請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92457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 | 賴怡辰 | zh_TW |
| dc.contributor.author | Yi-Chen Lai | en |
| dc.date.accessioned | 2024-03-22T16:35:49Z | - |
| dc.date.available | 2024-03-23 | - |
| dc.date.copyright | 2024-03-22 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-01-04 | - |
| dc.identifier.citation | [1] Angeler, D. G., & Allen, C. R. (2016). Quantifying resilience. Journal of Applied Ecology, 53(3), 617-624.
[2] Afshari, S., Tavakoly, A. A., Rajib, M. A., Zheng, X., Follum, M. L., Omranian, E., & Fekete, B. M. (2018). Comparison of new generation low-complexity flood inundation mapping tools with a hydrodynamic model. Journal of Hydrology, 556, 539-556. [3] Afifi, Z., Chu, H. J., Kuo, Y. L., Hsu, Y. C., Wong, H. K., & Zeeshan Ali, M. (2019). Residential flood loss assessment and risk mapping from high-resolution simulation.Water,11(4), 751. [4] Anni, A. H., Cohen, S., & Praskievicz, S. (2020). Sensitivity of urban flood simulations to stormwater infrastructure and soil infiltration. Journal of Hydrology, 588, 125028. [5] Arrighi, C., Carraresi, A., & Castelli, F. (2022). Resilience of art cities to flood risk: A quantitative model based on depth‐idleness correlation. Journal of Flood Risk Management, 15(2), e12794. [6] Blaikie, P., Cannon, T., Davis, I., & Wisner, B. (1994). At risk: natural hazards, people''s vulnerability and disasters. Routledge. [7] Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O''Rourke, T. D., Reinhorn, A. M., Shinozuka, M., Tierney, K., Wallace, W. A., & Von Winterfeldt, D. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake spectra, 19(4), 733-752. [8] Brand, F. S., & Jax, K. (2007). Focusing the meaning (s) of resilience: resilience as a descriptive concept and a boundary object. Ecology and society, 12(1). [9] Carrivick, J. L. (2006). Application of 2D hydrodynamic modelling to high-magnitude outburst floods: An example from Kverkfjöll, Iceland. Journal of Hydrology, 321(1-4), 187-199. [10] Chen, K.-F., & Leandro, J. (2019). A conceptual time-varying flood resilience index for urban areas: Munich city. Water, 11(4), 830. [11] Chang, Y. S., & Ho, H.-C. (2021). Role of Low Impact Development on Urban Flood Resilience Index. AGU Fall Meeting Abstracts. [12] Chikodzi, D., Nhamo, G., Dube, K., & Chapungu, L. (2022). Climate change risk assessment of heritage tourism sites within South African national parks. International Journal of Geoheritage and Parks, 10(3), 417-434. [13] Dassanayake, D., Burzel, A., & Oumeraci, H. (2012). Evaluation of cultural losses.XtremRisK Progress Report. Leichtweiß-Institute for Hydraulic Engineering and Water Resources, Technische Universität Braunschweig. [14] Daungthima, W., & Kazunori, H. (2013). Assessing the flood impacts and the cultural properties vulnerabilities in Ayutthaya, Thailand. Procedia Environmental Sciences, 17, 739-748. [15] Dahm, R., Hsu, C.-T., Lien, H.-C., Chang, C.-H., & Prinsen, G. (2014). Next generation flood modelling using 3Di: A case study in Taiwan. DSD international conference. [16] Das, S., Ghosh, A., Hazra, S., Ghosh, T., de Campos, R. S., & 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. [17] ECLAC - Economic Commission for Latin America and the Caribbean, (2003): Handbook for Estimating the Socio-economic and Environmental Effects of Disasters. United Nations, ECLAC and International Bank for Reconstruction and Development (The World Bank). [18] Erena, S. H., Worku, H., & De Paola, F. (2018). Flood hazard mapping using FLO-2D and local management strategies of Dire Dawa city, Ethiopia. Journal of Hydrology: Regional Studies, 19, 224-239. [19] Folke, C. (2006). Resilience: The emergence of a perspective for social–ecological systems analyses. Global environmental change, 16(3), 253-267. [20] Field, C. B. (2012). Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University Press. [21] Figueiredo, R., Romao, X., & Paupério, E. (2020). Flood risk assessment of cultural heritage at large spatial scales: Framework and application to mainland Portugal. Journal of Cultural Heritage, 43, 163-174. [22] Gunderson, L. (2010). Ecological and human community resilience in response to natural disasters. Ecology and society, 15(2). [23] Holling, C. S. (1973). Resilience and stability of ecological systems. Annual review of ecology and systematics, 4(1), 1-23. [24] Holling, C. S. (1996). Engineering resilience versus ecological resilience. Engineering within ecological constraints, 31(1996), 32. [25] Hsu, M.-H., Chen, S. H., & Chang, T.-J. (2000). Inundation simulation for urban drainage basin with storm sewer system. Journal of hydrology, 234(1-2), 21-37. [26] Haile, A. T., & Rientjes, T. (2005). Effects of LiDAR DEM resolution in flood modelling: A model sensitivity study for the city of Tegucigalpa, Honduras. Isprs wg iii/3, iii/4, 3, 12-14. [27] Hsu, Y.-C., Prinsen, G., Bouaziz, L., Lin, Y.-J., & Dahm, R. (2016). An investigation of DEM resolution influence on flood inundation simulation. Procedia Engineering, 154, 826-834. [28] Hemmati, M., Ellingwood, B. R., & Mahmoud, H. N. (2020). The role of urban growth in resilience of communities under flood risk. Earth''s Future, 8(3), e2019EF001382. [29] IPCC, C. C. (2007). Impacts, adaptation and vulnerability. contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Intergovernmental Panel on Climate Change (IPCC). [30] Imon, S. S., Dioko, L. A. N., & Ong, C. E. (2008). Tourism at cultural heritage sites in Asia: Cultural Heritage Specialist Guide Training and Certification Programme for UNESCO World Heritage Sites: a training manual for heritage guides, Core Module. [31] IPCC (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[Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp. [32] ISO14090 International Organization for Standardization (2019).Adaptation to climate change—Principles, requirements and guidelines. [33] Joakim, E. P., Mortsch, L., & Oulahen, G. (2015). Using vulnerability and resilience concepts to advance climate change adaptation. Environmental Hazards, 14(2), 137-155. [34] Lin, K.-H. E., Lee, H.-C., & Lin, T.-H. (2017). How does resilience matter? An empirical verification of the relationships between resilience and vulnerability. Natural Hazards, 88, 1229-1250. [35] Leandro, J., Chen, K.-F., Wood, R. R., & Ludwig, R. (2020). A scalable flood-resilience-index for measuring climate change adaptation: Munich city. Water Research, 173, 115502. [36] Manyena, S. B. (2006). The concept of resilience revisited. Disasters, 30(4), 434-450. [37] Malla, B. (2006). Classification of Cultural Property and Their Conservation. Orissa Review, 61-64. [38] Merz, B., Kreibich, H., Schwarze, R., & Thieken, A. (2010). Review article" Assessment of economic flood damage". Natural Hazards and Earth System Sciences, 10(8), 1697-1724. [39] McAllister, T. (2016). Research needs for developing a risk-informed methodology for community resilience. Journal of Structural Engineering, 142(8), C4015008. [40] Martyr-Koller, R. C., Kernkamp, H. W. J., Van Dam, A., van der Wegen, M., Lucas, L. V., Knowles, N., ... & Fregoso, T. A. (2017). Application of an unstructured 3D finite volume numerical model to flows and salinity dynamics in the San Francisco Bay-Delta. Estuarine, Coastal and Shelf Science, 192, 86-107. [41] Muthusamy, M., Casado, M. R., Butler, D., & Leinster, P. (2021). Understanding the effects of Digital Elevation Model resolution in urban fluvial flood modelling. Journal of Hydrology, 596, 126088. [42] Nicklin, H., Leicher, A. M., Dieperink, C., & Van Leeuwen, K. (2019). Understanding the costs of inaction–an assessment of pluvial flood damages in two European cities. Water, 11(4), 801. [43] NCDR.(2020)。Stochastic Frequency Analysis of Daily and Hourly Precipitation in Future Projection.國家災害防救科技中心,1-50. [44] Pachauri, R. K., Gomez-Echeverri, L., & Riahi, K. (2014). Synthesis report: summary for policy makers. [45] Rapporten, W. L. (2013). Testcase D-Flow FM. [46] Schmitt, T. G., Thomas, M., & Ettrich, N. (2004). Analysis and modeling of flooding in urban drainage systems. Journal of hydrology, 299(3-4), 300-311. [47] Stelling, G. S. (2012, November). Quadtree flood simulations with sub-grid digital elevation models. In Proceedings of the Institution of Civil Engineers-Water Management (Vol. 165, No. 10, pp. 567-580). Thomas Telford Ltd. [48] Timmermann, P. (1981). Vulnerability, resilience and the collapse of society. Environmental Monograph, 1, 1-42. [49] UNISDR, U. In Sendai Framework for Disaster Risk Reduction 2015–2030. In Proceedings of the 3rd United Nations World Conference on DRR, Sendai, Japan, 14–18 March 2015; pp. 14–18. [50] United Nations (2016). Report of the Open-ended Intergovernmental Expert Working Group on Indicators and Terminology Relating to Disaster Risk Reduction, Geneva: UN General Assembly. [51] Volp, N. D., Van Prooijen, B. C., & Stelling, G. S. (2013). A finite volume approach for shallow water flow accounting for high‐resolution bathymetry and roughness data. Water Resources Research,49(7), 4126-4135. [52] Wang, X., Li, H., Wang, Y., & Zhao, X. (2022). Assessing climate risk related to precipitation on cultural heritage at the provincial level in China. Science of The Total Environment, 835, 