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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 | 張淵舜 | zh_TW |
| dc.contributor.author | Yuan-Shun Chang | en |
| dc.date.accessioned | 2024-08-09T16:21:55Z | - |
| dc.date.available | 2024-08-10 | - |
| dc.date.copyright | 2024-08-09 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-31 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93910 | - |
| dc.description.abstract | 氣候變遷與都市化加劇極端降雨事件發生頻率，使都市面臨巨大的災害風險，防洪策略若能納入韌性概念，將有效減輕災害衝擊。關於韌性措施實際提升洪災韌性在過去文獻較少被提及，原因在於韌性是一個主觀的概念，其評估因子難以定義和量化。此外災前的預警、災中的應變以及災後的復原都會對於防災工作扮演重要角色，但既有方法難以完整評估所有階段，顯示在制定災害防治策略時需額外考量韌性的時變性。傳統上利用淹水潛勢地圖與風險矩陣的定性手段進行減災整備規劃及收容場所區位適宜性分析，然而上述方法僅提供物理淹水總量體的參考值，無法反映各區在災害不同階段的需求，也忽略了社會經濟和基礎設施對於韌性產生之影響。目前大部分相關研究對於都市防洪韌性僅提出質性評估架構，對於政策決策者在災害管理上較難提供有效且科學化的依據。
本研究以ISO 14091氣候變化風險評估的定義作為韌性評估框架，危害度、暴露度和敏感度為指標考量依據，有別於單純考慮物理因素因子，整合物理、社會經濟及基礎設施因子來建立洪災韌性指標(Flood Resilience Index, FRI)，並定義災害韌性衝擊值(FRI impact)評估FRI整體表現。透過3Di洪水演算模式模擬真實事件獲取淹水物理量的危害度子指標，然後結合非物理性因子影響的暴露度和敏感度之子指標來組成FRI。研究區域為新北市中永和地區、臺中市北屯區及臺南市新化都市計畫區，除探討不同因子對於FRI的影響，測試韌性防災措施低衝擊開發（Low Impact Development, LID)對於防災韌性的提升效益，藉由子指標與整體FRI之間的交互作用探討不同因子對於都市洪災韌性的影響與防災策略。 研究結果指出FRI和危害度曲線的最低點存在一時間差，此將為災害救援和減災策略制定提供重要根據，顯示僅考量物理因子將會低估災害所產生的衝擊。整體的防洪韌性會受到降雨型態的影響，高強度、長延時及左偏雨型將對應越大的韌性衝擊值。此外洪水期間，都市基礎設施的損壞程度會影響韌性表現，滯洪池和下水道系統的密集程度將影響非都市區域的韌性表現，而路網和電網系統在災中的損壞率對於都市區域的韌性影響更大。在高密度開發區域，經濟發展及自主防災程度越高將提升都市防洪韌性，顯著影響洪災回復期，產生更快的恢復速度和更高的韌性。 應用FRI探討都市計畫區的LID對於防災之效益，研究結果指出LID能在短延時降雨事件中減少淹水面積和深度，但長延時降雨的效果不佳。然而從防洪韌性角度來分析，即使在長延時降雨情境下LID還是能有效縮短洪水災害的回復期，增加都市區域的韌性表現。將研究區域根據是否為都市地區及洪水嚴重程度進行分類，本研究利用交叉相關函數(CCF)和耦合協調度(CCD)來建立標準化的防災策略，由量化FRI與子指標間的相互作用來智慧化輔助決策支援系統。在高危害度地區中，適應途徑的減災措施必須以減少物理洪水為主，並且考慮社會經濟因子，以提升災害回復效益減少風險。在輕度和中度危害地區，救援工作的策略需依賴主導危害度的因子和維持社會經濟穩定的因子。主導FRI的子指標會隨著開發程度不同而有所差異，不論在都市或非都市區域暴露度皆是韌性的主要決定因素，但在非都市區域中敏感度主導FRI程度會隨著淹水嚴重程度越高而降低。 本研究所提出的時變性FRI，能夠捕捉到各種韌性因子隨時間的複雜相互作用，改善目前韌性工具無法針對整個災害階段進行評估之不足，時變特性允許更適宜的規劃和防災策略，也可以適應洪災事件過程中的各種風險因素。未來洪災韌性策略可以考慮更多的適應性，且FRI提供一個通用的架構供後續研究者持續納入更全面的評估因子，透過不同情境中的FRI證實其可以實際使用於防災策略的制定，並將動態韌性措施整合到都市規劃和政策中，更能增強都市應對洪水的能力。 | zh_TW |
| dc.description.abstract | Climate change and urbanization have intensified the frequency of extreme rainfall events, exposing metropolitan areas to significant disaster risks. Incorporating resilience concepts into flood prevention strategies can effectively mitigate disaster impacts. Traditionally, qualitative methods utilizing flood hazard maps and risk matrices have been employed for disaster prevention decision-making and resource allocation. However, flood hazard maps only provide reference values for total volumes and cannot reflect the varying needs of different areas over time. Furthermore, conventional flood risk analyses typically only consider physical inundation conditions, neglecting socioeconomic and infrastructural disparities, leading to uneven resource distribution. This study establishes a quantitative assessment from a resilience perspective on both temporal and spatial scales, proposing time-varying indicators to enhance urban disaster prevention efficacy.
