AUTHOR=Brocca Luca , Barbetta Silvia , Camici Stefania , Ciabatta Luca , Dari Jacopo , Filippucci Paolo , Massari Christian , Modanesi Sara , Tarpanelli Angelica , Bonaccorsi Bianca , Mosaffa Hamidreza , Wagner Wolfgang , Vreugdenhil Mariette , Quast Raphael , Alfieri Lorenzo , Gabellani Simone , Avanzi Francesco , Rains Dominik , Miralles Diego G. , Mantovani Simone , Briese Christian , Domeneghetti Alessio , Jacob Alexander , Castelli Mariapina , Camps-Valls Gustau , Volden Espen , Fernandez Diego TITLE=A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations JOURNAL=Frontiers in Science VOLUME=1 YEAR=2024 URL=https://www.frontiersin.org/journals/science/articles/10.3389/fsci.2023.1190191 DOI=10.3389/fsci.2023.1190191 ISSN=2813-6330 ABSTRACT=

Climate change is profoundly affecting the global water cycle, increasing the likelihood and severity of extreme water-related events. Better decision-support systems are vital to accurately predict and monitor water-related environmental disasters and optimally manage water resources. These must integrate advances in remote sensing, in situ, and citizen observations with high-resolution Earth system modeling, artificial intelligence (AI), information and communication technologies, and high-performance computing. Digital Twin Earth (DTE) models are a ground-breaking solution offering digital replicas to monitor and simulate Earth processes with unprecedented spatiotemporal resolution. Advances in Earth observation (EO) satellite technology are pivotal, and here we provide a roadmap for the exploitation of these methods in a DTE for hydrology. The 4-dimensional DTE Hydrology datacube now fuses high-resolution EO data and advanced modeling of soil moisture, precipitation, evaporation, and river discharge, and here we report the latest validation data in the Mediterranean Basin. This system can now be explored to forecast flooding and landslides and to manage irrigation for precision agriculture. Large-scale implementation of such methods will require further advances to assess high-resolution products across different regions and climates; create and integrate compatible multidimensional datacubes, EO data retrieval algorithms, and models that are suitable across multiple scales; manage uncertainty both in EO data and models; enhance computational capacity via an interoperable, cloud-based processing environment embodying open data principles; and harness AI/machine learning. We outline how various planned satellite missions will further facilitate a DTE for hydrology toward global benefit if the scientific and technological challenges we identify are addressed.