水文模型参数敏感性分析—优化—区域化方法研究进展

doi:10.18306/dlkxjz.2022.07.016

Abstract

Abstract:

Hydrological models are an important scientific tool for understanding the basic theory of hydrology disciplines, analyzing hydrological processes, and studying hydrological cycle mechanisms. The uncertainty analysis of simulation results is a prerequisite for improving the reliability of a model and for conducting an effective hydrological regime forecast. Parameter uncertainty is one of the important factors that affect the uncertainty of simulation results from hydrological models, and the analysis of model parameter uncertainty and its impact factors has important practical significance for hydrological forecasting. The current parameter uncertainty analysis methods can be roughly divided into three categories: parameter sensitivity analysis, parameter optimization, and parameter regionalization method that consider the parameter estimation in ungauged catchments. This?article reviewed the current development of technique and operation status of parameter sensitivity analysis for hydrological models, as well as the advantages and disadvantages of different analysis methods. We also identified the potential development direction of future research on the method of uncertainty analysis of hydrological models, that is, to strengthen the study of the systematic method of uncertainty analysis for hydrological models with the help of multidisciplinary theories and technical methods.

Key words: hydrological model, parameter uncertainty, sensitivity analysis, parameter optimization, parameter regionalization

GOU Jiaojiao, MIAO Chiyuan, DUAN Qingyun. Progress in parameter sensitivity analysis-optimization-regionalization methods for hydrological models[J].PROGRESS IN GEOGRAPHY, 2022, 41(7): 1338-1348.

Figures/Tables 6

Fig.1

Tab.1

Summary of parameter sensitivity analysis approaches

类型	方法	特点	敏感性描述方式
局部	一次二阶矩法	利用模型输出变量的均值和标准差(即前二阶矩)来求解参数敏感性,简单快捷和可操作性强	参数相对敏感性
	蒙特卡洛抽样	计算简单但精度受模拟次数的影响较大	参数相对敏感性
全局/定性	拉丁超立方采样—随机OAT (LH-OAT)	对分析参数中的每个参数分层求取参数敏感性	各层参数相对敏感性均值
	Morris	对模型参数在整个变化范围内进行微小扰动,其他参数保持不变,以评估参数微小变化量引起的输出变量变化	根据参数相对敏感性及交互作用大小进行参数排序
	多元自适应回归样条(MARS)	利用线性回归、样条构造以及二元递归分区方法来构建敏感性分析模型	参数重要性相对得分
	Delta Test	一种基于最近邻理论的残差方差估计方法,可以识别连续函数中的参数依赖关系	参数重要性相对得分
	Sum-of-Trees	本质上为多变量累积模型,属于随机树方法的一种	参数重要性相对得分
	傅里叶幅度敏感性检验法(FAST)	利用傅里叶变换计算参数变化引起的响应,计算效率较高	输入参数占输出结果方差比例
全局/定量	Sobol′	基于方差分解方法计算出每个参数对模型输出总方差的贡献率数值,计算耗时较大	输入参数占输出结果方差比例
	RSM-Sobol′	基于响应曲面替代复杂模型系统求解敏感性,模型运行时耗较低	输入参数占输出结果方差比例
	RSA (regional sensitivity analysis)	考虑了参数空间分布的相关性和复杂性,可以评估模型模拟指标在参数空间中的频率分布	参数分布与原始均匀分布的距离
局部/全局混合	DELSA (distributed evaluation of local sensitivity analysis)	基于导数的“局部”方法来获得参数空间中参数敏感性的分布	参数重要性归一化分布

