Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions.

In 2011, Lake Erie experienced the largest harmful algal bloom in its recorded history, with a peak intensity over three times greater than any previously observed bloom. Here we show that long-term trends in agricultural practices are consistent with increasing phosphorus loading to the western basin of the lake, and that these trends, coupled with meteorological conditions in spring 2011, produced record-breaking nutrient loads. An extended period of weak lake circulation then led to abnormally long residence times that incubated the bloom, and warm and quiescent conditions after bloom onset allowed algae to remain near the top of the water column and prevented flushing of nutrients from the system. We further find that all of these factors are consistent with expected future conditions. If a scientifically guided management plan to mitigate these impacts is not implemented, we can therefore expect this bloom to be a harbinger of future blooms in Lake Erie.

Good modeling practice guidelines for applying multimedia models in chemical assessments.

Multimedia mass balance models of chemical fate in the environment have been used for over 3 decades in a regulatory context to assist decision making. As these models become more comprehensive, reliable, and accepted, there is a need to recognize and adopt principles of Good Modeling Practice (GMP) to ensure that multimedia models are applied with transparency and adherence to accepted scientific principles. We propose and discuss 6 principles of GMP for applying existing multimedia models in a decision-making context, namely 1) specification of the goals of the model assessment, 2) specification of the model used, 3) specification of the input data, 4) specification of the output data, 5) conduct of a sensitivity and possibly also uncertainty analysis, and finally 6) specification of the limitations and limits of applicability of the analysis. These principles are justified and discussed with a view to enhancing the transparency and quality of model-based assessments.

Estimating mercury concentrations and fluxes in the water column and sediment of Lake Ontario with HERMES model.

The HERMES model-predicted Hg concentrations and fluxes in Lake Ontario were based on twelve lake and drainage basin variables (i.e., water temperature, precipitation rate, air Hg, surface area, mean depth, water volume, water inflow rate, inflow water Hg, inflow and lake suspended particulate matter, air-water and water-air mass transfer coefficients, and sedimentation rate). The HERMES model-predicted Hg water and surface sediment concentrations were found to be significantly correlated (±20%) with measured values (r(2) = 0.94, p < 0.0001, n = 13) and mechanistic model predictions (LOTOX2-Hg, r(2) = 0.95, p < 0.0001, n = 10). The predictive capacity of HERMES was previously tested on smaller (≤1 km(2)) lakes in Nova Scotia and Ontario, Canada (i.e., water and sediment Hg concentrations were ±15% of measured data). Results suggest that HERMES could be applicable to a broad range of lake sizes. Uncertainty analyses on HERMES model input variables indicated a larger atmospheric Hg contribution for Lake Ontario when compared to previous predictions for smaller lakes.