探討高時間解析度降雨資料對臺灣淹水模擬之影響: 以宜蘭和高雄為例

簡易檢索 / 詳目顯示

回結果列表

研究生：	林芸安 Yun-An Lin
論文名稱：	探討高時間解析度降雨資料對臺灣淹水模擬之影響: 以宜蘭和高雄為例 Investigating the Impact of High Temporal-Resolution Precipitation Data on Flood Simulations in Taiwan: A Case Study of Yilan and Kaohsiung
指導教授：	鍾高陞 Kao-Shen Chung
口試委員:
學位類別：	碩士 Master
系所名稱：	地球科學學院 - 大氣科學學系 Department of Atmospheric Sciences
論文出版年：	2025
畢業學年度：	113
語文別：	中文
論文頁數：	93
中文關鍵詞：	淹水、3Di 水動力模式、淹水模擬、定量降水估計
外文關鍵詞：	Flood, QPE, Flood Simulation, 3Di
相關次數：	點閱：8 下載：0
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

台灣擁有豐沛的降雨，但也有致災的可能性，因此高解析度淹水模擬及預報對防災及水資源管理是重要的，但淹水模擬及預報的準確性有很多誤差來源，包括地形資料的準確度、模型的設定、以及降雨資料的不確定性。
本研究針對降雨資料的不確定性，將不同類型及時間解析度的降雨資料應用至3Di 水動力模式，包括理想情境下的降雨、定量降水估計、和MAPLE (McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation)的即時預報結果，以此檢視3Di模式對降雨資料的敏感度，並選擇宜蘭的冬山河流域及高雄市區，模擬2024年發生的多個颱風、東北季風造成的淹水事件，以此檢視不同集水區和降雨型態的差異性。過去臺灣用在水文分析的降雨資料大多都是一小時一筆累積降水資料，但當強降雨集中在更短時間例如半小時或十分鐘時，根據實驗結果，可能造成淹水情形更加嚴重，因此更高時間解析度的降雨資料是有存在的必要性。在即時預報方面，MAPLE的即時預報在每小時滾動更新的條件下，淹水歷線趨勢上和使用定量降水估計的模擬相似，但整體淹水面積低估。
此外，淹水模式時常因為真實資料不足或零散而難以驗證，但2024年的凱米颱風在台灣造成多處淹水而有相對充足的資料，因此本研究利用淹水感測器、衛星資料及Emergency Management Information Cloud (EMIC) 作為模式驗證的參考，也顯示了不同資料的優缺點及結果上的差異。根據校驗結果，3Di水動力模式的模擬結果和淹水感測器相比，準確度在不同事件下分布在0.6至0.8，和衛星資料相比，準確度分布在0.75和0.95之間。

The accuracy of flood simulations depends on multiple factors, with precipitation data uncertainty being a key influence. Therefore, selecting an appropriate combination of meteorological data and flood models is essential for effective flood management. This study presents a framework that emphasizes the importance of high-temporal-resolution precipitation data by integrating five types of precipitation datasets: designed rainfall, two quantitative precipitation estimation (QPE) products, and the nowcast system MAPLE (McGill Algorithm for Precipitation Nowcasting using Lagrangian Extrapolation). These datasets were used as inputs for the hydrodynamic model 3Di.
An idealized experiment revealed that flooding may be underestimated when heavy rainfall occurs within a short duration, particularly less than one hour, if only hourly rainfall data is used, underscoring the necessity of finer temporal resolution in flood forecasting. Six heavy rainfall events from 2024 in northeastern and southwestern Taiwan were analyzed, leveraging improved data availability from these events to validate the flood model. The results demonstrate that higher temporal resolution enables earlier flood detection, which is critical for early warning systems. Additionally, when rainfall intensity increases, the discrepancy between flood extents generated by different datasets becomes more pronounced. Furthermore, MAPLE provides reliable short-term forecasts within one hour, though any forecast errors may be amplified when incorporated into the flood model. These findings highlight the importance of precise precipitation data in flood simulations and the potential challenges associated with forecast uncertainty.

