提孜那甫河流域融雪径流模拟及不确定性分析
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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(SRM_K)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。
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(SRM_K) 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.
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(SRM_K) 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.
引文
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[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.
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[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] W?hling 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.
[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] W?hling 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.
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