Drought poses significant threats to humans and ecosystems, underscoring the need for robust and efficient monitoring systems for informed decision making and adaptive management. This study assessed the effectiveness of five indices in detecting historical droughts and low-flow periods in the upper and lower sub-basins of the Great Ruaha River Basin, Tanzania using hydrometeorological data (1981–2022). Historical droughts were obtained from government reports and scientific literature. The indices included the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Actual Evapotranspiration Index (SPAEI), Standardized Streamflow Index (SSI) and Water Scarcity Index (WSI). The standardized indices were calculated using basin monthly precipitation, evapotranspiration and discharge data at 3-, 6-, and 12-month timescales while the WSI was used to define low-flows based on a threshold of 0.001 m³/s, computed from the 10th percentile of daily discharge data. Drought characteristics were investigated using the run theory approach. The SPI captured 70–90% of historical droughts, SPEI identified 80–90%, SPAEI detected 50–60%, while SSI, and WSI each identified 80%. The SPI, SPEI and SSI classified majority of these events as severe while SPAEI identified them as moderate. The WSI was the most effective in detecting low flows, whereas all the standardized indices performed poorly. Between 1981 and 1989, discharge never fell below 0.001 m³/s. However, from 1990, lower discharges occurred almost every year, indicating a major shift in the hydrological system. Based on our research findings, we recommend using the SPI, SPEI, SSI and WSI for drought monitoring and more specific, the WSI for low flow analysis. Therefore, by using appropriate drought indices, water managers can achieve sustainable management of droughts and water resources in the basin.

Land use/landcover (LULC) changes and climate variability impact soil erosion; however, their combined long-term effects are poorly studied. Using remote sensing data, this study investigates changes in LULC and rainfall from 1985 to 2022 and their implications for soil erosion in the Lake Tana Basin, Ethiopia. The Global Land Cover Fine Classification System (GLC_FCS30D) data were used to analyze LULC changes; Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS-v2) were used to assess rainfall trends; and the RUSLE was used to estimate potential soil erosion. The GLC_FCS30D proved to have an overall accuracy of 77.3% for 2005, 80.2% for 2014, and 80.3% for 2022. The cropland area increased slightly, from 32.9% to 33.3%, while tree cover initially decreased from 31.2% to 27.8% before recovering to 29.9%. Overall, annual rainfall increased by 2.92 mm yr⁻¹, though it exhibited strong spatial variability, and rainfall erosivity rose by 1.25 MJ mm ha⁻¹ h⁻¹ yr⁻¹. Despite seemingly modest changes in landcover and rainfall, the combined effect on potential soil erosion was substantial. Potential soil loss in the Lake Tana Basin showed significant spatial and temporal variation, with a slight increase of 0.9% from 1985 to 1995, followed by a slight decrease of 0.12% from 1995 to 2005, and a more substantial decrease of 2.3% from 2005 to 2015 before a notable increase of 8.2% occurred from 2015 to 2022. By elucidating the intricate interactions between landcover changes and rainfall variability, this study enhances our understanding of landscape dynamics in the Lake Tana Basin. The findings highlight the importance of considering the interaction between rainfall and landcover changes in climate change studies, as well as when targeting soil conservation efforts and promoting sustainable land management and ecosystem resilience in the tropics.

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.

In recent decades, the Upper Great Ruaha River Basin (UGRRB) has experienced severe water scarcity due to alterations in its river discharge. However, the extent to which these alterations may be attributed to climate change versus land use/cover changes, water withdrawals, etc., remain uncertain. This study aims to assess the historical impact of climate change in the water balance and river discharge using a modelling approach. The Soil and Water Assessment Tool Plus (SWAT+) model was developed, calibrated and validated for the basin. By forcing the model with both factual (including anthropogenic climate change) and counterfactual (excluding anthropogenic climate change) climate data for 1901–2019, the impact of climate change was quantified as the difference between the two model simulations. Furthermore, long–term trends and magnitudes from simulations were assessed by Mann–Kendall and Sen’s slope statistical approaches, respectively at 5% significance level. The SWAT+ model yielded satisfactory performance with a Nash-Sutcliffe Efficiency of 0.76 and 0.67 during monthly calibration and validation periods, respectively. Attribution results from factual and counterfactual model simulations revealed increases in annual precipitation, evapotranspiration, surface runoff, lateral flow and percolation, as well as simulated annual river discharge attributable to climate change. Conversely, analysis of observed annual river discharge data revealed a consistent and significant decline during 1963–2017 (–1.029 m ³ s ⁻¹ yr ⁻¹ , p < 0.05). This discrepancy highlights the influence of non–climatic factors such as land use changes and increased water use. These findings underline the urgent need of integrating both climatic and non–climatic factors into basin’s water management frameworks to mitigate emerging water challenges.