. The first group consists of frequency indices: number of heavy rainfall days, very heavy rainfall days and extremely heavy rainfall days; and maximum number of Consecutive dry days and wet days. The second group concerns intensity: daily maximum rainfall (RX1day), maximum five-day rainfall (RX5day), annual total wet-day rainfall (PRCPTOT), simple daily intensity index (SDII), very wet day (R95P) and extremely wet day rainfall (R99P). The non-parametric Mann-Kendall test was used to assess trends in those indices. The results show that only 30% of the stations experienced decreasing trends for the number of heavy rainfall days (R10mm) and daily maximum rainfall (RX1day). For the annual total wet-day rainfall (PRCPTOT), the simple daily intensity index (SDII) and the very wet day rainfall (R95P), 20% of stations faced significant negative trends. In addition, the decreasing trends are observed for 10% stations considering the number of very heavy rainfall days (R20mm), the maximum five-day rainfall (RX5day) and the extremely wet day rainfall (R99P). About the increasing trend, 10% stations are identified for the number of consecutive dry days (CDD), very heavy rainfall days (R20mm), the daily maximum rainfall (RX1day), the simple daily intensity index, and the extremely wet day rainfall (R99P). These results show the absence of clear trend of climate indices evolution in almost all stations. Consequently, uncertainties in the evolution of rainfall indices must be taken into account in the definition of adaptation strategies for flood or drought risks. Similarly, these results show a slight drop in the dry sequences of the 1970s and 1980s revealed in the region by previous studies.