The Food and Agricultural Organization of the United Nations (FAO) portal to monitor water productivity through open‐access of remotely sensed derived data (WaPOR) offers continuous actual evapotranspiration and interception (ETIa‐WPR) data at a 10‐day basis across Africa and the Middle East from 2009 onwards at three spatial resolutions. The continental level (250 m) covers Africa and the Middle East (L1). The national level (100 m) covers 21 countries and 4 river basins (L2). The third level (30 m) covers eight irrigation areas (L3). To quantify the uncertainty of WaPOR version 2 (V2.0) ETIa‐WPR in Africa, we used a number of validation methods. We checked the physical consistency against water availability and the long‐term water balance and then verify the continental spatial and temporal trends for the major climates in Africa. We directly validated ETIa‐WPR against in situ data of 14 eddy covariance stations (EC). Finally, we checked the level consistency between the different spatial resolutions. Our findings indicate that ETIa‐WPR is performing well, but with some noticeable overestimation. The ETIa‐WPR is showing expected spatial and temporal consistency with respect to climate classes. ETIa‐WPR shows mixed results at point scale as compared to EC flux towers with an overall correlation of 0.71, and a root mean square error of 1.2 mm/day. The level consistency is very high between L1 and L2. However, the consistency between L1 and L3 varies significantly between irrigation areas. In rainfed areas, the ETIa‐WPR is overestimating at low ETIa‐WPR and underestimating when ETIa is high. In irrigated areas, ETIa‐WPR values appear to be consistently overestimating ETa. The relative soil moisture content (SMC), the input of quality layers and local advection effects were some of the identified causes. The quality assessment of ETIa‐WPR product is enhanced by combining multiple evaluation methods. Based on the results, the ETIa‐WPR dataset is of enough quality to contribute to the understanding and monitoring of local and continental water processes and water management.

Knowledge of evapotranspiration (ET) is essential for enhancing our understanding of the hydrological cycle, as well as for managing water resources, particularly in semi-arid regions. Remote sensing offers a comprehensive means of monitoring this phenomenon at different spatial and temporal intervals. Currently, several satellite methods exist and are used to assess ET at various spatial and temporal resolutions with various degrees of accuracy and precision. This research investigated the performance of three satellite-based ET algorithms and two global products, namely land surface temperature/vegetation index (TsVI), Penman–Monteith (PM), and the Meteosat Second Generation ET (MET) and the Global Land-surface Evaporation: the Amsterdam Methodology (GLEAM) global products, in two eco-regions of South Africa. Daily ET derived from the eddy covariance system from Skukuza, a sub-tropical, savanna biome, and large aperture boundary layer scintillometer system in Elandsberg, a Mediterranean, fynbos biome, during the dry and wet seasons, were used to evaluate the models. Low coefficients of determination (R²) of between 0 and 0.45 were recorded on both sites, during both seasons. Although PM performed best during periods of high ET at both sites, results show it was outperformed by other models during low ET times. TsVI and MET were similarly accurate in the dry season in Skukuza, as GLEAM was the most accurate in Elandsberg during the wet season. The conclusion is that none of the models performed well, as shown by low R² and high errors in all the models. In essence, our results conclude that further investigation of the PM model is possible to improve its estimation of low ET measurements.

This paper analyses the effect of the spatial assessment scale on irrigation performance indicators in small and medium-scale agriculture. Three performance indicators—adequacy (i.e., sufficiency of water use to meet the crop water requirement), equity (i.e., fairness of irrigation distribution), and productivity (i.e., unit of physical crop production/yield per unit water consumption)—are evaluated in five irrigation schemes for three spatial resolutions—250 m, 100 m, and 30 m. Each scheme has varying plot sizes and distributions, with average plot sizes ranging from 0.2 ha to 13 ha. The datasets are derived from the United Nations Food and Agricultural Organization (FAO) water productivity through open access of remotely sensed–derived data (the Water Productivity Open Access Portal—WaPOR) database. Irrigation indicators performed differently in different aspects; for adequacy, all three resolutions show similar spatial trends for relative evapotranspiration (ET) across levels for all years. However, the estimation of relative ET is often higher at higher resolution. In terms of equity, all resolutions show similar inter-annual trends in the coefficient of variation (CV); higher resolutions usually have a higher CV of the annual evapotranspiration and interception (ETIa) while capturing more spatial variability. For productivity, higher resolutions show lower crop water productivity (CWP) due to higher aboveground biomass productivity (AGBP) estimations in lower resolutions; they always have a higher CV of CWP. We find all resolutions of 250 m, 100 m, and 30 m suitable for inter-annual and inter-scheme assessments regardless of plot size. While each resolution shows consistent temporal trends, the magnitude of the trend in both space and time is smoothed by the 100 m and 250 m resolution datasets. This frequently results in substantial differences in the irrigation performance assessment criteria for inter-plot comparisons; therefore, 250 m and 100 m are not recommended for inter-plot comparison for all plot sizes, particularly small plots (<2 ha). Our findings highlight the importance of selecting the spatial resolution appropriate to scheme characteristics when undertaking irrigation performance assessment using remote sensing.

