Using a Bayesian hierarchical model to improve Lake Erie cyanobacteria bloom forecasts

Abstract

The last decade has seen a dramatic increase in the size of western Lake Erie cyanobacteria blooms, renewing concerns over phosphorus loading, a common driver of freshwater productivity. However, there is considerable uncertainty in the phosphorus load-bloom relationship, because of other biophysical factors that influence bloom size, and because the observed bloom size is not necessarily the true bloom size, owing to measurement error. In this study, we address these uncertainties by relating latesummer bloom observations to spring phosphorus load within a Bayesian modeling framework. This flexible framework allows us to evaluate three different forms of the load-bloom relationship, each with a particular combination of statistical error distribution and response transformation. We find that a novel implementation of a gamma error distribution, along with an untransformed response, results in a model with relatively high predictive skill and realistic uncertainty characterization, when compared to models based on more common statistical formulations. Our results also underscore the benefits of a hierarchical approach that enables assimilation of multiple sets of bloom observations within the calibration processes, allowing for more thorough uncertainty quantification and explicit differentiation between measurement and model error. Finally, in addition to phosphorus loading, the model includes a temporal trend component indicating that Lake Erie has become increasingly susceptible to large cyanobacteria blooms over the study period (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013). Results suggest that current phosphorus loading targets will be insufficient for reducing the intensity of cyanobacteria blooms to desired levels, so long as the lake remains in a heightened state of bloom susceptibility.

Key Factors Affecting Temporal Variability in Stream Water Quality

“…Convergence of the chains was checked using the Rhat value (Sturtz et al, ). We assessed the model fit using the Nash‐Sutcliffe coefficient (NSE; Nash & Sutcliffe, ) as per Obenour et al ().…”

Mapping the spatial distribution of the biomass and filter‐feeding effect of invasive dreissenid mussels on the winter‐spring phytoplankton bloom in Lake Michigan

“…Cladophora) and macrophytes to nuisance values (Vanderploeg et al, 2002;Hecky et al, 2004;Auer et al, 2010). Dreissena may also promote the dominance of cyanobacterial blooms through selective feeding and alteration of the phosphorus cycle (Vanderploeg et al, 2001;Obenour et al, 2014;Steffen et al, 2014), although such blooms are not presently an issue in the oligotrophic main body of Lake Michigan. In addition, Dreissena alters the abundance and distribution of fish and wildlife through effects on benthic habitat and prey species distribution (e.g.…”

Measuring the Contribution of Agricultural Conservation Practices to Observed Trends and Recent Condition in Water Quality Indicators in Ohio, USA

“…These efforts have led to significant reductions in suspended solids and total phosphorus (TP) in Ohio rivers (Richards et al, 2009), especially after 1990. Despite decreasing concentrations of TP, the fraction in dissolved form has been increasing (Ohio EPA, 2010; Baker et al, 2014; Obenour et al, 2014), contributing to a resurgence of algal blooms in Lake Erie. The return of eutrophic conditions in Lake Erie, along with the well‐documented problem of hypoxia in the Gulf of Mexico due to agriculturally derived nutrient loads from the Mississippi River basin, have called into question the efficacy of conservation programs for water quality (Sohngen et al, 2013).…”