Modeling the interaction between tides and storm surges for the Taiwan coast

Liu, Wen‐Cheng; Huang, Wei-Che; Chen, Weibo

doi:10.1007/s10652-015-9441-0

Cited by 17 publications

(12 citation statements)

References 59 publications

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“…Especially for the case of 292 • , the maximum storm surge with a dramatic increase up to 180 cm can be generated by the local high wind and low pressure. Overall, the result is quite consistent with the actual observation data (during Typhoon Krosa) as well as the estimation from empirical equations [20,21] and numerical simulations [76,77]. The result of maximum storm surge also indicates an interesting fact that the inverted barometer effect (due to pressure deficit) and wind setup (due to wind speed) roughly are in the same order of magnitude for Taiwan coastal waters, unlike some other global cases where the low pressure makes a minimal contribution (about 5% of total) in comparison with the wind-driven surge (e.g., [8]).…”

Section: Knowledge Of the Neural Networksupporting

confidence: 88%

Long-Lead-Time Prediction of Storm Surge Using Artificial Neural Networks and Effective Typhoon Parameters: Revisit and Deeper Insight

Chao

Young

Hsu

et al. 2020

Water

Self Cite

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Storm surge induced by severe typhoons has caused many catastrophic tragedies to coastal communities over past decades. Accurate and efficient prediction/assessment of storm surge is still an important task in order to achieve coastal disaster mitigation especially under the influence of climate change. This study revisits storm surge predictions using artificial neural networks (ANN) and effective typhoon parameters. Recent progress of storm surge modeling and some remaining unresolved issues are reviewed. In this paper, we chose the northeastern region of Taiwan as the study area, where the largest storm surge record (over 1.8 m) has been observed. To develop the ANN-based storm surge model for various lead-times (from 1 to 12 h), typhoon parameters are carefully examined and selected by analogy with the physical modeling approach. A knowledge extraction method (KEM) with backward tracking and forward exploration procedures is also proposed to analyze the roles of hidden neurons and typhoon parameters in storm surge prediction, as well as to reveal the abundant, useful information covered in the fully-trained artificial brain. Finally, the capability of ANN model for long-lead-time predictions and influences in controlling parameters are investigated. Overall, excellent agreement with observations (i.e., the coefficient of efficiency CE > 0.95 for training and CE > 0.90 for validation) is achieved in one-hour-ahead prediction. When the typhoon affects coastal waters, contributions of wind speed, central pressure deficit, and relative angle are clarified via influential hidden neurons. A general pattern of maximum storm surge under various scenarios is also obtained. Moreover, satisfactory accuracy is successfully extended to a much longer lead time (i.e., CE > 0.85 for training and CE > 0.75 for validation in 12-h-ahead prediction). Possible reasons for further accuracy improvement compared to earlier works are addressed.

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Section: Knowledge Of the Neural Networksupporting

confidence: 88%

Long-Lead-Time Prediction of Storm Surge Using Artificial Neural Networks and Effective Typhoon Parameters: Revisit and Deeper Insight

Chao

Young

Hsu

et al. 2020

Water

Self Cite

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“…A better understanding of storm surge processes is therefore important. Previous studies have focused on tidesurge interaction in the TS (Zhang et al, 2010;Liu et al, 2016), while the effects of wave-current interaction during storm surges remain relatively understudied.…”

Section: Introductionmentioning

confidence: 99%

Effects of wave-current interaction on storm surge in the Taiwan Strait: Insights from Typhoon Morakot

Pan

Zheng

et al. 2017

Continental Shelf Research

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The effects of wave-current interaction on storm surge are investigated by a two-dimensional wave-current coupling model through simulations of Typhoon Morakot in the Taiwan Strait. The results show that wind wave and slope of sea floor govern wave setup modulations within the nearshore surf zone. Wave setup during Morakot can contribute up to 24% of the total storm surge with a maximum value of 0.28 m. The large wave setup commonly coincides with enhanced radiation stress gradient, which is itself associated with transfer of wave momentum flux. Water levels are to leading order in modulating significant wave height inside the estuary. High water levels due to tidal change and storm surge stabilize the wind wave and decay wave breaking. Outside of the estuary, waves are mainly affected by the current-induced modification of wind energy input to the wave generation. By comparing the observed significant wave height and water level with the results from uncoupled and coupled simulations, the latter shows a better agreement with the observations. It suggests that wave-current interaction plays an important role in determining the extreme storm surge and wave height in the study area and should not be neglected in a typhoon forecast

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“…Figure 2 in [25]), yet despite their impact on the environment of the TWS, studies are still limited. Research within the last decade has focused on analysis of storm surges [26,27], the importance of tide-surge or wave-tide-surge interactions in predicting the storm tide [25], or consideration of volume transport [20], sediment transport [28]), and currents [29,30]. In this study, a fully coupled ocean-atmosphere-wave model system [31] with a high horizontal resolution (~1 km) is employed to investigate the response of the TWS to Typhoon Nesat (2017).…”

Section: Introductionmentioning

confidence: 99%

“…The volume transport, heat budget, and momentum balance will be discussed in detail. A fully coupled modelling system should present more complete results than uncoupled modelling systems used in previous studies (e.g., [26]) as it not only utilises ocean and wave models that are forced by atmospheric conditions with sufficiently high spatial and temporal resolution, but also provides feedback to the atmosphere model.…”

Section: Introductionmentioning

confidence: 99%

Response of Coastal Water in the Taiwan Strait to Typhoon Nesat of 2017

Yang

Tian

et al. 2019

Water

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The oceanic response of the Taiwan Strait (TWS) to Typhoon Nesat (2017) was investigated using a fully coupled atmosphere-ocean-wave model (COAWST) verified by observations. Ocean currents in the TWS changed drastically in response to significant wind variation during the typhoon. The response of ocean currents was characterised by a flow pattern generally consistent with the Ekman boundary layer theory, with north-eastward volume transport being significantly modified by the storm. Model results also reveal that the western TWS experienced the maximum generated storm surge, whereas the east side experienced only moderate storm surge. Heat budget analysis indicated that surface heat flux, vertical diffusion, and total advection all contributed to changes in water temperature in the upper 30 m with advection primarily affecting lower depths during the storm. Momentum balance analysis shows that along-shore volume acceleration was largely determined by a combined effect of surface wind stress and bottom stress. Cross-shore directional terms of pressure gradient and Coriolis acceleration were dominant throughout the model run, indicating that the effect of the storm on geostrophic balance was small. This work provides a detailed analysis of TWS water response to typhoon passage across the strait, which will aid in regional disaster management.

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Modeling the interaction between tides and storm surges for the Taiwan coast

Cited by 17 publications

References 59 publications

Long-Lead-Time Prediction of Storm Surge Using Artificial Neural Networks and Effective Typhoon Parameters: Revisit and Deeper Insight

Long-Lead-Time Prediction of Storm Surge Using Artificial Neural Networks and Effective Typhoon Parameters: Revisit and Deeper Insight

Effects of wave-current interaction on storm surge in the Taiwan Strait: Insights from Typhoon Morakot

Response of Coastal Water in the Taiwan Strait to Typhoon Nesat of 2017

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