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1.
Urban drainage models are important tools used by both practitioners and scientists in the field of stormwater management. These models are often conceptual and usually require calibration using local datasets. The quantification of the uncertainty associated with the models is a must, although it is rarely practiced. The International Working Group on Data and Models, which works under the IWA/IAHR Joint Committee on Urban Drainage, has been working on the development of a framework for defining and assessing uncertainties in the field of urban drainage modelling. A part of that work is the assessment and comparison of different techniques generally used in the uncertainty assessment of the parameters of water models. This paper compares a number of these techniques: the Generalized Likelihood Uncertainty Estimation (GLUE), the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), an approach based on a multi-objective auto-calibration (a multialgorithm, genetically adaptive multi-objective method, AMALGAM) and a Bayesian approach based on a simplified Markov Chain Monte Carlo method (implemented in the software MICA). To allow a meaningful comparison among the different uncertainty techniques, common criteria have been set for the likelihood formulation, defining the number of simulations, and the measure of uncertainty bounds. Moreover, all the uncertainty techniques were implemented for the same case study, in which the same stormwater quantity and quality model was used alongside the same dataset. The comparison results for a well-posed rainfall/runoff model showed that the four methods provide similar probability distributions of model parameters, and model prediction intervals. For ill-posed water quality model the differences between the results were much wider; and the paper provides the specific advantages and disadvantages of each method. In relation to computational efficiency (i.e. number of iterations required to generate the probability distribution of parameters), it was found that SCEM-UA and AMALGAM produce results quicker than GLUE in terms of required number of simulations. However, GLUE requires the lowest modelling skills and is easy to implement. All non-Bayesian methods have problems with the way they accept behavioural parameter sets, e.g. GLUE, SCEM-UA and AMALGAM have subjective acceptance thresholds, while MICA has usually problem with its hypothesis on normality of residuals. It is concluded that modellers should select the method which is most suitable for the system they are modelling (e.g. complexity of the model’s structure including the number of parameters), their skill/knowledge level, the available information, and the purpose of their study.  相似文献   
2.
林青  徐绍辉 《水利学报》2012,43(9):1017-1024
模型参数的不确定性分析是模型不确定性研究的重要内容之一。本文以示踪剂Br和反应性溶质Cu在石英砂中的运移为例,采用GLUE方法探讨了多孔介质中溶质运移模型参数的不确定性。研究结果表明,仅对水力学参数θs和λ进行识别时,θs和λ的可识别性较强。对耦合Freundlich等温吸附的模型参数进行识别时,由于参数间的相互作用,θs和λ的可识别性降低;吸附特性参数kF的后验分布基本呈均匀分布,可识别性较差,吸附特性参数β、ω、f的取值区间则相对收敛,可识别性较强。K-S检验结果表明,参数区域敏感度由高到底的排序为f、ω、β、kF、λ、θs,主要是因为石英砂对Cu的吸附以动力学反应为主,而f和ω是与动力学吸附反应相关的两个参数。上述结论有助于加深对溶质运移模型参数的理解和提高模型预测的可靠性。  相似文献   
3.
本文在Blasone研究工作的基础上,进一步提出了基于马尔科夫链-蒙特卡洛算法的改进通用似然不确定性估计方法(Markov Chain_Monte Carlo based Modified Generalized Likelihood Uncertainty Estimation,MMGLUE)。该方法结合近年来被广泛用于推求参数后验分布的MCMC方法,对基于Monte Carlo随机取样方法的传统GLUE方法进行改进,并以预测区间性质最优为标准,对可行参数组阈值进行判断与选择,提出了衡量预测区间对称性的标准,并就预测区间性质与可行参数组个数的相关关系进行了探索。在汉江玉带河流域的实例研究证明,MMGLUE方法较传统的GLUE方法能够推求出性质更为优良的预测区间,从而更真实合理地反映水文模型的不确定性。  相似文献   
4.
GLUE方法与信息熵结合,分析新安江水文模型参数及预报结果的不确定性。首先用信息熵公式初步判断参数的敏感程度,根据GLUE方法求出90%置信度下流量的不确定范围;其次应用信息熵和U不确定公式共同量化分析模型预报结果的不确定性。以资水水系的新宁站集水区为研究区域,得出结论:①GLUE方法能够用来分析新安江水文模型参数的不确定性;②信息熵公式能够初步判断模型参数的敏感程度;③模型预报结果的不确定性随着实测资料的增加而降低。  相似文献   
5.
GLUE方法分析新安江模型参数不确定性的应用研究   总被引:13,自引:0,他引:13  
李胜  梁忠民 《东北水利水电》2006,24(2):31-33,47
采用普适似然不确定估计(GLUE)方法,研究新安江流域水文模型参数的不确定性问题。通过江西乐安河流域资料的模拟分析表明,模型参数间存在“异参同效”现象,即存在许多可接受的似然估计值的参数值组合。利用这些参数组的概率分布,可以对模型模拟的不确定性范围进行分析。  相似文献   
6.