Tab.1

Fig.2

Tab.2

Summary of parameter optimization approaches

类型	方法	优点及适用范围	应用局限性
局部/手动	人工试错法	人工使用试错程序不断调整模型参数,过程直观、易于理解	需由模型经验丰富的水文专家执行,非常耗时,优化参数具有主观性
局部/自动	下山单纯型法	通过一系列几何操作移动单纯型,使其往最小值移动	优化性能很大程度上取决于初始值的质量,需进行一定的初值预处理,易陷入局部最优
全局/自动	遗传算法(genetic algorithm,GA)	具有群体搜索策略和不依赖梯度信息的计算方式,比传统优化算法更通用高效	个体没有记忆,遗传操作盲目无方向,所需要的收敛时间长
	差分进化算法(differential evolution)	采用实数编码、基于差分的简单变异操作和一对一的竞争生存策略,操作简单	优化迭代后期接近最优解时收敛速度缓慢,易陷入局部最优
	洗牌复合形演化算法(SCE-UA)	将基于确定性的复合型搜索技术和自然界中的生物竞争进化原理相结合,以随机方式构建子复合型,使得在可行域中的搜索更加彻底	采用下山单纯型算法进化每一个复合形,待优化参数的维数比较大时,进化过程就显得比较复杂,收敛速度相对缓慢
	粒子群优化算法(particle swarm optimization,PSO)	PSO与GA类似,但没有交叉和变异等进化算子,通过粒子间的相互作用发现复杂搜索空间中的最优区域,需要用户确定的参数并不多,操作简单	易陷入局部极小点,种群多样性差,搜索范围小
	贝叶斯优化算法	不仅提供“最佳拟合”校准参数集,还提供参数的置信带、集合和似然分布	参数实际先验分布难度较大,按照均匀分布代替总体不具有实用性,引入额外不确定性
	基于替代模型的自适应优化算法(ASMO)	以较少次数的真实模型运行寻找最优参数,有助于复杂模型的优化迭代求解	不同的替代模型适用于不同模型响应曲面构建,替代模型可能带来一定不确定性

