Integration of Bayesian analysis for eutrophication prediction and assessment in a landscape lake.

Abstract

Eutrophication models have been widely used to assess water quality in landscape lakes. Because flow rate in landscape lakes is relatively low and similar to that of natural lakes, eutrophication is more dominant in landscape lakes. To assess the risk of eutrophication in landscape lakes, a set of dynamic equations was developed to simulate lake water quality for total nitrogen (TN), total phosphorous (TP), dissolve oxygen (DO) and chlorophyll a (Chl a). Firstly, the Bayesian calibration results were described. Moreover, the ability of the model to reproduce adequately the observed mean patterns and major cause-effect relationships for water quality conditions in landscape lakes were presented. Two loading scenarios were used. A Monte Carlo algorithm was applied to calculate the predicated water quality distributions, which were used in the established hierarchical assessment system for lake water quality risk. The important factors affecting the lake water quality risk were defined using linear regression analysis. The results indicated that the variations in the landscape lake receiving recharge water quality caused considerable landscape lake water quality risk in the surrounding area. Moreover, the Chl a concentration in lake water was significantly affected by TP and TN concentrations; the lake TP concentration was the limiting factor for growth of plankton in lake water. The lake water TN concentration provided the basic nutritional requirements. Lastly, lower TN and TP concentrations in the receiving recharge water caused increased lake water quality risk.