吉林大学学报(地球科学版) ›› 2019, Vol. 49 ›› Issue (5): 1415-1424.doi: 10.13278/j.cnki.jjuese.20180031

• 地质工程与环境工程 • 上一篇    下一篇

提孜那甫河流域融雪径流模拟及不确定性分析

张爽, 曾献奎, 吴吉春   

  1. 南京大学地球科学与工程学院, 南京 210023
  • 收稿日期:2018-08-11 发布日期:2019-10-10
  • 通讯作者: 曾献奎(1985-),男,副教授,博士,主要从事地下水数值模拟研究,E-mail:zengxiankui@yeah.net E-mail:zengxiankui@yeah.net
  • 作者简介:张爽(1994-),女,硕士研究生,主要从事水文学及水资源研究,E-mail:zhang789k@126.com
  • 基金资助:
    国家重点研发计划项目(2016YFC0402802);国家自然科学基金项目(41672233,41571017);中央高校基本科研业务费专项资金(020614380040)

Snowmelt Runoff Simulation and Uncertainty Analysis in Tizinafu River Basin

Zhang Shuang, Zeng Xiankui, Wu Jichun   

  1. School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
  • Received:2018-08-11 Published:2019-10-10
  • Supported by:
    Supported by National Key R&D Program of China (2016YFC0402802), National Natural Science Foundation of China (41672233,41571017) and Fundamental Research Funds for the Central Universities (020614380040)

摘要: 为了开展寒旱山区典型流域融雪径流过程的研究,提高融雪径流模型(SRM)在山区融雪地区的水文过程模拟精度,本文选取新疆提孜那甫河流域作为典型研究区,在SRM径流计算基础上,加入合适的基流数据并进行不确定性分析。考虑4种常见的基流分割方法(数字滤波法、加里宁法、BFI法(滑动最小值法)和HYSEP(hydrograph separation program)法),基于贝叶斯理论,采用马尔科夫链蒙特卡洛(MCMC)模拟进行参数不确定性分析,对使用不同基流数据SRM的融雪径流模拟表现进行综合评价。分析结果表明,基于加里宁基流分割方法的模型(SRMK)能够最佳地模拟研究区融雪径流过程(纳什系数NSE在识别期和验证期分别为0.866和0.721,大于其他对比模型)。MCMC模拟能够较好地识别SRM参数,获得可靠的参数后验概率分布。当实测降水资料缺乏或其代表性较差时,TRMM(tropical rainfall measuring mission)卫星数据能够描述研究区的降水过程特征。

关键词: 融雪径流模型, 基流分割, 马尔科夫链蒙特卡洛法, 不确定性

Abstract: In order to improve the accuracy of the simulated daily streamflow by the snowmelt runoff model (SRM) and focus on the snowmelt runoff process in the typical watershed of cold and arid mountainous areas, the authors selected the Tizinafu River basin in Xinjiang as the study area, and conducted uncertainty analysis on the basis of streamflow calculated by SRM through adding base flow-data. Based on the Bayesian method, combined with the four commonly used base flow separation methods (digital filter,Kalinlin,BFI, and HYSEP(hydrograph separation program)methods), the parameter uncertainty analysis was carried out by using Markov chain Monte Carlo (MCMC) simulation, and the model performance was evaluated comprehensively. According to the results of the model evaluation, the model using Kalinlin base-flow data (SRMK) has higher accuracy in both calibration and validation periods (the values of NSE (Nash-Sutcliffe efficiency coefficient)during model calibration and prediction periods are 0.866 and 0.721, respectively). MCMC method can identify model parameters very well, and can accurately obtain the posterior probability distribution of parameters, while TRMM data can describe the characteristics of precipitation in the study area when the related data is lacking or poorly representative.

