Identification of flood seasonality using an entropy-based method

Identifying flood seasonality is critical in hydrologic applications as well as water resources management. We develop an entropy-based method (EBM) for identifying flood seasonality and partitioning the entire flood season into multiple sub-seasons. The performance of the proposed EBM is evaluated using a Monte Carlo simulation test and compared with current methods. The Three Gorges Reservoir (TGR) basin in the Yangtze River is selected as a case study to test the applicability of the proposed method. Results of Monte Carlo simulation test demonstrate that the EBM performs better than the probability change-point method and the improved relative frequency method with less bias and higher efficiency. The case study results illustrate that the EBM can appropriately divide the entire flood season of the TGR into pre-flood season (from June 1st to June 20th), main-flood season (from June 21th to September 10th) and post-flood season (from September 11th to September 30th). The flood limited water levels (FLWL) in these three sub-seasons can then be derived, which are 150 m, 145 m and 149 m, respectively. Compared with conventional operation rule, the seasonal FLWL scheme can generate more hydropower (0.93 billion KWh) annually with a reliability of 99.86%. Therefore, it is meaningful to divide the entire flood season into three sub-seasons and apply seasonal FLWL for TGR operation.     

Flood season segmentation based on the probability change-point analysis technique

Abstract

The segmentation of flood seasons has both theoretical and practical importance in hydrological sciences and water resources management. The probability change-point analysis technique is applied to segmenting a defined flood season into a number of sub-seasons. Two alternative sampling methods, annual maximum and peaks-over-threshold, are used to construct the new flow series. The series is assumed to follow the binomial distribution and is analysed with the probability change-point analysis technique. A Monte Carlo experiment is designed to evaluate the performance of proposed flood season segmentation models. It is shown that the change-point based models for flood season segmentation can rationally partition a flood season into appropriate sub-seasons. China's new Three Gorges Reservoir, located on the upper Yangtze River, was selected as a case study since a hydrological station with observed flow data from 1882 to 2003 is located 40 km downstream of the dam. The flood season of the reservoir can be reasonably divided into three sub-seasons: the pre-flood season (1 June–2 July); the main flood season (3 July–10 September); and the post-flood season (11–30 September). The results of flood season segmentation and the characteristics of flood events are reasonable for this region.

Citation Liu, P., Guo, S., Xiong, L. & Chen, L. (2010) Flood season segmentation based on the probability change-point analysis technique. Hydrol. Sci. J. 55(4), 540–554.     

考虑碳减排的水库多目标提前蓄水调度研究

针对当前的水库提前蓄水调度研究尚未考虑碳减排问题,本文基于水库碳排放和有机碳埋藏因子法,构建了考虑碳减排的水库提前蓄水调度模型,采用基于熵权重的逼近理想解排序法(TOPSIS)对提前蓄水调度方案进行了多目标评价,以优选调度方案,在三峡水库开展了实例研究。研究结果表明:三峡水库从9月1日起蓄,于9月30日逐步蓄至167 m的调度方案最优,相较于原设计方案,在不增加防洪风险的前提下,多年平均发电量增加29.91亿kW·h(8.80%),弃水量减少26.03亿m³(27.51%),碳排放量减少69.26亿g(3.94%),有机碳埋藏量增加1.93亿g(1.28%),温室气体的CO₂碳当量减少235.48亿g(3.85%),提前蓄水方案可显著提升三峡水库发电量、供水保障能力和减少碳排放量。本研究为水库提前蓄水的水碳协同调度提供了技术支撑。     

Study of the flood control scheduling scheme for the Three Gorges Reservoir in a catastrophic flood

