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In situ data in West Africa are scarce, and reanalysis datasets could be an alternative source to alleviate the problem of data availability. Nevertheless, because of uncertainties in numerical prediction models and assimilation methods, among other things, existing reanalysis datasets can perform with various degrees of quality and accuracy. Therefore, a proper assessment of their shortcomings and strengths should be performed prior to their usage. In this study, we examine the performance of ERA5 and ERA-interim (ERAI) products in representing the mean and extreme climates over West Africa for the period 1981–2018 using observations from CRU and CHIRPS. The major conclusion is that ERA5 showed a considerable decrease in precipitation and temperature biases and an improved representation of inter-annual variability in much of western Africa. Also, the annual cycle is better captured by ERA5 in three of the region’s climatic zones; specifically, precipitation is well-reproduced in the Savannah and Guinea Coast, and temperature in the Sahel. In terms of extremes, the ERA5 performance is superior. Still, both reanalyses underestimate the intensity and frequency of heavy precipitations and overestimate the number of wet days, as the numerical models used in reanalyses tend to produce drizzle more often. While ERA5 performs better than ERAI, both datasets are less successful in capturing the observed long-term trends. Although ERA5 has achieved considerable progress compared to its predecessor, improved datasets with better resolution and accuracy continue to be needed in sectors like agriculture and water resources to enable climate impact assessment.

Climate variability and change greatly affect agricultural and water resource management over West Africa. This paper presents the current characteristics and projected change in regional crop water demand (CWD), irrigation requirement (IR), and water availability (WA) over West Africa. Observed and simulated daily rainfall, minimum temperature, maximum temperature, and evapotranspiration are used to derive the above agro-meteorological and hydrological variables. For future periods, high-resolution climate data from three regional climate models under two different scenarios, i.e., representative concentration pathway (RCP) 4.5 and 8.5, are considered. Evaluation of the characteristics of present-day CWD, IR, and WA indicated that the ensemble mean of the model-derived outputs reproduced the prevailing spatial patterns of CWD and IR. Moreover, the wetter part of the domain, especially along the southern coast, was correctly delineated from the drier northern regions, despite having biases. The ensemble model also simulated the annual cycle of water supply and the bimodal pattern of the water demand curves correctly. In terms of future projections, the outcomes from the study suggest an average increase in the CWD by up to 0.808 mm/day and IR by 1.244 mm/day towards the end of the twenty-first century, compared to the baseline period. The hot-spot areas, where there is higher projected increment in CWD and IR, are over Senegal, Southern Mali, and Western Burkina Faso. In most cases, WA is projected to decrease towards the end of the twenty-first century by −0.418 mm/day. The largest decline in WA is found to be over Guinea and most of the eastern parts of West Africa. Despite the current under-utilization of the existing groundwater resources, the threat of global warming in reducing future WA and increasing CWD suggested caution on the scale of irrigation schemes and management strategies. The outcomes from the study could be a crucial input for the agricultural and water managers for introducing effective measures to ensure sustainability of irrigated farm lands.

Development times of eggs, larvae and pupae of vectors of onchocerciasis (Simulium spp.) and of Onchocerca volvulus larvae within the adult females of the vectors decrease with increasing temperature. At and above 25°C, the parasite could reach its infective stage in less than 7 days when vectors could transmit after only two gonotrophic cycles. After incorporating exponential functions for vector development into a novel blackfly population model, it was predicted that fly numbers in Liberia and Ghana would peak at air temperatures of 29°C and 34°C, about 3°C and 7°C above current monthly averages, respectively; parous rates of forest flies (Liberia) would peak at 29°C and of savannah flies (Ghana) at 30°C. Small temperature increases (less than 2°C) might lead to changes in geographical distributions of different vector taxa. When the new model was linked to an existing framework for the population dynamics of onchocerciasis in humans and vectors, transmission rates and worm loads were projected to increase with temperature to at least 33°C. By contrast, analyses of field data on forest flies in Liberia and savannah flies in Ghana, in relation to regional climate change predictions, suggested, on the basis of simple regressions, that 13–41% decreases in fly numbers would be expected between the present and before 2040. Further research is needed to reconcile these conflicting conclusions.

A novel approach for upscaling land-surface parameters based on inverse stochastic surface-vegetation-atmosphere transfer (SVAT) modelling is presented. It allows estimation of effective parameters that yield scale invariant outputs e.g. for sensible and latent heat fluxes and evaporative fraction. The general methodology is used to estimate effective parameters for the Oregon State University Land-Surface Model, including surface albedo, surface emissivity, roughness length, minimum stomatal resistance, leaf area index, vapour pressure deficit factor, solar insolation factor and the Clapp-Hornberger soil parameter. Upscaling laws were developed that map the mean and standard deviation of the distributed land-surface parameters at the subgrid scale to their corresponding effective parameter at the grid scale. Both linear and bi-parabolic upscaling laws were obtained for the roughness length. The bi-parabolic upscaling law fitted best for the remaining land-surface parameters, except surface albedo and emissivity, which were best fitted with linear upscaling laws.