請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98675完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 何昊哲 | zh_TW |
| dc.contributor.advisor | Hao-Che Ho | en |
| dc.contributor.author | 楊貽琇 | zh_TW |
| dc.contributor.author | Yi-Siou Yang | en |
| dc.date.accessioned | 2025-08-18T01:18:38Z | - |
| dc.date.available | 2025-08-18 | - |
| dc.date.copyright | 2025-08-15 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-06 | - |
| dc.identifier.citation | [1] 3Di Water Management. (2025). 3Di Documentation. https://docs.3di.live/
[2] Adger, W. (2000). Social and Ecological Resilience: Are They Related? Progress in Human Geography, 24, 347–364. https://doi.org/10.1191/030913200701540465 [3] Afifi, Z., Chu, Z., Kuo, Y.-L., Hsu, M., Wong, H.-K., & Ali, M. (2019). Residential Flood Loss Assessment and Risk Mapping from High-Resolution Simulation. Water, 11, 751. https://doi.org/10.3390/w11040751 [4] Alberti, M., & Marzluff, J. M. (2004). Ecological resilience in urban ecosystems: Linking urban patterns to human and ecological functions. Urban Ecosystems, 7(3), 241–265. https://doi.org/10.1023/B:UECO.0000044038.90173.c6 [5] Alkema, D. (2007). Simulating floods: on the application of a 2D hydraulic model for flood hazard and risk assessment. Research external, graduation external, Utrecht University, International Institute for Geo-Information Science and Earth Observation (ITC)]. https://dspace.library.uu.nl/handle/1874/23283 [6] Ashley, R., Balmforth, D., Saul, A., & Blanskby, J. (2005). Flooding in the future - Predicting climate change, risks and responses in urban areas. Water science and technology : a journal of the International Association on Water Pollution Research, 52, 265–273. https://doi.org/10.2166/wst.2005.0142 [7] Barros, V., Mastrandrea, M., Abdrabo, M., Adger, W., Federation, Y., Anisimov, O., Arent, D., Cai, R., Cohen, S., Dasgupta, P., Davidson, D., Denton, F., Doell, P., & Yohe, G. (2014). Climate change 2014: impacts, adaptation, and vulnerability – IPCC WGII AR5 summary for policymakers. In (pp. 1–32). [8] Becken, S., & Khazai, B. (2017). Resilience, tourism and disasters. In (pp. 96–104). https://doi.org/10.1079/9781780648330.0096 [9] Benedict, M., & MacMahon, E. (2002). Green Infrastructure: Smart Conservation for the 21st Century (Vol. 20). [10] Berke, P. R., & Campanella, T. J. (2006). Planning for postdisaster resiliency. The Annals of the American Academy of Political and Social Science, 604(1), 192–207. https://doi.org/10.1177/0002716205285533 [11] Carpenter, S., Walker, B., Anderies, J., & Abel, N. (2001). From Metaphor to Measurement: Resilience of What To What? Ecosystems, 4, 765–781. https://doi.org/10.1007/s10021-001-0045-9 [12] Chen, K.-F., & Leandro, J. (2019). A Conceptual Time-Varying Flood Resilience Index for Urban Areas: Munich City. Water, 11(4). [13] Cimellaro, G. P., & Solari, D. (2014). Considerations about the optimal period range to evaluate the weight coefficient of coupled resilience index. Engineering Structures, 69, 12–24. https://doi.org/https://doi.org/10.1016/j.engstruct.2014.03.003 [14] Cimellaro, G. P., Solari, D., Arcidiacono, V., Renschler, C. S., Reinhorn, A. M., & Bruneau, M. (2014). Community Resilience Assessment Integrating Network Interdependencies Tenth U.S. National Conference on Earthquake Engineering [15] Cutter, S. L., Burton, C. G., & Emrich, C. T. (2008). Disaster resilience indicators for benchmarking baseline conditions. Journal of Homeland Security and Emergency Management, 5(1). https://doi.org/10.2202/1547-7355.1732 [16] Dahm, R. J. (2014). Next