Tab.2

Fig.3

Tab.3

References 72

[1]	Huntington T G. Evidence for intensification of the global water cycle: Review and synthesis[J]. Journal of Hydrology, 2006, 319: 83-95.
[2]	United Nations Educational Scientific and Cultural Organization Managing water under uncertainty and risk:The united nations world water development report 4[M]. Paris, France: UNESCO, 2012.
[3]	方建, 杜鹃, 徐伟, 等. 气候变化对洪水灾害影响研究进展[J]. 地球科学进展, 2014, 29(9): 1085-1093. doi: 10.11867/j.issn.1001-8166.2014.09.1085
	[ Fang Jian, Du Juan, Xu Wei, et al. Advances in the study of climate change impacts on flood disaster. Advances in Earth Science, 2014, 29(9): 1085-1093. ] doi: 10.11867/j.issn.1001-8166.2014.09.1085
[4]	张利平, 陈小凤, 赵志鹏, 等. 气候变化对水文水资源影响的研究进展[J]. 地理科学进展, 2008, 27(3): 60-67. doi: 10.11820/dlkxjz.2008.03.009
	[ Zhang Liping, Chen Xiaofeng, Zhao Zhipeng, et al. Progress in study of climate change impacts on hydrology and water resources. Progress in Geography, 2008, 27(3): 60-67. ] doi: 10.11820/dlkxjz.2008.03.009
[5]	夏军, 左其亭. 国际水文科学研究的新进展[J]. 地球科学进展, 2006, 21(3): 256-261. doi: 10.11867/j.issn.1001-8166.2006.03.0256
	[ Xia Jun, Zuo Qiting. Advances in international hydrological science research. Advances in Earth Science, 2006, 21(3): 256-261. ]
[6]	陈腊娇, 朱阿兴, 秦承志, 等. 流域生态水文模型研究进展[J]. 地理科学进展, 2011, 30(5): 535-544.
	[ Chen Lajiao, Zhu Axing, Qin Chengzhi, et al. Review of eco-hydrological models of watershed scale. Progress in Geography, 2011, 30(5): 535-544. ]
[7]	杨大文, 徐宗学, 李哲, 等. 水文学研究进展与展望[J]. 地理科学进展, 2018, 37(1): 36-45. doi: 10.18306/dlkxjz.2018.01.005
	[ Yang Dawen, Xu Zongxue, Li Zhe, et al. Progress and prospect of hydrological sciences. Progress in Geography, 2018, 37(1): 36-45. ] doi: 10.18306/dlkxjz.2018.01.005
[8]	Sorooshian S, Chu W. Review of parameterization and parameter estimation for hydrologic models[M]// LiX, XieX H. Land surface observation, modeling and data assimilation, Singapore, Singapore: World Scientific, 2013: 127-140.
[9]	王浩, 李扬, 任立良, 等. 水文模型不确定性及集合模拟总体框架[J]. 水利水电技术, 2015, 46(6): 21-26.
	[ Wang Hao, Li Yang, Ren Liliang, et al. Uncertainty of hydrologic model and general framework of ensemble simulation. Water Resources and Hydropower Engineering, 2015, 46(6): 21-26. ]
[10]	张静文, 郭家力, 王敬斌, 等. 水文模型参数选取对模拟径流的年际年内分布影响评估[J]. 气候变化研究进展, 2020, 16(3): 325-335.
	[ Zhang Jingwen, Guo Jiali, Wang Jingbin, et al. Appraising the effect of choosing hydrological model parameters on the inter-annual and intra-annual distribution of simulated runoff. Climate Change Research, 2020, 16(3): 325-335. ]
[11]	Gou J J, Miao C Y, Duan Q Y, et al. Sensitivity analysis-based automatic parameter calibration of the VIC model for streamflow simulations over China[J]. Water Resources Research, 2020, 56(1): e2019WR025968. doi: 10.1029/2019WR025968. doi: 10.1029/2019WR025968
[12]	Blöschl G, Bierkens M F P, Chambel A, et al. Twenty-three unsolved problems in hydrology (UPH): A community perspective[J]. Hydrological Sciences Journal, 2019, 64(10): 1141-1158.
[13]	Duan Q Y, Schaake J, Andréassian V, et al. Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops[J]. Journal of Hydrology, 2006, 320(1/2): 3-17.
[14]	van Griensven A, Meixner T, Grunwald S, et al. A global sensitivity analysis tool for the parameters of multi-variable catchment models[J]. Journal of Hydrology, 2006, 324: 10-23.
[15]	Beven K. How far can we go in distributed hydrological modelling?[J]. Hydrology and Earth System Sciences, 2001, 5(1): 1-12.
[16]	Oudin L, Andréassian V, Perrin C, et al. Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments[J]. Water Resources Research, 2008, 44(3): W03413. doi: 10.1029/2007WR006240.
[17]	Samaniego L, Kumar R, Attinger S. Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale[J]. Water Resources Research, 2010, 46(5): W05523. doi: 10.1029/2008WR007327. doi: 10.1029/2008WR007327
