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The watershed analysis risk management framework (WARMF) model was applied to Wetland $6 of the Marcell Experimental Forest, using the data from a field experiment, conducted to investigate the effect of sulfate additions on mercury methylation in the wetland. The wetland was modeled as interconnected land catchments. Actual meteorology data and mercury and sulfate concentrations of precipitation were input to the model. To simulate the sulfate sprinkling, the experimental section of the bog was irrigated with sulfate water on the actual dates of sulfate additions. The model simulated wetland outflows that matched the measured outflows with an R-square of 0.856. WARMF also simulated other phenomena observed in the experiment: higher sulfate and MeHg levels at the wetland outlet after every sulfate addition, and higher sulfate and MeHg levels in the pore water of the bog after only the May addition, not the July and September additions. According to WARMF, the low groundwater table in May allowed the sprinkled sulfate to percolate to the soil stratum 10-30 cm below the ground level of the bog, where the pore water was sampled. In July and September, the sulfate could not reach that zone because the percolation was blocked by high groundwater tables. The sampled soil stratum was not the site of methylation that contributed MeHg to the wetland outlet. The saturated zone of the top 10 cm of bog was the site that produced MeHg, which was flushed to the outlet after all sulfate additions. WARMF predicted that quadrupling the sulfate deposition would increase the MeHg output by 216%, which might become lower with more data and better model calibration. DOI: 10.1061/(ASCE)EE. 1943-7870.0000176 CE Database subject headings: Wetlands; Water pollution; Water quality; Mercury; Sulfates; Simulation models. Author keywords: Wetland; Atmospheric deposition; Mercury; Sulfate; Methylmercury output; Model simulation.

Watershed Analysis Risk Management Framework (WARMF) is a comprehensive watershed model and decision support system designed for stakeholders to formulate alternatives for point and nonpoint source pollution controls, evaluate their technical ability to meet water quality criteria, and make changes to negotiate the preferred plan. WARMF uses physically based model input parameters to describe the watershed. Many of these are known from data or watershed-specific studies, but others are determined through the calibration process. The model calibration is performed by systematically adjusting model input parameters within their normal ranges to match the simulated results to the observed data. The GUI enables users to make changes to model input parameters and/or data and graphically compare the simulated results and observed data. WARMF calculates correlation coefficient, relative error, absolute error, cumulative volume balance, and other measures to evaluate the calibration quantitatively. Relative error and absolute error are the preferred measures of accuracy and precision used in WARMF calibration. Long-term observed data may be split into two periods, one for model calibration and the other for validation. New data may also be collected for model validation. A case study is presented to demonstrate the calibration of flow and electrical conductivity (EC) for the San Joaquin River watershed in California. Soil properties were adjusted so the simulated flow would match the measured flow data during both the winter rainy season and summer irrigation season. Soil chemistry inputs were calibrated to ensure long-term stability of pore water concentrations and so the simulated water quality of the San Joaquin River followed the magnitude and pattern of in-stream monitoring data. The simulation of flow produced a 1% relative error and 13% absolute error over the eight-year calibration period. The EC calibration had relative error of 5% and absolute error of 15%. [ABSTRACT FROM AUTHOR]