Understanding the variability of rainfall is important for sustaining rain-dependent agriculture and driving the local economy of Nigeria. Paucity and inadequate rain gauge network across Nigeria has made satellite-based rainfall products (SRPs), which offer a complete spatial and consistent temporal coverage, a better alternative. However, the accuracy of these products must be ascertained before use in water resource developments and planning. In this study, the performances of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT), were evaluated to investigate their ability to reproduce long term (1983–2013) observed rainfall characteristics derived from twenty-four (24) gauges in Nigeria. Results show that all products performed well in terms of capturing the observed annual cycle and spatial trends in all selected stations. Statistical evaluation of the SRPs performance show that CHIRPS agree more with observations in all climatic zones by reproducing the local rainfall characteristics. The performance of PERSIANN and TAMSAT, however, varies with season and across the climatic zones. Findings from this study highlight the benefits of using SRPs to augment or fill gaps in the distribution of local rainfall data, which is critical for water resources planning, agricultural development, and policy making.

The Kabul River basin (KRB) of Afghanistan, a lifeline of around 10 million people, has multiplicity of governance, management, and development-related challenges leading to inequity, inadequacy, and unreliability of irrigation water distribution. Prior to any uplifting intervention, there is a need to evaluate the performance of irrigation system on a long term basis to identify the existing bottlenecks. Although there are several indicators available for the performance evaluation of the irrigation schemes, we used the coefficient of variation (CV) of actual evapotranspiration (ET_a) in space (basin, sub-basin, and provincial level), relative evapotranspiration (RET), and temporal CV of RET, to assess the equity, adequacy, and reliability of water distribution, respectively, from 2003 to 2013. The ET_a was estimated through a surface energy balance system (SEBS) algorithm and the ET_a estimates were validated using the advection aridity (AA) method with a R² value of 0.81 and 0.77 at Nawabad and Sultanpur stations, respectively. The global land data assimilation system (GLDAS) and moderate-resolution imaging spectroradiometer (MODIS) products were used as main inputs to the SEBS. Results show that the mean seasonal sub-based RET values during summer (May–September) (0.37 ± 0.06) and winter (October–April) (0.40 ± 0.08) are below the target values (RET ≥ 0.75) during 2003–2013. The CV of the mean ET_a, within sub-basins and provinces for the entire study period, has an equitable distribution of water from October–January (0.09 ± 0.04), whereas the highest inequity (0.24 ± 0.08) in water distribution is during early summer. The range of the CV of the mean ET_a (0.04–0.06) on a monthly and seasonal basis shows the unreliability of water supplies in several provinces or sub-basins. The analysis of the temporal CV of mean RET highlights the unreliable water supplies across the entire basin. The maximum ET_a during the study period was estimated for the Shamal sub-basin (552 ± 43 mm) while among the provinces, Kunar experienced the highest ET_a (544 ± 39 mm). This study highlights the dire need for interventions to improve the irrigation performance in time and space. The proposed methodology can be used as a framework for monitoring and implementing water distribution plans in future.

Water scarcity for smallholder farming in West Africa has led to the shift of cultivation from uplands to inland valleys. This study investigates the impacts of climate and land use/land cover (LULC) change on water resources in an intensively instrumented inland valley catchment in Southwestern Burkina Faso. An ensemble of five regional climate models (RCMs) and two climate scenarios (RCP 4.5 and RCP 8.5) was utilized to drive a physically-based hydrological model WaSiM after calibration and validation. The impact of climate change was quantified by comparing the projected period (2021–2050) and a reference period (1971–2000). The result showed a large uncertainty in the future change of runoff between the RCMs. Three models projected an increase in the total runoff from +12% to +95%, whereas two models predicted a decrease from −44% to −24%. Surface runoff was projected to show the highest relative change compared to the other runoff components. The projected LULC 2019, 2025, and 2030 were estimated based on historical LULC change (1990–2013) using the Land Change Modeler (LCM). A gradual conversion of savanna to cropland was shown, with annual rates rom 1 to 3.3%. WaSiM was used to simulate a gradual increase in runoff with time caused by this land use change. The combined climate and land use change was estimated using LULC-2013 in the reference period and LULC-2030 as future land use. The results suggest that land use change exacerbates the increase in total runoff. The increase in runoff was found to be +158% compared to the reference period but only +52% without land use change impacts. This stresses the fact that land use change impact is not negligible in this area, and climate change impact assessments without land use change analysis might be misleading. The results of this study can be used as input to water management models in order to derive strategies to cope with present and future water scarcities for smallholder farming in the investigated area.