基于经验频率曲线的湖泊富营养化随机评价方法及其验证

本文提出了基于经验频率曲线的湖泊富营养化随机评价方法.该法通过建立水体富营养化评价标准中各项水质指标和评价级别的经验频率曲线,推求实际水体各项水质指标的经验频率,并采用加权平均法推求湖泊富营养化评价级别的经验频率,从而得出湖泊富营养化的评价级别,具有方法简单、图形直观、计算工作量小的特点.经全国30个湖库湖泊实测水质资料的验证,随机评价方法与模糊评价方法和灰色评价方法的计算精度相当.

A Lake Eutrophication Stochastic Assessment Method by Using Empirical Frequency Curve and Its Verification

In order to correctly evaluate the trophic state of a lake and then to establish a scientific basis for the prevention of lake eutrophication, a lake eutrophication stochastic assessment method (LESAM) by using empirical frequency curve (EFC) is proposed. In this method, the EFC of each water quality index of an actual lake is firstly derived through establishing the EFCs of each of those water quality indices and assessment orders used in the lake eutrophication assessment criteria. Secondly, the assessment order of lake eutropication is derived by using the weighting average methok to calculate the EFC of assessment order of lake eutrophication. The characteristics of LEGAM are that the method is simple , the visual graphs are informative, and the computation work is moderate. LESAM, together with other two methods, i. e. the lake eutrophication (LEGAM), has been tested against the measured data of 30 lakes all over the country, it is found that the difference of assessment results between those three methods is not greater than 1 order in 29 lakes, i. e. 96. 6% of the total lakes. It is concluded that all these three methods (LESAM, LEFAM, and LEGAM)have comparable computation precision. For there is a lot of uncertainty involved both in determining the number and type of water quality index in lake eutrophication assessment and in determining the assessment order and its corresponding standard water quality concentration, the lake eutrophication assessment methods also have uncertainty. It is suggested that the lake eutrophication assessment should be studied with various approaches in order to overcome the weakness of one particular method and to make assessment results more reasonable.