A watershed-scale model for depressional wetland-rich landscapes

Printer-friendly versionPDF version
Dec 05, 2018
Author: 
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

 

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.

Read the full article in Journal of Hydrology.

Associated SESYNC Researcher(s): 
DOI for citing: 
https://doi.org/10.1016/j.hydroa.2018.10.002
Share: Facebook Icon Twitter Icon Linked Icon