The Three Gorges Project is the world's largest water conservancy project. According to the design standards for the 1,000‐year flood, flood diversion areas in the Jingjiang reach of the Yangtze River must be utilized to ensure the safety of the Jingjiang area and the city of Wuhan. However, once these areas are used, the economic and life loss in these areas may be very great. Therefore, it is vital to reduce this loss by developing a scheme that reduces the use of the flood diversion areas through flood regulation by the Three Gorges Reservoir (TGR), under the premise of ensuring the safety of the Three Gorges Dam. For a 1,000‐year flood on the basis of a highly destructive flood in 1954, this paper evaluates scheduling schemes in which flood diversion areas are or are not used. The schemes are simulated based on 2.5‐m resolution reservoir topography and an optimized model of dynamic capacity flood regulation. The simulation results show the following. (a) In accord with the normal flood‐control regulation discharge, the maximum water level above the dam should be not more than 175 m, which ensures the safety of the dam and reservoir area. However, it is necessary to utilize the flood diversion areas within the Jingjiang area, and flood discharge can reach 2.81 billion m³. (b) In the case of relying on the TGR to impound floodwaters independently rather than using the flood diversion areas, the maximum water level above the dam reaches 177.35 m, which is less than the flood check level of 180.4 m to ensure the safety of the Three Gorges Dam. The average increase of the TGR water level in the Chongqing area is not more than 0.11 m, which indicates no significant effect on the upstream reservoir area. Comparing the various scheduling schemes, when the flood diversion areas are not used, it is believed that the TGR can execute safe flood control for a 1,000‐year flood, thereby greatly reducing flood damage.     

Characteristics of sedimentation and channel adjustment linked to the Three Gorges Reservoir

Construction of large dams is attractive because of their great benefits in flood control,hydropower generation,water resources utilization,navigation improvement,etc.However,dam construction may bring some negative impacts on sediment transport and channel dynamics adjustments.Due to the effects of recent water and soil conservation projects,sediment retention in the newly constructed large upstream reservoirs,and other factors,the sedimentation in the Three Gorges Reservoir(TGR)is quite different from the amount previously predicted in the demonstration stage.Consequently,based on the measured data,characteristics of sedimentation and the related channel deformation in the TGR were analyzed.The results imply that sediment transport tended to be reduced after the Three Gorges Project(TGP).Sedimentation slowed dramatically after 2013 and indicated obvious seasonal characteristics.Due to the rising water level in the TGR in the flood season,the yearly sediment export ratio(Eratio)was prone to decrease.The water level near the dam site should be reasonably regulated according to the flow discharge to improve the sediment delivery capacity and reduce sedimentation in the TGR,and to try to avoid situations where the flood retention time is close to 444 h.The depositional belt was discontinuous in the TGR and was mainly distributed in the broad reaches,and only slight erosion or deposition occurred in the gorge reaches.Sedimentation in the broad and gorge reaches accounted for 93.8% and 6.2% of the total sedimentation,respectively.The estuarine reach located in the fluctuating backwater area experienced alternate erosion-deposition,with a slight accumulative deposition in the curved reach.Sedimentation mainly occurred in the perennial backwater area.The insight gained in this study can be conducive to directly understanding of large reservoir sedimentation and mechanism of channel adjustment in the reservoir region in the main channel of large river.     

Quantifying differences between reservoir inflows and dam site floods using frequency and risk analysis methods

Reservoirs are the most important constructions for water resources management and flood control. Great concern has been paid to the effects of reservoir on downstream area and the differences between inflows and dam site floods due to the changes of upstream flow generation and concentration conditions after reservoir’s impoundment. These differences result in inconsistency between inflow quantiles and the reservoir design criteria derived by dam site flood series, which can be a potential risk and must be quantificationally evaluated. In this study, flood frequency analysis (FFA) and flood control risk analysis (FCRA) methods are used with the long reservoir inflow series derived from a multiple inputs and single output model and a copula-based inflow estimation model. The results of FFA and FCRA are compared and the influences on reservoir flood management are also discussed. The Three Gorges Reservoir (TGR) in China is selected as a case study. Results show that the differences between the TGR inflow and dam site floods are significant which result in changes on its flood control risk rates. The mean values of TGR’s annual maximum inflow peak discharge and 3 days flood volume have increased 5.58 and 3.85% than the dam site ones, while declined by 1.82 and 1.72% for the annual maximum 7 and 15 days flood volumes. The flood control risk rates of middle and small flood events are increased while extreme flood events are declined. It is shown that the TGR can satisfy the flood control task under current hydrologic regime and the results can offer references for better management of the TGR.     

Reservoir operation by staging due to climate variability

The changing environment enhances the hydrological cycle and increases the frequency of extreme floods. In this paper, the impacts of climate variability on flood season segmentation are determined and the scientific basis for determining corresponding flood limiting water levels (FLWLs) is provided. Climate variation was determined and then the flood season was divided into several sub-seasons using the results of the set pair analysis method (SPAM) and four indices; peak floods crossing the transitional periods were sampled to obtain a design flood hydrograph; and, finally, seasonal FLWLs were determined for reservoir operation. The performance of this reservoir staging operation was evaluated for a case study in the Chengbihe Reservoir, China.     

The impact of Three Gorges Reservoir refill operation on water levels in Poyang Lake,China

The refill operation of Three Gorges Reservoir (TGR) in the end of flood season significantly alters the water level regimes in Poyang Lake by reducing Yangtze River flow discharge. This study aims to investigate the impact of TGR refill operation on water level probability distribution of the Poyang Lake. The multiple linear regression model was established to estimate the water level with catchment inflow and Yangtze River flow as explanatory variables. A probability distribution of water level was derived and the refill operation effects were quantified by comparing the water level distribution at Xingzi station in the Poyang Lake before and after TGR. It is revealed that Yangtze River flow, rather than the catchment inflow is the dominant factor affecting the water level of Poyang Lake during TGR refill operation period. Results also show that the water level distribution estimated by the derived distribution method can be accepted as a theoretical distribution and has a comparable accuracy as the directly fitted distribution method before TGR. The derived method can be adapted to the environment change, thus is well suited for estimating the water level distribution after TGR. It is observed that Xingzi water levels with different design frequencies have been reduced due to the TGR refill operation. The water level reductions induced by TGR refill operation are 1.28, 0.87, and 0.50 m corresponding with design frequencies of 50, 90 and 99 %, respectively. The results from this work would improve the understanding of the TGR effects on the downstream river–lake system and provide scientific evidences for formulating better scheme for water resources management in this region.     

1990年以来北京密云水库主要水环境因子时空分布特征

根据19902011年密云水库共12个监测点的月监测资料,采用聚类分析研究各监测点水环境相似性及空间分布特征,采用因子分析识别影响水质的主要因子并评价各采样点的综合水质.通过绝对主成分多元回归分析,获得汛期和非汛期各因子对各水质指标的贡献率.利用季节性Kendall检验及流量调节检验对密云水库库区水化学特征和水质状况时空分布特征进行了研究.结果表明：汛期水质主要受到农业营养物质的影响,其次为生物化学因素和有机物的影响.非汛期水质主要受到农牧业排放因素的影响,其次为人类活动和生物化学因素的影响.因子得分综合评价显示,汛期辛庄桥、内湖和大关桥综合水质较差,潮河、库西和白河综合水质较好.非汛期辛庄桥、石佛桥和大关桥综合水质较差,库东、套里和恒河综合水质较好.主要水质指标的年际变化规律不同,但最终都趋于平稳.与潮河、白河入库水质变化相比,库区水质变化趋势较小,上游入库水质和库区水质都整体趋好.除白河入库的总氮和总磷外,其他监测指标的变化趋势经流量调节前后基本一致,表明流量并不是引起水质趋势变化的主要因素,水质的变化主要是由于污染源变化而引起.     

三峡小江回水区透明度季节变化及其影响因子分析

三峡成库后其季节调蓄过程使该水域湖沼学特征具有独特性.根据三峡小江流域回水区段为期2年的定位跟踪观测,对透明度(SD)和主要环境指标的相互关系进行分析研究.研究期间,小江回水区透明度均值为170±7cm,各采样点透明度差异不明显且季节变化过程一致,自春末夏初开始降至最低水平,夏季汛期相对稳定,夏末入秋持续升高,冬季维持在较高状态,入春后下降.对透明度和主要环境指标的相关性分析发现,无机悬浮颗粒(PIM)是影响透明度的主要指标.透明度同PIM、Chl.a多元回归模型为:SD=(-89.389±8.101)·lg(PIM)+(-84.008±8.624)·lg(Chl.a)+(264.132±8.232).汛期低水位状态下(145-150m)小江回水区水动力条件趋于天然河道,河道输沙量增加使无机悬浮颗粒含量远高于藻类生物量而成为影响透明度的主要环境指标.在中水位(150-156m)和高水位(156m以上),虽然藻类进入非生长季节,但水位抬升和水体滞留时间的延长促使悬浮颗粒物大量沉淀,悬浮生长于表层水体的藻类成为影响透明度的主要环境指标,生物作用对透明度的影响明显.     

