Improving process-consistency of an ecohydrological model through inclusion of spatial patterns of satellite-derived land surface temperature

Distributed hydrological models can correctly simulate spatial patterns if the model parameters adequately represent the spatial heterogeneity of the basin. However, determining realistic values of these parameters is often difficult. Considering remote sensing-derived land-surface states and fluxes in combination with streamflow observations is a suitable strategy to better constrain parameters and improve process-consistency. For ecohydrological modelling, an accurate representation of vegetation characteristics is important to capture the vegetation response to varying moisture availability. Land surface temperature (Ts) may serve as a valuable diagnostic because it is pivotal to the surface energy and water balance, and conveys information about ecosystem stress and water use. This study aims at assessing the benefits of integrating spatial patterns of Landsat-derived Ts into calibration of a process-based ecohydrological model to improve process representation of catchment-scale energy fluxes and vegetation response to moisture deficits. We explicitly analyze the trade-off between streamflow and Ts performance, and explore the value of adding an increasing number of Ts images in the calibration process. The study is performed in a mixed land cover catchment in Germany using the ecohydrological model EcH2O. Our results demonstrate the value of satellite-derived Ts data for reducing uncertainties of energy-balance related vegetation pa-rameters, which are hardly constrained in streamflow-only calibration. Including satellite-derived Ts for model calibration reduced the mean absolute error of spatial anomalies in simulated Ts patterns by 15 % with negligible deterioration of streamflow performance. Inclusion of spatial Ts patterns improved capturing the dif-ferences in Ts between vegetation types, and affected simulated evapotranspiration fluxes. Improvements in simulated Ts could already be achieved by including only few (4-5) images of satellite-derived Ts in calibration. Based on our results, we advocate a wider use of satellite-based Ts data for multivariate calibration to improve model parameter identifiability and process consistency of highly parameterised, process-based distributed ecohydrological models, especially when capturing hydrology-vegetation interactions is crucial.