Hydrologic Risk Analysis Research Articles

Compound Impact of Storm Surge and Flood Characteristics in Coastal Area Based on Copula

In low-lying coastal areas, the interplay of various factors including precipitation, river flow, and storm surge can lead to greater influence on floods when they occur simultaneously. The copula method was used in this study to investigate the bivariate flood risk of compounding storm surge and river discharge events in the Pearl River Delta (PRD). Our results indicate that while the correlation between storm surge and flood peak (S-Q) was weak, there was a strong dependence between the pairs of storm surge–flood volume (S-V) and storm surge–flood duration (S-D). For these three pairs, the Clayton copula was the optimal function for S-Q, while the Frank copula was the optimal function for S-V and S-D, respectively. When the flood volume exceeds 2.0 × 104 m3/s and the flood duration is more than 10 days, the bivariate hydrologic risk for S-V and S-D is observed to decrease rapidly. Furthermore, the failure probability (FP) would be underestimated when the combined impact of river flow and storm surge is ignored in coastal flood risk assessment. Such bivariate hydrologic risk analysis implies that when determining design values in coastal flood risk assessment, the combined impact of river flow and storm surge should be taken into account.

Technical note: Modeling spatial fields of extreme precipitation – a hierarchical Bayesian approach

Abstract. We introduce a hierarchical Bayesian model for the spatial distribution of rainfall corresponding to an extreme event of a specified duration that could be used with regional hydrologic models to perform a regional hydrologic risk analysis. An extreme event is defined if any gaging site in the watershed experiences an annual maximum rainfall event and the spatial field of rainfall at all sites corresponding to that occurrence is modeled. Applications to data from New York City demonstrate the effectiveness of the model for providing spatial scenarios that could be used for simulating loadings into the urban drainage system. Insights as to the homogeneity in spatial rainfall and its implications for modeling are provided by considering partial pooling in the hierarchical Bayesian framework.

Bivariate hydrologic risk analysis for the Xiangxi River in Three Gorges Reservoir Area, China

BackgroundHydrological extremes such as floods generally have multidimensional attributes with complex dependence structures. This leads to the urgent demand of hydrological risk analysis within a multivariate context. In this study, the bivariate hydrologic risk framework is proposed based on the bivariate copula method. In the proposed risk analysis framework, bivariate flood frequency would be analyzed for different flood variable pairs (i.e., flood peak-volume, flood peak-duration, flood volume-duration), and the bivariate hydrologic risk is then derived based on the joint return period of a flood variable pair. The distribution of one flood variable conditional on another flood variable can also be obtained through the copula method.ResultsThe proposed method is applied to the risk analysis for the Xiangxi River in the Three Gorges Reservoir area, China, based on 50 years streamflow measurements. The results indicate that the bivariate risk for flood peak flow-duration would keep constant for some time and then decrease as the increase of the flood duration. The bivariate risk for flood peak-volume holds a similar trend with the bivariate risk of flood peak-duration. The probability density functions (PDFs) of the flood volume and duration conditional on flood peak can also be generated through the best fitted copula function.ConclusionThe results indicate that the distributions of flood volume would be highly influenced by the flood peak flows, in which the flood volume would be expected to increase as the increase of flood return period. Conversely, the distribution of the flood duration would not change significantly with the variation in the flood peak return period. The obtained conclusions from the bivariate hydrologic analysis can provide decision support for flood control and mitigation.

Multivariate Hydrologic Risk Analysis for River Thames

This study analyzed the multivariate flood risk for the river Thames at Kingston based on historical flood data from the National River Flow Archive (NRFA) website. The bivariate risk analysis framework was prepared from the joint return periods of the peak flow (m3/s) and 3-day annual maximum flow (m3/s) flood pair. A total of 137 samples of flood pairs from 1883 to 2019 were adopted for risk analysis. The multivariate return periods were characterized depending on the quantification of the bivariate flood frequency analysis of the pair through copulas methods. The unknown parameter of each copula was estimated using the method-of-moment (MOM) estimator based on Kendall’s tau inversion, in which the Clayton copula performed best to model the dependence of the two flood variables. Then, the bivariate hydrologic risk was characterized based on the joint return period in AND, established from the Clayton copula method. The results reveal that the flood pair would keep a constant hydrologic risk value for some time then moderately decrease as the 3-day AMAX flow increases from 700 m3/s. This hydrologic risk indicator was analyzed under four service time scenarios and three peak flows whose return periods were positioned at 50, 100, and 150 years. The outcomes from the bivariate risk analysis of the flood pairs can be used as decision support during the design of flood defenses and hydraulic facilities.

An exceedance model based on bivariate order statistics

In hydrologic risk analysis, the use of exceedance statistics are very important. In this sense, we construct a random threshold model based on bivariate order statistics. The exact distribution of exceedance statistics is calculated under some well-known copulas such as independent and Farlie-Gumbel-Morgenstern (FGM) copulas. Furthermore, numerical results are provided for expected value and variance of exceedance statistics under independent and Farlie-Gumbel-Morgenstern copulas. The application of the model in hydrology is also discussed.

An uncertainty partition approach for inferring interactive hydrologic risks

Abstract. Extensive uncertainties exist in hydrologic risk analysis. Particularly for interdependent hydrometeorological extremes, the random features in individual variables and their dependence structures may lead to bias and uncertainty in future risk inferences. In this study, an iterative factorial copula (IFC) approach is proposed to quantify parameter uncertainties and further reveal their contributions to predictive uncertainties in risk inferences. Specifically, an iterative factorial analysis (IFA) approach is developed to diminish the effect of the sample size and provide reliable characterization for parameters' contributions to the resulting risk inferences. The proposed approach is applied to multivariate flood risk inference for the Wei River basin to demonstrate the applicability of IFC for tracking the major contributors to resulting uncertainty in a multivariate risk analysis framework. In detail, the multivariate risk model associated with flood peak and volume will be established and further introduced into the proposed iterative factorial analysis framework to reveal the individual and interactive effects of parameter uncertainties on the predictive uncertainties in the resulting risk inferences. The results suggest that uncertainties in risk inferences would mainly be attributed to some parameters of the marginal distributions, while the parameter of the dependence structure (i.e. copula function) would not produce noticeable effects. Moreover, compared with traditional factorial analysis (FA), the proposed IFA approach would produce a more reliable visualization for parameters' impacts on risk inferences, while the traditional FA would remarkably overestimate the contribution of parameters' interaction to the failure probability in AND (i.e. all variables would exceed the corresponding thresholds) and at the same time underestimate the contribution of parameters' interaction to the failure probabilities in OR (i.e. one variable would exceed its corresponding threshold) and Kendall (i.e. the correlated variables would exceed a critical multivariate threshold).

Vine copula selection using mutual information for hydrological dependence modeling

Hydrological risk analysis and management entails multivariate modeling which requires modeling the structure of dependence among different variables. Vine copulas have been increasing applied in multivariate modeling wherein the selection of vine copula structure plays a critical role. Inspired by the relationship between Mutual information (MI) and copula entropy (CE), this study discussed the connection between conditional mutual information (CMI) and CE and developed a mutual information-based sequential approach to select a vine structure which was based on original observations, and model-independent. Then, to reduce the complexity of R-vine copulas, a statistical method-based truncation procedure was applied. Finally, an MI-based approach for hydrological dependence modeling was developed. Two types of hydrological processes with different dependence structures were utilized to show the performance of the proposed approach: (i) drought characterization: showing a D-vine structure; and (ii) multi-site streamflow dependence: showing a C-vine structure. Results indicated that the MI-based approach satisfactorily modeled different kinds of dependence structure and yielded more information on variables in comparison with traditional tau-based approach.

