Lake water eutrophication has become one of the most important factors impeding sustainable economic development in China. Knowledge of the current status of lake water eutrophication and determination of its mechanism are prerequisites to devising a sound solution to the problem. Based on reviewing the literature, this paper elaborates on the evolutional process and current state of shallow inland lake water eutrophication in China. The mechanism of lake water eutrophication is explored from nutrient sources. In light of the identified mechanism strategies are proposed to control and tackle lake water eutrophication. This review reveals that water eutrophication in most lakes was initiated in the 1980s when the national economy underwent rapid development. At present, the problem of water eutrophication is still serious, with frequent occurrence of damaging algal blooms, which have disrupted the normal supply of drinking water in shore cities. Each destructive bloom caused a direct economic loss valued at billions of yuan. Nonpoint pollution sources, namely, waste discharge from agricultural fields and nutrients released from floor deposits, are identified as the two major sources of nitrogen and phosphorus. Therefore, all control and rehabilitation measures of lake water eutrophication should target these nutrient sources. Biological measures are recommended to rehabilitate eutrophied lake waters and restore the lake ecosystem in order to bring the problem under control.
In many hydrological studies and applications, it is desirable to know the absorption property of water bodies. In order to derive this inherent optical property of waters from remote sensing reflectance, a multiband quasi-analytical algorithm (QAA) was calibrated and validated for the highly turbid water of Taihu Lake in China. A data set collected on November 8, 2007, from Meiliang Bay of Lake Taihu was first used to calibrate a regional QAA algorithm for this area, and other two independent data sets, which were collected on August 22, 2006, and November 10, 2008, from the same area, were used to further validate the local algorithm. By shifting the reflectance wavelength from the red region to near infrared, the local QAA algorithm works well for this highly turbid water, the percent difference between the derived and measured absorption coefficients is less than 20% for all 13 samples in the data set of 2007, and most of them are less than 10%. The regional calibrated algorithm also has great result in deriving the absorption for the data set of 2008. However, the performance of the local algorithm also has various seasonal properties. It failed in deriving absorption for the data set of August 2006, unless the reference wavelength is shifted to even more long ones. It has been suggested in this paper that the seasonal and regional information is necessary for using the QAA algorithm in different optical property waters.
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