| 摘要: | 乾旱是一種嚴重的自然災害，對全球的生態系統、環境和人類生活產生廣泛的影響。近期台灣的乾旱情況引起了重大關注，尤其是對半導體晶片生產這樣的重要產業。本研究使用衛星基礎指數來監測及分析乾旱狀態，因為準確乾旱評估在有效的水資源管理中起著關鍵作用。溫度-植被乾旱指數（TVDI），使用經驗技術結合地表溫度（LST）和植被覆蓋分數（FVC），被廣泛使用。然而，其在植被稀疏地區的適用性受到限制，促使我們探索替代方法。最近發展的溫度-土壤水分乾旱指數（TMDI），用標準化差異潛熱指數（NDLI）替代植被指數，顯示出做為一種合適替代品的潛力。研究的第一個關鍵部分介紹了使用從NDLI衍生出的新的表面水分可用性（FSWA）對TMDI的改進。該部分深入探討了在LST-FSWA空間內精煉邊緣選擇以觀察乾旱。已經採用了一種實用的方法來準確地識別這個空間內的乾和濕邊緣。為了評估TMDI的可靠性，該研究使用各種指標進行了全面的評估，包括由地表能量平衡算法（SEBAL）衍生的蒸散量（ET）、作物水分壓力指數（CWSI）、初級生產力（GPP）和降水數據。結果顯示，TMDI與SEBAL衍生的CWSI、ET和GPP之間的相關性高於TVDI。此外，TMDI與降水之間的強關聯突顯了其捕捉乾旱模式的有效性。該部分還提出了一個標準的TMDI閾值，用於評估2014年至2021年台灣西南部的乾旱。本文的後續部分應用新穎的 FSWA 來監測全國範圍內的農業缺水和乾旱狀況。 FSWA 與其他兩個指數一起用於分析 2001 年至 2022 年澳洲的年度乾旱模式。在澳洲的大多數農業地區，FSWA 顯示出與土壤濕度 (SM)、ET 和降雨量的強大時間相關性。鑑於SWAT的簡化計算和全國範圍的適用性，其實際利用已在全球範圍內進行評估。研究發現SWAT可以代表土壤水分並生成高分辨率的乾旱地圖。此外，SWAT被應用於評估2011年至2022年的全球乾旱狀況。基於SWAT指數的乾旱分佈和趨勢分析顯示出不同土地覆蓋類型之間的廣泛變化。總的來說，TMDI和SWAT都作為實際運行的乾旱指數。先進的TMDI，依賴於僅兩個變量的整合：FSWA和LST，在區域範圍的乾旱監測中表現出色，特別是在農業區。另一方面，由於其更廣泛的規模適用性和更簡單的方法，基於衛星的SWAT指數提供了一種實用的替代方案。;Drought, a detrimental natural disaster, has extensive impacts on ecosystems, the environment, and human life globally. Its recent emergence in Taiwan has sparked significant concerns, especially for vital industries like semiconductor chip production. This dissertation uses satellite-based indices to monitor drought status, given the crucial role of accurate drought assessment in effective water management. The Temperature-Vegetation Dryness Index (TVDI), which combines Land Surface Temperature (LST) and Fractional Vegetation Cover (FVC) using empirical techniques, is widely utilized. However, its applicability is limited in regions with sparse vegetation, prompting the exploration of alternative methods. The recently introduced Temperature-Soil Moisture Dryness Index (TMDI), which substitutes the vegetation index with the Normalized Difference Latent Heat Index (NDLI), shows potential as a suitable alternative. The first key section of the dissertation presents advancements in the TMDI using the new Fractional Surface Water Availability (FSWA) derived from the NDLI. This section delves into refining edge selection within the LST–FSWA space to observe drought. A practical method has been adopted to accurately identify the dry and wet edges within this space. To evaluate the reliability of TMDI, the study conducted comprehensive assessments using various indicators, including the evapotranspiration (ET), Crop Water Stress Index (CWSI) derived from the Surface Energy Balance Algorithm for Land (SEBAL), Gross Primary Productivity (GPP), and precipitation data. The results reveal high correlations between the TMDI and SEBAL-derived CWSI, ET, and GPP, surpassing those obtained with TVDI. Moreover, strong associations between the TMDI and precipitation underscore its effectiveness in capturing drought patterns. This section also proposes a standard TMDI threshold for assessing drought in southwestern Taiwan from 2014 to 2021. The subsequent section of this dissertation applies the novel FSWA to monitor agricultural water stress and drought status at the national scale. The FSWA is employed alongside two other indices to analyze annual drought patterns in Australia from 2001 to 2022. The analyses reveal high correlations between the FSWA against soil moisture (SM), ET, and rainfall. Across most agricultural regions in Australia, the FSWA shows robust temporal correlations with the SM, ET, and rainfall. Furthermore, given the SWAT’s simplified calculations and wide applicability, its practical utilization has been evaluated at the global scale. It is found that the SWAT can represent SM and generate high-resolution drought maps. The SWAT was applied to assess global drought conditions from 2011 to 2022. The analysis of drought distributions and trends based on the SWAT index exhibited wide variations across different land cover types. In conclusion, both the TMDI and SWAT function as practically operational drought indices. The advanced TMDI, relying on the integration of only two variables: FSWA and LST, excels in regional-scale drought monitoring, particularly in agricultural zones. On the other hand, the satellite-based SWAT index, due to its broader scale applicability and more straightforward methodology, offers a viable alternative to empirical models. |