Using the ISO 14091 definition of climate change risk as a resilience assessment framework, hazard, exposure, and sensitivity serve as the basis for indicator consideration. Diverging from indicators that solely consider immediate hydrological factors, this study integrates hydrological, socioeconomic, and infrastructural factors to construct a Flood Resilience Index (FRI). The 3Di flood simulation model is employed to simulate real events and obtain hazard sub-indicators of physical flood quantities. These are then combined with exposure and sensitivity sub-indicators influenced by social, economic, and infrastructural factors to compose the FRI. The study areas include the Zhonghe-Yonghe district of New Taipei City, the Beitun district of Taichung City, and the Xinhua urban planning area of Tainan City. In addition to exploring the impact of different factors on the FRI, the study identifies dominant factors through the interactions between sub-indicators and the overall FRI across different regions, while also investigating the influence of various factors on urban flood resilience and disaster prevention strategies. Under the interplay of hazard, exposure, and sensitivity, the research results indicate a temporal discrepancy between the FRI and the lowest point of the hazard curve, providing crucial basis for disaster relief and mitigation strategy formulation. Moreover, during flood events, the extent of urban infrastructure damage affects the overall disaster impact. Compared to retention ponds and drainage systems, road and power grid networks have a more significant influence on disasters, implying that infrastructure substantially impacts the FRI. In high-density development areas, economic development and autonomous disaster prevention have a certain influence on resilience. The overall disaster impact is affected by rainfall intensity, with rainfall skewness dominating in the initial stages of a disaster. The FRI curve shape is determined by the position of its lowest point, while population age distribution and economic conditions significantly influence the flood recovery period, with better socioeconomic conditions leading to faster recovery rates and higher resilience. Comparing urban and non-urban areas reveals that exposure is the primary determinant of resilience in both contexts, with sensitivity having a relatively greater influence in urban areas. Classifying the