城市暴雨径流模拟的参数不确定性研究

基于暴雨管理模型(SWMM),构建了澳门半岛雅廉访实验小区降雨径流模拟系统,采用generalized likelihood uncertainty estimation(GLUE)方法对复杂降雨径流模型参数的不确定性进行了分析,采用均匀分布为参数的先验分布(prior distribution),选取拉丁超立方法作为采样方法,Nash-Sutcliffe效率系数作为似然评判准则。结果表明该方法可以有效的分析降雨径流模型参数的不确定性,对降雨径流模型参数识别提供了深入分析与理解模型系统的有效途径。     

Quantifying the uncertainty of semiarid flash floods using generalized likelihood uncertainty estimation

This paper aims to address the question of how the parameter uncertainty associated with a mixed conceptual and physical based rainfall-runoff model (AFFDEF) has influences on flood simulation of the semiarid Abolabbas catchment (284 km²), in Iran. AFFDEF was modified and coupled with the generalized likelihood uncertainty estimation (GLUE) algorithm to simulate four flash flood events. Analysis suggests that AFFDEF parameters showed non-unique posterior distributions depending on the magnitudes and duration of flash flood events. Model predictive uncertainty was heavily dominated by error and bias in soil antecedent moisture condition that led to large storage effect in simulation. Overall, multiplying parameter for the infiltration reservoir capacity and multiplying parameter for the interception reservoir capacity along with potential runoff contributing areas were identified the key model parameters and more influential on flood simulation. Results further revealed that uncertainty was satisfactorily quantified for the event with low to moderate flood magnitudes while high magnitude event exhibited unsatisfactory result.     

Estimation of Hydraulic Conductivity and Its Uncertainty from Grain-Size Data Using GLUE and Artificial Neural Networks

Various approaches exist to relate saturated hydraulic conductivity (K _s) to grain-size data. Most methods use a single grain-size parameter and hence omit the information encompassed by the entire grain-size distribution. This study compares two data-driven modelling methods??multiple linear regression and artificial neural networks??that use the entire grain-size distribution data as input for K _s prediction. Besides the predictive capacity of the methods, the uncertainty associated with the model predictions is also evaluated, since such information is important for stochastic groundwater flow and contaminant transport modelling. Artificial neural networks (ANNs) are combined with a generalised likelihood uncertainty estimation (GLUE) approach to predict K _s from grain-size data. The resulting GLUE-ANN hydraulic conductivity predictions and associated uncertainty estimates are compared with those obtained from the multiple linear regression models by a leave-one-out cross-validation. The GLUE-ANN ensemble prediction proved to be slightly better than multiple linear regression. The prediction uncertainty, however, was reduced by half an order of magnitude on average, and decreased at most by an order of magnitude. This demonstrates that the proposed method outperforms classical data-driven modelling techniques. Moreover, a comparison with methods from the literature demonstrates the importance of site-specific calibration. The data set used for this purpose originates mainly from unconsolidated sandy sediments of the Neogene aquifer, northern Belgium. The proposed predictive models are developed for 173 grain-size K _s-pairs. Finally, an application with the optimised models is presented for a borehole lacking K _s data.     

Uncertainty Analysis of the Parameters of the Temperature-index Method: A Case Study of Shiyi Glacier in Qilian Mountains

In order to assess the performance of the common temperature-index melt model at both spatial and temporal scale in Qilian Mountains, we performed the sensitivity and uncertainty analysis on the parameters of a common temperature-index method and evaluated the glacier mass balance on a small alpine glacier, which is separated into two relatively independent branches, with the daily mass balance and the meteorological data in the summer of 2011 and 2012. Sensitivity analysis was conducted by perturbation analysis and uncertainty analysis was carried out by Generalized Likelihood Uncertainty Estimation (GLUE) for different conditions. The results showed that the temperature-index method could properly capture the diurnal variability of the glacier mass balance. But strong equifinality of model parameter existsed in model calibration due to the uncertainty in the parameters. The model was very sensitive to changes in the value of K_ice, followed by the K_snow and T_s. It was also found that the GLUE approaches could estimate and derive the posterior distributions of 3 parameters properly. Moreover, there existed an acceptable range, which ensured high precision under different conditions.     

