基于MCMC和ES-MDA方法的地下水数值模型非均 质参数场及开采量的反演研究

马尔科夫链蒙特卡罗方法(MCMC)和多重数据同化集合平滑器方法(ES-MDA)近年来在地下水参数反演得到广泛应用，但对三维多层非均质含水层参数反演精度和计算效率还缺乏对比分析。本文构建了含有基于Karhunen-Loève展开的非均质参数场的潜水和多层承压水含水层案例，并建立了地下水数值模型和基于Kriging方法的替代模型，模拟含水层分层水头变化，探讨了基于替代模型的MCMC、替代模型和数值模型相结合的两阶段MCMC以及ES-MDA方法反演的含水层渗透系数以及开采量。结果表明，针对本文算例，在非均质参数和开采量的反演中，相比而言，两阶段MCMC反演参数精度更高，ES-MDA方法计算效率更高。本研究为地下水数值模型参数反演方法选择提供参考依据。     

应用正交试验法分析地下水流模型参数灵敏度

在地下水数值模拟中,实际水文地质条件的复杂性、概念模型和相关拟合数据的不确定性,往往造成数值模型和率定参数的随机性,从而降低模拟的精度。对参数进行敏感性分析,分清随机模型中参数的主次作用,可以有效提高模拟精度。以北京市朝阳区为研究区,选取与地下水流场相关性较大的渗透系数k、给水度u及贮水率s三种参数作为灵敏度分析因子,采用正交试验法中的极差分析和方差分析两种方法分析参数对模拟结果的影响程度。结果显示:含水层的渗透系数灵敏度值最大,给水度灵敏度次之,贮水率灵敏度最小。因此,在进行数值模拟时应重点提高含水层渗透系数的精度,以确保数值模拟的准确性。     

供水工程设计中的决策分析风险

介绍决策分析法在小规模供水和地下含水层污染问题中的应用。在不同风险条件下，提出了一系列确保连续供水的优选方案。在风险分析中，假定渗透系数为不确定因素。为了评估所有设计方案在系统运作中不确定性的本质，使用了地下水径流和污染物运动随机模拟模型。通过综合考虑实际费用和概率费用，确定了优选方案，并运用风险－费用－效益目标函数进行了比选。     

改进的新安江模型多目标参数优化 ——以临涣集流域为例

平原灌区水文过程受人类开采灌溉影响较大,地表水与地下水物质能量交换频繁,单目标率定方法难以同时有效模拟该区流量和地下水埋深过程。对此,在新安江模型基础上加入地下水蓄水库,构建地表-地下水耦合模型,建立包含流量与地下水埋深信息的多目标函数。采用MCAT分析流量和埋深单目标以及多目标下的参数敏感性,用SCE-UA算法率定遴选出的敏感参数,比较单目标和多目标率定结果。结果表明,该多目标参数优化方法显著提高了工作效率,解决了单目标率定中无法同时达到流量与地下水埋深过程模拟高精度的问题。模型在临涣集流域的耦合模拟中取得较好效果,为平原灌区地表水与地下水动态模拟和水资源评价提供参考。     

灌区地表水—地下水耦合模型的构建

为了定量描述灌区水平衡要素及其转化关系,构建了灌区地表水-地下水分布式模拟耦合模型。通过改进SWAT模型的稻田及旱作物水分循环、蒸发蒸腾量和渠系渗漏计算等模块,建立了灌区地表水分布式模拟模型;以SWAT模型中的水文响应单元(HRU)和MODFLOW模型中的有限差分网格(cells)作为基本交换单元,将改进SWAT模型的地下水补给量计算值加载到MODFLOW模型的地下水补给模块,实现了灌区地表水-地下水分布式模拟模型的耦合。耦合模型在柳园口灌区的应用结果表明,该模型能够准确模拟和预测灌区地表水和地下水的动态变化,为灌区水管理提供了科学依据。     

地下水污染健康风险模拟预测评估中参数的敏感度分析

随着地下水污染健康风险评估逐渐被重视,其风险的影响程度和发展趋势也越来越被关注,因此解析解模拟预测评估地下水污染健康风险也逐渐被应用。本文旨在研究解析解模型对污染物健康风险模拟的敏感度,为该方法更广泛的应用提供依据。选取解析解模型中的渗透系数(K)、纵向弥散度(αx)、污染物有机碳分配系数(Koc)和半衰期(t1/2)4个参数,在其他参数不变的情况下,依次改变各参数的值并进行解析解计算,记录对应的参数值与模拟结果,绘出模拟预测值与各个分析参数的变化曲线,分析解析解对各个参数的响应程度,从而为处理不确定因素提供依据。     

地下水与地表水耦合模拟模型研究与展望

地下水与地表水耦合模拟模型可以合理地反映水文循环过程,为水资源规划管理提供可靠依据.地下水与地表水耦合模拟模型按求解方法可分为水均衡法模型、解析法模型和数值法模型,按耦合方式可分为边界条件耦合模型、交换通量耦合模型、整体边界模合模型,目前各类模型存在问题主要有数值求解不稳定、失真、适应性差、参数率定困难等,"3S"及其他新技术的应用是今后的发展热点.     

地表水地下水的交互与耦合模拟研究现状与进展

在介绍"四水"转化概念模型的基础上,引出了地表水与地下水之间的联系及相互作用过程,归纳地表水和地下水交互作用研究的基本规律和方法;系统介绍和对比国内外典型地表水与地下水耦合模型的总体结构、特点、适用领域及模型的局限性。对地表水地下水交互作用过程及耦合模拟研究中存在的问题提出看法,认为集成模型应该解决时空尺度整合、参数的不确定性等关键问题,指出综合水循环和水质各组成部分的复杂系统模拟是模型发展的必然趋势,对今后研究工作的发展趋势和研究前景作了探讨。     

基于HYDRUS-MODFLOW的大洋河岫岩流域地表水与地下水耦合模型

流域内的水循环是一个完整的过程,只有综合考虑地表水和地下水的影响方可获得较高精度的模拟成果。以辽宁省大洋河岫岩流域为例,构建基于HYDRUS-MODFLOW的地表水和地下水耦合模型,结果显示地表水和地下水耦合模型具有较高的模拟精度和适应性,可用于研究区的相关研究。     

水环境风险评价的随机模拟与三角模糊数耦合模型

探讨了将三角模糊数理论用于描述和处理水环境风险评价系统中的随机性、模糊性,以及资料信息的不完整、不精确性等不确定性特征的具体实现途径。采用Monte Carlo方法模拟三角模糊数,把三角模糊数及其函数之间的运算转化为普通的实数之间的运算,建立了水环境风险评价的随机模拟与三角模糊数耦合模型(SS-TFN)。应用结果表明:SS-TFN通过Monte Carlo方法可得到水环境系统的质量状态变量的大量模拟序列,其计算结果比三角模糊数的截集技术的计算结果更简便、全面和合理;用SS-TFN通过调整水环境系统的质量状态变量的控制参数,可方便地获得水环境风险管理的对策方案。     

地下水数值模拟不确定性的贝叶斯分析

本文在常用的确定性地下水流数值模型的基础上,提出了基于贝叶斯原理的不确定性分析方法,并通过算例,针对几种常见的不确定性因素,定量地描述了其对模型参数和模拟结果的影响。相较于传统的蒙特卡罗法等不确定性分析方法,该方法不仅考虑了先验的参数不确定性,而且考虑了模拟结果与观测资料的相似度,因此其结果更加符合实际情况。此外,该方法能够被用于风险分析,可为地下水资源评价及开发利用提供科学的依据。     

土石坝边坡小概率失效计算方法

针对蒙特卡罗随机有限元方法求解土石坝边坡小概率失效问题计算时间过长这一弊端,提出了基于链表筛分法和Kriging代理模型的混合子集模拟法。首先,利用链表筛分法计算初始样本点,并以初始样本点作为Kriging代理模型的训练样本点。然后在子集模拟法的分层模拟中采用Kriging代理模型预测条件样本点的响应值,从而可计算出土石坝边坡失效概率。最后,通过一个土石坝边坡算例来说明本方法相比其他方法所需要的计算时间更少,更适合计算土石坝边坡小概率失效问题。     

Predicting Variability in a Lake Ontario Phosphorus Model

The prediction of a model always has a degree of uncertainty. Because the level of uncertainty is inversely related to the value of information contained in the prediction, there is a need to quantify the uncertainty. One approach to estimate prediction uncertainty is first-order error analysis. In this method, the error in a characteristic (variable or parameter) is defined by its first nonzero moment (the variance). Errors are propagated through the model using first-order terms in the Taylor series, and the variances are then combined to yield the total prediction uncertainty. An alternative approach to model prediction error analysis is Monte Carlo simulation. In this technique, probability density functions are assigned to each characteristic (variable or parameter), reflecting the uncertainty in that characteristic. Then, values are randomly selected from the distribution for each term and inserted into the model, to calculate a prediction. Repeating this process a number of times produces a distribution of predicted values, which reflects the combined uncertainties. These two approaches (first-order error analysis and Monte Carlo simulation) are applied to Lake Ontario data using a steady state mass balance phosphorus model. Comparisons are made which suggest guidelines for the use of each.     

Stochastic and Robust Multi-Objective Optimal Management of Pumping from Coastal Aquifers Under Parameter Uncertainty

Combined simulation-optimization approaches have been used as tools to derive optimal groundwater management strategies to maintain or improve water quality in contaminated or other aquifers. Surrogate models based on neural networks, regression models, support vector machies etc., are used as substitutes for the numerical simulation model in order to reduce the computational burden on the simulation-optimization approach. However, the groundwater flow and transport system itself being characterized by uncertain parameters, using a deterministic surrogate model to substitute it is a gross and unrealistic approximation of the system. Till date, few studies have considered stochastic surrogate modeling to develop groundwater management methodologies. In this study, we utilize genetic programming (GP) based ensemble surrogate models to characterize coastal aquifer water quality responses to pumping, under parameter uncertainty. These surrogates are then coupled with multiple realization optimization for the stochastic and robust optimization of groundwater management in coastal aquifers. The key novelty in the proposed approach is the capability to capture the uncertainty in the physical system, to a certain extent, in the ensemble of surrogate models and using it to constrain the optimization search to derive robust optimal solutions. Uncertainties in hydraulic conductivity and the annual aquifer recharge are incorporated in this study. The results obtained indicate that the methodology is capable of developing reliable and robust strategies for groundwater management.     

Risk Probability Assessment of Sudden Water Pollution in the Plain River Network Based on Random Discharge from Multiple Risk Sources

Starting from the time variance and uncertainty of accidental discharge, this paper describes the probability of the occurrence of the “normal-accident” alternate state for a risk source using the Markov state transfer model, simulates the behaviour of pollutants in rivers using the hydrodynamic and water quality models for non-conservative substances, and tracks the transport path of pollutants in rivers using the water quality model for conservative substances. The above models are coupled with the Sequential Monte Carlo algorithm, and the risk probability analysis model for sudden water pollution in the plain river network is established and applied to the Yixing river network. The results show that (a) the risk probability of exceeding ammonia nitrogen standard (PES of ammonia nitrogen) is lower in the upper reaches and higher in the middle and lower reaches; (b) dynamic changes in pollutant concentration lead to different changes in the PES of ammonia nitrogen in each reach; (c) the differences in the simulated PES values between the sudden scheme and the stable scheme (NPES of ammonia nitrogen) in the upper and middle reaches show a patchy distribution of high and low values, which are related to the risk source location, the water movement direction and the concentration change in the reach after accepting pollutant loads from the risk sources; (d) the NPES of ammonia nitrogen in the lower reaches results from the coupling effect caused by accidental discharges from multiple risk sources; and (e) the different effects of the lower boundary hydrological conditions on the upstream water inflow lead to the different coupling effect on the water quality probability of sections in the downstream area.

     

河流水质参数估计的蒙特卡罗方法

考虑到水环境系统的不确定性,把河流水质观测数据和水质模型参数都作为随机变量来处理。采用贝叶斯方法计算出模型参数的后验概率密度函数,通过蒙特卡罗方法对其进行采样来获得参数的估计值。最后通过对一个实际河道的水质模型参数进行了估计,取得了较好的效果,可为进一步使用水质模型预测河流浓度分布提供依据。     

Risk analysis of slope instability of levees under river sand mining conditions

Levees are affected by over-exploitation of river sand and river adjustments after the formation of sand pits. The slope stability is seriously threatened, drawing wide concern among experts and scholars in the area of water conservancy. This study analyzed the uncertainties of slope stability of levees under river sand mining conditions, including uncertainty caused by interest- driven over-exploitation by sand mining contractors, and uncertainty of the distance from the slope or sand pit to the bottom of the levee under the action of cross-flow force after the sand pit forms. Based on the results of uncertainty analysis, the distribution and related parameters of these uncertainties were estimated according to the Yangtze River sand mining practice. A risk model of the slope instability of a levee under river sand mining conditions was built, and the possibility of slope instability under different slope gradients in a certain reach of the Yangtze River was calculated with the Monte Carlo method and probability combination method. The results indicated that the probability of instability risk rose from 2.38% to 4.74% as the pits came into being.     

干旱内陆河地区地表水和地下水集成模型及应用

基于水动力学基础建立了地表水和地下水集成模型,分析了地表水和地下水模型集成中的尺度耦合、转化量计算和移动自由液面法等关键技术问题.并采用该模型对一维明渠地表水流、一维和二维饱和一非饱和地下水流典型算例进行了模拟,计算结果符合实际.最后将模型应用于黑河干流中游地区地表水和地下水的转化分析,结果表明,黑河干流在黑河大桥以上约有17%的渗漏,在黑河大桥以下地下水向黑河的排泄量占莺落峡来水的30%左右,而地下水向黑河干流的河泉流量逐年减少.     

基于新安江模型的降雨不确定性传播

基于模糊隶属度理论,结合蒙特卡罗方法,定量分析了降雨不确定性在新安江模型中的传播特性及其对流量过程的影响。用模糊隶属度函数表示降雨量的不确定性,用蒙特卡罗方法把3 h雨量随机解集为1 h数据,以表征降雨时程分布的不确定性影响。应用新安江模型对褒河流域进行径流模拟。结果表明,雨量量级大小的不确定性在降雨不确定性中占据主导位置,降雨时程分布的影响次之。     

Integrated modelling under uncertainty in watershed-level assessment and management.

Uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modelling system (modified-BASINS) under uncertainty is described and demonstrated for use in receiving-water quality prediction and watershed management. A Monte Carlo simulation was used to investigate the effect of various uncertainty types on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorus (T-P) in the Hwaong Reservoir, considering three uncertainty types, would be less than about 4.4 and 0.23 mg L(-1), respectively, in 2012, with 90% confidence. The effects of two watershed management practices, wastewater treatment plants (WWTP) and constructed wetlands (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaong Reservoir to less than 3.4 and 0.14 mg L(-1), 24 and 41% improvements, respectively, with 90% confidence. Overall, the Monte Carlo simulation in the integrated modelling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on the probability and level of risk, and its application is recommended.