The influence of the correlation-covariance structure of measurement
errors over uncertainties propagation in online monitoring: application
to environmental indicators in SUDS
Abstract
This paper presents a methodology to assess the influence of the
correlation-covariance structure of measurement errors in online
monitoring over the propagation of uncertainties, applied to wet-weather
environmental indicators in Sustainable Urban Drainage Systems (SUDS).
The effect of autocorrelated and heteroscedastic errors in measured
time-series over the estimated Probability Density Function (PDF) of
different environmental indicators is analyzed for a wide variety of
possible error structures in the data. For this purpose, multiple
correlation-covariance structures are randomly generated from exploring
the parametric space of a Linear Exponent Autoregressive (LEAR) model,
employing a Bayesian-based Markov Chain Monte Carlo sampling technique.
Significant differences tests are proposed to identify the most
correlated parameters of the correlation-covariance error model with
statistics of the environmental indicators PDFs. The methodology is
applied to Total Suspended Solids (TSS) and Chemical Oxygen Demand (COD)
time-series recorded during 13 rainfall events at the inlet and outlet
of a SUDS train (stormwater settling tank - horizontal constructed
wetland). For this case, results showed that the total error in the
estimation of the analyzed environmental indicators is mostly explained
by standard uncertainties (flattening of the PDFs) rather than bias
contributions (displacement of the PDFs). The correlation-covariance
model parameters related to the temporal delimitation of
hydrographs/pollutographs and the intensity of the autocorrelation
showed to have the strongest influence in the propagation of measurement
errors (flattening/displacement of the PDFs).