集水區主要的營養鹽負荷來自暴雨逕流沖刷地表所形成的非點源污染，而流入營養過量會造成水庫及湖泊優養而致使各種水質惡化。因此非點源污染及水質的關聯性不僅是水質管理的基礎，也是改善人類最主要飲用水來源的重要研究課題。為了對非點源污染及水質的關聯性做更廣泛的評估及分析，本研究以台灣最大的曾文水庫為案例，分為兩個部分進行研究：第一部份調查集水區非點源污染資料，然後利用這些資料及適當的污染負荷模式估計集水區非點源污染負荷；第二部分研發新的遙測方法以產生水體表面水質分布影像，以能更完善評估水體狀態。
　　第一部分選擇曾文水庫集水區中的大埔壩及塔庫布央溪攔砂壩做為非點源污染調查點，大埔壩為曾文溪流入水庫最後一個攔砂壩，其逕流量及水質可代表水庫承受之污染負荷；塔庫布央溪集水區為天然林地，其逕流量及水質不僅可代表曾文水庫最大土地利用類型之流出污染負荷，也可使吾人對於台灣山區熱帶天然林地的污染輸出特性更為瞭解。在調查期間中(2002~2003)，採樣點之逕流量以量水堰及水位計連續量測，水質則分別在晴雨天採樣。另外，因台灣地區年降雨變異十分顯著，為了使推估結果更具代表性，本研究以美國環保署的HSPF模式進行水理模式之建置，使收集之降雨及逕流資料擴充為5年(模式模擬2001，2004及2005年)。以調查所得之非點源污染資料建立對數型態回歸模式以描述其流量與負荷的關係，最後結合此負荷模式及HSPF模擬結果，可得到集水區每小時污染負荷歷線。研究結果顯示大部分水質項目之雨天濃度高於晴天濃度；在塔庫布央溪，其逕流平均濃度及負荷均高於國外天然林地之調查值。全年集水區污染負荷歷線呈現相當大的波動，例如在塔庫布央溪平均每年總歷時僅6.4天的6場較大降雨，即貢獻了42%的年雨量以及至少40%的污染負荷。這些特性將形成非點源污染控制之重大挑戰。
　　在第二部分，本研究發展了兩種以遙測產生水質分佈影像的方法。在水面有高光譜影像的情況下，例如海洋水色衛星感測器(MODIS)或機載高光譜掃描影像(ISIS)，可採用本研究結合基因演算法(GA)及海洋水色半解析模式(SA)所發展之GA-SA由水色反算水面物質含量。以國際海洋光學水色組織評比其他反算法的同樣程序評估GA-SA，評估結果顯示GA-SA除了誤差低之外還具有許多反算水質的優勢，主要特點包括可單獨分離出複雜水體中5種光學固有性質、具有彈性調整不同水體生光模式的空間以及可跨遙測平台使用。研究也以GA-SA實際處理MODIS影像，可產生水面Chl-a、NAP及CDOM之水質分佈。
　　當水面僅有多頻譜影像時，如福衛二號影像，本研究以現地水面反射光譜量測及水質資料發展經驗反算法(FORTW-EM)，可利用福衛影像的綠光對藍光訊號比及紅光對藍光訊號比，分別推估水面表層Chl-a及SS濃度。2006年不同時間拍攝之曾文水庫之有效福衛影像經處理套用FORTW-EM反算水質，得Chl-a平均相對誤差絕對值為51%，SS為33%，此誤差同時涵蓋大氣輻射校正及影像與水質非同取得所造成之誤差。
　　因為曾文水庫目前沒有適合的多時期高光譜影像，所以本研究以FORTW-EM處理2006年15幅福衛二號影像以產生Chl-a及SS之水質分布圖，再配合非點源污染負荷模式所導出的2006年污染歷線、集水區平均降雨量及水庫有效蓄水量等資料，影像顯示之表面水質流布狀態可合理的分類成雨季前的低水位期(約1到5月)，連續暴雨期(約6到7月)，滿水位期(約8到10月)及水位漸退期(約11月到隔年)等四個階段，且影像所顯示的流佈狀態也與Chl-a及SS本身的特性相符。結論顯示以遙測所產生的水質分佈及時空趨勢對於評估及描述流出污染負荷及水質的關聯性具有相當大的潛力。

Nutrient overenrichment (eutrophication) is the major cause of decline in overall water qualities of lakes and reservoirs. Since the watershed nonpoint sources (NPS) pollution is the primary source of nutrient and sediment loads, the study of the linkage between NPS pollution and lake/reservoir water quality is essential and of importance. With an intention to perform better assessments of the linkage, this study was divided into two parts: for a better estimation of NPS loads, collecting representative data and using appropriate models; and for a better evaluation of the water status, mapping the water quality parameters using remote sensing (RS) techniques. This new approach was carried out at Tsengwen Reservoir (TWR), the largest artificial freshwater source in Taiwan.