The effects of ionic strength and organic matter on virus inactivation at low temperatures: general likelihood uncertainty estimation (GLUE) as an alternative to least-squares parameter optimization for the fitting of virus inactivation models

This study examined how the inactivation of bacteriophage MS2 in water was affected by ionic strength (IS) and dissolved organic carbon (DOC) using static batch inactivation experiments at 4 °C conducted over a period of 2 months. Experimental conditions were characteristic of an operational managed aquifer recharge (MAR) scheme in Uppsala, Sweden. Experimental data were fit with constant and time-dependent inactivation models using two methods: (1) traditional linear and nonlinear least-squares techniques; and (2) a Monte-Carlo based parameter estimation technique called generalized likelihood uncertainty estimation (GLUE). The least-squares and GLUE methodologies gave very similar estimates of the model parameters and their uncertainty. This demonstrates that GLUE can be used as a viable alternative to traditional least-squares parameter estimation techniques for fitting of virus inactivation models. Results showed a slight increase in constant inactivation rates following an increase in the DOC concentrations, suggesting that the presence of organic carbon enhanced the inactivation of MS2. The experiment with a high IS and a low DOC was the only experiment which showed that MS2 inactivation may have been time-dependent. However, results from the GLUE methodology indicated that models of constant inactivation were able to describe all of the experiments. This suggested that inactivation time-series longer than 2 months were needed in order to provide concrete conclusions regarding the time-dependency of MS2 inactivation at 4 °C under these experimental conditions.     

HSPF模型在流域水文与水环境研究中的进展

HSPF （Hydrological Simulation Program-Fortran）模型是在Stanford水文模型的基础上发展起来的。作为半分布式水文水质模型的优秀代表, HSPF模型能够综合模拟河道水力、 流域径流、 土壤流失、 污染物迁移等过程, 从而被广泛应用于流域水文与水环境研究中。通过概述HSPF模型研发与整合的发展历程, 以及模型中PERLND、 IMPLND与RCHRES等3个主要模块结构, 且综述了模型在土地利用变化、 水文过程、 非点源污染模拟以及模型参数校验等方面的研究进展。研究表明： HSPF模型对于经验公式和假设估计仍然存在改进与完善的空间; 对参数的深入分析研究参有助于降低模拟结果的不确定性; HSPF模型对采集数据精度和规范性的要求也较高。最后, 从经验公式完善、 参数不确定性探讨和模拟精度提高等方面对模型的完善提出相关建议, 供相关研究提供参考。     

前期土壤含水量对水文模拟不确定性影响分析

前期土壤含水量是影响洪水形成的重要流域物理因素,在该次水文模拟中土壤初始含水量采用新安江日模型计算得出.研究中,通过改变日模型模拟的时间长度TP,来研究初始时刻土壤含水量的变化对洪水模拟精度的影响.结果表明:初始土壤含水量对于洪水模拟的精度有显著影响,不同日模型计算时间可以使次洪初始土壤蓄水量W的变化达到42%,而相应的洪峰偏差达到57.7%.土壤和降雨观测数据分析显示,土壤体积含水量与前期累积降雨量有一定地相关关系.为有效解决传统模型与流域真实物理属性数据的融合问题,初步建立了实测土壤体积含水量数据与模型中土壤湿度参数及前期降雨的经验关系.     

