Efficient Strategies for the Joint Probability Evaluation of Storm Surge Hazards

Niedoroda, Alan W.; Resio, Donald T.; Toro, Gabriel R.; Divoky, David; Reed, C.W.

doi:10.1061/40968(312)22

Cited by 10 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimization procedure involves an iterative loop where n storms are chosen each time, their weights and associated estimation variance are calculated, and then an adjusted set of storms is chosen for the next iteration. We utilize the NEWUOA numerical optimization algorithm developed by Powell (2004) as in previous JPM‐OS‐BQ studies (Niedoroda et al., 2008; Toro, Resio, et al., 2010; Yin et al., 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Using this approach, we select storm characteristics that are correlated with either peak storm surge and peak rainfall rate or both, and thus are likely to provide a reasonable representation of the compound flood hazard. The storm parameters we select based on the lasso regression are similar to the storm parameters used in many previous JPM‐OS studies (Bilskie et al., 2019; Niedoroda et al., 2008; Toro, Niedoroda, et al., 2010). Therefore, in the absence of pre‐simulated rainfall and storm tide data, the storm parameters could be specified based on the set up from previous studies.…”

Section: Methodsmentioning

confidence: 99%

“…Since the joint density is normal for some variables, non‐normal for others, and because some of the variables are correlated, it would be computationally costly to numerically compute the integral in the physical parameter space. To overcome this challenge, previous studies (Nadal‐Caraballo et al., 2015; Niedoroda et al., 2008; Sheng et al., 2022; Toro, Niedoroda, et al., 2010; Toro, Resio, et al., 2010; Yin et al., 2018) have used the Rosenblatt transformation (Rosenblatt, 1952) to convert the joint density from the physical parameter space to an equivalent uncorrelated, normal space (which we call the optimization space). Conversion of the joint density to an uncorrelated joint normal distribution allows analytical computation of Equation 5, thereby ensuring computational efficiency of the optimization algorithm.…”

Section: Methodsmentioning

confidence: 99%

“…In general, variables that are more important should have smaller specified correlation distances so that the optimization algorithm spaces the storm points more closely together. The correlation distances can be specified based on the results from previous studies (Niedoroda et al, 2008;Toro, Niedoroda, et al, 2010;Toro, Resio, et al, 2010) and modified based on trial-and-error analysis. Given a set of n support points (i.e., number of synthetic storms whose characteristics are represented in m-dimensional space) represented as an m by n dimensional matrix (D), the vector of weights ( 𝐴𝐴 𝑾𝑾 ) are calculated by solving a system of equations involving the joint density ( 𝐴𝐴 𝐴𝐴𝑿𝑿(𝒙𝒙) in Equation 1) and the autocorrelation function (…”

Section: Jpm-os For Compound Floodingmentioning

confidence: 99%

See 3 more Smart Citations

Projecting Compound Flood Hazard Under Climate Change With Physical Models and Joint Probability Methods

Gori

Lin

2022

Earth's Future

View full text Add to dashboard Cite

Accurate delineation of compound flood hazard requires joint simulation of rainfall‐runoff and storm surges within high‐resolution flood models, which may be computationally expensive. There is a need for supplementing physical models with efficient, probabilistic methodologies for compound flood hazard assessment that can be applied under a range of climate and environment conditions. Here we propose an extension to the joint probability optimal sampling method (JPM‐OS), which has been widely used for storm surge assessment, and apply it for rainfall‐surge compound hazard assessment under climate change at the catchment‐scale. We utilize thousands of synthetic tropical cyclones (TCs) and physics‐based models to characterize storm surge and rainfall hazards at the coast. Then we implement a Bayesian quadrature optimization approach (JPM‐OS‐BQ) to select a small number (∼100) of storms, which are simulated within a high‐resolution flood model to characterize the compound flood hazard. We show that the limited JPM‐OS‐BQ simulations can capture historical flood return levels within 0.25 m compared to a high‐fidelity Monte Carlo approach. We find that the combined impact of 2100 sea‐level rise (SLR) and TC climatology changes on flood hazard change in the Cape Fear Estuary, NC will increase the 100‐year flood extent by 27% and increase inundation volume by 62%. Moreover, we show that probabilistic incorporation of SLR in the JPM‐OS‐BQ framework leads to different 100‐year flood maps compared to using a single mean SLR projection. Our framework can be applied to catchments across the United States Atlantic and Gulf coasts under a variety of climate and environment scenarios.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Jpm-os For Compound Floodingmentioning

confidence: 99%

See 2 more Smart Citations

Projecting Compound Flood Hazard Under Climate Change With Physical Models and Joint Probability Methods