Generation Flood Modelling using 3Di: A Case Study in Taiwan. [17] Das, S., Ghosh, A., Hazra, S., Ghosh, T., Safra de Campos, R., & Samanta, S. (2020). Linking IPCC AR4 & AR5 frameworks for assessing vulnerability and risk to climate change in the Indian Bengal Delta. Progress in Disaster Science, 7, 100110. https://doi.org/10.1016/j.pdisas.2020.100110 [18] Dobie, S., Schneider, J., Kesgin, M., & Lagiewski, R. (2018). Hotels as Critical Hubs for Destination Disaster Resilience: An Analysis of Hotel Corporations’ CSR Activities Supporting Disaster Relief and Resilience. Infrastructures, 3, 46. https://doi.org/10.3390/infrastructures3040046 [19] Elmqvist, T., Folke, C., Nyström, M., Peterson, G., Bengtsson, J., Walker, B., & Norberg, J. (2003). Response Diversity, Ecosystem Change, and Resilience. Frontiers in Ecology and the Environment, 1, 488–494. https://doi.org/10.2307/3868116 [20] Ernstson, H., van der Leeuw, S. E., Redman, C. L., Meffert, D. J., Davis, G., Alfsen, C., & Elmqvist, T. (2010). Urban transitions: On urban resilience and human-dominated ecosystems. AMBIO, 39, 531–545. https://doi.org/10.1007/s13280-010-0081-9 [21] Ferreira, C., Potočki, K., Kapovic Solomun, M., & Kalantari, Z. (2021). Nature-Based Solutions for Flood Mitigation and Resilience in Urban Areas. In (pp. 1–20). https://doi.org/10.1007/698_2021_758 [22] Field, C. B., Barros, V. R., Dokken, D. J., & Katharine J. Mach, M. D. M. (2014). Climate Change 2014 Impacts, Adaptation, and Vulnerability Part A: Global and Sectoral Aspects. [23] Fletcher, T., Andrieu, H., & Hamel, P. (2013). Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art. Advances in Water Resources, 51, 261–279. https://doi.org/10.1016/j.advwatres.2012.09.001 [24] Folke, C., Carpenter, S., Walker, B., Scheffer, M., Elmqvist, T., Gunderson, L., & Holling, C. S. (2004). Regime Shifts, Resilience, and Biodiversity in Ecosystem Management. Annual Review of Ecology, Evolution, and Systematics, 35(Volume 35, 2004), 557–581. https://doi.org/10.1146/annurev.ecolsys.35.021103.105711 [25] Gunderson, L. (2010). Ecological and Human Community Resilience in Response to Natural Disasters. ECOLOGY AND SOCIETY, 15. https://doi.org/10.5751/ES-03381-150218 [26] Hemmati, M., Ellingwood, B., & Mahmoud, H. (2020). The Role of Urban Growth in Resilience of Communities Under Flood Risk. Earth's Future, 8, e2019EF001382. https://doi.org/10.1029/2019EF001382 [27] Holling, C. S. (1973). Resilience and Stability of Ecological Systems. Annual Review of Ecology, Evolution, and Systematics, 4(Volume 4, 1973), 1–23. https://doi.org/https://doi.org/10.1146/annurev.es.04.110173.000245 [28] Hsu, M. H., Tsai, M. Y., Lin, Y. C., Chen, A. S., Hammond, M. J., Djordjevic, S., & Butler, D. (2012). Flood damage assessment in Taipei city Taiwan. [29] IPCC. (2014). Climate change 2014: Impacts, adaptation, and vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, New York, NY, USA, 1820 pp. [30] Joakim, E., Mortsch, L., & Oulahen, G. (2015). Using Vulnerability and Resilience Concepts to Advance Climate Change Adaptation. Environmental Hazards, 14. https://doi.org/10.1080/17477891.2014.1003777 [31] Keeley, A. T. H., Beier, P., & Jenness, J. S. (2021). Connectivity metrics for conservation planning and monitoring. Biological Conservation, 255, 109008. https://doi.org/https://doi.org/10.1016/j.biocon.2021.109008 [32] Kron, W. (2005). Flood Risk = Hazard • Values • Vulnerability. Water International - WATER INT, 30, 58–68. https://doi.org/10.1080/02508060508691837 [33] Leandro, J., Chen, K.