Applying ANN emulators in uncertainty assessment of flood inundation modelling: a comparison of two surrogate schemes

Abstract

A generalized likelihood uncertainty estimation (GLUE) framework coupling with artificial neural network (ANN) models in two surrogate schemes (i.e. GAE-S1 and GAE-S2) was proposed to improve the efficiency of uncertainty assessment in flood inundation modelling. The GAE-S1 scheme was to construct an ANN to approximate the relationship between model likelihoods and uncertain parameters for facilitating sample acceptance/rejection instead of running the numerical model directly; thus, it could speed up the Monte Carlo simulation in stochastic sampling. The GAE-S2 scheme was to establish independent ANN models for water depth predictions to emulate the numerical models; it could facilitate efficient uncertainty analysis without additional model runs for locations concerned under various scenarios. The results from a study case showed that both GAE-S1 and GAE-S2 had comparable performances to GLUE in terms of estimation of posterior parameters, prediction intervals of water depth, and probabilistic inundation maps, but with reduced computational requirements. The results also revealed that GAE-S1 possessed a slightly better performance in accuracy (referencing to GLUE) than GAE-S2, but a lower flexibility in application. This study shed some light on how to apply different surrogate schemes in using numerical models for uncertainty assessment, and could help decision makers in choosing cost-effective ways of conducting flood risk analysis.     

Tidal straining effect on the suspended sediment transport in the Huanghe (Yellow River) Estuary, China

Tidal straining effect on sediment transport dynamics in the Huanghe (Yellow River) estuary was studied by field observations and numerical simulations. The measurement of salinity, suspended sediment concentration, and current velocity was conducted during a flood season in 1995 at the Huanghe river mouth with six fishing boats moored at six stations for 25-h hourly time series observations. Based on the measurements, the intra-tidal variations of sediment transport in the highly turbid river mouth was observed and the tidal straining effect occurred. Our study showed that tidal straining of longitudinal sediment concentration gradients can contribute to intra-tidal variability in sediment stratification and to asymmetries in sediment distribution within a tidal cycle. In particular, the tidal straining effect in the Huanghe River estuary strengthened the sediment-induced stratification at the flood tide, thus producing a higher bottom sediment concentration than that during the ebb. A sediment transport model that is capable of simulating sediment-induced stratification effect on the hydrodynamics in the bottom boundary layers and associated density currents was applied to an idealized estuary to demonstrate the processes and to discuss the mechanism. The model-predicted sediment processes resembled the observed characteristics in the Huanghe River estuary. We concluded that tidal straining effect is an important but poorly understood mechanism in the transport dynamics of cohesive sediments in turbid estuaries and coastal seas.     

Practical flood frequency estimates

The best information on which to base estimates of future flood frequencies is records of past flood events. Where there is a substantial record at the location for which estimates are desired the estimation process is generally straighforward, although a variety of methods is used and there is major uncertainty in the estimates. In general, the frequency of future events is assumed to be indicated by the observed frequency of past events under constant controlling watershed conditions.Techniques are available for using information on historical (pre-record) flood data to improve the reliability of flood frequency estimates. There are methods for detecting and managing extremely unusual actual events (outliers) and for improving the reliability of short-record estimates based on long-record data at related locations. Regional correlation analysis is usable for establishing flood frequency estimates for locations where records are not available.Detailed hydrologic analysis, usually involving rainfall-runoff studies, is required for establishing flood frequency relationships for modified conditions of the watershed or, in many cases, for establishing flood frequency estimates for newly formed drainage systems such as in urban areas and airports.The principal use of flood frequency functions is to compare expected changes in flood damages (due to a contemplated action) with the economic and social costs or benefits of the contemplated action.     

Comparison of parameter uncertainty analysis techniques for a TOPMODEL application

