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.

scholarly journals Compounding effects of sea level rise and fluvial flooding

2017 ◽  
Vol 114 (37) ◽  
pp. 9785-9790 ◽  
Author(s):  
Hamed R. Moftakhari ◽  
Gianfausto Salvadori ◽  
Amir AghaKouchak ◽  
Brett F. Sanders ◽  
Richard A. Matthew
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Hazard Assessment ◽  
Failure Probability ◽  
River Flow ◽  
Flood Hazard ◽  
High Tide ◽  
Probability Method ◽  
Flood Hazards ◽  
Flood Hazard Assessment

Sea level rise (SLR), a well-documented and urgent aspect of anthropogenic global warming, threatens population and assets located in low-lying coastal regions all around the world. Common flood hazard assessment practices typically account for one driver at a time (e.g., either fluvial flooding only or ocean flooding only), whereas coastal cities vulnerable to SLR are at risk for flooding from multiple drivers (e.g., extreme coastal high tide, storm surge, and river flow). Here, we propose a bivariate flood hazard assessment approach that accounts for compound flooding from river flow and coastal water level, and we show that a univariate approach may not appropriately characterize the flood hazard if there are compounding effects. Using copulas and bivariate dependence analysis, we also quantify the increases in failure probabilities for 2030 and 2050 caused by SLR under representative concentration pathways 4.5 and 8.5. Additionally, the increase in failure probability is shown to be strongly affected by compounding effects. The proposed failure probability method offers an innovative tool for assessing compounding flood hazards in a warming climate.

scholarly journals An objective and efficient method for estimating probabilistic coastal inundation hazards

2019 ◽  
Vol 99 (2) ◽  
pp. 1105-1130 ◽  
Author(s):  
Kun Yang ◽  
Vladimir Paramygin ◽  
Y. Peter Sheng
Keyword(s):  
High Resolution ◽  
Storm Surge ◽  
Computational Cost ◽  
Joint Probability ◽  
Water Levels ◽  
Computational Time ◽  
Probability Method ◽  
Highly Efficient ◽  
Inundation Maps ◽  
Flood Elevation

Abstract The joint probability method (JPM) is the traditional way to determine the base flood elevation due to storm surge, and it usually requires simulation of storm surge response from tens of thousands of synthetic storms. The simulated storm surge is combined with probabilistic storm rates to create flood maps with various return periods. However, the map production requires enormous computational cost if state-of-the-art hydrodynamic models with high-resolution numerical grids are used; hence, optimal sampling (JPM-OS) with a small number of (~ 100–200) optimal (representative) storms is preferred. This paper presents a significantly improved JPM-OS, where a small number of optimal storms are objectively selected, and simulated storm surge responses of tens of thousands of storms are accurately interpolated from those for the optimal storms using a highly efficient kriging surrogate model. This study focuses on Southwest Florida and considers ~ 150 optimal storms that are selected based on simulations using either the low fidelity (with low resolution and simple physics) SLOSH model or the high fidelity (with high resolution and comprehensive physics) CH3D model. Surge responses to the optimal storms are simulated using both SLOSH and CH3D, and the flood elevations are calculated using JPM-OS with highly efficient kriging interpolations. For verification, the probabilistic inundation maps are compared to those obtained by the traditional JPM and variations of JPM-OS that employ different interpolation schemes, and computed probabilistic water levels are compared to those calculated by historical storm methods. The inundation maps obtained with the JPM-OS differ less than 10% from those obtained with JPM for 20,625 storms, with only 4% of the computational time.

scholarly journals VEGETATED HYDRODYNAMIC SYSTEM: PARAMETERIZATION AND STOCHASTIC DEPENDENCE MODELLING

2020 ◽  
pp. 25
Author(s):  
Muhammad Hassan Khan Niazi ◽  
Oswaldo Morales Nápoles ◽  
Bregje K. Van Wesenbeeck
Keyword(s):  
Numerical Modeling ◽  
Flood Hazard ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Load Reduction ◽  
Wave Load ◽  
Stochastic Dependence ◽  
Dependence Modeling ◽  
Coastal Systems ◽  
Dependence Modelling

