title: Rainfall-induced fecal indicator organisms transport from manured fields: model sensitivity analysis.
abstract: Microbial quality of surface waters attracts attention due to food- and waterborne disease outbreaks. Fecal indicator organisms (FIOs) are commonly used for the microbial pollution level evaluation. Models predicting the fate and transport of FIOs are required to design and evaluate best management practices that reduce the microbial pollution in ecosystems and water sources and thus help to predict the risk of food and waterborne diseases. In this study we performed a sensitivity analysis for the KINEROS/STWIR model developed to predict the FIOs transport out of manured fields to other fields and water bodies in order to identify input variables that control the transport uncertainty. The distributions of model input parameters were set to encompass values found from three-year experiments at the USDA-ARS OPE3 experimental site in Beltsville and publicly available information. Sobol' indices and complementary regression trees were used to perform the global sensitivity analysis of the model and to explore the interactions between model input parameters on the proportion of FIO removed from fields. Regression trees provided a useful visualization of the differences in sensitivity of the model output in different parts of the input variable domain. Environmental controls such as soil saturation, rainfall duration and rainfall intensity had the largest influence in the model behavior, whereas soil and manure properties ranked lower. The field length had only moderate effect on the model output sensitivity to the model inputs. Among the manure-related properties the parameter determining the shape of the FIO release kinetic curve had the largest influence on the removal of FIOs from the fields. That underscored the need to better characterize the FIO release kinetics. Since the most sensitive model inputs are available in soil and weather databases or can be obtained using soil water models, results indicate the opportunity of obtaining large-scale estimates of FIO transport from fields based on publicly available rather than site-specific information.
authors.affiliation: Department of Agronomy, University of Cordoba, 14071, Cordoba, Spain; USDA-ARS- Environmental Microbial and Food Safety Lab, Beltsville, MD, 20705, USA.
authors.forename: Gonzalo
authors.initials: G
authors.lastname: Martinez
authors.affiliation: USDA-ARS- Environmental Microbial and Food Safety Lab, Beltsville, MD, 20705, USA.
authors.forename: Yakov A
authors.initials: YA
authors.lastname: Pachepsky
authors.affiliation: National Exposure Research Laboratory, Ecosystem Research Division, Environmental Protection Agency, Athens, GA, USA.
authors.forename: Gene
authors.initials: G
authors.lastname: Whelan
authors.affiliation: Zuckerberg Institute for Water Research, J. Blaustein Institutes For Desert Research, Ben-Gurion University of Negev, Sede Boqer Campus, 84990, Israel.
authors.forename: Alexander M
authors.initials: AM
authors.lastname: Yakirevich
authors.affiliation: Michigan State University, Dep. of Plant, Soil and Microbial Sciences, East Lancing, MI 48824, USA.
authors.forename: Andrey
authors.initials: A
authors.lastname: Guber
authors.affiliation: USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD, 20705, USA.
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Gish
mesh_terms: D004784:Environmental Monitoring; D005243:Feces / Q000382:microbiology*; D008372:Manure / Q000382:microbiology*; D015233:Models, Statistical; D011891:Rain; D012988:Soil Microbiology; D035501:Uncertainty
chemical_list: D008372:Manure
keywords: Fecal indicator organisms; Global sensitivity analysis; Regression trees; Release and transport