Relative importance of geostatistical and transport models in describing heavily tailed breakthrough curves at the Lauswiesen site.
We analyze the relative importance of the selection of (1) the geostatistical model depicting the structural heterogeneity of an aquifer, and (2) the basic processes to be included in the conceptual model, to describe the main aspects of solute transport at an experimental site. We focus on the results of a forced-gradient tracer test performed at the "Lauswiesen" experimental site, near Tübingen, Germany. In the experiment, NaBr is injected into a well located 52 m from a pumping well. Multilevel breakthrough curves (BTCs) are measured in the latter. We conceptualize the aquifer as a three-dimensional, doubly stochastic composite medium, where distributions of geomaterials and attributes, e.g., hydraulic conductivity (K) and porosity (phi), can be uncertain. Several alternative transport processes are considered: advection, advection-dispersion and/or mass-transfer between mobile and immobile regions. Flow and transport are tackled within a stochastic Monte Carlo framework to describe key features of the experimental BTCs, such as temporal moments, peak time, and pronounced tailing. We find that, regardless the complexity of the conceptual transport model adopted, an adequate description of heterogeneity is crucial for generating alternative equally likely realizations of the system that are consistent with (a) the statistical description of the heterogeneous system, as inferred from the data, and (b) salient features of the depth-averaged breakthrough curve, including preferential paths, slow release of mass particles, and anomalous spreading. While the available geostatistical characterization of heterogeneity can explain most of the integrated behavior of transport (depth-averaged breakthrough curve), not all multilevel BTCs are described with equal success. This suggests that transport models simply based on integrated measurements may not ensure an accurate representation of many of the important features required in three-dimensional transport models.
Riva
Monica
M
DIIAR, Politecnico di Milano, Piazza L. Da Vinci 32, 20133 Milano, Italy. monica.riva@polimi.it
Guadagnini
Alberto
A
Fernandez-Garcia
Daniel
D
Sanchez-Vila
Xavier
X
Ptak
Thomas
T
D016000:Cluster Analysis; D004867:Equipment Design; D007700:Kinetics; D015233:Models, Statistical; D008962:Models, Theoretical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D013269:Stochastic Processes; D013997:Time Factors; D014872:Water Movements; D014873:Water Pollutants / Q000032:analysis; D014881:Water Supply
D014873:Water Pollutants