Process-based primary production modeling in Chesapeake Bay

Abstract

A primary production model is described and compared to 3 observational data bases: light-saturated carbon fixation, net phytoplankton primary production, and gross phytoplankton primary production. The model successfully reproduces the observations while maintaining realistic calculations of algal carbon, chlorophyll, limiting nutrient, and light attenuation. Computed primary production in light-limited regions is proportional to the algal growth rate. Successful computation of primary production in nutrient-depleted waters depends on the formulation and magnitude of the model predation term. Our quadratic formulation mimics a predator population that is closely coupled to algal biomass and rapidly recycles nutrients from algal biomass to available form. KEY WORDS: Chesapeake Bay · Primary production · Phytoplankton · Mathematical modelsResale or republication not permitted without written consent of the publisher

Analysis of the Chesapeake Bay Hypoxia Regime Shift: Insights from Two Simple Mechanistic Models

“…Following Scavia and Liu (2009), Susquehanna River nitrogen load was converted to phytoplankton carbon production by multiplying the load by a factor α that encompasses the C/N ratio for nitrogen-limited production and an "estuarine conversion efficiency factor" intended to capture the myriad processes involved in converting the nitrogen load to algal production. Phytoplankton sinking is modeled as a first-order loss rate and zooplankton grazing is modeled as a quadratic term in phytoplankton biomass similar to approaches for zooplankton mortality (Edwards and Yool 2000;Cerco and Noel 2004;Cranford et al 2007) under the assumption that zooplankton grazer abundance will vary with phytoplankton abundance. The rate of change of surface mixed-layer phytoplankton carbon (B) is:…”

“…In regime shift modeling, one needs to consider the ability of the model structure to reflect the fundamental ecological processes, the method for estimating parameter values, and model uncertainties assessment. Several models have been developed for the Chesapeake Bay, ranging from complex mechanistic models (e.g., Cerco and Cole 1993;Cerco and Noel 2004) to simple statistical ones (Hagy et al 2004). Complex mechanistic models can resolve important ecosystem processes; however, they are often over-determined (i.e., more coefficients than state variables and data) and require use of literature values and expert-judgment to select key parameter values.…”

The potential impact of an inter-basin water transfer project on nutrients (nitrogen and phosphorous) and chlorophyll a of the receiving water system

“…Detailed calculating process of each growth limitation can be found in previous literature (Bunch et al, 2000;Cerco and Noel, 2004). For this scenario, the light intensity is sufficient at the water surface such that it would not become a major limiting factor for the growth of algae.…”

“…In past decades, water quality models have been successfully developed to predict the water quality and algae responses to a change in external loadings (Cerco and Noel, 2004;Wang et al, 2014;Zhao et al, 2013). A calibrated model could be used to analyze and predict water quality changes in multiple scenarios, to provide possible explanations for the principal factors of water deterioration and to help discover an optimized solution.…”

Modeling the seasonal autochthonous sources of dissolved organic carbon and nitrogen in the upper Chesapeake Bay

“…In addition, our model did not account for the ability of mixotrophic species, which can be present at this station in high concentrations during the spring (Adolf et al, 2006;Keller, personal observation in 2007 and2008), to supplement their photosynthetic growth with grazing and, thus, grow better than strict phototrophs at low spring temperatures and light levels. The exponential temperature function that we used to set the maximum attainable daily growth rate of phytoplankton may have also contributed to the delay in the beginning of the spring bloom as it has often led models to underestimate primary production at lower temperatures (Brush et al, 2002;Cerco and Noel, 2004).…”