[18]	Shafii M, Tolson B A. Optimizing hydrological consistency by incorporating hydrological signatures into model calibration objectives[J]. Water Resources Research, 2015, 51(5): 3796-3814.
[19]	Tang Y, Reed P, van Werkhoven K, et al. Advancing the identification and evaluation of distributed rainfall-runoff models using global sensitivity analysis[J]. Water Resources Research, 2007, 43(6): W06415. doi: 10.1029/2006WR005813. doi: 10.1029/2006WR005813
[20]	贺缠生, 田杰, 张宝庆, 等. 土壤水文属性及其对水文过程影响研究的进展、挑战与机遇[J]. 地球科学进展, 2021, 36(2): 113-124. doi: 10.11867/j.issn.1001-8166.2021.016
	[ He Chansheng, Tian Jie, Zhang Baoqing, et al. A review of advances in impacts of soil hydraulic properties on hydrological processes, challenges and opportunities. Advances in Earth Science, 2021, 36(2): 113-124. ] doi: 10.11867/j.issn.1001-8166.2021.016
[21]	刘丽芳, 刘昌明, 王中根, 等. HIMS模型参数的不确定性及其影响因素[J]. 地理科学进展, 2013, 32(4): 532-537. doi: 10.11820/dlkxjz.2013.04.005
	[ Liu Lifang, Liu Changming, Wang Zhonggen, et al. Parameter uncertainty of HIMS model and its influence factor analysis. Progress in Geography, 2013, 32(4): 532-537. ] doi: 10.11820/dlkxjz.2013.04.005
[22]	孔凡哲, 宋晓猛, 占车生, 等. 水文模型参数敏感性快速定量评估的RSMSobol方法[J]. 地理学报, 2011, 66(9): 1270-1280.
	[ Kong Fanzhe, Song Xiaomeng, Zhan Che-sheng, et al. An efficient quantitative sensitivity analysis approach for hydrological model parameters using RSMSobol method. Acta Geographica Sinica, 2011, 66(9): 1270-1280. ]
[23]	Gong W, Duan Q, Li J, et al. Multi-objective parameter optimization of common land model using adaptive surrogate modeling[J]. Hydrology and Earth System Sciences, 2015, 19(5): 2409-2425.
[24]	田雨, 雷晓辉, 蒋云钟, 等. 水文模型参数敏感性分析方法研究评述[J]. 水文, 2010, 30(4): 9-12, 62.
	[ Tian Yu, Lei Xiaohui, Jiang Yunzhong, et al. Comment on parameter sensitivity analysis of hydrological model. Journal of China Hydrology, 2010, 30(4): 9-12, 62. ]
[25]	Campolongo F, Cariboni J, Saltelli A. An effective screening design for sensitivity analysis of large models[J]. Environmental Modelling & Software, 2007, 22(10): 1509-1518.
[26]	Friedman J H. Multivariate adaptive regression splines[J]. The Annals of Statistics, 1991, 19(1): 1-67.
[27]	Sobol′ I M. Sensitivity estimates for nonlinear mathematical models[J]. Mathematical modelling and computational experiments, 1993, 4: 407-414.
[28]	Di Z H, Duan Q Y, Gong W, et al. Assessing WRF model parameter sensitivity: A case study with 5 day summer precipitation forecasting in the greater Beijing area[J]. Geophysical Research Letters, 2015, 42: 579-587.
[29]	徐会军, 陈洋波, 李昼阳, 等. 基于LH-OAT分布式水文模型参数敏感性分析[J]. 人民长江, 2012, 43(7): 19-23.
	[ Xu Huijun, Chen Yangbo, Li Zhouyang, et al. Analysis on parameter sensitivity of distributed hydrological model based on LH-OAT Method. Yangtze River, 2012, 43(7): 19-23. ]
[30]	任启伟, 陈洋波, 周浩澜, 等. 基于Sobol法的TOPMODEL模型全局敏感性分析[J]. 人民长江, 2010, 41(19): 91-94, 107.
	[ Ren Qiwei, Chen Yangbo, Zhou Haolan, et al. Global sensitivity analysis of TOPMODEL parameters based on Sobol method. Yangtze River, 2010, 41(19): 91-94, 107. ]
[31]	Pappenberger F, Beven K J, Ratto M, et al. Multi-method global sensitivity analysis of flood inundation models[J]. Advances in Water Resources, 2008, 31(1): 1-14.
[32]	Li J, Duan Q Y, Gong W, et al. Assessing parameter importance of the Common Land Model based on qualitative and quantitative sensitivity analysis[J]. Hydrology and Earth System Sciences, 2013, 17: 3279-3293.
[33]	Gan Y J, Duan Q Y, Gong W, et al. A comprehensive evaluation of various sensitivity analysis methods: A case study with a hydrological model[J]. Environmental Modelling & Software, 2014, 51: 269-285.
[34]	Christopher Frey H, Patil S R. Identification and review of sensitivity analysis methods[J]. Risk Analysis, 2002, 22(3): 553-578. pmid: 12088234
[35]	Herman J D, Kollat J B, Reed P M, et al. Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models[J]. Hydrology and Earth System Sciences, 2013, 17(7): 2893-2903.
