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• W4377101694 abstract "Water environment-water resources carrying capacity (WERCC) is a sustainability indicator, rendering it significant to measure the water systems covering the water environment and water resources. Currently, water environment carrying capacity (WECC) accounting and assessment mostly tend to the deterministic results of a single element under a design hydrological condition. However, the WERCC, as well as its components WECC and water resources carrying capacity (WRCC), is a random variable with a certain probability distribution. To account for that, we established a Bayesian network-based WERCC overloading risk assessment method system. It can calculate the water system carrying state and its corresponding probability after determining the WECC and WRCC probability distributions. Among them, the probability distributions of the WECC and WRCC were determined by the Bayesian formula, and the spatiotemporal law, respectively. Additionally, based on the above quantitative uncertainty analysis in the basin, a Bayesian network-based WERCC overloading risk assessment path was built by modeling the causal chain of the WERCC internal impact mechanism, and the risk assessment results were obtained by the software Netica. Taking the North Canal Basin in China as an example, the results of WERCC overloading risk zoning showed that CU6 and CU9 were severe risk areas; CU2, CU7, CU8, and CU10 had moderate risks; and the remaining 40% control units were slight or mild risks. According to the overloading risk characteristics, the uncertain quantitative assessment has provided a scientific basis for ensuring the sustainable development of the North Canal from the perspective of reducing the water environment pressure (WEP) and improving the WERCC." @default.
• W4377101694 created "2023-05-20" @default.
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• W4377101694 date "2023-07-01" @default.
• W4377101694 modified "2023-09-27" @default.
• W4377101694 title "Overloading risk assessment of water environment-water resources carrying capacity based on a novel Bayesian methodology" @default.
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• W4377101694 doi "https://doi.org/10.1016/j.jhydrol.2023.129697" @default.
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