基于SWAT模型的径流还原方法研究—以大汶河流域为例
DOI:
作者:
作者单位:

作者简介:

陈佳蕾 (1993-),女,江苏苏州人,硕士研究生,主要从事水资源规划与管理研究。 E-mail:18251829035@163.com

通讯作者:

中图分类号:

TV121

基金项目:

国家自然科学基金项目(51579068,51379055);


Research on Runoff Restoration Method Based on SWAT Model: A Case Study in Dawenhe River Basin
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:

    在现有下垫面条件下还原天然径流是水资源配置的重要基础性工作。利用SWAT分布式水文模型进行径流还原计算。以大汶河流域为例,选取流域内受人类活动影响较小、能大致反映流域天然径流情况的雪野水库、黄前水库以及东周水库所控制的3个子流域,采用SUFI-2方法进行模型参数率定、验证和不确定性分析;根据就近性与相似性原则,进行全流域参数展布,并通过Arc SWAT2012分析计算大汶河流域内泰安市各分区地表水资源量。结果表明:3个典型子流域的P-factor均大于0.64,Rfactor均小于0.72,率定期和验证期的相关性系数和纳什效率系数均高于0.77,径流模拟值和实测值拟合程度高。通过SWAT模型还原天然径流是可行的。

    Abstract:

    Restoring natural runoff is an important basic work for water resources allocation under the existing conditions of the underlying surface. In this paper, SWAT as a distributed hydrological model was used to calculate natural runoff quantity. We applied the methodology to the Dawenhe River Basin, and selected three typical sub-basins named the Xueye Reservoir, Huangqian Reservoir and Dongzhou Reservoir, which can generally reflect the natural runoff situation of the basin because of less impact by hu man activities. Then we calibrated and validated the model parameters and analyzed uncertainty of the model by using the SUFI-2 method. After that, we distributed all the sub-basin parameters into the whole basin based on the proximity and similarity principle and analyzed surface water resources of Tai’an City in the Dawenhe River Basin with ArcSWAT2012. The results show that P -factor in three typical sub -basins is larger than 0.64, and R -factor is less than 0.72. The relative correlationand Nash efficiency coefficient is larger than 0.77 during the periodic and validation periods. The higher fitting degree between the runoff simulation value and the measured data demonstrate that restoring natural runoff by SWAT model is feasible.

    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:2016-06-06
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2022-06-22
  • 出版日期: