基于SWAT模型的绍兴城市径流时空演变规律分析
DOI:
作者:
作者单位:

作者简介:

陈成广(1983-),男,浙江苍南人,硕士,讲师, 主要从事环境污染检测与治理研究。 E-mail:salen1983@163.com

通讯作者:

中图分类号:

P333

基金项目:

绍兴市科技计划项目(2014B70041,2014B70049);


Study on Temporal-spatial Changes of Urban Runoff in Shaoxing City
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    为克服较大尺度城市径流的定量难题,引入了分布式流域水文模型(SWAT),对绍兴城市径流开展了定量模拟及时空演变规律分析。经DEM建模分析,将绍兴城市置于合适尺度的流域内建立SWAT模型;在敏感性分析的基础上,利用径流监测数据对模型进行了校准与验证,模型评价结果表明,所有站点校准期与验证期的平均相对误差Re(-5.29%8.81%)、判定系数R2(0.910.96)和效率系数Ens(0.900.94)均满足年水平和月水平径流定量要求。在此基础上,以19922011年的降雨为驱动模拟得到了子流域的月径流数据;最后利用地统计学方法(GSM)对绍兴城市径流的时空演变规律进行了分析,并得到了关键源区,为绍兴城市水资源保护与利用提供科学依据。

    Abstract:

    To solve the quantitative problem of urban runoff at large scale, a distributed watershed hydrological model named soil and water assessment tool (SWAT) was employed to quantitatively simulate the urban runoff and analyze the temporal-spatial changes in Shaoxing City. The SWAT model was established by putting the Shaoxing City in a suitable scale watershed based on DEM analysis. And then the SWAT model was calibrated and validated using monitoring data from hydrological stations based on the parameter sensitivity analysis. The results show that the calibrated SWAT model perform well on modeling the annual and monthly runoff in both calibration and validation periods with an average relative error (from -5.29% to 8.81%), coefficient of determination (from 0.91 to 0.96) and coefficient of efficiency (from 0.90 to 0.94). Meanwhile, monthly runoff data from 1992 to 2011 at sub -watershed scale were obtained using the calibrated model. Besides, temporal -spatial changes of urban runoff was quantitatively analyzed and the critical source areas (CSAs) was finally gained by means of geo-statistic method (GSM), which will provide the scientific basis for water resources protection and utilization.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2016-07-08
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2022-06-22
  • 出版日期: