Deterministic model to quantify pathogen and faecal indicator loads in drinking water catchments.

Catchments are the first potential barrier to pathogen hazards in the water supply system. Reducing pathogen loads exported from catchments to drinking water reservoirs is thus an important priority in applying a risk-based approach to managing water supplies. Although predictive models are available to estimate sediment and nutrient loads, few models are available to predict either bacterial indicator or pathogen loads exported from catchments. This paper describes the application of a process-based mathematical model to predict pathogen (Cryptosporidium) and faecal indicator (E. coli) loads generated within and exported from the Sydney drinking water catchments. The model was derived from a conceptual model that identified key processes for microbial sources from animals, on-site systems and sewage treatment plants (STPs) and their subsequent transport within drinking water catchments (Ferguson et al. 2003). Inputs to the model include GIS land use and hydrologic data and catchment specific information. The model was initially applied to the Wingecarribee catchment in the Sydney drinking water catchment and a sensitivity analysis of the model was undertaken to determine components of the model that required further investigation (Ferguson et al. submitted). The model was then applied to all 27 individual catchments (and the 196 subcatchments) within the Sydney Catchment Authority (SCA) area of operations. The model predicts pathogen catchment budgets (PCB) and ranks the sub-catchments that generate the highest loads of pathogens and indicators (per km), as well as the sub-catchments that export the greatest load of pathogens to the downstream storages. Ranking the sub-catchments enables quick identification of those areas that are generating the highest pathogen and indicator loads facilitating the implementation of control measures. The outputs from the model show that in dry weather the highest daily loads of Cryptosporidium were predicted to be generated in Kellys Creek and Mittagong Creek subcatchments in the Wingecarribee catchment. These sub-catchments are heavily impacted by the effluent discharged from Bowral and Moss Vale STPs, respectively. However, in wet weather the wash off of faecal material into surface runoff predicts that large loads of Cryptosporidium are generated in sub-catchments dominated by improved pasture grazed by cattle. The slow decay of protozoan pathogens combined with their rapid transport in water during wet weather events results in a cumulative export of Cryptosporidium to downstream sub-catchments. For example, the PCB model predicts that Warragamba reservoir would receive 4 x 10 Cryptosporidium oocysts following a 100 mm in 24 h rainfall event in the Sydney catchment. The model predicts that in dry weather approximately 1 x 10 E. coli per day were generated in subcatchments that contain improved pasture with agricultural livestock with additional inputs from sub-catchments receiving STP effluent. The rapid die-off and limited transport of this microorganism in dry weather results in fairly localized impacts. However in wet weather significant loads of faecal indicator bacteria are mobilised to the stream network and transported to downstream sub-catchments with Warragamba reservoir and the Lower Wollondilly predicted to receive up to 5.4 x 10 E. coli following a 100 mm in 24 h rain event in the Sydney catchment. The pathogen and indicator wet weather export loads predicted by the PCB model can be used as input variables to the hydrodynamic reservoir model developed by Hipsey et al. (2005) thus enabling the estimation of the risk of their subsequent transport to the water storage offtake point in Warragamba Reservoir.