Remotely sensed latent heat fluxes for model error diagnosis: a case study

Abstract. This study shows that remotely sensed ET_act is useful in hydrological modelling for the procedure of model calibration and shows it potential to update soil moisture predictions. Comparison of modeled and remotely sensed ET_act together with the outcomes of our data assimilation procedure points out potential model errors, both conceptual and flux-related. Assimilation of remotely sensed ET_act results in a realistic spatial adjustment of soil moisture, except for the area where the model suffers from conceptual errors (forest with deep groundwater levels). By using operational (i.e. available for community in practice) data and models we aim to show the potential and limitations of using remotely sensed ET_act in the practice of hydrological modelling. We use satellite data of both ASTER and MODIS for the same two days in the summer of 2006 that, in association with the Surface Energy Balance Algorithm for Land (SEBAL), provides us the spatial distribution of daily ET_act. The model, used by the local water board, is a physically based distributed hydrological model of a small catchment (70 km²) in The Netherlands that simulates the water flow in both the unsaturated and saturated zone. Model outcomes of ET_act show values that are at least 20% lower than those estimated by SEBAL, which is due to the fact that different evapotranspiration methods are used. The spatial pattern of ET_act from the hydrological model resembles the soil map, whereas the ET_act from SEBAL resembles the land use map. As both ASTER and MODIS images were available for the same days, this study provides an opportunity to compare the worth of these two satellite sources. It is shown that, although ASTER provides better insight in the spatial distribution of ET_act due to its higher spatial resolution than MODIS, they appeared in this study just as useful.