, 1981a, Scavia et al., 1981b and Scavia et al., 1988), a new generation of models has emerged more recently (e.g., Bierman et al., 2005, Fishman et al., 2009,

Leon et al., 2011, LimnoTech, 2010, Rucinski et al., 2010, Rucinski et al., 2014, Zhang et al., 2008 and Zhang et al., 2009). For Lake Erie, Zhang et al. (2008) developed a two-dimensional ecological model to explore potentially important ecosystem processes and the contribution of internal Enzalutamide cost vs. external P loads. Rucinski et al. (2010) developed a one-dimensional model to examine the inter-annual variability in DO dynamics and evaluate the relative roles of climate and P loading. Leon et al. (2011) developed a three-dimensional model to capture the temporal and spatial variability of phytoplankton and nutrients. LimnoTech (2010) developed a fine-scale linked hydrodynamic, sediment transport, advanced eutrophication model for the WB that relates nutrient, sediment, and phytoplankton temporal and spatial profiles to external loads and forcing functions. Stumpf et see more al. (2012) developed a model to predict the likelihood of cyanobacteria blooms as a function of average discharge of the Maumee River. As part of EcoFore-Lake Erie, Rucinski et al. (2014) developed and tested a model specifically for establishing the relationship between P loads and CB hypoxia. This model is driven by a one-dimensional

hydrodynamic model that provides temperature and vertical mixing ADP ribosylation factor profiles as described in Rucinski et al. (2010). The Ekman pumping effect described above and in Beletsky et al., 2012 and Beletsky et al., 2013 was in essence parameterized as additional diffusion in the one-dimensional hydrodynamic model.

The biological portion of the model is a standard eutrophication model that used constant sediment oxygen demand (SOD) of 0.75 gO2∙ m− 2·d− 1 because it has not varied significantly over the analysis period (Matisoff and Neeson, 2005, Schloesser et al., 2005, Snodgrass, 1987 and Snodgrass and Fay, 1987). Earlier analysis (Rucinski et al., 2010) indicated that SOD represented on average 63% of the total hypolimnetic oxygen demand, somewhat larger than the 51% and 53% contribution that Bouffard et al. (2013) measured in 2008 and 2009, respectively. However, for load-reduction scenarios, a new formulation was needed to adjust SOD as a function of TP load. This relationship (Rucinski et al., 2014), while ignoring the 1-year time lag suggested by Burns et al. (2005), was based on an empirical relationship between SOD and deposited organic carbon (Borsuk et al., 2001). The model was calibrated over 19 years (1987–2005) using chlorophyll a, zooplankton abundance, phosphorus, and DO concentrations, and was compared to key process rates, such as organic matter production and sedimentation, DO depletion rates, and estimates of hypoxic area ( Zhou et al., 2013) by taking advantage of a new empirical relationship between bottom water DO and area ( Zhou et al., 2013).