優養化為臺灣水庫常見的水質問題，大部分被判定為優養化水庫的主因是由於低透明度所造成，低透明度又與水中懸浮固體物有關，因此為改善水庫優養化問題，在磷與懸浮物質削減的選擇上，需透過完整的污染負荷到透明度的整合模式進行評估。台灣年降雨量大且集中，加上地形陡峭等先天因素，每逢雨季便會造成大量地表沖刷流入水庫，造成水庫水質劣化，提高水處理成本、影響用水標的，須為此建立完整的水庫水質模式、針對水庫污染來源進行分析、制定污染改善方案，以改善水庫水質。本研究結合本實驗室之前期研究成果，以阿公店水庫為例，結合集水區模式、承受水體模式與透明度光學物理模式，整合為水庫光學水質模式。
阿公店水庫由於屬青灰泥岩，每逢颱風暴雨將會造成集水區土壤侵蝕流失而進入水庫，造成水質惡化與水庫大量淤積，因此設有每年定期實施空庫排淤之操作，以延長水庫使用壽命，平時則引流自旗山溪進行水庫蓄水為主。由於阿公店水庫的操作特性，本研究亦將模擬時間分為空庫排淤期與蓄水期分開討論。
阿公店水庫集水區水理模式有良好的模擬結果，率定年與驗證年之R2分別為0.953及0.757，唯逢降雨量大的情況下，入流量模擬植接有高估的情況。推估阿公店集水區污染負荷來源，包含懸浮固體物、氨氮、硝酸鹽氮、亞硝酸鹽氮、總磷、TOC、BOD等七項污染物，以入庫溪流(點源)、非點源及越域引水三者當中，非點源與越域引水佔95%以上，而入庫溪流(點源均不到5%，顯示阿公店水庫污染主要受集水區非點源污染與越域引水所影響。CE-QUAL-W2模擬結果顯示，當水庫進流水來源主要為降雨時，水庫污染物濃度除葉綠素a與總有機碳外，其他污染物濃度會有明顯短暫提高；蓄水期初期主要進水來源為越域引水，各項污染物濃度亦有短暫提高跡象。
透明度光學水質模式為根據水中光敏物質與當時環境光場特性進行模擬。以阿公店模擬水質進行透明度演算，空庫排淤期有較好的模擬結果，而蓄水期較不理想，原因為透明度物理模式在高透明度情況常有準確性不佳的情況發生，且由於W2模擬大量出流水導致的底泥再懸浮現象較不理想，模擬透明度值於蓄水期後期有明顯上升的異常情況，因此本研究建立之整合模式不適用模擬出現出流水導致底泥再懸浮之情況。
由三項阿公店水庫負荷主要來源進行污染削減模擬，發現以營養鹽為削減目標的策略無法有效改善透明度與優氧化指數，而透過削減越域引水之懸浮固體物與總磷，最能有效改善透明度。

Eutrophication is a common water quality problem of reservoirs in south Taiwan, and most of reservoirs which was identified as the eutrophic reservoir because of low transparency instead of chlorophyll-a. The cause of low transparency are mainly high concentration of suspended solids instead of chlorophyll-a. The strategy of restoring eutrophic reservoirs must be based on science as integrating watershed and bio-optical models for liking catchment loading to the depth of Secchi disk to simulate different load reduction scenarios. Therefore, this study was integrating watershed model, receiving water model and Secchi disk depth physical model to develop an integrating model.
The result of HSPF watershed hydrological modeling is acceptable. The simulated total inflow rate is close to observed inflow rate. The R2 value of calibration and validation are 0.5929 and 0.7574 respectively. According to loading estimation result, the loading constituents includes SS, NH4-N, NO3-N, NO2-N, TP, TOC, and BOD¬. The sources of pollutant loading in Agongdian Reservoir are point source from residents, nonpoint source, and cross-watershed diversion. Nonpoint source and cross-watershed diversion are the main resources with more than 95% proportion, and point source with less than 5% proportion. The variation trend of total loading is similar to precipitation change.
There are three loading reduction scenarios are simulated in this study. (1) Point source control: SS is reduced from 200mg/L to 20mg/L. TOC is reduced from 3.93 to 0.8 mg/L. PO43- is reduced from 1.33 mg/L to 0.16 mg/L. BOD is reduced from 5.92 to 2 mg/L (2) Nonpoint source control: TP and TN emission are reduced to 70% in fruit land. (3) Cross-watershed diversion control: SS is reduced to 60% and TP is reduced to 80%. In the result, the cross–watershed diversion control is the most effective one. Agongdian Reservoir is identified as eutrophic due to overloading of SS loading which leads to low transparency.

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