Gori

Lin

2022

Earth's Future

View full text Add to dashboard Cite

show abstract

“…Conventional approaches to this assessment are based on parametric or non-parametric analysis of data from historical storms (Borgman et al 1992) or on simulation of hurricane design events. A different methodology (Myers 1975), frequently referenced as the Joint Probability (JPM) Method, relies on a simplified description of hurricane scenarios through a small number of model parameters (Niedoroda et al 2008;Resio et al 2009). Description of the uncertainty in these parameters, through appropriate probability models, leads to a probabilistic characterization of the hurricane risk.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Assessment of Wave and Surge Risk Due to Landfalling Hurricanes

Taflanidis

Kennedy

Westerink

et al. 2012

Int. Conf. Coastal. Eng.

View full text Add to dashboard Cite

In this work, a probabilistic framework is presented for real-time assessment of wave and surge risk for hurricanes approaching landfall. This framework has two fundamental components. The first is the development of a surrogate model for the rapid evaluation of hurricane waves, water levels, and runup based on a small number of parameters describing each hurricane: hurricane landfall location and heading, central pressure, forward speed, and radius of maximum winds. This surrogate model is developed using a response surface methodology fed by information from hundreds of pre-computed, high-fidelity model runs. For a specific set of hurricane parameters (i.e., a specific landfalling hurricane), the surrogate model is able to evaluate the maximum wave height, water level, and runup during the storm at a cost that is more than seven orders of magnitude less than the high fidelity models and thus meet time constraints imposed by emergency managers and decision makers. The second component to this framework is a description of the uncertainty in the parameters used to characterize the hurricane, through appropriate probability models, which then leads to quantification of hurricane-risk in terms of a probabilistic integral. This integral is then efficiently computed using the already established surrogate model by analyzing thousands of different scenarios (based on the aforementioned probabilistic description). Finally, by leveraging the computational simplicity and efficiency of the surrogate model, a simple stand-alone PC-based risk assessment tool is developed that allows non-expert end users to take advantage of the full potential of the framework. An illustrative example is presented that considers applications of these tools for hurricane risk estimation for Oahu. The development of cyber-infrastructure at the University of Notre Dame to further support these initiatives is also discussed.

show abstract

A surge response function approach to coastal hazard assessment – part 1: basic concepts

2009

Self Cite

View full text Add to dashboard Cite

This paper reviews historical methods for estimating surge hazards and concludes that the class of solutions produced with Joint Probability Method (JPM) solutions provides a much more stable estimate of hazard levels than alternative methods. We proceed to describe changes in our understanding of the winds in hurricanes approaching a coast and the physics of surge generation that have required recent modifications to procedures utilized in earlier JPM studies. Of critical importance to the accuracy of hazard estimates is the ability to maintain a high level of fidelity in the numerical simulations while allowing for a sufficient number of simulations to populate the joint probability matrices for the surges. To accomplish this, it is important to maximize the information content in the sample storm set to be simulated. This paper introduces the fundamentals of a method based on the functional specification of the surge response for this purpose, along with an example of its application in the New Orleans area. A companion paper in this special issue (Irish et al. 2009) provides details of the portion of this new method related to interpolating/extrapolating along spatial dimensions.

show abstract

Efficient Strategies for the Joint Probability Evaluation of Storm Surge Hazards

Cited by 10 publications

References 4 publications

Projecting Compound Flood Hazard Under Climate Change With Physical Models and Joint Probability Methods

Projecting Compound Flood Hazard Under Climate Change With Physical Models and Joint Probability Methods

Real-Time Assessment of Wave and Surge Risk Due to Landfalling Hurricanes

A surge response function approach to coastal hazard assessment – part 1: basic concepts

Contact Info

Product

Resources

About