Water data science

The Al Murunah project seeks to improve water security in Egypt, Jordan, Lebanon, and the Occupied Palestinian Territories (OPT) by integrating nature-based solutions for water and agricultural water management strategies (Resilient Nature-Based Water Solutions, RNBWS). The “High-Resolution Climate Change Downscaling for Resilient Nature-Based Water Solutions in the MENA Region” webinar showcased the Al Murunah Project’s groundbreaking findings and insights that can shape the future of sustainable water management in the region. The webinar’s aims were to: 1) disseminate technical findings from high-resolution, bias-corrected climate change downscaling activities (full report and data available here) and 2) describe how they are being used in the Al Murunah pilot projects and can be used more widely. It focused on how data-driven insights are informing the planning and implementation of resilient nature-based water solutions (RNBWS) demonstration projects in the four pilot basins of Abu Al-Matamir (Egypt), Wadi Seer (Jordan), Ras Baalbeck Basin (Lebanon) and Wadi Al-Faria’a (OPT). The webinar highlighted the need for localized, high-resolution (temporal and spatial) climate data to address the MENA region’s unique challenges. The study data is instrumental not only for sustainable water management but also for driving broader climate resilience initiatives rooted in community engagement. A key highlight of the webinar was the presentation of the Bias-Corrected Statistical Disaggregation (BCSD) method, a cutting-edge approach for generating precise and more accurate climate projections. The model outputs explain the rising intensity of extreme weather events such as heatwaves and droughts, underscore the need for proactive planning and interventions. The importance of partnerships in turning these findings into actionable solutions was also underscored. Local communities play a central role in the project, with their insights ensuring that interventions are context-specific and sustainable, while gender inclusion and diverse stakeholder consultations are critical for equitable outcomes, particularly in addressing the needs of vulnerable populations.

VegDischarge v1, which covers over 64,000 river segments in Africa, is a natural river discharge dataset produced by coupled modeling; the agro-hydrologic VegET model and the mizuRoute routing model for the period 2001-2021. Using remote sensing data and hydrological modeling system, the 1-km runoff field simulated by VegET, was routed with mizuRoute. Performance metrics show strong model reliability, with R² of 0.5–0.9, NSE of 0.6–0.9, and KGE of 0.5–0.8 at the continental scale. The total average annual discharge for Africa is quantified at 3271.4 km³year-1, with contributions to oceanic basins: 1000.0 km³year-1 to the North Atlantic, primarily from the Senegal, Gambia, Volta, and Niger Rivers; 1327.2 km³year-1 to the South Atlantic, largely from the Congo River; 214.7 km³year-1 to the Mediterranean Sea, predominantly from the Nile River; and 729.4 km³year-1 to the Indian Ocean, with inputs from rivers such as the Zambezi. The dataset is valuable for stakeholders and researchers to understand water availability, its temporal and spatial variations that affect water-related infrastructure planning, sustainable resource allocation, and the development of climate resilience strategies.

Greenhouse gas (GHG) emission from tropical large hydropower reservoirs (LHRs) is the highest among all climatic zones due to the combinatory effect of elevated content of flooded organic matter and high temperatures. Traditional methods for GHG emission estimation involve extensive fieldwork, topographic surveys, hydrological analyses, and environmental assessments with high-end instrument requirements. In a country like India, where the hydropower sector is mushrooming rapidly, implementing these techniques on such a large scale is challenging. Alternatively, cloud-based tools like Google Earth Engine (GEE), G-res, and Earth Observation (EO) data related to biophysical and climatic conditions with in-situ reservoir water levels provide an opportunity to quantify GHG emissions from LHRs efficiently. In the present study, Maithon, one of the oldest LHRs in India, situated in a tropical climatic zone, has been studied by integrating site-specific parameters to estimate GHG emissions. The results from this study, which show that at the mean operating level (146.31 m) of the reservoir, net GHG emission is 1,024 - 1,271 gCO2e/m2/yr (with a 95% confidence interval), are of significant importance. This study highlights the GHG emissions varying greatly between the full reservoir level (786 gCO2e/m2/yr) and near the dead storage level (3,855 gCO2e/m2/yr), indicating the role of reservoir operating level in mitigating GHG emissions to achieve global goals like net zero emissions. There has been limited work globally using the G-res tool, and this is the first comprehensive study of initial GHG emission estimation of a tropical reservoir using G-res and GEE incorporating updated high-resolution land use land cover and Sentinel-1 images.