High-resolution modeling for development of nearshore ecosystem objectives in eastern Lake Erie
Introduction
Nuisance algal blooms that plagued Lake Erie in the 1960s and 70s were largely controlled by collaborative binational efforts to reduce loadings of phosphorus (P), primarily from point sources. Currently, however, the majority of phosphorus loads to Lake Erie are from non-point sources (Maccoux et al., 2016--in this issue). Since the mid-1990s, algal blooms have again returned to Lake Erie (cyanobacteria in the western basin, Cladophora in the eastern basin). While it is generally agreed that excess P is the primary cause of these blooms, there is uncertainty regarding the mechanisms responsible for the apparent increase in P availability. The establishment of expansive populations of filter-feeding dreissenid mussels has greatly increased water clarity (Binding et al., 2007), and mussels have likely altered the retention and cycling of nutrients such as P (Hecky et al., 2004), particularly in nearshore shallow areas where the water column is well-mixed. Furthermore, the form of P entering the lake from many surrounding catchments appears to be changing, with increases in the proportion of bio-available forms (i.e., soluble reactive phosphorus, SRP) in some watersheds (Baker et al., 2014).
In 2012, the Great Lakes Water Quality Agreement was amended to include an Annex on nutrients (Annex 4), specifically focused on achieving Lake Ecosystem Objectives (LEOs) through management of nutrient loadings. For Lake Erie, the LEOs included a reduction in the extent of the hypoxic zone in the central basin and a goal to maintain algal biomass below levels deemed to be a hazard in the western basin (cyanobacteria) and below nuisance levels in the eastern basin (Cladophora). As part of the commitments under the Protocol of 2012 amending the GLWQA, the governments of the United States and Canada were charged with setting target loads for P to Lake Erie to achieve the desired LEOs. An ensemble modeling approach was initiated to generate ecological response curves to various P loading scenarios. The water year for 2008 (October 1, 2007, to September 30, 2008) was selected as the base year for calculating response curves as loading data are considered accurate and the water year load for 2008 (10,675 metric tons; MT) is close to the annual target load of 11,000 MT set in the 1978 Amendment to the GLWQA. Load–response curves have been developed for cyanobacterial blooms in the western basin and hypoxic extent in the central basin of Lake Erie (DePinto et al., 2013, Stumpf et al., 2012, Bocaniov et al., 2014), but the paucity of suitable data from the nearshore in the eastern basin in 2008 and the coarse definition grid size (2 km in current models) has thus far rendered modeling of Cladophora growth in response to reductions in P loading challenging.
In Lake Erie, water quality dynamics in the nearshore are regulated by wind, surface runoff, the degree of stratification, and basin scale physical processes, including coastal upwelling events (period of 5–10 days), seiches (~ 14 h), and near-inertial waves (~ 17 h) (Bouffard et al., 2012, Rao et al., 2008, Valipour et al., 2015a, Valipour et al., 2015b). Previous high-resolution modeling efforts to resolve nearshore water quality have used nested approaches with simulations at coarse resolution (2 km) to derive boundary conditions that supply higher resolution meshes with inputs (e.g., Leon et al., 2012); however, there is a recognition that these may not adequately capture the interplay of the offshore predominant physical processes because 2 km is not fine enough to resolve the processes within the lake's internal Rossby radius of deformation (~ 3 km), which influences the offshore–nearshore exchange and transport (Rao and Murthy, 2001, Rao and Schwab, 2007, Valipour et al., 2015b).
The ability of the three-dimensional hydrodynamic Estuary and Lake Computer Model (ELCOM) to resolve the Lake Erie's predominant hydrodynamic physical processes (and interactions between the offshore and nearshore) has been examined (Leon et al., 2005, Leon et al., 2011, Valipour et al., 2015b). Here, the dynamic coupling between ELCOM and the Computational Aquatic Ecosystem Dynamics Model CAEDYM (Hipsey et al., 2007) was used to study water quality conditions with a particular emphasis on simulating Cladophora growth along the northern shoreline of the eastern basin, which has been a recurring phenomenon since the late 1990s (Higgins et al., 2005a). The objectives of this study are (i) to develop a lake-wide high-resolution water quality model capable of simulating the predominant offshore and nearshore nutrient dynamics in eastern Lake Erie using an extensive water quality dataset collected in 2013, (ii) to use the water quality model output to simulate Cladophora growth in the eastern basin using a Cladophora growth model (Auer and Canale, 1982), and (iii) to generate phosphorus load–response curves for Cladophora growth to evaluate projected changes in TP and SRP concentrations and Cladophora biomass under different P reduction scenarios for 2008 and 2013 to provide guidance for managers to develop P loading strategies for Lake Erie that will address Cladophora blooms.
