scholarly journals Joint probability analysis of flood hazard at river confluences using bivariate copulas

2018 ◽  
Vol 70 (04) ◽  
pp. 267-275 ◽  
Keyword(s):  
Flood Hazard ◽  
Joint Probability ◽  
Probability Analysis ◽  
River Confluences

scholarly journals Measuring compound flood potential from river discharge and storm surge extremes at the global scale

2020 ◽  
Vol 20 (2) ◽  
pp. 489-504 ◽  
Author(s):  
Anaïs Couasnon ◽  
Dirk Eilander ◽  
Sanne Muis ◽  
Ted I. E. Veldkamp ◽  
Ivan D. Haigh ◽  
...  
Keyword(s):  
Storm Surge ◽  
River Discharge ◽  
Flood Hazard ◽  
Joint Probability ◽  
Global Scale ◽  
Statistical Dependence ◽  
Dependence Structure ◽  
Meteorological Forcing ◽  
Continental Scale ◽  
Bivariate Dependence

Abstract. The interaction between physical drivers from oceanographic, hydrological, and meteorological processes in coastal areas can result in compound flooding. Compound flood events, like Cyclone Idai and Hurricane Harvey, have revealed the devastating consequences of the co-occurrence of coastal and river floods. A number of studies have recently investigated the likelihood of compound flooding at the continental scale based on simulated variables of flood drivers, such as storm surge, precipitation, and river discharges. At the global scale, this has only been performed based on observations, thereby excluding a large extent of the global coastline. The purpose of this study is to fill this gap and identify regions with a high compound flooding potential from river discharge and storm surge extremes in river mouths globally. To do so, we use daily time series of river discharge and storm surge from state-of-the-art global models driven with consistent meteorological forcing from reanalysis datasets. We measure the compound flood potential by analysing both variables with respect to their timing, joint statistical dependence, and joint return period. Our analysis indicates many regions that deviate from statistical independence and could not be identified in previous global studies based on observations alone, such as Madagascar, northern Morocco, Vietnam, and Taiwan. We report possible causal mechanisms for the observed spatial patterns based on existing literature. Finally, we provide preliminary insights on the implications of the bivariate dependence behaviour on the flood hazard characterisation using Madagascar as a case study. Our global and local analyses show that the dependence structure between flood drivers can be complex and can significantly impact the joint probability of discharge and storm surge extremes. These emphasise the need to refine global flood risk assessments and emergency planning to account for these potential interactions.

scholarly journals Measuring compound flood potential from river discharge and storm surge extremes at the global scale and its implications for flood hazard

2019 ◽  
Author(s):  
Anaïs Couasnon ◽  
Dirk Eilander ◽  
Sanne Muis ◽  
Ted I. E. Veldkamp ◽  
Ivan D. Haigh ◽  
...  
Keyword(s):  
Storm Surge ◽  
River Discharge ◽  
Flood Hazard ◽  
Joint Probability ◽  
Global Scale ◽  
Statistical Dependence ◽  
Dependence Structure ◽  
Meteorological Forcing ◽  
Continental Scale ◽  
Bivariate Dependence

Abstract. The interaction between physical drivers from oceanographic, hydrological, and meteorological processes in coastal areas can result in compound flooding. Compound flood events, like Cyclone Idai and Hurricane Harvey, have revealed the devastating consequences of the co-occurrence of coastal and river floods. A number of studies have recently investigated the likelihood of compound flooding at the continental scale based on simulated variables of flood drivers such as storm surge, precipitation, and river discharges. At the global scale, this has only been performed based on observations, thereby excluding a large extent of the global coastline. The purpose of this study is to fill this gap and identify potential hotspots of compound flooding from river discharge and storm surge extremes in river mouths globally. To do so, we use daily time-series of river discharge and storm surge from state-of-the-art global models driven with consistent meteorological forcing from reanalysis datasets. We measure the compound flood potential by analysing both variables with respect to their timing, joint statistical dependence, and joint return period. We find many hotspot regions of compound flooding that could not be identified in previous global studies based on observations alone, such as: Madagascar, Northern Morocco, Vietnam, and Taiwan. We report possible causal mechanisms for the observed spatial patterns based on existing literature. Finally, we provide preliminary insights on the implications of the bivariate dependence behaviour on the flood hazard characterisation using Madagascar as a case study. Our global and local analyses show that the dependence structure between flood drivers can be complex and can significantly impact the joint probability of discharge and storm surge extremes. These emphasise the need to refine global flood risk assessments and emergency planning to account for these potential interactions.

scholarly journals Estimations of statistical dependence as joint return period modulator of compound events. Part I: storm surge and wave height

2017 ◽  
Author(s):  
Thomas I. Petroliagkis
Keyword(s):  
Storm Surge ◽  
Wave Height ◽  
Return Period ◽  
Flood Hazard ◽  
Joint Probability ◽  
Irish Sea ◽  
Statistical Dependence ◽  
Joint Return ◽  
Compound Events ◽  
Joint Return Period

