Increasingly, satellite-derived rainfall data is used for climate research and action in Africa. In this study, we use six years of rain gauge data from 596 stations operated by the Trans-African Hydro-Meteorological Observatory (TAHMO) to validate three gauge-calibrated satellite rainfall products – CHIRPS, TAMSAT and GSMaP_wGauge – and one satellite-only rainfall product – GSMaP. Validations are stratified to evaluate performance across the continent and in East Africa, Southern Africa, and West Africa at daily, pentadal, and monthly timescales. For daily mean rainfall over Africa, CHIRPS has the highest bias at 15.5 % (0.5 mm) whereas GSMaP_wGauge has the lowest bias at 0.02 mm (0.7 %). We find higher daily rainfall event detection scores in the GSMaP products than in CHIRPS or TAMSAT. Generally, for every two rainfall events predicted by CHIRPS and TAMSAT, the GSMaP products predict three or more events. The highest mean monthly biases are produced by CHIRPS in East Africa (29 %; 26.3 mm wet bias), TAMSAT in Southern Africa (13 %; 10.4 mm dry bias) and GSMaP in West Africa (23 %; 19.6 mm wet bias). Considerable biases in seasonal rainfall are observed in all sub-regions for every satellite product. There is an increase of 0.6–1.3 mm in satellite rainfall RMSE for a 1 km increase in elevation revealing the influence of elevation on rainfall estimation by satellite models. Overall, satellite-derived rainfall products have notable errors, while GSMaP products produce comparable or better results at multiple timescales relative to CHIRPS and TAMSAT.

Increasing climate variability and change coupled with steady population growth is threatening water resources and livelihoods of communities living in the Wami-Ruvu and Rufiji basins in Tanzania. These basins are host to three large urban centers, namely Dar es Salaam, Dodoma and Morogoro, with a combined total of more than 7 million people. Increased demand for ecosystem services from the available surface water resources and a decreasing supply of clean and safe water are exacerbating the vulnerability of communities in these basins. Several studies have analyzed climate projects in the two basins but little attention has been paid to identify locations that have vulnerable communities in a spatially-explicit form. To address this gap, we worked with stakeholders from national and local government agencies, basin water boards and the Water Resources Integration Development Initiative (WARIDI) project funded by USAID to map the vulnerability of communities to climate variability and change in the two basins. A generalized methodology for mapping social vulnerability to climate change was used to integrate biophysical and socioeconomic indicators of exposure, sensitivity and adaptive capacity and produced climate vulnerability index maps. Our analysis identified vulnerability “hotspots” where communities are at a greater risk from climate stressors. The results from this study were used to identify priority sites and adaptation measures for the implementation of resilience building interventions and to train local government agencies and communities on climate change adaptation measures in the two basins.

Flooding, an increasing risk in Rwanda, tends to isolate and restrict the mobility of rural communities. In this work, we developed a streamflow model to determine whether floods and rainfall anomalies explain variations in rural trail bridge use, as directly measured by in-situ motion-activated digital cameras. Flooding data and river flows upon which our investigation relies are not readily available because most of the rivers that are the focus of this study are ungauged. We developed a streamflow model for these rivers by exploring the performance of process-based and machine learning models. We then selected the best model to estimate streamflow at each bridge site to enable an investigation of the associations between weather events and pedestrian volumes collected from motion-activated cameras. The Gradient Boosting Machine model (GBM) had the highest skill with a Kling-Gupta Efficiency (KGE) score of 0.79 followed by the Random Forest model (RFM) and the Generalized Linear Model (GLM) with KGE scores of 0.73 and 0.66, respectively. The physically-based Variable Infiltration Capacity model (VIC) had a KGE score of 0.07. At the 50% flow exceedance threshold, the GBM model predicted 90% of flood events reported between 2013 and 2022. We found moderate to strong positive correlations between total monthly crossings and the total number of flood events at four of the seven bridge sites (r = 0.36–0.84), and moderate negative correlations at the remaining bridge sites (r = -0.33– -0.53). Correlation with monthly rainfall was generally moderate to high with one bridge site showing no correlation and the rest having correlations ranging between 0.15–0.76. These results reveal an association between weather events and mobility and support the scaling up of the trail bridge program to mitigate flood risks. The paper concludes with recommendations for the improvement of streamflow and flood prediction in Rwanda in support of community-based flood early warning systems connected to trail bridges.

Effective management of water resources and mitigation of water-related disasters are essential for human well-being, ecosystem health, and socio-economic development. However, the challenge lies in translating scientific insights into actions and decisions, resulting in a disconnect between knowledge production and practical decision-making. SERVIR, a collaborative initiative by the U.S. Agency for International Development (USAID) and the U.S. National Aeronautics & Space Administration (NASA), addresses this gap by partnering with regional organizations worldwide to integrate Earth Observation (EO) data into practical applications. This paper evaluates SERVIR’s experience translating cutting-edge science into actionable information for water security/water resources management through a collaborative approach, co-developing tools within diverse cultural contexts, and emphasizing capacity building. By discussing case studies and engagement strategies from SERVIR’s extensive experience, we highlight its collaborative efforts with regional bodies, governmental agencies, and other partners to transform water resources research into practical insights, supporting decision-making at various levels. This paper underscores the importance of continuous capacity-building workshops, stakeholder engagement, and adapting to technological advancements, such as cloud computing, for sustained impact. It also addresses the need for effective translators to navigate the complex EO toolkit, ensuring the appropriate application of tools for specific water management decisions. By reflecting on SERVIR’s journey, this paper offers guidance for decision-makers, practitioners, and researchers, encouraging dialogue, innovation, and collective action to support reliable access to water for all and sustainable water management. The insights and opportunities derived from SERVIR’s experience provide a framework for future initiatives, advancing water security and water resource management strategies.