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研究生:卜維拉
研究生(外文):Marsha Savira Agatha Putri
論文名稱:臺灣水庫優養化之成因分析
論文名稱(外文):Identifying the factors affecting eutrophication level in Taiwan major reservoirs
指導教授:王玉純王玉純引用關係
指導教授(外文):Yu-Chun Wang
學位類別:碩士
校院名稱:中原大學
系所名稱:環境工程學系
學門:工程學門
學類:環境工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:英文
論文頁數:155
中文關鍵詞:優養化預測氣候變遷水質臺灣主要水庫
外文關鍵詞:Eutrophicationpredictionclimate changewater qualityTaiwan major reservoirs
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氣候變遷將使得水溫較高並可能會導致水體污染濃度上升及優養化的風險。 而近年來,優養化已成為水庫管理的其中一項關鍵議題,尤其是當水庫用為重要的供水來源. 本研究旨在評估卡爾森指數,因此選定四個臺灣主要水庫,即石門水庫、鯉魚潭水庫、烏山頭水庫和澄清湖水庫作為研究對象。應用主成分分析-絕對主成分分析(Principal Component Analysis - Absolute Principal Component Scores, PCA - APCS)以確認導致優養化的關鍵因素,並利用多元線性迴歸(Multiple Linear Regression, MLR) 及分類與迴歸樹(Classification and Regression Tree, CART)以預測未來卡爾森指數的趨勢。
由交通部中央氣象局及行政院環境保護署分別收集2000年至2017年的相對濕度、總降水量、日照百分比和雲量5項氣象參數及葉綠素-a(Chl-a)、總磷酸鹽(TP)、透明度(SD)、pH值、化學需氧量(COD)、懸浮固體(SS)、氨、總硬度、硝酸鹽、亞硝酸鹽和水温(WT) 11項水質參數之數據。另外由科技部臺灣氣候變遷推估資訊與調適知識平台(Taiwan Climate Change Projection and Information Platform, TCCIP)選用代表濃度途徑(Representative Concentration Pathways, RCPs)的RCP 2.6、RCP 4.5、RCP 6.0、RCP 8.5 四種情境下及臺灣地區適合的全球氣候模式(Global Climate Model, GCM)所預測之平均氣溫數據與降雨量,來推估21世紀中每一水庫於近未來(2016-2035)、中未來(2045-2065)與遠未來(2081-2100)三個時段的未來溫度。
研究發現依照2000年至2017年四個水庫的卡爾森指數大多可將水庫區分為貧養狀態的鯉魚潭水庫及烏山頭水庫、普養狀態的石門水庫、優養狀態的澄清湖水庫。其中利用PCA/APCS模式解析水庫優養化的來源分配如下:石門水庫分別以營養鹽因素(16%)、降雨強度因素(51%)、溫度因素(3%);鯉魚潭水庫分別以營養鹽因素(35%)、降雨強度因素(38%)、溫度因素(27%);烏山頭水庫分別以營養鹽因素(19%)、降雨強度因素(41%)、溫度因素(40%);澄清湖水庫則分別以營養鹽因素(25%)、降雨強度因素(58%)、溫度因素(17%)。依據21世紀近未來、中未來與遠未來的卡爾森指數之程度,優養化的情形將會出現在石門水庫、 烏山頭水庫及澄清湖水庫。而鯉魚潭水庫未來則可能會發生中度優養化的狀況,在下列條件下將使水庫產生優養化:石門水庫(Chl-a > 4.5、 μg/L、 TP > 0.0309 mg/L與SD < 1 m);鯉魚灘水庫(TP > 5.9 mg / L、Chl-a > 15.6 μg/ L 與 SD < 0.8 m) ;烏山頭水庫(Chl-a > 1.24μg/ L 、SD < 1.2 m 與 TP > 4.97 mg/L);澄清湖水庫(TP > 22.86 mg / L、Chl-a > 5.2μg/ L與SD < 1.1 m)。
本研究認為對於臺灣主要水庫來說氣候變遷與營養鹽攝取量可能是影響優養化的因素之一,並證明極端高溫、人為活動及自然因素將會提升整個主要水庫歷史及預測的卡爾森指數的潛在水平。因此,建議可透過持續監測評估、適當水處理措施、建置表面流人工溼地及嚴格規範以控管臺灣水庫的優養化程度。
Climate change may increase the risk of eutrophication in water bodies due to higher water temperatures. Eutrophication has become a critical issue for reservoir management in recent years especially when the reservoir serves as a major water supply source. This study aims to evaluate the Carlson’s trophic states index (CTSI), identify the key factors affecting eutrophication and their source apportionment using principal component analysis-absolute principal component scores (PCA-APCS), to predict the Carlson’s trophic states index (CTSI) tendency using linear regression prediction and classification and regression tree (CART) and suggest the appropriate water management strategy to control the eutrophication in four Taiwan major reservoirs, namely Shihmen, Liyutan, Wushantou, and Chengchinghu Reservoirs.
This study used 5 weather parameters (air temperature, relative humidity, total precipitation, sunshine percentage and cloud amount) obtaining from Taiwan Central Weather Bureau and 11 water quality parameters datasets (chlorophyll-a (Chl-a), total phosphorus (TP), transparency (SD), pH, chemical oxygen demand (COD), suspended solid (SS), ammonia, total hardness, nitrate, nitrite and water temperature) obtaining from Taiwan Environmental Protection Administration from 2000 to 2017. The mean air temperature and rainfall intensity prediction dataset under the representative concentration pathways (RCP) 2.6, 4.5, 6.0 and 8.5 with 5 general circulation models (GCMs) for each reservoir for early (2016-2035), middle (2046-2065), and end (2081-2100) of 21st century were collected from Taiwan Climate Change Projection and Information Platform Project (TCCIP).
This study found the trophic states index status were mostly defined as the mesotrophic reservoir in Shihmen, Liyutan, Wushantou Reservoir from 2000 to 2017, and Chengchinghu Reservoir was defined as the eutrophic reservoir. The eutrophication source apportionment in Shihmen, Liyutan, Wushantou and Chengchinghu Reservoir are 16%, 35%, 19% and 25% from nutrient factor; 51%, 38%, 41% and 58% from rainfall intensity factor; 33%, 27%, 40% and 17% from temperature factor, respectively. The eutrophic condition would be potentially occurred in Shihmen, Wushantou, and Chengchinghu Reservoir, and mesotrophic condition would be in Liyutan Reservoir in association with ambient temperature effect in the future. However, the eutrophic condition would be potentially occurred in all reservoirs in association with mean rainfall intensity in the future. The eutrophic condition would be happened if Chl-a > 4.5 μg/L or TP > 0.0309 mg/L or SD < 1 m in Shihmen Reservoir; TP > 5.9 mg/L or Chl-a > 15.6 μg/L or SD < 0.8 m in Liyutan Reservoir; Chl-a > 1.24 μg/L or SD < 1.2 m or TP > 4.97 mg/L in Wushantou Reservoir; and TP > 22.86 mg/L or Chl-a > 5.2 μg/L or SD < 1.1 m in Chengchinghu Reservoir.
This study concludes that a lot of nutrient intake amounts and climate change could be the factors affecting the eutrophication in the Taiwan major reservoirs. The potential increment of CTSI tendency in whole major reservoirs would be caused by the anthropogenic activity, extreme high-temperature and rainfall in the future. Thus, the continuous monitoring assessment, appropriate water treatment process, construct the free water surface wetlands, and strict regulation were highly recommended to control the trophic state level in Taiwan major reservoirs.
Table of Contents
摘要 I
Abstract II
Acknowledgement IV
Table of Contents V
List of Figures VIII
List of Tables X
Abbreviations XI
1. Introduction 1
1.1 Research background 1
1.2 Research objectives 2
2. Literature reviews 3
2.1 Limnology 3
2.2 Reservoir morphology 3
2.3 Eutrophication 5
2.4 Occurrences of reservoir eutrophication 6
2.5 Factor influencing trophic state level in water bodies 8
2.5.1 Nutrient enrichment 8
2.5.2 Hydrodynamic 9
2.5.3 Environmental factors 10
2.5.4 Microbial and biodiversity 12
2.6 Effect of climate change on eutrophication in reservoir 12
2.6.1 Temperature 13
2.6.2 Precipitation 14
2.6.3 Wind 15
2.6.4 Solar radiation 16
2.7 The future scenario of climate change in Taiwan 17
3. Materials and methods 22
3.1 Flowchart of this study 22
3.2 Study area 22
3.3 Dataset 26
3.3.1 Water quality parameters 26
3.2.2 Weather parameters 28
3.2.3 Future climate projection 29
3.4 Analytical and statistical method 30
3.4.1 Trophic state index 30
3.4.2 Pearson’s correlation analysis 32
3.4.3 Principle component analysis – absolute principal component scores 33
3.4.4 Linear regression 35
3.4.5 Classification and regression tree 36
3.5 Data displays and analysis tools 38
4. Results and discussions 39
4.1 Long-term trends of water quality evaluation 39
4.1.1 Shihmen Reservoir 39
4.1.2 Liyutan Reservoir 42
4.1.3 Wushantou Reservoir 45
4.1.4 Chengchinghu Reservoir 47
4.2 Long-term trends of weather evaluation 51
4.3 Trophic state level evaluation 54
4.3.1 Shihmen Reservoir 54
4.3.2 Liyutan Reservoir 54
4.3.3 Wushantou Reservoir 55
4.3.4 Chengchinghu Reservoir 56
4.4 Relationship between trophic states, water quality and weather factors 61
4.4.1 Shihmen Reservoir 61
4.2.2 Liyutan Reservoir 62
4.2.3 Wushantou Reservoir 63
4.2.4 Chengchinghu Reservoir 65
4.5 Source apportionment of the significant factors affecting the trophic states level 66
4.6 Prediction of trophic states level 75
4.6.1 The effect of air temperature on trophic states tendency in the future 75
4.6.2 The effect of rainfall intensity on trophic states tendency in the future 91
4.6.3 Prediction of trophic states classification 101
4.7 Water management strategy to control the eutrophication in major reservoirs 108
4.7.1 Shihmen Reservoir 108
4.7.2 Liyutan Reservoir 109
4.7.3 Wushantou Reservoir 110
4.7.4 Chengchenghu Reservoir 111
5. Conclusions and suggestions 113
5.1 Conclusions 113
5.2 Suggestions 115
6. Study limitation 116
References 117
Supplementary materials 130
Response to committees’ questions and suggestions 142
Prof. Lin, Jr-Lin 142
Prof. Wang, Yu-Chun 143
Prof. Chiang Hsieh, Lin-Han 144

List of Figures

Figure 2.1 Longitudinal zonation in reservoir 4
Figure 2.2 All forcing agents'' atmospheric CO2-equivalent concentrations (ppm) in every Regional Circulation Models (RCM) scenario 20
Figure 3.1 Framework of the statistical models’ integration 22
Figure 3.2 Four studied reservoirs, land use and monitoring station points map 24
Figure 3.3 Learning process (a) and prediction process (b) of MLR 35
Figure 4.1 Long-term of water quality trends in Shihmen Reservoir 41
Figure 4.2 Long-term of water quality trends in Liyutan Reservoir 44
Figure 4.3 Long-term of water quality trends in Wushantou Reservoir 47
Figure 4.4 Long-term of water quality trends in Chengchinghu Reservoir 50
Figure 4.5 Average long-term trends of weather factor parameters from 2000 to 2017 52
Figure 4.6 Trophic state level evaluation of Shihmen Reservoir 57
Figure 4.7 Trophic state level evaluation of Liyutan Reservoir 58
Figure 4.8 Trophic state level evaluation of Wushantou Reservoir 59
Figure 4.9 Trophic state level evaluation of Chengchinghu Reservoir 60
Figure 4.10 Pearson''s correlation plot among water quality parameters in Shihmen Reservoir 61
Figure 4.11 Pearson''s correlation plot among water quality parameters in Liyutan Reservoir 63
Figure 4.12 Pearson''s correlation plot among water quality parameters in Wushantou Reservoir 64
Figure 4.13 Pearson''s correlation plot among water quality parameters in Chengchinghu Reservoir 65
Figure 4.14 Source apportionment of factors affecting trophic states level in each reservoir 74
Figure 4.15 The effect of air temperature prediction for CTSI of Shihmen Reservoir in the beginning, middle, and end of 21st century 79
Figure 4.16 The effect of air temperature prediction for CTSI of Liyutan Reservoir in the beginning, middle, and end of 21st century 80
Figure 4.17 The effect of air temperature prediction for CTSI of Wushantou Reservoir in the beginning, middle, and end of 21st century 81
Figure 4.18 The effect of air temperature prediction for CTSI of Chengchinghu Reservoir in the beginning, middle, and end of 21st century 82
Figure 4.19 The difference percentage between observed data and CTSI parameters prediction affected by air temperature in Shihmen Reservoir 85
Figure 4.20 The difference percentage between observed data and CTSI parameters prediction affected by air temperature in Liyutan Reservoir 86
Figure 4.21 The difference percentage between observed data and CTSI parameters prediction affected by air temperature in Wushantou Reservoir 87
Figure 4.22 The difference percentage between observed data and CTSI parameters prediction affected by air temperature in Chengchinghu Reservoir 88
Figure 4.23 The effect of rainfall prediction for CTSI of Shihmen Reservoir in the beginning, middle, and end of 21st century 92
Figure 4.24 The effect of rainfall prediction for CTSI of Liyutan Reservoir in the beginning, middle, and end of 21st century 93
Figure 4.25 The effect of rainfall prediction for CTSI of Wushantou Reservoir in the beginning, middle, and end of 21st century 94
Figure 4.26 The effect of rainfall prediction for CTSI of Chengchinghu Reservoir in the beginning, middle, and end of 21st century 95
Figure 4.27 The difference percentage between observed data and CTSI parameters prediction affected by rainfall intensity in Shihmen Reservoir 97
Figure 4.28 The difference percentage between observed data and CTSI parameters prediction affected by rainfall intensity in Liyutan Reservoir 98
Figure 4.29 The difference percentage between observed data and CTSI parameters prediction affected by rainfall intensity in Wushantou Reservoir 99
Figure 4.30 The difference percentage between observed data and CTSI parameters prediction affected by rainfall intensity in Chengchinghu Reservoir 100
Figure 4.31 Trophic states prediction classification in Shihmen Reservoir 104
Figure 4.32 Trophic states prediction classification in Liyutan Reservoir 105
Figure 4.33 Trophic states prediction classification in Wushantou Reservoir 106
Figure 4.34 Trophic states prediction classification in Chengchinghu Reservoir 107

List of Tables

Table 2.1 The example of lake and reservoir eutrophication occurrences in the world 7
Table 2.2 AR5 global warming increase (°C) projections 19
Table 2.3 Fourty GCMs RCP scenario which suitable for Taiwan 21
Table 3.1 Reservoirs characteristic 25
Table 3.2 Water quality description and measurement method 26
Table 3.3 CWB stations for weather data collection 28
Table 3.4 TCCIP selected point for air temperature and rainfall prediction 30
Table 3.5 Ranges of variable values associated with Trophic Levels in reservoir 32
Table 3.6 Linear regression equation model resulted from TSI factor against air temperature to predict the trophic states level in the future 36
Table 3.7 Linear regression equation model resulted from TSI factor against rainfall to predict the trophic states level in the future 36
Table 4.1 Descriptive statistic of water quality in Shihmen Reservoir 39
Table 4.2 Descriptive statistic of water quality in Liyutan Reservoir 43
Table 4.3 Descriptive statistic of water quality in Wushantou Reservoir 46
Table 4.4 Descriptive statistic of water quality in Chengchinghu Reservoir 49
Table 4.5 Descriptive statistic of weather data from 2000 to 2017 53
Table 4.6 KMO and Barlett''s test of 4 major reservoirs 66
Table 4.7 Total variance explained of major reservoirs 67
Table 4.8 Varimax rotated factor of PCA of Shihmen Reservoir 68
Table 4.9 Contribution percentage of each factor from Stepwise method of linear regression 72
Table 4.10 The historical observed data for CTSI parameters in four major reservoirs 83

Supplementary materials

Supplementary Table 1. The average of observed air temperature in four reservoirs used for TCCIP future data prediction
Supplementary Table 2. The average of observed rainfall intensity (mm) in four reservoirs used for TCCIP future data prediction
Supplementary Table 3. The effect of air temperature prediction for Chl-a, TP and SD level in the future
Supplementary Table 4. The effect of rainfall intensity prediction for Chl-a, TP and SD level in the future
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