|Martinez, Gonzalo - Forest Service (FS)|
|Whelan, Gene - Us Environmental Protection Agency (EPA)|
|Yakirevich, Alexander - Ben Gurion University Of Negev|
|Guber, Andrey - Michigan State University|
|Gish, Timothy - Retired ARS Employee|
Submitted to: Environment International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/1/2014
Publication Date: 11/1/2014
Citation: Martinez, G., Pachepsky, Y.A., Whelan, G., Yakirevich, A., Guber, A., Gish, T. 2104. Rainfall-induced fecal indicator organisms transport from animal waste applied fields: Model sensitivity analysis. Environment International. 63:121-129.
Interpretive Summary: Microbial quality of irrigation waters depends to a large extent on the release and transport of microorganisms from field-deposited animal waste and manures in runoff. This in turn is affected by the interplay of soil, vegetation, weather and manure properties. It is not always clear which of these properties should receive priority in characterizing microbial runoff, when there are limited resources? We addressed this question using the model STWIR, which we previously developed and tested. We observed that the more sensitive the model prediction was to site condition properties, the higher the priority it should receive. We found that fate and runoff transport of manure-borne microbes and microorganism release rates are controlled by weather conditions to a substantially larger extent than by soil properties. These results are of importance for the microbial risk assessment of irrigation water quality in that they point to the opportunity of using readily available, rather than on-site obtained, soil data. In addition, they illustrate the importance of improving the currently limited information of microbe release from manure and animal waste. This information should be useful to other scientists and regulatory agencies.
Technical Abstract: The microbial quality of surface waters warrants attention because of associated food- and waterborne-disease outbreaks, and fecal indicator organisms (FIOs) are commonly used to evaluate levels of microbial pollution. Models that predict the fate and transport of FIOs are required for designing and evaluating best management practices that reduce microbial pollution in ecosystems and water sources and predict the risk of outbreaks. In this study, we performed a sensitivity analysis for the KINEROS2/STWIR model, developed to predict FIO transport out of manured fields to identify input variables that control transport uncertainty. Model input distributions were defined, when available, to encompass values determined from three-year experiments at the USDA-ARS OPE3 experimental site in Beltsville, Maryland, USA. Sobol’ indices and complementary regression trees were used to perform a global sensitivity analysis of the model and to explore interactions of model input influences on the proportion of FIOs removed from fields. Regression trees provided a useful visualization of the differences in sensitivity of model output in different parts of the input variable domain. A Montecarlo filtering showed different distributions for most of the parameters for the cases with and without transport of FIOs. Environmental controls, such as soil saturation and rainfall duration and intensity, had the largest influence in model behavior, while soil and manure properties ranked lower; field length had only a moderate effect on the model output sensitivity to inputs. Among manure-related properties, the parameter determining the shape of the FIO release kinetic curve had the greatest influence on removal of FIO from the fields which underscores the need for better characterization of FIO release kinetics.