In the first part of this study, two sampling site in the TWR watershed, Tapu Dam and Takubuyan Dam, were selected to estimate the NPS loads, which represents the NPS loads from the entire TWR watershed and an undistributed forest watershed, respectively. The flow rates were measured with weirs and samples taken for water quality analysis in both non-rainy and rainy days for 2 years (2002 and 2003). The subroutine of the Hydrological Simulation Program - FORTRAN (HSPF) was used to simulate runoff for additional 3 years (2000, 2001 and 2004). Total annual loads of various water quality parameters were then estimated by a regression model. The results indicate that most of the parameter concentrations in both sites are higher during the rainy days. In Takubuyan Dam, the values of parameter concentrations and loads are typically higher as compared to data from other undisturbed forest areas. The fluctuation of annual load from TWR watershed is significant. For example, in Takubuyan Dam, six major events of the entire year, for which the total duration is merely 6.4 days, contribute 42% of the annual precipitation and at least 40% of the annual NPS loads.
In the second part of this study, two approaches had been developed for mapping water qualities from remotely sensed imagery. Whenever the hyperspectral image is available, e.g., images taken by MODerate resolution Imaging Spectroradiometer (MODIS) onboard AQUA satellite or the hyperspectral imager-ISIS from an airborne platform, a novel approach that integrates a semi-analytical (SA) model and a genetic algorithm (GA) to retrieve the constituents of water bodies from remote sensing of ocean color (GA-SA) was suggested. Following the same procedures the International Ocean-Color Coordinating Group (IOCCG) employed in evaluating various algorithms, this GA-SA approach is validated against a synthetic dataset (N=500) and an in-situ dataset (N=656) compiled by the IOCCG. The results of validation indicate that the GA-SA technique is accurate and robust, easy to use, and sufficiently fast to process satellite imagery on a regional scale. This novel approach is applied in processing the images taken by MODIS and generates maps of water constituents and IOPs, including concentrations of chlorophyll-a (Chl-a), non-algal particle (NAP), and the absorption coefficient of color dissolved organic matter (CDOM) at 443nm.
On the other hand, an empirical approach (FORTW-EM) that transform the ratio of Green to Blue band and Red to Blue band of the imagery to respective retrieve the concentration of Chl-a and suspended solids (SS) was suggested, whenever there are only multispectral images available, e.g., images taken by FORMOSAT-2. During the development of FORTW-EM, both the in-situ measurement of spectral reflectance in TWR and the techniques of image processing are applied to the time series of imagery collected by FORMOSAT-2. The result of FORTW-EM shows that the relative percentage differences (RPD) for retrieving Chl-a and SS from the FORMOSAT-2 imagery are 51% and 33%, respectively. The error might be caused by the different level of atmospheric effect on different date. Another possibility is that the FORMOSAT-2 imagery and the in-situ measurement were not collected on the same time.
Because the most appropriate hyperspectral image for mapping reservoir water quality is not available at present. Therefore, based on fifteen FORMOSAT-2 images in TWR taken in 2006, we apply the FORTW-EM approach to derive maps of Chl-a and SS. With the daily runoff loads derived by the NPS regression model and the ancillary information of averaged precipitation and effective storage volume of TWR, the imagery revealed surface dispersal patterns can be reasonably categorized as low-level, stormwater, full-level and water level decreasing periods. The results demonstrated that the different characteristics of Chl-a and SS make them ideal tracers for observing large-scale dispersal patterns. Mapping the water constituents from remotely sensed data enables a better understanding of the linkage of river-borne substances in TWR.

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