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EVALUATING REGIONAL PREDICTIVE CAPACITY OF A PROCESS-BASED MERCURY EXPOSURE MODEL, REGIONAL-MERCURY CYCLING MODEL (R-MCM), APPLIED TO 91 VERMONT AND NEW HAMPSHIRE LAKES AND PONDS, USA
Citation:
KNIGHTES, C. D. AND R. B. AMBROSE. EVALUATING REGIONAL PREDICTIVE CAPACITY OF A PROCESS-BASED MERCURY EXPOSURE MODEL, REGIONAL-MERCURY CYCLING MODEL (R-MCM), APPLIED TO 91 VERMONT AND NEW HAMPSHIRE LAKES AND PONDS, USA. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, 26(4):807-815, (2007).
Impact/Purpose:
The objective of this task is to develop, support and transfer a wide variety of tools and mathematical models that can be used to support watershed and water quality protection programs in support of OW, OSWER, and the Regions.
Description:
Regulatory agencies must develop fish consumption advisories for many lakes and rivers with limited resources. Process-based mathematical models are potentially valuable tools for developing regional fish advisories. The Regional Mercury Cycling model (R-MCM) was specifically designed to model a series of lakes for a given region with site-specific data and parameterization for each application. In this paper, we explore the feasibility of R-MCM application to develop regional fish advisories from existing data by testing model performance across 91 Vermont (VT) and New Hampshire (NH) (both in USA) lakes. We use a progressive method of parameter refinement ranging from simple defaults specified by the model to site-specific parameterization to evaluate potential improvements in model prediction. Model applications and parameter refinement tiers are based on Regional Environmental Monitoring Assessment Program (REMAP) data. Results show that R-MCM generally under-predicts water column methylmercury and total mercury concentrations and over-predicts sediment methylmercury concentrations. Default level input parameterization produced the largest amount of random scatter in model forecasted values. Using site-specific values for the default level characteristics reduced this variability but did not improve overall model performance. By separating the observed and predicted data by lake characteristics, we identify some overall trends in bias and fit, but are unable to identify systematic biases in model performance by lake type. This analysis suggests that process based models like R-MCM cannot be used for a priori predictive applications at the regional scale at this time. Further, this work reinforces the need for additional research on the transport and transformation of mercury to elucidate parameterization useable in a modeling framework to help refine predictive capabilities of process based models.