優養化複雜系統分析與管理策略研究

This research was to analysis the complex system in eutrophication, and our research place were Te-Chi reservoir and Shuili-stream watershed. We based on results of a series of analysis to discuss the management strategies in eutrophiction. We wished the result of this study could build a standard method to analysis a complex system, and be as a reference for management. This study could taken apart to three parts, the first part was to optimize the prediction ability of time series models. We used the one factor design method to reach the target. We used additive and multiplicative time series models to predict the eutrophication condition. The result showed that we could reduce 64.14% error in best situation. We also found that the additive model had better predition ability for eutrophication. Although the multiplicative model worked worse, it might be used to find out outliers to improve prediction ability in models. The second part of this study was to find out the key parameter for eutrophication, and the research method we used was multivariate statistical analysis. The statistical methods were including of descriptive statistics and simple regression, multiple regression, regression model selection methods, principle component analysis, discriminant analysis, cluster analysis. (1) descriptive statistics and simple regression: the result of the study showed that water in the Te-Chi reservoir had a trend to be trophic. We looked for the most relative factors with eutrophication by using simple regression. When we considered all data of watershed, the most important eutrophication factors including total phosphorous, suspend solid, water transparency and chlorophyll-a could be obtained. This result was corresponding to Carlson's eutrophication index. (2) multiple regression: The results showed that the R2 value in the multiple regression model was 0.9116 that could highly explain the model variance. And we found that there was collinearity condition between suspending solid, COD, and chlorophyll-a. Otherwise, the results of analysis of variance showed that there were 5 possible outlets. After calibrating the outlets, we could get the total model of eutrophication. This study result showed the model could explain the trend of eutropjication completely, and could be the foundation of reservoir management in the future. (3) regression model selection methods: Three regression methods including forward, backward, and stepwise were used to analyze the important water quality factors that had been screened by simple regression analysis. The results showed that all standardized R2 values of three methods were better than that of the orginal multiple regression model, and the best method was forward regression analysis. Consequently, regression methods could screen efficiently the important water quality factors of eutrophication to promote the explanatory ability of models. (4) principle component analysis: The results showed that the first three principal components were nutrition index of phosphorous, nitrogen, sodium and concentration of alga. And those principal components could explain 75.92% variance of the model in our study. This results proved that the major reason of eutrophication in the Te-Chi reservoir was the nutrient. After comparing principal components with original variables by regression model selection method, we proved the model formed by original variables had better ability to explain the variance of the model, but the principal component could solve the colinearity problem in the model. But if we looked the colinearity like an important subject, we still suggested to use the principal component regression model. (5) discriminant analysis: We used the TSI to be the basis of classification , and three kinds of methods including of discriminant analysis, canonical discriminant analysis and stepwise discriminant analysis. The results showed that the dicrrminant analysis classification had 63.16% correct rate and 0.03% stratified error rate after cross-validating. The best result of stepwise discriminant analysis was using forward or stepwise selection, and its result had 57.89% correct rate . The first canonical factor in the canonical discriminant analysis could properly classified the original data, and could explain 98.06% variance of the model. The first canonical factor was major formed by water temperature, suspending sand, ammonia nitrogen, total phosphorus, cod, chlorophyl-a, and secchi disk. After using discriminant analysis, the results proved that the major water quality factor of eutrophication we chosen in the Te-Chi reservoir could properly classified the data. And the classification result could have the same with the TSI, it also showed that the water quality factors we used could explain the trend of eutrophication. (6) cluster analysis: We used the TSI to be the standard of classification , and two kinds of methods including of hierarchical clustering and disjoint clustering. The results showed that the The best result of hierarchical clustering was using McQuitty's method, and its result best fitted for the trend of eutrophication . The McQuitty's method suggested we could classify the data into 4 clusters. The outliers would affect the result of cluster analysis, and it would have better result after calibrating outliers. In addition the result of disjoint clustering could properly classified the original data when we used 4 clusters as the correct clusters. Variables in the model were arranged by R2 and the order was as follows: phosphate, chlorophyl-a, cod, suspending sand, turbidity, total phosphorus, water temperature, secchi disk, nitrate, dissolved oxygen, ammonia nitrogen, and sodium. The most important variable of all is phosphate. Comparing cluster analysis with discriminant analysis, the results proved that the major water quality factor of eutrophication for classification is phosphorus and had better effect between the middle and heavy eutrophication condition. Both results of cluster and discriminant analysis showed that it would have better index for classification of eutrophication. The third part was to try to decide the landuse management strategies by TMDL. Because we cooperated the plan of National Science Council, we chosen the Shuili-stream as our study area. In this study, we will simulate the TMDL in the watershed by BASINS model developed by USEPA. This stud could divided to four parts. (1)The simulation of best basins delineation: Four Digital Elevation Models (DEMs) were chosen to compare simulation differences. Three DEMs came from the simulation of ArcGis, Surfer, and WinGrid system, respectively. The other one was provided by National Central University. The simulation results, real environmental conditions, and research requirements, etc., were used to decide the best subwatershed numbers and the optimal simulation efficiency. The best results of 17 subwatersheds obtained from the DEM of Surfer system were corresponding to the original boundary ranges and the basic research requirements in the rivernet of 300 hectares. Basing on this result we suggest to proceed watershed analysis more deeply. (2)The simulation of meteorology data and calibration of hydrology parameters in the model: We will use the WDMUtil tool in the BASINS model to simulate the weather data we need. It includes of evapotranspiration every hour and rainfall every hour. After building the database that the model needs, we will calibrate the hydrology factor in the model. The results showed that we could calibrate the AGWRC, LZSN, UZSN parameters in the model to improve the flow simulation result. Finally our simulation result show the error in total flow is 10%, and it is fit the standard in the model. But the simulation result also show that the 50% low flow and seasonal flow error are very large. The large error still needs to discuss and try to improve in the future. (3)The simulation of sediment TMDL: The simulation was taken apart into two parts. The first part was to simulate sediment load in hillside field, including of pervious and impervious land. The simulation result of sediment TMDL was 63642(ton/year) on hillside field. This result was proved reasonable after comparing with reference. The second part was to simulate suspended solid in the rivers. Our simulation was based on the sediment load that was delivered to rivers. The simulation results were calibrated and validated by observations. The sum of squared error was 603.617 after calibrating the model. And the validated simulation results were almost fitted the observation. (4)Deciding the strategies of sedimend TMDL: The study processes were taken apart into four parts. The first part was to choose a standard of TMDL of sediment ,and our choices were including of soil loss tolerance and water quality standards used in Taiwan EPA. The second part was to choose management strategies which were suitable for this watershed. After thinking about the characteristic of the watershed and management target, we will focus on landuse management. The third part of this study was to find out point area. We based on the result of detail lanuse analysis to decide to let the dry farmland in NO. 5 and 14 subbasins be our first management target. The fourth part was to simulate the benefit of landuse management strategies. According to the simulation results by BASINS model, we found out the best result will happen when we managed the No. 5 and 14 subbasins at the same time. The efficiency of management for reducing soil loss was 4.79% and 0.325% for suspended load in the river. But the benefit of management was not obvious in the watershed. Above all, we found out the condition of soil loss was close to balance in this watershed. So we have not to implement the management strategies. But the average suspended load was still higher than water quality standard in the river. Because of our strategies had not enough benefit, we still should enhance management for water quality in this watershed with other strategies in the future.本研究以自然優養複雜系統為研究對象，以大甲溪流域之德基水庫及南投縣水里溪流域為研究區位進行分析，研究中透過一系列分析之結合，以優養化管理為主題進行探討，希望可藉由研究成果建立一複雜系統分析標準流程，並供未來管理工作之依據及參考。研究中共可分為三大部份進行，第一部份為優養化模式預測能力之最佳化研究，此部份研究希望透過單因子試驗設計分析方法，進行優養模式預測之最佳化，使預測誤差能將到最低，研究中主要採用相加性與相乘性時間序列模式進行預測，初步研究成果將預測誤差最大降低64.14%；而在預測模式比較中發現以相加性模式表現較佳，較符合優養變化之趨勢；但相乘性模式具放大誤差之特性，適合進行優養變化之變異點探討，可助於未來尋求離群值進而降低誤差之研究。 第二部份研究為優養化之動力分析研究，研究中希望藉由多變量統計分析方法進行動力分析，若以所採用之分析法來分，內容又可分為(1)敘述統計與單迴歸分析、(2)複迴歸分析、(3)迴歸模式選擇分析、(4)主成份分析、(5)鑑別分析、(6)群集分析等六部份。