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Agriculture consumes the largest share of freshwater globally; therefore, distinguishing between rainfed and irrigated croplands is essential for agricultural water management and food security. In this study, a framework incorporating the Budyko model was used to differentiate between rainfed and irrigated cropland areas in Africa for eight remote sensing landcover products and a high-confidence cropland map (HCCM). The HCCM was generated for calibration and validation of the crop partitioning framework as an alternative to individual cropland masks which exhibit high disagreement. The accuracy of the framework in partitioning the HCCM was evaluated using an independent validation dataset, yielding an overall accuracy rate of 73 %. The findings of this study indicate that out of the total area covered by the HCCM (2.36 million km2), about 461,000 km2 (19 %) is irrigated cropland. The partitioning framework was applied on eight landcover products, and the extent of irrigated areas varied between 19 % and 30 % of the total cropland area. The framework demonstrated high precision and specificity scores, indicating its effectiveness in correctly identifying irrigated areas while minimizing the misclassification of rainfed areas as irrigated. This study provides an enhanced understanding of rainfed and irrigation patterns across Africa, supporting efforts towards achieving sustainable and resilient agricultural systems. Consequently, the approach outlined expands on the suite of remote sensing landcover products that can be used for agricultural water studies in Africa by enabling the extraction of irrigated and rainfed cropland data from landcover products that do not have disaggregated cropland classes.

Study Region: Continental Africa Study Focus: This study evaluates the accuracy of eight gauge-corrected rainfall products across Africa through direct comparisons with in situ observations for the period 2001–2020. The effect of validation datasets on the performance of the rainfall products was also quantified in ten African countries. Four categorical and five continuous metrics were estimated at multiple spatial and temporal scales as part of the evaluation. New hydrological insights for the Region: Results indicate that the performance of the rainfall products varied in space and time. Evaluation at temporal scales revealed that, on average, most rainfall products showed poor results (KGE < 0.35) at the daily timescale. In contrast, RFE v2.0, ARC v2.0, and MSWEP v2.8 were reliable (KGE > 0.75) at the monthly and annual timescales. Among the rainfall products, the performance of TAMSATv3.1, PERSIANN-CDR, and ERA 5 was relatively poor in capturing in situ observations. Evaluation at various spatial scales revealed mixed results. The ARC v2.0 and CHIRPS v2.0 rainfall products were reliable in detecting no rains (< 1 mm/day) for all 19 spatial scales, indicating a high level of confidence for drought studies. IMERG-F v6B and RFE v2.0 were reliable in detecting heavy and high-intensity rainfall events for all spatial scales. Using the KGE performance metrics at the regional level, MSWEP v2.8 in the Northern Africa region, RFE v2.0 in the Western and Southern Africa regions, ARC v2.0 in Central Africa, and CHIRPS v2.0 in the Eastern Africa region showed better performances at monthly timescale. Moreover, the performance of the gauge-corrected rainfall datasets was reduced when compared with independent validation data (gauge data not used by rainfall products) than dependent validation data. This study provides several new insights into choosing a rainfall product for continental to regional applications and identifies the need for bias correction.