Watershed scale models simulating hydrological and water quality processes have advanced rapidly in sophistication, process representation, flexibility in model structure, and input data. With calibration being an inevitable step prior to any model application, there is need for a simple procedure to assess whether or not a parameter should be adjusted for calibration. We provide a rationale for a hierarchical selection of parameters to adjust during calibration and recommend that modelers progress from parameters that are most uncertain to parameters that are least uncertain, namely starting with pure calibration parameters, followed by derived parameters, and finally measured parameters. We show that different information contained in time and frequency domains can provide useful insight regarding the selection of parameters to adjust in calibration. For example, wavelet coherence analysis shows time periods and scales where a particular parameter is sensitive. The second component of the paper discusses model performance evaluation measures. Given the importance of these models to support decision-making for a wide range of environmental issues, the hydrology community is compelled to improve the metrics used to evaluate model performance. More targeted and comprehensive metrics will facilitate better and more efficient calibration and will help demonstrate that the model is useful for the intended purpose. Here, we introduce a suite of new tools for model evaluation, packaged as an open-source Hydrologic Model Evaluation (HydroME) Toolbox. We apply these tools in the calibration and evaluation of Soil and Water Assessment Tool (SWAT) models of two watersheds, the Le Sueur River Basin (2880 km2) and Root River Basin (4300 km2) in southern Minnesota, USA.
Calibration Parameter Selection and Watershed Hydrology Model Evaluation in Time and Frequency DomainsWater
Citation InformationKumarasamy, K.; Belmont, P. Calibration Parameter Selection and Watershed Hydrology Model Evaluation in Time and Frequency Domains. Water 2018, 10, 710.