Key words: snowmelt runoff model, base flow separation, Markov chain Monte Carlo, uncertainty

中图分类号: 

  • TV121.6
[1] 张利平,夏军,胡志芳.中国水资源状况与水资源安全问题分析[J].长江流域资源与环境, 2009, 18(2):116-120. Zhang Liping, Xia Jun, Hu Zhifang. Situation and Problem Analysis of Water Resource Security in China[J]. Resources and Environment in the Yangtze Basin, 2009, 18(2):116-120.
[2] 朱玉仙,黄义星,王丽杰. 水资源可持续开发利用综合评价方法[J].吉林大学学报(地球科学版), 2002,32(1):55-57,63. Zhu Yuxian, Huang Yixing, Wang Lijie. Synthetical Evaluating Method of Water Resources Sustainable Development and Using Status[J]. Journal of Jilin University (Earth Science Edition), 2002,32(1):55-57,63.
[3] 唐数红. 对新疆水问题的基本认识[J]. 干旱区研究, 2010, 27(5):657-662. Tang Shuhong. Basic Understanding to the Water Related Issues in Arid Lands of Xinjiang[J]. Arid Zone Research, 2010, 27(5):657-662.
[4] 陶希东, 石培基, 巨天珍,等. 西部干旱区水资源利用与生态环境重建研究[J]. 干旱区资源与环境, 2001, 15(1):18-22. Tao Xidong, Shi Peiji, Ju Tianzhen, et al. Studies on Ecological Environment Rebuilding and Utilization of Water Resources in Arid Area of Northwest China[J]. Journal of Arid Land Resources and Environment, 2001, 15(1):18-22.
[5] 甘容. 中国西北干旱区和中亚天山地区流域基流过程特征及气候变化影响研究[D]. 北京:中国科学院研究生院, 2014. Gan Rong. Baseflow Characteristics and Impact of Climate Change on River Basins in arid Northwest China and Tianshan, Central Asia[D]. Beijing:The University of Chinese Academy of Sciences, 2014.
[6] Martinec J, Rango A, Roberts R, et al. Snowmelt Runoff Model (SRM) User's Manual[M]. Berne:Department of Geography, University of Berne, 1998.
[7] 怀保娟,李忠勤,孙美平,等. SRM融雪径流模型在乌鲁木齐河源区的应用研究[J]. 干旱区地理, 2013, 36(1):41-48. Huai Baojuan, Li Zhongqin, Sun Meiping, et al. Snowmelt Runoff Model Applied in the Headwaters Region of Urumqi River[J]. Arid Land Geography, 2013, 36(1):41-48.
[8] 李兰海,尚明,张敏生,等. APHRODITE降水数据驱动的融雪径流模拟[J].水科学进展, 2014, 25(1):53-59. Li Lanhai, Shang Ming, Zhang Minsheng, et al. Snowmelt Runoff Simulation Driven by APHRODITE Precipitation Dataset[J]. Advances in Water Science, 2014, 25(1):53-59.
[9] 熊立华, 郭生练. 采用非线性水库假设的基流分割方法及应用[J]. 武汉大学学报(工学版), 2005, 38(1):27-29. Xiong Lihua, Guo Shenglian. A Baseflow Separation Method Based on Nonlinear Reservoir Assumption[J]. Engineering Journal of Wuhan University, 2005, 38(1):27-29.
[10] Eckhardt K. A Comparison of Baseflow Indices, Which Were Calculated with Seven Different Baseflow Separation Methods[J]. Journal of Hydrology, 2008,352(1/2):168-173.
[11] 张玉芳. 提孜那甫河流域卫星雪盖时空分布研究[D]. 南京:南京大学, 2014. Zhang Yufang. Spatial and Temporal Characteristics of Satellite Snow Cover in the Tizinafu Watershed[D]. Nanjing:Nanjing University, 2014.
