Development of a Field Scale SWAT+ Modeling Framework for the Contiguous U.S.

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.

A Framework for Parameter Estimation, Sensitivity Analysis, and Uncertainty Analysis for Holistic Hydrologic Modeling Using SWAT+

“…In this study, we use SWAT+ models that have been created within the National Agroecosystem Model (NAM) (White et al, 2022;Arnold et al, 2020), a national effort for improving environmental assessments and conservation strategies. Within the NAM, a SWAT + model is constructed for each of the 2,139 HUC8 (8-digit hydrologic unit code) watersheds within the conterminous United States, simulating hydrologic processes and management according to five domains: main rivers (>150 km 2 ), tributaries (15-150 km 2 ), headwaters (1-15 km 2 ), transitions (0.2-2.0 km 2 ), and fields (1-50 ha).…”

“…Each NHD+ channel segment is designated as a unique channel in SWAT+. White et al (2022) provides detailed information on model construction and input data sets. We use these model set-ups for the four study watersheds.…”

Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks

“…However, this assumption no longer holds under changing environmental conditions, as the non-stationarity of climate conditions has not been considered in future climate predictions (Yang et al 2020). The temporal variability of hydrological parameters has become a key topic of extensive research interest in recent years (White et al 2022). The effects of different climate conditions and human activities prevent the parameters obtained during the calibration period from representing the parameter values over the entire time series.…”

From a test case to a trusted tool: Lithuania’s evolving SWAT system for water and agricultural management

“…However, the benefits of using the SWAT model extend far beyond academia. For example, the United States demonstrated its successful application for calculating total maximum daily loads (Borah et al, 2006), evaluating conservation programs (White et al, 2014) or environmental decision--making (White et al, 2022).…”