(1)敘述統計與單迴歸分析部份，，而敘述統計分析結果發現德基水庫有傾向優養化的趨勢；在透過單迴歸分析結果顯示，以整體水庫為考量時，最能掌握與優養化相關的重要水質因子，包括總磷、懸浮固體、透明度與葉綠素a，與卡爾森優養指標理論相符合。(2)複迴歸分析結果得知複迴歸模式解釋能力達0.9116高解釋力，而各個水質因子中以懸浮固體、COD及葉綠素a有較嚴重的共線性；另外殘差分析結果表示存有5個可能的離群值，在經過離群值修正後，即可得到最後的優養全模式，研究顯示此模式可充分解釋優養化變化情形，可作為未來水庫管理的參考依據。 (3)迴歸模式選擇分析研究中共使用三種迴歸分析選擇法，包括順向、逆向選擇迴歸分析與逐步迴歸分析，分別針對先經過單迴歸分析篩選的優養化重要因子進行分析，以挑選出最適宜的因子。研究結果顯示，三種分析法的標準化R2值均較原始複迴歸表現好，其中又以順向選擇分析最佳，表示使用迴歸分析選擇法可有效選出與優養化最相關的因子，進而提升模式的解釋能力。(4)主成份分析結果顯示前3個主成分分別為營養指標磷、營養指標氮、營養指標鈉與藻類濃度，此3個主成分共可解釋模式之75.92%的變異度，表示德基水庫之優養化主要原因為營養鹽所造成。而將主成分分析結果與原始變數透過迴歸模式選擇法來相互比較後，可證明原始變數之迴歸模式選擇結果對模式變異度解釋能力較高，而主成分分析則是可解決模式變項間之共線問題。但若以共線性消除為重要考量下，仍建議使用主成分迴歸模式為較佳的模式。(5)鑑別分析中，共採用(A)分類鑑別分析、(B)典型鑑別分析、(C)逐步鑑別分析等3種方法來區分資料。研究結果顯示，分類鑑別分析經過交叉驗證後，分類正確率可達63.16%，分層後錯誤率估算為0.03%；而逐步鑑別分析中以順向或逐步選擇法分類效果最佳，經交叉驗證後正確率達57.89%。另外，典型鑑別分析結果中的第1鑑別因素可有效的分類原始資料，可解釋98.06%的變異度，而其主要組成為水溫、懸浮固體、氨氮、總磷、COD、葉綠素a、透明度等因子。而最後鑑別結果發現以優養全模式可有效鑑別優養變化，達到與TSI同樣效果，這表示所選用參數可完成解釋優養變化趨勢。(6)群集分析共採用(A)階層群集分析、(B)非階層群集分析等2種方法來分群資料。研究結果顯示，階層分析結果中以馬氏法最符合優養指標TSI的變化趨勢，而該方法建議將觀測組分為4群；另外，非階層分析結果顯示以4群為準時，其分群效果尚稱顯著，其中變數以磷酸鹽為最重要。而若將群集分析結果與鑑別分析比較，結果顯示分群以磷因子最為重要，中高度優養的分群效果較為明顯，但兩者分析結果的差異亦指出應該有更佳的分類指標存在。 第三部份研究為土地利用總量管制策略制定之研究，因配合國科會計畫之執行，而選用水里溪集水區為研究對象，研究中主要使用美國環保署所發展之整合型整體集水區管理模式BASINS來進行模擬，研究流程共可分為(1)最佳集水分區模擬、(2)氣象資料模擬與模式水文校正、(3)泥砂總量模擬、(4)泥砂總量管理策略研擬等4個部份進行。(1)最佳集水分區模擬部份，在數值等高線模型 (DEM) 方面，除了由中央大學提供外，還選用了ArcGis、Surfer及WinGrid模式模擬的DEM來進行分析，以比較不同DEM所模擬出來的結果。最後，再加上配合現場實際情形與研究所需等指標，來決定最佳集水分區數目，以達到最佳模擬效率之目的。研究顯示，以Surfer模式模擬的DEM為基礎，在門檻值為300公頃所決定的水系下，17個子集水區的分區結果最符合原始邊界範圍與研究之基本需求，因此，建議以此為基礎，進一步進行集水區分析工作。(2)氣象資料模擬與模式水文校正研究中，使用BASINS模式下之WDMUtil工具進行氣象資料模擬，模擬項目包括(A)逐時蒸發散量、(B)逐時降雨量。水文校正結果證實調校模式中AGWRC、LZSN、UZSN等3個變數有助於提升模式模擬之精確度，而最後模式模擬結果總逕流量誤差為10%，尚在模式標準之內，而50%最低流量與季節流量誤差等2個指標則是誤差過大，需進一步的研究探討。(3)泥砂總量模擬方面，模擬分為兩部份進行，第一部份首先針對坡地泥砂產量進行模擬，模擬過程中又將坡地分為可滲透區與非滲透區兩區，最後計算結果為63642(ton/year)，與相關研究比較後證實為合理。第二部份進行河道懸浮質之模擬，以坡地泥砂產量運移至河道部份為依據進行，最後模擬結果與實測值比較，校正後其誤差平方和為603.617，而驗證後模擬值亦大部分符合實測值。(4)泥砂總量管理策略研擬部份，研究中共可分為四部份進行，(A)選擇適當總量管理目標，本研究決定以容許土壤流失量與環保署水質相關指標為依據。(B)管理策略之選擇，在考量集水區與管理目標之特性後，決定以土地管理為重點。(C)選擇管理重點區，藉由細部土地利用分析，最後決定以第5、14號子集水區之農地中旱田耕種為管理目標。(D)管理效益之評估，透過模式模擬結果，發現以同時治理第5、14號子集水區之效果最好，泥砂產量控制效益為4.79%，河道懸浮質則為0.325%，但成效並不顯著。最後整合模擬結果，在泥砂產量方面集水區已接近土砂平衡狀況，因此不需管理策略之執行；而懸浮質方面則因未達總量管理標準，且管理策略成效不顯著，未來仍需配合其他管理辦法，加強此區之水質管理。目 錄 中文摘要............................ i 英文摘要............................ iii 目錄................................ v 表目錄.............................. viii 圖目錄.............................. xviii 第一章 前言........................................... 1 第二章 文獻回顧....................................... 3 2.1 時間數列預測優養化相關文獻........................ 3 2.2 多變量分析尋找優養動力相關理論文獻................ 8 2.3 總量管理模式應用於集水區水質管理.................. 25 第三章 研究方法....................................... 27 3.1優養化模式預測能力之最佳化研究..................... 27 3.1.1初步模式選擇研究................................. 27 3.1.2預測時距選擇..................................... 28 3.1.3模式時距選擇..................................... 28 3.2優養化之動力分析研究............................... 30 3.2.1敘述統計......................................... 31 3.2.2單迴歸分析....................................... 31 3.2.3複迴歸分析....................................... 31 3.2.4複迴歸模式選擇................................... 31 3.2.5主成分分析....................................... 31 3.2.6鑑別分析......................................... 31 3.2.7群集分析......................................... 31 3.3土地利用總量管制策略之研究......................... 32 3.3.1 資料蒐集........................................ 32 3.3.2 模式選擇........................................ 33 3.3.3 集水區管理模式學習.............................. 33 3.3.4 建立基本總量管理模式............................ 33 第4章 優養化模式預測能力之最佳化研究.................. 33 4.1不同模式之預測能力研究............................. 33 4.2不同預測時距對模式預測能力影響之研究............... 44 4.3不同模式時距對模式預測能力影響之研究............... 56 4.4本章結論........................................... 70 4.4.1前言............................................. 71 4.4.2單因子實驗設計................................... 71 4.4.3綜合討論......................................... 71 4.4.5結論與建議....................................... 72 第五章 優養化之動力分析研究........................... 74 5.1德基水庫優養水質因子之研究......................... 74 5.2以複迴歸分析法探討水質因子與優養化全模式之研究..... 85 5.3不同優養水質共線性分析及模式選擇之研究............. 106 5.4以主成分分析法探討水庫優養化之動力研究............. 125 5.5以鑑別分析法探討水庫優養化之動力研究............... 149 5.6以群集分析法探討水庫優養化之動力研究............... 177 5.7以整合多變量分析法探討水庫優養化之動力研究......... 210 5.8本章結論........................................... 229 5.8.1前 言........................................... 230 5.8.2 動力因素探討.................................... 230 5.8.3 結論與建議...................................... 239 第六章 土地利用總量管制策略制定之研究................. 242 6.1 最佳集水分區模擬之研究............................ 242 6.2 總量管理模式氣象模擬與水文校正之研究.............. 267 6.3 總量管理模式集水區泥砂總量推估之研究.............. 288 6.4 集水區泥砂總量管理策略之研究...................... 317 6.5 本章結論.......................................... 332 6.5.1 前言............................................ 332 6.5.2 總量管理模式模擬流程............................ 333 6.5.3 結論與建議...................................... 339 第七章 結論與建議..................................... 341 7.1 優養化模式預測能力之最佳化研究之小結.............. 341 7.2 優養化之動力分析研究之小結........................ 342 7.3 土地利用總量管制策略研究之小結.................... 344 7.4 總結.............................................. 346 參考文獻.............................................. 34

Comparison+Study+of+the+Best+Water+Quality+Index+by+Regression+Analysis

The main purpose of this study was to find out the best water quality index with simple and multiple regression analysis, which could be helpful for monitoring and decision-making in watershed management. We selected the Dansuie River Watershed as a study site and collected all water quality data available. Regression analysis was applied to find out the regression relationship between water quality indexes (RPI and WQI) and water quality parameters. Also we make a comparison study between RPI and WQI. The results could help us to evaluate these two water quality indexes. We find out several important results in this study: (1) In simple regression, the design of RPI, dissolved oxygen, BOD and ammonia were significant parameters but suspending solid was not. Besides the nutrient pollution, WQI could point out the potential problem of pH. (2) The result of full model selection for multiple regression showed that the most suitable model was formed with parameters selected by simple regression. And (3) After comparing all the regression analyses, WQI exhibited a better performance for water quality. WQI also offered better sensitivity than RPI. Therefore, WQI is a better index for water quality management.本研究主要目的在於探討最佳水質指標，以協助集水區水質管理策略之訂定與監測。研究中選用淡水河流域為研究區域，在蒐集水質資料後，嘗試使用迴歸分析法，分別分析RPI(River Pollution Index)、WQI (Water Quality Index)兩種水質指標與本區16項水質參數之迴歸關係，藉此了解水質指標之解釋機制，並依據分析結果來評斷水質指標之優缺點。研究結果顯示在單迴歸分析中RPI之組成溶氧、BOD、氨氮均為顯著參數，而懸浮固體則不顯著；而在WQI分析結果則顯示此流域除營養鹽污染外，可能存有水中酸鹼平衡遭破壞的問題。透過不同複迴歸全模式選擇法發現，以單迴歸顯著參數所建構之複迴歸全模式，最符合相關理論，且具有合理之模式解釋能力，RPI模式R^2=0.682，WQI模式R^2=0.88。經過比較RPI與WQI之單迴歸結果後，發現WQI明顯含括範圍較廣，相較於RPI可更完整的解釋水質狀態。而在複迴歸分析方面，結果同樣顯示WQI可解讀較多的水質資訊，因此本研究在綜合所有迴歸分析結果後，明顯指出WQI的敏感度優於RPI，因此建議未來水質管理可將WQI指標納入優先考量