Integration of GRACE mass variations into a global hydrological model |
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| Authors: | S Werth A Güntner S Petrovic R Schmidt |
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| Institution: | 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China;2. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China;1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China;4. Northeast Institute of Geography and Agricultural Ecology, Chinese Academy of Sciences, Changchun 130012, China;1. State Key Laboratory of Geodesy and Earth''s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China;2. Institute of Earth Sciences, Academia Sinica, Taipei, Taiwan;3. Center for Space Research, University of Texas at Austin, Austin, USA;4. Department of Geological Sciences, Jackson School of Geosciences, University of Texas at Austin, Austin, USA |
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| Abstract: | Time-variable gravity data of the GRACE (Gravity Recovery And Climate Experiment) satellite mission provide global information on temporal variations of continental water storage. In this study, we incorporate GRACE data for the first time directly into the tuning process of a global hydrological model to improve simulations of the continental water cycle. For the WaterGAP Global Hydrology Model (WGHM), we adopt a multi-objective calibration framework to constrain model predictions by both measured river discharge and water storage variations from GRACE and illustrate it on the example of three large river basins: Amazon, Mississippi and Congo. The approach leads to improved simulation results with regard to both objectives. In case of monthly total water storage variations we obtained a RMSE reduction of about 25 mm for the Amazon, 6 mm for the Mississippi and 1 mm for the Congo river basin. The results highlight the valuable nature of GRACE data when merged into large-scale hydrological modeling. Furthermore, they reveal the utility of the multi-objective calibration framework for the integration of remote sensing data into hydrological models. |
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