[12] Lyne V, Hollick M. Stochastic Time-Variable Rainfall-Runoff Modelling[C]//Institute of Engineers Australia National Conference. Barton:Institute of Engineers Australia, 1979:89-93.
[13] Nathan R J, Mcmahon T A. Evaluation of Automated Ttechniques for Base Flow and Recession Analyses[J]. Water Resources Research, 1990, 26(7):1465-1473.
[14] Chen L Q, Zheng H X, Chen Y Q, et al. Base-Flow Separation in the Source Region of the Yellow River[J]. Journal of Hydrologic Engineering, 2008, 13(7):541-548.
[15] 丁志立,胡魁德,方园园.用加里宁改进法分割河川基流分析与探讨[J].江西水利科技, 2003, 29(4):211-215. Ding Zhili, Hu Kuide, Fang Yuanyuan.Analysis and Discussion of Dividing up Ground Water by the Kalinlin Improving Method[J]. Jiangxi Hydraulic Science and Technology, 2003, 29(4):211-215.
[16] Gustard A, Bullock A, Dixon J M. Low Flow Estimation in the United Kingdom[M]. Oxford:Institute of Hydrology, 1992.
[17] Sloto R A, Crouse M Y. HYSEP:A Computer Program for Streamflow Hydrograph Separation and Analysis[J]. Water Resources Investigations Report, 1996, 96:4040.
[18] Simpson J, Joanne, Adler R F, et al. A Proposed Tropical Rainfall Measuring Mission (TRMM) Satellite[J]. Bulletin of the American Meteorological Society, 1988, 69(3):278-295.
[19] Huffman G J, Adler R F, Bolvin D T, et al. The TRMM Multi-Satellite Precipitation Analysis (TMPA)[J]. Journal of Hydrometeorology, 2007, 90(3):237-247.
[20] Dezfuli A K, Zaitchik B F, Gnanadesikan A. Regional Atmospheric Circulation and Rainfall Variability in South Equatorial Africa[J]. Journal of Climate, 2015, 28(2):809-818.
[21] Tahir A A, Chevallier P, Arnaud Y, et al. Modeling Snowmelt-Runoff under Climate Scenarios in the Hunza River basin, Karakoram Range, Northern Pakistan[J]. Journal of Hydrology, 2011, 409(1):104-117.
[22] Sanjay K J, Goswami A, Saraf A K. Snowmelt Runoff Modelling in a Himalayan Basin with the Aid of Satellite Data[J]. International Journal of Remote Sensing, 2010, 31(24):6603-6618.
[23] Zhang J L, Li Y P, Huang G H, et al. Evaluation of Uncertainties in Input Data and Parameters of a Hydrological Model Using a Bayesian Framework:A Case Study of a Snowmelt-Precipitation-Driven Watershed[J]. Journal of Hydrometeorology, 2015, 17(8):2333-2350.
[24] Box G E P, Tiao G C. Bayesian Inference in Statistical Analysis[M]. New York:John Wiley & Sons, 2011.
[25] Brooks S P, Roberts G O. Convergence Assessment Techniques for Markov Chain Monte Carlo[J]. Statistics and Computing, 1998, 8(4):319-335.
[26] Haario H, Saksman E, Tamminen J. An Adaptive Metropolis Algorithm[J]. Bernoulli, 2001, 7(2):223-242.