基于GLUE方法的城市雨洪模型参数不确定性分析

以北京市大红门排水片为例,构建基于SWMM(Storm Water Management Model)的城市雨洪模型,并基于GLUE方法(Generalized Likelihood Uncertainty Estimation)对模型参数的不确定性进行了分析。结果表明,所建城市雨洪模型能够较好的模拟区域的雨洪过程。在模型参数中,管道曼宁系数、不透水区洼蓄量以及河道曼宁系数的不确定性对模型结果的影响较为显著。GLUE方法能够在分析模型结果不确定性的同时给出敏感性参数,为城市雨洪模型的参数率定和进一步研制提供了有效工具。     

水文模型参数区域化研究进展

参数区域化方法是解决资料缺乏地区水文模拟和预报的有效手段,主要包括回归法、空间邻近法和属性相似法三类方法,可将有资料流域的水文模型参数移用到资料缺乏流域。首先回顾了区域化方法的基本原理和应用方法,并分析了三类主要区域化方法的适用性。从流域特征因子、水文模型及参数、不确定性探讨三个方面综述了区域化方法的研究进展。分析发现,当前区域化方法缺乏完善的理论基础,流域特征因子选择存在主观性,水文模型及参数的适用性方面研究不足。最后展望了未来的研究重点：（1）多维度适用性比较;（2）水文过程和参数的空间分布规律;（3）参数的尺度问题;（4）参数区域化的不确定性问题。     

考虑局部趋势的非一致性水文频率分析方法

变化环境下水文序列常表现出非一致性,导致基于独立同分布假设的频率分析方法受到严峻挑战,故亟需研究适用于非一致性水文序列的频率分析方法。针对具有趋势变异的水文序列,提出了一种考虑局部趋势的非一致性水文频率分析方法:以具有局部与整体趋势识别能力的滑动窗口趋势分析法诊断序列;构造时变概率分布模型组,采用融合了贝叶斯理论的广义极大似然法估计模型参数;优选模型并按照等可靠度原则推求设计值及置信区间。应用该方法对甘肃石羊河流域红崖山水库站年径流量序列开展了实例研究,结果表明从设计值及95%置信区间角度看,所提方法与现行的一致性频率分析方法及考虑整体趋势的时变分布方法相比存在一定的差异,揭示了在准确识别序列局部或整体趋势成分的基础上,开展非一致性水文频率分析有助于合理估计设计值。     

Geochemical and qualitative assessment of groundwater of the High Mekerra watershed,NW Algeria

Groundwater constitutes the main source of water supply in the High Mekerra watershed of northwestern Algeria. This resource is currently under heavy pressures to meet the growing needs of drinking water and irrigation. This study assesses the geochemical characteristics of groundwater of the High Mekerra watershed at 21 points distributed across the two main aquifers (Ras El Ma and Mouley Slissen) in the region. Hydrochemical facies of Ras El Ma groundwater are dominantly MgCl and CaCl type, while those of Mouley Slissen groundwater are of CaHCO₃ type. Principal component analysis shows a strong correlation between groundwater mineralization and Ca²⁺, Na⁺, Cl^? and SO₄ ^2? ions stemming from the dissolution of carbonates, gypsum and anhydrite. Groundwater mineralization evolves from south to north. Geochemical modeling shows that the High Mekerra groundwater is saturated with respect to calcite and dolomite and undersaturated with respect to gypsum and anhydrite. Nitrate concentrations that exceed the WHO standard (50 mg L^?1) at several points are linked to the agro-pastoral activities in this region.     

基于贝叶斯模型平均的径流模拟及不确定性分析

水文模型是模拟水循环过程重要手段,依靠单个模型进行模拟往往存在很大的不确定性,使通过多模型进行组合模拟成为必然趋势。选取3个集总式水文模型应用贝叶斯模型平均(BMA)进行流域月径流量的多模型模拟,采用期望最大化算法推求BMA分布参数以得到BMA均值模拟序列和90%不确定性区间。以武烈河实测数据为例进行分析,结果表明:BMA方法既能通过均值模拟提供更高精度的模拟效果,还可通过不确定性置信区间定量评价模型结构不确定性,为径流模拟提供丰富信息。     

