A watershed-scale model for depressional wetland-rich landscapes

Grey R Evenson; C Nathan Jones; Daniel L McLaughlin; Heather E Golden; Charles R Lane; Ben DeVries; Laurie C Alexander; Megan W Lang; Gregory W McCarty; Amirreza Sharifi

doi:10.1016/j.hydroa.2018.10.002

A watershed-scale model for depressional wetland-rich landscapes

J Hydrol X. 2018 Dec 1:1:100002. doi: 10.1016/j.hydroa.2018.10.002.

Authors

Affiliations

¹ Department of Food, Agricultural, and Biological Engineering, The Ohio State University, Columbus, OH, USA.
² The National Socio-Environmental Synthesis Center, University of Maryland, Annapolis, MD, USA.
³ Department of Forest Resources and Environmental Conservation, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
⁴ US Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Cincinnati, OH, USA.
⁵ Department of Geographical Sciences, University of Maryland, College Park, MD, USA.
⁶ US Environmental Protection Agency, Office of Research and Development, Washington, DC, USA.
⁷ USFWS National Wetlands Inventory Program, Falls Church, VA, USA.
⁸ US Department of Agriculture - Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA.
⁹ Government of the District of Columbia, Department of Energy and Environment, Water Quality Division, Washington, DC, USA.

Abstract

Wetlands are often dominant features in low relief, depressional landscapes and provide an array of hydrologically driven ecosystem services. However, contemporary models do not adequately represent the role of spatially distributed wetlands in watershed-scale water storage and flows. Such tools are critical to better understand wetland hydrological, biogeochemical, and biological functions and predict management and policy outcomes at varying spatial scales. To develop a new approach for simulating depressional landscapes, we modified the Soil and Water Assessment Tool (SWAT) model to incorporate improved representations of depressional wetland structure and hydrological processes. Specifically, we refined the model to incorporate: (1) water storage capacity and surface flowpaths of individual wetlands and (2) local wetland surface and subsurface exchange. We utilized this model, termed SWAT-DSF (DSF for Depressional Storage and Flows), to simulate the ~289 km² Greensboro watershed within the Delmarva Peninsula of the US Coastal Plain. Model calibration and verification used both daily streamflow observations and remotely sensed surface water extent data (ca. 2-week temporal resolution), allowing us to assess model performance with respect to both streamflow and watershed inundation patterns. Our findings demonstrate that SWAT-DSF can successfully replicate distributed wetland processes and resultant watershed-scale hydrology. SWAT-DSF provides improved temporal and spatial characterization of watershed-scale water storage and flows in depressional landscapes, providing a new tool to quantify wetland functions at broad spatial scales.

Keywords: Agricultural ditches; Delmarva Peninsula; Depressional wetlands; Geographically isolated wetlands; Inundation mapping; Non-floodplain wetlands; Surface water extent.

Grants and funding

EPA999999/Intramural EPA/United States