[36]	Vazquez-Cruz M A, Guzman-Cruz R, Lopez-Cruz I L, et al. Global sensitivity analysis by means of EFAST and Sobol' methods and calibration of reduced state-variable TOMGRO model using genetic algorithms[J]. Computers and Electronics in Agriculture, 2014, 100: 1-12.
[37]	刘松, 佘敦先, 张利平, 等. 基于Morris和Sobol的水文模型参数敏感性分析[J]. 长江流域资源与环境, 2019, 28(6): 1296-1303.
	[ Liu Song, She Dunxian, Zhang Li-ping, et al. Global sensitivity analysis of hydrological model parameters based on Morris and Sobol methods. Resources and Environment in the Yangtze Basin, 2019, 28(6): 1296-1303. ]
[38]	张兰影, 庞博, 徐宗学, 等. 基于PSUADE的VIC模型参数敏感性分析: 以太湖西苕溪流域为例[J]. 资源科学, 2014, 36(5): 929-936.
	[ Zhang Lanying, Pang Bo, Xu Zongxue, et al. Parameter sensitivity analysis of a variable infiltration capacity model based on PSUADE. Resources Science, 2014, 36(5): 929-936. ]
[39]	杨晓华, 杨志峰, 郦建强, 等. 水文模型参数识别算法研究及展望[J]. 自然科学进展, 2006, 16(6): 657-661.
	[ Yang Xiaohua, Yang Zhifeng, Li Jianqiang, et al. Research and progress of hydrological model parameter identification algorithm. Progress in Natural Science, 2006, 16(6): 657-661. ]
[40]	Duan Q Y, Di Z H, Quan J P, et al. Automatic model calibration: A new way to improve numerical weather forecasting[J]. Bulletin of the American Meteorological Society, 2017, 98(5): 959-970.
[41]	Gupta H V, Sorooshian S, Yapo P O. Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration[J]. Journal of Hydrologic Engineering, 1999, 4(2): 135-143.
[42]	Brouziyne Y, Abouabdillah A, Bouabid R, et al. SWAT manual calibration and parameters sensitivity analysis in a semi-arid watershed in North-western Morocco[J]. Arabian Journal of Geosciences, 2017, 10: 1-13.
[43]	郑巍斐, 杨肖丽, 程雪蓉, 等. 基于CMIP5和VIC模型的长江上游主要水文过程变化趋势预测[J]. 水文, 2018, 38(6): 48-53.
	[ Zheng Weifei, Yang Xiaoli, Cheng Xue-rong, et al. Prediction of major water cycle change over upper Yangtze River based on CMIP5 and VIC Model. Journal of China Hydrology, 2018, 38(6): 48-53. ]
[44]	徐杰, 李致家, 马亚楠, 等. 基于TOPKAPI模型的湿润流域洪水模拟[J]. 南水北调与水利科技, 2020, 18(1): 18-25.
	[ Xu Jie, Li Zhijia, Ma Yanan, et al. Flood simulation based on TOPKAPI model in a humid basin. South-to-North Water Transfers and Water Science & Technology, 2020, 18(1): 18-25. ]
[45]	Dawdy D R, O'Donnell T. Mathematical models of catchment behavior[J]. Journal of the Hydraulics Division, 1965, 91(4): 123-137.
[46]	Ibbitt R. Systematic parameter fitting for conceptual models of catchment hydrology[D]. London, UK: University of London, 1970.
[47]	Nash J E, Sutcliffe J V. River flow forecasting through conceptual models part I: A discussion of principles[J]. Journal of Hydrology, 1970, 10(3): 282-290.
[48]	Johnston P R, Pilgrim D H. Parameter optimization for watershed models[J]. Water Resources Research, 1976, 12(3): 477-486.
[49]	Press W H, Teukolsky S A, Vetterling W T, et al. FORTRAN numerical recipes:Vol. 1[M]. Cambridge, UK: Cambridge University Press, 1996.
[50]	Holland J H. Adaptation in natural and artificial systems:An introductory analysis with applications to biology, control, and artificial intelligence[M]. Cambridge, USA: MIT Press, 1992.
[51]	Duan Q Y, Sorooshian S, Gupta V. Effective and efficient gobal optimization for conceptual rainfall-runoff models[J]. Water Resources Research, 1992, 28(4): 1015-1031.
[52]	马海波, 董增川, 张文明, 等. SCE-UA算法在TOPMODEL参数优化中的应用[J]. 河海大学学报(自然科学版), 2006, 34(4): 361-365.
	[ Ma Haibo, Dong Zengchuan, Zhang Wenming, et al. Application of SCE-UA algorithm to optimization of TOPMODEL parameters. Journal of Hohai University ( Natural Sciences), 2006, 34(4): 361-365. ]
[53]	雷晓辉, 贾仰文, 蒋云钟, 等. WEP模型参数自动优化及在汉江流域上游的应用[J]. 水利学报, 2009, 40(12): 1481-1488.
	[ Lei Xiaohui, Jia Yangwen, Jiang Yunzhong, et al. Parameter optimization of WEP model and its application to the upstream of Han River. Journal of Hydraulic Engineering, 2009, 40(12): 1481-1488. ]
[54]	宋晓猛, 占车生, 夏军. 集成统计仿真技术和SCE-UA方法的水文模型参数优化[J]. 科学通报, 2012, 57(26): 2530-2536.
	[ Song Xiaomeng, Zhan Chesheng, Xia Jun. Integration of a statistical emulator approach with the SCE-UA Method for parameter optimization of a hydrological model. Chinese Science Bulletin, 2012, 57(26): 2530-2536. ]
[55]	Wang C, Duan Q Y, Gong W, et al. An evaluation of adaptive surrogate modeling based optimization with two benchmark problems[J]. Environmental Modelling & Software, 2014, 60: 167-179.
[56]	安永凯, 卢文喜, 董海彪, 等. 基于克里格法的地下水流数值模拟模型的替代模型研究[J]. 中国环境科学, 2014, 34(4): 1073-1079.
	[ An Yongkai, Lu Wenxi, Dong Haibiao, et al. Surrogate model of numerieal simulation model of groundwater based on Kriging. China Environmental Science, 2014, 34(4): 1073-1079. ]
[57]	Razavi S, Tolson B A, Burn D H. Numerical assessment of metamodelling strategies in computationally intensive optimization[J]. Environmental Modelling & Software, 2012, 34: 67-86.
[58]	Mousavi S J, Shourian M. Adaptive sequentially space-filling metamodeling applied in optimal water quantity allocation at basin scale[J]. Water Resources Research, 2010, 46: W03520. doi: 10.1029/2008WR007076. doi: 10.1029/2008WR007076
[59]	Gan Y J, Liang X Z, Duan Q Y, et al. A systematic assessment and reduction of parametric uncertainties for a distributed hydrological model[J]. Journal of Hydrology, 2018, 564: 697-711.
[60]	刘金涛, 宋慧卿, 王爱花. 水文相似概念与理论发展探析[J]. 水科学进展, 2014, 25(2): 288-296.
	[ Liu Jintao, Song Huiqing, Wang Aihua. Advances in the theories of hydrologic similarity: A discussion. Advances in Water Science, 2014, 25(2): 288-296. ]
[61]	徐宗学, 程磊. 分布式水文模型研究与应用进展[J]. 水利学报, 2010, 41(9): 1009-1017.
	[ Xu Zongxue, Cheng Lei. Progress on studies and applications of the distributed hydrological models. Journal of Hydraulic Engineering, 2010, 41(9): 1009-1017. ]
[62]	Parajka J, Merz R, Blöschl G. A comparison of regionalisation methods for catchment model parameters[J]. Hydrology and Earth System Sciences, 2005, 9: 157-171.
[63]	Gou J J, Miao C Y, Samaniego L, et al. CNRDv1.0: A high-quality natural runoff dataset for hydrological and climate studies in China[J]. Bulletin of the American Meteorological Society, 2021, 102(5): E929-E947.
[64]	Seibert J. Regionalisation of parameters for a conceptual rainfall-runoff model[J]. Agricultural and Forest Meteorology, 1999, 98/99: 279-293.
[65]	刘庆, 张友静, 刘祺, 等. SWAT模型参数区域化在黄河源区的应用[J]. 河海大学学报(自然科学版), 2012, 40(5): 491-497.
	[ Liu Qing, Zhang Youjing, Liu Qi, et al. Application of parameter regionalization of SWAT model to source region of Yellow River. Journal of Hohai University (Natural Sciences), 2012, 40(5): 491-497. ]
[66]	Peel M C, Chiew F H S, Western A W, et al. Extension of unimpaired monthly streamflow data and regionalisation of parameter values to estimate streamflow in ungauged catchments[R/OL]. 2020-07-01 [2021-10-11]. https://people.eng.unimelb.edu.au/mpeel/NLWRA.pdf.
[67]	Hundecha Y, Bárdossy A. Modeling of the effect of land use changes on the runoff generation of a river basin through parameter regionalization of a watershed model[J]. Journal of Hydrology, 2004, 292: 281-295.
[68]	Mizukami N, Clark M P, Newman A J, et al. Towards seamless large-domain parameter estimation for hydrologic models[J]. Water Resources Research, 2017, 53(9): 8020-8040.
[69]	Kumar R, Livneh B, Samaniego L. Toward computationally efficient large-scale hydrologic predictions with a multiscale regionalization scheme[J]. Water Resources Research, 2013, 49(9): 5700-5714.
[70]	Beck H E, de Roo A, van Dijk A I J M. Global maps of streamflow characteristics based on observations from several thousand catchments[J]. Journal of Hydrometeorology, 2015, 16(4): 1478-1501.
[71]	Lin P R, Pan M, Beck H E, et al. Global reconstruction of naturalized river flows at 2.94 million reaches[J]. Water Resources Research, 2019, 55: 6499-6516.
[72]	Vrugt J A, Sadegh M. Toward diagnostic model calibration and evaluation: Approximate Bayesian computation[J]. Water Resources Research, 2013, 49(7): 4335-4345.

