Risk Prediction and Optimal Control of Waterlogging in Urban Land Based on PLUS-SCS Model —A Case Study in Changsha City, Hunan Province
Author:
Affiliation:

Clc Number:

F301.2, P333.2, X43

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    [Objective] The urban land use in the future and corresponding waterlogging risk intensity was predicted, and the optimal control of the waterlogging risk and its implementation effects was explored in order to provide references for improving the prevention and control level of urban waterlogging and optimizing the layout of urban land use. [Methods] The study was conducted in Changsha City, Hunan Province. Urban land use layout and its waterlogging risk under a baseline scenario were predicted using the PLUS and SCS models. High-risk waterlogging areas were used as limiting conversion factors in the PLUS model to simulate the urban land layout and its waterlogging risk under a waterlogging control scenario by coupling the PLUS and SCS models. The implementation of the optimal management and control measures was verified by comparing the waterlogging risk differences under the two scenarios. [Results] The high-risk waterlogging area of construction land was predicted to be 96.47 km2 in 2035 under the baseline scenario, and the total waterlogging risk area of the urban construction land under the waterlogging control scenario would be reduced by 36.94 km2 compared with the baseline scenario without reducing construction land area. Furthermore, all high-risk areas in the new construction land would be avoided. [Conclusion] The waterlogging risk in urban land will increase significantly in the future. An optimization control method based on the PLUS-SCS model will help cities avoid waterlogging risk.

    Reference
    Related
    Cited by
Get Citation

焦胜,操婷婷,牛彦合,关惠嫦.基于耦合PLUS-SCS模型的城市用地内涝风险预测及优化管控——以湖南省长沙市为例[J].水土保持通报英文版,2023,43(5):195-202

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:December 14,2022
  • Revised:February 15,2023
  • Adopted:
  • Online: November 30,2023
  • Published: