Parameter uncertainty in a highly parameterized model of Lake Okeechobee

ISSN： 1040-2381

Source： Lake and Reservoir Management, Vol.27, Iss.4, 2011-12, pp. : 376-389

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

Abstract

The uncertainty in input parameters and management predictions of a complex eutrophication model were defined using a likelihood-based Monte Carlo uncertainty analysis. Results indicate that substantial uncertainty exists in the posterior distributions for model input parameters, despite 18 years of calibration data. Marginal posterior parameter distributions were not significantly different than priors for 95 of the 114 input parameters. The consideration of likelihood did reduce the uncertainty of model predictions of future lake response to nutrient loads by 68–79% compared to predictions based using prior uncertainty alone. These results indicate that consideration of likelihood does provide important information on the necessary correlation structure between inputs, and that selection of an acceptable set of parameters is much more important than the selection of any particular parameter value. Computational requirements were extensive when applying Monte Carlo analysis to a complex model with 18 years of continuous calibration data. Application of a regionalized sensitivity analysis provided close agreement to the full uncertainty analysis with an order of magnitude less computational effort, and is likely a preferred alternative for computationally intensive models with informal likelihood functions and/or poorly defined priors.