http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98863 MS Program in Disaster Risk Reduction and Resilience Fri, 09 Oct 2026 00:02:28 GMT 2026-10-09T00:02:28Z http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99020 標題: 臺北都會區北部地區地表覆蓋變遷下之氣溫變化分析; Analysis of Temperature Change Under Land Cover Change in Northern Taipei Metropolitan Area 作者: 卡吾妲; Undrakh Ganzorig 摘要: 過去三十年來，臺北都會區北部的都市發展導致了大量綠地與農地轉為建成區的地表覆蓋變化。為探討此類變化如何影響夏季白天與夜間的近地表空氣溫度，本研究分析1990年至2023年間的氣溫變化趨勢。研究方法結合衛星影像進行的土地覆蓋分類、臺灣氣象觀測站資料，以及臺灣歷史氣候重建資料（TReAD），並依據不同土地覆蓋類型進行分類分析。 研究結果顯示，新興都市化區域，特別是林口與龜山，出現最顯著的升溫，夜間氣溫上升超過2 °C。相較之下，綠地比例較高或海拔較高的區域則呈現較小的氣溫增幅，凸顯綠地與地形在緩和氣溫上升方面的作用。1990年代至2000年代初期的資料顯示，夜間升溫相對較強，而2010年代以後則顯示白天升溫趨勢明顯上升，在都市核心區中更與夜間升溫持平甚至超越，可能與不透水鋪面增加、綠地減少以及通風不良導致的日間熱累積效應有關。 整體而言，研究結果顯示地表覆蓋組成與在地環境條件共同影響都市熱環境的時空變化。雖然TReAD為基於觀測資料建構的模擬資料，其格網化的特性有助於掌握區域性氣候趨勢，但氣象觀測站則能補足模型對微氣候變異的限制。整合兩種資料來源有助於更全面理解都市氣溫變化機制，為未來都市發展、綠地配置與熱風險緩解策略提供實證參考。; Urban development in the northern Taipei Metropolitan Area has resulted in extensive conversion of vegetated and agricultural land to built-up surfaces over the past three decades. This study investigates how such land cover changes have influenced summer daytime and nighttime air temperatures from 1990 to 2023. Using satellite-derived land cover classifications, Taiwan’s weather station observations, and the Taiwan Re-Analysis Downscaling dataset (TReAD), temperature trends were assessed across clustered land cover types. The findings show that newly urbanized areas—especially Linkou and Guishan—experienced the highest warming, with nighttime temperatures rising by over 2 °C. In contrast, vegetated and higher elevation areas showed less warming, suggesting that green space and topography help moderate temperature increases. While the 1990s to early 2000s were marked by stronger nighttime warming, recent years (2010s onward) show a shift toward intensified daytime warming, particularly in long-urbanized city centers where built-up surfaces and reduced airflow may amplify heat buildup. These findings suggest that both land cover and local environmental conditions shape temperature changes over time. This study shows the value of combining gridded model outputs with station data to better understand urban heat warming trends. Insights from the findings may help inform decision makers regarding development consequences and potential land use interventions. Wed, 01 Jan 2025 00:00:00 GMT http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99020 2025-01-01T00:00:00Z http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/102557 標題: 植被與氣候驅動因子對尼羅河流域水儲量動態之時空影響：2003–2022年; Spatiotemporal Influence of Vegetation and Climate Drivers on Water Storage Dynamics in the Nile Basin, 2003-2022. 作者: 翁珊益; Saed Mohamed Omar 摘要: 水儲量變化是評估水文變遷的重要指標，因為其反映降雨、蒸發散、土壤水分、地下水、地表水、植被活動及人類用水等多種過程的綜合影響。本研究探討2003年至2022年間，植被與氣候驅動因子對尼羅河流域水儲量動態之影響。研究分析三項水儲量目標變數，包括GRACE/GRACE-FO陸地水儲量異常（TWSA）、GLDAS土壤水分異常（SMA）及GLDAS地下水儲量異常（GWSA）。本研究選取四項解釋變數，分別為降水量、降水吉尼指數（GI）、校正後正向蒸發需求（PETd）及MODIS常態化差異植生指數（NDVI）。 所有資料均處理至共同的每月0.25°尼羅河流域網格，並轉換為去季節化異常值。分析以網格尺度進行，並進一步彙整至九個HydroBASINS Level-04子流域。首先，本研究採用0至3個月最佳滯後相關分析，以考量水文系統可能存在的延遲反應。接著，使用逐步多元線性迴歸辨識具統計意義的解釋變數，並利用標準化係數判讀水儲量與驅動因子之間的關係方向。最後，透過優勢分析量化各解釋變數的相對重要性，並採用Theil–Sen斜率與Kendall顯著性檢定進行水儲量趨勢熱點分析。 研究結果顯示，尼羅河流域水儲量動態具有明顯的空間異質性，且不同水儲量組成之間呈現不同反應。GRACE TWSA在流域多數區域呈現整體增加趨勢，而GLDAS土壤水分與地下水儲量異常則呈現較為區域化且混合的變化型態。降水量與土壤水分及地下水相關儲量多呈正相關，顯示其作為主要水分輸入因子的作用。