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Evaluating uncertainty in areas contributing recharge to wells for water-quality network design

This study explores the sources of uncertainty in groundwater flow simulation and their impact on the estimation of areas contributing recharge to wells. The findings show that by considering these uncertainties, more effective monitoring networks can be developed for water-quality network design. The study emphasizes the importance of collecting more data and refining models to decrease the level of uncertainty.

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Evaluating uncertainty in areas contributing recharge to wells for water-quality network design

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  1. Evaluating uncertainty in areas contributing recharge to wells for water-quality network design Jeff Starn, Hydrologist, Connecticut National Water Quality Assessment (NAWQA)

  2. Take-home message • “…science aims to separate the probably true from the definitely false…”1 • More effective monitoring networks can be developed by considering uncertainty in ground-water-flow paths 1Robert Lempert, EOS, v84, n30, p 285

  3. Three sources of uncertainty that affect simulation of areas contributing recharge • Boundary and internal flow rates, including recharge • Often estimated outside model • May vary in time, such as flows from irrigation wells • Not discussed in this presentation • Heterogeneity of hydraulic conductivity • Briefly illustrated using 3 examples • Uncertainty caused by model calibration to sparse data • Illustrated using a Monte Carlo model with parameter correlation • Main topic of this presentation

  4. Sources of uncertainty in 4 modeled areas

  5. Heterogeneity in the Florida study area Small-scale hydraulic conductivity variation Sinkholes that penetrate vertically Dissolution along fractures in a specific horizontal layer Large-scale hydraulic conductivity variation Geologic layers (not shown) Vertical only potential sinkholes

  6. Heterogeneity in the Nebraska study area Small-scale hydraulic conductivity variation Locations of wells Screened depths of wells Active or abandoned wells Large-scale hydraulic conductivity variation Geologic layers Vertical only

  7. Heterogeneity in the California study area Small-scale hydraulic conductivity variation Relative amounts of coarse and fine material Alluvial fans modeled using probabilistic geologic model Clay unit important Large-scale hydraulic conductivity variation Hydrofacies category Additional clay layer (not shown)

  8. Heterogeneity in the Connecticut study area Large-scale hydraulic conductivity variation by zone Fluvial Deltaic Till Streambed Basalt—underlying glacial units

  9. Model is used to estimate mean and standard deviation of hydraulic conductivity

  10. Hydraulic conductivity estimates are correlated to various degrees Examples: • Till and basalt are strongly correlated (low scatter/high slope) • Till and streambed are weakly independent (high scatter/low slope)

  11. Uncertainty in hydraulic conductivity zones was assessed using a probabilistic model • Probabilistic model did account for • correlation of hydraulic conductivity • standard deviation of hydraulic conductivity • large-scale heterogeneity • But did not account for • small-scale heterogeneity

  12. Probabilistic modeling allowed a more effective monitoring network • A coarse model was constructed using existing data • Predictions from the coarse model were not accurate • A coarse probabilistic model showed that monitoring wells should be installed over a broad area • New monitoring wells were installed • Refined model made more accurate predictions • Uncertainty in predictions using the refined model was less than in the coarse model

  13. Recharge area to a public-supply well using a coarse ground-water-flow model Areas contributing recharge Supply well Coarse model

  14. MTBE was detected in the supply well, but simulated plume does not reach well MTBE source and simulated plume Coarse model

  15. Probabilistic model showed a 20-40% chance that the MTBE source was in the simulated recharge area MTBE source < 20 % 20-40 % > 40 % Coarse model

  16. Refined model showed a different recharge area than the coarse model 5-23 years 1–5 years < 1 year Refined model

  17. Refined model showed >40 % chance MTBE was in the simulated recharge area < 20 % MTBE source 20-40 % Area contributing recharge > 40 % Refined model

  18. Probabilistic model can be used to put error bounds on age distributions Age distributions from 13 model runs using parameter mean, standard deviation, and correlation

  19. Conclusions • More effective monitoring networks can be developed by considering uncertainty • Level of uncertainty can be decreased by collecting more data and refining model

  20. Conclusions • Source-area uncertainty increases with distance and travel time from the well • Error on vulnerability indicators such as ground-water age can be quantified

  21. Acknowledgments • Brian Clark, Nebraska study area • Christy Crandall, Florida study area • Karen Burow-Fogg, California study area • Leon Kauffman, NAWQA • Matthew Landon, Nebraska study area • Sandra Eberts, NAWQA

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