Predicting Streambed Sediment and Water Column Escherichia coli Levels at Watershed Scale

Abstract

A sub‐model for the Soil and Water Assessment Tool (SWAT) is developed to predict Escherichia coli levels in the streambed sediment as well as in the water column. New formulations to estimate the levels of E. coli in streambed sediment and the water column are derived. These equations include calculations of E. coli resuspension from the streambed sediment to the water column, E. coli deposition from the water column to the streambed sediment, E. coli growth in the streambed sediment and the water column, and instream E. coli routing. These formulations were programmed in FORTRAN and integrated into SWAT. The modified SWAT model was applied to Squaw Creek Watershed, Iowa, to predict E. coli levels in the stream. Escherichia coli concentrations in the streambed sediment and the water column were monitored extensively in this watershed, and observations were used to verify the model predictions. The model proposed here can predict E. coli concentrations in streambed sediment as well as in the water column. Approximately 58% of the predictions of E. coli levels in the bed sediment were within 1 order of magnitude from the observed value, and in the water column 83% of the predictions of E. coli levels were within 1 order of magnitude. Results suggest that the proposed model will help predictions of instream bacterial contamination.

Optimal strategies for monitoring irrigation water quality

“…No previous studies were identified that measured bacterial concentrations in irrigation canal transects. However, previous research conducted in non-irrigation water matrices demonstrated point source pollution and sediment resuspension produced rapid microbial concentration fluxes within the water column which return to a homogenous state over space and time (McDaniel et al, 2013;Pandey and Soupir, 2014;Pandey et al, 2016;Rehmann and Soupir, 2009). In the current study, the homogeneity of E. coli concentrations likely indicates minimal direct fecal contamination, sediment presence, or sediment resuspension in the studied canals.…”

Hydrological modeling of Fecal Indicator Bacteria in a tropical mountain catchment

“…We used the temperature dependent bacterial regrowth rate which varies between the minimum and maximum growth temperatures (Hipsey et al, 2008;Pandey et al, 2016). The bacterial regrowth model is expressed as the below:…”

“…However, previous studies have explored the effect of regrowth on FIB concentration in surface waters (Cho et al, 2016b;Pandey et al, 2016). In-stream regrowth process was further implemented to improve the model prediction.…”

16S rRNA analysis of diversity of manure microbial community in dairy farm environment

“…It has been reported that bacteria loads associated with enormous amount of animal waste produced in the U.S. are the leading cause of impairment for rivers and streams [ 1 , 2 , 3 ]. However, the impact of animal waste-borne microbiomes on environment including soil, water, and plant is not well-understood.…”

“…In such systems, a dairy barn is flushed with water, and flushed manure is passed through solid-manure separation systems, where liquid manure is separated from solid ( Fig 1 ). Solid streams are stored in the form of piles, and liquid manure streams are stored in lagoon systems prior to the application of manure into the cropland as fertilizers [ 2 ]. Although both lagoon systems and compost piles are used extensively to manage dairy manure in California, the efficacies and effectiveness of these manure handling processes for regulating microbial population are not well-understood.…”