Regional climate models (RCMs) are crucial for climate studies and may be an alternative source of meteorological data in data-scarce regions. However, the effectiveness of the numerical weather prediction (NWP) models applied in RCMs is hampered by the parameterization of unresolved physical processes in the model. A major source of uncertainties in NWP models is the parameterization of the planetary boundary layer (PBL). This study evaluates the influence of seven PBL parameterization schemes in the Weather Research and Forecasting (WRF) model on the retrieval of four meteorological variables over the Kenyan highlands. The seven PBL schemes consist of four local schemes: the Mellor-Yamada-Janjic (MYJ), Mellor-Yamada-Nakanishi-Niino (MYNN), Bougeault-Lacarrere (BouLac), quasinormal scale elimination (QNSE), and three nonlocal schemes: asymmetrical convective model version 2 (ACM2), Shin and Hong (SHIN) and Yonsei University (YSU). The forcing data for the WRF model was obtained from the fifth generation of the European ReAnalysis (ERA5) dataset. The results were validated against observational data from the Trans-African Hydro-Meteorological Observatory (TAHMO). WRF was found to simulate surface meteorological variables with spatial details coherent with the complex topography within the Kenyan highlands, irrespective of the PBL scheme. A comparison between 2-meter temperature (T2) derived from the YSU scheme and T2 from the land component of ERA5 (ERA5-Land) indicates that surface meteorological variables derived from WRF are better suited for applications over the Kenyan highlands. The choice of the PBL scheme was found to primarily influence the simulation of the 10-meter wind speed (WS10) and rainfall as opposed to T2 and the 2-meter relative humidity (RH2). The insensitivity of the 2-meter variables to the choice of the PBL scheme is attributed to the influence of the surface layer parameterization near the surface. Results from the rainfall simulation indicate that the YSU scheme provides a more realistic depiction of PBL dynamics within the study area. Hence, the YSU scheme is best suited for simulating surface meteorological variables over the Kenyan highlands.

Understanding the dynamics of pesticide transport in the Malewa River and Lake Naivasha, a major fresh water resource, is critical to safeguard water quality in the basin. In this study, the soil and water assessment tool (SWAT) model was used to simulate the discharge of sediment and pesticides (notably the organochlorine residues of lindane, methoxychlor and endosulfan) into the Malewa River Basin. Model sensitivity analysis, calibration and validation were performed for both daily and monthly time steps using the sequential uncertainty fitting version 2 (SUFI-2) algorithm of the SWAT-CUP tool. Water level gauge data as well as a digital turbidity sensor (DTS-12) for suspended sediment transport were used for the SWAT calibration. Pesticide residues were measured at Upper and Down Malewa locations using a passive sampling technique and their quantity was determined using laboratory gas chromatography. The sensitivity analysis results showed that curve number (CN2), universal soil loss equation erodibility factor (USLE-K) and pesticide application efficiency (AP_EF) formed the most sensitive parameters for discharge, sediment and pesticide simulations, respectively. In addition, SWAT model calibration and validation showed better results for monthly discharge simulations than for daily discharge simulations. Similarly, the results obtained for the monthly sediment calibration demonstrated more match between measured and simulated data as compared to the simulation at daily steps. Comparison between the simulated and measured pesticide concentrations at upper Malewa and down Malewa locations demonstrated that although the model mostly overestimated pesticide loadings, there was a positive association between the pesticide measurements and the simulations. Higher concentrations of pesticides were found between May and mid-July. The similarity between measured and simulated pesticides shows the potential of the SWAT model as initial evaluation modelling tool for upstream to downstream suspended sediment and pesticide transport in catchments.

Land surface temperature (LST) plays a fundamental role in various geophysical processes at varying spatial and temporal scales. Satellite-based observations of LST provide a viable option for monitoring the spatial-temporal evolution of these processes. Downscaling is a widely adopted approach for solving the spatial-temporal trade-off associated with satellite-based observations of LST. However, despite the advances made in the field of LST downscaling, issues related to spatial averaging in the downscaling methodologies greatly hamper the utility of coarse-resolution thermal data for downscaling applications in complex environments. In this study, an improved LST downscaling approach based on random forest (RF) regression is presented. The proposed approach addresses issues related to spatial averaging biases associated with the downscaling model developed at the coarse resolution. The approach was applied to downscale the coarse-resolution Satellite Application Facility on Land Surface Analysis (LSA-SAF) LST product derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor aboard the Meteosat Second Generation (MSG) weather satellite. The LSA-SAF product was downscaled to a spatial resolution of ~30 m, based on predictor variables derived from Sentinel 2, and the Advanced Land Observing Satellite (ALOS) digital elevation model (DEM). Quantitatively and qualitatively, better downscaling results were obtained using the proposed approach in comparison to the conventional approach of downscaling LST using RF widely adopted in LST downscaling studies. The enhanced performance indicates that the proposed approach has the ability to reduce the spatial averaging biases inherent in the LST downscaling methodology and thus is more suitable for downscaling applications in complex environments.