Assimilation of remotely sensed latent heat flux in a distributed hydrological model

Abstract

This paper addresses the question of whether remotely sensed latent heat flux estimates over a catchment can be used to improve distributed hydrological model water balance computations by the process of data assimilation. The data used is a series of noaa-avhrr satellite images for the Drentse Aa catchment in the Netherlands for the year 1995. These 1×1 km resolution images are converted into latent heat flux estimates using sebal ( surface energy balance algorithm for land [J Hydrol 2000;229:87]). The physically-based distributed model simgro ( simulation of groundwater flow and surface water levels [J Hydrol 1997;192:158]) is used to compute the water balance of the Drentse Aa catchment for that same year. Comparison between model-derived and remotely sensed area-averaged evapotranspiration estimates show good agreement, but spatial analysis of the model latent heat flux estimates indicate systematic underestimation in areas with higher elevation. A constant gain Kalman filter data assimilation algorithm is used to correct the internal state variables of the distributed model whenever remotely sensed latent heat flux estimates are available. It was found that the spatial distribution of model latent heat flux estimates in areas with higher elevation were improved through data assimilation.