Advancing Surface Water Science Through the Integration of Satellite Observations and Hydrological Modeling

Surface water is fundamental to the Earth system, and it is undergoing rapid transformation driven by climate variability, extreme events, land use change, and water regulation. Yet, despite its importance, our ability to observe, understand, and predict surface water dynamics across spatial and temporal scales remains limited. Recent satellite missions (e.g., SWOT, Sentinel-3A/B, ICESat-2, PACE, BIOMASS, NISAR) are expanding observations of water extent, elevation, storage, and water quality across scales. In parallel, advances in water modeling, including hydrological, hydraulic, and data-driven approaches, provide increasingly powerful tools to represent runoff generation, river–floodplain dynamics, and human–water interactions, and to support prediction and scenario analysis. Integrating these complementary approaches is a key frontier for surface water science: satellite observations can constrain model states and parameters, while models provide process understanding and predictive capability beyond observational limits. Together, this integration enables improved estimation of discharge and storage, reduced uncertainty, and enhanced understanding and prediction of surface water dynamics and extremes in a changing world.The goal of this Research Topic is to advance the integration of multi-mission satellite observations with water modeling to improve understanding, monitoring, and prediction of surface water dynamics across scales. We seek contributions that translate advanced satellite measurements into robust hydrological variables, including river discharge, lake storage change, inundation extent, bathymetric information, and water quality indicators.A central objective is to strengthen the linkage between satellite observation systems and modeling frameworks through data assimilation, model calibration, uncertainty quantification, and hybrid modeling approaches. Studies that integrate observations from SWOT, Sentinel-3A/B, ICESat-1/2, PACE, BIOMASS, and complementary optical or radar missions are particularly encouraged. Contributions addressing hydrological extremes, climate-driven variability, anthropogenic regulation, and ecosystem responses are also welcome. By fostering collaboration between remote sensing and hydrological communities, this collection aims to enhance predictive capability, improve environmental assessment, and support sustainable water resource management in a changing world.We invite original research articles, reviews, and methodological contributions that advance the integration of satellite observations and water modeling for improved understanding and prediction of surface water systems across scales. Contributions may address, but are not limited to the following topics: • Multi-mission integration: including SWOT, Sentinel-3A/B, ICESat-1/2, PACE, BIOMASS, NISAR,and complementary sensors.• Retrieval and derivation of hydrologic state variables such as surface water elevation, discharge, storage change, and inundation extent.• Characterization of surface water system structure and processes, including bathymetry proxies, channel–floodplain morphology, vegetation–water interactions, and biogeochemical properties, and their integration into hydrological and hydraulic modeling frameworks.• Integration with modeling frameworks, including data assimilation, model calibration, uncertainty quantification, and coupled hydrological and hydraulic modeling approaches.• Analysis and prediction of surface water variability and extremes, including floods, droughts, climate variability, human regulation, and applications for water resources management, environmental assessment, and decision support.We particularly welcome studies demonstrating methodological innovation and strong linkage between satellite observations and hydrological modeling. Topic editors: Jiaqi Yao, Yuan Yang, Huanyu Chang, Zhiqing Peng, Fan Mo Source: https://www.frontiersin.org/research-topics/78889/advancing-surface-water-science-through-the-integration-of-satellite-observations-and-hydrological-modeling