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系統識別號 U0026-0508201910051900
論文名稱(中文) 利用序率方法進行乾旱特徵評估與預測
論文名稱(英文) Using the stochastic methods for drought characteristics evaluation and forecasting
校院名稱 成功大學
系所名稱(中) 資源工程學系
系所名稱(英) Department of Resources Engineering
學年度 107
學期 2
出版年 108
研究生(中文) 許心藜
研究生(英文) Hsin-Li Hsu
學號 N46061012
學位類別 碩士
語文別 英文
論文頁數 81頁
口試委員 指導教授-葉信富
口試委員-李明旭
口試委員-羅偉誠
口試委員-徐國錦
口試委員-蔡瑞彬
中文關鍵字 馬可夫鏈  乾旱特徵  小波分析  大氣環流  乾旱預測  時間序列模型 
英文關鍵字 Markov chain  drought characteristic  wavelet analysis  atmospheric circulation  drought forecasting  time series model 
學科別分類
中文摘要 近年來,由於氣候變遷的影響,臺灣一直面臨水資源短問題,導致許多嚴重的乾旱事件發生,特別是在臺灣南部地區。本研究蒐集臺灣南部地區25個雨量站及17個地下水位站之長期監測資料,利用序率方法如馬可夫鏈模型、時間序列模型進行乾旱特徵的評估與預測,其中乾旱特徵包括乾旱穩態機率、乾旱平均延時以及乾旱的傾向,並且利用小波分析探討大氣環流對臺灣氣候的影響。由乾旱特徵的分析結果顯示,鹽水溪流域是一個乾旱程度較高的地區,其乾旱穩態機率高於其他地區且乾旱平均延時也相對較長;另外,在乾旱傾向分析中,本研究發現當降雨乾旱持續時間越長時,未來發生地下水乾旱的傾向較高。而小波分析的結果則指出大氣環流確實與臺灣降雨有一定的相關程度。最後,本研究使用ARIMA時間序列模型進行乾旱的預測,在進行模型的建立時,模型殘差皆有符合白噪音的性質,意味著ARIMA適合作為本研究地區的乾旱預測模型,而預測結果顯示在2020年夏季有相對較低的SPI值。本研究延續前人使用乾旱指標評估乾旱事件,並加入新工具分析乾旱特徵及進行乾旱預測,本研究的成果可作為未來水資源管理的參考。
英文摘要 In recent years, Taiwan has been facing water shortages due to the impact of climate change, which has resulted in many serious drought events, especially in southern Taiwan. Long-term records from 25 rainfall stations and 17 groundwater stations in the southern Taiwan basin were used in this study. We used the stochastic methods including Markov chain and time series model based on drought indexes to determine the drought characteristics and predict the drought events. In addition, wavelet analysis was used to explore the linkage between atmospheric circulation and precipitation. Overall, based on the results of Markov chain analysis, the Yanshui River basin is a region with a high degree of drought where the steady state probability of the meteorological drought is higher than that in other basins and the drought mean duration is relatively longer. In addition, the results from the drought proneness analysis indicated that when rainfall causes a longer drought duration, there will be a higher degree of proneness to groundwater drought in the future. The results of the wavelet analysis revealed a positive correlation between precipitation and groundwater at long-term scales, which may be related to large-scale atmospheric circulation. Finally, we confirmed that the ARIMA model is suitable for drought forecasting in southern Taiwan and the results show that there may be relatively dry during summer in the year of 2020. This study continues the work of predecessors using a drought index analysis to assess drought events, where the new tool was added for the drought characteristics analysis and drought forecasting. The information from this research could be used as a reference for water resource management in the future.
論文目次 Table of Contents
Abstract I
摘要 II
致謝 III
List of Figures IV
List of Tables V
Chapter 1 Introduction 1
1.1 Motivation and Purpose 1
1.2 Scopes of Study 3
1.3 Literature Review 4
1.3.1 Definition of drought 4
1.3.2 Drought index 6
1.3.3 Drought characteristics identification 9
1.3.4 Periodic analysis: wavelet transform 11
1.3.5 Time series model for drought forecasting 14
1.4 Outlines of Thesis 15
Chapter 2 Methodology 19
2.1 Standardized Precipitation Index (SPI) 19
2.2 Standardized Groundwater Level Index (SGI) 20
2.3 Markov Chain Model 21
2.3.1 Steady state probability (p) 22
2.3.2 Mean duration (D) 22
2.3.3 Drought proneness index (DI) 23
2.4 Wavelet Analysis 24
2.5 Time Series Model 26
2.5.1 Non-seasonal autoregressive integrated moving average (ARIMA) model 26
2.5.2 Seasonal ARIMA model 27
2.5.3 ARIMA model development 27
2.5.3.1 Model identification 28
2.5.3.2 Parameter estimation 29
2.5.3.3 Diagnostic checking 29
Chapter 3 Study Area 31
Chapter 4 Results 34
4.1 Spatial Pattern of Drought Characteristics 34
4.1.1 Meteorological drought steady state probability 34
4.1.2 Drought Mean Duration 36
4.1.2.1 Meteorological drought mean duration 36
4.1.2.2 Groundwater drought mean duration 37
4.1.3 Groundwater drought proneness induced by precipitation 38
4.2 Links Between Precipitation and Groundwater Drought 40
4.3 Links Between Drought and Climate Indices 42
4.3.1 El niño-southern oscillation, ENSO 43
4.3.2 Pacific decadal oscillation, PDO 44
4.4 Drought Forecasting 46
4.4.1 Model identification 46
4.4.2 Parameter estimation 49
4.4.3 Diagnostic checking 54
4.4.4 Model validation 60
4.4.5 Forecasting 61
Chapter 5 Conclusion and suggestion 64
5.1 Conclusion 64
5.2 Future work 65
Reference 67
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