Modeling hydrologic responses of the Zwalm catchment using the REW approach: propagation of uncertainty in soil properties to model output

The research presented in this paper focuses on the application of a newly developed physically based watershed modeling approach, which is called representative elementary watershed approach. The study stressed the effects of uncertainty of input parameters on the watershed responses (i.e., simulated discharges). The approach was applied to the Zwalm catchment, which is an agriculture-dominated watershed with a drainage area of 114 km² located in East Flanders, Belgium. Uncertainty analysis of the model parameters is limited to the saturated hydraulic conductivity because of its high influence on the watershed hydrologic behavior and availability of the data. The assessment of output uncertainty is performed using the Monte Carlo method. The ensemble statistical watershed responses and their uncertainties are calculated and compared with measurements. The results show that the measured discharges fall within the 95% confidence interval of the modeled discharge. This provides the uncertainty bounds of the discharges that account for the uncertainty in saturated hydraulic conductivity. The methodology can be extended to address other uncertain parameters as far as the probability density function of the parameter is defined.     

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

基于气候变化影响的水资源评价对水资源规划和管理具有重要意义,随着全球气候变化影响的加剧,这一研究显得越来越紧迫。在目前的气候变化研究中,很少考虑气候自然波动的影响(气候自然变异),常将所有的变化单独归因于气候变化的影响,这在气候变化的影响评价中可能导致错误的理解与判断。气候自然变异分析由于缺乏超长系列的数据资料而长期被人为避开。针对这一问题,本研究提出模型方法体系,通过历史基准期的长系列模拟来分析气候自然变异的影响。选取常用的1961~1990年水文系列作为基准期,提出一种基于拉丁超立方体抽样技术的季节分段抽样模拟方法,实现对气候自然变异的模拟。应用水文模型TOPMODEL对基准期的径流系列进行模拟,基于不确定性分析GLUE方法对基准期内水文模型参数不确定性进行分析,并探讨了气候自然变异的影响。研究结果表明,在气候变化影响评价中,气候自然变异的影响不可忽略,应在气候变化的影响中加以区分和界定。     

DEM空间分辨率的初步分析

分布式模型的输入及其参数具有时空变异性,模型的校正也依赖于网格单元的大小,因此需要确定适当的空间分辨率来描述和控制空间变化。随着分辨率的不同, DEM的精度以及由此提取的流域特征值（如高程、坡度、地形指数、河网长度）在统计特性上也会随之变化。对50 m分辨率的DEM平均取样获得150~950 m的9组DEM,对不同分辨率下提取的流域特征值进行了统计分析,并采用信息熵度量不同分辨率的信息量。     

新安江模型参数多目标优化研究

水文模型的参数优化率定一直以来是水文预报领域的重要研究内容,当水文模型的结构确定后,水文模型参数的选择对水文模型整体性能和水文预报结果的好坏有着至关重要的影响.针对传统水文模型参数优选采用单一目标不能充分全面挖掘水文观测资料中蕴含的水文特征信息的缺陷,本文以新安江三水源模型为例,尝试采用多目标优化算法优化率定水文模型,算例应用分析表明,通过合理的选择目标函数的种类和数目,采用多目标进化算法优化率定模型参数,可以获得相对于单目标率定模型参数更优的结果.进一步,研究工作针对模型参数优化的结果进行分析,可以明显看出模型参数优化中存在“异参同效”现象,为后续模型参数不确定性分析等相关研究工作的开展做好了铺垫.     