摘要    i
Abstract    ii
Acknowledgment    iii
Outline    iv
List of Tables    vi
List of Figures    vii
Chapter 1. Introduction    1
Chapter 2. Study area and cases overview    6
2.1 Study area    6
2.2 cases overview    7
Chapter 3. Data and Methodology    9
3.1 3Di Hydrodynamic Model    9
3.1.1 Governing equation    9
3.1.2 Computational grid    10
3.1.3 Model configuration    11
3.2 Observed rainfall data    12
3.2.1 Radar network in Taiwan    12
3.2.2 QPESUMS    13
3.3 MAPLE nowcasting system    14
3.3.1 Variational Echo Tracking (VET) technique    14
3.3.2 Semi-Lagrangian advection    15
3.4 Experiment setting    16
Chapter 4. Verification strategies    18
4.1 Observed flood data    18
4.1.1 EMIC    19
4.1.2 Flood sensor    20
4.1.3 SAR    20
4.2 Verification approaches    22
4.2.1 Continuous Verification    22
4.2.2 Categorical verification    22
4.2.3 Neighborhood method    24
Chapter 5. Results and discussion    25
5.1 Idealized experiment    25
5.2 Validation of 3Di with observational flood data    27
5.2.1 Using EMIC and flood sensors as a reference    27
5.2.2 Using SAR data as a reference    29
5.3 Comparison between QPESUMS and QPESUMS10M    31
5.4 Evaluation of MAPLE's forecast performance    33
5.5 Hydrological value of MAPLE forecasts    34
Chapter 6. Conclusion and future work    38
6.1 Conclusion    38
6.2 Future work    42
Reference    44
Table    48
Figure    51


                                

