Rainfall and Streamflow Effects on Estuarine Staphylococcus aureus and Fecal Indicator Bacteria Concentrations. Issue 6 (1st November 2019) - Economy, Louise M.; Wiegner, Tracy N.; Strauch, Ayron M.; Awaya, Jonathan D.; Gerken, Tyler
Abstract : Pathogenic bacteria in nearshore waters are a public health threat, and many have watershed sources. Hence, understanding direct and indirect causes of bacterial loading can improve awareness and watershed management. Rainfall‐driven runoff influences river discharge, affecting pathogen transport to the ocean. This study assessed pathogen loading to nearshore waters under varying weather conditions within Hilo Bay, Hawaii, from 2014 to 2017. Staphylococcus aureus, methicillin‐resistant S. aureus (MRSA), and fecal indicator bacteria (FIB) were quantified in the bay, rivers, and potential watershed sources using culture‐based methods. Relationships between their concentrations with rainfall, river discharge, and water quality data were examined. Staphylococcus aureus, MRSA, and FIB were present within Hilo Bay and its rivers, as well as road runoff, sewage, and soils; MRSA was less prevalent. Staphylococcus aureus and FIB concentrations increased with rainfall and river discharge. Turbidity and salinity were the best water quality parameters for predicting bacteria concentrations, with positive and negative relationships, respectively. Our results suggest that more intense storms, especially after longer dry periods between events, will increase S. aureus and FIB loads to nearshore waters, as storms comprise >80% of annual river loads. Our models can be used to assess recreational water users' health risks and predict future water quality conditions with changing rainfall patterns. Core Ideas: Staphylococcus aureus and FIB were present in the bay, rivers, road runoff, sewage, and soils. MRSA was less prevalent in the environment than S. aureus and FIB. S. aureus and FIB bay concentrations increased with rainfall and river discharge. S. aureus and FIB bay concentrations varied with bay salinity and turbidity. Bay bacteria concentrations can be modeled from hydrologic and water quality data.