Abstract

Many of the present problems in hydrogeology such as old waste disposal sites and the risk of the infiltration of contaminated riverwater concern the protection of groundwater. Solutions of qualitative and quantitative, site-specific groundwater problems require the knowledge of the site-specific heterogeneity of the subsurface. Therefore, (1) descriptive, (2) structure-imitating, and (3) process-imitating methods are combined: (1) Sedimentlogical and geophysical data – outcrop, drill-core, and georadar data – are combined in a lithofacies-based interpretation and processed to be used for stochastic simulations of sedimentary structures. This interpretation respects differences in data uncertainty and provides lithofacies probabilities for points along boreholes and grid nodes with arbitrary mesh sizes along georadar sections. The estimation of probabilities that drill-core layer descriptions and radarfacies patterns represent specified lithofacies types is based on the significance of the information included in drill-core layer descriptions and the structural information of radarfacies patterns. The specification of the lithofacies types is based on outcrop data. (2) GEOSSAV (Geostatistical Environment fOr Subsurface Simulation And Visualization) has been developed for the integration of hard and soft data into the stochastic simulation and visualization of distributions of geological structures and hydrogeological properties in the subsurface. GEOSSAV, an interface to selected geostatistical modules (bicalib, gamv, vargplt, and sisim) from the Geostatistical Software LIBrary, GSLIB (Deutsch and Journel, 1998), can be used for data analysis, variogram computation of regularly or irregularly spaced data, and sequential indicator simulation of subsurface heterogeneities. Sequential indicator simulation, based on various kriging techniques (simple, ordinary, and Bayesian), is suitable for the simulation of either continuous variables such as hydraulic conductivity of an aquifer or chemical concentrations at a contaminated site, or categorical variables which indicate the presence or absence of a particular lithofacies. Export options for finite-difference groundwater models allow either files that characterize single model layers or files that characterize the complete 3D flow model set-up for MODFLOW-based groundwater simulation systems. GEOSSAV has been successfully tested on Microsoft Windows NT 4.0/2000/XP and on SuSE Linux 7.3. The current version is available at http://www.unibas.ch/earth/pract. (3) The developed lithofacies-based interpretation of geological and geophysical data and the software GEOSSAV was applied on a field example in the groundwater recharge and production area Lange Erlen, a formerly braided river environment near Basel, Northwestern Switzerland. Two different groundwater models are used to simulate a capture zone of a well located near the infiltrating river Wiese, depending on the hydrological variations (river discharge, hydraulic conductivity of the riverbed), the water supply operation, the progress of river restoration, and the heterogeneity of the subsurface. A deterministic, large-scaled groundwater model (1.8 km x 1.2 km) is used to simulate the average behavior of groundwater flow and advective transport. It is also used to assign the hydraulic boundary conditions for a small-scaled groundwater model (550 m x 400 m), which relies on stochastically generated aquifer properties based on sitespecific drill-core and georadar data. The stochastic approach in the small-scaled groundwater model does not lead to a clearly defined well capture zone, but to a well capture zone distribution reflecting the uncertainty of the knowledge of the aquifer parameters. The developed methods and tools allow the integration of geological and geophysical data of different quality into the stochastic description of aquifers. They can be used, e.g., to define and evaluate groundwater protection zones in heterogeneous aquifers associated with infiltration from rivers under changing boundary conditions and under the uncertainty of subsurface heterogeneity.

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