长江科学院院报 ›› 2019, Vol. 36 ›› Issue (4): 19-26.DOI: 10.11988/ckyyb.20170624

• 水资源与环境 • 上一篇    下一篇

雨量站网分布对雨量插值算法及径流响应的影响

汪青静1,杨欣玥2,陈华1,许崇育1,3,曾强1,徐坚1   

  1. 1.武汉大学 水资源与水电工程科学国家重点实验室,武汉 430072;
    2.河海大学 水文与水资源学院,南京 210098;
    3.奥斯陆大学 地学系,挪威奥斯陆 0317
  • 收稿日期:2017-06-08 修回日期:2017-06-08 出版日期:2019-04-01 发布日期:2019-04-18
  • 通讯作者: 陈华(1977-),男,福建建瓯人,教授,博士,研究方向为流域水文模拟。E-mail:chua@whu.edu.cn
  • 作者简介:汪青静(1993-),女,湖北咸宁人,硕士研究生,研究方向为水文水资源。E-mail:964182661@qq.com
  • 基金资助:
    国家自然科学基金重点项目(51539009)

Influence of Rain Gauges Network Configuration on the Accuracy of Rainfall Spatial Interpolation and Hydrological Modeling

WANG Qing-jing1, YANG Xin-yue2, CHEN Hua1, XU Chong-yu1,3, ZENG Qiang1, XU Jian1   

  1. 1.State Key Laboratory of Water Resources and Engineering Science,Wuhan University, Wuhan 430072, China;
    2.College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China;
    3.Department of Geosciences, University of Oslo, Oslo 0317, Norway
  • Received:2017-06-08 Revised:2017-06-08 Online:2019-04-01 Published:2019-04-18

摘要: 为比较雨量站网密度及分布对不同空间插值算法的影响,选取6种雨量站密度的不同分布,采用4种空间插值算法对研究区2006—2014年的日降雨进行插值,并将面均雨量作为新安江模型的输入,分析和比较其降雨径流响应。结果表明:①雨量站网空间分布越均匀,降雨插值误差越小,其径流模拟的精度也越高;②在雨量站网均匀布置的情况下,各空间插值算法的插值结果差异较小;雨量站网布置不均匀时,站点数目越少各空间插值算法插值结果差异越大;③计算点雨量时,考虑空间变量的克里金法能更准确地计算日降雨的结果;计算面雨量时,不同插值算法间差异较小,建议选用计算简便的插值算法,比如泰森多边形、反距离权重法。

关键词: 雨量站网, 空间插值算法, 日降雨, 新安江模型, 克里金

Abstract: To investigate the influence of rain gauges network configuration on the accuracy of rainfall spatial interpolation and hydrological modelling,we selected different distributions of six rain gauges over the study basin and compared the results of four interpolation methods for 9-year(2006-2014) daily rainfall data. Then we calculated the mean areal daily rainfall series for different network configurations and used as the inputs of Xinanjiang model to obtain the simulated runoff. The results show that: 1) evenly distributed rain gauges lead to smaller rainfall interpolation error and higher hydrological modeling efficiency; 2)when the rain gauges are evenly distributed, no significant differences among the four rainfall interpolation results are found, while when the rain gauges are unevenly distributed, such differences are obvious especially for small rain gauge density networks; and 3) for point rainfall interpolation, Kriging interpolation method in consideration of spatial autocorrelation in residuals performs better than the other methods in calculating actual daily rainfall; for mean areal rainfall,the difference between different interpolation methods is small so we suggest to adopt a simple interpolation method, such as Thiessen Polygons or inverse distance weighted method.

Key words: rain gauges, spatial interpolation, daily rainfall, Xinanjiang Model, Kriging

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