Cryptosporidium is an environmentally robust pathogen that has caused severe waterborne disease outbreaks worldwide. The main source of zoonotic Cryptosporidium parvum oocysts in human drinking water is likely to be from farm animals via catchment pathways with water as the main transport vector. The vast majority of small agricultural catchments are ungauged therefore it is difficult to use a process model to predict and understand the mechanisms and activities that regulate the risk of surface water contamination from agricultural areas. For this study, two ungauged agricultural catchments in Ireland were used to model Cryptosporidium oocyst transport using SWAT2005 on a daily basis with reference data from adjacent catchment gauging stations. The results indicated that SWAT2005 could simulate stream flow with good agreement between prediction and observation on a monthly basis (R(2) from 0.94 to 0.83 and E (efficiency) from 0.92 to 0.66), but Cryptosporidium oocyst concentration results were less reliable (R(2) from 0.20 to 0.37, P < 0.05; with poor E -0.37 to -2.57). A sensitivity analysis using independent parameter perturbation indicated that temperature was the most important parameter regulating oocyst transport in the study catchments and that the timing of manure application relative to the occurrence of water runoff event was critical. The results also showed that grazing management had little influence on predicted oocyst transport while fields fertilized with manure were the key critical source areas for microbial contaminations in the study catchments. It was concluded that the approach presented could be used to assist with understanding the epidemiology of waterborne cryptosporidiosis outbreaks and to improve catchment management for the safety of the general public health.
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