Section snippets
Study area
Lake Erie (Fig. 1; 388 km long and 92 km maximum width) is the shallowest of the Laurentian Great Lakes and consists of distinct western, central, and eastern basins with maximum depths of 11, 25, and 64 m, respectively (Valipour et al., 2015a). Lake Erie has the shortest residence time of ~ 3 years compared to the other Great Lakes (Chapra, 1997) and receives nutrient loads from one-third of the Great Lakes' population (Rao et al., 2008). The majority of the inflow is from the Detroit River flowing
ELCOM-CAEDYM (ELCD)
ELCOM solves the unsteady Reynolds-averaged Navier–Stokes equations for heat and momentum transfer across the water surface due to wind and atmospheric thermodynamics (Hodges et al., 2000) to simulate the spatial and temporal variations of the physical and transport processes in the water body. The hydrodynamic model setup and lake-wide calibration has been described elsewhere (Leon et al., 2005, Leon et al., 2011, Valipour et al., 2015b). It has been demonstrated to be capable of resolving the
ELCD validation
Comparison of ELCD model results with observations in 2013 (Fig. 4) shows that the model is able to successfully reproduce spatial and temporal variability of TP, SRP, TN, DO, and TCHLA, and the results were in acceptable ranges (RMSE as shown) and in agreement with previous modeling efforts (Leon et al., 2011, Oveisy et al., 2014). Overall, there were no systematic geographic patterns of deviation between modeled and observed concentrations although the performance was observed to be better in
Distribution of TP, SRP, DO, and TCHLA
Distributions of depth-averaged model results during May to September 2013 for TP, SRP, TN, DO, and TCHLA are shown in Fig. 5 (see also Electronic Supplementary Material, ESM Fig. S1). The model produced major features that have been noted in previous Lake Erie modeling efforts (e.g., Boegman et al., 2008, Schwab et al., 2009, Leon et al., 2011) and in water quality evaluations (Dove, unpublished data). For example, well-delineated nutrient plumes (TP, SRP, and TN) are identified along the
Cladophora: biomass and internal phosphorus (QP)
The use of the ELCD results for prediction of Cladophora biomass is based on reasonable simulation of PAR attenuation (kPAR) over the domain of interest. ELCD predictions and observation of kPAR were acceptable (range and time series were well reproduced; Fig. 7). We did observe some disagreement at shallow (i.e., 3 m) sites within a few km of the Grand River and speculate that this is a reflection of the importance of re-suspended material from the lake bed in mediating light penetration (
Load–response curves
For both TP and SRP concentrations, the responses for the study area varied between 2008 and 2013 (Fig. 8). In 2013, although the total TP load was ~ 20% lower than in 2008 (Table 1), average nearshore TP and SRP concentrations in the model domain were higher (Fig. 8). Similarly, response curves for TP and SRP in 2013 are steeper relative to 2008 and indicate a proportionately larger reduction in nearshore TP and SRP concentration per unit reduction in external load. The contrasting response
Discussion and conclusion
This is the first time the offshore–nearshore nutrient exchange is assessed for the northeastern Lake Erie coastal waters, and this highlights the influence of hydrodynamics on nearshore water quality. Of the predominant physical processes in eastern Lake Erie during May to September (upwelling events, surface seiches, and near-inertial waves), which were successfully resolved by this high-resolution model, we demonstrate that coastal upwelling events (period of 5–10 days) are dominant drivers
Acknowledgments
The authors are grateful to ECCC's Research Support group and the captain and crew of the CCGS LIMNOS for logistical support with the field measurements. We thank Isaac Wong and Craig McCrimmon for their support and helpful discussions during the modeling and manuscript preparation. RV was supported by the National Sciences and Engineering Council of Canada visiting fellow program. We also thank two anonymous reviewers for their constructive comments.
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