| dc.description.abstract | Spring droughts in Taiwan significantly impact the socio-economic, affecting local agriculture, industry, and domestic sectors. These droughts are particularly concerning due to their far-reaching implications for both local and global economies, especially the semiconductor industry, a cornerstone of global technology markets. This is particularly evident in the disruption of chip production by Taiwan Semiconductor Manufacturing Company Limited (TSMC) in Spring 2021. Despite the critical nature of these droughts, there is a notable gap in the integration of various drought indices in local studies, which is essential for comprehensive drought risk assessment and management. This dissertation addresses this gap by assessing the characteristics of spring droughts and proposing an innovative framework that utilizes an ensemble learning method to comprehensively evaluate drought risk in Taiwan.
Firstly, Chapter 3 employs Standardized Drought Indices (SDI), the Standardized Temperature Index (STI), and the Normalized Difference Water Index (NDWI) to investigate the characteristics of spring droughts from 1982 to 2021 in Taiwan. It utilizes a combination of remote sensing, machine learning techniques, and statistical analysis, enhanced by the assimilation of ground-validated remote sensing data. The results reveal that drought dynamics in Taiwan are distinct from typical global patterns. Hydrological droughts in Taiwan show a rapid response to rainfall deficits, with strong correlations (r > 0.8), whereas agricultural droughts demonstrate moderate correlations (0.4 < r < 0.6). Additionally, while agricultural droughts have seen a decline, meteorological and hydrological droughts have increased since the early 2000s, featuring a significant change point. The central and southern regions of Taiwan emerge as drought hotspots, experiencing notable air temperature-drought coherence in 4–6-year cycles. This chapter underscores the importance of advancing drought management strategies, particularly vital for Taiwan′s industrial economy. Secondly, Chapter 4 introduces a comprehensive framework to assess drought risk in Taiwan, utilizing a combination of the Analytic Network Process (ANP) and Artificial Neural Network (ANN). The research objectives of this study are to generate a detailed map of drought risk while considering the impacts of drought hazards on the socio-economic domains of agriculture, industry, and domestic sectors. The final drought risk map was thoroughly validated through fieldwork and statistical analysis, achieving high validation accuracies ranging from 0.717 to 0.851 in evaluating crop damage, area conversion, and estimated economic losses. This chapter highlights the effectiveness of the ANP-ANN ensemble method, proving its robustness in rapidly and accurately predicting drought risk under various ecological and socioeconomic conditions. Finally, Chapter 5 concludes the dissertation by summarizing its principal discoveries and contributions. The chapter also discusses the potential for expanding the methodologies employed, to address emerging challenges in the field. | en_US |