The technical case for optimism is genuine. Constellations of satellites now track soil moisture, surface water extent, snow cover, groundwater storage and precipitation at spatial and temporal resolutions that were unthinkable a generation ago. Machine learning models trained on these observations can forecast floods days in advance, reconstruct streamflow histories in ungauged basins and detect subtle shifts in water availability across entire continents. For regions where ground-based monitoring networks are sparse or deteriorating, remote sensing combined with AI appears to promise a shortcut around decades of underinvestment in physical instrumentation. The commentary acknowledges that these capabilities are transforming hydrology from a data-scarce discipline into one awash with information.

But the authors caution that the apparent abundance conceals deep asymmetries. Satellite missions are designed, launched and operated overwhelmingly by wealthy nations and space agencies, and the raw observations they collect are processed into usable products largely by institutions in North America, Europe and East Asia. Training and running the large AI models that convert raw spectral data into hydrological insight requires enormous computational resources, and recent research on the geography of computing power shows that access to high-performance computing is heavily concentrated in the so-called global compute north. Scientists in Africa, South Asia and Latin America often find themselves consuming data products generated elsewhere rather than shaping the questions those products are designed to answer.

This asymmetry matters because hydrological data are never neutral. The commentary draws on the FAIR principles, which hold that research data should be findable, accessible, interoperable and reusable, and points out that formal openness does not guarantee equitable participation. A dataset may be freely downloadable, yet the expertise, bandwidth, software and computing infrastructure required to work with it may be unavailable to the very communities whose rivers and aquifers it describes. The authors argue that unequal access to data and the underrepresentation of diverse perspectives risk reinforcing existing disparities in who gets accurate flood warnings, who can anticipate drought, and whose water grievances are visible to policymakers.

The problem extends beyond infrastructure to the epistemic foundations of AI itself. Citing influential critiques of large language models, the authors note that machine learning systems trained on biased or incomplete datasets can reproduce and amplify those biases at scale. In hydrology, training data are disproportionately drawn from well-monitored basins in Europe and North America, meaning models may perform poorly, and fail silently, when applied to tropical monsoon systems, arid endorheic basins or heavily modified urban catchments in the global South. When such models inform flood zoning, reservoir operation or drought compensation schemes, errors fall hardest on populations that lack the technical capacity to audit the predictions or contest the decisions built upon them.

To illustrate what a more equitable alternative might look like, the commentary highlights participatory action research initiatives in the Sundarbans, the vast mangrove delta where the Ganges, Brahmaputra and Meghna rivers meet the Bay of Bengal across India and Bangladesh. Supported by the ENGAGE4Sundarbans project, funded through the Swiss Agency for Development and Cooperation and the Swiss National Science Foundation, researchers are working directly with delta communities to co-produce knowledge about flooding, salinity intrusion and environmental change. Rather than treating local residents as passive subjects of satellite surveys, these initiatives position them as active generators and interpreters of data, combining Earth observations with lived experience of a landscape that shifts with every tide and cyclone.

The Sundarbans example underscores a broader argument the authors make about data justice. Hydrological knowledge has historically been produced through colonial and post-colonial power relations, with monitoring systems designed to serve irrigation departments, navigation interests and state control rather than the communities most exposed to water hazards. Modern digital infrastructures, the commentary warns, can quietly reproduce these hierarchies under a veneer of technological neutrality. Datafication, the conversion of social and environmental phenomena into machine-readable data, creates new forms of visibility and invisibility: a flood captured by radar becomes a global data point, while the displacement of a village may never enter any database at all.

The authors also situate their argument within what they describe as a broader crisis of living beyond hydrological means, referencing recent work on global water bankruptcy in the post-crisis era. As climate change intensifies floods and droughts and as groundwater depletion accelerates in major agricultural regions, the demand for reliable water information will only grow. If the emerging AI-mediated observation system distributes its benefits unevenly, the result could be a two-tier hydrology: sophisticated, near-real-time prediction for those who can afford it, and crude or absent information for those who cannot. Such a divide would not merely be unfair, the authors contend; it would be scientifically corrosive, because the ground truth needed to validate global models depends on diverse, well-documented environments, including those currently least represented.

The commentary does not call for abandoning Earth observation or AI, which the authors regard as indispensable tools. Instead, it proposes a set of orientations for the hydrological community. These include investing in local monitoring capacity and data sovereignty so that communities and national institutions in data-poor regions can own and govern the information generated about their territories; designing AI systems with attention to the provenance and representativeness of training data; fostering South-South and North-South scientific partnerships in which priorities are set jointly rather than imposed; and treating participatory, community-based data collection not as a supplement to high technology but as an integral component of credible hydrological science. The researchers argue that equity should be understood as a condition for scientific quality, not merely an ethical add-on.

The paper concludes with a challenge to journals, funders and research institutions. Peer review, the authors suggest, should ask not only whether a new model improves predictive skill but also whose realities it captures and whom it may exclude. Funding agencies should support the slow, relational work of building trust with communities, which rarely fits neatly into short project cycles but is essential for knowledge that endures. As satellites multiply and algorithms grow more powerful, the question the commentary poses is deceptively simple: will the age of big water data be one in which everyone’s water is seen, or one in which only some people’s water counts? The answer, the authors insist, depends on choices being made now, in laboratories, agencies and boardrooms far from the deltas and drylands where the consequences will be felt.

Subject of Research: Equity and justice in global hydrological data production amid the rise of Earth observation and artificial intelligence

Article Title: Towards equitable hydrological data in the age of Earth observations and artificial intelligence

Article References: Di Baldassarre, G., Brandimarte, L., de Brito, M. M., Mukherjee, J., & Rusca, M. (2026). Towards equitable hydrological data in the age of Earth observations and artificial intelligence. Nature Water. https://doi.org/10.1038/s44221-026-00706-w

Image Credits: AI Generated

DOI: 10.1038/s44221-026-00706-w

Keywords: hydrology, Earth observation, artificial intelligence, data equity, water data, Sundarbans, participatory research, data justice, machine learning, flood monitoring, global South, Nature Water