[27] Laloy E, Vrugt J A. High-Dimensional Posterior Exploration of Hydrologic Models Using Multiple-Try DREAM (ZS) and High-Performance Computing[J]. Water Resources Research, 2012, 50(3):182-205.
[28] Zeng X K, Wu J C, Wang D, et al. Assessing the Pollution Risk of a Groundwater Source Field at Western Laizhou Bay under Seawater Intrusion[J]. Environmental Research, 2016, 148:586-594.
[29] Fan Y R, Huang G H, Baetz B W, et al. Development of a Copula-Based Particle Filter (CopPF) Approach for Hydrologic Data Assimilation Under Consideration of Parameter Interdependence[J]. Water Resources Research, 2017, 53(6):4850-4875.
[30] Wöhling T, Vrugt J A. Multiresponse Multilayer Vadose Zone Model Calibration Using Markov Chain Monte Carlo Simulation and Field Water Retention Data[J]. Water Resources Research, 2011, 47(4):W04510.
[31] Vrugt J A, Ter Braak C J F. DREAM(D):An Adaptive Markov Chain Monte Carlo Simulation Algorithm to Solve Discrete, Noncontinuous, and Combinatorial Posterior Parameter Estimation Problems[J]. Hydrology and Earth System Sciences, 2011, 15(12):3701-3713.
[1] 束龙仓, 许杨, 吴佩鹏. 基于MODFLOW参数不确定性的地下水水流数值模拟方法[J]. 吉林大学学报(地球科学版), 2017, 47(6): 1803-1809.
[2] 侯卫生, 杨翘楚, 杨亮, 崔婵婕. 基于Monte Carlo模拟的三维剖面地质界线不确定性分析[J]. 吉林大学学报(地球科学版), 2017, 47(3): 925-932.
[3] 蒋其峰, 荣棉水, 彭艳菊. 动剪切模量比对反应谱影响的定量分析[J]. 吉林大学学报(地球科学版), 2015, 45(3): 876-885.
[4] 苏小四,杜守营,杜尚海,宋宪宗,邵广凯,王璜. 基于随机模拟的浑河冲洪积扇地区地下水压采风险预报[J]. 吉林大学学报(地球科学版), 2014, 44(3): 986-994.
[5] 刘佩贵, 陶月赞. 均衡法评价地下水可开采量的风险率[J]. J4, 2012, 42(4): 1125-1129.
[6] 卢文喜, 罗建男, 鲍新华. 贝叶斯网络在水资源管理中的应用[J]. J4, 2011, 41(1): 153-158.
[7] 林学钰, 廖资生, 钱云平, 苏小四. 基流分割法在黄河流域地下水研究中的应用[J]. J4, 2009, 39(6): 959-967.
[8] 刘佩贵,束龙仓. 傍河水源地地下水水流数值模拟的不确定性[J]. J4, 2008, 38(4): 639-0643.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 葛玉辉,孙春林,刘茂修. 鄂尔多斯盆地东北缘中侏罗统延安组植物群与古气候分析[J]. J4, 2006, 36(02): 164 -0168 .
[2] 何朋朋,姚磊华,刘立鹏,陈庆. 地下水位随机性影响下的边坡可靠性分析[J]. J4, 2009, 39(2): 288 -0293 .
[3] 鲍庆中,张长捷,吴之理,王宏,李伟,桑家和,刘永生. 内蒙古白音高勒地区石炭纪石英闪长岩SHRIMP锆石U-Pb年代学及其意义[J]. J4, 2007, 37(1): 15 -0023 .
[4] 周燕,郑培玺,王铁夫,张延洁. 招平断裂带上盘金矿床氢氧同位素地质特征[J]. J4, 2007, 37(4): 668 -0671 .
[5] 杜新强,齐素文,廖资生,李砚阁. 人工补给对含水层水质的影响[J]. J4, 2007, 37(2): 293 -297 .
[6] 张春艳, 张兴洲, 邱殿明. 延边地区青龙村群斜长角闪岩中锆石U-Pb同位素年龄及地质意义[J]. J4, 2007, 37(4): 672 -0677 .
[7] 张文权,翁爱华. 地面核磁共振正则化反演方法研究[J]. J4, 2007, 37(4): 809 -0813 .
[8] 闵飞琼,王璞珺,于世泉,黄玉龙,吴颜雄,李喆,任利军. 营城组三段及二段岩性岩相和储层物性的精细刻画--基于标准剖面营三D1井全取心钻孔资料[J]. J4, 2007, 37(6): 1203 -1216 .
[9] 叶栋成,慕山, 陶月赞. 地下水补给对河流水质模型的影响[J]. J4, 2008, 38(4): 644 -0648 .
[10] 郝立波, 刘海洋, 陆继龙, 孙淑梅, 潘志恒, 赵玉岩. 松花湖沉积物137Cs和210Pb分布及沉积速率[J]. J4, 2009, 39(3): 470 -473 .