A framework for automated and spatially-distributed modeling with the Agricultural Policy Environmental eXtender (APEX) model. (October 2021)

• Record Type: Journal Article
• Authors: Pan, Feng; Feng, Qingyu; McGehee, Ryan; Engel, Bernard A.; Flanagan, Dennis C.; Chen, Jingqiu
• Is Part Of: Environmental modelling & software. Volume 144(2021)
• Publication Date: 2021-10
• Subjects: APEX -- Distributed-modeling -- Best management practices -- Nonpoint source pollution -- Parallel computing; 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.2021.105147
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Agricultural Best Management Practices (BMPs) are popular approaches to reduce nonpoint source (NPS) pollutant losses. Hydrologic models that can simulate impacts of BMPs at the field-scale can help guide the selection of BMPs. Furthermore, high-performance computing techniques have significant potential for scaling spatial simulations and reducing model runtimes. In this study, a parallel modeling framework for the Agricultural Policy Environmental eXtender (APEX) model was developed for large-scale, high-resolution, spatially-distributed model simulations. It provides a tool for conducting BMP evaluations at field-scale with a distributed architecture and automatic model setup of APEX. Sample results demonstrated the capability of the framework for distributed and semi-distributed modeling and illustrated the performance of parallelization. This framework can help provide guidance for decision makers on agricultural BMPs with large-scale water quality assessments and NPS nutrient loading reductions. Highlights: A framework for spatially-distributed modeling of APEX was developed. Included input database and automated functionality make APEX modeling efficient. The script is modular and can be readily maintained, modified, and expanded. Parallelization drastically reduces computational time for spatially-distributed tasks.