In this study the impacts of the data assimilation of the SMOS soil moisture neural network trained on ECMWF product upon streamflow estimates were investigated. Two hydrological experiments were performed, one which used hydro-meteorological forcings produced with the assimilation of the SMOS data, the other using forcings which excluded the assimilation of the SMOS data. Both hydrological simulations produced streamflow estimates usingthe Global Flood Awareness System [GloFAS], run at ECMWF on behalf of the European Commission Copernicus Emergency Management Service. Both sets of experiment results were verified against streamflow observations in the United States and Australia, and were also analysed globally with respect to a GloFAS simulation forced with ERA-5 re-analysis which provided a benchmark acting as global proxy observations. Skill scores were computed for each experiment against the observation datasets, the differences in the skill scores were used to identify where hydrological skill may be affected by the assimilation of SMOS soil moisture data. Results found that skill score differences between the two GloFAS data assimilation experiments were pronounced within a tropical band of latitude. Differences were also present in areas such as south east Asia and the Himalayas. There was no clear spatial trend to these differences, so it is not possible to conclude whether aparticular region’s hydrological skills improved by the assimilation of SMOS soil moisture. Investigating the differences between the simulation sat individual gauging stations found that they often only occurred during a single flood event, for the remainder of the simulation period the experiments were almost identical. Future work could further understand the impact of SMOS soil moisture data assimilation by focusing the analysis on individual flood events and correlating any differences to the analysis increments. Therefore it is not possible to conclude whether the assimilation of SMOS soil moisture improves the hydrological skill of GloFAS streamflow predictions. However the assimilation may affect individual flood peaks but further analysis is required.
BT - ESA Contract Report DA - 08/2019 DO - 10.21957/4qgjv63e LA - eng M3 - ESA Contract Report N2 -In this study the impacts of the data assimilation of the SMOS soil moisture neural network trained on ECMWF product upon streamflow estimates were investigated. Two hydrological experiments were performed, one which used hydro-meteorological forcings produced with the assimilation of the SMOS data, the other using forcings which excluded the assimilation of the SMOS data. Both hydrological simulations produced streamflow estimates usingthe Global Flood Awareness System [GloFAS], run at ECMWF on behalf of the European Commission Copernicus Emergency Management Service. Both sets of experiment results were verified against streamflow observations in the United States and Australia, and were also analysed globally with respect to a GloFAS simulation forced with ERA-5 re-analysis which provided a benchmark acting as global proxy observations. Skill scores were computed for each experiment against the observation datasets, the differences in the skill scores were used to identify where hydrological skill may be affected by the assimilation of SMOS soil moisture data. Results found that skill score differences between the two GloFAS data assimilation experiments were pronounced within a tropical band of latitude. Differences were also present in areas such as south east Asia and the Himalayas. There was no clear spatial trend to these differences, so it is not possible to conclude whether aparticular region’s hydrological skills improved by the assimilation of SMOS soil moisture. Investigating the differences between the simulation sat individual gauging stations found that they often only occurred during a single flood event, for the remainder of the simulation period the experiments were almost identical. Future work could further understand the impact of SMOS soil moisture data assimilation by focusing the analysis on individual flood events and correlating any differences to the analysis increments. Therefore it is not possible to conclude whether the assimilation of SMOS soil moisture improves the hydrological skill of GloFAS streamflow predictions. However the assimilation may affect individual flood peaks but further analysis is required.
PY - 2019 T2 - ESA Contract Report TI - SMOS Operational Emergency Services - Floods UR - https://www.ecmwf.int/node/19530 ER -