类型	方法	特点	参数传递	参数值计算方法
后参数区域化	算数统计法	原理简单,参数区域化精度很大程度取决于有资料流域密度	无	局部或全局平均值/中位数
	物理相似性	根据流域下垫面信息来定义流域的相似程度,简单易行	相似流域	相似流域参数平均值
	距离相似性	根据流域间距离来定义流域的相似程度,简单易行	相似流域	相似流域参数平均值
	参数放缩法	根据有资料流域与无资料流域的面积比率缩放参数	流域面积比例	按比例估算参数
	相似气候法	根据气候区对有资料流域进行分类	气候区	同一气候区有资料流域参数平均值
	多元线性/非线性回归	对有资料流域参数与流域物理特征建立回归模型	参数与流域属性的回归函数	根据无资料流域物理属性及传递函数估算模型参数
同步参数区域化	同步多元线性/非线性回归	对有资料流域参数与流域物理特征建立先验回归模型	参数与流域属性的回归函数	通过优化传递函数参数实现同步参数区域化
	多尺度参数区域化	对有资料流域参数与流域物理特征建立先验回归模型	精细尺度下参数与流域属性的回归函数	通过优化精细尺度下传递函数参数、参数升尺度实现同步参数区域化
	水文特征指数法	对有资料流域参数与水文特征指数建立先验回归模型	参数与水文特征指数的回归函数	通过优化传递函数参数实现同步参数区域化

Progress in parameter sensitivity analysis-optimization-regionalization methods for hydrological models

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