Improved ecohydrologic modelling using spatial patterns of remotely sensed land surface temperature

Considering different types of hydrologic observations for model calibration in addition to streamflow is a suitable strategy to better constrain model parameters and improve process-consistency of hydrologic models. In this regard, land surface temperature (T_s) is an interesting variable as it is at the core of the surface energy and water balance. This study aims at evaluating the benefits of integrating spatial patterns of satellite-derived T_s into calibration of the process-based ecohydrologic model EcH₂O. We furthermore explore the value of an increasing number of T_s images in the calibration period. The study is performed in a mixed land cover catchment in NE Germany and makes use of Landsat-derived T_s data. Our results show that satellite-derived T_s is useful for reducing uncertainties of energy-balance related vegetation parameters, which are hardly constrained when the model is calibrated to streamflow only. Good model performance with respect to streamflow does not preclude low performance in terms of T_s and including satellite-derived T_s for model calibration clearly improves simulated spatial patterns of T_s. Spatial patterns in observed T_s are shown to be strongly related to land cover class and a vegetation index, and our results indicate that further model improvements may be possible by better representing observed variations of leaf area index within the ecohydrologic model.