Application Of Dynamic Parametric
Sensitivity Analysis For Identifying
Contaminant Sources In A Watershed
Author(s)
H. Manolopoulos, B. E. Halbert, C. R. Paton & J. M. Scharer
Abstract
Lakes of the Fulton Creek watershed in Northern Saskatchewan, Canada, possess
constituents associated with uranium mining. Although mining activity ceased
over 24 years ago and site decommissioning was largely completed in 1985, the
discharge from this former tailings management watershed continues to have
elevated levels of three specific constituents, namely dissolved radium-226,
selenium and uranium, as well as total dissolved solids (TDS). The objective of
the current study was to identify and rank the sources of constituents in the
watershed. Constituent dispersion modelling was carried out employing a
proprietary computer code called LAKEVIEW (developed by SENES
Consultants Limited). Using the Metropolis-Hastings algorithm of a Markov
chain computational procedure, parameter calibration was performed at two
locations in the watershed with 25 years of more or less regular water quality and
occasional sediment monitoring data. The calibrated model captured the time
dependent trends for all variables. Employing the Metropolis-Hastings parameter
sampler, dynamic parametric (source) sensitivity analysis was carried out for a
300-year period. The sensitivity responses showed strong temporal variability.
Therefore, the normalized gradient sensitivity values were integrated and
averaged over time for use as the measure of constituent source loads. Sediments
in two lakes were shown to be both the current and future principal sources of all
constituents. An adjacent surface tailings area was identified as a relatively
minor contributor of all constituents but uranium.
Keywords: constituent source modelling, parameter distribution, constituent flux
apportionment, normalized gradient sensitivity.
Keywords
constituent source modelling, parameter distribution, constituent flux
apportionment, normalized gradient sensitivity.
Related Book
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