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Final Project

Final Project. Summary of Results & Conclusions. 2008 Project“ TRUTH”. Hydraulic Conductivity. Layer 1. PW2 discharge reduced To 0.90E8 ft3/year. Layer 2. Layer 3. All these complicated details may not matter. What matters is to capture the essential features

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Final Project

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  1. Final Project Summary of Results & Conclusions

  2. 2008 Project“ TRUTH” Hydraulic Conductivity Layer 1 PW2 discharge reduced To 0.90E8 ft3/year

  3. Layer 2

  4. Layer 3 All these complicated details may not matter. What matters is to capture the essential features of the system for the purposes of predicting the response to pumping and the movement of the contaminant particles.

  5. Recharge rates Extinction depths = 10, 30 ft Leakance = 4 ft/yr

  6. PW2. Dry cell. Reduce pumping rate from -0.99E8 ft3/year to -0.90E8 ft3/year

  7. PW1 doesn’t capture any particles.

  8. PW1 No cone of depression. PW1 doesn’t look like a sink and doesn’t capture particles.

  9. Particles by-pass PW1 and exit in PW2. PW 2 All the particles exit in wells; none end up in the playa.

  10. All of the particles that enter in layer 1, stay in layer 1.

  11. *Dry cells. PW2 went dry. Predicted ARM > Calibrated ARM

  12. ** Doesn’t include PW2 since it is also a target. *Dry cells. PW2 went dry. • 1. Predicted ARM > Calibrated ARM • Generally predicted ARM at non-target cells > predicted ARM at target cells

  13. 724 Project Results ? Includes results from 2006 and 4 other years A “good” calibration does not guarantee an accurate prediction.

  14. Calibrated ARM of around 1.0 is a good calibration. 2006 Project Results • 1. Predicted ARM > Calibrated ARM • Predicted ARM at pumping wells > predicted ARM at targets *Does not include PW2 since it is also a target.

  15. Calibrated ARM of around 1.0 is a good calibration. • 1. Predicted ARM > Calibrated ARM • Predicted ARM at pumping wells > predicted ARM at targets Predicted ARM at targets > predicted ARM at pumping wells

  16. 2006 Project Results Despite the relatively poor calibration, groups 4 and 5 managed to capture the essential features of the system for the purpose of the prediction.

  17. 2008 Results ** Doesn’t include PW2 since it is also a target. *Dry cells. PW2 went dry.

  18. 2008 Particle Tracking

  19. 6 hits or Luck? PEST? 2008 Results Particle Tracking Results travel time (yr) & exit location     *PW2 went dry. Low porosity gives high velocity which yields short travel times.

  20. 2006 Project Results Despite the relatively poor calibration, groups 4 and 5 managed to capture the essential features of the system for the purpose of the prediction.

  21. 2006 Project Results Particle Tracking Results travel time (yr) & exit location     6 hits 5 hits

  22. 6 hits or Luck? PEST? 2008 Results Group 3 managed to capture the essential features of the system for the best pumping prediction and the best prediction of particle exit points, but not travel times.

  23. Observations Generally predicted ARM at targets > Calibrated ARM Generally, predicted ARM at pumping wells > Predicted ARM at nodes with targets Head predictions are more robust (consistent among different calibrated models) than transport (particle tracking) predictions.

  24. To use conventional inverse models/parameter estimation models in calibration, you need to have a pretty good idea of zonation (of K, for example). (New version of PEST with pilot points does not need zonation as it works with continuous distribution of parameter values.) Also need to identify reasonable ranges for the calibration parameters.

  25. Calibration to Fluxes When recharge rate (R) is a calibration parameter, calibrating to fluxes can help in estimating K and/or R. R was not a calibration parameter in our problem.

  26. In this example, flux information helps calibrate K. q = KI K = ? H1 H2

  27. or discharge information helps calibrate R. R = ?

  28. In our example, total recharge is known/assumed to be 7.14E08 ft3/year and discharge = recharge. All water discharges to the playa. Calibration to ET merely fine tunes the discharge rates within the playa area. Calibration to ET does not help calibrate the heads and K values except in the immediate vicinity of the playa.

  29. Conclusions • Calibrations are non-unique. • A good calibration (even if ARM = 0) • does not ensure that the model will make • good predictions. • Field data are essential in constraining the model • so that the model can capture the essential • features of the system. • Modelers need to maintain a healthy skepticism • about their results. • Need for an uncertainty analysis to accompany • calibration results and predictions.

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