Parameter uncertainty in hydrologic modeling is crucial to the flood simulation and forecasting. The Bayesian approach allows one to estimate parameters according to prior expert knowledge as well as observational data about model parameter values. This study assesses the performance of two popular uncertainty analysis (UA) techniques, i.e., generalized likelihood uncertainty estimation (GLUE) and Bayesian method implemented with the Markov chain Monte Carlo sampling algorithm, in evaluating model parameter uncertainty in flood simulations. These two methods were applied to the semi-distributed Topographic hydrologic model (TOPMODEL) that includes five parameters. A case study was carried out for a small humid catchment in the southeastern China. The performance assessment of the GLUE and Bayesian methods were conducted with advanced tools suited for probabilistic simulations of continuous variables such as streamflow. Graphical tools and scalar metrics were used to test several attributes of the simulation quality of selected flood events: deterministic accuracy and the accuracy of 95 % prediction probability uncertainty band (95PPU). Sensitivity analysis was conducted to identify sensitive parameters that largely affect the model output results. Subsequently, the GLUE and Bayesian methods were used to analyze the uncertainty of sensitive parameters and further to produce their posterior distributions. Based on their posterior parameter samples, TOPMODEL’s simulations and the corresponding UA results were conducted. Results show that the form of exponential decline in conductivity and the overland flow routing velocity were sensitive parameters in TOPMODEL in our case. Small changes in these two parameters would lead to large differences in flood simulation results. Results also suggest that, for both UA techniques, most of streamflow observations were bracketed by 95PPU with the containing ratio value larger than 80 %. In comparison, GLUE gave narrower prediction uncertainty bands than the Bayesian method. It was found that the mode estimates of parameter posterior distributions are suitable to result in better performance of deterministic outputs than the 50 % percentiles for both the GLUE and Bayesian analyses. In addition, the simulation results calibrated with Rosenbrock optimization algorithm show a better agreement with the observations than the UA’s 50 % percentiles but slightly worse than the hydrographs from the mode estimates. The results clearly emphasize the importance of using model uncertainty diagnostic approaches in flood simulations.     

Improvement of mid- to long-term runoff forecasting based on physical causes: application in Nenjiang basin,China

Abstract

An artificial neural network, mid- to long-term runoff forecasting model of the Nenjiang basin was established by deciding predictors using the physical analysis method, combined with long-term hydrological and meteorological information. The forecasting model was gradually improved while considering physical factors, such as the main flood season and non-flood season by stage, runoff sources and hydrological processes. The average relative errors in the simulation tests of the prediction model were 0.33 in the main flood season and 0.26 in the non-flood season, indicating that the prediction accuracy during the non-flood season was greater than that in the main flood season. Based on these standards, forecasting accuracy evaluation was conducted by comparing forecasting results with actual conditions: for 2001 to 2003 data, the pass rate of forecasting in the main flood season was 50%, while it was 93% in the non-flood season; for 2001–2010, the respective values were 45% and 72%. The accuracy of prediction was found to decrease as the length of record increases.

Editor D. Koutsoyiannis, Associate editor A. Viglione

Citation Li, H.-Y. Tian, L., Wu, Y., and Xie, M., 2013. Improvement of mid- to long-term runoff forecasting based on physical causes: application in Nenjiang basin, China. Hydrological Sciences Journal, 58 (7), 1414–1422.     

Seasonal ensemble generator for radar rainfall using copula and autoregressive model

Uncertainty analysis of radar rainfall enables stakeholders and users have a clear knowledge of the possible uncertainty associated with the rainfall products. Long-term empirical modeling of the relationship between radar and gauge measurements is an efficient and practical method to describe the radar rainfall uncertainty. However, complicated variation of synoptic conditions makes the radar-rainfall uncertainty model based on historical data hard to extend in the future state. A promising solution is to integrate synoptic regimes with the empirical model and explore the impact of individual synoptic regimes on radar rainfall uncertainty. This study is an attempt to introduce season, one of the most important synoptic factor, into the radar rainfall uncertainty model and proposes a seasonal ensemble generator for radar rainfall using copula and autoregressive model. We firstly analyze the histograms of rainfall-weighted temperature, the radar-gauge relationships, and Box and Whisker plots in different seasons and conclude that the radar rainfall uncertainty has strong seasonal dependence. Then a seasonal ensemble generator is designed and implemented in a UK catchment under a temperate maritime climate, which can fully model marginal distribution, spatial dependence, temporal dependence and seasonal dependence of radar rainfall uncertainty. To test its performance, 12 typical rainfall events (4 for each season) are chosen to generate ensemble rainfall values. In each time step, 500 ensemble members are produced and the values of 5th to 95th percentiles are used to derive the uncertainty bands. Except several outliers, the uncertainty bands encompass the observed gauge rainfall quite well. The parameters of the ensemble generator vary considerably for each season, indicating the seasonal ensemble generator reflects the impact of seasons on radar rainfall uncertainty. This study is an attempt to simultaneously consider four key features of radar rainfall uncertainty and future study will investigate their impacts on the outputs of hydrological models with radar rainfall as input or initial conditions.     

