In regions lacking sufficient data, remote sensing (RS) offers a reliable alternative for precipitation estimation, enabling more effective drought management. This study comprehensively evaluates four commonly used RS datasets—Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS), Tropical Applications of Meteorology using Satellite data (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) against ground-based data—with respect to their performance in detecting precipitation and drought patterns in the Great Ruaha River Basin (GRRB), Tanzania (1983–2020). Statistical metrics including the Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and bias were employed to assess the performance at daily, monthly, seasonal (wet/dry), and annual timescales. Most of the RS products exhibited lower correlations (r < 0.5) at daily timestep and low RMSE, bias, and ME. Monthly performance improved substantially (r > 0.8 at most stations) particularly during the wet season (r = 0.52–0.82) while annual and dry-season performance declined (r < 0.5 and r < 0.3, respectively). Performance under RMSE, bias, and ME declined at higher timescales, particularly during the wet season and annually. CHIRPS, MSWEP, and PERSIANN generally overestimated precipitation while TAMSAT consistently underestimated it. Spatially, CHIRPS and MSWEP reproduced coherent basin-scale patterns of drought persistence, with longer dry-spells concentrated in the northern, central, and western parts of the basin and shorter dry-spells in the eastern and southern regions. Trend analysis further revealed that most products captured consistent large-scale changes in dry-spell characteristics, although localized drought events were more variably detected. CHIRPS and MSWEP showed superior performance especially in capturing monthly precipitation patterns and major drought events in the basin. Most products struggled to detect extreme dry conditions with the exception of CHIRPS and MSWEP at certain stations and periods. Based on these findings, CHIRPS and MSWEP are recommended for drought monitoring and water resource planning in the GRRB. Their appropriate use can help water managers make informed decisions, promote sustainable resource use, and strengthen resilience to extreme weather events.

Drought is a pervasive and intensifying climate hazard with profound implications for food security, water availability, and socioeconomic stability, particularly in sub-Saharan Africa. In Kenya, where over 80% of the landmass comprises arid and semi-arid lands (ASALs), recurrent droughts have become a critical threat to agricultural productivity and climate resilience. This study presents a comprehensive spatiotemporal analysis of seasonal drought dynamics in Kenya for June–July–August–September (JJAS) from 2000 to 2024, leveraging remote sensing-based drought indices and geospatial analysis for climate risk planning. Using the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Condition Index (VCI), Soil Moisture Anomaly (SMA), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) anomaly, a Combined Drought Indicator (CDI) was developed to assess drought severity, persistence, and impact across Kenya’s four climatological seasons. Data were processed using Google Earth Engine and visualized through GIS platforms to produce high-resolution drought maps disaggregated by county and land-use class. The results revealed a marked intensification of drought conditions, with Alert and Warning classifications expanding significantly in ASALs, particularly in Garissa, Kitui, Marsabit, and Tana River. The drought persistence analysis revealed chronic exposure in drought conditions in northeastern and southeastern counties, while cropland exposure increased by over 100% while rangeland vulnerability rose nearly 56-fold. Population exposure to drought also rose sharply, underscoring the socioeconomic risks associated with climate-induced water stress. The study provides an operational framework for integrating remote sensing into early warning systems and policy planning, aligning with global climate adaptation goals and national resilience strategies. The findings advocate for proactive, data-driven drought management and localized adaptation interventions in Kenya’s most vulnerable regions.

