Watershed sustainable phosphorus management involving the resilience assessment: Framework and application

被引:0
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作者
Fan, Yu [1 ,2 ]
Ni, Zhaokui [3 ,4 ,5 ]
Dong, Yue [3 ]
Wang, Shengrui [3 ,4 ,5 ]
Zhang, Jian [1 ,2 ,6 ]
机构
[1] School of Management Science and Engineering, Beijing Information Science and Technology University, Beijing,100192, China
[2] Beijing Key Laboratory of Big Data Decision Making for Green Development, Beijing,100192, P.R., China
[3] Guangdong-Hong Kong Joint Laboratory for Water Security, Center for Water Research, Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai,519087, China
[4] College of Water Sciences, Beijing Normal University, Beijing,100875, China
[5] Yunnan Key Laboratory of Pollution Process and Management of Plateau Lake- Watershed, Yunnan Province, Kunming,650034, China
[6] Beijing International Science and Technology Cooperation Base for Intelligent Decision and Big Data Application, Beijing,100192, China
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D O I
10.1016/j.resconrec.2024.107907
中图分类号
学科分类号
摘要
Phosphorus (P) is an important nutrient for human society development and a central factor to pollution issues, especially causing lake eutrophication in the watershed. However, a management method considering both the resource attributes and pollution issues is absent, resulting in disorder and uncertainty governance of P flows. We present a two-dimensions and multiple-nodes distributed management framework of P transport process in the watershed, which incorporates resilience assessment, material flow analysis and scenario analysis. The framework was validated and applied to the Erhai Lake of China. Results show that the imbalance governance exists in the whole-watershed P flows. And it was overly vulnerable to dramatic changes with the key links and nodes related to food production. This framework can be used to identify where and how to improve watershed sustainable P management for reduced pollution and increased food security. Besides, it offers an effective approach for governance of nutrient flows. © 2024 Elsevier B.V.
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