The magnitude of variability produced by methods used to estimate annual stormwater contaminant loads for highly urbanised catchments.
Stormwater contaminant loading estimates using event mean concentration (EMC), rainfall/runoff relationship calculations and computer modelling (Model of Urban Stormwater Infrastructure Conceptualisation--MUSIC) demonstrated high variability in common methods of water quality assessment. Predictions of metal, nutrient and total suspended solid loadings for three highly urbanised catchments in Sydney estuary, Australia, varied greatly within and amongst methods tested. EMC and rainfall/runoff relationship calculations produced similar estimates (within 1 SD) in a statistically significant number of trials; however, considerable variability within estimates (∼50 and ∼25 % relative standard deviation, respectively) questions the reliability of these methods. Likewise, upper and lower default inputs in a commonly used loading model (MUSIC) produced an extensive range of loading estimates (3.8-8.3 times above and 2.6-4.1 times below typical default inputs, respectively). Default and calibrated MUSIC simulations produced loading estimates that agreed with EMC and rainfall/runoff calculations in some trials (4-10 from 18); however, they were not frequent enough to statistically infer that these methods produced the same results. Great variance within and amongst mean annual loads estimated by common methods of water quality assessment has important ramifications for water quality managers requiring accurate estimates of the quantities and nature of contaminants requiring treatment.
Environmental Geology Group, School of Geosciences, Sydney University, Sydney, NSW 2006, Australia. hayden.j.beck@student.uts.edu.au
H J
HJ
Beck
G F
GF
Birch
D002947:Cities; D004784:Environmental Monitoring / Q000379:methods*; D011891:Rain; D062065:Wastewater / Q000706:statistics & numerical data*; D014874:Water Pollutants, Chemical / Q000032:analysis; D014877:Water Pollution, Chemical / Q000706:statistics & numerical data*; D014887:Weather
D062065:Wastewater; D014874:Water Pollutants, Chemical