本研究探討吐瓦魯動態降水及其對乾旱管理的影響，研究重點為富納富提島。研究中使用橫跨72年的數據（1951年至2022年），分析降水指標，特別是在不同時間尺度之標準化降水指數（SPI）的變化。較短時間尺度的SPI 1和SPI 3可提供關於短期波動的即時觀察。對於依賴雨水的吐瓦魯來說，這類水資源的可利用性極為重要。相反地，較長時間尺度的SPI 12則提供了更廣泛的視野，有助於預測長期乾旱的影響。此外，本研究還探討了以日為基底的SPI與熱帶氣旋（TC）事件之間的關連，分析了2015至2018四年時間期。結果顯示較長的日基底時間尺度（少於一個月）與TC事件之間具有較強的相關性。研究並採用新進的數據分析技術，建構長短期記憶（LSTM）模型。在120個月的序列中，LSTM模型對乾旱預測顯示出很高的準確性，此結果有助於強化氣候災害的抵禦韌性。研究最後對先進分析技術整合於乾旱管理、社區能力建設以及與可持續發展目標相符的國家和全球政策提出建議，期能加強吐瓦魯對氣候變異的韌性，以保障人民和環境的福祉。

This thesis investigates the dynamics of precipitation in Tuvalu and its implications for drought management, focusing on the island of Funafuti. Utilizing a dataset spanning 72 years (1951 – 2022), it examines precipitation indices, particularly the Standardized Precipitation Index (SPI), across various timescales. Shorter timescales such as SPI 1 and SPI 3 provide timely insights into short-term fluctuations crucial for water availability in rain-dependent Tuvalu. Conversely, longer timescales like SPI 12 offer a broader perspective, aiding in anticipating long-term drought impacts. Additionally, the study explores the relationship between daily SPI and Tropical Cyclone (TC) events, analyzing a four-year period (2015 – 2018). Stronger correlations with TC events are observed with longer daily timescales (less than a month). Advanced data analytics techniques, notably Long Short-Term Memory (LSTM) modeling, in the 120-month sequence demonstrate high accuracy in drought prediction, contributing to enhanced resilience against climate hazards. The thesis concludes by offering suggestions for further integration of advanced analytics into drought management, community capacity-building, and policy alignment with national and global sustainability goals are provided. These efforts aim to strengthen Tuvalu's resilience to climate variability, safeguarding the well-being of its population and environment.

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