PETd與水儲量之間則呈現一致的負向關係，尤其在SMA與GWSA中更為明顯，表示大氣蒸發需求對水儲量消耗具有重要影響。NDVI是解釋GRACE TWSA變化最主要的因子，顯示總水儲量與植被綠度之間存在緊密耦合關係。相較之下，SMA與GWSA受到降水量、PETd與NDVI較為平衡的共同控制。GI則較多扮演調節降雨有效性的角色，而非主要主導因子。 整體而言，本研究指出尼羅河流域水儲量變化無法僅由降水量解釋，而是受到降雨供給、降雨分布、蒸發需求、植被動態及區域水文條件交互作用的共同影響。; Water storage variability is an important indicator of hydrological change because it reflects the combined effects of rainfall, evapotranspiration, soil moisture, groundwater, surface water, vegetation activity, and human water use. This study examines how vegetation and climate drivers influence water storage dynamics across the Nile Basin from 2003 to 2022. The analysis focuses on three storage targets: GRACE/GRACE-FO terrestrial water storage anomaly (TWSA), GLDAS soil moisture anomaly (SMA), and GLDAS groundwater storage anomaly (GWSA). Four explanatory drivers were considered: precipitation, precipitation Gini Index (GI), corrected positive evaporative demand (PETd), and MODIS NDVI. All datasets were processed to a common monthly 0.25° Nile Basin grid and converted into deseasonalized anomalies. The analysis was conducted at the grid-cell scale and summarized across nine HydroBASINS Level-04 subbasins. A 0–3 month best-lag correlation analysis was used to account for delayed hydrological response. Stepwise multiple linear regression was then applied to identify statistically selected predictors. Standardized coefficients were used to interpret the direction of storage–driver relationships, dominance analysis was used to quantify relative predictor importance, and Theil–Sen/Kendall hotspot analysis was used to detect significant storage trends. The results show that Nile Basin storage dynamics are spatially heterogeneous and differ among storage components. GRACE TWSA shows broad increasing tendencies across much of the basin, while GLDAS soil moisture and groundwater storage anomalies show more regionally mixed patterns. Precipitation is strongly and positively associated with soil moisture and groundwater-related storage, confirming its role as a primary water-input driver. PETd shows a consistent negative relationship with storage, especially for SMA and GWSA, indicating the importance of atmospheric drying pressure. NDVI is the strongest explanatory factor for GRACE TWSA, suggesting close coupling between total storage and vegetation greenness. In contrast, SMA and GWSA are controlled by a more balanced combination of precipitation, PETd, and NDVI. GI contributes more as a modifier of rainfall effectiveness than as a dominant driver. Overall, this study shows that Nile Basin water storage cannot be explained by rainfall amount alone. Storage variability reflects the interaction of rainfall supply, rainfall distribution, evaporative demand, vegetation dynamics, and regional hydrological conditions. Thu, 01 Jan 2026 00:00:00 GMT http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/102557 2026-01-01T00:00:00Z http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/103179 標題: 基於脆弱性函數之地震經濟損失機率估計：以2024年能登半島地震為例; Probabilistic Estimation of Earthquake Economic Losses Based on Vulnerability Functions: Application to the 2024 Noto Peninsula Earthquake 作者: 小川 憲人; Kento Ogawa 摘要: 既有關於日本地震損失估計的研究，多以震度等級對應固定損壞率的方式進行推估。這種方法有助於災後快速掌握初步損失規模，但在處理損壞率的不確定性，或將經濟損失表示為機率分布時，仍有其限制。基於此，本論文導入機率式脆弱性函數，嘗試改進災後地震損失估計方法，並以2024年1月能登半島地震作為實證應用案例。 本文以2024年能登半島地震的受災區域為研究對象，分析範圍主要涵蓋石川縣、富山縣與新潟縣。