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DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 何昊哲 | zh_TW |
dc.contributor.advisor | Hao-Che Ho | en |
dc.contributor.author | 張雅婷 | zh_TW |
dc.contributor.author | Ya-Ting Chang | en |
dc.date.accessioned | 2023-03-19T23:48:30Z | - |
dc.date.available | 2023-11-10 | - |
dc.date.copyright | 2022-08-29 | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2002-01-01 | - |
dc.identifier.citation | [1] Adger WN (2000) Social and ecological resilience: are they related? Prog Hum Geogr 24:347–364
[2] 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. [3] 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, e12794. [4] Batica, J., 2013. METHODOLOGY FOR FLOOD RESILIENCE INDEX. International Conference on Flood Resilience Experiences in Asia and Europe, Sep. 5-7, 2013, Exeter, United Kingdom. [5] Batica, J., 2014. Flood Resilience Index - Methodology And Application. 11th International Conference on Hydroinformatics HIC, Aug., 2014, New York, City, USA. [6] Carpenter SR and Gunderson LH (2001) Coping with collapse: ecological and social dynamics in ecosystem management. Bioscience 51:451–457 [7] Chang YS, Ho HC, Huang LY. (2021). Role of Low Impact Development on Urban Flood Resilience Index. Advancing Earth and space science Fall Meeting 2021, Dec. 13-17(2021), New Orleans, USA. [8] Chang, T. J., Wang, C. H. and Chen, A. S., “A novel approach to model dynamic flow interactions between storm sewer system and overland surface for different land covers in urban areas,” Journal of Hydrology, 524, pp. 662-679, 2015 [9] Chen, K. F., & Leandro, J. (2019). A conceptual time-varying flood resilience index for urban areas: Munich city. Water, 11(4), 830. [10] Cheng, T., Xu, Z., Hong, S., & Song, S. (2017). Flood risk zoning by using 2D hydrodynamic modeling: A case study in Jinan City. Mathematical Problems in Engineering, 2017. [11] Chia-Ho Wang, Hsiang-Lin Yu, Yi-Cyuan Liang, Jia-Yu Wang, Tsang-Jung Chang. (2019) .Rapid Flood Inundation Simulation of Dual Drainage System between Streets and Sewers. Journal of Taiwan Agricultural Engineering Vol. 65, No. 1, March 2019. [12] Chin-Yu He, Ching-Pin Tung, Wan-Ya Wang. (2020). Study on the Integration of Disaster Risk Reduction and Climate Change Adaptation. Journal of Taiwan Agricultural EngineeringVol. 66, No. 2, JUNE 2020 [13] Climate Change 2014, the Fourth Assessment Report (AR5) of the United Nations Intergovernmental Panel on Climate Change [14] Dahm, R., Hsu, C. T., Lien, H. C., Chang, C. H., & Prinsen, G. (2014, November). Next generation flood modelling using 3Di: A case study in Taiwan. In DSD international conference. [15] Dasallas, L., Kim, Y., & An, H. (2019). Case study of HEC-RAS 1D–2D coupling simulation: 2002 Baeksan flood event in Korea. Water, 11(10), 2048. [16] 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. [17] Ferguson, C., & Fenner, R. (2020). The impact of Natural Flood Management on the performance of surface drainage systems: A case study in the Calder Valley. Journal of Hydrology,590. [18] Füssel H-M (2007) Vulnerability: a generally applicable conceptual framework for climate change research. Glob Environ Chang 17:155–167 [19] Gallopín GC (2006) Linkages between vulnerability, resilience, and adaptive capacity. Glob Environ Chang 16:293–303. doi:10.1016/j.gloenvcha.2006.02.004 [20] Gunderson LH (2010) Ecological and human community resilience in response to natural disasters. Ecol Soc 15:18 [21] Haile, A. T., & Rientjes, T. H. M. (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. [22] 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. [23] Hsu, M. H., Chen, S. H. and Chang, T. J. Inundation simulation for urban drainage basin with storm sewer system. Journal of Hydrology, 234(1), pp. 21-37, 2000. [24] 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. [25] Huff, F.A., 1967. Time distribution of rainfall in heavy storms.Water Resources Research, 3(4), pp. 1007-1019. [26] IPCC. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL, editors. Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2014 p. 1048. [27] Joakim E, Mortsch L, Oulahen G (2015) Using vulnerability and resilience concepts to advance climate change adaptation. Environ Hazard 14:137–155 [28] John T. Delaney, Kristen L. Bouska, Josh D. Eash, Patricia J. Heglund, Andrew J. Allstadt. (2021). Mapping climate change vulnerability of aquatic-riparian ecosystems using decision-relevant indicators. Ecological Indicators 125 (2021) 107581. [29] Kong, J., & Simonovic, S. P. (2016, June). An original model of infrastructure system resilience. In Proceedings of the CSCE Annual Meeting: Resilient Infrastructure, London, ON, Canada (pp. 1-4). [30] Kuan-Hui Elaine Lin, Hsiang- Chieh Lee & Thung-Hong Lin. How does resilience matter? An empirical verification of the relationships between resilience and vulnerability. Natural Hazards volume 88, pages1229–1250 (2017). [31] 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. [32] Li, J., Zhang, B., Mu, C., & Chen, L. (2018). Simulation of the hydrological and environmental effects of a sponge city based on MIKE FLOOD. Environmental earth sciences, 77(2), 1-16. [33] Margherita Giuzio, Dejan Krusec, Anouk Levels, Ana Sofia Melo, Katri Mikkonen and Petya Radulova. Climate change and financial stability. European Central Bank. 2021.05 [34] Marina Batalini de Macedo, Thalita Raquel Pereira de Oliveira, Tassiana Halmenschlager Oliveira, Marcus Nóbrega Gomes Junior, José Artur Teixeira Brasil, Cesar Ambrogi Ferreira do Lago, Eduardo Mario Mendiondo. Evaluating low impact development practices potentials for increasing flood resilience and stormwater reuse through lab-controlled bioretention systems. Water Sci Technol (2021) 84 (5): 1103–1124. [35] Miller F et al (2010) Resilience and vulnerability: complementary or conflicting concepts? Ecol Soc 15:11 [36] 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. [37] Schmitt, T.G., Thomas, M., & Ettrich, N. (2004). Analysis and modeling of flooding in urban drainage systems. Journal of Hydrology,299,300-311. [38] Schmitt, T.G., Thomas, M., & Ettrich, N. (2004). Analysis and modeling of flooding in urban drainage systems. Journal of Hydrology,299,300-311. [39] Schneider SH, Semenov S, Patwardhan A, Burton I, Magadza CHD, Oppenheimer M, et al. Assessing key vulnerabilities and the risk from climate change. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE, editors. Climate change 2007: impacts, adaptation and vulnerability, contribution of working group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press; 2007. p. 779–810. [40] Shouvik Das, Amit Ghosh, Sugata Hazra, Tuhin Ghosh, Ricardo Safra de Campos, Sourav Samanta. (2020). Linking IPCC AR4 & AR5 frameworks for assessing vulnerability and risk to climate change in the Indian Bengal Delta. Progress in Disaster Science 7 (2020) 100110. [41] Smit B and Wandel J (2006) Adaptation, adaptive capacity and vulnerability. Glob Environ Chang 16:282–292. [42] Wong PP, Losada IJ, Gattuso J-P, Hinkel J, Khattabi A, McInnes KL, et al. Coastal systems and low-lying areas. In: Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, Chatterjee M, Ebi KL, Estrada YO, Genova RC, Girma B, Kissel ES, Levy AN, MacCracken S, Mastrandrea PR, White LL, editors. Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of working group II to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press; 2014. p. 361–409. [43] 內政部營建署(2015年3月)。水環境低衝擊開發操作手冊總報告。 [44] 內政部營建署下水道誌政府自辦雨水篇。2011 年7月。 [45] 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 [46] 出流管制計畫書與規劃書檢核基準及洪峰流量計算方法(2019年2月)。行政院公報 第 025 卷 第 028 期。 [47] 台灣大學慶齡工業研究中心(2019)。應用高速降雨逕流模式協助擬定都市積水防治策略。 [48] 何媚華(2014)。中永和地區都市排洪系統最佳管理措施之探討。國立台灣大學土木工程學研究所學位論文,1-95。 [49] 余濬。由雨型推估流量方法之差異探討-以河川治理與區域排水整治為例。水利會訊 第十二期。 [50] 余濬。雨水下水道與河川重現期距差異之研究-以台北市雨水下水道與景美溪為例。水利會訊 第十二期。 [51] 李明儒(2010)。雨水下水道淤積對於都市淹水之影響評估。國立交通大學土木工程研究所碩士論文。 [52] 林士惟(2018)。多目標基因演算法於韌性城市評估之研究。國立台灣大學土木工程學研究所學位論文,1-98。 [53] 邱詩婷(2016)。HEC-RAS 5.0二維逕流模式之應用探討。國立臺灣海洋大學河海工程學系碩士學位論文。 [54] 雨水下水道系統維護缺失態樣與改善對策之探討。戴俊地(Tai,Chunti)、陳昶良(Chen Chang liang)、蔡得時(Tsay Dershys)(2014)。中華民國營建工程學會第十二屆產業永續發展研討會。 [55] 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。國立台灣大學土木工程學研究所學位論文, 1-126。 [56] 張天豪(2019)。淺水波慣性力對都市淹水模擬之影響。國立成功大學水利及海洋工程學系研究所博碩士論文。 [57] 許雅淳(2009)。台灣人口密度之最「永和市」的都市發展研究。臺北市立教育大學歷史與地理系。 [58] 童裕翔、劉俊志、鄭兆尊、陳正達、連琮勛。氣候變遷之日雨量以及時雨量頻率分析(2020年1月)。國家災害防救科技中心。 [59] 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。國立台灣大學土木工程學研究所學位論文,1-108。 [60] 新北市中和區區公所官方網站 [61] 新北市地政局(2021年)。110年第1季新北市不動產市場分析季報。 [62] 新北市政府(2019年7月)。變更中和主要計畫(第二次通盤檢討)(第一階段)書。 [63] 新北市政府(2020年1月)。變更永和都市計畫(部分住宅區區)書為衛生福利特定專用。 [64] 新北市區域計畫政策評估說明書 附件七新北市歷史災例清冊。(2015) [65] 經濟部水利署(2020年12月)。因應氣候變遷洪災韌性提升策略建構(2/2)。 [66] 經濟部水利署水利規劃試驗所(2018年5月)。淡水河水系水文水理論證報告。 [67] 經濟部水利署水利規劃試驗所(2013年12月)。易淹水地區水患治理計畫第三階段實施計畫 縣管河川蘇澳溪水系分洪道規劃。 [68] 經濟部水利署第十河川局 官方網站。 [69] 劉子明、鄧澤宇。以 IPCC 風險定義探討氣候變遷下水資源風險評估與調適應用(2019年1月)。國家災害防救科技中心。 [70] 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。 | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86313 | - |
dc.description.abstract | 氣候變遷使極端事件的發生機率提高,加上都市化改變了土地利用類型,兩者對都市的排水系統造成很大的挑戰。都市土地面積有限,既無法無限增加抽水容量及堤防高度等集中式的基礎工程,面對高重現期的降雨也難以透過分散式的低衝擊開發(LID) 有效減洪,加上政府救災資源有限,需透過科學化的工具謹慎評估洪水事件中各區域所配給的資源多寡與時間。
本研究以脆弱度(vulnerability)與回復力(recovery)的概念作為韌性的切入點,設計具有時間變異性的洪患韌性指標(Flood Resilience Index, FRI) ,參酌 ISO14090 對氣候變遷風險的定義,取危害度、暴露度與敏感度為 FRI 內部的三大指標,依照各指標的定義選擇適合研究區域中永和的因子及設計量化公式。 有鑑於防災管理的決策調度時間緊湊,本研究選擇以高精度且計算快速的3Di 來模擬中永和面臨 RCP8.5 情境雨量的淹水程度,透過 Arc GIS 後處理取得具有時間變異性的物理性因子;而社會性因子則依照公式定義,隨著各里社經條件和淹水延時而改變。 本研究證實社會性因子的時變對 FRI 的作用為分數降低、回復期拉長且出現時間延後。根據危害度指標內超過臨界值的因子數量可將風險程度歸納為三類,FRI 亦可依其趨勢線圖像區分為三類,並與危害度指標的分類做對照。最後本研究透過改變降雨量、降雨延時、降雨型態比較不同設計雨型之 FRI,其中韌性較佳的降雨特徵為小雨量、短延時、左偏雨型的情境。決策者可透過 FRI 事件期中相鄰兩時間的梯度差,判斷區域致災時間點並即時給予救援,以調適災害衝擊,增加韌性。 | zh_TW |
dc.description.abstract | Climate change has increased the incidence of extreme events, and moreover, urbanization has changed land use patterns, both of which pose significant challenges to urban drainage systems. The space in urban area is too limited to increase in pumping capacity and embankment height for centralized infrastructure unlimitedly. In the face of rainfall with high return period, the decentralized low-impact development (LID) isn’t effective of flooding reduction, so under limited resources in the flooding events, government needs a scientific tool to carefully evaluate the amount and timing of resources for each district.
