岩土工程风险分析及应用综述

系统分析了岩土工程不确定性的根源及其分析方法,总结了降低不确定性的途径。系统评述了岩土参数随机场估计、随机变量描述方法以及模型不确定性研究。对几类典型岩土工程问题的风险分析方法、研究现状及其工程应用进行了总结。对目前岩土工程风险理论及其应用的前沿问题进行了研究和展望。研究指出岩土工程风险分析待研问题主要有：系统的风险源辨识方法包括风险源辨识的可视化研究,不确定性因子的合理描述研究,极限状态包括失效模式的仿真研究,不确定性计算包括应用信息融合技术数据挖掘技术研究,目标可靠度研究,风险转嫁风险交换的量化及风险跟踪的实施研究,风险的反分析研究等。展望岩土工程风险分析与应用：风险分析反映了对工程设计的综合性要求,表现了安全与经济的统一;定量风险分析作为决策工具或传统设计的补充,可给决策者提供更多的辅助评价信息,提高了结果的置信程度;但现在能描述的还只是“随机性的确定性模式”,工程师们只有使用“保守的”选择,以适应和弥补不完全的认识与有限的资料的条件。     

DEM提取黄土高原地面坡度的不确定性

选择陕北黄土高原6个典型地貌类型区为试验样区,采用野外实测及高精度的1:1万比例尺DEM为基准数据,研究栅格分辨率及地形粗糙度对DEM所提取地面平均坡度精度的影响。结果显示,对于1:1万比例尺DEM,5 m是保证该地区地形描述精度的理想分辨率尺度;多要素逐步回归模拟的方法进一步揭示了DEM所提取的地面平均坡度误差E与栅格分辨率X以及地形起伏的代表性因子-沟壑密度S之间存在的量化关系为E = (0.0015S2+0.031S-0.0325)X-0.0045S2-0.155S+0.1625,该结果也为确定适用的DEM分辨率提供了理论依据。     

气候变化国家评估报告（II）：气候变化的影响与适应

已经观测到的气候变化影响是显著的、多方面的。各个领域和地区都存在有利和不利影响,但以不利影响为主,未来的气候变暖将会对中国的生态系统、农业以及水资源等部门和沿海地区产生重大的不利影响。采取适应措施可以减轻气候变化的不利影响,应将适应气候变化的行动逐步纳入国民经济和社会发展的中长期规划中。由于我国科学研究的相对不足和科学认识能力的局限,目前的气候变化影响评估方法和结果还存在很大的不确定性。应当加强区域适应气候变化的案例研究、扩大研究领域、加强极端天气、气候事件影响的研究,以降低影响评估的不确定性,并提出切实可行的适应对策。     

The comparative study of multi‐site uncertainty evaluation method based on SWAT model

With the recent development of distributed hydrological models, the use of multi‐site observed data to evaluate model performance is becoming more common. Distributed hydrological model have many advantages, and at the same time, it also faces the challenge to calibrate over‐do parameters. As a typical distributed hydrological model, problems also exist in Soil and Water Assessment Tool (SWAT) parameter calibration. In the paper, four different uncertainty approaches – Particle Swarm Optimization (PSO) techniques, Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting algorithm (SUFI‐2) and Parameter Solution (PARASOL) – are taken to a comparative study with the SWAT model applied in Peace River Basin, central Florida. In our study, the observed river discharge data used in SWAT model calibration were collected from the three gauging stations at the main tributary of the Peace River. Behind these approaches, there is a shared philosophy; all methods seek out many parameter set to fit the uncertainties due to the non‐uniqueness in model parameter evaluation. On the basis of the statistical results of four uncertainty methods, difficulty level of each method, the number of runs and theoretical basis, the reasons that affected the accuracy of simulation were analysed and compared. Furthermore, for the four uncertainty method with SWAT model in the study area, the pairwise correlation between parameters and the distributions of model fit summary statistics computed from the sampling over the behavioural parameter and the entire model calibration parameter feasible spaces were identified and examined. It provided additional insight into the relative identifiability of the four uncertainty methods Copyright © 2014 John Wiley & Sons, Ltd.     

