Castles built on sand or predictive limnology in action? Part B: Designing the next monitoring-modelling-assessment cycle of adaptive management in Lake Erie

Abstract In Lake Erie, a wide variety of statistical and process-based models have significantly advanced our understanding of the major causal linkages/ecosystem processes underlying the local water quality problems. In this study, our aim is to identify knowledge gaps, monitoring assessment objectives, and management recommendations that should be critically reviewed through the iterative monitoring-modelling-assessment cycles of adaptive management. In the watershed, the presence of multiple SWAT applications provides assurance that a wide array of physical, chemical, and biological processes with distinct characterizations are used to reproduce the patterns of flow and nutrient export in agricultural lands. While there are models with more advanced mechanistic representation of certain facets of the hydrological cycle (surface runoff, groundwater and sediment erosion) or better equipped to depict urban settings, we believe that greater insights will be gained by revisiting several influential assumptions (tile drainage, fertilizer/manure application rates, land-use/landcover data) and recalibrating the existing SWAT models to capture both baseline and event-flow conditions and daily nutrient concentration (not loading) variability in multiple locations rather than a single downstream site. It is also critical to redesign land-use management scenarios by accommodating recent conceptual and technical advancements of their life-cycle effectiveness, the variability in their starting operational efficiency, and differential response to storm events or seasonality, as well as the role of legacy phosphorus. In the receiving waterbody, the development of data-driven models to establish causal linkages between the trophic status of Lake Erie and external phosphorus loading represents a pragmatic means to draw forecasts regarding the phytoplankton community response to different management actions. Two critical next steps to further augment the empirical modelling work is the iterative updating as more data are acquired through monitoring and the introduction of additional explanatory variables that are likely associated with the occurrence of cyanobacteria-dominated blooms. The majority of the process-based models are not constrained by the available data, and therefore their primary value is their use as heuristic tools to advance our understanding of Lake Erie. The validation of their predictive power should become one of the overarching objectives of the iterative monitoring-modelling-assessment cycles. With respect to the projected responses of the system to nutrient loading reduction, we express our skepticism with the optimistic predictions of the extent and duration of hypoxia, given our limited knowledge of the sediment diagenesis processes in the central basin along with the lack of data related to the vertical profiles of organic matter and phosphorus fractionation or sedimentation/burial rates. Our study also questions the adequacy of the coarse spatiotemporal (seasonal/annual, basin- or lake-wide) scales characterizing the philosophy of both water quality management objectives and modelling enterprise in Lake Erie, as this strategy seems somewhat disconnected from the ecosystem services targeted. We conclude by emphasizing that the valuation of ecosystem services should be integrated into the decision-making process, as we track the evolution of the system over time.