Towards evaluating the conditional probability of waterborne microbial risks associated with critical rainfall events following land application of animal waste - A focus on Irish pastures

Modelling surface runoff and infiltration in an agricultural watershed is essential for understanding the nutrient cycle and water quality management. This research evaluates the critical rainfall events causing maximum surface runoff and infiltration. Empirical models such as Soil Conservation Service (SCS) curve number and Explicit Green-Ampt (EGA) were analysed for different soil hydraulic conditions and storm events with specific return periods. The present study forecasted the maximum daily rainfall and intensity as 113.6 mm and 19.9 cm h-1, respectively, considering a 100-year return period storm. The deterministic part of the model calculates the daily maximum surface runoff as 5.51 cm (from the SCS model). The EGA model revealed that the rainfall intensity of 0.143, 0.163, 0.175, 0.179, 0.181, 0.182, and 0.182 cm h-1 (duration of 24 h) was found critical for 1, 2, 5, 10, 25, 50, 100 year return period of the storm, respectively in different soil texture classes (sand, loamy sand, sandy loam, loam, silt loam, sandy clay loam, clay loam, silty clay soils). Exceptions imply for three occasions where the intensity of 0.407 cm h-1 and duration of 18 h produced maximum infiltration for silty clay loam, and intensity of 0.352 and 0.354 cm h-1 with 9 h rainfall (each) produced maximum infiltration for clay. The probabilistic model revealed that a daily storm event probability of 2.74 x 10-5 is associated with a simulated mean runoff depth of 0.69 cm and infiltration of 20.7 cm. Critical rainfall probabilities calculated in this study can be multiplied with the overall microbial/pollutant human health risk assessment framework to calculate conditional probability and better estimate microbial risk through the drinking water pathway. This study is very relevant for the wet climatic condition of Ireland, where most land use is pastures.