Estimation of uncertainty propagation in flood inundation mapping using a 1‐D hydraulic model

A need for more accurate flood inundation maps has recently arisen because of the increasing frequency and extremity of flood events. The accuracy of flood inundation maps is determined by the uncertainty propagated from all of the variables involved in the overall process of flood inundation modelling. Despite our advanced understanding of flood progression, it is impossible to eliminate the uncertainty because of the constraints involving cost, time, knowledge, and technology. Nevertheless, uncertainty analysis in flood inundation mapping can provide useful information for flood risk management. The twin objectives of this study were firstly to estimate the propagated uncertainty rates of key variables in flood inundation mapping by using the first‐order approximation method and secondly to evaluate the relative sensitivities of the model variables by using the Hornberger–Spear–Young (HSY) method. Monte Carlo simulations using the Hydrologic Engineering Center's River Analysis System and triangle‐based interpolation were performed to investigate the uncertainty arising from discharge, topography, and Manning's n in the East Fork of the White River near Seymour, Indiana, and in Strouds Creek in Orange County, North Carolina. We found that the uncertainty of a single variable is propagated differently to the flood inundation area depending on the effects of other variables in the overall process. The uncertainty was linearly/nonlinearly propagated corresponding to valley shapes of the reaches. In addition, the HSY sensitivity analysis revealed the topography of Seymour reach and the discharge of Strouds Creek to be major contributors to the change of flood inundation area. Copyright © 2014 John Wiley & Sons, Ltd.     

Modelling soil water dynamics considering measurement uncertainty

In shallow water table‐controlled environments, surface water management impacts groundwater table levels and soil water dynamics. The study goal was to simulate soil water dynamics in response to canal stage raises considering uncertainty in measured soil water content. Water and Agrochemicals in the soil, crop and Vadose Environment (WAVE) was applied to simulate unsaturated flow above a shallow aquifer. Global sensitivity analysis was performed to identify model input factors with the greatest influence on predicted soil water content. Nash–Sutcliffe increased and Root Mean Square Error reduced when uncertainties in measured data were considered in goodness‐of‐fit calculations using measurement probability distributions and probable asymmetric error boundaries, implying that appropriate model performance evaluation should be carried out using uncertainty ranges instead of single values. Although uncertainty in the experimental measured data limited evaluation of the absolute predictions by the model, WAVE was found a useful exploratory tool for estimating temporal variation in soil water content. Visual analysis of soil water content time series under proposed changes in canal stage management indicated that sites with land surface elevation of less than 2.0‐m NGVD29 were predicted to periodically experience saturated conditions in the root zone and shortening of the growing season if canal stage is raised more than 9 cm and maintained at this level. The models developed could be combined with high‐resolution digital elevation models in future studies to identify areas with the greatest risk of experiencing saturated root zone. The study also highlighted the need to incorporate measurement uncertainty when evaluating performance of unsaturated flow models. Copyright © 2014 John Wiley & Sons, Ltd.     

Uncertainty analysis of flood inundation modelling using GLUE with surrogate models in stochastic sampling

A generalized likelihood uncertainty estimation (GLUE) method incorporating moving least squares (MLS) with entropy for stochastic sampling (denoted as GLUE‐MLS‐E) was proposed for uncertainty analysis of flood inundation modelling. The MLS with entropy (MLS‐E) was established according to the pairs of parameters/likelihoods generated from a limited number of direct model executions. It was then applied to approximate the model evaluation to facilitate the target sample acceptance of GLUE during the Monte‐Carlo‐based stochastic simulation process. The results from a case study showed that the proposed GLUE‐MLS‐E method had a comparable performance as GLUE in terms of posterior parameter estimation and predicted confidence intervals; however, it could significantly reduce the computational cost. A comparison to other surrogate models, including MLS, quadratic response surface and artificial neural networks (ANN), revealed that the MLS‐E outperformed others in light of both the predicted confidence interval and the most likely value of water depths. ANN was shown to be a viable alternative, which performed slightly poorer than MLS‐E. The proposed surrogate method in stochastic sampling is of practical significance in computationally expensive problems like flood risk analysis, real‐time forecasting, and simulation‐based engineering design, and has a general applicability in many other numerical simulation fields that requires extensive efforts in uncertainty assessment. Copyright © 2014 John Wiley & Sons, Ltd.