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.

农业最佳管理实践 (BMP) 是减少非点源 (NPS) 污染物损失的流行方法。可以在现场规模模拟 BMP 影响的水文模型有助于指导 BMP 的选择。此外，高性能计算技术在扩展空间模拟和减少模型运行时间方面具有巨大潜力。在本研究中，为大规模、高分辨率、空间分布的模型模拟开发了农业政策环境扩展器 (APEX) 模型的并行建模框架。它提供了一个工具，用于在现场规模上进行 BMP 评估，具有分布式架构和 APEX 的自动模型设置。示例结果证明了分布式和半分布式建模框架的能力，并说明了并行化的性能。