Keywords
Microbial water quality; water quality modelling; temporal scales
Start Date
26-6-2018 2:00 PM
End Date
26-6-2018 3:20 PM
Abstract
Microbial water quality can have profound effects on human health. Microbial water quality modelling can be a useful tool for water quality management in catchments. Since flow drives water quality, water quality models rely on estimates of flow from hydrological and systems models. However, these different hydrological and systems models operate over different time steps, which may be mismatched to those of flow processes affecting microbial water quality. Therefore, the aim of this study was to investigate the accuracy of microbial water quality modelling across different time steps, thereby assessing which flow time steps are suitable for driving microbial water quality modelling. Escherichia coli was modelled as an indicator of microbial water quality. For monthly modelling, the Water Evaluation and Planning Model (WEAP) was applied to the upper catchment of the Crocodile River in Mpumalanga, South Africa. For the above application, E. coli was modelled in WEAP using first-order degradation. For daily modelling, monthly flows from the upper Crocodile River were disaggregated to daily and modelled using the Water Quality Systems Assessment Model (WQSAM). Model simulations were assessed against available observed data. WQSAM managed to represent the full variability of the observed data at a daily time step. This was not the case for WEAP at a monthly time step. The results indicate that for integration of water management and water quality models for microbial water quality modelling, integration will have to take place at a daily or sub-daily time step.
Modelling microbial water quality across multiple temporal scales
Microbial water quality can have profound effects on human health. Microbial water quality modelling can be a useful tool for water quality management in catchments. Since flow drives water quality, water quality models rely on estimates of flow from hydrological and systems models. However, these different hydrological and systems models operate over different time steps, which may be mismatched to those of flow processes affecting microbial water quality. Therefore, the aim of this study was to investigate the accuracy of microbial water quality modelling across different time steps, thereby assessing which flow time steps are suitable for driving microbial water quality modelling. Escherichia coli was modelled as an indicator of microbial water quality. For monthly modelling, the Water Evaluation and Planning Model (WEAP) was applied to the upper catchment of the Crocodile River in Mpumalanga, South Africa. For the above application, E. coli was modelled in WEAP using first-order degradation. For daily modelling, monthly flows from the upper Crocodile River were disaggregated to daily and modelled using the Water Quality Systems Assessment Model (WQSAM). Model simulations were assessed against available observed data. WQSAM managed to represent the full variability of the observed data at a daily time step. This was not the case for WEAP at a monthly time step. The results indicate that for integration of water management and water quality models for microbial water quality modelling, integration will have to take place at a daily or sub-daily time step.
Stream and Session
D3: Modelling Ecological Public Health Risks Across Scales