155489. [53] Yang, L., Li, J., Zhou, K., Feng, P., & Dong, L. (2021). The effects of surface pollution on urban river water quality under rainfall events in Wuqing district, Tianjin, China. Journal of Cleaner Production, 293, 126136. [54] Yang, K., Hou, H., Li, Y., Chen, Y., Wang, L., Wang, P., & Hu, T. (2022). Future urban waterlogging simulation based on LULC forecast model: A case study in Haining City, China. Sustainable Cities and Society, 87, 104167. [55] 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 洋工程學系研究所學位論文。1-117 [56] 王嘉和, 游翔麟, 梁益詮, 王嘉瑜, & 張倉榮. (2019). 街道及下水道之雙排水系統快速淹水模擬. 農業工程學報, 65(1), 1-17. [57] 李冠曄. (2009). 氣候變異對於都市淹水影響之評估與應用研究. [58] 李明儒. (2010). 雨水下水道淤積對於都市淹水之影響評估. [59] 徐硯庭. (2014). 低衝擊開發運用在高都市化地區的減洪效益-以新北市中永和地區為例. [60] 郭炳宏, & 劉宏亮. (2011). 文化資產概念的轉變歷程與認定標準. 文化資產保存學刊, (17), 41-60. [61] 張雅婷. (2022). 時變性洪患韌性指標於高都市化區域之研究—以中永和為例. [62] 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. [63] 賴桂文(2016). HEC-RAS 水理模式 2D 模組介紹及應用。學術天地-工程技術 新知,1-17. [64] 鄧屬予(2006).台北盆地之地質研究.WESTERN PACIFIC EARTH SCIENCES, 6, 1-28. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92457 | - |
| dc.description.abstract | 在全球極端氣候與都市化的影響下,強降雨引發的洪澇災害日益頻繁且難以預測,加上防救災資源的有限性,要降低都市高暴露度下的洪水風險需要透過低衝擊開發與風險評估的非結構性防災調適策略。UNDRR及過去的洪水風險研究定義了韌性城市建構要項,同時提供給決策者於災害期間拯救生命和財產的資源分配建議,但這些評估當中的保護標的卻很少針對文化資產。其原因為文化資產具有無法以貨幣計算的無形價值(歷史、文化、藝術、紀念價值),當受到衝擊後難以透過維修或重建而回復其歷史價值,遭遇損壞或遺失也會間接造成歷史地區經濟損失。正因為它的不可替代與老舊特性,在估算其韌性時,需要有別於一般建物韌性計算方法並提供相對的治理對策。
本研究以「經破壞後難以復原」及「受洪水影響」之有形文化資產為研究對象,參考 ISO 14090 針對氣候變遷定義之風險,由危害度、暴露度和敏感度為韌性指標中的三大次指標,並整合脆弱度與回復力的概念建立一個具有時變性的洪災韌性指標(Flood Resilience Index, FRI),評估洪水對歷史地區的文化韌性衝擊。此研究配合高精度且計算快速的3Di模式,模擬臺灣臺北市五個行政區在不同降雨情境下的淹水物理因子,並針對文化資產定義具有時變性與空間變化性的韌性因子,最後,計算FRI供後續擬定調適行動的依據。 研究結果發現區域之間危害度與韌性分數並非成一致的正向關係,證實區域韌性需考量文化損失的必要性,實際風險與地區防洪效率和資產特性密切相關。經比較區域差異,發現中正區為最受文化韌性影響的區域,同時也是FRI分數最低的區域;相反地,最不受文化韌性影響的區域為萬華區,也正好為韌性最佳的區域。根據FRI時變分析,事件期前期受到暴露度影響最多,事件期後期和恢復期則受到危害度影響最多,當區域暴露度影響越小,此轉換會越提前發生,而敏感度在整個事件中影響皆屬相對較低。FRI可幫助決策者(decision makers) 了解區域之間特質的差異,於災前預測指標變化作為災前或災時應變的依據,經由調適行動,提升歷史地區的韌性。 | zh_TW |
| dc.description.abstract | With extreme global climate and urbanization, increasingly frequent and unpredictable floods caused by heavy rainfall, and limited resources for disaster prevention and relief, reducing flood risk in highly exposed cities requires non-structural adaptation strategies for disaster prevention through low-impact development and risk assessment. Integrating UNDRR and past flood risk studies, the Constructive Resilient Cities (CR) definition provides policymakers with recommendations for allocating resources to save lives and property during disasters, but the protection targets of these assessments rarely target cultural heritage (CH). The reason for this lack of attention is that they have intangible values (historical, cultural, artistic, and commemorative) which can not be measured in monetary terms and are difficult to restore through repairs or reconstruction when they are impacted. Because of its irreplaceable and old nature, it requires a different method of calculating its resilience from that of ordinary buildings and provides a relative solution for its management.