《工程(英文)》2018,4(5):643-652
The Ganga River, the longest river in India, is stressed by extreme anthropogenic activity and climate change, particularly in the Varanasi region. Anticipated climate changes and an expanding populace are expected to further impede the efficient use of water. In this study, hydrological modeling was applied to Soil and Water Assessment Tool (SWAT) modeling in the Ganga catchment, over a region of 15 621.612 km2 in the southern part of Uttar Pradesh. The primary goals of this study are: ① To test the execution and applicability of the SWAT model in anticipating runoff and sediment yield; and ② to compare and determine the best calibration algorithm among three popular algorithms—sequential uncertainty fitting version 2 (SUFI-2), the generalized likelihood uncertainty estimation (GLUE), and parallel solution (ParaSol). The input data used in the SWAT were the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), Landsat-8 satellite imagery, soil data, and daily meteorological data. The watershed of the study area was delineated into 46 sub-watersheds, and a land use/land cover (LULC) map and soil map were used to create hydrological response units (HRUs). Models utilizing SUFI-2, GLUE, and ParaSol methods were constructed, and these algorithms were compared based on five categories: their objective functions, the concepts used, their performances, the values of P-factors, and the values of R-factors. As a result, it was observed that SUFI-2 is a better performer than the other two algorithms for use in calibrating Indian watersheds, as this method requires fewer runs for a computational model and yields the best results among the three algorithms. ParaSol is the worst performer among the three algorithms. After calibrating using SUFI-2, five parameters including the effective channel hydraulic conductivity (CH_K2), the universal soil-loss equation (USLE) support parameter (USLE_P), Manning’s n value for the main channel (CH_N2), the surface runoff lag time (SURLAG), and the available water capacity of the soil layer (SOL_AWC) were observed to be the most sensitive parameters for modeling the present watershed. It was also found that the maximum runoff occurred in sub-watershed number 40 (SW#40), while the maximum sediment yield was 50 t·a−1 for SW#36, which comprised barren land. The average evapotranspiration for the basin was 411.55 mm·a−1. The calibrated model can be utilized in future to facilitate investigation of the impacts of LULC, climate change, and soil erosion.  相似文献   
7.
结合新安江模型在东洋河流域的应用,提出了基于GLUE方法的新安江模型参数不确定性分析。采用GLUE算法抽样结果对东洋河流域进行不确定性预报,选用水文模拟中常用的确定性系数作为似然判据,通过设定0.7为阀值,得到的90%置信区间的流量过程,实例研究表明,以该结果进行不确定预报是可行的。  相似文献   
8.
针对分布式城市雨洪模型的研究现状,分析讨论了城市雨洪模型及其模拟结果的不确定性问题,并以深圳市某独立排水片区为例,采用GLUE方法分析SWMM模型参数的不确定性。首先构建排水区的SWMM模型,针对敏感性参数随机生成多个参数方案,通过MATLAB编程调用SWMM动态链接库多次进行参数率定,以Nash-Sutcliffe效率系数(NS)大于0.8为阈值进行最佳参数方案的筛选,得到置信度为90%的排水区径流量区间。研究结果表明,GLUE方法能够分析出模型敏感性参数的不确定性,此种模型不确定性研究方法可为实际工程提供更加科学的预测。  相似文献   
9.
Parameter uncertainty and sensitivity for a watershed-scale simulation model in Portugal were explored to identify the most critical model parameters in terms of model calibration and prediction. The research is intended to help provide guidance regarding allocation of limited data collection and model parameterization resources for modelers working in any data and resource limited environment. The watershed-scale hydrology and water quality simulation model, Hydrologic Simulation Program – FORTRAN (HSPF), was used to predict the hydrology of Lis River basin in Portugal. The model was calibrated for a 5-year period 1985–1989 and validated for a 4-year period 2003–2006. Agreement between simulated and observed streamflow data was satisfactory considering the performance measures such as Nash–Sutcliffe efficiency (E), deviation runoff (Dv) and coefficient of determination (R2). The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to establish uncertainty bounds for the simulated flow using the Nash–Sutcliffe coefficient as a performance likelihood measure. Sensitivity analysis results indicate that runoff estimations are most sensitive to parameters related to climate conditions, soil and land use. These results state that even though climate conditions are generally most significant in water balance modeling, attention should also focus on land use characteristics as well. Specifically with respect to HSPF, the two most sensitive parameters, INFILT and LZSN, are both directly dependent on soil and land use characteristics.  相似文献   
10.
水文模型两种不确定性研究方法的比较   总被引:2,自引:0,他引:2  
水文模型结构本身的缺陷、模型输入输出误差、水文模型参数冗余及其复杂的非线性联系等,导致了流域水文模型的不确定性.基于贝叶斯理论的MCMC方法及GLUE方法近年来被广泛应用于流域水文模型的不确定性研究工作中.为比较上述2种模型不确定性分析方法的分析效果及其优劣,以位于汉江流域的牧马河流域作为研究对象,采用集总式概念性水文模型SMAR模型作为实验模型,推求其模型参数的不确定性及参数的后验分布.采用基于实测流量资料估计的置信区间可靠性作为评判标准,实验结果表明:就SMAR模型而言,MCMC方法能够更好地推求模型参数的后验分布.  相似文献   
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