Lake Victoria, the world’s second-largest freshwater lake, is vital for regional ecosystems and the livelihoods of millions across East Africa. However, the basin is increasingly vulnerable to hydroclimatic extremes, such as floods and droughts, exacerbated by climate variability and human activities. This research aims to address these challenges through the development and application of a Inland Lakes Integrated Water Balance Model (ILIWaB Model) for the lake. The ILIWaB Model, which has been successfully developed, integrates hydrological, meteorological, and socioeconomic data to simulate lake inflows, outflows, and net balances. The model serves as a foundation for flood simulations and climate projections, with the latter performed under a suite of Shared Socioeconomic Pathways (SSPs) to capture diverse future scenarios. Key outputs include flood extent simulations, scheduled for completion by May 2025, and the assessment of risks to surrounding populations. These simulations aim to predict the intensity and frequency of flooding events and evaluate their implications for population safety, infrastructure, and economic stability. Preliminary results demonstrate the model’s capability to accurately replicate historical water balance conditions and predict potential flooding hotspots. Long-term projections suggest a significant increase in flood risks under high-emission scenarios, threatening over 5 million residents in low-lying areas. The study underscores the importance of adopting adaptive management strategies and informed policymaking to mitigate future risks. This research offers a robust framework for climate-resilient planning in the Lake Victoria basin and provides transferable insights for other transboundary water systems globally.

In regions lacking sufficient data, remote sensing (RS) offers a reliable alternative for precipitation estimation, enabling more effective drought management. This study comprehensively evaluates four commonly used RS datasets-Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS), Tropical Applications of Meteorology using Satellite data (TAMSAT), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) against ground-based data-with respect to their performance in detecting precipitation and drought patterns in the Great Ruaha River Basin (GRRB), Tanzania (1983-2020). Statistical metrics including the Pearson correlation coefficient (r), Mean Error (ME), Root Mean Square Error (RMSE), and Bias were employed to assess the performance at daily, monthly, seasonal (wet/dry), and annual timescales. Most of the RS products exhibited lower correlations (r 0.8 at most stations) particularly during wet season (r = 0.52-0.82) while annual and dry-season performance declined (r < 0.5 and r < 0.3, respectively). Performance under RMSE, Bias, and ME declined at higher timescales, particularly during wet season and annually. CHIRPS, MSWEP, and PERSIANN generally overestimated precipitation while TAMSAT consistently underestimated it. CHIRPS and MSWEP showed superior performance especially in capturing monthly precipitation patterns and major drought events in the basin. Most products struggled to detect extreme dry conditions with exception of CHIRPS and MSWEP at certain stations and periods. Based on these findings, CHIRPS and MSWEP are recommended for drought monitoring and water resource planning in the GRRB. Their appropriate use can help water managers make informed decisions, promote sustainable resource use and strengthen resilience to extreme weather events.

Large tropical lakes are highly sensitive to climate variability and anthropogenic warming, yet their future hydroclimatic responses remain insufficiently quantified. This study assesses future changes in Lake Victoria using the Lake Victoria Integrated Water Balance (LaVIWaB) model driven by bias-adjusted ISIMIP3b/CMIP6 climate projections under SSP1-2.6, SSP3-7.0, and SSP5-8.5 for 2015–2100. Ten global climate model simulations were integrated to project changes in precipitation, evaporation, inflows, outflows, lake levels, surface area, and storage dynamics.Results indicate an intensifying hydrological cycle, with increasing precipitation and moderate increases in lake evaporation. By 2100, mean annual precipitation is projected to increase by approximately 0.2 mm day⁻¹ under SSP1-2.6 and 0.6 mm day⁻¹ under SSP5-8.5, while evaporation increases by 0.1 and 0.4 mm day⁻¹, respectively. Assuming outflow management continues under the Agreed Curve, mean annual lake levels rise by approximately 0.5 m under SSP1-2.6 and up to 1.0 m under SSP5-8.5. Surface area expands nonlinearly, with annual maxima approaching 71,000–73,000 km², representing an increase of approximately 2% under high emissions. Projected lake levels range from 1135.3 to 1137.0 m above sea level, with peaks exceeding 1137.5 m, while lake area ranges from 67,000 to 73,000 km². Increasing interannual variability and model spread highlight substantial uncertainty and potential hydroclimatic extremes. These findings have important implications for flood risk, shoreline inundation, water resources management, and climate adaptation across the Lake Victoria Basin.