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基于生态补偿理念的生态效益约束下洪水资源分配的区间两阶段随机规划模型。

An Interval Two-Stage Stochastic Programming Model for Flood Resources Allocation under Ecological Benefits as a Constraint Combined with Ecological Compensation Concept.

机构信息

Northeast Asian Studies College, Jilin University, Changchun 130012, China.

Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing 100875, China.

出版信息

Int J Environ Res Public Health. 2019 Mar 21;16(6):1033. doi: 10.3390/ijerph16061033.

DOI:10.3390/ijerph16061033
PMID:30901949
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6466455/
Abstract

The Momoge National Nature Reserve (MNNR) is located at the intersection of Nenjiang and Taoer Rivers in Baicheng City, Jilin Province, where the Taoer River is the main source of water for the nature reserve. However, due to the construction of the water control project in the upper reaches of the Taoer River, the MNNR has been in a state of water shortage for a long time. To guarantee the wetland function of the nature reserve, the government planned to carry out normal and flood water supply from Nenjiang River through the West Water Supply Project of Jilin Province. Therefore, how to improve the utilization of flood resources effectively has become one of the key issues of ecological compensation for the MNNR. In this paper, a flood resources optimal allocation model that is based on the interval two-stage stochastic programming method was constructed, and the corresponding flood resource availability in different flow scenarios of Nenjiang River were included in the total water resources to improve their utilization. The results showed that the proportion of flood resources that were used in the MNNR after optimization was more than 70% under different flow scenarios, among which the proportion of flood resources under a low-flow scenario reached 77%, which was 23% higher than the proposed increase. In addition, the ecological benefits of low, medium, and high flow levels reached the range of 26.30 (10⁶ CNY) to 32.14(10⁶ CNY), 28.21(10⁶ CNY) to 34.49(10⁶ CNY) and 29.41(10⁶ CNY) to 35.94(10⁶ CNY), respectively. According to the results, flood resources significantly reduce the utilization of normal water resources, which can be an effective supplement to the ecological compensation of nature reserves and provide a basis for the distribution of transit flood resources in other regions.

摘要

莫莫格国家自然保护区(MNNR)位于吉林省白城市嫩江和洮儿河的交汇处,洮儿河是自然保护区的主要水源。然而,由于洮儿河上游的水利工程建设,MNNR 长期处于缺水状态。为了保证自然保护区的湿地功能,政府计划通过吉林省西部供水工程,从嫩江正常和洪水供水。因此,如何有效提高洪水资源的利用效率已成为 MNNR 生态补偿的关键问题之一。本文构建了基于区间两阶段随机规划方法的洪水资源优化配置模型,将嫩江不同流量情景下的洪水资源可利用量纳入总水资源中,以提高其利用效率。结果表明,在不同流量情景下,MNNR 优化后洪水资源的利用率超过 70%,其中低流量情景下洪水资源的利用率达到 77%,比建议增加 23%。此外,低、中、高流量水平的生态效益分别达到 26.30(10⁶ CNY)至 32.14(10⁶ CNY)、28.21(10⁶ CNY)至 34.49(10⁶ CNY)和 29.41(10⁶ CNY)至 35.94(10⁶ CNY)。根据结果,洪水资源显著减少了对正常水资源的利用,可以有效补充自然保护区的生态补偿,为其他地区过境洪水资源的分配提供依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/71c12309778f/ijerph-16-01033-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/476c47d1c842/ijerph-16-01033-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/1ca5b7cda361/ijerph-16-01033-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/710202b33523/ijerph-16-01033-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/8349d60c72e3/ijerph-16-01033-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/0225d8f9b17b/ijerph-16-01033-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/ccea123ec1fb/ijerph-16-01033-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/71c12309778f/ijerph-16-01033-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/476c47d1c842/ijerph-16-01033-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/1ca5b7cda361/ijerph-16-01033-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/710202b33523/ijerph-16-01033-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/8349d60c72e3/ijerph-16-01033-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/0225d8f9b17b/ijerph-16-01033-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/ccea123ec1fb/ijerph-16-01033-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef3f/6466455/71c12309778f/ijerph-16-01033-g007.jpg

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