Using nocturnal water level fluctuations for estimating seepage from stormwater detention systems

A simple method, modified from White's method, was developed and verified for estimating seepage from two stormwater detention areas (SDAs) for 2 years, using night‐time changes in surface water levels. The SDAs were located in warm sub‐tropical Florida where the assumption of negligible night‐time evaporation for White's method does not hold true. Daily seepage was estimated using the nocturnal water level fluctuations on no flow days during winter when evaporation losses were insignificant. Specific yield, rather than the composite specific yield, provided accurate seepage estimates. The average annual seepage from the two SDAs was 2.03 m/year. At almost 70% of surface flows, seepage is a significant contributor to regional surface and sub‐surface flows. Comparison of seepage estimates from the night‐time method (NM) and the water balance (WB) method showed that the NM‐based estimates were within the range of the estimates from the WB method. At SDA1, the differences between the NM and WB estimates were 1% and 11%, for the 2 years. The discrepancy between the two estimates became higher (27% and 23%) at SDA2. Larger differences at SDA2 were because of higher error in quantifying pumped inflows for the WB method. Successful performance of NM combined with its low resource (single well monitoring) requirements will help quantify seepage from detention areas and other similar features (e.g. ponds, constructed wetlands) in warmer climates. A scale‐up for the Caloosahatchee River basin showed that seepage from SDA's accounted for 15% of annual river flows indicating the importance of seepage in evaluating water and chemical balances. Copyright © 2015 John Wiley & Sons, Ltd.     

Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty

Stream flow predictions in ungauged basins are one of the most challenging tasks in surface water hydrology because of nonavailability of data and system heterogeneity. This study proposes a method to quantify stream flow predictive uncertainty of distributed hydrologic models for ungauged basins. The method is based on the concepts of deriving probability distribution of model's sensitive parameters by using measured data from a gauged basin and transferring the distribution to hydrologically similar ungauged basins for stream flow predictions. A Monte Carlo simulation of the hydrologic model using sampled parameter sets with assumed probability distribution is conducted. The posterior probability distributions of the sensitive parameters are then computed using a Bayesian approach. In addition, preselected threshold values of likelihood measure of simulations are employed for sizing the parameter range, which helps reduce the predictive uncertainty. The proposed method is illustrated through two case studies using two hydrologically independent sub‐basins in the Cedar Creek watershed located in Texas, USA, using the Soil and Water Assessment Tool (SWAT) model. The probability distribution of the SWAT parameters is derived from the data from one of the sub‐basins and is applied for simulation in the other sub‐basin considered as pseudo‐ungauged. In order to assess the robustness of the method, the numerical exercise is repeated by reversing the gauged and pseudo‐ungauged basins. The results are subsequently compared with the measured stream flow from the sub‐basins. It is observed that the measured stream flow in the pseudo‐ungauged basin lies well within the estimated confidence band of predicted stream flow. Copyright © 2013 John Wiley & Sons, Ltd.     

Estimation of soil hydraulic properties and their uncertainty through the Beerkan infiltration experiment

The Beerkan method based on in situ single‐ring water infiltration experiments along with the relevant specific Beerkan estimation of soil transfer parameters (BEST) algorithm is attractive for simple soil hydraulic characterization. However, the BEST algorithm may lead to erroneous or null values for the saturated hydraulic conductivity and sorptivity especially when there are only few infiltration data points under the transient flow state, either for sandy soil or soils in wet conditions. This study developed an alternative algorithm for analysis of the Beerkan infiltration experiment referred to as BEST‐generalized likelihood uncertainty estimation (GLUE). The proposed method estimates the scale parameters of van Genuchten water retention and Brooks–Corey hydraulic conductivity functions through the GLUE methodology. The GLUE method is a Bayesian Monte Carlo parameter estimation technique that makes use of a likelihood function to measure the goodness‐of‐fit between modelled and observed data. The results showed that using a combination of three different likelihood measurements based on observed transient flow, steady‐state flow and experimental steady‐state infiltration rate made the BEST‐GLUE procedure capable of performing an efficient inverse analysis of Beerkan infiltration experiments. Therefore, it is more applicable for a wider range of soils with contrasting texture, structure, and initial and saturated water content. Copyright © 2015 John Wiley & Sons, Ltd.     