In this study, tangible CH that are "difficult to recover from damage" and "affected by floods" are used as the target, and the ISO 14090 definition of climate change risk is used to define hazard, exposure, and sensitivity as the three major internal indicators of resilience. Integrating the concepts of resilience and vulnerability, this study establishes a time-varying Flood Resilience Index (FRI) to assess the impact of floods on the cultural resilience of historical areas, This research employs the 3Di model, a numerical model known for its high resolution and computational efficiency, to simulate the physical factors of flooding in five districts of Taipei City under various rainfall scenarios. Additionally, resilient factors, characterized by temporal and spatial variability, are defined specifically for CH. In conclusion, FRI provides a basis for formulating subsequent adaptation strategies. The results indicate an inconsistency between the rankings of hazard and resilience. This underscores the necessity to consider cultural losses when comparing resilience among different areas, as the actual risk is intricately linked to regional flood prevention efficiency and asset characteristics. When we compare the differences among regions, it turns out that Zhongzheng District is the area most affected by cultural resilience, and it also has the lowest FRI score. Conversely, Wanhua District stands out as the area least affected by cultural resilience, coinciding with the highest resilience scores. According to the time-varying analysis of FRI, the early stages of the event phase are most affected by exposure, while the later stages, including the post-event phase and recovery phase, are predominantly shaped by the severity of hazards. The smaller the impact of regional exposure, the earlier this transition occurs, and sensitivity remains relatively low throughout the entire event. The FRI serves as a useful tool for decision-makers to comprehend the distinctive characteristics among regions. It offers a basis for pre-disaster or disaster-time response strategies by anticipating changes in indicators. Through adaptive actions, the FRI contributes to the enhancement of the regional resilience of the ancient city. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-03-22T16:35:49Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-03-22T16:35:49Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 目次
致謝 I 摘要 II ABSTRACT III 目次 V 圖次 VIII 表次 XI 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 4 2.1 都市淹水模式 4 2.1.1 D-flow FM 5 2.1.2 HEC-RAS 6 2.1.3 FLO - 2D 7 2.1.4 InfoWork ICM 8 2.1.5 Mike 9 2.1.6 SWMM 10 2.1.7 SOBEK 12 2.1.8 3Di 13 2.2 氣候變遷下風險評估與韌性調適 14 2.2.1 AR4 16 2.2.2 AR5 17 2.2.3 ISO 14090 19 2.3 洪患韌性指標 21 2.4 文化資產保存價值 24 第三章 研究方法與理論 29 3.1 3Di模式 29 3.1.1 控制方程式 29 3.1.2 數值計算方法 30 3.1.3 模型設定與模擬一二維耦合過程 33 3.2 文化資產的價值與保護策略 35 3.3 洪患韌性指標(FRI) 37 3.3.1 危害度指標 38 3.3.2 暴露度指標 39 3.3.3 敏感度指標 41 第四章 研究區域與模型建置 44 4.1 地理位置與氣候條件 44 4.1.1 地理位置 44 4.1.2 氣候條件 46 4.2 3Di模型設置 48 4.2.1 建置流程 48 4.2.2 二維參數資料設定 50 4.2.3 一維水文測站及排水系統設置 54 4.2.4 模式驗證 56 4.3 文化資產條件設定 58 4.3.1 歷史條件與資產分布 58 4.3.2 細部資料 60 4.4 模擬情境與指標因子計算流程 63 4.4.1 模擬情境 63 4.4.2 指標因子計算流程 64 第五章 結果與討論 67 5.1 不同淹水定義下的區域韌性 67 5.2 三大次指標於FRI中的作用 71 5.3 影響因子於FRI中的作用 73 5.3.1 物理性危害因子 73 5.3.2 資產韌性因子 75 5.4 調適策略 78 5.4.1 區域調適 78 5.4.2 資產調適 79 第六章 結論與建議 82 6.1 結論 82 6.2 建議 83 參考文獻 85 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 3Di | zh_TW |
| dc.subject | 風險評估 | zh_TW |
| dc.subject | 文化資產 | zh_TW |
| dc.subject | 洪災韌性指標 | zh_TW |
| dc.subject | 都市韌性 | zh_TW |
| dc.subject | Urban Resilience | en |
| dc.subject | Flood Resilience Index | en |
| dc.subject | Cultural Heritage | en |
| dc.subject | Risk Assessment | en |
| dc.subject | 3Di | en |
| dc.title | 歷史地區面對洪水災害之韌性評估 | zh_TW |
| dc.title | Resilience Assessment of Historic Areas Facing Flood Disasters | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| 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 | 都市韌性,洪災韌性指標,文化資產,風險評估,3Di, | zh_TW |
| dc.subject.keyword | Urban Resilience,Flood Resilience Index,Cultural Heritage,Risk Assessment,3Di, | en |
| dc.relation.page | 89 | - |
| dc.identifier.doi | 10.6342/NTU202400008 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-01-05 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2029-01-04 | - |
| 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-112-1.pdf 未授權公開取用 | 7.36 MB | Adobe PDF | 檢視/開啟 |
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。
