A hydroinformatics framework for multisource precipitation merging and SPI-based drought monitoring in Eswatini

ABSTRACT Graphical summary of the study. A map of Eswatini shows elevation, agroecological zones, and training and testing rain gauges. AgERA5, ERA5-Land, ARC2, and CHIRPS are combined in four precipitation configurations and modelled using Random Forest and XGBoost, with and without empirical quantile mapping. The selected New Hybrid Precipitation Product is evaluated using station quantile plots, SHAP analysis, regional SPI time series, spatial drought characteristics, and NDVI-SPI correlations. The results indicate that CHIRPS is the leading predictor, NHPP and GPCC identify similar major drought periods, and NHPP shows higher agreement with vegetation response for wet-season SPI-6 and annual SPI-12. Reliable drought monitoring in gauge-limited regions requires precipitation products that reproduce both monthly rainfall and the distributions used to derive drought indices. This study developed a 0.1° monthly New Hybrid Precipitation Product (NHPP) for Eswatini for 1986–2024 by merging AgERA5, ERA5-Land, ARC2 and CHIRPS at various staged configurations using Random Forest and XGBoost, followed by seasonal quantile mapping. Performance was assessed through fully nested leave-one-station-out validation and three independent test stations, supported by SHAP interpretation, station quantile analysis, SPI evaluation and NDVI response. The selected model configuration achieved KGE = 0.82, RMSE = 38.86 mm, RBIAS = −2.38% and R2 = 0.70. GPCC showed slightly better mean station-level Q–Q agreement than NHPP (0.15 versus 0.17), while NHPP was the best-performing machine-learning product. Both datasets reproduced the principal drought periods, with SPI correlations of 0.70–0.84. NHPP identified droughts more frequently, whereas GPCC generally produced longer and more severe SPI-6 and SPI-12 events. NHPP also showed higher NDVI correlations for wet-season SPI-6 and annual SPI-12. NHPP therefore provides finer spatial detail and locally responsive drought information, while GPCC remains a stable regional benchmark; their complementary use improves drought monitoring in data-scarce environments.