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A Hybrid Framework for Improving Recharge and Discharge Estimation for a Three-Dimensional Groundwater Flow Model

Authors

  • Scott C. Meyer,

    Corresponding author
      Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820; smeyer@illinois.edu
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  • Yu-Feng Lin,

    1. Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820
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  • George S. Roadcap

    1. Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820
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Illinois State Water Survey, Prairie Research Institute, University of Illinois at Urbana-Champaign, Champaign, IL 61820; smeyer@illinois.edu

Abstract

We employed the ArcGIS plug-in package PRO-GRADE (Lin et al. 2009), developed for zonation of recharge/discharge (R/D) for modeling two-dimensional aquifer systems, to develop alternative R/D zonations for an existing three-dimensional groundwater flow model of a complex hydrogeologic setting. Our process began by intersecting PRO-GRADE output with the existing model's 4-zone R/D representation to develop a model having 12 R/D zones (R12) and then calibrating the resulting model using PEST. We then revised the R12 zonation using supplementary GIS data to develop a 51-zone R/D zonation (R51). From R51, we developed a series of daughter models having 40, 30, 28, and 18 R/D zones by removing zones from R51 if calibration resulted in little change in the zone's starting R/D rate and/or if the model was insensitive to the zone's R/D rate. For these models (R40N, R30N, R28N, and R18N), we used the ArcGIS Nibble tool to rapidly and consistently reassign model cells within eliminated zones of R51 to the zone of the nearest model cell in a retained zone having the same starting value. R12, R51, R40N, R30N, R28N, and R18N are all more accurate than the original model (R4), although improvements relative to stream discharge targets exceeded improvements relative to head targets. The models also executed with better numerical stability and less mass balance discrepancy than R4. These improvements demonstrate that R/D estimation in a complex shallow three-dimensional steady-state model can be improved with PRO-GRADE estimates of R/D when guided by calibration statistics and supplemental geographic data.

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