風暴潮為天氣系統 (如熱帶氣旋、溫帶氣旋) 引起之海面異常升降現象，常造成沿海區域設施損壞與人員傷亡，形成機制包含有：大氣壓力變化、風剪力、海底地形與海岸線形貌、與波浪和潮汐交互作用等。本研究室利用數值模式、潮位暨浮標資料處理與分析、風場模擬與分析等方法，嘗試量化風暴潮對海岸帶區域之影響，並進行歷史個案分析與討論，取得重要資訊以及風暴潮增幅機制等研究成果。

Storm surge is an abnormal water level rise, generated by a meteorological system (e.g., tropical/extra-tropical storms) and usually affects coastal facilities and causes casualties. Its mechanisms are: differences in surface air pressure, wind shear stress, and interactions with tides and waves. Our group uses numerical modeling tools and data analysis to further quantify the influence of storm surge on coastal areas. Case studies in various countries are used to understand storm surge amplification.

延伸文獻 / Extended Reference:

Tsai et al. (2025): Coastal Storm Surge Amplification by Wave Radiation Stress: The Case Study of 2015 Typhoon Soudelor in East Taiwan. https://doi.org/10.1016/j.apor.2024.104370

Tsai et al. (2024): Storm Surge Induced by Tropical Storm Pabuk (2019) and Its Impact by Track Variation Scenarios on the Thailand Coast. https://doi.org/10.1007/s11069-024-06717-8

Tsai et al. (2022): Parallel-Computing Two-Way Grid-Nested Storm Surge Model with a Moving Boundary Scheme and Case Study of the 2013 Super Typhoon Haiyan. https://doi.org/10.3390/w14040547

紅樹林作為自然解方 (Nature-based Solutions) 之一，常被運用於生態防減災 (Ecosystem-based Disaster Risk Reduction, Eco-DRR) 工法，降低海岸天然災害 (如海嘯、風暴潮、越波等) 對海岸帶區域之侵害。本研究室利用水動力數值模式、水工試驗、野外資料搜集、影像處理等方法，嘗試量化紅樹林對波浪消能作用，並且包含紅樹林特殊之樹木特徵 (例如 Rhizophora 紅樹林支持根) 等細節。

As one of the nature-based solutions, mangroves are widely used as ecosystem-based disaster risk reduction against coastal natural hazards. Our research group aims to use numerical modeling tools, lab experiments, field data collection, and image analysis to quantify wave attenuation by mangroves, including the complex characteristics of tree geometry and prop roots.

延伸文獻 / Extended Reference:

Tsai et al. (2026): Investigation of Wave Attenuation by Rhizophora apiculata Mangroves: Coupled Laboratory Experiments and Boussinesq Modeling. https://doi.org/10.1029/2025JC022836

Mori et al. (2025): Field experiment study to assess critical wave conditions leading to failure of mangrove Rhizophora stylosa. https://doi.org/10.1016/j.coastaleng.2025.104749

改進現有之淺水波方程式模式 (Shallow Water Equation Model)，配合多重耦合巢狀網格 (Multiple Grid Nesting) 以及乾溼網格移動邊界法 (Wet-and-Dry Cell Treatment)，計算海洋長波 (例如海嘯或風暴潮) 於海底地形之溯升 (Runup) 與溯降 (Rundown) 過程。

By improving the existing shallow water equation model with a multiple grid nesting scheme and a wet-and-dry cell treatment, the calculation of runup and rundown for long waves, such as tsunamis and storm surges, can be satisfied.

延伸文獻 / Extended Reference:

Tsai et al. (2022): Parallel-Computing Two-Way Grid-Nested Storm Surge Model with a Moving Boundary Scheme and Case Study of the 2013 Super Typhoon Haiyan. https://doi.org/10.3390/w14040547

利用二維/三維納維-斯托克斯方程式模式 (Navier-Stokes Equation Model) 配合流體體積法 (Volume of Fluid)，可將所發展之數值模擬工具，運用於討論海岸工程暨海洋工程中之複雜工程問題，輔助工程設計與防減災策略之制訂。

By utilizing the 2D/3D Navier-Stokes Equation model with the volume-of-fluid (VOF) method, the developed numerical model can be employed to address coastal and ocean engineering problems, supporting engineering design and hazard mitigation.

延伸文獻 / Extended Reference:

Wu et al. (2021): Solitary Wave Interacting with a Submerged Circular Plate. https://doi.org/10.1061/(ASCE)WW.1943-5460.0000605

配合使用 CPU 多執行緒 OpenMP 技術或 GPU 運算技術，實現利用線性淺水波方程式模式進行海嘯速算之可能性，以期於地震發生前期，掌握可能之海嘯影響範圍以及海嘯波能量等重要資訊，提供預警預報之用。此外，海嘯傳遞時，會引致大氣電離層產生擾動，此訊號可被地表 GPS 接收站獲取，此電離層擾動可領先於大洋傳播之海嘯首達波 (Leading Tsunami Waves) 數分鐘，作為輔助海嘯預警之用。

By using a CPU OpenMP technique or a GPU acceleration skill, the linear shallow water equation model can be used in tsunami fast calculations. Additionally, transoceanic tsunami propagation will induce traveling ionospheric disturbances (TTIDs), which travel faster than leading tsunami waves and can be used to assist in tsunami early warning.

延伸文獻 / Extended Reference:

Lin et al. (2015): Development of a Tsunami Early Warning System for the South China Sea. https://doi.org/10.1016/j.oceaneng.2015.02.003

Yen et al. (2020): Knowledge-Building Approach for Tsunami Impact Analysis Aided by Citizen Science. https://doi.org/10.3389/feart.2020.00315

Liu et al. (2019): Ionospheric GNSS Total Electron Content for Tsunami Warning. https://doi.org/10.1142/S1793431119410070