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研究生:曾宏偉
研究生(外文):Hung-WeiTseng
論文名稱:氣候變遷對乾旱特性之衝擊與其不確定性探討
論文名稱(外文):Impact of Climate Change on Drought Characteristics and Its Uncertainty
指導教授:游保杉游保杉引用關係
指導教授(外文):Pao-Shan Yu
學位類別:博士
校院名稱:國立成功大學
系所名稱:水利及海洋工程學系
學門:工程學門
學類:河海工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:103
語文別:英文
論文頁數:101
中文關鍵詞:空間統計降尺度隨機森林變量刪減乾旱指標單調特性
外文關鍵詞:spatial statistical downscalingRandom Forestvariable eliminationdrought indicesmonotonic behavior
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本研究目的在於評估氣候變遷對於乾旱事件特性之衝擊及其不確定性。為此,本研究先提出一個兩階段變量刪減流程以精簡大尺度變量,然後利用隨機森林進行月雨量空間降尺度分析以連結大尺度變量與集水區月雨量資料,再藉由5種全球環流模式(general circulation model, GCM)模擬資料推估未來情境下可能發生之雨量變化。接著透過整合氣象繁衍模式、水文模式、水庫操作模擬以及乾旱缺水指標進行未來乾旱事件特性之量化。本研究重要成果為:(1)本研究提出的兩階段變量刪減流程,第一階段可挑選出前50個相對重要之網格,而第二階段則可在不影響模式表現的前題下再進行一次篩選,可顯著減少資料量高達96.4%;(2)豐水期雨量在未來皆有增加的情況,枯水期情境雨量變異在未來有增加之情況,而大多推估值皆顯示雨量有減少的情況。綜合而言,氣候變遷可能增強南部原本就降雨豐枯懸殊的降雨情況;(3)本研究提出一個新的複合型乾旱缺水指標,該指標不但可提供更多元之乾旱事件訊息(強度、延時和事件數量),且具有良好之單調特性,可合理量化缺水事件特性;(4)氣候變遷將造成豐水期流量增加,帶給水資源系統正面之影響,即使將水庫淤積納入考慮,缺水案例增加數量亦不明顯;(5)研究推估結果僅基於5種GCM推估值,而政府間氣候變遷委員會(Intergovernmental Panel on Climate Change, IPCC)第5次評估報告採用GCM數量高達50幾種,故此研究結果僅能代表部分可能結果。
This study aims to assess the impact of climate change on a water resources system of southern Taiwan. For this purpose, firstly, the study proposed a two-step variable elimination procedure for monthly precipitation spatial downscaling based on Random Forests (RF) for finding out the possible impact on hydrology. The proposed procedure not only can reduce the variable dimension but also preserve the model performance simultaneously. Two variable importance (VI) estimates (i.e., error-based and impurity-based estimates) provided by RF were applied to judge whether a variable should be kept or not. The minimized important variables were regarded as the predictors for precipitation downscaling. The precipitation projections under various emission scenarios were made according to the latest climate change experiments of five general circulation models. The downscaling results show that the future precipitation may increase in the wet season (from May to October) but decrease in the dry season (from November to April). Secondly, based on these downscaling results, a weather generator was applied to work with the downscaling information for synthesizing daily weather data under various climate change scenarios. Since a stochastic process is involved in weather generation, the study deals with this uncertainty by using ensemble mean approach. Then, the daily weather data were input to a hydrological model for deriving the scenario runoffs under various climate change scenarios. Based on these projection results, a large prime variation in scenario runoffs occurs in the wet season but a slight variation in the dry season. In terms of quantity, the scenario projections indicate scenario runoffs probably increase in the wet season. Lastly, the scenario runoffs were routed through the reservoir operation model to collect all failure events (when the water supply does not meet the water demand) of the water resources system. The study proposed a composite drought index, water shortage index (WSI), to quantify these failure events of the water resources system. The results show that there is a 26.6% chance (four out of the fifteen sets) that climate change impact could lead to more severe droughts in the study site in the near-term period. Most WSI values show that the impact of climate change has a positive effect on then water resources system due to a rise in runoffs in the wet season. However, since all of the future scenario projections were made based on the five selected GCMs from the CMIP5 in which more than fifty GCMs are included, the results of this study may therefore partly represent the possible future. It is suggested including more GCMs for assessing the uncertainty among GCMs.
Considering sedimentation is a serious problem for the Tsengwen Reservoir, the additional effect of sedimentation to the impact of future drought events to the study site was also considered. Under the combined impact of climate change and the effect of sedimentation which could decrease the storage capacity of the Tsengwen Reservoir by 12%, more severe drought impacts will be expected in five of the fifteen cases. In the worst case, WSI turns out to be 4.979 which is more than triple that of the baseline value of 1.597 when the total amount of water shortage is projected to be 1.9 times that of the baseline case. Moreover, the water resources system in the worst case scenario will not be able to provide enough water around two thirds of the operation time, and it has a lower probability to return to a satisfactory state once a drought event of that magnitude has occurred. The reduction of the reservoir capacity by sedimentation aggravates the potential impact of drought events, leading to more serious water deficits, than if only the hydrologic impact of climate change on the study site is considered.
Abstract I
Dedication IV
Acknowledgements V
Content VI
Tables VIII
Figures IX
1 Introduction 1
1.1 Motivation and Objective 1
1.2 Overview of the Dissertation 2
1.3 Literature Review 3
1.3.1 Downscaling Approaches 3
1.3.2 Weather Generators 7
1.3.3 Drought Indices 9
1.4 Study Area and Dataset 11
1.5 General Circulation Models 13
2 Downscaling 17
2.1 Introduction 17
2.2 Methodology 17
2.2.1 Randomness 18
2.2.2 Ensemble Learning 19
2.3 Results and Discussions 20
2.3.1 VI Sensitivity 21
2.3.2 Step-1 Variable Elimination 23
2.3.3 Step-2 Variable Elimination 25
2.3.4 Benefit of Dimension Reduction 29
2.3.5 Data Interpretation Based on VI Estimates 29
2.3.6 Monthly Precipitation Downscaling 30
2.4 Summary and Conclusions 33
3 Weather Generator 36
3.1 Introduction 36
3.2 Methodology 37
3.2.1 Occurrence of Wet and Dry Days 37
3.2.2 Precipitation Amount 38
3.2.3 Temperature Generation 39
3.3 Results and Discussions 39
3.3.1 Model Performance 39
3.3.2 Model Uncertainty in Baseline Generation 42
3.3.3 Precipitation Generation under Various RCPs 43
3.4 Summary and Conclusions 48
4 Hydrological Model 50
4.1 Introduction 50
4.2 Methodology 51
4.2.1 Modified Hydrologiska Byråns Vattenbalansavdelning 51
4.2.2 Bias Correction 55
4.3 Results and Discussions 56
4.3.1 Calibration and Validation of MHBV Model 56
4.3.2 Results of Bias Correction 59
4.3.3 Statistics of Runoffs 61
4.4 Summary and Conclusions 68
5 Drought Indices 69
5.1 Introduction 69
5.2 Methodology 70
5.2.1 Simulation of Reservoir System Operations 70
5.2.2 Drought Indices for Water Resources Systems 71
5.2.3 Composite Drought Indices 74
5.3 Results and Discussions 75
5.3.1 Simulation of Reservoir System Operation 75
5.3.2 Applications of Drought Indices 76
5.3.3 An Aggravation of Drought Events 81
5.3.4 Characteristics of Drought Events under Climate Change Scenarios 82
5.4 Summary and Conclusions 88
6 Conclusions and Future Works 91
6.1 Monthly Precipitation Spatial Downscaling 91
6.2 Weather Generation 92
6.3 Hydrological Model 93
6.4 Drought Indices 94
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