Pywr-DRB:特拉华河流域水资源可用性和干旱风险评估的开源 Python 模型

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
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引用次数: 0

摘要

美国大西洋中部地区的特拉华河流域(DRB)是一个体制复杂的水资源系统,为 1350 万人提供饮用水,此外还为能源、工业、娱乐和生态系统提供用水。本文介绍了 Pywr-DRB,这是一个开源 Python 模型,用于探索水库运行、跨流域引水和最小流量目标对 DRB 中水资源可用性和干旱风险的影响。Pywr-DRB 利用了新兴数据资源中的流量估算值,将大规模水文建模的进展与流域不断发展的水利基础设施和管理机构的改进表示相结合。我们详细的模型诊断评估表明,Pywr-DRB 在捕捉 DRB 的动态方面比单纯使用水文模型有很大改进。我们还探讨了水资源管理如何改变模型对低流量和水资源需求短缺的风险估计。我们的诊断基准和水系统建模方法广泛适用于其他主要流域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pywr-DRB: An open-source Python model for water availability and drought risk assessment in the Delaware River Basin

The Delaware River Basin (DRB) in the Mid-Atlantic region of the United States is an institutionally complex water resources system that provides drinking water for 13.5 million people, plus water for energy, industry, recreation, and ecosystems. This paper introduces Pywr-DRB, an open-source Python model exploring the impacts of reservoir operations, transbasin diversions, and minimum flow targets on water availability and drought risk in the DRB. Pywr-DRB draws on streamflow estimates from emerging data resources, bridging advances in large-scale hydrologic modeling with an improved representation of the basin's evolving water infrastructure and management institutions. Our detailed model diagnostic assessment demonstrates that Pywr-DRB provides substantial improvements over sole use of hydrologic models in capturing the DRB's dynamics. We also explore how water management alters model-derived risk estimates for low flows and water demand shortfalls. Our approach to diagnostic benchmarking and water systems modeling is broadly applicable to other major basins.

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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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