Digital Twin Earth for Hydrology – Transforming Water Management in the Anthropocene

The concept of a Digital Twin Earth (DTE) represents the current frontier of Earth system science. As climate change accelerates, traditional hydrological models—often predicated on the assumption of stationarity—are increasingly failing to predict extreme events and manage dwindling resources. The DTE Hydrology initiative marks a paradigm shift toward a virtual, high-fidelity replica of our planet. By integrating a 4D reconstruction of the global water cycle with real-time satellite observations, the framework allows for predictive simulation and "what-if" scenario testing. This digital evolution is no longer merely a scientific exercise but a critical survival tool for the 21st century, designed to bridge the gap between global big data and local actionable intelligence for flood, landslide, and drought mitigation.The transition from conceptual models to functional digital twins is currently hindered by significant technical and methodological hurdles. This Research Topic seeks to address the "resolution gap," where the demand for kilometer-scale data has outpaced our ability to validate algorithms on the ground. Furthermore, it aims to tackle the complexity of the "Anthropocene water cycle," where human interventions—such as irrigation and reservoir management—non-linearly alter natural flows. To achieve a truly operational DTE, we must advance toward probabilistic digital twins that quantify uncertainty across compounding data layers. We also seek to explore the integration of AI-Hybrid models that maintain the speed of machine learning while strictly adhering to the fundamental laws of physics, such as mass and energy conservation.We invite contributions that explore the development, validation, and application of digital twin technologies within the hydrological sciences. We particularly encourage submissions that address the following themes:• High-Resolution Modeling: Advances in sub-kilometer scale hydrological simulations and global validation strategies.• Human-Water Interactions: Modeling irrigation, dam operations, and socio-economic feedback loops.• Uncertainty Quantification: Methods for developing probabilistic twins and managing error propagation in satellite data.• Computational Innovation: AI-Hybrid models, High-Performance Computing (HPC) applications, and physics-informed machine learning.• Decision Support: Web platforms and "what-if" scenarios for flood, landslide, and drought management. Topic editors: Luca Brocca, Wei Zhao, Mehdi Rahmati Source: https://www.frontiersin.org/research-topics/81096/digital-twin-earth-for-hydrology---transforming-water-management-in-the-anthropocene