study areas based on urbanization and flood severity, this research employs cross-correlation functions and coupling coordination degree to establish standardized disaster prevention strategies, utilizing quantified FRI and sub-indicator interactions to intelligently assist decision support systems. In high-hazard areas, adaptive mitigation measures must primarily focus on reducing physical flooding while considering socioeconomic factors to enhance disaster recovery efficacy and reduce risks. In low and moderate hazard areas, rescue strategies should rely on factors dominating hazard levels and maintaining socioeconomic stability. Applying the FRI to examine the disaster prevention benefits of Low Impact Development (LID) in urban planning areas, the results indicate that LID can reduce flood area and depth in short-duration rainfall events but is less effective for long-duration rainfall. However, FRI analysis shows that even under long-duration conditions, LID can effectively shorten the recovery period of flood disasters, thereby increasing flood resilience. The time-varying FRI proposed in this study can capture the complex temporal interactions among various resilience factors. Its temporal characteristics allow for more appropriate planning and disaster prevention strategies, adapting to various risk factors throughout the flood event process. Future flood resilience strategies could consider greater adaptability, including comprehensive risk assessments, integrating dynamic resilience measures into urban planning and policies to further enhance urban flood response capabilities. | en |
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| dc.description.provenance | Made available in DSpace on 2024-08-09T16:21:55Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
誌謝 II 中文摘要 IV 英文摘要 VI 目 次 IX 圖 次 XV 表 次 XXIV 第一章 緒論 1 1.1 研究動機 1 1.2 研究目的 4 1.3 研究流程 6 第二章 文獻回顧 8 2.1 不同國際氣候變遷風險定義下的韌性評估架構 8 2.1.1 災害的定義、種類與特性 8 2.1.2 洪水風險之定義 9 2.1.3 災害管理與韌性防災概念 11 2.1.4 臺灣淹水風險圖資之發展 20 2.2 洪災韌性指標(FRI) 22 2.3 不同影響韌性的評估層面 36 2.3.1 物理淹水層面 36 2.3.2 社會經濟層面 36 2.3.3 關鍵基礎設施 37 2.3.4 都市與非都市區域的韌性差異 38 2.4 都市淹水模式 39 2.4.1 SOBEK 40 2.4.2 MIKE 43 2.4.3 FLO-2D 45 2.4.4 InfoWorks ICM 47 2.4.5 SWMM 48 2.4.6 HEC-RAS 49 2.4.7 3Di 51 2.4.8 各淹水模式之比較 55 2.5 低衝擊開發設施與對於韌性之效益 56 2.5.1 低衝擊開發設施 56 2.5.2 低衝擊開發之水理模擬 62 第三章 FRI理論與研究方法 66 3.1 FRI基本結構建立 66 3.2 物理淹水因子的確立 68 3.3 社會經濟因子的確立 70 3.4 社會經濟因子作用範疇的改變及FRI架構的修正 72 3.4.1 危害度指標 74 3.4.2 暴露度指標 74 3.4.3 敏感度指標 76 3.5 最終修正之FRI架構及評估因子 79 3.6 FRI表現之模式評估 80 3.6.1 FRI表現程度之評估方式 80 3.6.2 FRI子指標間的相互作用程度評估 82 第四章 模式建置 85 4.1 3Di 模式說明 85 4.1.1 模式技術及特性簡介 85 4.1.2 水理運算基本原理 87 4.1.3 數值計算方法 88 4.2 模式建置 96 4.2.1 模擬降雨情境設計 96 4.2.2 不同區域模式一二維參數設定 97 4.2.3 研究區域排水系統設置 98 4.2.4 網格設定 102 