On the link between contaminant source release conditions and plume prediction uncertainty.
The initial width of contaminant plumes is known to have a key influence on expected plume development, dispersion and travel time statistics. In past studies, initial plume width has been perceived identical to the geometric width of a contaminant source or injection volume. A recent study on optimal sampling layouts (Nowak et al., 2009) showed that a significant portion of uncertainty in predicting plume migration stems from the uncertain total hydraulic flux through the source area. This result points towards a missing link between source geometry and plume statistics, which we denote as the effective source width. We define the effective source width by the ratio between the actual and expected hydraulic fluxes times the geometric source width. The actual hydraulic flux through the source area is given by individual realizations while the expected one represents the mean over the ensemble. It is a stochastic quantity that may strongly differ from the actual geometric source width for geometrically small sources, and becomes identical only at the limit of wide sources (approaching ergodicity). We derive its stochastic ensemble moments in order to explore the dependency on source scale. We show that, if the effective source width is known rather than the geometric width, predictions of plume development can greatly increase in predictive power. This is illustrated on plume statistics such as the distribution of plume length, average width, transverse dispersion, total mass flux and overall concentration variance. The analysis is limited to 2D depth-averaged systems, but implications hold for 3D cases.
de Barros
Felipe P J
FP
Institute for Applied Analysis and Numerical Simulation/SRC SimTech, Pfaffenwaldring 57, University of Stuttgart, 70569 Stuttgart, Germany. simbarros@simtech.uni-stuttgart.de
Nowak
Wolfgang
W
D004787:Environmental Pollution / Q000032:analysis*; D008962:Models, Theoretical; D009010:Monte Carlo Method; D013269:Stochastic Processes; D035501:Uncertainty; D014872:Water Movements; D014873:Water Pollutants / Q000032:analysis*
D014873:Water Pollutants