Transmissivity estimation for highly heterogeneous aquifers: comparison of three methods applied to the Edwards Aquifer,Texas, USA |
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| Authors: | Scott L Painter Allan D Woodbury Yefang Jiang |
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| Institution: | (1) Geosciences and Engineering Division, Southwest Research Institute, 6220 Culebra Rd, San Antonio, TX 78228-0510, USA;(2) Department of Civil Engineering, University of Manitoba, 15 Gillson St., Winnipeg, Manitoba, R3T 3V5, Canada;(3) Water Management Division, Department of Environment and Energy, 11 Kent Street, P.O. Box 2000, Charlottetown, Prince Edward Island, C1A 7N8, Canada |
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| Abstract: | Obtaining reliable hydrological input parameters is a key challenge in groundwater modeling. Although many quantitative characterization techniques exist, experience applying these techniques to highly heterogeneous real-world aquifers is limited. Three geostatistical characterization techniques are applied to the Edwards Aquifer, a limestone aquifer in south-central Texas, USA, for the purposes of quantifying the performance in an 88,000-cell groundwater model. The first method is a simple kriging of existing hydraulic conductivity data developed primarily from single-well tests. The second method involves numerical upscaling to the grid-block scale, followed by cokriging the grid-block conductivity. In the third method, the results of the second method are used to establish the prior distribution for a Bayesian updating calculation. Results of kriging alone are biased towards low values and fail to reproduce hydraulic heads or spring flows. The upscaling/cokriging approach removes most of the systematic bias. The Bayesian update reduced the mean residual by more than a factor of 10, to 6 m, approximately 2.5% of the total head variation in the aquifer. This agreement demonstrates the utility of automatic calibration techniques based on formal statistical approaches and lends further support for using the Bayesian updating approach for highly heterogeneous aquifers. |
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| Keywords: | Inverse modeling Geostatistics Numerical modeling Edwards Aquifer Bayesian methods |
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