Vegetation as a nature-based solution for increasing flood risk has convincingly shown potential for flood hazard (wave load) reduction but lacks generalized results. In this study we have introduced stochastic dependence modeling using non-parametric Bayesian networks (NPBN) for vegetated coastal systems where the system was parametrized using continuous marginal distributions, and likely (conditional) correlations among variables. The model represented a consistent joint probability distribution and hence can be used to generate physically realistic conditions in data-scare environments. It adds value to numerical modeling by reducing the number of simulations required to get meaningful generalized results. Main findings, that were derived by using a NPBN, help to pave way for implementation of nature-based solutions for a range of realistic conditions that can be found across global coastal foreshores.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/T6TP0DH0qMw

scholarly journals On the Flood Peak Distributions over China

2019 ◽  
Author(s):  
Long Yang ◽  
Lachun Wang ◽  
Xiang Li ◽  
Jie Gao
Keyword(s):  
Tropical Cyclones ◽  
Southern China ◽  
Flood Hazard ◽  
Critical Role ◽  
Northern China ◽  
Drainage Area ◽  
Flood Hazards ◽  
Flood Peak ◽  
Flood Peaks ◽  
Landfalling Tropical Cyclones

Abstract. Time series of annual maximum instantaneous peak discharge from 1120 stations with record lengths of at least 50 years are used to examine flood peak distributions across China. Abrupt change rather than slowly varying trend is the dominant mode of the violation of stationary assumption for annual flood peaks over China. The dominance of decreasing trends in annual flood peak series indicates a weakening tendency of flood hazard over China in recent decades. Delayed (advanced) occurrence of annual flood peaks in southern (northern) China point to a tendency for seasonal clustering of floods across the entire country. We model the upper tails of flood peaks based on the Generalized Extreme Value (GEV) distributions for the stationary series, and evaluate the scale-dependent properties of flood peaks. The relations of GEV parameters and drainage area show spatial contrasts between northern and southern China. Weak dependence of the GEV shape parameter on drainage area highlights the critical role of space-time rainfall organizations in dictating the upper tails of flood peaks. Landfalling tropical cyclones play an important role in characterizing the upper-tail properties of flood peak distributions especially in northern China and southeastern coast, while the upper tails of flood peaks are dominated by extreme monsoon rainfall in southern China. Severe flood hazards associated with landfalling tropical cyclones are characterized with tropical cyclones experiencing extratropical transition, and persistent moisture transport/interactions with regional topography as demonstrated by Typhoon Nina (1975).

Statistical Analysis for the Duration and Time Intervals of Tropical Cyclones, Hong Kong

2019 ◽  
Author(s):  
Shanshan Tao ◽  
Jialing Song ◽  
Zhifeng Wang ◽  
Yong Liu ◽  
Sheng Dong
Keyword(s):  
Hong Kong ◽  
Tropical Cyclone ◽  
Probability Distribution ◽  
Tropical Cyclones ◽  
Conditional Probability ◽  
Probability Distributions ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Time Interval ◽  
Time Intervals

