Impact of Satellite and In Situ Data Assimilation on Hydrological Predictions

• Published in: Remote Sensing
• Main Authors: Musuuza, Jude Lubega, Gustafsson, David, Pimentel, Rafael, Crochemore, Louise, Pechlivanidis, Ilias
• Other Authors: Swedish Meteorological and Hydrological Institute (SMHI), Universidad de Córdoba = University of Córdoba Córdoba, Swedish Meteorological and Hydrological Institute (SMHI), Hydrology Research, Norrköping, Hydrosystèmes et Bioprocédés (UR HBAN), Centre national du machinisme agricole, du génie rural, des eaux et forêts (CEMAGREF), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), RiverLy - Fonctionnement des hydrosystèmes (RiverLy), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: [SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology; Umeälven; Northern Sweden
• Online Access: https://hal.science/hal-04670901; https://hal.science/hal-04670901/document; https://hal.science/hal-04670901/file/remotesensing-12-00811-v2.pdf; https://doi.org/10.3390/rs12050811

International audience The assimilation of different satellite and in situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we used the European-scale Hydrological Predictions for the Environment hydrological model in the Umeälven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimised model performance when the ensemble Kalman filter method is used. We further assessed the effect of assimilating different satellite products; namely, snow water equivalent, fractional snow cover, and actual and potential evapotranspiration, as well as in situ measurements of river discharge and local reservoir inflows. We finally investigated the combinations of those products that improved model predictions of the target variables and how the model performance varied through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without evapotranspiration products improved the model performance.