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A Comparison of Six Transport Models of the MADE-1 Experiment Implemented With Different Types of Hydraulic Data

AuthorsZech, Alraune; Attinger, Sabine; Bellin, Alberto; Cvetkovic, Vladimir; Dagan, Gedeon; Dentz, Marco CSIC ORCID; Dietrich, Peter; Fiori, Aldo; Teutsch, Georg
KeywordsTransport Models
Hydraulic Data
Issue Date28-Apr-2021
PublisherAmerican Geophysical Union
CitationWater Resources Research 57 (5): e2020WR028672 (2021)
AbstractSix conceptually different transport models were applied to the macrodispersion experiment (MADE)-1 field tracer experiment as a first major attempt for model comparison. The objective was to show that complex mass distributions in heterogeneous aquifers can be predicted without calibration of transport parameters, solely making use of structural and flow data. The models differ in their conceptualization of the heterogeneous aquifer structure, computational complexity, and use of conductivity data obtained from various observation methods (direct push injection logging, DPIL, grain size analysis, pumping tests and flowmeter). They share the same underlying physical transport process of advection by the velocity field solely. Predictive capability is assessed by comparing results to observed longitudinal mass distributions of the MADE-1 experiment. The decreasing mass recovery of the observed plume is attributed to sampling and no physical process like mass transfer is invoked by the models. Measures like peak location and strength are used in comparing the modeled and measured plume mass distribution. Comparison of models reveals that the predictions of the solute plume agree reasonably well with observations, if the models are underlain by a few parameters of close values: mean velocity, a parameter reflecting log-conductivity variability, and a horizontal length scale related to conductivity spatial correlation. The robustness of the results implies that conservative transport models with appropriate conductivity upscaling strategies of various observation data provide reasonable predictions of plumes longitudinal mass distribution, as long as key features are taken into account.
Publisher version (URL)https://doi.org/10.1029/2020WR028672
Appears in Collections:(IDAEA) Artículos
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