近年來，氣候變遷日益嚴重，導致頻發極端水文事件。根據中央氣象局的資料，臺灣的年平均氣溫自1911年至2020年間上升了約1.6攝氏度，這一趨勢在過去三十年持續加劇。基於聯合國政府間氣候變遷專門委員會（IPCC）的第六次評估報告（AR6），科技部臺灣氣候變遷推估與資訊平台（TCCIP）預測，臺灣將在本世紀末經歷一系列極端事件，包括濕季降雨增加和乾季降雨減少，這將對本國的農業造成重大影響。
為了應對這些氣候相關變化帶來的挑戰，本研究探討農業乾旱指標理論，分析重要作物歷史乾旱事件之趨勢，評估重要作物的產區在氣候變遷下之乾旱雨水情趨勢，探究農業乾旱指標與農產業之應用。未能評估農業乾旱及其等級，本研究整理常見之乾旱指標，包括：標準化降雨指標(SPI)、標準化降水爭發散指標(SPEI)、標準化流量指標(SSI)、河川乾旱指標(SDI)、標準化地下水指標(SGI)、地表供水指標(SWSI)，分析曾文溪流域各乾旱指標所呈現之乾旱趨勢，與歷史乾旱事件或水稻(產量或耕作面積)之關係，病例用水文因子推估水稻需水量，提供各生育期之灌溉水量，以探討後續各乾旱指標尺度反映不同乾旱等級之訂定。
本研究以曾文溪流域作為研究區域，為呈現乾旱指標在時間與空間上之分布變化，將歷史資料與AR6氣候變遷情境之所分析之SPI與SPEI網格數值繪製於曾文溪流域，再以自主特徵映射網路(SOM)進行拓樸分類，探討曾文溪流域灌區豐枯變化情形與對重要作物之影響。根據AR6資料與歷史資料結果顯示，各區域乾旱程度表現上時距6個月相較於時距3個月更為顯著，且AR6乾旱指標數值整體有上升現象，隨著溫室氣體排放增加，發生頻率隨之提升，未來發生極端乾旱與豐水的情形有增加趨勢，應用SOM模式分類乾旱指標數值，有效區分乾旱程度並取得乾旱發生時間，未來將作物需水量、缺水耐受性與乾旱指標結合SOM模式，根據水稻各生長階段之推估需水量得知，皆有降低灌溉量，最大為225毫米，說明以實際降雨量與推估的灌溉量，能有效運用農業水資源，可提供農民進行實際應用。

In recent years, climate change has been increasingly severe, leading to frequent occurrences of extreme hydrological events. According to data from the Central Weather Administration, Taiwan's annual average temperature has risen by approximately 1.6 degrees Celsius from 1911 to 2020, a trend that has intensified over the past thirty years. Based on the Intergovernmental Panel on Climate Change's Sixth Assessment Report (AR6) and forecasts from Taiwan's Climate Change Projection and Information Platform (TCCIP), Taiwan is projected to experience a series of extreme events by the end of this century. These events include increased rainfall during wet seasons and decreased rainfall during dry seasons, significantly impacting the country's agriculture.
In response to the challenges posed by climate-related changes, this study explores agricultural drought index theories. It analyzes the trends of significant historical drought events for key crops, assesses the trends of drought and precipitation in major crop-producing regions under climate change, and investigates the application of agricultural drought indices in the agricultural industry. Failing to evaluate agricultural drought and its severity, this study compiles common drought indices, including the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Streamflow Index (SSI), Streamflow Drought Index (SDI), Standardized Groundwater Index (SGI), and Surface Water Supply Index (SWSI). It analyzes the drought trends presented by each drought index in the Zengwen River Basin, along with their relationship to historical drought events or rice (yield or cultivation area). Hydrological factors are utilized to estimate rice water requirements and provide irrigation amounts during different growth stages to investigate the subsequent establishment of different drought index scales reflecting various drought severity levels. 
This study focuses on the Zengwen River Basin to illustrate the temporal and spatial variations of drought indices. The historical data and SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) values derived from AR6 climate change scenarios were mapped within the Zengwen River Basin. Subsequently, a Self-Organizing Map (SOM) was employed for topological classification to investigate the variations in water abundance within the irrigation area and their impact on crucial crops. Results derived from AR6 data and historical records indicate that the severity of drought in different regions is more pronounced over a six-month period compared to a three-month duration. Moreover, there is an overall increase in AR6 drought index values, which correlates with the rise in greenhouse gas emissions, thereby escalating the frequency of extreme droughts and periods of excess water. By utilizing the SOM model to classify drought index values, it effectively discerns drought severity and identifies the onset of droughts. Future endeavors will integrate crop water requirements and drought tolerance with the SOM model to understand water needs across various growth stages of rice. The findings revealed reduced irrigation amounts, with the maximum reduction being 225 millimeters. This underscores the effective utilization of agricultural water resources by aligning actual rainfall with estimated irrigation needs, providing practical implications for farmers.

