CRESP III Management Board Meeting February 27, 2012 PI: Shlomo P. Neuman

1. CRESP III RNL 03: Quantifying and Reducing Uncertainties in Characterization, Flow-Transport Analysis and Monitoring of Subsurface Remediation and Waste Storage Sites CRESP III Management Board Meeting February 27, 2012 PI: Shlomo P. Neuman Dept of Hydrology and Water Resources, University of Arizona, Tucson

2. Project Objectives • Develop/demonstrate tools that would provide quantitative information to decision makers about • uncertainties associated with characterization, flow-transport analysis and monitoring of subsurface remediation and waste storage sites • potential of additional characterization and monitoring data to help reduce these uncertainties and risks associated with particular decisions

3. Relevance and Impact to DOE Accounting for scale phenomena and the worth of data within the framework of a comprehensive risk and uncertainty assessment methodology, such as we propose, would greatly enhance confidence in DOE decisions concerning subsurface remediation and waste storage sites

4. Recent Accomplishments • Pumping test inference of deep vadose zone properties • Multimodel Bayesian method to assess the worth • of data • Characterizing the scaling properties of hydrologic quantities varying randomly in space – time

5. Pumping Test Inference of Deep Vadose Zone Properties • The Problem: • There presently is no good way to assess large (field) scale vadose zone hydraulic properties at depth • Infiltration experiments and laboratory samples limited mostly to shallow depths • The Solution: • Infer such properties by pumping water from • saturated zone beneath deep vadose zone • Work Products: • 1 doctoral dissertation, 2 papers in archival • journal, 1 paper in WM2011

6. Pumping Test Inference of Deep Vadose Zone Properties Borden Test Layout:

7. Pumping Test Inference of Deep Vadose Zone Properties Borden Best-Fit Solution:

8. Pumping Test Inference of Deep Vadose Zone Properties Borden Best-Fit Parameter Estimates:

9. Pumping Test Inference of Deep Vadose Zone Properties Borden Vadose Zone Characteristic Estimates:

10. Multimodel Bayesian Method to Assess the Worth of Data • The Problem: • Traditional worth of data analyses do not consider conceptual & parameter uncertainties • Bias and underestimation of uncertainty • The Solution: • Multimodel Bayesian approach in cost-risk- • benefit framework • Work Products: • 1 doctoral dissertation, 2 papers in archival • journals (1 invited in special issue on risk and • uncertainty assessment), 1 paper in • WM2011, 1 invited paper in International • Groundwater Conference proceedings

11. Multimodel Bayesian Method to Assess the Worth of Data Apache Leap Research Site (ALRS) example: • Unsaturated fractured tuff • 1-m-scale packer tests • Conducted with air • Matrix virtually saturated • Tests see mainly fractures • 184 log10k data • k in m2

12. Multimodel Bayesian Method to Assess the Worth of Data ALRS cross validation exercise: Cross Validation Cases CV I: D = W2a, Y3, Z2 C1 = X2 C2 = Y2 CV II: D = W2a, X2, Y2 C1 = V2 C2 = Z2 D = given data; C = new Given funds to drill / test only one hole in each CV, should it be C1 or C2?

13. Multimodel Bayesian Method to Assess the Worth of Data ALRS alternative model fits: • Multimodel (variogram) • geostatistical analysis: • Power (Pow0) • Exponential (Exp0) • Spherical (Sph0) • Fits based on • – (b) D • (c) – (d) D + C’1 • (e) – (f) D + C’2

14. Multimodel Bayesian Method to Assess the Worth of Data ALRS prior & preposterior uncertainty measures:

15. Multimodel Bayesian Method to Assess the Worth of Data ALRS posterior & preposterior uncertainty reduction measures: Though preposterior and posterior measures differ, both select borehole X2 in CV I and V2 in CV II

16. Scaling Properties of Space – Time Variables • The Problem: • Earth and environmental variables span multiple space – time scales • Their multiscale statistics remain poorly understood • The Solution: • New model that unifies seemingly disparate fractal / multifractal Gaussian / non-Gaussian power-law / breakdown scaling behaviors • New statistical inference method based on it • Application to synthetic / field / lab data • Work Products: • Multiple papers in varied archival journals; • invited / keynote talks at AGU / PEDOFRACT

17. Scaling Properties of Space – Time Variables • AGU Invited Talk: Are log permeabilities Gaussian? Their increments may tell. • The Problem: • Log permeabilities appear to be Gaussian or nearly so (say beta) • Their increments are often heavy tailed • Can these be reconciled? • The Solution: • Demonstrate consistency with our scaling model • Apply model to ALRS log permeability data

18. Scaling Properties of Space – Time Variables • ALRS log k data are close to Gaussian • Their increments show heavy tails

19. Scaling Properties of Space – Time Variables • Can fit Levy distributions to increments • Levy index increases with separation • scale (lag) s toward Gaussian value of 2 • Consistent with • our model and • Hurst scaling • exponent H = 0.33

20. Scaling Properties of Space – Time Variables Model generated signal:

21. Scaling Properties of Space – Time Variables Model generated signal:

22. Scaling Properties of Space – Time Variables ALRS log k signal (highly irregular, not unlike synthetic signal):

23. Scaling Properties of Space – Time Variables • We conclude: • ALRS log k is Levy with index slightly • smaller than Gaussian value of 2 • Statistics of earth and environmental • variables should be inferred jointly from • data and their increments in a mutually • consistent manner

24. Scaling Properties of Space – Time Variables • Additional key findings: • Multifractal scaling, exhibited by many earth • and environmental variables, is fully reproduced • by our (truncated monofractal) signals; as such • it is likely an artifact of sampling • Our model reproduces observed power-law • breakdown at small / large lags • Our model is the first to explain the widely • observed phenomenon of Extended Self • Similarity (ESS)

25. Current / Future Efforts(with Co-PI Prof. Marcel Schaap) • Explore extreme value statistics of measured and • synthetic signals that scale in the above manner • Develop a data base of pedologic and hydraulic properties of samples from the Hanford 200 Area vadose zone • Use neural network, statistical and inverse methods to estimate vadose zone hydraulic properties at Hanford 200 Area and at Maricopa, AZ.

26. Comment by PNNL Colleague • There was some effort to develop a database of physical and hydraulic properties and to port these to the HEIS database. That work was supported by one of the site contractors, CHPRC. • Unfortunately the project was discontinued in Jan 2011, after CHPRC over-ran their budget on a large-scale pump-and-treat system on site, and no data were actually put into HEIS. There has been no mention of restarting that effort. • Unless DOE/CHPRC/other decides to fund that effort again, it will not happen.