1a. Objectives (from AD-416):
Objective 1 - Elucidate spatial variability of indicator bacteria concentrations in surface waters (e.g., streams, ponds, reservoirs), and describe factors responsible for this variability. Sub-objective 1.A. Research and quantify lateral patterns of indicator bacteria concentrations in ponds and reservoirs, and evaluate the effect of algal populations, flow patterns and water quality parameters on these patterns. Sub-objective 1.B. Research and quantify patterns of vertical indicator bacteria distributions in water column in ponds and reservoirs. Sub-objective 1.C. Develop a model to estimate indicator bacteria concentrations at the intake of irrigation water based on vertical and lateral indicator bacteria distributions in the water of pond or reservoir. Objective 2 - Elucidate temporal variability of indicator bacteria concentrations in watersheds as a function of land use and meteorological conditions, and develop/validate predictive models. Sub-objective 2.A. Develop a model to evaluate stream bottom sediment as an indicator bacteria source between rainfall events. Sub-objective 2.B. Research survival of manure-borne indicator bacteria in soil to predict contribution of soil E. coli reservoir to runoff leaving fields and pastures. Sub-objective 2.C. Develop a modeling-based method for site-specific optimization of stream water sampling scheduling to provide the most representative indicator bacteria concentrations in irrigation water for a given annual number of samples.
1b. Approach (from AD-416):
Taken as a whole, this project strives to acquire, package and disseminate the knowledge about microbial quality of irrigation water in the way that offers wide applicability of results. No resources can currently be made available to monitor a large enough number of sites across the country to build a reliable statistical model that would relate microbial water quality to a multitude of environmental variables that vary based on prevailing local conditions at specific sites. This project relies on mechanistic rather than statistical models. It is designed on the premise that processes affecting microbial water quality stay the same whereas rates of those processes vary as they reflect local conditions. The project will develop observation methods that will improve data collection to fine-tune the model to a specific site by finding the site-specific rates. Models will be tested to make sure that simulation results are quantitatively and qualitatively similar to results of measurements. Data for such testing will be collected at field sites that reflect represent major contrasting combinations of environmental and management factors affecting water quality in irrigation water sources. The satisfactory performance of the models will provide confidence that the models and the corresponding data collection will be applicable at sites other than observed. As a disclaimer, it is realized that the current knowledge about microbial water quality controls still is far from being exhaustive, and some sites may exhibit microbial water quality features that are not understood and modeled well. The project is designed to efficiently utilize the best current knowledge about the processes controlling the microbial water quality of surface water. The integrated monitoring and modeling approach of this project can be re-applied as new knowledge will become available about the processes and factors controlling the microbial quality of surface water used for irrigation.
3. Progress Report:
The field water sampling in combination with drone-based multi-spectral imaging continues successfully. Regular photography, photos in two narrow wavelength ranges, and a five-wavelength multispectral imagery are obtained in conjunction with each water sampling. Classification algorithms are tested and compared to optimize the subdivision of ponds into zones with relatively homogeneous E. coli concentrations. Results of this work contribute to the improvement in design of microbial water quality monitoring in that they will guide and justify the decrease in the number of water samples. Six years of data of field experiments with manure application and subsequent simulated rainfall have been reanalyzed to establish dominant factors of E. coli export from manured fields. In experiments at the USDA ARS experimental site OPE3 in Beltsville, Maryland, solid manure was applied on the field at the BARC OPE3 research watershed, irrigation was applied, and runoff and E. coli concentrations in runoff were monitored. Irrigation events were repeated after one week. It has been found that the lag time between rainfall start and runoff start is the dominant variable that controls the E. coli export rate, and can be used to predict the export with a good accuracy. Results of this work will improve the estimates of the microbial loads to freshwater sources due to the radical improvement in estimation of microbial export rates which are the most sensitive parameters in the microbial load computations. The USDA ARS model APEX with the microbial submodel developed in the previous project research period was successfully calibrated and validated with the unique dataset from rural watershed where pasture is the predominant land use. The seven years of monitoring data have been provided by the New Zealand collaborators. Concentrations of E. coli in streambed sediments and rates of their release to water column were the most influential model parameters. Results of this work are expected to bring the substantial improvement in the estimation of microbial quality in irrigation water sources since the APEX model accounts for much more management details than other watershed scale water quality models. A novel method has been proposed to measure rates of E. coli bacteria release from streambed sediments to the water column during baseflow. The method consists in using conservative tracers to create a mass balance volume of stream water and to determine the release rates from the data of bacteria number change in the mass balance volume. High E. coli release rates were found from the results of application of this method at the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) creek at the Beltsville Agricultural Research Center (BARC). Results of this work may eventually lead to the radical revision of the current concept of the need and feasibility of change in management practices for microbial water quality improvement.
1. Better assessment of microbial quality of surface waters used in irrigation. Microbial quality of water in irrigation water sources must be assessed to prevent spread of microbes that can cause disease in humans because of the produce consumption. Microbial quality of irrigation water is evaluated based on concentrations of the indicator bacterium E. coli. No recommendations have existed so far on where and when water samples should be taken for microbial analysis. ARS scientists from Beltsville, Maryland, discovered the presence of stable spatial patterns in the E. coli concentration distributions across irrigation ponds and along streams in Maryland and Pennsylvania. All studied water sources appeared to have zones where E. coli concentrations were mostly higher than average and zones where concentrations were mostly lower than average over the observation period when water was used for irrigation. Levels of E. coli concentrations in streams were strongly affected by E. coli residing in bottom sediments. Results of this work provide the knowledge base for taking representative microbial water quality samples and will be used by water resource managers and consultants who design and implement microbial water quality monitoring.
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Pachepsky, Y.A., Shelton, D.R., Dorner, S., Wjelan, G. 2014. Can E. coli or thermotolerant coliform concentrations predict pathogen presence or prevalence in irrigation waters? Critical Reviews in Microbiology. 42(3):384-393. doi:10.3109/1040841X.2014.954524.
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Park, Y., Pachepsky, Y.A., Hong, E., Shelton, D.R., Coppock, C.R. 2016. E. coli release from streambed to water column during base flow periods: a modeling study. Journal of Environmental Quality. 46(1):219-226.