目　錄
謝誌	I
目　錄	VIII
圖目錄	X
表目錄	XIII
第一章　前言	1
1.1	研究緣起	1
1.2	研究目的	1
1.3	研究架構	2
第二章　文獻回顧	4
2.1	氣候變遷相關研究	4
2.2	自組特徵映射網路應用於水文之研究	5
2.3	乾旱指標相關應用	5
2.4	農業水資源管理相關研究	7
第三章　理論概述	8
3.1	乾旱指標	8
3.2	類神經網路	16
第四章　研究案例	22
4.1	研究區域	22
4.2	資料蒐集	23
4.3相關參數推估	36
第五章　結果與討論	38
5.1	農業乾旱指標與水稻歷史乾旱事件趨勢分析	38
5.2	AR6氣候變遷情境下農業乾旱趨勢分析	56
5.3	評估氣候變遷下之乾旱與水情趨勢	64
5.4	水稻生育期灌溉量分析	79
第六章　結論與建議	86
6.1	結論	86
6.2	建議	87
參考文獻	88
附錄一、水稻收穫面積與生產量	93
附錄二、嘉南地區各式乾旱指標歷程圖	109
附錄三、曾文溪流域AR6情境SOM拓樸圖	112
附錄四、曾文溪流域AR6情境二次分類拓樸圖	116


