水體透明度(depth of Secchi disk, SD)是人類最早有觀測記錄的水質參數、也是湖庫澄清度與優養評估的重要指標之一，觀測迄今兩百年以上尚未被其他先進儀器所取代。一般湖庫水質模式或優養評估均透過經驗關係從水質估算透明度，例如使用相當普遍的卡爾森優養指標中的葉綠素a (Chl-a) vs SD經驗式，雖然學界早有質疑此經驗關係的適用性，但仍鮮少研究以光學物理模式探討內陸湖庫水質與SD間的關連性。古典光學理論假設水下的沙奇盤為一個點目標，進而以特定波長光束衰減係數(beam attenuation coefficient, c(λ))為核心推導水質與SD的關係。近年來海洋水色學界提出新的光學物理理論，假設沙奇盤不屬於點目標，其在水下隨深度漸深到由人眼視界消失的過程中，主要與人眼感知特徵波長之光衰係數(diffuse attenuation coefficient for downward irradiance, Kd(λ))及水下遙測反射率(remote sensing reflectance, rrs(λ))有關。
本研究以台灣澄清度最高且最具景觀遊憩價值之天然湖泊–日月潭為研究目標，分析其光敏性物質濃度、固有光學性質(inherent optical properties, IOPs)、外顯光學性質(apparent optical properties, AOPs)，以建立比濃度IOP關係及四成分生光模式(bio-optical models, BOM)。其次，搭配半解析輻射傳輸模式及新的透明度物理模式，發展水質–透明度物理模式，並針對日月潭內陸湖庫光學特性，修正其中Kd(λ)及rrs(λ)之特徵波長設定。最後，以建立之透明度模式從歷年觀測水質重建日月潭透明度，分析其透明度模擬效果，並與卡爾森經驗關係及古典光學理論模擬結果進行比較。
本研究三次採樣之懸浮固體物濃度(SS)、總溶解性有機碳濃度(DOC)、Chl-a濃度及濁度(Turb)分別為0.7-2.9 mg/L、0.45-1.12 mg/L、0.296-2.962 μg/L及1.55-7.21 NTU；測得之全白及半黑半白沙奇盤深度分別為2.0-3.8 m及1.8-3.17 m。綜合前期分析數據，更新後之比濃度IOP關係採用SS估計顆粒背向散射係數〖bb〗_p (550) (R2=0.77)、以Chl-a估計浮游植物顆粒吸收係數a_ph (440) (R2=0.79)、以DOC估計有色溶解性有機物質吸收係數a_g (440) (R2=0.88)，並以Turb估計無機顆粒吸收係數a_dm (400) (R2=0.91)。BOMs部分，本研究調整參數修正前期研究BOMs在總吸收係數a(λ)及總背向散射係數bb(λ)於400-550 nm模擬值高估的偏差，使其更符合觀測值。根據日月潭歷年透明度觀測與模擬比較結果顯示，Kd(λ)之特徵波長(wavelength for Kd transparent window)率定在527-532 nm之間；rrs(λ) 之特徵波長(perceive color of rrs)率定在500-537 nm間可得到最佳透明度模擬結果。
本研究修正後之透明度物理模式，以實測日月潭水質模擬SD，R2可達0.84，誤差為0.27 m，其模擬效果較卡爾森經驗關係及前期研究採用之古典光學理論為佳。此外，本研究也以在日月潭率定之物理模式套用在本島其他20座主要水庫，其平均透明度模擬相對誤差為35%，其中除白河、石門與寶二水庫誤差超過40%以外，其他水庫的模擬結果都遠比卡爾森經驗關係為佳。
本研究發展的透明度物理模式演繹過程均為代數及顯式解，可輕易將其編碼套用在現有水庫水質模式上，加強管理單位對景觀遊憩型湖庫之水質管理與污染防治。

SUMMARY
Depth of Secchi disk(SD) is the most earlier water quality parameter of record by human observation. Now, SD is still the important parameter to analyze clarity and eutrophication of reservoirs. In this study, we will connect water quality and SD by the physical model of clarity, BOM and semi-analytical model. The physical clarity model is related to a new theory of underwater visibility, which says that Secchi disk should not be seen as a point target in water, and water color will effect the observation of human eyes. Compared with the classic visible theory, the classic theory assumes that Secchi disk is a point target in water, and is related to beam attenuation coefficient(c). The physical model is related to the characteristic wavelength of diffuse attenuation coefficient for downward irradiance(Kd) and remote sensing reflectance,(rrs). According to references and analytical data of other research, we update the regression relationship of IOPs and water quality. The relationship is Turd and adm(400)(R2=0.88), DOC and ag(440)(R2=0.91),Chla and aph(440)(R2=0.79), TSS and bbp(550)( R2=0.77). We use QAA algorithm to simulate remote-sensing reflectance. QAA algorithm has three types of parameter numbers to simulate rrs(λ), the result we test shows that Case1 has better results than others. The result compares with Obs_ rrs(pc), R2=0.563. We use two semi-analytical model to simulate K_d (λ), and theres is no difference of results. We choose the semi-analytical model calculated by solar zenith angle because the model is easier to code. The result compares with Obs_ K_d(tr), R2=0.976. According to the analyzing results and Obs_data, if we would like to get the best result of simulating clarity, tr wavelength is at 500-537nm, pc wavelength is at 527-532nm in Sun Moon Lake reservoir. According to the result of SD, the result from the physical model of clarity is better than classic clarity model. The best result of classic clarity model shows R2=0.79, RMSE=6.98m, and the best result of physical clarity model shows R2=0.84, RMSE=0.27m. Finaaly, we use the physical model to simulate SD from water quality of years(2005-2013) in Taiwan reservoirs. The result from physical clarity model of Sun Moon Lake shows R2=0.5, RMSE=0.84m. On the other hand, we simulate clarity of other twenty reservoirs. The average RPD=0.35, RMSE=0.63m. The results are better than the results from Calson experimental algorithm.

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