-F., Wood, R., & Ludwig, R. (2020). A scalable flood-resilience-index for measuring climate change adaptation: Munich city. Water Research, 173, 115502. https://doi.org/10.1016/j.watres.2020.115502 [34] Lin, K. H., & Lin, T. (2017). How does resilience matter? An empirical verification of the relationships between resilience and vulnerability. Natural Hazards, 88. https://doi.org/10.1007/s11069-017-2916-1 [35] Luthar, S., Cicchetti, D., & Becker, B. (2000). The Construct of Resilience: A Critical Evaluation and Guidelines for Future Work. Child development, 71, 543–562. https://doi.org/10.1111/1467-8624.00164 [36] Masten, A. (2001). Masten AS. Ordinary magic. Resilience processes in development. Am Psychol.56(3):227-238. The American psychologist, 56, 227–238. https://doi.org/10.1037/0003-066X.56.3.227 [37] McClymont, K., David, M., Lindsay, B., & and Carmen, E. (2020). Flood resilience: a systematic review. Journal of Environmental Planning and Management, 63(7), 1151–1176. https://doi.org/10.1080/09640568.2019.1641474 [38] NCDR. (2025). 全球災害事件簿資料庫. 國家災害防救科技中心. https://den.ncdr.nat.gov.tw/1330/1334/ [39] Niemczynowicz, J. (1999). Urban hydrology and water management – present and future challenges. Urban Water, 1(1), 1–14. https://doi.org/https://doi.org/10.1016/S1462-0758(99)00009-6 [40] O'Brien, K., Hayward, B., & Berkes, F. (2009). Rethinking Social Contracts: Building Resilience in a Changing Climate. ECOLOGY AND SOCIETY, 14. https://doi.org/10.5751/ES-03027-140212 [41] Pachauri, R. K. A., Myles R. ; Barros, Vicente R. et al. (2014). Climate change 2014 synthesis report. contribution of working groups I, II, and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. IPCC. . [42] Parry, M. L., Canziani, O., Palutikof, J. P., van der Linden, P., & Hanson, C. E. (2007). Climate Change 2007: Impacts, Adaptation and Vulnerability. [43] Prashar, N., Lakra, H. S., Shaw, R., & Kaur, H. (2023). Urban Flood Resilience: A comprehensive review of assessment methods, tools, and techniques to manage disaster. Progress in Disaster Science, 20, 100299. https://doi.org/10.1016/j.pdisas.2023.100299 [44] Qi, W., Ma, C., Xu, H., Chen, Z., Zhao, K., & Han, H. (2021). A review on applications of urban flood models in flood mitigation strategies. Natural Hazards, 108. https://doi.org/10.1007/s11069-021-04715-8 [45] Rose, A. (2007). Economic resilience to natural and man-made disasters: Multidisciplinary origins and contextual dimensions. Environmental Hazards, 7(4), 383–398. https://doi.org/10.1016/j.envhaz.2007.10.001 [46] Saura, S., & Pascual-Hortal, L. (2007). A new habitat availability index to integrate connectivity in landscape conservation planning: Comparison with existing indices and application to a case study. Landscape and Urban Planning, 83(2), 91–103. https://doi.org/https://doi.org/10.1016/j.landurbplan.2007.03.005 [47] Saura, S., & Pascual-Hortal, L. (2007). Conefor Sensinode 2.2 User’s Manual: Software for quantifying the importance of habitat patches for maintaining landscape connectivity through graphs and habitat availability indices. University of Lleida. http://www.conefor.org [48] Smit, B., & Wandel, J. (2006). Adaptation, Adaptive Capacity and Vulnerability. Global Environmental Change, 16, 282–292. https://doi.org/10.1016/j.gloenvcha.2006.03.008 [49] Stelling, G. S. (2012). Quadtree flood simulations with sub-grid digital elevation models. . Thomas Telford Ltd. . [50] Taylor, P. D., Fahrig, L., Henein, K., & Merriam, G. (1993). Connectivity Is a Vital Element of Landscape Structure. Oikos, 68(3), 571–573. https://doi.org/10.2307/3544927 [51] Timmerman, P. (1981). Vulnerability, resilience and the collapse of society: A review of models and possible climatic applications. [52] Tsatsou, A., Frantzeskaki, N., & Malamis, S. (2023). Nature-based solutions for circular urban water systems: A scoping literature review and a proposal for urban design and planning. Journal of Cleaner Production, 394, 136325. https://doi.org/https://doi.org/10.1016/j.jclepro.2023.136325 [53] Vincenzo, C. (2009). A high‐resolution wetting and drying algorithm for free‐surface hydrodynamics. International Journal for Numerical Methods in Fluids, 60, 391–408. https://doi.org/10.1002/fld.1896 [54] Vincenzo, C., & Stelling, G. (2013). A semi-implicit numerical model for urban drainage systems. International Journal for Numerical Methods in Fluids, 73, 600–614. https://doi.org/10.1002/fld.3817 [55] Volp, N. D., Prooijen, B. C. v., & Stelling, G. S. (2013). A finite volume approach for shallow water flow accounting for high-resolution bathymetry and roughness data. Water Resour. Res., 49, 4126–4135,. https://doi.org/10.1002/wrcr.20324. [56] Wagner, P., & Breil, M. (2013). The role of ecosystem services in climate change adaptation. ETC/CCA Technical Paper. [57] Walker, B., Salt, D., & Reid, W. (2006). Resilience Thinking: Sustaining Ecosystems and People in A Changing World. Bibliovault OAI Repository, the University of Chicago Press. [58] 內政部戶政司.(2025). 人口統計資料. https://www.ris.gov.tw/app/portal/346 [59] 內政部消防署.(2023). 臺灣地區天然災害損失統計表（民國47年至111年12月）. https://www.nfa.gov.tw/cht/index.php?code=list&ids=233 [60] 王福杰. (2018). 3Di應用於淹水即時預報之研究 [國立成功大學水利及海洋工程學系研究所]. https://hdl.handle.net/11296/rqvbh9 [61] 台灣大學慶齡工業慶齡工齡工業工業研究中心. (2019). 高速降雨逕流模式協助擬定都市積水防治策略. [62] 行政院公報. (2019). 出流管制計畫書與規劃書檢核基準及洪峰流量計算方法. [63] 何謹余, 童慶斌, & 王涴雅. (2020). 減災與氣候變遷調適在區域治理上之融合研究－以坡地災害為例 [Study on the Integration of Disaster Risk Reduction and Climate Change Adaptation for Regional Governance-Taking the risk of sloping land disaster as an example]. 農業工程學報, 66(2), 12–25. https://doi.org/10.29974/JTAE.202006_66(2).0002 [64] 黃莉雅. (2021). 考量內外水動態模擬下之逕流分擔策略成效評估. [碩士論文。國立臺灣大學]. https://hdl.handle.net/11296/23c85r。 [65] 經濟部水利署. (2005). 烏溪水系支流筏子溪治理基本計畫(第一次修訂). [66] 經濟部水利署水利規劃試驗分署. (2020). 淡水河流域國土計畫特定區域計畫推動(1/2). [67] 臺中市政府. (2020). 變更臺中市都市計畫（西屯地區）細部計畫（第二次通盤檢討）（第一階段）書. [68] 臺中市政府. (2022). 111年臺中市公園綠地清冊V2. [69] 臺中市政府. (2022). 變更臺中市都市計畫（西屯地區）細部計畫（第二次通盤檢討）（第二階段）書. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98675 | - |
| dc.description.abstract | 在全球氣候變遷和城市化加速的背景下，極端降雨和洪水災害的頻率和強度不斷增加，對自然和人類系統構成了重大威脅。本研究旨在建構洪水災害韌性指數 (Flood Resilience Index, FRI)，以不同的觀點來量化和評估研究區域在面對洪水災害的韌性。
目前大部分評估韌性的文獻都是從人類視角來看，所考量的因子大多集中在社會及經濟方面。本研究納入環境觀點來評估韌性，採用 ISO 14090 氣候變遷風險評估框架，將危害度、暴露度和敏感度作為三個關鍵的韌性子指標。透過 3Di 水動力學模型模擬暴雨事件造成的洪水災害，並根據研究區域的洪水物理特性推導出危害度指標。此外，本研究使用 Conefor Sensinode 2.2 計算棲地連通機率(Probability of Connectivity, PC)來評估棲地連續性，並將其作為暴露度指標中的因子之一，結合生物多樣性、土壤排水等級、生物密度等環境因子來評估臺中市西屯區內各個研究單元的環境韌性表現，從而建立了不同觀點的FRI。 研究結果顯示，環境因子敏感度普遍大於社會經濟因子，在高危害度的地區尤為明顯。環境韌性的耦合協調度(CCD)與僅加入社會因子的韌性指標耦合度大部分大於0.6，顯示出良好的協同性，當耦合較差時，可以進一步使用互相關函數(CCF)來量化FRI與不同子指標間的相互作用，找出主導FRI的指標，進而提供不同地區的防災策略，也突顯了多元觀點評估的重要性。本研究引入環境觀點的FRI框架，納入棲地連續性等新的韌性因子，並為決策者提供了識別關鍵主導因子的分析方法，有助於制定更精確的洪水易發地區適應性管理策略。 | zh_TW |
| dc.description.abstract | In the context of global climate change and accelerating urbanization, the frequency and intensity of extreme rainfall and flooding events are continuously increasing, posing significant threats to both natural and human systems. This study aims to develop a Flood Resilience Index (FRI) to quantify and assess regional resilience to flood disasters from different perspectives.
