Contribution de la future mission altimétrique à large fauchée SWOT pour la modélisation hydrologique à grande échelle

Abstract : Scientific objective of this PhD work is to improve water fluxes estimation on the continental surfaces, at interanual and interseasonal scale (from few years to decennial time period). More specifically, it studies contribution of remotely-sensed measurements to improve hydrology model. Notably, this work focuses on the incoming SWOT mission (Surface Water and Ocean Topography, launch scheduled for 2021) for the study of the continental water cycle at global scale, and using the land surface model ISBA-TRIP. In this PhD work, I explore the potential of satellite data to correct both input parameters of the river routing scheme TRIP and its state variables. To do so, a data assimilation platform has been set to assimilate SWOT virtual observation as well as discharge estimated from real nadir altimetry data. Beforehand, it was necessary to do a sensibility analysis of TRIP model to its parameters. The aim of such study was to highlight what are the most impacting parameters on SWOT-observed variables and therefore select the ones to correct via data assimilation. The sensibility analysis (ANOVA) has been led on TRIP main parameters. The study has been done over the Amazon basin. The results showed that the simulated water levels are sensitive to local geomorphological parmaters exclusively. On the other hand, the simulated discharges are sensitive to upstream parameters (according to the TRIP river routing network) and more particularly to the groundwater time constant. Finally, water anomalies present sensitivities similar to those of the water levels but with more pronounced temporal variations. These results also lead me to do some choices in the implementation of the assimilation scheme and have been published. Therefore, in the second part of my PhD, I focused on developing a data assimilation platform which consists in an Ensemble Kalman Filter (EnKF). It could either correct the model input parameters or directly its state. A series of twin experiments is used to test and validate the parameter estimation module of the platform. SWOT virtual-observations of water heights and anomalies along SWOT tracks are assimilated to correct the river manning coefficient, with the possibility to easily extend to other parameters. First results show that the platform is able to recover the "true" Manning distribution assimilating SWOT-like water heights and anomalies. In the state estimation mode, daily assimilation cycles are realized to correct TRIP river water storage initial state by assimilating ENVISAT-based discharge. Those observations are derived from ENVISAT water elevation measures, using rating curves from the MGB-IPH hydrological model (calibrated over the Amazon using in situ gages discharge). Using such kind of observation allows going beyond idealized twin experiments and also to test contribution of a remotely-sensed discharge product, which could prefigure the SWOT discharge product. The results show that discharge after assimilation are globally improved : the root-mean-square error between the analysis discharge ensemble mean and in situ discharges is reduced by 28 \%, compared to the root-mean-square error between the free run and in situ discharges (RMSE are respectively equal to 2.79 x 103 m3/s and 1.98 x 103 m3/s).
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Charlotte Emery. Contribution de la future mission altimétrique à large fauchée SWOT pour la modélisation hydrologique à grande échelle. Sciences de la Terre. Université Paul Sabatier - Toulouse III, 2017. Français. ⟨NNT : 2017TOU30034⟩. ⟨tel-01523481v2⟩

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