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ABSTRACT:

Traditional approaches to quantify uncertainty & explore teleconnections in process-based models of coupled natural and human systems (CHANS) range from global sensitivity analysis of model parameters to Monte Carlo simulation experiments, decom-position analyses and propagation of errors analysis. We hypothesize that the application of machine learned emulator models to simulate process-based CHANS enables discovery of teleconnections & quantification of relative importance of natural versus human drivers of change in CHANS. We test this hypothesis by applying machine learning algorithms (Random Forest Models) to the simulation outputs derived from 332 scenarios of an integrated process-based CHANS model that predicts water quality in Missisquoi Bay of Lake Cham-plain under alternate hydro-climatic, and nutrient management scenarios for the 2001-2047 timeframe. Relative importance and partial dependence plots are derived from Random Forest models to quantify relative uncertainty & importance of (external to lake) climatic, hydrological, nutrient management and (internal to lake) P and N sediment re-lease drivers of Harmful Algal Blooms (HABs) in Missisquoi Bay. We discover that predictor variables representing snow, evaporation and transpiration dynamics tele-connect hydro-climatic processes occurring in terrestrial watersheds with the biogeochemical processes occurring in the freshwater lakes. We find that 14 predictors, representing both internal and external to lake processes, successfully predict four alternate trophic states of the Missisquoi Bay with ~93% accuracy rate.

ABSTRACT:

With mounting scientific evidence demonstrating adverse global climate change (GCC) impacts to water quality, water quality policies, such as the Total Maximum Daily Loads (TMDLs) under the U.S. Clean Water Act, have begun accounting for GCC effects in setting nutrient load-reduction policy targets. These targets generally require nutrient reductions for attaining prescribed water quality standards (WQS) by setting safe levels of nutrient concentrations that curtail potentially harmful cyanobacteria blooms (CyanoHABs). While some governments require WQS to consider climate change, few tools are available to model the complex interactions between climate change and benthic legacy nutrients. We present a novel process-based integrated assessment model (IAM) that examines the extent to which synergistic relationships between GCC and legacy Phosphorus release could compromise the ability of water quality policies to attain established WQS. The IAM is calibrated for simulating the eutrophic Missisquoi Bay and watershed in Lake Champlain (2001-2050). Water quality impacts of seven P-reduction scenarios, including the 64.3% reduction specified under the current TMDL, were examined under 17 GCC scenarios. The TMDL WQS of 0.025 mg/L total phosphorus is unlikely to be met by 2035 under the mandated 64.3% reduction for all GCC scenarios. IAM simulations show that the frequency and severity of summer CyanoHABs increased or minimally decreased under most climate and nutrient reduction scenarios. By harnessing IAMs that couple complex process-based simulation models, the management of water quality in freshwater lakes can become more adaptive through explicit accounting of GCC effects on both the external and internal sources of nutrients.