Development of a Field Scale SWAT+ Modeling Framework for the Contiguous U.S. (23rd August 2022)

• Record Type: Journal Article
• Authors: White, Michael J.; Arnold, Jeffrey G.; Bieger, Katrin; Allen, Peter M.; Gao, Jungang; Čerkasova, Natalja; Gambone, Marilyn; Park, Seonggyu; Bosch, David D.; Yen, Haw; Osorio, Javier M.
• Is Part Of: Journal of the American Water Resources Association. Volume 58:Number 6(2022)
• Page Start: 1545
• Page End: 1560
• Publication Date: 2022-08-23
• Subjects: SWAT -- simulation -- non‐point source pollution -- rivers/streams -- streamflow -- surface water hydrology -- spatially distributed modeling; Water-supply -- Periodicals; Hydrology -- Periodicals; Water resources development -- Periodicals; Water resources development -- Environmental aspects -- Periodicals; 333.9100973
• DOI: 10.1111/1752-1688.13056
• Languages: English
• ISSNs: 1093-474X

Abstract

ABSTRACT: The Soil and Water Assessment Tool (SWAT) model is commonly used to predict the impacts of agricultural practices on water quality and quantity. Although widely applied, the data framework used to drive SWAT in the United States (U.S.) is fragmented and inconsistent, varying by user and model interface. This research describes the development of the National Agroecosystems Model (NAM), which provides a unified field to national scale modeling computational framework for research and decision support by using the latest SWAT platform, dubbed SWAT+. NAM has sufficient detail to capture field‐level processes and management actions and spans the full extent of the contiguous U.S. NAM contains 7 million computational units, 4 million of which represent specific cultivated fields. It contains 3 million individually identifiable stream segments and more than 5, 000 reservoirs. NAM is intended to serve as a reasonable base framework to be refined for specific applications. This work describes the individual data sources, assumptions, and the processing steps for their inclusion. NAM is constructed with 2, 121 individual SWAT+ models which can be executed in a parallel hierarchical structure to dramatically improve runtime. This framework was tested in a case study of the Little River Watershed, Tifton, GA. NAM is developed using only publicly available data sources such that subsets of it can be shared to support research with other government agencies, universities, and others in the public domain.