1a. Objectives (from AD-416):
Objective 1: Elucidate and quantify mechanisms and factors of pathogen and indicator bacteria fate and transport from animal sources to irrigation waters. Evaluate the effects of soil and vegetation properties on parameters of pathogen fate and transport with surface runoff to irrigation water sources. Assess contribution of bottom sediments as potential pathogen reservoirs in agricultural landscapes. Research the fate and transport of pathogen and indicator bacteria in irrigation water delivery systems. Objective 2: Develop models and computer-based tools to recommend and implement site-specific diagnostics, monitoring, and prediction of the fate and transport of pathogen and indicator bacteria that affect the microbiological condition of irrigation water. Develop bacteria fate and transport components for USDA-ARS hydrologic models to simulate the effect of bottom sediments, periphyton, and bank soils on microbiological quality of surface waters intended for irrigation. Develop a farm-scale irrigation system model that will use site specific environmental and management data to provide input data for quantitative microbial risk assessment of irrigation waters.
1b. Approach (from AD-416):
An integrated approach including laboratory research, field research on irrigation systems, and mathematical modeling will be used. Experiments and monitoring will be carried out to (a) evaluate the effects of soil and vegetation properties on pathogen fate and transport with surface runoff to irrigation water sources, (b) understand and quantify pathogen and indicator bacteria fate in potential pathogen reservoirs associated with irrigation systems, such as bottom sediments in surface waters, and biofilms in irrigation equipment, and (b) microbial exchange between these reservoirs and flowing or stagnant waters. Mechanistic models will be developed to allow for (a) analyzing possible changes in pathogen and indicator bacteria concentrations along hydrologic pathways from animal sources to fields, and (b) improving resource allocation to monitor pathogen and indicator bacteria occurrence along the pathways.
3. Progress Report:
Research was carried out to elucidate the distribution of manure-borne microorganisms in soils after irrigation or precipitation events. In collaborative work with the University of Maryland, ARS researchers in Beltsville, Maryland carried out controlled indoor experiments on release of fecal indicator bacteria from solid and liquid manure applied on the grassland subject to different durations of aging between consecutive rainfalls. It was the first research to quantify the effect of aging on release and removal of manure-borne bacteria from manured lands. ARS researchers found that aging can strongly reduce rainfall-induced bacterial release and removal, and that neglecting manure aging can distort estimated effect of manure application on microbial quality of the surface water. Research was carried out to explain the presence of the high concentration of indicator bacteria E. coli in slow moving creek waters between rainfall events in absence of input of bacteria from animal waste and residential sources. ARS researchers applied a novel and unique experimental setup at the 600-m creek stretch in Beltsville, Maryland that slowed accurate measuring of bacteria concentration changes in a particular water slug as it moved along the creek. ARS researchers found that the increase of bacteria concentrations most probably occurred due to the influx of bacteria from creel sediment without any agricultural or other sources. This reinforced earlier conclusions about the role of creek sediment as a source of the indicator bacteria in creek waters that can substantially distort the evaluation of animal husbandry management practices as sources of fecally-derived microorganisms in surface waters used for recreation and irrigation. Modeling was carried out to elucidate mechanisms of fecal indicator bacteria transfer from stream bed sediment to water column. Data collected in cooperation with Wilson College (Pennsylvania) at the Cove Mountain Creek in Pennsylvania were used to calibrate and validate the Beltsville version of the USDA-ARS surface water quality model Soil Water Assessment Tool (SWAT). Transport of E. coli from the bottom sediment to water column during the low-flow periods was simulated (a) assuming active transfer due to bacteria motility and (b) passive transport with groundwater carrying bacteria from sediment to the creek. ARS researchers in Beltsville, Maryland found (a) that accounting for the transport from sediment to water column is necessary to explain the observed high concentrations during low-flow periods, and (b) that the assumption of active transport provides more realistic modeling results. Modeling was carried out to evaluate the effect of weather conditions of a particular year on microbial water quality of creek water used for irrigation. Weather records from 1927 to 2016 in the area of Cove Mountain Creek were used to simulate E. coli dynamics at three locations in zones of different land use. The USDA-ARS water quality model SWAT was validated using the water quality threshold metrics established by U.S. Food and Drug Administration (FDA) to implement the Food Safety Modernization Act with data collected in collaboration with Wilson College. ARS researchers in Beltsville, Maryland found that weather conditions in the study area had a critical role in classifying water by its microbial water quality and deciding on permitted practices of produce irrigation and harvest. Results of this work indicate that assessing microbial water quality of irrigation water sources by the data collected in arbitrary two years may be misleading for specific year conditions, and correction of permitted practices may be needed for a specific year. This is the final report for the project 8042-12630-008-00D which terminated in February 2016. Key findings/outcome of the project are as follows. A new experimental design demonstrated that large amounts of microbial water quality indicator bacteria are routinely suspended from bottom sediments into the water column, and that the increased concentrations can be substantial (up to 100 times) after rainfall events. Without accounting for the bacterial transport from sediment to water, assessment of the effect of animal waste and manure management practices on microbial water quality can be grossly inaccurate. Models were developed and tested to estimate these sediment to water column bacterial fluxes. It was demonstrated that biofilms in irrigation pipes can harbor large numbers of indicator organisms that can be released into irrigation water, and that biofilms can facilitate changes in the antibiotic resistance of bacteria that are released to irrigation water. Therefore, testing of irrigation water should be conducted at the field rather than at the intake. The first farm-scale model was developed to simulate microbial water quality of irrigation water. The model uses site specific environmental and management data, as well as exhaustive databases on indicator bacteria survival in surface water, soil, manure, and animal waste. This model can be used as a screening tool to estimate site specific ranges in microbial quality of irrigation waters, as well as to provide guidance in designing water quality monitoring protocols to conduct quantitative microbial risk assessment of irrigation waters.
