Analysis of storm surge characteristics based on stochastic process

Chen, Baiyu; Kou, Yi; Wang, Yufeng; Zhao, Daniel; Liu, Shaoxun; Liu, Guilin; Wang, Liping; Han, Xuefeng

doi:10.3934/math.2021072

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…Secondly, the violent atmospheric disturbances caused by the approaching typhoon can bring heavy rainfall and trigger secondary flooding. In addition, typhoons moving into shallow offshore waters can also trigger storm surges if they meet with astronomically high tides, causing abnormal sea level rise and triggering the risk of seawater spillover [31,[34][35][36]. Thus, it shows that the hazard of typhoons mainly comes from high winds, huge waves, heavy rainfall and storm surges, which can be characterized by three causative factors, namely wind speed, wave height and water increase.…”

Section: Definition 1 If N Typhoons Occur In the Study Area Within A ...mentioning

confidence: 99%

Risk Level Assessment of Typhoon Hazard Based on Loss Utility

Liu,

Yang,

Nong

et al. 2023

JMSE

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In the context of climate change with frequent natural disasters, disaster risk assessment can provide great help for related risk decision-making. Based on the theory of loss expectation, this paper presents a quantitative method to assess typhoon disaster risk. Among them, the probability of typhoon occurrence is calculated by fitting the optimal structure function of the sample to the joint distribution of wave height, water increment and wind speed. Then, the loss expectation is expressed as the product of typhoon occurrence probability and loss utility, which is used to quantify the loss result of a typhoon disaster. Using the loss utility theory, the risk grade chart is drawn with the direct economic loss rate and the proportion of the affected population as indicators. The results show that the absolute loss value considering the loss utility is slightly higher than the loss value of the quantitative algorithm by 2% to 25%, indicating that the new model reflects the social group’s aversion to typhoon disaster risk. As can be seen from the risk level zoning map, the highest combined risk level typhoons are Prapiroon 0606 and Chanthu 1003, with a risk level of Category 5. The typhoon comprehensive risk level before 2011 was ≥3, and the typhoon comprehensive risk level from 2012 to 2015 was ≤3. The evaluation model has certain feasibility and practicability, and the results can provide a basis and reference for typhoon risk assessment and decision-making.

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Section: Definition 1 If N Typhoons Occur In the Study Area Within A ...mentioning

confidence: 99%

Risk Level Assessment of Typhoon Hazard Based on Loss Utility

Liu,

Yang,

Nong

et al. 2023

JMSE

Self Cite

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“…Globally, typhoons (hurricanes) are one of the natural disasters that have caused the greatest losses to human society. With global warming, the frequency of strong typhoons (hurricanes) is increasing, and hydrological events (including rainfall, runoff, evaporation, flood, drought, tide, storm surge, huge wave) caused by typhoons are more serious and frequent than ever (see Figure 1), which has attracted the close attention of scholars in related fields [1][2][3]. Typhoon Hato brought a Class 10 gale to Hong Kong on 23 August 2017, causing economic losses of USD 1.02 billion and injuring nearly 100 people.…”

Section: Research Backgroundmentioning

confidence: 99%

Design Wave Height Parameter Estimation Model Reflecting the Influence of Typhoon Time and Space

Liu

Kou

et al. 2021

JMSE

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Typhoon storm surge disasters are one of the main restrictive factors of sustainable development in coastal areas. They are one of several important tasks in disaster prevention and reduction in coastal areas and require reasonable and accurate calculations of wave height in typhoon-affected sea areas to predict and resist typhoon storm surge disasters. In this paper, the design wave height estimation method based on the stochastic process and the principle of maximum entropy are theoretically advanced, and it can provide a new idea as well as a new method for the estimation of the return level for marine environmental elements under the influence of extreme weather. The model uses a family of random variables to reflect the influence of a typhoon on wave height at different times and then displays the statistical characteristics of wave height in time and space. At the same time, under the constraints of the given observations, the maximum uncertainty of the unobtainable data is maintained. The new model covers the compound extreme value distribution model that has been widely used and overcomes the subjective interference of the artificially selected distribution function—to a certain extent. Taking the typhoon wave height data of Naozhou Observatory as an example, this paper analyzes the probability of typhoon occurrence frequency at different times and the characteristics of typhoon intensity in different time periods. We then calculate the wave height return level and compare it with traditional calculation models. The calculation results show that the new model takes into account the time factor and the interaction between adjacent time periods. Furthermore, it reduces the subjective human interference, so the calculated results of the typhoon’s influence on wave height return level are more stable and accurate.

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“…Deriving deposition rates from storms is not trivial because these events are intermittent (Chen et al., 2021; Zhong et al., 2014) and are characterized by different magnitudes and return periods. It is not realistic to simulate all the storms occurring during long‐term accumulation measurements at high temporal resolution, since they can span decades.…”

Section: Introductionmentioning

confidence: 99%

Storm Impacts on Mineral Mass Accumulation Rates of Coastal Marshes

Cortese,

Zhang,

Simard

et al. 2024

JGR Earth Surface

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Coastal marsh survival may be compromised by sea‐level rise, limited sediment supply, and subsidence. Storms represent a fundamental forcing for sediment accumulation in starving marshes because they resuspend bottom material in channels and tidal flats and transport it to the marsh surface. However, it is unrealistic to simulate at high resolution all storms that occurred in the past decades to obtain reliable sediment accumulation rates. Similarly, it is difficult to cover all possible combinations of water levels and wind conditions in fictional scenarios. Thus, we developed a new method that derives long‐term deposition rates from short‐term deposition generated by a finite number of storms. Twelve storms with different intensity and frequency were selected in Terrebonne Bay, Louisiana, USA and simulated with the 2D Delft3D‐FLOW model coupled with the Simulating Waves Nearshore (SWAN) module. Storm impact was analyzed in terms of geomorphic work, namely the product of deposition and frequency. To derive the long‐term inorganic mass accumulation rates, the new method generates every possible combination of the 12 chosen storms and uses a linear model to fit modeled inorganic deposition with measured inorganic mass accumulation rates. The linear model with the best fit (highest R2) was used to derive a map of inorganic mass accumulation rates. Results show that a storm with 1.7 ± 1.6 years return period provides the largest geomorphic work, suggesting that the most impactful storms are those that balance intensity with frequency. Model results show higher accumulation rates in marshes facing open areas where waves can develop and resuspend sediments. This method has the advantage of considering only a few real scenarios and can be applied in any marsh‐bay system.

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Analysis of storm surge characteristics based on stochastic process

Cited by 10 publications

References 22 publications

Risk Level Assessment of Typhoon Hazard Based on Loss Utility

Risk Level Assessment of Typhoon Hazard Based on Loss Utility

Design Wave Height Parameter Estimation Model Reflecting the Influence of Typhoon Time and Space

Storm Impacts on Mineral Mass Accumulation Rates of Coastal Marshes

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