基于径流丰枯分类的月水量平衡模型参数响应研究
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摘要
针对变化环境下水文模型参数的时间变异性如何影响径流模拟的准确性问题,提出依据径流丰枯分类后对水文模型分别率定和验证。通过常规法和集对分析法对清江流域径流丰枯分类,并将不同径流丰枯年份对应降水、蒸发输入到两参数月水量平衡模型中,率定各丰枯类型下的模型参数值,并分析不同频率的径流差异情况。结果表明:集对分析法分类后两参数月水量平衡模型的C参数值规律为枯水年>平水年>丰水年,常规法则为枯水年<平水年<丰水年; SC参数值的规律在两种方法下表现类似。从年际变化看,模型效率系数基于集对分析法分类时较大,且对于不同丰枯年份下的径流模拟更接近实测值。从年内分配看,基于集对分析法的不同频率高、中、低水相对误差综合表现优于常规法。基于此进行的交叉模拟进一步发现,使用比当前状态偏湿润年份的参数值,得到的径流响应均为增大;反之则径流响应均为减小,且所使用参数值条件与基准条件变化的幅度越大,径流响应增大或者减小的幅度就越大。
Aiming at the problem of that how the temporal variability of hydrological model parameter under changing environment to impact the accuracy of runoff simulation,it is proposed herein that the parameters of hydrological model are calibrated and verified respectively in accordance with the runoff( wet-dry) classification. Through traditional method( TRA) and set pair analysis method( SPA),the runoff( wet-dry) classification of Qingjiang River Basin is made,and then the precipitations and evaporations corresponding to the runoffs of different wet-dry years are input into the relevant two-parameter monthly water balance modelfor calibrating the model parameters under the conditions of all the wet-dry types and analyzing the runoff differences with different frequencies. The result shows that the law of the parameter value C of the two-parameter monthly water balance model after the classification made by set pair analysis method( SPA) is dry year > normal year > wet year and that from traditional method( TRA) is the same,while the laws of the parameter value SC from both the methods are similar. From the aspect of the intra-annual variation,the classification of set pair analysis method( SPA)-based model efficiency coefficient is larger and is more close to the measured value. From the aspect of the annual distribution,the relative errors of high,middle and lower waters from set pair analysis method( SPA) are comprehensively better than those from traditional method( TRA). It is further found out from the relevant crossing simulations made on the basis of this that all the runoff responses obtained from the adoptions of the parameter values of the year which is a little bit wet are increased,otherwise,they are decreased. Moreover,the larger the changing amplitudes of the conditions of the both the parameter values and the references are,the larger the amplitudes of the increase or decrease of the runoff response are to be.
Aiming at the problem of that how the temporal variability of hydrological model parameter under changing environment to impact the accuracy of runoff simulation,it is proposed herein that the parameters of hydrological model are calibrated and verified respectively in accordance with the runoff( wet-dry) classification. Through traditional method( TRA) and set pair analysis method( SPA),the runoff( wet-dry) classification of Qingjiang River Basin is made,and then the precipitations and evaporations corresponding to the runoffs of different wet-dry years are input into the relevant two-parameter monthly water balance modelfor calibrating the model parameters under the conditions of all the wet-dry types and analyzing the runoff differences with different frequencies. The result shows that the law of the parameter value C of the two-parameter monthly water balance model after the classification made by set pair analysis method( SPA) is dry year > normal year > wet year and that from traditional method( TRA) is the same,while the laws of the parameter value SC from both the methods are similar. From the aspect of the intra-annual variation,the classification of set pair analysis method( SPA)-based model efficiency coefficient is larger and is more close to the measured value. From the aspect of the annual distribution,the relative errors of high,middle and lower waters from set pair analysis method( SPA) are comprehensively better than those from traditional method( TRA). It is further found out from the relevant crossing simulations made on the basis of this that all the runoff responses obtained from the adoptions of the parameter values of the year which is a little bit wet are increased,otherwise,they are decreased. Moreover,the larger the changing amplitudes of the conditions of the both the parameter values and the references are,the larger the amplitudes of the increase or decrease of the runoff response are to be.