Hierarchical approach to hydrological model calibration

Hydrological models have been widely used for water resources management. Successful application of hydrological models depends on careful calibration and uncertainty analysis. Spatial unit of water balance calculations may differ widely in different models from grids to hydrological response units (HRU). The Soil and Water Assessment Tool (SWAT) software uses HRU as the spatial unit. SWAT simulates hydrological processes at sub-basin level by deriving HRUs by thresholding areas of soil type, land use, and slope combinations. This may ignore some important areas, which may have great impact on hydrological processes in the watershed. In this study, a hierarchical HRU approach was developed in order to increase model performance and reduce computational complexity simultaneously. For hierarchical optimization, HRUs are first divided into two-HRU types and are optimized with respect to some relevant influence parameters. Then, each HRU is further divided into two. Each child HRU inherits the optimum parameter values of the parent HRU as its initial value. This approach decreases the total calibration time while obtaining a better result. The performance of the hierarchical methodology is demonstrated on two basins, namely Sarisu-Eylikler and Namazgah Dam Lake Basins in Turkey. In Sarisu-Eylikler, we obtained good results by a combination of curve number (CN2), soil hydraulic conductivity, and slope for generating HRUs, while in Namazgah use of only CN2 gave better results.     

数字高程模型分辨率对流域地形特征参数的影响

地形特征(如高程和坡度)和水文特征(如河流长度和河流坡度)是分布式流域水文水质模型的基础输入参数,用于量化描述模型模拟流域的自然特征。这些特征参数的准确性直接影响水文水质过程模拟的准确性。应用数字高程模型(Digital Elevation Model,DEM)在4个不同地形的子流域研究了10种不同分辨率DEM对平均高程、流域面积、坡度、河流坡度、最长河长等参数的影响。结果表明,随着DEM分辨率降低,流域地形变缓,流域平均坡度逐渐减小;随着DEM网格分辨率的变化,子流域划分范围和河道位置也都可能发生变化,且该变化在地形起伏较小的丘陵平原地区较明显,子流域集水面积和河长进一步随之改变;河流坡度随DEM分辨率降低则呈无规则变化。从地形和水文参数两方面揭示了DEM 分辨率在分布式流域模型中的不确定性影响。     

流域地貌特征对流域水文响应的影响分析

针对目前研究流域地貌特征与流域水文响应的指标具有随意性、主观性以及地貌特征与流域水文响应之间相关性较多、定量化较少等现象,求解出河道和坡面水流运动方程的格子玻尔兹曼方法解,进而构建了基于LBM的汇流模型,计算出黑龙江流域25个研究流域的单位线。分析了单位线要素与流域面积、流域长度和流域宽度之间的相关性,并给出了定量公式。研究结果表明:时间面积曲线由于受到水动力扩散作用比距离面积曲线变得平滑,可以用单位化的时间面积曲线定量单位线,但当一个流域的时间面积曲线有多个大的极值接近时峰现时差和洪峰拟合的不好。研究结果可为定量化流域水文响应和流域水文模型的参数率定提供方法和定量公式。     

The storage potential of different surface coverings for various scale storms on Wu-Tu watershed,Taiwan

An impervious surface cover is continuously spreading over the Wu-Tu upstream watershed due to the concentrated population and raised economical demands, while that area also frequently suffers from heavy storms or typhoons during the summer season. The increased flood volume due to this extended imperviousness causes a greater potential hazard than that of the past. In order to evaluate the urbanized impacts on the watershed, a set of methods were used to estimate the changes of the watershed storage. This research chose 51 observed events from three raingauges on the Wu-Tu upstream watershed, Taiwan, to study the volume characteristic of abstracted rainwater. In the study, the block Kriging method was used to estimate the area rainfall and the hourly excess was derived through the non-linear programing (NLP). A total of 40 samples were calibrated through the hydrological model and the Soil Conservation Service (SCS) model using the optimum seeking method in order to search out and establish the best parameters that illustrate the hydrological and geomorphic conditions at that time. Eleven cases were used to examine the established relationship of the parameters and the impervious coverings. A design storm approach was used to view the changes of the volume for various scale storms/typhoons because of the different degrees of urbanization. Then, a diagram was designed to show the relationships that exist among the runoff coefficient, return period, and impervious surface. The satisfactory results show that storage capability of rainwater for various scale storms on the Wu-Tu watershed would be respectively reduced about 42–156 cms in different decrements up to now.