Augmenting the National agroecosystem model with physically based spatially distributed groundwater modeling. (February 2023)

• Record Type: Journal Article
• Authors: Bailey, Ryan T.; Abbas, Salam; Arnold, Jeffrey; White, Michael; Gao, Jungang; Čerkasova, Natalja
• Is Part Of: Environmental modelling & software. Volume 160(2023)
• Publication Date: 2023-02
• Subjects: SWAT -- SWAT+ -- Groundwater modeling -- Aquifers -- Agroecosystem; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105589
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: We present an integrated hydrologic modeling approach for the conterminous United States using the SWAT + models of the National Agroecosystem Model (NAM). The NAM is a field-based, national scale hydrologic model to aid in conservation planning and policy. We augment the NAM by simulating groundwater storage and flow in a physically based spatially distributed manner using the gwflow module. We also present a scripting tool that automates the creation of gwflow inputs for each 8-digit hydrologic unit code (HUC8) SWAT + model. Key calculated groundwater inflows and outflows include recharge, pumping, groundwater-lake exchange, groundwater-surface water exchange, transfer to soils, and boundary inflow. We demonstrate the approach for six HUC8 watersheds located in various regions of the United States, focusing on areas of groundwater pumping, tile drainage, intensive groundwater-surface water interactions, and high precipitation. The presented framework can be used to establish models for scenario analysis of climate, management, and land use. Highlights: Implemented groundwater modeling into SWAT+ within the National Agroecosystem Model. Python script developed for automating processing of groundwater input data. Method presented for 6 8-digit watersheds across the United States. Model results compared against monthly streamflow and groundwater head.