A Flood Forecasting Model based on Wireless Sensor and Actor Networks

Flood forecasting is a challenging area of research that can help to save precious lives by timely intimating about the flood possibilities. The role of advancements in computing and allied technologies has moved the research towards a new horizon. Researchers from all over the world are using Artificial Neural Networks (ANN), Global Information Systems (GIS), and Wireless Sensor Networks (WSN) based schemes for flash flood forecasting and hydrological risk analysis. ANN and GIS-based solutions are much costly whereas the analysis and prediction using WSN require much less cost for infrastructure deployment. It will enable the use of flood prediction mechanisms in the third world and poor countries. New variation in storage capacity can be a vital source to eliminate or reduce the chance of flood. By considering this observation, it is proposed to develop a generic flood prediction scheme that can manage the system as per resources and environmental conditions. A heterogeneous WSN has considered where powerful Collector Nodes (CN) continuously takes values from member sensor nodes in the region. CN transmits the alerts to Administration Unit (AU) when threshold values are crossed. It is proposed to save the threshold values from all water sources like storage capacity, water inflow, and outflow in the repository for decision making. Moreover, environmental parameters including water level, humidity, temperature, air pressure, rainfall, soil moisture, etc. are considered for the analysis of flood prediction. We have also considered the evaluation of these parameters in specified boundary values that were not considered in existing schemes. In this research study, we have used Arc GIS and NS2 simulation tools to analyze the parameters and predict the probability of the occurrence of a flood.

Compound effects of rainfall and storm tides on coastal flooding risk

Rainfall and storm tides are both flood drivers in coastal zones. The complex interplay between them can lead to or exacerbate the impacts of flooding. While the dependence between rainfall and storm tides has been extensively studied, the compound effects of them on coastal flood risk have not been well researched. With Haikou city as the case study, this study investigates the bivariate return period of compounding rainfall and storm tide events based on copula functions and the failure probability is used to assess the variation of bivariate flood risk during the entire project lifetime. The results show that (1) there is a significant correlation between rainfall and storm tides. Therefore, bivariate RP analysis can provide more adequate and comprehensive information about risks than univariate RP analysis. Kendall RP can describe the bivariate RP more accurately since the dangerous region of Kendall RP is divided by the joint probability value. (2) Neglecting the compounding impacts and the dependence of rainfall and storm tides causes significant underestimation of the joint RP and failure probability. (3) The bivariate hydrologic risk value will decrease quickly when the design rainfall is higher than 100 mm and it becomes small and decrease slowly when the design rainfall exceeds 450 mm. Furthermore, the bivariate hydrologic risk value would not decrease until the storm tide is higher than 2 m and the values would become quite small as the storm tide exceeds 4.5 m. Such bivariate hydrologic risk analysis can provide decision support for hydraulic facility design as well as actual flood control and mitigation.

Hydrological Risk Analysis of Dams: The Influence of Initial Reservoir Level Conditions

In this paper, we present a method to assess the influence of the initial reservoir level in hydrological dam safety and risk analysis. Traditionally, in professional practice, the procedures applied are basically deterministic. Several physical processes are defined deterministically, according to the criteria of the designer (usually in the conservative side), although there is a high degree of uncertainty regarding these processes. A relevant variable is the reservoir level considered at the beginning of flood events. Hydrological dam safety assessment methods traditionally assume that the reservoir is initially full when it receives the design flood, thus, staying in the conservative side when designing a new dam. However, the distribution of reservoir levels at the beginning of flood episodes takes more importance for evaluating the real risk for the dams in operation. We analyzed three different scenarios—initial reservoir level equal to maximum normal level, equal to a maximum conservation level, and following the probability distribution from the historical records. To do so, we presented a method applied to a gated-spillway dam located in the Tagus river basin. A set of 100,000 inflow hydrographs was generated through a Monte Carlo procedure, by reproducing the statistics of the main observed hydrograph characteristics—peak flow, volume, and duration. The set of 100,000 hydrographs was routed through the reservoir applying the Volumetric Evaluation Method as a flood control strategy. In order to compare the three scenarios, we applied an economic global risk index. The index combines the hydrological risk for the dam, linked to the maximum water level reached in the reservoir, during the flood routing, and the flood risk in the downstream river reach, linked to the discharge releases from the dam. The results showed the importance of accounting for the fluctuation of initial reservoir levels, for assessing the risk related to hydrological dam safety. Furthermore, a procedure to quantify the uncertainty associated with the effects of initial reservoir level on hydrological dam safety, has been proposed.

An assessment of temporal effect on extreme rainfall estimates

Abstract. This study assesses the temporal behaviour in terms of inter-decadal variability of extreme daily rainfall of stated return period relevant for hydrologic risk analysis using a novel regional parametric approach. The assessment is carried out based on annual maximum daily rainfall series of 180 meteorological stations of Yangtze River Basin over a 50-year period (1961–2010). The outcomes of the analysis reveal that while there were effects present indicating higher quantile values when estimated from data of the 1990s, it is found not to be noteworthy to exclude the data of any decade from the extreme rainfall estimation process for hydrologic risk analysis.

Hydrological risk analysis of dam overtopping using bivariate statistical approach: a case study from Geheyan Reservoir, China

Hydrological risk analysis is essential and provides useful information for dam safety management and decision-making. This study presents the application of bivariate flood frequency analysis to risk analysis of dam overtopping for Geheyan Reservoir in China. The dependence between the flood peak and volume is modelled with the copula function. A Monte Carlo procedure is conducted to generate 100,000 random flood peak-volume pairs, which are subsequently transformed to corresponding design flood hydrographs (DFHs) by amplifying the selected annual maximum flood hydrographs (AMFHs). These synthetic DFHs are routed through the reservoir to obtain the frequency curve of maximum water level and assess the risk of dam overtopping. Sensitive analysis is performed to investigate the influence of different AMFH shapes and correlation coefficients of flood peak and volume on estimated overtopping risks. The results show that synthetic DFH with AMFH shape characterized by a delayed time to peak results in higher risk, and therefore highlight the importance of including a range of possible AMFH shapes in the dam risk analysis. It is also demonstrated that the overtopping risk is increased as the correlation coefficient of flood peak and volume increases and underestimated in the independence case (i.e. traditional univariate approach), while overestimated in the full dependence case. The bivariate statistical approach based on copulas can effectively capture the actual dependence between flood peak and volume, which should be preferred in the dam risk analysis practice.

Maximum Entropy-Copula Method for Hydrological Risk Analysis under Uncertainty: A Case Study on the Loess Plateau, China

Copula functions have been extensively used to describe the joint behaviors of extreme hydrological events and to analyze hydrological risk. Advanced marginal distribution inference, for example, the maximum entropy theory, is particularly beneficial for improving the performance of the copulas. The goal of this paper, therefore, is twofold; first, to develop a coupled maximum entropy-copula method for hydrological risk analysis through deriving the bivariate return periods, risk, reliability and bivariate design events; and second, to reveal the impact of marginal distribution selection uncertainty and sampling uncertainty on bivariate design event identification. Particularly, the uncertainties involved in the second goal have not yet received significant consideration. The designed framework for hydrological risk analysis related to flood and extreme precipitation events is exemplarily applied in two catchments of the Loess plateau, China. Results show that (1) distribution derived by the maximum entropy principle outperforms the conventional distributions for the probabilistic modeling of flood and extreme precipitation events; (2) the bivariate return periods, risk, reliability and bivariate design events are able to be derived using the coupled entropy-copula method; (3) uncertainty analysis highlights the fact that appropriate performance of marginal distribution is closely related to bivariate design event identification. Most importantly, sampling uncertainty causes the confidence regions of bivariate design events with return periods of 30 years to be very large, overlapping with the values of flood and extreme precipitation, which have return periods of 10 and 50 years, respectively. The large confidence regions of bivariate design events greatly challenge its application in practical engineering design.

Ecohydrologic modelling of water resources and land use for watershed conservation

Land use land cover is an important determinant of ecohydrologic processes in watershed systems. Continued urbanization changes the very nature of ecohydrological regimes of watersheds and increases their vulnerability to flooding, soil loss, and water pollution. To integrate hydrologic risk and suitability analysis into land use decisions, we used an ecohydrologic risk criteria for land use impacts on water quality and quantity in the Riva Creek watershed located to the east of Istanbul, Turkey. Hydrological risks are modeled for each sub-watershed using a spatial analysis. A spatial quantitative assessment is used to rank sub-factors for evaluating suitability for agricultural, residential and forest operations. An expert focus group is used to quantify weights. The results revealed that about 58% of the watershed is prone to hydrologic risk in medium to severe levels. We concluded that ecohydrological evaluations should form the background of landscape assessments in watersheds. An integrated approach and spatial results can provide the basis for long-term planning.

Hydrologic risk analysis in the Yangtze River basin through coupling Gaussian mixtures into copulas

In this study, a bivariate hydrologic risk framework is proposed through coupling Gaussian mixtures into copulas, leading to a coupled GMM-copula method. In the coupled GMM-Copula method, the marginal distributions of flood peak, volume and duration are quantified through Gaussian mixture models and the joint probability distributions of flood peak-volume, peak-duration and volume-duration are established through copulas. The bivariate hydrologic risk is then derived based on the joint return period of flood variable pairs. The proposed method is applied to the risk analysis for the Yichang station on the main stream of the Yangtze River, China. The results indicate that (i) the bivariate risk for flood peak-volume would keep constant for the flood volume less than 1.0 × 105 m3/s day, but present a significant decreasing trend for the flood volume larger than 1.7 × 105 m3/s day; and (ii) the bivariate risk for flood peak-duration would not change significantly for the flood duration less than 8 days, and then decrease significantly as duration value become larger. The probability density functions (pdfs) of the flood volume and duration conditional on flood peak can also be generated through the fitted copulas. The results indicate that the conditional pdfs of flood volume and duration follow bimodal distributions, with the occurrence frequency of the first vertex decreasing and the latter one increasing as the increase of flood peak. The obtained conclusions from the bivariate hydrologic analysis can provide decision support for flood control and mitigation.

Multivariate flood risk analysis for Wei River

In this study, bivariate hydrologic risk analysis was conducted based on the daily streamflow discharge at the Xianyang station on the Wei River. This bivariate hydrologic risk analysis was conducted based on copula methods, in which the bivariate hydrologic frequency was firstly quantified through copulas, and the bivariate hydrologic risk analysis was then characterized based on the joint return period of flood pairs. The maximum likelihood estimation (MLE) and the method-of-moments-like (MOM) estimator were compared in estimating the unknown parameters in copula. The results showed that the Gumbel–Hougaard copula was most appropriate for modelling the dependence for all three flood pairs, in which the parameter of the copula for flood peak–volume was estimated by MLE and the parameters of the copulas for flood peak–duration and volume–duration were needed to be obtained by MOM. The bivariate hydrologic risk values are then obtained based on the AND-joint return period. The results show that the bivariate hydrologic values will not decrease until the corresponding volume for a flood is larger than 1.0 × 104 m3/s. For the bivariate hydrologic risk for flood peak–duration, the value will decrease quickly when the duration is longer than 5 days. Such bivariate hydrologic risk analysis can provide decision support for hydraulic facility design as well as actual flood control and mitigation.

초기 변동수위를 고려한 제방 월류에 따른 안정성 분석

지반공학적으로 제방(또는 댐) 월류에 대한 근거를 제시하기 어렵다. 수문학적인 안정성 평가에서 댐의 초기수위(만수위)를 고정시키고 강우량을 계산하기 때문에 월류 가능성은 매우 희박하다. 그러나 Copula 함수를 사용하여 초기수위가 고정된 댐의 만수위가 아닌 변동성 있는 확률수위를 적용해서 국내 40년간의 빈도를 고려할 때, 월류 가능성을 확인할 수 있었다. 수문학적 댐의 위험성 분석은 다양한 불확실성 인자 중 댐 초기수위에 대한 모의기법 개발이 필요한 복잡한 수문학적 해석을 요구한다. 본 연구에서는 기존 댐 위험도 분석 시 초기수위는 상시만수위 또는 홍수기 제한수위로 가정하지만, 이러한 보수적인 가정에 의한 연구는 기상변동성 및 기후변화의 영향을 고려하지 못하며, 댐의 월류확률 및 이에 따른 붕괴확률을 추정하는데 있어서 지반공학적인 접근이 필요하다. Copula 함수를 이용하여 댐 특성에 맞는 초기수위를 결정하였으며, HEC-5 모형을 활용하여 강우-유출 모형 매개변수의 사후분포를 정량적으로 추정하여 댐 월류확률을 산정하였다. 지반공학적인 측면에서 댐 안정성 해석은 상류사면(upstream)의 수위급강하(drawdown)에 대한 안전율과 하류사면(downstream) 월류상태에서의 불안정성을 비교하여 지반공학적 위험도를 비교 분석하였다. It is not possible to provide resonable evidence for embankment (or dam) overtopping in geotechnical engineering, and conventional analysis by hydrologic design has not provided the evidence for the overflow. However, hydrologic design analysis using Copula function demonstrates the possibility that dam overflow occurs when estimating rainfall probability with rainfall data for 40 years based on fluctuating water level of a dam. Hydrologic dam risk analysis depends on complex hydrologic analyses in that probabilistic relationship needs to be established to quantify various uncertainties associated with modeling process and inputs. The systematic approaches to uncertainty analysis for hydrologic risk analysis have not been addressed yet. In this paper, the initial level of a dam for stability of a dam is generally determined by normal pool level or limiting the level of the flood, but overflow of probability and instability of a dam depend on the sensitivity analysis of the initial level of a dam. In order to estimate the initial level, Copula function and HEC-5 rainfall-runoff model are used to estimate posterior distributions of the model parameters. For geotechnical engineering, slope stability analysis was performed to investigate the difference between rapid drawdown and overtopping of a dam. As a result, the slope instability in overtopping of a dam was more dangerous than that of rapid drawdown condition.