The study introduced vulnerability and recovery as the concepts of resilience, and designed a time-varying Flood Resilience Index (FRI). Based on the definition of climate change risk in ISO 14090, the three main indicators of FRI are hazard, exposure, and sensitivity. The factors are suitable for the study area are selected according to the definition of each indicator and the quantification formula is designed. In view of the tight activation time for decision making in disaster prevention and management, we simulated the rainfall scenario of RCP8.5 by 3Di, a numerical model with high resolution, fast calculation speed. Subsequently, we obtained the physical factors with time variation through post-processing in Arc GIS. In the study, the effect of time dependent social factor on FRI was found to be a decrease in score, a longer recovery period, and a delay in occurrence. The number of factors exceeding the reference value in the hazard indicators could be classified into three types from high to low risk level; furthermore, FRI is categorized according to its trend and compared with the level of hazard. Finally, the study contrasted the FRI of different rainfall designs by changing the total precipitations, rainfall duration, and rainfall pattern, among which the more resilient are smaller amount of precipitation, shorter duration, and left-skewed rainfall pattern. By calculating the gradient difference between two adjacent hours in the event phase of FRI, the decision maker could determine the timing of disaster and provide immediate relief to adjust the impact and increase the resilience. | en |
dc.description.provenance | Made available in DSpace on 2023-03-19T23:48:30Z (GMT). No. of bitstreams: 1 U0001-1608202211413000.pdf: 10656823 bytes, checksum: b340ceda7e0ac52d3531547183867af4 (MD5) Previous issue date: 2022 | en |
dc.description.tableofcontents | 目錄
誌謝I 摘要II ABSTRACT III 目錄V 圖目錄VIII 表目錄XII 第一章 緒論 1 1.1 研究動機 1 1.2 研究目的 3 1.3 研究流程 4 第二章 文獻回顧 5 2.1 都市淹水與模式介紹 5 2.1.1 SWMM 6 2.1.2 FLO – 2D 8 2.1.3 SOBEK 9 2.1.4 Mike 系列 10 2.1.5 InfoWorks ICM 11 2.1.6 HEC-RAS 12 2.1.7 3Di 13 2.2 不同氣候變遷風險下的韌性 15 2.3.1 AR4(2007) 17 2.3.2 AR5(2014) 18 2.3.3 ISO 14090 21 2.3 洪災韌性指標 22 第三章 研究方法與理論 27 3.1 3Di 模式 27 3.1.1 控制方程式 28 3.1.2 網格技術 29 3.1.3 模擬過程 34 3.2 氣候變遷實體風險 37 3.2.1 氣候相關風險 37 3.2.2 氣候實體風險模板 39 3.3 洪患韌性指標(FRI) 40 3.3.1 危害度指標 41 3.3.2 暴露度指標 43 3.3.3 敏感度指標 43 第四章 研究區域 46 4.1 地理位置與地文條件 46 4.2 氣候與水文測站 48 4.3 排水系統 50 4.4 都市計畫與人口發展 53 4. 5 經濟發展 56 4. 6 自主防災社區與社區參與 58 第五章 模式設置 62 5.1 3Di 模式建置 62 5.1.1 二維參數資料 62 5.1.2 一維參數資料與全域設定 67 5.2 模式驗證 69 5.3 模擬情境 72 5.4 計算危害度指標 74 第六章 結果與討論 77 6.1. 社會因子在 FRI 的作用 77 6.2 FRI 分類及其內部指標型態 81 6.2.1 危害度指標型態 82 6.2.2 FRI 分類 84 6.3 不同設計雨型之 FRI 比較 86 6.3.1 同降雨延時及降雨型態、不同降雨量 87 6.3.2 同降雨量及降雨型態、不同降雨延時 91 6.3.3 同降雨量及降雨延時、不同降雨型態 95 第七章 結論與建議 101 7.1 結論 101 7.2 建議 102 參考文獻 104 | - |
dc.language.iso | zh_TW | - |
dc.title | 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 | zh_TW |
dc.title | Time-dependent Flooding Resilience Index Applied in Highly Urbanized Areas — A Case Study of Zhonghe and Yonghe District | en |
dc.type | Thesis | - |
dc.date.schoolyear | 110-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 李鴻源;葉克家 | zh_TW |
dc.contributor.oralexamcommittee | HONG-YUAN LI;Keh Chia Yeh | en |
dc.subject.keyword | 都市韌性,洪患韌性指標,氣候變遷實體風險,淹水模擬,3Di 模式, | zh_TW |
dc.subject.keyword | Urban resilience,Flood resilience index,Climate physical risks,Flooding simulation,3Di mode, | en |
dc.relation.page | 111 | - |
dc.identifier.doi | 10.6342/NTU202202440 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2022-08-26 | - |
dc.contributor.author-college | 工學院 | - |
dc.contributor.author-dept | 土木工程學系 | - |
dc.date.embargo-lift | 2025-08-31 | - |
顯示於系所單位: | 土木工程學系 |
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