Most existing resilience assessment literature adopts a human-centered viewpoint, with factors primarily focused on social and economic aspects. This study incorporates an environmental perspective for resilience assessment, adopting the ISO 14090 climate change risk assessment framework and establishing hazard, exposure, and sensitivity as three key resilience sub-indicators. The 3Di hydrodynamic model is employed to simulate flooding events caused by extreme rainfall, and hazard indicators are derived based on the physical flood characteristics of the study area. Additionally, this study uses Conefor Sensinode 2.2 to calculate the Probability of Connectivity (PC) for assessing habitat connectivity, incorporating it as one of the factors in the exposure indicator. By integrating environmental factors such as biodiversity, soil drainage levels, and biological density, the environmental resilience performance of various research units in Xitun District, Taichung City is evaluated, thereby establishing FRI from different perspectives. Results indicate that the sensitivity of environmental factors is generally greater than that of social-economic factors, particularly pronounced in high-hazard areas. The Coupling Coordination Degree (CCD) between environmental resilience and social-economic resilience mostly exceeds 0.6, demonstrating good synergy. When coupling is poor, Cross-Correlation Functions (CCF) can be further employed to quantify interactions between FRI and different sub-indicators, identifying dominant factors and thereby providing location-specific disaster prevention strategies. These findings highlight the importance of multi-perspective assessment. This study introduces an environmental perspective FRI framework, incorporates new resilience factors such as habitat connectivity, and provides decision-makers with analytical methods for identifying key dominant factors, contributing to the development of more precise adaptive management strategies for flood-prone areas. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-18T01:18:38Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-18T01:18:38Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 I
摘要 II ABSTRACT III 目次 V 圖次 VIII 表次 XII 第一章 緒論 1 1.1 研究動機 1 1.2 研究目的 2 1.3 研究流程 3 第二章 文獻回顧 5 2.1 不同氣候變遷風險下的韌性 5 2.1.1 AR4 7 2.1.2 AR5 8 2.1.3 ISO 14090 10 2.2 洪災韌性指標 11 2.3 棲地連續性 14 第三章 研究方法與理論 17 3.1 3Di模式 17 3.1.1 控制方程式 18 3.1.2 數值方法 19 3.1.3 模式設定 24 3.2 Conefor Sensinode 2.2 27 3.2.1棲地計算方式 28 3.2.2模式設定 29 3.3 洪患韌性指標(FRI) 30 3.3.1危害度指標 32 3.3.2暴露度指標 34 3.3.3敏感度指標 37 3.3.4韌性子指標間相互作用 40 第四章 研究區域與模式建置 42 4.1地理位置與地文條件 42 4.2氣候條件 44 4.3 排水系統 47 4.4都市計畫與發展 48 4.5經濟發展 50 4.6自主防災社區與社區參與 53 4.7自然環境條件 57 第五章 模式建置 61 5.1 3Di模式建置 61 5.1.1二維參數資料 61 5.1.2一維參數資料與全域設定 65 5.2 模式驗證 66 5.3 模擬情境 68 5.4 計算危害度指標 69 第六章 結果與討論 71 6.1 多元觀點之FRI建構與分析 71 6.1.1 因子選擇結果 72 6.1.2 FRI之分類 73 6.1.3 因子敏感度分析 75 6.2 相同降雨延時、相同降雨量不同觀點之FRI比較 80 6.3 FRI子指標間相互作用 83 6.3.1 耦合協調度(CCD)與互相關函數(CCF)分析 83 6.3.2 防災策略 89 第七章 結論與建議 91 7.1 結論 91 7.2 建議 92 參考文獻 93 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 洪災韌性指標 | zh_TW |
| dc.subject | 棲地連續性 | zh_TW |
| dc.subject | 淹水模擬 | zh_TW |
| dc.subject | 3Di 模式 | zh_TW |
| dc.subject | 棲地連通機率 | zh_TW |
| dc.subject | Flood simulation | en |
| dc.subject | 3Di model | en |
| dc.subject | Probability of Connectivity (PC) | en |
| dc.subject | Habitat Connectivity | en |
| dc.subject | Flood Resilience Index (FRI) | en |
| dc.title | 環境系統面對洪水災害衝擊之韌性評估 | zh_TW |
| dc.title | Resilience Assessment of Environmental Systems Under Flood Impacts | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 李鴻源;葉克家 | zh_TW |
| dc.contributor.oralexamcommittee | Hong-Yuan Lee;Keh-Chia Yeh | en |
| dc.subject.keyword | 洪災韌性指標,棲地連續性,淹水模擬,3Di 模式,棲地連通機率, | zh_TW |
| dc.subject.keyword | Flood Resilience Index (FRI),Habitat Connectivity,Flood simulation,3Di model,Probability of Connectivity (PC), | en |
| dc.relation.page | 99 | - |
| dc.identifier.doi | 10.6342/NTU202503546 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2025-08-12 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | N/A | - |
| 顯示於系所單位： | 土木工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-2.pdf 未授權公開取用 | 6.16 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。