Eutrophication research of West Lake, Hangzhou, China: modeling under uncertainty.

The models such as the eutrophication ecosystem model of West Lake, Hangzhou (EEM), are always used to make policy decisions for eutrophication management. Thus it is important to know the uncertainty in the model predictions due to the combined effects of uncertainty in the full set of input variables, and the individual input parameters whose variations have the greatest effect on variations in model predictions. In this study, randomized methods based on Monte Carlo technique have been developed and applied to the model (EEM). The technique consists of parameter sensitivity analysis, randomly sampling from underlying probability distributions and multivariate regression analysis. With this technique, model uncertainties during modeling are clarified and their propagation evaluated. Results show that among the five input parameters selected for uncertainty analysis, the settling rate of algae SVS and water temperature TEM have the largest contribution to model prediction uncertainty of the model outputs (PC, PS and PHYT).