圖目錄
圖1-1　研究流程圖	3
圖3-1　累積機率轉換圖	10
圖3-2　SPI常態機率分布圖	10
圖3-3　自組特徵映射網路架構圖	17
圖3-4　優勝神經元與鄰近神經元示意圖	19
圖3-5　SOM網路神經元的拓樸座標	20
圖3-6　SOM網路演算方法流程圖	21
圖4-1　曾文溪流域研究區域圖	22
圖4- 2　曾文溪流域測站位置圖	23
圖4-3　曾文溪流域各縣市稻作產量百分比	27
圖4-4　水稻一期作最小與最大面積分布	29
圖4-5　水稻二期作最小與最大面積分布	29
圖4-6　AR6於曾文溪流域和蓋範圍網格點	31
圖4-7　曾文水庫 SSP5-8.5年平均雨量變化圖	32
圖4-8　曾文水庫 SSP5-8.5年平均溫度變化圖	32
圖4-9　曾文水庫 SSP1-2.6年平均雨量變化圖	33
圖4-10　曾文水庫 SSP1-2.6年平均溫度變化圖	33
圖5-1　嘉義市、嘉義縣與臺南市SPI-6趨勢圖	40
圖5-2　嘉義市、嘉義縣與臺南市SPEI-6趨勢圖	40
圖5-3　嘉南平原SSI-6趨勢圖	41
圖5-4　嘉南平原SDI-6趨勢圖	41
圖5-5　嘉南平原SGI-6趨勢圖	42
圖5-6　嘉南平原SWSI趨勢圖	42
圖5-7　各縣市SPEI-3與產量趨勢圖	45
圖5-8　各縣市SPEI-6與產量趨勢圖	46
圖5-9　各縣市SPI-3與產量趨勢圖	47
圖5-10　各縣市SPI-6與產量趨勢圖	48
圖5-11　嘉南平原SSI-1與SWSI與產量趨勢圖	49
圖5-12　曾文溪流域SPI-3與SPEI-3空間分布圖	52
圖5-13　曾文溪流域SPI-6與SPEI-6空間分布圖	52
圖5-14　SPI-6 SOM 2×2拓樸圖	53
圖5-15　SPEI-6 SOM 2×2拓樸圖	53
圖5-16　SPI-6 SOM 2×2二次分類拓樸圖	54
圖5-17　曾文溪流域SSP1-2.6情境SPI-3、6圖	59
圖5-18　曾文溪流域SSP1-2.6情境SPEI-3、6圖	60
圖5-19　曾文溪流域SSP2-4.5情境SPI-3、6圖	60
圖5-20　曾文溪流域SSP2-4.5情境SPEI-3、6圖	60
圖5-21　曾文溪流域SSP3-7.0情境SPI-3、6圖	61
圖5-22　曾文溪流域SSP3-7.0情境SPEI-3、6圖	61
圖5-23　曾文溪流域SSP5-8.5情境SPI-3、6圖	61
圖5-24　曾文溪流域SSP5-8.5情境SPEI-3、6圖	62
圖5-25　曾文溪流域灌區示意與網格點分布	66
圖5-26　SSP1-2.6情境下SPI-6 SOM 2×2拓樸圖	67
圖5-27　SSP1-2.6情境下SPEI-6 SOM 2×2拓樸圖	67
圖5-28　SSP2-4.5情境下SPI-6 SOM 2×2拓樸圖	68
圖5-29　SSP2-4.5情境下SPEI-6 SOM 2×2拓樸圖	68
圖5-30　SSP3-7.0情境下SPI-6 SOM 2×2拓樸圖	69
圖5-31　SSP3-7.0情境下SPEI-6 SOM 2×2拓樸圖	69
圖5-32　SSP5-8.5情境下SPI-6 SOM 2×2拓樸圖	70
圖5-33　SSP5-8.5情境下SPEI-6 SOM 2×2拓樸圖	70
圖5-34　SPEI-6豐枯水期2×2資料比例(以SSP-1.26為例)	71
圖5-35　AR6情境下SPI3與SPI-6乾旱情勢分布比例	74
圖5-36　SSP1-2.6情境下SPI-3 SOM 2×2二次分類拓樸圖	75
圖5-37　SSP1-2.6情境下SPI-6 SOM 2×2二次分類拓樸圖	75
圖5-38　SSP2-4.5情境下SPI-3 SOM 2×2二次分類拓樸圖	76
圖5-39　SSP2-4.5情境下SPI-6 SOM 2×2二次分類拓樸圖	76
圖5-40　SSP3-7.0情境下SPI-3 SOM 2×2二次分類拓樸圖	77
圖5-41　SSP3-7.0情境下SPI-6 SOM 2×2二次分類拓樸圖	77
圖5-42　SSP5-8.5情境下SPI-3 SOM 2×2二次分類拓樸圖	78
圖5-43　SSP5-8.5情境下SPI-6 SOM 2×2二次分類拓樸圖	78
圖5-44　歷史與推估整地時間灌溉量比較歷程圖	85

表目錄
表3-1　SPI乾旱程度分級表	9
表3-2　SPEI乾旱程度對照表	11
表3-3　SWSI乾旱程度分級表	14
表4-1　曾文溪流域內水利署雨量站資訊	24
表4-2　曾文溪流域流量站測站資訊	24
表4-3　曾文溪流域水位站測站資訊	25
表4-4　曾文溪流域地下水位測站資訊	25
表4-5　水稻一期作與二期作種植面積	28
表4-6　AR-6日資料模式總表	34
表5-1　三縣市SPI與SPEI乾旱程度統計表	40
表5-2　嘉南平原SSI與SDI乾旱程度統計表	42
表5-3　嘉南地區一期作停灌年份與面積	44
表5-4　嘉南地區一期作停灌年前乾旱指標數值	44
表5-5　SOM二次分類各神經元分類時間	55
表5-6　歷史雨量與不同情境下之雨量分析	57
表5-7　 SSP1-2.6 SPEI與SPI乾旱分析統計表	64
表5-8　SSP2-4.5情境下SPI與SPEI與乾旱程度對照表	64
表5-9　SSP3-7.0情境下SPI與SPEI與乾旱程度對照表	64
表5-10　SSP5-8.5情境下SPI與SPEI與乾旱程度對照表	64
表5-11　水田土壤特性	80
表5-12　水稻各生育期時間與深度	80
表5-13　1960至2021年水稻一期作各生育期推估水量與總雨量	81
表5-14　歷史與推估整地時間灌溉量比較	83

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