Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter

Guillaume Thirel,Peter Salamon,Peter Burek,Milan Kalas

doi:10.3390/rs5115825

Abstract

Snow is an important component of the water cycle, and its estimation in hydrological models is of great significance concerning the simulation and forecasting of flood events due to snow-melt. The assimilation of Snow Cover Area (SCA) in physical distributed hydrological models is a possible source of improvement of snowmelt-related floods. In this study, the assimilation in the LISFLOOD model of the MODIS sensor SCA has been evaluated, in order to improve the streamflow simulations of the model. This work is realized with the final scope of improving the European Flood Awareness System (EFAS) pan-European flood forecasts in the future. For this purpose daily 500 m resolution MODIS satellite SCA data have been used. Tests were performed in the Morava basin, a tributary of the Danube, for three years. The particle filter method has been chosen for assimilating the MODIS SCA data with different frequencies. Synthetic experiments were first performed to validate the assimilation schemes, before assimilating MODIS SCA data. Results of the synthetic experiments could improve modelled SCA and discharges in all cases. The assimilation of MODIS SCA data with the particle filter shows a net improvement of SCA. The Nash of resulting discharge is consequently increased in many cases.

Highlights

Snow is an important component of the water cycle and of the climate evolution [1]
The main goal of this paper is to illustrate the incorporation of MODIS Snow Cover Area (SCA) data into a distributed hydrological model (LISFLOOD) using the particle filter and to evaluate the effects on the simulation of snow and discharges
The assimilation timestep was variable, from seven days—which we considered a reasonable compromise between the computing time and the time evolution of the snow cover—to a daily time-step, including 2-day and 3-day time-steps

Summary

Introduction

Snow is an important component of the water cycle and of the climate evolution [1]. Snowpacks can contain a huge quantity of water that can be released suddenly during the spring. Its characteristics, like the albedo, can have an important impact on surface energy fluxes. Research in recent years has been aimed at a better understanding of snowpack evolution and its simulation (e.g., [2,3,4]). Further investigation is needed in particular with respect to incorporating this information into hydrologic and Land Surface Models (LSMs, e.g., [5,6]). The assimilation of observed data in order to keep physical states of the models up-to-date, is a widely used technique in meteorology (e.g., [7,8,9])

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Conclusion