1. Improved capability to estimate microbial water quality. Reliability of modeling microbial water quality in irrigation water sources depends on the accuracy of simulating microorganism exchange between bottom sediment and water column. ARS scientists from Beltsville, Maryland used both field experiments and modeling to demonstrate that microorganism exchange between bottom sediment and water column occurs during the baseflow, or low-flow periods. These results will lead to substantial improvements in the accuracy of microbial water quality models used for assessment and predictions for recreational and irrigation water sources. Also, the role of indicator organisms as markers for grazing and manure application effects on water quality needs to be scrutinized and possibly re-evaluated.
5. Significant Activities that Support Special Target Populations:
Hong, E., Nam, W., Choi, J., Pachepsky, Y.A. 2016. Projected irrigation requirements for upland crops using soil moisture model under climate change in South Korea. Agricultural Water Management. 165:163-180.
Cho, K., Pachepsky, Y.A., Kim, M., Pyo, J., Park, M., Kim, J., Kim, J. 2016. Modeling seasonal variability of fecal coliform in natural surface waters using the modified SWAT. Journal of Hydrology. 535:377-385.
Park, Y., Pachepsky, Y.A., Cho, K., Jeon, J., Kim, J. 2015. Stressor-response modeling using the 2D water quality model and regression trees to predict chlorophyll-a in a reservoir system. Journal of Hydrology. 529:805-815.
Cho, K., Pachepsky, Y.A., Oliver, D., Muirhead, R., Park, Y., Quilliam, R., Shelton, D.R. 2016. Modeling fate and transport of fecally-derived microorganisms at the watershed scale: state of the science and future opportunities. Water Research. 100:38-56.
Stocker, M.D., Rodriguez-Valentin, J., Pachepsky, Y.A., Shelton, D.R. 2016. Spatial and temporal variation of fecal indicator organisms in two creeks in Beltsville, Maryland. Water Quality Research Journal of Canada. 51(2):167-179.
Oliver, D., Reaney, S., Porter, K., Pachepsky, Y.A., Muirhead, R., Coffey, R., Kay, D., Hong, E. 2016. Predicting microbial water quality with models: over-arching questions for managing risk in agricultural catchments. Science of the Total Environment. 544:39-47.
Blaustein, R., Hill, R., Micallef, S., Shelton, D.R., Pachepsky, Y.A. 2016. Rainfall intensity effects on removal of fecal indicator bacteria from solid dairy manure applied over grass-covered soil. Science of the Total Environment. 539:583-591.
Blaustein, R., Shelton, D.R., Van Kessel, J.S., Karns, J.S., Matt, S., Pachepsky, Y.A. 2015. Irrigation waters and pipe-based biofilms as sources for antibiotic-resistant bacteria. Environmental Monitoring and Assessment. 199(1):1-12.
Keesstra, S., Bouma, J., Wallinga, J., Tittonel, P., Smith, P., Cerda, A., Montanarella, L., Quinton, J., Pachepsky, Y.A., Van Der Putten, W., Bargett, R., Moolenaar, S., Mol, G., Fresco, L. 2016. FORUM paper: The significance of soils and soil science towards realization of the United Nations sustainable development goals. Soil. 2:111-128.