期刊
JOURNAL OF HYDROLOGY
卷 596, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jhydrol.2020.125711
关键词
Reliability theory; Probability; Physics-based; Watershed; Water quality; Sustainability metrics
资金
- National Science Foundation [CBET-1351361]
- U.S. Environmental Protection Agency (USEPA), through its Office of Research and Development
Microbial surface water contamination poses a threat to critical ecosystem services, and predicting and assessing water contamination and sustainability is challenging due to the complexity of environmental systems. This paper uses reliability theory to study the reliability of components in the surface water network and assess their impact on overall water quality and sustainability, obtaining spatially distributed measures. GIS is used to present these measures as geospatial products to promote public acceptance of probability-based methods in contaminant hydrology.
Microbial surface water contamination can disrupt critical ecosystem services such as recreation and drinking water supply. Prediction of water contamination and assessment of sustainability of water resources in the context of water quality are needed but are difficult to achieve - with challenges arising from the complexity of environmental systems, and stochastic variability of processes that drive contaminant fate and transport. In this paper we use reliability theory as a framework to address these issues. We define failure as exceedance of regulatory water contamination limits, and system components as reaches in the surface water network. We then methodically study the reliability of each component in the context of water quality, as well as the impact of individual components on overall water quality and sustainability. We obtain spatially distributed probability- and physics-based sustainability measures of reliability, vulnerability, resilience and the sustainability index. Finally, we use GIS as a platform to present these measures as geospatial products in an effort to foster public acceptance of probability-based methods in contaminant hydrology.
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