A comprehensive approach to evaluating watershed models for predicting river flow regimes critical to downstream ecosystem services Seyoum Y. Gebremariam a, e, * , Jay F. Martin a , Carlo DeMarchi b , Nathan S. Bosch c , Remegio Confesor d , Stuart A. Ludsin e a Food, Agricultural and Biological Engineering Department, The Ohio State University, Columbus, OH 43210, USA b Department of Earth, Environmental, and Planetary Sciences, Case Western Reserve University, Cleveland, OH 44106, USA c Environmental Science, Grace College, Winona Lake, IN 46590, USA d National Center for Water Quality Research, Heidelberg University, Tiffin, OH 44883, USA e Aquatic Ecology Laboratory, Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43212, USA article info Article history: Received 10 October 2013 Received in revised form 2 July 2014 Accepted 3 July 2014 Available online Keywords: Watershed modeling Eutrophication Runoff Great Lakes Lake Erie Allochthonous inputs Non-point source pollution Algal bloom abstract 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 Hydro- logical Simulation ProgrameFORTRAN (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. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction Many of the world's coastal and lake ecosystems that drain large agricultural watersheds are experiencing degraded water quality, including noxious algal blooms, hypoxia, and reduced water clarity (Cloern, 2001; O'Neil et al., 2012; Diaz and Rosenberg, 2008; Rabalais et al., 2009; Michalak et al., 2013). Watershed flow regimes have been shown to be drivers of such conditions by influencing nutrient runoff into the downstream environment (Donner et al., 2002; Vidon et al., 2009), and therefore need to be considered in nutrient mitigation or rehabilitation strategies (Royer et al., 2006; Scavia et al., 2014). Numerous factors interact to govern river outflows from the water- shed, including topography, meteorology (e.g., precipitation, tem- perature), soil characteristics, and land-use practices and management (DeFries and Eshleman, 2004). Owing to the complexity of factors that control hydrologic processes, finding a way to reliably model flow regimes that are critical to stream ecology and downstream ecosystem services can be challenging. However, doing so is absolutely critical, if land-use planners and water-quality managers are to succeed in protecting downstream water bodies (DeFries and Eshleman, 2004; Royer et al., 2006). To help research and management communities make well- informed choices regarding hydrology models, we describe a comprehensive approach to evaluate model performance in pre- dicting river flow regimes critical to downstream ecosystem ser- vices. The approach was used to evaluate three commonly used Abbreviations: SWAT, Soil and Water Assessment Tool; DLBRM, Distributed Large Basin Runoff Model; HSPF, Hydrology Simulation ProgrameFortran; GOF, goodness-of-fit; SCS, Soil Conservation Service; HRU, hydraulic response unit; STATSGO, state soil geographic; USGS, U.S. Geological Survey; BMP, best manage- ment practice. * Corresponding author. Aquatic Ecology Laboratory, Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH 43212, USA. Tel.: þ1 208 596 5165; fax: þ1 614 292 9448. E-mail address: gebremariam.6@osu.edu (S.Y. Gebremariam). Contents lists available at ScienceDirect Environmental Modelling & Software journal homepage: www.elsevier.com/locate/envsoft http://dx.doi.org/10.1016/j.envsoft.2014.07.004 1364-8152/© 2014 Elsevier Ltd. All rights reserved. Environmental Modelling & Software 61 (2014) 121e134