在地震危害方面，本文採用日本氣象廳震度，並使用依地震動加速度換算而得的連續近似震度值，以呈現地震動強度。暴露量部分則以市町村層級的資本存量表示，並將全國及縣層級的資本存量分配至六類資產，包括住宅、非住宅建築、電力、瓦斯與自來水等公用事業、港灣、港灣以外之社會基礎設施，以及其他社會資本。上述資本存量的分配，主要依據戶數、從業人數、基礎設施相關指標與設施位置等行政統計資料進行推估。 在脆弱性函數的建構上，本文分別處理住宅與非住宅資產。住宅資產部分，本文利用觀測到的損壞率資料估計probit迴歸模型，並將地震動強度、1981年建築基準修正前後之木造住宅比例，以及是否位於沿海地區等因素納入分析。非住宅資產部分，則依據震度別損壞率設定，以probit函數近似各資產類別的損壞率曲線。經濟損失估計則是將市町村層級的資本存量暴露量，與各資產類別相對應的損壞率函數結合計算；此外，本文亦透過蒙地卡羅模擬，檢視估計結果所包含的不確定性。 估計結果顯示，研究區域的確定性點估計總損失為2.14兆日圓。蒙地卡羅模擬結果則顯示，總損失分布呈現正偏態，其中位數為2.05兆日圓，95%區間介於1.47兆至3.30兆日圓之間。若從機率分布觀察，總損失低於2兆日圓的機率為44.9%，而超過3兆日圓的機率為5.37%。整體而言，本文估計結果與既有地震損失估計研究大致一致；同時，機率式估計也能呈現單一確定性估計難以處理的不確定性與上尾風險。本文的分析可作為未來以機率式方法檢討地震損失估計的參考，並有助於改進日本災後快速經濟損失評估方法。; Previous seismic loss estimation studies in Japan have often relied on fixed damage-rate assumptions for each seismic intensity class. While this approach is useful for rapid estimation, it has limited ability to evaluate uncertainty in damage rates or express economic loss as a probabilistic distribution. This thesis aims to improve post-earthquake seismic loss estimation by introducing probabilistic vulnerability functions and applying them to the January 2024 Noto Peninsula Earthquake. The study area covers the affected area of the 2024 Noto Peninsula Earthquake, focusing on Ishikawa, Toyama, and Niigata Prefectures. Seismic hazard is represented by Japan Meteorological Agency seismic intensity and its continuous approximation based on ground acceleration. Capital stock is calculated as municipal-level exposure by allocating national and prefectural stock values to six asset categories: housing, nonresidential buildings, electricity/gas/water utilities, ports, social infrastructure excluding ports, and other social capital. The allocation uses administrative statistics such as households, employees, infrastructure indicators, and facility locations. Vulnerability functions are constructed separately for housing and non-housing assets. For housing, a probit regression model is estimated from observed damage-rate data, incorporating ground-motion intensity, wooden housing shares before and after the 1981 building code revision, and coastal location. For non-housing assets, asset-specific damage-rate curves are approximated using probit functions based on seismic-intensity damage-rate settings. Economic loss is estimated by combining municipal capital stock exposure with the corresponding damage-rate functions, and Monte Carlo simulation is conducted to examine uncertainty. The deterministic point estimate for the three-prefecture study area is 2.14 trillion yen. The Monte Carlo simulation showed a positively skewed distribution, with a median total loss of 2.051 trillion yen and a 95% interval ranging from 1.469 to 3.302 trillion yen. The probability that total loss remained below 2 trillion yen was 44.9%, while the probability of exceeding 3 trillion yen was 5.37%. These results are broadly consistent with previous seismic loss estimation studies, while also quantifying uncertainty and upper-tail risk that cannot be obtained from a deterministic estimate alone. These findings suggest that probabilistic vulnerability functions can extend conventional seismic loss estimation by adding information on uncertainty, supporting more informative post-earthquake economic loss assessment in Japan. Thu, 01 Jan 2026 00:00:00 GMT http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/103179 2026-01-01T00:00:00Z