Assessing within-lake nutrient cycling through multi-decadal Bayesian mechanistic modeling

Nutrient recycling from bottom sediments can provide substantial internal loading to eutrophic lakes and reservoirs, potentially exceeding external watershed loads. However, measurements of sediment nutrient fluxes are rare for most waterbodies in the United States, causing many modeling studies to parameterize these fluxes in simplistic ways or else make assumptions about complex sediment diagenetic rates. Here we propose an alternative approach to understanding internal cycling, using a mass-balance model combined with Bayesian inference to rigorously update prior information on nutrient flux parameters. The approach is applied to Jordan Lake, a major water supply reservoir in North Carolina (USA) that has been highly eutrophic since impoundment in the early 1980s, with chlorophyll a concentrations occasionally exceeding 100 µg/L. We simulate monthly nitrogen and phosphorus dynamics in the water column and sediment layer of four longitudinal reservoir segments, forced by watershed flows, nutrient loads, and meteorology. The model is calibrated within the Bayesian framework and validated using a multi-decadal record of surface nutrient concentration data. We compare multiple versions of the model to assess the importance of prior knowledge from previous literature, the multi-decadal calibration period, and the mechanistic formulation for obtaining accurate and robust predictive performance. Overall, the model explains from 40-60% of the variability in observed nutrient concentrations. Model results indicate that a large fraction (>40%) of phosphorus is lost in the upstream reaches of the reservoir, likely due to rapid settling and burial of particulate material. Within the main body of the reservoir, phosphorus recycling rates were found to be higher than expected a priori, particularly in the summer season. Results show how nutrients stored in lacustrine sediment have been an important source of internal loading to the reservoir for multiple decades, and will dampen the effects of external watershed loading reductions, at least in the near term. To better understand potential time scales for reservoir recovery, we perform future simulations over a multi-decadal period and characterize forecast uncertainties.