Abstract Hong Kong is impacted by tropical cyclones from April to December each year. The duration of tropical cyclones is one key factor to impact the normal operation of port or coastal engineering, and longer time interval between two tropical cyclones can provide longer operation or construction time. Therefore, it is quite important to study on the long-term laws of the duration and time intervals of tropical cyclones which attacked Hong Kong. The Hong Kong Observatory issues the warning signals to warn the public of the threat of winds associated with a tropical cyclone. Choose the tropical cyclones with warning signal No. 3 or above as the research object. A statistical study was conducted on the duration of each tropical cyclone, the time interval between every two continuous tropical cyclones during the year, and the time interval between the last cyclone of each year and the first cyclone of the following year. Poisson compound extreme value distributions are constructed to calculate the return values, which can make people know how long a tropical cyclone with a fixed duration or time interval occurs once in statistical average sense. Based on bivariate copulas, the joint probability distribution of duration and time intervals of tropical cyclones are presented. Then when the duration of a tropical cyclone is known, the conditional probability that the time interval before the next tropical cyclone occurs is greater than a certain value can be calculated. The results provide corresponding conditional probability distributions. Similarly, for the sum of the duration of tropical cyclones each year, and the time interval between the last cyclone of each year and the first cyclone of the following year, their joint probability distribution and conditional probability distributions are also presented. The conditional probability can provide the probabilistic prediction of the length of the stationary period (with no impact of tropical cyclones).

Multi-Criteria Decision Making for Vulnerability Mapping of Flood Hazard: A Case Study of Pune City

2019 ◽  
Vol 2 (1) ◽  
pp. 41-52
Author(s):  
Nitin Mundhe
Keyword(s):  
Decision Making ◽  
Flood Hazard ◽  
Geographical Information ◽  
Multi Criteria Decision Making ◽  
Flood Hazards ◽  
Very High Frequency ◽  
Flood Vulnerability ◽  
Natural Risk ◽  
The Town

Floods are natural risk with a very high frequency, which causes to environmental, social, economic and human losses. The floods in the town happen mainly due to human made activities about the blockage of natural drainage, haphazard construction of roads, building, and high rainfall intensity. Detailed maps showing flood vulnerability areas are helpful in management of flood hazards. Therefore, present research focused on identifying flood vulnerability zones in the Pune City using multi-criteria decision-making approach in Geographical Information System (GIS) and inputs from remotely sensed imageries. Other input data considered for preparing base maps are census details, City maps, and fieldworks. The Pune City classified in to four flood vulnerability classes essential for flood risk management. About 5 per cent area shows high vulnerability for floods in localities namely Wakdewadi, some part of the Shivajinagar, Sangamwadi, Aundh, and Baner with high risk.

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 Probability elicitation using geostatistics in hydrocarbon exploration

2021 ◽  
Author(s):  
André Luís Morosov ◽  
Reidar Brumer Bratvold
Keyword(s):  
Value Creation ◽  
Decision Process ◽  
Conceptual Models ◽  
Joint Probability ◽  
Decision Makers ◽  
Hydrocarbon Exploration ◽  
Joint Probability Distribution ◽  
Proof Of Concept ◽  
Geological Information ◽  
Decision Supporting

AbstractThe exploratory phase of a hydrocarbon field is a period when decision-supporting information is scarce while the drilling stakes are high. Each new prospect drilled brings more knowledge about the area and might reveal reserves, hence choosing such prospect is essential for value creation. Drilling decisions must be made under uncertainty as the available geological information is limited and probability elicitation from geoscience experts is key in this process. This work proposes a novel use of geostatistics to help experts elicit geological probabilities more objectively, especially useful during the exploratory phase. The approach is simpler, more consistent with geologic knowledge, more comfortable for geoscientists to use and, more comprehensive for decision-makers to follow when compared to traditional methods. It is also flexible by working with any amount and type of information available. The workflow takes as input conceptual models describing the geology and uses geostatistics to generate spatial variability of geological properties in the vicinity of potential drilling prospects. The output is stochastic realizations which are processed into a joint probability distribution (JPD) containing all conditional probabilities of the process. Input models are interactively changed until the JPD satisfactory represents the expert’s beliefs. A 2D, yet realistic, implementation of the workflow is used as a proof of concept, demonstrating that even simple modeling might suffice for decision-making support. Derivative versions of the JPD are created and their effect on the decision process of selecting the drilling sequence is assessed. The findings from the method application suggest ways to define the input parameters by observing how they affect the JPD and the decision process.

Sign in / Sign up

Export Citation Format

Share Document