Year: 2026
Type: article
Abstract: . The proliferation and diversification of hydrological datasets have significantly advanced hydrological research. However, the coherence across these datasets remains poorly understood, hindering the comparability of findings derived from different data sources and variables. Here, we demonstrate that state-of-the-art hydrological datasets exhibit overall low consistency when evaluated through the lens of water balance – specifically, the relationship between variations in soil moisture and the difference between precipitation, evapotranspiration, and runoff. Our analysis reveals that satellite-based precipitation datasets generally show the highest consistency, while gauge-based datasets perform better in densely monitored regions of the Northern Hemisphere. For evapotranspiration, runoff, and soil moisture, reanalysis datasets demonstrate broader areas of higher consistency compared to gauge- or satellite-based products. Spatial patterns of consistency are strongly influenced by aridity and temperature, which affect measurement and modelling accuracy, while vegetation cover further modulates the performance of soil moisture datasets. Notably, dataset consistency has improved significantly in northern mid-latitudes over recent decades, likely reflecting advancements in observational technologies and the effects of climate warming. These findings underscore the importance of continued efforts to enhance dataset coherence and reliability for robust hydrological assessments.
Source: Earth system science data
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,509
Funders: U.S. Department of Energy, Deutsche Forschungsgemeinschaft, National Natural Science Foundation of China, Albert-Ludwigs-Universität Freiburg, China Scholarship Council, Higher Education Discipline Innovation Project, Graduate School, University of Maryland, International Cooperation and Exchange Programme, Overseas Expertise Introduction Project for Discipline Innovation, High-end Foreign Experts Recruitment Plan of China
OpenAlex ID: W4412640222