Deterministic and stochastic modelling for protection zone delineation

Protection zones delimited by isochrones are often computed using calibrated groundwater flow and transport models. In heterogeneous formations, all direct (hard) and indirect (soft) data must be used optimally. Approaches involving in situ pumping and tracer tests, combined with geophysical and/or other geological observations, should be developed. In a deterministic framework, the calibrated model is considered to be the best representation of reality at the current investigation stage, but uncertainty of the results is not quantified. Using stochastic methods, a range of equally likely isochrones can be produced, allowing us to quantify the influence of our knowledge on the aquifer parameters on protection-zone uncertainty. Furthermore, integration of soft data in a conditioned stochastic generation process, possibly associated with an inverse modelling procedure, can reduce the resulting uncertainty. Proposed is a stochastic methodology for protection-zone delineation, integrating hydraulic conductivity measurements (hard data), head observations and electrical resistivity data (soft data).

[1]  G. de Marsily,et al.  Spatial Variability of Properties in Porous Media: A Stochastic Approach , 1984 .

[2]  Matthijs van Leeuwen,et al.  Stochastic determination of well capture zones conditioned on regular grids of transmissivity measurements , 2000 .

[3]  E. Poeter,et al.  Field example of data fusion in site characterization , 1995 .

[4]  Wolfgang Kinzelbach,et al.  Determination of groundwater catchment areas in two and three spatial dimensions , 1992 .

[5]  Keith Beven,et al.  Stochastic capture zone delineation within the generalized likelihood uncertainty estimation methodology: Conditioning on head observations , 2001 .

[6]  Matthijs van Leeuwen,et al.  Stochastic determination of well capture zones , 1998 .

[7]  A. Sahuquillo,et al.  Stochastic simulation of transmissivity fields conditional to both transmissivity and piezometric data—I. Theory , 1997 .

[8]  E. Scott Bair,et al.  A Monte Carlo-Based Approach for Determining Traveltime-Related Capture Zones of Wells Using Convex Hulls as Confidence Regions , 1991 .

[9]  Jonathan Levy,et al.  Uncertainty Quantification for Delineation of Wellhead Protection Areas Using the Gauss‐Hermite Quadrature Approach , 2000 .

[10]  L. Nunes,et al.  Permeability field estimation by conditional simulations of geophysical data. , 2000 .

[11]  W. Kinzelbach,et al.  Computation of stochastic wellhead protection zones by combining the first-order second-moment method and Kolmogorov backward equation analysis , 2000 .

[12]  Estimating aquifer transmissivities-- on the value of auxiliary data , .

[13]  S. Brouyère,et al.  Integrating geophysical and tracer test data for accurate solute transport modelling in heterogeneous porous media , 2002 .

[14]  David N. Lerner,et al.  How Uncertain Is Our Estimate of a Wellhead Protection Zone? , 1998 .

[15]  J. M. Shafer,et al.  Assessment of Uncertainty in Time‐Related Capture Zones Using Conditional Simulation of Hydraulic Conductivity , 1991 .

[16]  A. Dassargues,et al.  Delineation of groundwater protection zones based on tracer tests and transport modeling in alluvial sediments , 1998 .

[17]  C. Welty,et al.  A Critical Review of Data on Field-Scale Dispersion in Aquifers , 1992 .

[18]  A. Mantoglou,et al.  The Turning Bands Method for simulation of random fields using line generation by a spectral method , 1982 .

[19]  Alberto Guadagnini,et al.  Time‐Related Capture Zones for Contaminants in Randomly Heterogeneous Formations , 1999 .

[21]  A. J. Desbarats,et al.  Subsurface Flow and Transport: A Stochastic Approach , 1998 .