What can we learn from multi-data calibration of a process-based ecohydrological model?. (March 2018) - Kuppel, Sylvain; Tetzlaff, Doerthe; Maneta, Marco P.; Soulsby, Chris
Abstract: We assessed whether a complex, process-based ecohydrological model can be appropriately parameterized to reproduce the key water flux and storage dynamics at a long-term research catchment in the Scottish Highlands. We used the fully-distributed ecohydrological model EcH2 O, calibrated against long-term datasets that encompass hydrologic and energy exchanges, and ecological measurements. Applying diverse combinations of these constraints revealed that calibration against virtually all datasets enabled the model to reproduce streamflow reasonably well. However, parameterizing the model to adequately capture local flux and storage dynamics, such as soil moisture or transpiration, required calibration with specific observations. This indicates that the footprint of the information contained in observations varies for each type of dataset, and that a diverse database informing about the different compartments of the domain, is critical to identify consistent model parameterizations. These results foster confidence in using EcH2 O to contribute to understanding current and future ecohydrological couplings in Northern catchments. Highlights: Ecohydrological model captures multi-process response in wet, steep catchment. Data diversity constrains feasible parameter sets more than data quantity. Using all observation types for calibration yields best model performance. Riparian soil moisture and transpiration observations are most informative.