On the Interest of Optical Remote Sensing for Seasonal Snowmelt Parameterization, Applied to the Everest Region (Nepal)
2019
Bouchard, Benjamin | Eeckman, Judith | Dedieu, Jean-Pierre | Delclaux, François | Chevallier, Pierre | Gascoin, Simon | Arnaud, Yves | Université Laval [Québec] (ULaval) | Centre national de recherches météorologiques (CNRM) ; Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) ; Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS) | Hydrosciences Montpellier (HSM) ; Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS) | Institut des Géosciences de l’Environnement (IGE) ; Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]) | Centre d'études spatiales de la biosphère (CESBIO) ; Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP) ; Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
International audience
Mostrar más [+] Menos [-]Inglés. In the central part of the Hindu Kush Himalayan region, snowmelt is one of the main inputs that ensures the availability of surface water outside the monsoon period. A common approach for snowpack modeling is based on the degree day factor (DDF) method to represent the snowmelt rate. However, the important seasonal variability of the snow processes is usually not represented when using a DDF method, which can lead to large uncertainties for snowpack simulation. The SPOT-VGT and the MODIS-Terra sensors provide valuable information for snow detection over several years. The aim of this work was to use those data to parametrize the seasonal variability of the snow processes in the hydrological distributed snow model (HDSM), based on a DDF method. The satellite products were corrected and combined in order to implement a database of 8 day snow cover area (SCA) maps over the northern part of the Dudh Koshi watershed (Nepal) for the period 1998–2017. A revisited version of the snow module of the HDSM model was implemented so as to split it into two parameterizations depending on the seasonality. Corrected 8 day SCA maps retrieved from MODIS-Terra were used to calibrate the seasonal parameterization, through a stochastic method, over the period of study (2013–2016). The results demonstrate that the seasonal parameterization reduces the error in the simulated SCA and increases the correlation with the MODIS SCA. The two-set version of the model improved the yearly RMSE from 5.9% to 7.7% depending on the basin, compared to the one-set version. The correlation between the model and MODIS passes from 0.73 to 0.79 in winter for the larger basin, Phakding. This study shows that the use of a remote sensing product can improve the parameterization of the seasonal dynamics of snow processes in a model based on a DDF method.
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