Special Column:Celebration of the 70th Anniversary of IGSNRR, CAS

An Improved Precipitation-Runoff Model Based on MMS and Its Application in the Upstream Basin of the Heihe River

Expand
  • 1. Cold and Arid Regions Environmental and Engineering Research Institute, CAS, Lanzhou 730000, China;
    2. U.S. Geological Survey, Carson City, Nevada, USA

Received date: 2007-11-19

  Revised date: 2008-05-07

  Online published: 2008-07-28

Abstract

The Heihe river upstream basin was selected as a case study, the mechanisms of runoff yield and concentration in upstream mountain areas were cognized by using the rainfall-runoff relevant modules in MMS (modular modeling system) model. Considering hysteresis of rainfall seepage in non-saturated soil, and integrated the snowmelt models and frost-soil area identify models together, the existing tested-basin PRMS (precipitation-runoff models) was modified and improved in this study. The improved new PRMS is used to simulate and forecast the hydrological process in the upstream of the Heihe River, and the results showed that the improved new PRMS was more suitable for the dry and cold mountain areas of inland basins. Due to considering the frost-soil conditions, the runoff simulation and forecast error is less than 2.7%, and each element of runoff composition was simulated and forecasted more veracious than the original PRMS. Finally, the stream flow changing trends were analyzed under different climate and land cover change scenarios in future, which provided the scientific basis for the rational use and management of water resources in the Heihe River Basin.

Cite this article

ZHOU Jian, LI Xin, WANG Gen-xu, HU Hong-chang, CHAO Zhen-hua, George Leavesley, Steve Markstrom, Roland Viger . An Improved Precipitation-Runoff Model Based on MMS and Its Application in the Upstream Basin of the Heihe River[J]. JOURNAL OF NATURAL RESOURCES, 2008 , 23(4) : 724 -736 . DOI: 10.11849/zrzyxb.2008.04.020

References

[1] Freeze R A, Harlan R L. Blueprint for physically-based digital-simulated hydrological response model [J]. Journal of Hydrology,1969,9:237-258. [2] Abbott M B, Bathurst J C. An introduction to the European Hydrological System "SHE" [J]. Journal of Hydrology,1986,87:61-77. [3] 贾仰文,王浩.分布式流域水文模拟研究进展及未来展望[J].水科学进展,2003,14(增刊):118~123. [4] 贾仰文,王浩.分布式流域水文模型原理与实践[M].北京:中国水利水电出版社,2005. [5] 康尔泗,程国栋,董增川.中国西北干旱区冰雪水资源与出山径流[M].北京:科学出版社,2002. [6] 陈仁升,康尔泗,杨建平.Topmodel模型在黑河干流出山径流模拟中的应用[J].中国沙漠,2003,23(4):429~431. [7] Leavesley G H, Striffler W D. A mountain watershed simulation model . U.S Army Corps of Engineers, Cold Region Research and Engineering Laboratory Report. 1978. [8] Leavesley G H , Lichty R W, Troutman B M, et al. Precipitation-runoff modeling system — User's manual . U.S. Geological Survey Water Resources Investigation Report 83-4238,1983.207. [9] Anderson E A. Development and testing of snowpack energy balance equations[J]. Water Resources Research,1968, 4(1):19-38. [10] Riley J P , Israelsen E K, Eggleston K O .Some approaches to snowmelt prediction in the role of snow and ice in hydrology[J]. International Association of Hydrological Sciences Publication,1973,107:956-971. [11] 赵仁俊.流域水文模拟[M].北京:中国水利水电出版社,1984. [12] 骆祖江.非饱和带水气二相渗流动力学模型[J].煤田地质与勘探,1999,(1):43~45. [13] Martin T Hagan, Howard B Demuth, Mark H Beale.蔡葵(译).神经网络设计[M].北京:机械工业出版社, 2002. [14] Eagleson P S. Climate, soil, and vegetation — 6. Dynamics of the annual water balance[J]. Water Resources Research,1978,14(5):749-764. [15] Restrepo P J, Bras R L. Automatic parameter estimation of a large conceptual rainfall-runoff model: A maximum-likelihood approach.Massachusetts Institute of Technology, Department of Civil Engineering, Ralph M. Parsons Laboratory Report,1982.267. [16] Rosenbrock H H. An automatic method of finding the greatest or least value of a function[J]. Computer Journal,1960,3:175-184. [17] U.S. Army Corps of Engineers. Geographical Resources Analysis Support System (GRASS) Version 4.0 User's Reference Manual[Z]. U.S. Army Corps of Engineers Research Laboratory,1991.513. [18] Fletcher R, Powell M J D. A rapidly convergent descent method for minimization[J]. Computer Journal,1963,6: 163-168. [19] 楚永伟,蓝永超.黑河莺落峡站年径流长期预报模型研究[J].中国沙漠,2005,25(6):77~81. [20] 蓝永超,丁永建.全球气候变暖情景下黑河山区流域水资源的变化[J].中国沙漠,2005,25(6):71~76. [21] 蓝永超,康而泗,张济世,等.祁连山区近50a来的气温序列及变化趋势[J].中国沙漠,2001,(s1):55~59. [22] 陈仁升,康而泗,张济世.基于小波变换和GRNN神经网络的黑河出山径流模型[J].中国沙漠,2001,(s1):12~16. [23] 徐中民,蓝永超,程国栋.人工神经网络方法在径流预报中的应用[J].冰川冻土,2000,22(4):372~374. [24] 蔡煜东,姚林声.径流长期预报的人工神经网络方法[J].水科学进展,1995,6(1):61~65. [25] 贾仰文,王浩.分布式流域水文模型原理与实践[M].北京:中国水利水电出版社,2005. [26] 李纪人.遥感和地理信息系统在分布式流域水文模型研制中的应用[J].水文,1997,9(3):8~12. [27] 郭方,刘新仁,任立良.以地形为基础的流域水文模型——TOPMODEL及其拓宽应用[J].水科学进展,2000,11(3):69~74. [28] 贾仰文.WEP模型的开发与分布式水循环模拟.中国水利学会2003年论文集.北京:中国三峡出版社,2003. [29] 余钧辉,张万昌,朱求安.河西黑河流域水文研究的若干进展[J].江西师范大学学报,2005,29(1):85~89.
Outlines

/