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An Interannual Probabilistic Assessment of Subsurface Water Storage Over Europe Using a Fully Coupled Terrestrial Model
Author(s) -
Hartick Carl,
FurushoPercot Carina,
Goergen Klaus,
Kollet Stefan
Publication year - 2021
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2020wr027828
Subject(s) - environmental science , forcing (mathematics) , predictability , probabilistic logic , water cycle , anomaly (physics) , groundwater , water resources , water storage , current (fluid) , climatology , meteorology , hydrology (agriculture) , computer science , geology , statistics , mathematics , oceanography , geotechnical engineering , inlet , ecology , physics , condensed matter physics , geomorphology , artificial intelligence , biology
The years 2018 and 2019 were two of the hottest and driest in Mid‐Europe, highlighting the need for a comprehensive assessment of available water resources. In this study, we propose a probabilistic, terrestrial water assessment method, which utilizes a terrestrial forward model that closes the coupled water and energy cycles, from groundwater to the top of the atmosphere. In this methodology, the model is initialized with the current state of the water year and forced with a climatologic ensemble of atmospheric forcing to account for atmospheric uncertainty and natural variability. The simulations result in an ensemble of ensuing water years that are analyzed for subsurface water storage anomalies. The methodology was applied to the water years 2011/2012 and 2018/2019 and showed an increased probability of a significant water deficit in regions that had a water deficit in the previous year. This was also observed in an evaluation simulation. The results were compared to simulations with perfect forcing and uncertain initial conditions, and showed predictability at the interannual timescale and beyond, depending on the strength of the anomaly. The methodology was then applied to 2019/2020 to provide an outlook of the evolution of the current anomalies. The results emphasize the importance of accounting for groundwater dynamics in applied terrestrial models to account for long‐term memory effects in the terrestrial water cycle in forward simulations, over large spatial scales. This method of probabilistic subsurface water storage assessment may provide crucial information to public and industrial sectors for long‐term water resource planning.