Abstract
Elevated phosphorus (P) loading from the watersheds draining into Lake Erie, particularly from agricultural (53%) and urban (43%) sources, is identified as one of the main drivers of the severe eutrophication. In this study, we present a comprehensive evaluation of 11 process-based models to characterize the water cycle as well as nutrient fate and transport within a watershed context, and to find a robust and replicable way to optimize the modelling strategy for the Lake Erie watershed. Our primary objective is to review the conceptual/technical strengths and weaknesses of the individual models for reproducing surface runoff, groundwater, sediment transport, nutrient cycling, and channel routing, and to collectively guide the management of the Lake Erie Basin. Our analysis suggested that the available models either opted for simpler approximations of the multifaceted, nonlinear dynamics of nutrient fate and transport, and instead placed more emphasis on the advanced representation of the water cycle or, introduced a greater degree of biogeochemical complexity but simplified their strategies to recreate the roles of critical hydrological processes. Notwithstanding its overparameterization problem, the MIKE SHE model provides the most comprehensive 3D representation of the interplay between surface and subsurface hydrological processes with a fully dynamic description, whereby we can recreate the solute transport that infiltrates from the surface to the unsaturated soil layer and subsequently percolates into the saturated layer. Likewise, the physically based submodels designed to represent the sediment detachment and erosion/removal processes (DWSM, HBV-INCA, HSPF, HYPE, and MIKE SHE), offer a distinct alternative to USLE-type empirical strategies. The ability to explicitly simulate the daily plant growth (SWAT and APEX) coupled with a dynamic representation of soil P processes can be critical when evaluating the long-term watershed responses to various agricultural management strategies. Drawing parallels with the (sub)surface and sediment erosion processes, a more complicated physically based approach, e.g., the dynamic wave model provided by MIKE SHE (coupled with MIKE URBAN or MIKE HYDRO) and SWMM may be more appropriate for realistically simulating the pressurized flow and backwater effects of water routing in both open channels and closed pipes. While our propositions seem to favor the consideration of complex models that may lack the commensurate knowledge to properly characterize the underlying processes, we contend this issue can be counterbalanced by the joint consideration of simpler empirical models under an ensemble framework, which can both constrain the plausible values of individual processes and validate macroscale patterns. Finally, our study discusses critical facets of the watershed modelling work in Lake Erie, such as the role of legacy P, the challenges in reproducing spring-freshet or event-flow conditions, and the dynamic characterization of water/nutrient cycles under the nonstationarity of a changing climate.
Highlights
IntroductionLake Erie, the shallowest of the Great Lakes, has been the most severely impacted by eutrophication-related issues including excessive harmful algal blooms or HABs (Stumpf et al., 2012; Bertani et al, 2016), dissolved oxygen depletion (Zhou et al, 2013; Rucinski et al, 2014), and excessive Cladophora growth in the eastern basin (Higgins et al, 2008; Depew et al, 2011; Watson et al, 2016)
Our examination of eleven spatially distributed models is suggestive of a tradeoff between the representation of the water cycle and the associated biogeochemical processes within a watershed context; namely, either simpler approximations of the nutrient fate and transport are
The presence of multiple models on its own cannot ensure that the decision-making process is reliably supported, as there are several methodological steps required in order (i) to identify the conceptual or structural differences of the existing models, and determine the actual diversity collectively characterizing a multi-model ensemble; (ii) to determine the most suitable calibration/validation domain and resolution for evaluating model performance in time and space; and (iii) to establish an optimal weighting scheme in order to assign weights to individual models, when integrating over their corresponding predictions, and subsequently determine the most likely outcome along with the associated uncertainty bounds
Summary
Lake Erie, the shallowest of the Great Lakes, has been the most severely impacted by eutrophication-related issues including excessive harmful algal blooms or HABs (Stumpf et al., 2012; Bertani et al, 2016), dissolved oxygen depletion (Zhou et al, 2013; Rucinski et al, 2014), and excessive Cladophora growth in the eastern basin (Higgins et al, 2008; Depew et al, 2011; Watson et al, 2016). Notwithstanding the multitude of factors (e.g., supply and chemical speciation of nitrogen, iron availability, enhanced water clarity in the nearshore zone mediated by dreissenid mussels, water column stability, and water temperature) involved on HAB formation (Chaffin et al, 2013), a popular notion is that cyanobacterial blooms are more strongly associated with the phosphorus loading in Lake Erie (Obenour et al, 2014; Bertani et al, 2016; Maccoux et al, 2016). Except from the inflowing nutrient masses from Grand River, nutrient-rich hypolimnetic waters transported to the nearshore zone through upwelling events, excreted metabolic wastes, and/or egesta of non-edible algae by dreissenids could be suppliers of nutrients in the benthic environment (Wilson et al, 2006; Valipour et al, 2016)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