引文
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[19] ONI S,FUTTER M,LEDESMA J,et al. Using dry and wet yearhydroclimatic extremes to guide future hydrologic projections[J].Hydrology&Earth System Sciences,2016,20(7):2811-2825.
[20]熊华康.清江对荆江的防洪作用分析[J].华中电力,2005,18(1):40-43.
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[22]郭生练,郭家力,侯雨坤,等.基于Budyko假设预测长江流域未来径流量变化[J].水科学进展,2015,26(2):151-160.
[23]王文圣,向红莲,李跃清,等.基于集对分析的年径流丰枯分类新方法[J].四川大学学报(工程科学版),2008(5):1-6.
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[25]熊立华,郭生练,付小平,等.两参数月水量平衡模型的研制和应用[J].水科学进展,1996(S1):80-86.
[26]郭生练,王国庆.半干旱地区月水量平衡模型[J].人民黄河,1994(12):13-16.
[27]刘艳丽,张建云,王国庆,等.气候自然变异在气候变化对水资源影响评价中的贡献分析:Ⅰ.基准期的模型与方法[J].水科学进展,2012,23(2):147-155.
[28]康玲,何小聪,熊其玲.基于贝叶斯网络理论的南水北调中线工程水源区与受水区降水丰枯遭遇风险分析[J].水利学报,2010,41(8):908-913.
[29]谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策,2003,18(2):129-134.
[30]胡庆芳,王银堂,刘克琳,等.基于改进的两参数月水量平衡模型的月径流模拟[J].河海大学学报(自然科学版),2007,35(6):638-642.
[31]董磊华,熊立华,万民.基于贝叶斯模型加权平均方法的水文模型不确定性分析[J].水利学报,2011,42(9):1065-1074.
[32] TROCH P A,CARRILLO G,SIVAPALAN M,et al. Climate-vege-tation-soil interactions and long-term hydrologic partitioning:signa-tures of catchment co-evolution[J]. Hydrology&Earth System Sci-ences,2013,17(6):2209-2217.
[33]孙瑜.变化环境下流域水文模型参数动态规律研究[D].北京:清华大学,2015.
[2]张洪波,黄强,李文英,等.气候变化和人类活动对黄河中游水文要素的影响研究[J].安全与环境学报,2011,11(4):134-138.
[3]冯平,付军,李建柱.下垫面变化对洪水影响的水文模型分析[J].天津大学学报(自然科学与工程技术版),2015(3):189-195.
[4]李占玲,徐宗学,巩同梁.雅鲁藏布江流域径流特性变化分析[J].地理研究,2008,27(2):353-361.
[5]丁志宏,冯平,牛军宜.黑河莺落峡年径流量时序变化的趋势特性及丰枯演化规律研究[J].干旱区资源与环境,2009,23(10):59-63.
[6]郭家力.变化环境下水文极值事件和水量平衡响应研究[D].武汉:武汉大学,2014.
[7]姚文艺,高亚军,安催花,等.百年尺度黄河上中游水沙变化趋势分析[J].水利水电科技进展,2015,35(5):112-120.
[8]孙悦,李栋梁,朱拥军.渭河径流变化及其对气候变化与人类活动的响应研究进展[J].干旱气象,2013,31(2):396-405.
[9]周研来,郭生练,李天元,等. Copula-SAR模型及其在洪水地区组成随机模拟中的应用[J].武汉大学学报(工学版),2013,46(2):137-142.
[10]李鸿雁,刘寒冰,苑希民,等.人工神经网络峰值识别理论及其在洪水预报中的应用[J].水利学报,2002,33(6):15-20.
[11]符传君,黄国如,陈永勤.用TOPMODEL模型模拟流域枯水径流[J].应用基础与工程科学学报,2007,15(4):72-72.
[12]黄国如,陈永勤.基于新安江水文模型的东江流域枯水径流模拟[J].华南理工大学学报(自然科学版),2006,34(11):93-98.
[13]段超宇,张生,李锦荣,等.基于SWAT模型的内蒙古锡林河流域降水:径流特征及不同水文年径流模拟研究[J].水土保持研究,2014,21(5):292-297.
[14] ZHU Q,ZHANG X,MA C,et al. Investigating the uncertainty andtransferability of parameters in SWAT model under climate change[J]. Hydrological Sciences Journal,2015,61(5):914-930.
[15] CORON L,ANDREASSIAN V,PERRIN C,et al. Crash testinghydrological models in contrasted climate conditions:An experimenton 216 Australian catchments[J]. Water Resources Research,2012,48(5):213-223.
[16]梁忠民,胡义明,王军.非一致性水文频率分析的研究进展[J].水科学进展,2011,22(6):864-871.
[17]李超群,郭生练,张俊. hv-block交叉验证法在率定水文模型参数中的比较研究[J].水文,2006,26(5):25-28.
[18] LUO J,WANG E,SHEN S,et al. Effects of conditional parameteri-zation on performance of rainfall-runoff model regarding hydrologicnon-stationarity[J]. Hydrological Processes,2012,26(26):3953-3961.
[19] ONI S,FUTTER M,LEDESMA J,et al. Using dry and wet yearhydroclimatic extremes to guide future hydrologic projections[J].Hydrology&Earth System Sciences,2016,20(7):2811-2825.
[20]熊华康.清江对荆江的防洪作用分析[J].华中电力,2005,18(1):40-43.
[21] HAMON W R. Estimating potential evapotranspiration[C]//Pro-ceedings of the American Society of Civil Engineers. New York:American Society of Civil Engineering,1961:107-120.
[22]郭生练,郭家力,侯雨坤,等.基于Budyko假设预测长江流域未来径流量变化[J].水科学进展,2015,26(2):151-160.
[23]王文圣,向红莲,李跃清,等.基于集对分析的年径流丰枯分类新方法[J].四川大学学报(工程科学版),2008(5):1-6.
[24] GUO SL,WANG JX,XIONG LH,et al. A macro-scale and semi-distributed monthly water balance model to project climate changeimpacts in China[J]. Journal of Hydrology,2002,268:1-15.
[25]熊立华,郭生练,付小平,等.两参数月水量平衡模型的研制和应用[J].水科学进展,1996(S1):80-86.
[26]郭生练,王国庆.半干旱地区月水量平衡模型[J].人民黄河,1994(12):13-16.
[27]刘艳丽,张建云,王国庆,等.气候自然变异在气候变化对水资源影响评价中的贡献分析:Ⅰ.基准期的模型与方法[J].水科学进展,2012,23(2):147-155.
[28]康玲,何小聪,熊其玲.基于贝叶斯网络理论的南水北调中线工程水源区与受水区降水丰枯遭遇风险分析[J].水利学报,2010,41(8):908-913.
[29]谢晓锋,张文俊,杨之廉.微粒群算法综述[J].控制与决策,2003,18(2):129-134.
[30]胡庆芳,王银堂,刘克琳,等.基于改进的两参数月水量平衡模型的月径流模拟[J].河海大学学报(自然科学版),2007,35(6):638-642.
[31]董磊华,熊立华,万民.基于贝叶斯模型加权平均方法的水文模型不确定性分析[J].水利学报,2011,42(9):1065-1074.
[32] TROCH P A,CARRILLO G,SIVAPALAN M,et al. Climate-vege-tation-soil interactions and long-term hydrologic partitioning:signa-tures of catchment co-evolution[J]. Hydrology&Earth System Sci-ences,2013,17(6):2209-2217.
[33]孙瑜.变化环境下流域水文模型参数动态规律研究[D].北京:清华大学,2015.
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