Estimating ADWF at Sewage Treatment Plants

Abstract

Estimating and understanding Average Dry Weather Flow (ADWF) is fundamental to the planning, design, and operation of sewage treatment plants (STPs). This paper reviewed methods for estimation of ADWF, in four general groups: Rainfall-based; Equivalent person (EP) based; Basic statistical (Percentiles); and ‘Novel’. The ‘Novel’ methods identified were: Histogram/ Mode; Antecedent Precipitation Index (API); Ratio of Short Term and Long-Term Moving Averages; K-means Clustering; Diurnal Profile Smoothing; and Kernel Density Estimation. EP-based methods were not considered useful because they shift the uncertainty from rainfall and/or flow data to population and/or loading data. The other methods were tested using datasets for two STPs of similar size (ADWF approximately 1.2 to 1.3 ML/d) in northern New South Wales, one of which is more prone to wet weather inflow/ infiltration (I/I). On balance of simplicity and performance against more complex methods, we recommend the Histogram/ Mode and/or the Percentile methods for routine reporting. For larger and more complex assignments (e.g., design projects, planning studies), it is recommended that one or more of the alternative high-performing methods described in this paper (e.g., Ratio of moving averages; Kernel Density Estimation) be employed for ADWF checks. Relatively large datasets (at least one year of daily flow totals) should be used and the results compared against the estimates from simpler methods.