Alfieri, L., P. Salamon, F. Pappenberger, F. Wetterhall, and J. Thielen, 2012: Operational early warning systems for water-related hazards in Europe. Environmental Science & Policy, 21, 35–49.
Bellon, A., I. Zawadzki, A. Kilambi, H. C. Lee, Y. H. Lee, and G. Lee, 2010: McGill algorithm for precipitation nowcasting by lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part I: Sensitivity studies of the Variational Echo Tracking (VET) technique. Asia-Pacific Journal of Atmospheric Sciences, 46, 369–381.
Cloke, H. L., and F. Pappenberger, 2009: Ensemble flood forecasting: A review. Journal of Hydrology, 375, 613–626.
Chang, C.-H., Rahmad, R., Wu, S.-J., Hsu, C.-T., & Chung, P.-H. (2024). Enhancing flood verification using Signal Detection Theory (SDT) and IoT Sensors: A spatial scale evaluation. Journal of Hydrology, 636, 131308.
Chang, P.-L., and Coauthors, 2020: An operational Multi-Radar Multi-Sensor QPE system in Taiwan. Bulletin of the American Meteorological Society, 102, E555–E577.
Chung, K.-S., and I.-A. Yao, 2019: Improving Radar Echo Lagrangian Extrapolation Nowcasting by blending numerical model wind information: Statistical performance of 16 typhoon cases. Monthly Weather Review, 148, 1099–1120.
Emmanuel, I., H. Andrieu, E. Leblois, and B. Flahaut, 2012: Temporal and spatial variability of rainfall at the urban hydrological scale. Journal of Hydrology, 430–431, 162–172.
Germann, U., and I. Zawadzki, 2002: Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology. Monthly Weather Review, 130, 2859–2873.
Germann, U., I. Zawadzki, and B. Turner, 2006: Predictability of Precipitation from Continental Radar Images. Part IV: Limits to Prediction. J. Atmos. Sci., 63, 2092-2108.
Grimaldi, S., Y. Li, V. R. N. Pauwels, and J. P. Walker, 2016: Remote Sensing-Derived Water Extent and Level to Constrain Hydraulic Flood Forecasting Models: Opportunities and Challenges. Surveys in Geophysics, 37, 977–1034.
Hooker, H., S. L. Dance, D. C. Mason, J. Bevington, and K. Shelton, 2022: Spatial scale evaluation of forecast flood inundation maps. Journal of Hydrology, 612, 128170.
Hsu, Y.-C., G. Prinsen, L. Bouaziz, Y.-J. Lin, and R. Dahm, 2016: An investigation of DEM resolution influence on flood inundation simulation. Procedia Engineering, 154, 826–834.
Laroche, S., and I. Zawadzki, 1994: A Variational Analysis Method for Retrieval of Three-Dimensional Wind Field from Single-Doppler Radar Data. J. Atmos. Sci., 51, 2664-2682.
Lawrence, J., A. Reisinger, B. Mullan, and B. Jackson, 2013: Exploring climate change uncertainties to support adaptive management of changing flood-risk. Environmental Science & Policy, 33, 133–142.
Lee, H. C., Y. H. Lee, J.-C. Ha, D.-E. Chang, A. Bellon, I. Zawadzki, and G. Lee, 2010: McGill algorithm for precipitation nowcasting by lagrangian extrapolation (MAPLE) applied to the South Korean radar network. Part II: Real-time verification for the summer season. Asia-Pacific Journal of Atmospheric Sciences, 46, 383–391.
Mandapaka, P. V., U. Germann, L. Panziera, and A. Hering, 2011: Can Lagrangian Extrapolation of Radar Fields Be Used for Precipitation Nowcasting over Complex Alpine Orography? Weather and Forecasting, 27, 28–49.
Ochoa-Rodriguez, S., and Coauthors, 2015: Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation. Journal of Hydrology, 531, 389–407.
Pan, J.-W., K.-S. Chung, H.-H. Lin, T.-C. C. Wang, and I.-A. Yao, 2018: Feasibility Assessment of Applying Variational Radar Echo Tracking Method over Complex Terrain in Taiwan. Atmospheric Sciences, 46, 1-34.
Papaioannou, G., L. Vasiliades, A. Loukas, and G. T. Aronica, 2017: Probabilistic flood inundation mapping at ungauged streams due to roughness coefficient uncertainty in hydraulic modelling. Advances in Geosciences, 44, 23–34.
Roberts, N. M., and H. W. Lean, 2008: Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events. Monthly Weather Review, 136, 78–97.
Roebber, P. J., 2008: Visualizing multiple measures of forecast quality. Weather and Forecasting, 24, 601–608.
Saadi, M., C. Furusho-Percot, A. Belleflamme, S. Trömel, S. Kollet, and R. Reinoso-Rondinel, 2023: Comparison of three Radar-Based precipitation nowcasts for the extreme July 2021 flooding event in Germany. Journal of Hydrometeorology, 24, 1241–1261.
Schumann, G., P. D. Bates, M. S. Horritt, P. Matgen, and F. Pappenberger, 2009: Progress in integration of remote sensing–derived flood extent and stage data and hydraulic models. Reviews of Geophysics, 47.
Stelling, G. S., 2012: Quadtree flood simulations with sub-grid digital elevation models. Proceedings of the Institution of Civil Engineers - Water Management, 165, 567–580.
Trenberth, K., 2010: Changes in precipitation with climate change. Climate Research, 47, 123–138.
Turner, B. J., I. Zawadzki, and U. Germann, 2004: Predictability of Precipitation from Continental Radar Images. Part III: Operational Nowcasting Implementation (MAPLE). Journal of Applied Meteorology, 43, 231–248.
Wing, O. E. J., P. D. Bates, C. C. Sampson, A. M. Smith, K. A. Johnson, and T. A. Erickson, 2017: Validation of a 30 m resolution flood hazard model of the conterminous United States. Water Resources Research, 53, 7968–7986.
Zwiers, F. W., and Coauthors, 2013: Climate Extremes: Challenges in estimating and understanding recent changes in the frequency and intensity of extreme climate and weather events. Springer eBooks, 339–389.
Nelen & Schuurmans. (n.d.). 3Di documentation. Retrieved April 21, 2025, from https://docs.3di.live/
陳如瑜, 張偉裕, and 陳台琦, 2017: 北台灣 S 與 C 波段雙偏極化雷達定量降雨估計之比較. 大氣科學, 45, 57-81.
邱俊穎, 謝嘉聲, 黃宗仁, 葉堃生, 管立豪, & 胡植慶. (2019). 合成孔徑雷達影像於颱風豪雨後淹水之偵測.航測及遙測學刊, 24(4), 211-222.
許育蕎。利用三維回波移動場改善即時降雨預報並建構系集即時預報系統：臺灣梅雨鋒面及秋季降水個案分析。碩士論文，國立中央大學，民國 112 年 6 月。
賴茂修。應用洪災指標於二維HEC-RAS及3Di模式之評估。碩士論文，國立臺灣大學，民國 111 年 8 月

簡易檢索 / 詳目顯示

相關論文