The seasonal nitrogen cycle in Wilkinson Basin, Gulf of Maine, as estimated by 1-D biological model optimization

Author Posting. © Elsevier B.V., 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 78 (2009): 77-93, doi:10.1016/j.jmarsys.2009.04.001.The objective of this study was to fit a simple ecosystem model to climatological nitrogen cycle data in the Gulf of Maine, in order to calibrate the biological model for use in future 3-D modelling studies. First depth-dependent monthly climatologies of nitrate, ammonium, chlorophyll, zooplankton, detritus and primary production data from Wilkinson Basin, Gulf of Maine, were created. A 6-box nitrogen-based ecosystem model was objectively fitted to the data through parameter optimization. Optimization was based on weighted least squares with model-data misfits nondi- mensionalized by assigned uncertainties in the monthly climatological estimates. These uncertainties were estimated as the standard deviations of the raw data from the 6-meter and monthly bin averages. On average the model fits the monthly means almost within their assigned uncertainties. Several statistics are examined to assess model-data misfit. Pattern statistics such as the correlation coefficient lack practical significance when data errors are large relative to the signal, as in this application. Thus Taylor diagrams were not found to be useful. The RMSE and model bias normalized by the data error were found to be the most useful skill metrics as they indicate whether the model fits the data within its estimated error. The optimal simulated nitrogen cycle budgets are presented, as an estimate of the seasonal nitrogen cycle in Wilkinson Basin, and discussed in context of the available data.Wilkinson Basin has spring and fall phytoplankton blooms, and strong summer stratification with a deep chlorophyll maximum near 21 m, just above the nitracline. The mean euphotic zone depth is estimated to be 25 m, relatively constant with season. The model estimates annual primary production as 176 g C m−2 yr−1, annual new production as 71 g C m−2 yr−1 and sinking PON fluxes of 9.7 and 4.7 g N m−2 yr−1 at 24 and 198 m respectively. Areas for improvement in the biological model, the model optimization method, and significant data gaps are identified.This work was supported by ONR, NSF, and NOAA grant to Dennis McGillicuddy

Metal mixture modeling evaluation project: 2. Comparison of four modeling approaches

As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the US Geological Survey (USA), HDR|HydroQual (USA), and the Centre for Ecology and Hydrology (United Kingdom) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME workshop in Brussels, Belgium (May 2012), is provided in the present study. Overall, the models were found to be similar in structure (free ion activities computed by the Windermere humic aqueous model [WHAM]; specific or nonspecific binding of metals/cations in or on the organism; specification of metal potency factors or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single vs multiple types of binding sites on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong interrelationships among the model parameters (binding constants, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed