RAWS (Re-Analysis of Water for Society) aims to develop the first high-resolution global reanalysis of terrestrial water resources, reconstructing water availability and use over the past 60 years at approximately 1 km spatial resolution and daily time steps. The project combines a global hydrological model with satellite observations, climate datasets, and artificial intelligence–enhanced data assimilation to generate a consistent picture of the evolution of water systems worldwide. RAWS will integrate information on groundwater, water quality, crop growth, and human water use, with a focus on water scarcity hotspots. The project is part of the Schmidt Sciences VIEW programme and brings together leading research institutions to advance knowledge and tools for sustainable water management.
60 months from 01/03/2026 to 28/02/2031
General aims
RAWS aims to improve the understanding of global water dynamics by integrating natural and human components of the water system into a unified analytical framework. The project will reconstruct long-term patterns of water availability and use, identify the main drivers of water scarcity and ecosystem degradation, and analyse interactions across the water–food–energy–ecosystem nexus. These insights will support scientific research and help inform policies and investments that enhance water security and climate resilience.
CMCC role
CMCC contributes to the development of the RAWS global water reanalysis by supporting data processing, modelling, and analysis of water system dynamics. The Foundation brings expertise in water and environmental intelligence, integrating artificial intelligence and machine learning techniques with earth system and hydrological modelling. CMCC supports the processing and integration of large datasets, including information on water demand and reservoir operations, and contributes to the analysis of water scarcity dynamics and sectoral interactions within the water–food–energy–ecosystem nexus. The Foundation also contributes to the identification of global water-stress hotspots and to the assessment of potential adaptation pathways, helping translate scientific outputs into insights relevant for water management and climate resilience.
Activities
The project develops a global water reanalysis framework integrating hydrological modelling, climate data, satellite observations, and AI-supported data assimilation. RAWS will combine datasets on groundwater, water quality, crop growth, and human water use to analyse long-term trends in water availability and demand. The project will also investigate selected water scarcity hotspots and engage regional stakeholders through dedicated dialogues to ensure that the resulting knowledge products respond to regional needs.
RAWS holistic reanalysis framework is detailed in the following WPs:
- WP1. Global datasets to understand human water use and infrastructure (Lead: OU)
- WP2. State-of-the-art Global Water Model (Lead: UU)
- WP3. Global Water Model calibration and reanalysis (Lead: UU)
- WP4. Global water impact modelling (Lead: CMCC)
- WP5. Deep dives and dialogues (Lead: VT)
- WP6. Data analytics and dissemination (Lead: CMCC)
Expected results
RAWS will deliver a global, high-resolution (~1 km, daily) dataset describing terrestrial water dynamics over the past six decades. The project will generate new insights into water scarcity, sectoral competition for water, and feedbacks within the water–food–energy–ecosystem nexus. These results will support improved water management strategies, climate adaptation planning, and evidence-based decision-making.
Partners
Euro-Mediterranean Center on Climate Change (CMCC)
Virginia Tech (VT)
Radboud University (RU)
Politecnico di Milano (POLIMI)