Abstract. The possibility of utilising statistical dependence methods in coastal flood hazard calculations is investigated, since flood risk is rarely a function of just one source variable but usually two or more. Source variables in most cases are not independent as they may be driven by the same weather event, so their dependence, which is capable of modulating their joint return period, has to be estimated before the calculation of their joint probability. Dependence and correlation may differ substantially from one another since dependence is focused heavily on tail (extreme) percentiles. The statistical analysis between surge and wave is performed over 32 river ending points along European coasts. Two sets of almost 35-year hindcasts of storm surge and wave height were adapted and results are presented by means of analytical tables and maps referring to both correlation and statistical dependence values. Further, the top 80 compound events were defined for each river ending point. Their frequency of occurrence was found to be distinctly higher during the cold months while their main low-level flow characteristics appear to be mainly in harmony with the transient nature of storms and their tracks. Overall, significantly strong values of positive correlations and dependencies were found over the Irish Sea, English Channel, south coasts of the North Sea, Norwegian Sea and Baltic Sea, with compound events taking place in a zero-lag mode. For the rest, mostly positive moderate dependence values were estimated even if a considerable number of them had correlations of almost zero or even negative value.

Co-Occurrence Probability Analysis of Sea Ice at Yingkou and Huludao Observation Stations of China

2017 ◽  
Author(s):  
Shanshan Tao ◽  
Zhifeng Wang ◽  
Ri Zhang ◽  
Sheng Dong
Keyword(s):  
Sea Ice ◽  
Probability Distributions ◽  
Joint Probability ◽  
Offshore Structures ◽  
Early Spring ◽  
Probability Analysis ◽  
Ice Thickness ◽  
Occurrence Probability ◽  
Sea Ice Thickness ◽  
Ice Conditions

Co-occurrence probability analysis of sea ice between adjacent areas is very helpful for the hazard prevention and protection strategy making of coastal and offshore engineering. Yingkou and Huludao with similar latitudes are located on the opposite sides of Liaodong Bay of China. Their sea ice conditions are both apparent in winter and early spring, so it is useful to study on the co-occurrence situations of sea ice conditions between these two areas. Based on the annual maximum sea ice thickness of Yingkou and Huludao observation stations, the co-occurrence probability analysis of sea ice thickness is conducted. The joint probability distributions of sea ice thickness between these adjacent areas are constructed by using univariate maximum entropy distributions and four bivariate copulas. Both marginal curve fittings are very well, and the model determined by Gumbel-Hougaard copula describes the bivariate sea ice thickness data best. Then different cases of co-occurrence probabilities of sea ice thickness between Yingkou and Huludao are presented, and they can provide references to the hazard protection of the coastal and offshore structures between these two areas.

Long-Term Probability Prediction on the Extreme Sea States Induced by Typhoon of the South China Sea

2013 ◽  
Vol 726-731 ◽  
pp. 833-841 ◽  
Author(s):  
Liang Pang ◽  
Xuan Chen ◽  
Yu Long Li
Keyword(s):  
South China Sea ◽  
Wave Height ◽  
South China ◽  
Joint Probability ◽  
The South China Sea ◽  
Probability Analysis ◽  
Wave Period ◽  
China Sea ◽  
Long Term Prediction

The sea state of the South China Sea is influenced by tropical cyclone obviously. It is important to carry out the long-term prediction and probability analysis of typhoon wind, wave height and wave period for the coastal and offshore engineering. In this paper the measured wind and wave data during typhoon processes from 1964-1989 are used to predict the long-term extreme sea states by using Grey Markov Chain Model. And the joint probability analysis of extreme wave height with concomitant wave period and wind speed is performed by using Multivariate Compound Extreme Distribution model which involves typhoon occurrence frequency and corresponding joint probability distribution of typhoon induced extreme sea environmental events. The proposed model shows that the mean value of typhoon occurring frequency per year plays the significant role in long term prediction of typhoon induced joint return values of extreme sea events.

scholarly journals ADVANCES AND ISSUES IN UNCERTAINTY QUANTIFICATION FOR COASTAL FLOOD HAZARDS

2018 ◽  
pp. 19
Author(s):  
Taylor G. Asher ◽  
Jennifer L. Irish ◽  
Donald T. Resio
Keyword(s):  
Tropical Cyclones ◽  
Historical Data ◽  
Flood Hazard ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Probability Method ◽  
Flood Hazards ◽  
Significant Cost ◽  
Flooding Risk ◽  
Flood Elevation

Probabilistic flood hazard assessments have advanced substantially, with modern methods for dealing with the risk from tropical cyclones utilizing either a variation of the joint probability method with optimal sampling (JPM-OS)2,3 or the statistical deterministic track method (SDTM)1,4. In the JPM-OS, tropical cyclones are reduced to a set of 5 to 9 parameters, whose characteristics are analyzed statistically to develop a joint probability distribution for tropical cyclones of given characteristics. In the SDTM, cyclogenesis of a large number of storms is seeded via a statistical model from historical data, then storms are propagated using one of several different methods, incorporating varying degrees of the physics of cyclone transformation as the storms propagate. Due to the significant cost of storm surge simulations, some form of optimization or selection is then performed to reduce the number of synthetic storms that must be simulated to determine the flood elevation corresponding to a given recurrence interval (e.g. the so-called 100-year flood). In both methods, substantial uncertainties exist, which have a tendency to increase the estimated flooding risk. Efforts to account for these uncertainties have varied, and there remains significant work to be done. Here, we demonstrate how these uncertainties tend to increase the flood risk and show that additional sources of uncertainty remain to be accounted for.

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