A comprehensive approach to evaluating watershed models for predicting river flow regimes critical to downstream ecosystem services

• Published in: Environmental modelling & software : with environment data news 2014-11, Vol.61, p.121-134
• Main Authors: Gebremariam, Seyoum Y., Martin, Jay F., DeMarchi, Carlo, Bosch, Nathan S., Confesor, Remegio, Ludsin, Stuart A.
• Format: Article
• Language: English
• Subjects: Algal bloom; Allochthonous inputs; Animal and plant ecology; Animal, plant and microbial ecology; Biological and medical sciences; Eutrophication; Fresh water ecosystems; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Great Lakes; Lake Erie; Methods and techniques (sampling, tagging, trapping, modelling...); Non-point source pollution; Runoff; Synecology; Watershed modeling
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2014.07.004

Selection of strategies that help reduce riverine inputs requires numerical models that accurately quantify hydrologic processes. While numerous models exist, information on how to evaluate and select the most robust models is limited. Toward this end, we developed a comprehensive approach that helps evaluate watershed models in their ability to simulate flow regimes critical to downstream ecosystem services. We demonstrated the method using the Soil and Water Assessment Tool (SWAT), the Hydrological Simulation Program–FORTRAN (HSPF) model, and Distributed Large Basin Runoff Model (DLBRM) applied to the Maumee River Basin (USA). The approach helped in identifying that each model simulated flows within acceptable ranges. However, each was limited in its ability to simulate flows triggered by extreme weather events, owing to algorithms not being optimized for such events and mismatched physiographic watershed conditions. Ultimately, we found HSPF to best predict river flow, whereas SWAT offered the most flexibility for evaluating agricultural management practices. •A new approach was developed and used to evaluate the SWAT, HSPF model, and DLBRM.•The methodology allowed improved identification of model strengths and weaknesses.•Daily and monthly flow prediction was found to be “very good” to “excellent”.•All models performed less well at simulating extreme low-flow events.•Ease of use and flexibility make the SWAT most suitable for scenario-testing.