Water data science

The International Water Management Institute (IWMI) strengthens water security through innovative data analysis and application, as well as novel digital tools.

To address critical information gaps, IWMI collects and analyzes data using technologies like remote sensing and compiles fragmented datasets from various public and private sources. This comprehensive approach equips researchers, policymakers, and communities with the data and information they need to make informed decisions and manage water resources effectively.

To improve access and analysis, IWMI makes its data publicly available through Open Access platforms and offers user-friendly digital dashboards that integrate real-time monitoring and practical insights. These platforms empower experts to address water- and climate-related challenges, and engage citizens in water management and climate adaptation activities. Custom tools enable communities to track water quality, predict floods, and prepare for droughts. IWMI also enhances capacity at local, national, and regional levels.

IWMI’s science-based tools support diverse applications such as water accounting, irrigation management, and climate impact and adaptation assessments. These data-driven solutions include forecasts to anticipate future conditions, scenario analyses to explore potential outcomes, and impact assessments that evaluate policy effects on water, food, and land systems. This ensures that stakeholders have access to the right data for both immediate needs and long-term planning.

Artificial intelligence (AI) further amplifies the effectiveness of IWMI’s data tools, with machine learning models assisting in real-time monitoring and precise water resource mapping. By incorporating AI technologies within frameworks that prioritize gender equity and social inclusion, IWMI ensures that digital tools support equitable participation and inclusive water management for all communities.

Latest publications

Displaying 4 publications

Advancing water security in Africa with new high-resolution discharge data
Scientific Data, 2024
More... [DOI] | Fulltext (10.8 MB)

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.

Spatial distribution / Modelling / Runoff / River basins / Datasets / Discharge / Water security
Record No:H053276

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Why indicators matter for advancing the global goal on adaptation: the case of water and sanitation targets in the UAE-Belm work programme
2024
More... | Fulltext (1.08 MB)

Vulnerability / Institutions / Data management / Monitoring / Communities / Stakeholders / Socioeconomic aspects / Infrastructure / Climate change impacts / Climate change adaptation / Climate resilience / Indicators / Sanitation / Water scarcity
Record No:H053242

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Assessing GHG emissions of a tropical large hydropower reservoir using G-res and GEE
Journal of the Indian Society of Remote Sensing, 2024
More... [DOI]

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.

Rainfall / Climate change / Land cover / Land use / Datasets / Satellite imagery / Water levels / Reservoirs / Hydropower / Estimation / Greenhouse gas emissions
Record No:H053199

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Assessment of critical water-related data and skills needs to improve anticipatory actions in selected districts of the Somali Region, Ethiopia
2024
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The brief identifies critical gaps in water-related data and skills necessary for the effective implementation of anticipatory water management actions in response to climate change in the Dolo Ado and Bokolmayo districts, Ethiopia. It evaluates current community and local government practices and recommends improvements in data collection, capacity building, and integrated water management to enhance resilience against droughts and floods. Key findings highlight the need for better data monitoring, early warning systems, and infrastructure investment to mitigate water-related hazards and improve local adaptive capacities.

Local communities / Disaster risk reduction / Water availability / Infrastructure / Early warning systems / Monitoring / Flooding / Drought / Resilience / Capacity development / Climate change / Water management / Assessment
Record No:H053055

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