EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Doris Duethmann1, Martha Anderson2, Marco Maneta3, and Doerthe Tetzlaff1,4
Doris Duethmann et al.
  • 1IGB Leibniz Institute of Freshwater Ecology and Inland Fisheries, Department Ecohydrology and Biogeochemistry, Berlin, Germany (
  • 2USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705 USA
  • 3University of Montana, Department of Geosciences, Missoula, MT, USA
  • 4Humboldt University Berlin, Geography Department, Berlin, Germany

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 (Ts) 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 Ts into calibration of the process-based ecohydrologic model EcH2O. We furthermore explore the value of an increasing number of Ts images in the calibration period. The study is performed in a mixed land cover catchment in NE Germany and makes use of Landsat-derived Ts data. Our results show that satellite-derived Ts 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 Ts and including satellite-derived Ts for model calibration clearly improves simulated spatial patterns of Ts. Spatial patterns in observed Ts 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.

How to cite: Duethmann, D., Anderson, M., Maneta, M., and Tetzlaff, D.: Improved ecohydrologic modelling using spatial patterns of remotely sensed land surface temperature, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13592,, 2023.