Using Ground-Water Model Predictions and the ppr and opr Statistics to Guide Data Collection - PowerPoint PPT Presentation

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Using Ground-Water Model Predictions and the ppr and opr Statistics to Guide Data Collection

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  1. Using Ground-Water Model Predictions and the ppr and opr Statistics to Guide Data Collection

  2. Motivation • Ground-water model predictions are always uncertain. Hydrogeologic Data Parameter Distribution Calibrated Model & Predictions Incomplete Data Parameter Uncertainty Prediction Uncertainty

  3. Motivation • What hydrogeologic data could be collected to reduce this prediction uncertainty most effectively? Hydrogeologic Data Parameter Distribution Calibrated Model & Predictions Additional Data Reduced Parameter Uncertainty Reduced Prediction Uncertainty

  4. Approach Calibrated Model & Predictions Parameter Distribution Use calibrated model to identify parameters important to predictions.

  5. Approach Calibrated Model & Predictions Parameter Distribution Hydrogeologic Data Use calibrated model to identify parameters important to predictions. • Collect hydrogeologic data: • Parameter values • Flow system characteristics

  6. Approach Incorporate these data into the model to reduce parameter and prediction uncertainty. Use calibrated model to identify parameters important to predictions. • Collect hydrogeologic data: • Parameter values • Flow system characteristics

  7. Parameter-Prediction Statistic (PPR) Observation Sensitivities • Calculate prediction uncertainty (sZ) using the calibrated model. 2. Assume improved information on one or more parameters, and recalculatesZ. 3. PPR statistic equals the percent decrease in sZ from step 1 to step 2. Parameter Uncertainty Prediction Sensitivities Prediction Uncertainty(standard deviations sZ )

  8. DVRFS Model Parameters 9 Hydraulic Conductivities 4 Recharge Parameters

  9. Predictions: Advective-Transport Paths • Advective transport used as a surrogate for regional contaminant transport. • Advective transport paths are 10 km. • Predictions are the distances traveled in the N-S, E-W, and vertical directions.

  10. Uncertainty in Path Position Black bars: Prediction standard deviations calculated using calibrated model. Advective path

  11. Uncertainty in Path Position Black bars: Prediction standard deviations calculated using calibrated model. Red bars: Prediction standard deviations calculated with improved information on a parameter. Advective path

  12. PPR Statistic: Individual Parameters • Specify improved information on one parameter, so that its uncertainty decreases by 10 percent. • Calculate resulting decrease in prediction standard deviations sZ. • Repeat for all model parameters.

  13. PPR: Individual Parameters PPR Hydraulic Conductivity Recharge Parameter with Improved Information

  14. PPR: Individual Parameters PPR Hydraulic Conductivity Recharge Parameter with Improved Information

  15. PPR: Multiple Parameters • Specify improved information on three parameters. • Calculate PPR statistic (decrease in prediction standard deviation sZ). • Repeat for all possible sets of three model parameters.

  16. PPR: Multiple Parameters VOII on Individual Parameters VOII on 3 Parameters PPR PPR

  17. VOII: Multiple Parameters VOII on Individual Parameters VOII on 3 Parameters PPR PPR

  18. Advective Path from Yucca Flat Site K zones, layer 1 Recharge zones layer 2 10 km K1 R1 K3 R4 K5

  19. Collect hydrogeologic data related to important parameters System State Observations Using the PPR Results Improve Model & Parameters Recalibrate Model Improved Predictions, Reduced Uncertainty Societal decisions

  20. Observation-Prediction (opr) Statistic Observation Sensitivities • Calculate prediction uncertainty (sZ) using the calibrated model and all 517 observations. 2. Add or omit one or more observations, and recalculate sZ. 3. opr statistic equals the change in sZ from step 1 to step 2. Parameter Uncertainty Prediction Sensitivities Prediction Uncertainty(standard deviations sZ )

  21. Predictions evaluated for assessing observations Hill and Tiedeman, 2007, fig. 15.7. p. 366

  22. Which existing observations are important(or not) to predictions? Use opr(-1)to rank the 501 existing observation locations by their importance to predictions • Averaged values of opr(-1) for all the predictions are used, to obtain a measure indicating the importance of a single observation to all the predictions of interest. • Calculate opr(-100)by removing the 100 least important observations • opr(-100)= mean prediction uncertainty increase = 0.6% Hill and Tiedeman, 2007, fig. 15.9. p. 368

  23. What new observations would be important(or not) to predictions? Consider one potential new head observation in each cell of model layer 1. Determine weights for the potential observations. Here, same weighting strategy used as for weighting existing observations – weights smaller for heads in high-gradient areas. Calculate opr(+1) for each cell in the layer, even those with an existing observation, so that opr(+1) is continuous over the whole map. Hill and Tiedeman, 2007, fig. 15.10. p. 369

  24. Using the OPR results for potential new observations Hydrologic and Hydrogeologic Data Improve Model & Parameters Collect additional observation data Recalibrate Model Improved Predictions, Reduced Uncertainty Societal decisions

  25. Summary • Parameters and observations most important to the predicted advective-transport paths do not necessarily lie near the paths themselves. • Best to not use ppr and opr results alone for making decisions about future data collection – consider other criteria such as geologic insight about important subsurface units to investigate, maintaining good geographic and depth coverage of monitoring network, etc. • The ppr and opr results are only as good as the model itself!