Prediction of fecal indicator organism concentrations in rivers: the shifting role of environmental factors under varying flow conditions

Background: Fecal indicator organisms such as Escherichia coli, enterococci, and coliphages are important to assess, monitor, and predict microbial water quality in natural freshwater ecosystems. To improve predictive modelling of fecal indicators in surface waters, it is vital to assess the influence of autochthonous and allochthonous environmental factors on microbial water quality in riverine systems. To better understand how environmental conditions influence the fate of fecal indicators under varying weather conditions, the interdependencies of environmental parameters and concentrations of E. coli, intestinal enterococci, and somatic coliphages were studied at two rivers (Rhine and Moselle in Rhineland-Palatinate, Germany) over a period of 2 years that exhibited contrasting hydrological conditions. Both riverine sampling sites were subject to similar meteorological conditions based on spatial proximity, but differed in hydrodynamics and hydrochemistry, thus providing further insight into the role of river-specific determinants on fecal indicator concentrations. Furthermore, a Bayesian multiple linear regression approach that complies with the European Bathing Water Directive was applied to both rivers’ datasets to test model transferability and the validity of microbial water quality predictions in riverine systems under varying flow regimes. Results: According to multivariate statistical analyses, rainfall events and high water discharge favored the input and dissemination of fecal indicators in both rivers. As expected, concentrations declined with rising global solar irradiance, water temperature, and pH. While variations in coliphage concentrations were predominantly driven by hydro-meteorological factors, bacterial indicator concentrations were strongly influenced by autochthonous biotic factors related to primary production. This was more pronounced under low flow conditions accompanied by strong phytoplankton blooms. Strong seasonal variations pointed towards bacterial indicator losses due to grazing activities. The Bayesian linear regression approach provided appropriate water quality predictions at the Rhine sampling site based on discharge, global solar irradiance, and rainfall as fecal indicator distributions were predominantly driven by hydro-meteorological factors. Conclusions: Assessment of microbial water quality predictions implied that rivers characterized by strong hydrodynamics qualify for multiple linear regression models using readily measurable hydro-meteorological parameters. In rivers where trophic interactions exceed hydrodynamic influences, such as the Moselle, viral indicators may pose a more reliable response variable in statistical models.