Individual-based modelling of cyanobacteria blooms: Physical and physiological processes

Lakes and reservoirs throughout the world are increasingly adversely affected by cyanobacterial harmful algal blooms (CyanoHABs). The development and spatiotemporal distributions of blooms are governed by complex physical mixing and transport processes that interact with physiological processes affecting the growth and loss of bloom-forming species. Individual-based models (IBMs) can provide a valuable tool for exploring and integrating some of these processes. Here we contend that the advantages of IBMs have not been fully exploited. The main reasons for the lack of progress in mainstreaming IBMs in numerical modelling are their complexity and high computational demand. In this review, we identify gaps and challenges in the use of IBMs for modelling CyanoHABs and provide an overview of the processes that should be considered for simulating the spatial and temporal distributions of cyanobacteria. Notably, important processes affecting cyanobacteria distributions, in particular their vertical passive movement, have not been considered in many existing lake ecosystem models. We identify the following research gaps that should be addressed in future studies that use IBMs: 1) effects of vertical movement and physiological processes relevant to cyanobacteria growth and accumulations, 2) effects and feedbacks of CyanoHABs on their environment; 3) inter and intra-specific competition of cyanobacteria species for nutrients and light; 4) use of high resolved temporal-spatial data for calibration and verification targets for IBMs; and 5) climate change impacts on the frequency, intensity and duration of CyanoHABs. IBMs are well adapted to incorporate these processes and should be considered as the next generation of models for simulating CyanoHABs

Development of a biogeochemical modeling system to estimate fluxes and controls of estuarine organic matter cycling

This dissertation is an analysis of organic matter cycling using a biogeochemical modeling system to estimate a comprehensive organic carbon budget in an estuary. New processes were built into the model, including sediment-water column dissolved organic matter (DOM) fluxes, wetland input of DOM, and a more sophisticated representation of DOM reactions in the water column. First, the Sediment Flux Model was updated to include DOM as a diagenesis intermediate in the breakdown of organic matter. Long term time series of sediment-water column nitrogen and oxygen fluxes constrained the updated sediment model. On average, subtidal sediment was a net source of 1.00 mol C m-2 yr-1 and 0.19 mol N m-2 yr-1, substantially larger than previous estimates. Wetland derived DOM undergoes transformations due to absorbing large quantities of UV-Visible light during estuarine transport. To account for this in the model, the light absorbed by DOM drives mechanistic photochemical degradation reactions in a new module in the organic carbon reaction suite. The reaction equations were parameterized and tested by recreating bench top photochemical degradation experiments using the model. Predicted organic carbon transformation rates ranged from 0.59 to 4.86 μmol C L-1 hr-1 and a test data set was recreated with 3.66% mean percent error. The enhanced modeling system was implemented in the Rhode River, MD, USA, a well studied tributary of Chesapeake Bay. Coupled observations and 3-D modeling results at the outflow of the Kirkpatrick Marsh creek showed that wind variability was important in driving variations in salinity and was strongly correlated with fluorescent DOM. Finally, the fully coupled organic carbon cycle model was implemented and constrained by water column observations. Numerical experiments with and without the tidal wetland input showed that the marsh contributed 20.5% to the total DOC stock within the tributary and 20.7% to the total flux of DOC from the Rhode River to the Chesapeake Bay. A geographic relationship derived from the Rhode River predicts that tidal wetlands contribute 3.0% to the total DOC inputs in Chesapeake Bay and 13.4% to the total DOC stock