Quantifying fine‐grained sediment sources in the River Axe catchment,southwest England: application of a Monte Carlo numerical modelling framework incorporating local and genetic algorithm optimisation

Increasing recognition of the deleterious environmental effects of excessive fine sediment delivery to watercourses means that reliable sediment source assessment represents a fundamental component of catchment planning targeting the protection of freshwater resources and their ecological integrity. Sediment tracing or fingerprinting approaches have been increasingly used to provide catchment scale sediment source information, but there is a need to continue refining existing procedures especially with respect to uncertainty analysis during mass balance modelling. Consequently, an updated Monte Carlo numerical modelling framework was devised and tested, incorporating both conventional and robust statistics coupled with random and Latin Hypercube Sampling (LHS) together with local and genetic algorithm (GA) optimisation. A sediment sourcing study undertaken in the River Axe catchment, southwest England, suggested that the use of robust statistics and LHS with GA optimisation generated the best performance with respect to predicting measured bed sediment geochemistry in six out of eight model applications. On this basis, the catchment‐wide average median sediment source contributions were predicted to be 38 ± 1% (pasture topsoils), 3 ± 1% (cultivated topsoils), 37 ± 1% (damaged road verges) and 22 ± 1% (channel banks/subsurface sources). Using modelling frameworks which provide users with flexibility to compare local and global optimisation during uncertainty analysis is recommended for future sediment tracing studies. Copyright © 2011 John Wiley & Sons, Ltd.     

Using response surfaces to estimate impacts of climate change on flood peaks: assessment of uncertainty

The potential impacts of climate change are an increasing focus of research, and ever‐larger climate projection ensembles are available, making standard impact assessments more onerous. An alternative way of estimating impacts involves response surfaces, which present the change in a given indicator for a large number of plausible climatic changes defined on a regular sensitivity domain. Sets of climate change projections can then be overlaid on the response surface and impacts estimated from the nearest corresponding points of the sensitivity domain, providing a powerful method for fast impact estimation for multiple projections and locations. However, the effect of assumptions necessary for initial response surface development must be assessed. This paper assesses the uncertainty introduced by use of a sensitivity framework for estimating changes in 20‐year return period flood peaks in Britain. This sensitivity domain involves mean annual and seasonal precipitation changes, and a number of simplifications were necessary for consistency and to reduce dimensionality. The effect of these is investigated for nine catchments across Britain, representing nine typical response surfaces (response types), using three sets of climate projections. The results show that catchments can have different causes of uncertainty and some catchments have an overall higher level of uncertainty than others. These differences are compatible with the underlying climatological and hydrological differences between the response types, giving confidence in generalization of the results. This enables the development of uncertainty allowances by response type, to be used alongside the response surfaces to provide more robust impact estimates. Copyright © 2013 John Wiley & Sons, Ltd.     

Simulating wetland impacts on stream flow in southern Africa using a monthly hydrological model

The processes that occur in wetlands and natural lakes are often overlooked and not fully incorporated in the conceptual development of many hydrological models of basin runoff. These processes can exert a considerable influence on downstream flow regimes and are critical in understanding the general patterns of runoff generation at the basin scale. This is certainly the case for many river basins of southern Africa which contain large wetlands and natural lakes and for which downstream flow regimes are altered through attenuation, storage and slow release processes that occur within the water bodies. Initial hydrological modelling studies conducted in some of these areas identified the need to explicitly account for wetland storage processes in the conceptual development of models. This study presents an attempt to incorporate wetland processes into an existing hydrological model, with the aim of reducing model structural uncertainties and improving model simulations where the impacts of wetlands or natural lakes on stream flow are evident. The approach is based on relatively flexible functions that account for the input–storage–output relationships between the river channel and the wetland. The simulation results suggest that incorporating lake and wetland storage processes into modelling can provide improved representation (the right results for the right reason) of the hydrological behaviour of some large river basins, as well as reducing some of the uncertainties in the quantification of the original model parameters used for generating the basin runoff. Copyright © 2013 John Wiley & Sons, Ltd.