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

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 pote...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news 2021-10, Vol.144, p.105147, Article 105147
Main Authors: Pan, Feng, Feng, Qingyu, McGehee, Ryan, Engel, Bernard A., Flanagan, Dennis C., Chen, Jingqiu
Format: Article
Language:English
Subjects:
Agricultural policy
Agricultural practices
APEX
Apexes
Best management practices
Distributed-modeling
Hydrologic models
Hydrology
Nonpoint source pollution
Nutrient loading
Parallel computing
Parallel processing
Pollutants
Pollution sources
Quality assessment
Simulation
Water quality
Water quality assessments
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Summary: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. •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.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2021.105147