Bivariate hydrologic risk analysis based on a coupled entropy-copula method for the Xiangxi River in the Three Gorges Reservoir area, China

In this study, a bivariate hydrologic risk framework is proposed based on a coupled entropy-copula method. In the proposed risk analysis framework, bivariate flood frequency would be analyzed for different flood variable pairs (i.e., flood peak-volume, flood peak-duration, flood volume-duration). The marginal distributions of flood peak, volume, and duration are quantified through both parametric (i.e., gamma, general extreme value (GEV), and lognormal distributions) and nonparametric (i.e., entropy) approaches. The joint probabilities of flood peak-volume, peak-duration, and volume-duration are established through copulas. The bivariate hydrologic risk is then derived based on the joint return period to reflect the interactive effects of flood variables on the final hydrologic risk values. The proposed method is applied to the risk analysis for the Xiangxi River in the Three Gorges Reservoir area, China. The results indicate the entropy method performs best in quantifying the distribution of flood duration. Bivariate hydrologic risk would then be generated to characterize the impacts of flood volume and duration on the occurrence of a flood. The results suggest that the bivariate risk for flood peak-volume would not decrease significantly for the flood volume less than 1000 m3/s. Moreover, a flood in the Xiangxi River may last at least 5 days without significant decrease of the bivariate risk for flood peak-duration.

Projected increases in intensity and frequency of rainfall extremes through a regional climate modeling approach

AbstractGlobal warming is changing the hydrological cycle in multiple ways such as increased cloudiness, latent heat fluxes, and intense precipitation events. How extreme rainfall events will be influenced by the changing climate is becoming one of the most important problems for hydrological risk analysis and engineering design. In this study, a regional climate modeling approach based on the Providing REgional Climates for Impacts Studies modeling system is proposed for investigating the potential impacts of climate change induced by increased greenhouse gases on the intensity and frequency of extreme rainfall events in the context of Ontario, Canada. An ensemble of high‐resolution climate projections is first developed under both current and future forcing conditions. Validation of the ensemble simulations is then conducted through comparing the simulated rainfall annual extremes for 1960–1990 to the observed ones. Following that, the rainfall projections for future periods are used to develop projected intensity‐duration‐frequency curves and their plausible changes in 2030s, 2050s, and 2080s for the City of Toronto. The results suggest that intensities of rainfall extreme events versus various durations with different return periods are all likely to increase over time: [5, 17]% in 2030s, [11, 22]% in 2050s, and [25, 50]% in 2080s. Such a consistent increase would lead to an overall uplift in the exceedance values of rainfall intensity of extreme events, implying that intense rainfall events are likely to occur more frequently in the future. In addition, more significant changes in the rainfall intensity are projected for extreme events with longer return periods at all given durations.

수문해석과정의 불확실성을 고려한 수문학적 댐 위험도 해석 기법 개선

수문해석과정의 불확실성을 고려한 수문학적 댐 위험도 해석 기법 개선