Final Project Summary of Results & Conclusions. Generally predicted ARM at targets > Calibrated ARM Generally, predicted ARM at pumping wells > Predicted.

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Presentation transcript:

Final Project Summary of Results & Conclusions

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. Observations

Calibration Prediction GroupARM h ARM ET (x10e7) ARM h (at targets) ARM h (at pumping wells)* A good calibration does not guarantee an accurate prediction. A calibrated ARM of around 1 is a good calibration.

GroupP1P2P3P4P5P6P playa2561 PW25060 playa1098 PW4 401 PW41465 playa 1220 PW PW2606 PW2709 playa474 PW4 595 PW4608 playa310 PW PW21088 PW21599 playa361 PW4 510 PW42317 PW playa playa1226 PW2592 playa623 PW4 846 PW4968 playa1194 PW playa1660 PW21513 playa1412 PW4 744 PW4817 PW14410 playa 61.20E5 PW5 820 PW21.82 E4 PW5587 PW4 576 PW41.19 E5 PW PW playa1039 PW2618 playa647 PW4 629 PW4908 playa2484 PW PW2986 PW2752 playa659 PW4 577 PW4359 PW1502 PW PW1534 PW21156 playa402 PW playa 91 PW11170 PW2 Truth 672 PW1 549 PW E5 playa 359 PW4 650 PW4 238 PW playa  Particle Tracking Results travel time (yr) & exit location number of “hits”

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

K distribution Layer 1 Layer 2 Layer 3 Note anisotropy Kx/Kz n Truth

Group 4Group 7 Layer 1 - Zonation

Truth Group 4Group 7 Layer 1 - Zonation Group 2 Hyd. Conductivity (ft/yr) K x & K y KzKz

Truth Group 1 Layer 1 - Zonation Group 6

Truth Group 1 Group 5 Layer 1 - Zonation Group 3 Group 6

Layers 2, 3 layer 2 Truth layer 3 Group 7 Truth layer 2 layer 3 Group 4

Leakance array Representing K V of confining bed 1 ft/day 10 ft/day Truth

0.35 ft/yr 0.67 ft/yr Recharge Truth

ET – extinction depth 10 ft 15 ft Truth

GroupP1P2P3P4P5P6P playa2561 PW25060 playa1098 PW4 401 PW41465 playa 1220 PW PW2606 PW2709 playa474 PW4 595 PW4608 playa310 PW PW21088 PW21599 playa361 PW4 510 PW42317 PW playa playa1226 PW2592 playa623 PW4 846 PW4968 playa1194 PW playa1660 PW21513 playa1412 PW4 744 PW4817 PW14410 playa 61.20E5 PW5 820 PW21.82 E4 PW5587 PW4 576 PW41.19 E5 PW PW playa1039 PW2618 playa647 PW4 629 PW4908 playa2484 PW PW2986 PW2752 playa659 PW4 577 PW4359 PW1502 PW PW1534 PW21156 playa402 PW playa 91 PW11170 PW2 Truth 672 PW1 549 PW E5 playa 359 PW4 650 PW4 238 PW playa  Particle Tracking Results travel time (yr) & exit location number of “hits”

p3 1 p7

Calibration Prediction GroupARM h ARM ET (x10e7) ARM h (at targets) ARM h (at pumping wells)* Calibration to ET doesn’t improve prediction for this problem

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.

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

or discharge information helps calibrate R.

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. In our example, total recharge is known/assumed to be 7.14E08 ft 3 /year and discharge = recharge.

Conclusions Calibrations are non-unique. A good calibration (even if ARM = 0) does not ensure that the model will make good predictions. Need for an uncertainty analysis to accompany calibration results and predictions. You can never have enough field data. Modelers need to maintain a healthy skepticism about their results.