4.2.5 二維參數設定 110 4.2.6 一維下水道設定 117 4.2.7 研究區域洪水模式建置完成 123 4.3 模式率定及驗證 125 4.3.1 新化都市計畫區模式驗證 125 4.3.2 新北市中永和區域模式驗證 127 4.3.3 臺中市北屯區域模式驗證 129 4.4 數值模擬完成後之物理淹水因子結果產出 130 4.5 低衝擊開發模式建置 132 4.5.1 低衝擊開發對於都市洪災韌性之潛在效益 132 4.5.2 低衝擊開發元件 133 4.5.3 低衝擊開發於水理模式中之設定 134 第五章 研究區域選擇與模擬降雨情境 139 5.1 臺南市新化都市計畫區 139 5.1.1 地理位置與地文條件 139 5.1.2 人口趨勢與經濟發展 140 5.1.3 淹水風險與模擬情境 141 5.1.4 關鍵議題 144 5.2 新北市中永和地區 144 5.2.1 地理位置與地文條件 144 5.2.2 人口發展、經濟發展與自我防災政策 146 5.2.3 淹水風險與模擬情境 150 5.2.4 關鍵議題 153 5.3 臺中市北屯區 154 5.3.1 地理位置與地文條件 154 5.3.2 人口發展、經濟發展與自我防災政策 155 5.3.3 關鍵基礎設施 156 5.3.4 淹水風險與模擬情境 157 5.3.5 關鍵議題 159 5.4 關鍵議題統整與對應研究結果章節 159 第六章 時變性因子對於FRI之影響 161 6.1 不同降雨型態對於FRI產生之影響 161 6.1.1 降雨強度 161 6.1.2 降雨延時 162 6.1.3 降雨偏態 163 6.2 社會經濟因子在FRI扮演之角色 165 6.2.1 考量社會經濟因子以及時變性 166 6.2.2 社會經濟因子之重要性 169 6.3 關鍵基礎設施因子的導入對於FRI的影響及應用性 170 第七章 FRI應用於防災決策 175 7.1 低衝擊開發作為防災決策 175 7.1.1 LID對減洪的影響 175 7.1.2 LID對FRI的影響 178 7.1.3 FRI隨時間的變化 181 7.2 臨界值變化應用於不同特性之區域 184 7.3 不同土地利用中FRI的表現形態 185 7.4 利用FRI的分數曲線與各子指標做為防災策略制定之依據 188 7.4.1 以FRI 分類為根據制定防災策略路徑圖 188 7.4.2 CCD與CCF函數量化各因子對於FRI之影響來制定防災策略 193 7.5 FRI的時空變異性實際用於防災策略 196 第八章 討論 198 8.1 流速因子之適用性評估 198 8.2 評估因子對於FRI之敏感度 199 第九章 結論與建議 201 9.1 結論 201 9.2 建議 203 參考文獻 205 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 洪災韌性指標(Flood Resilience Index) | zh_TW |
| dc.subject | 動態風險地圖 | zh_TW |
| dc.subject | 洪災風險管理 | zh_TW |
| dc.subject | 耦合協調 | zh_TW |
| dc.subject | 低衝擊開發(LID) | zh_TW |
| dc.subject | Flood Resilience Index | en |
| dc.subject | Flood Risk Management | en |
| dc.subject | Coupling Coordination | en |
| dc.subject | Dynamic Risk Map | en |
| dc.subject | Low Impact Development | en |
| dc.title | 考量時空變化之都市洪災韌性指標定量化研究 | zh_TW |
| dc.title | Evaluation of Multi-Factor Flood Resilience Index for Resilient Capacity and Guiding Disaster Mitigation | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.coadvisor | 李鴻源 | zh_TW |
| dc.contributor.coadvisor | Hong-Yuan Lee | en |
| dc.contributor.oralexamcommittee | 游景雲;施上粟;林鎮洋;葉克家;陳憲宗 | zh_TW |
| dc.contributor.oralexamcommittee | Jiing-Yun You;Shang-Shu Shih;Jen-Yang Lin;Keh-Chia Yeh;Shien-Tsung Chen | en |
| dc.subject.keyword | 洪災韌性指標(Flood Resilience Index),耦合協調,洪災風險管理,低衝擊開發(LID),動態風險地圖, | zh_TW |
| dc.subject.keyword | Flood Resilience Index,Low Impact Development,Coupling Coordination,Flood Risk Management,Dynamic Risk Map, | en |
| dc.relation.page | 226 | - |
| dc.identifier.doi | 10.6342/NTU202402627 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-08-02 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2029-07-29 | - |
| 顯示於系所單位： | 土木工程學系 | |
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| ntu-112-2.pdf 未授權公開取用 | 15.96 MB | Adobe PDF | 檢視/開啟 |
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