Estimating extreme sea levels in Yangtze Estuary by quadrature Joint Probability Optimal Sampling Method

Yin, Kai; Xu, Sudong; Huang, Wenrui

doi:10.1016/j.coastaleng.2018.08.007

Cited by 17 publications

(8 citation statements)

References 50 publications

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“…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%

“…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%

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

Gori

Lin

2022

Earth's Future

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Gori

Lin

2022

Earth's Future

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“…It has been shown that the JPM is more suitable for consistency of estimates for different designs and is more complicated than the univariate extreme models [26]. The JPM thus has been widely used in estimating extreme water levels and waves [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…Chen et al [32] applied the JPM to nine selected stations along the coast of China and found that the traditional empirical method overestimates the joint extreme surge levels and waves as compared to the JPM, and suggested the use of the JPM to achieve realistic estimations. Based on coupled model results and the Quadrature Probability Method, Yin et al [29] estimated the 100-year and 200-year annual maximum water levels at three selected stations in the CRE. However, the generation of synthetic typhoon tracks were relatively limited.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Extreme Storm Surges over the Changjiang River Estuary from a Wave-Current Coupled Model

Chi

Rong

2021

JMSE

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Disastrous storm surges and waves caused by typhoons are major marine dynamic disasters affecting the east China coast and the Changjiang River Estuary, especially when they occur coincidentally. In this study, a high-resolution wave–current coupled model consisting of ADCIRC (Advanced Circulation) and SWAN (Simulating Waves Nearshore) was established and validated. The model shows reasonable skills in reproducing the surge levels and waves. The storm surges and associated waves are then simulated for 98 typhoons affecting the Changjiang River Estuary over the past 32 years (1987–2018). Two different wind fields, the ERA reanalysis and the ERA-based synthetic wind with a theoretical typhoon model, were adopted to discern the potential uncertainties associated with winds. Model results forced by the ERA reanalysis show comparative skills with the synthetic winds, but differences may be relatively large in specific stations. The extreme surge levels with a 50-year return period are then presented based on the coupled model results and the Gumbel distribution model. Higher risk is presented in Hangzhou Bay and the nearshore region along the coast of Zhejiang. Comparative runs with and without wave effects were conducted to discern the impact of waves on the extreme surge levels. The wave setup contributes to 2–12.5% of the 50-year extreme surge level. Furthermore, the joint exceedance probabilities of high surge levels and high wave height were evaluated with the Gumbel–logistic statistic model. Given the same joint return period, the nearshore region along the coast of Zhejiang is more vulnerable with high surges and large waves than the Changjiang River Estuary with large waves and moderate surges.

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“…Dong et al (2017) studied the joint return probability of the wind speed and rainfall intensity in a typhoon-affected sea area close to Shanghai using the Weibull distribution and GH copula. More recently, Yin et al (2018) estimated the extreme sea levels in the Yangtze estuary using the quadrature joint probability optimal sampling method (JPM-OS) with consideration of the typhoon field, wave height, and sea level in the studied region. Yang and Qian (2019) analysed the joint probability of typhoon-induced surges and rainstorms at Shenzhen and derived trivariate joint distributions and conditional distributions of these variables based on the copula method.…”

Section: Introductionmentioning

confidence: 99%

The performance of the copulas in estimating the joint probability of extreme waves and surges along east coasts of the mainland China

Pan

Chen

et al. 2021

Ocean Engineering

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Estimating extreme sea levels in Yangtze Estuary by quadrature Joint Probability Optimal Sampling Method

Cited by 17 publications

References 50 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

Assessment of Extreme Storm Surges over the Changjiang River Estuary from a Wave-Current Coupled Model

The performance of the copulas in estimating the joint probability of extreme waves and surges along east coasts of the mainland China

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