cresp iii management board meeting february 27, 2012 pi: shlomo p. neuman

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 MeetingFebruary 27, 2012

PI: Shlomo P. Neuman

Dept of Hydrology and Water Resources, University of Arizona, Tucson

Project ObjectivesDevelop/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

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

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

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 zoneWork Products: 1 doctoral dissertation, 2 papers in archival journal, 1 paper in WM2011

Pumping Test Inference of Deep Vadose Zone Properties

Borden Test Layout:

Pumping Test Inference of Deep Vadose Zone Properties

Borden Best-Fit Solution:

Pumping Test Inference of Deep Vadose Zone Properties

Borden Best-Fit Parameter Estimates:

Pumping Test Inference of Deep Vadose Zone Properties

Borden Vadose Zone Characteristic Estimates:

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 frameworkWork 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

Multimodel Bayesian Method to Assess the Worth of Data

Apache Leap Research Site (ALRS) example:

-25

-20

-15

-10

-5

0

Z( m)

-100

1020

3040

-100

1020

30

W2aV2

X2Y2

Z2

Y3 Unsaturated fractured tuff 1-m-scale packer tests Conducted with air Matrix virtually saturated Tests see mainly fractures 184 log10 k data k in m2

Multimodel Bayesian Method to Assess the Worth of Data

ALRS cross validation exercise:

-25

-20

-15

-10

-5

0

Z( m)

-100

1020

3040

-100

1020

30

W2aV2

X2Y2

Z2

Y3

Cross Validation CasesCV I: D = W2a, Y3, Z2 C1 = X2 C2 = Y2CV II: D = W2a, X2, Y2 C1 = V2 C2 = Z2D = given data; C = newGiven funds to drill / testonly one hole in each CV,should it be C1 or C2?

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(a)– (b) D(c) – (d) D + C’1

(e) – (f) D + C’2

0 5 10 15 20 250

0.2

0.4

0.6

0.8

1

Separation distance (m)

Var

iogr

am

(a)

Sample variogramExp0Sph0Pow 0

0 5 10 15 20 250

0.2

0.4

0.6

0.8

1

Separation distance (m)

Var

iogr

am

(c)

Sample variogramExp0Sph0Pow 0

0 5 10 15 20 250

0.2

0.4

0.6

0.8

1

Separation distance (m)

Var

iogr

am

(e)

Sample variogramExp0Sph0Pow 0

0 5 10 15 20 250

0.2

0.4

0.6

0.8

Separation distance (m)

Var

iogr

am

(b)

Sample variogramExp0Sph0Pow 0

0 5 10 15 20 250

0.2

0.4

0.6

0.8

Separation distance (m)

Var

iogr

am

(d)

Sample variogramExp0Sph0Pow 0

0 5 10 15 20 250

0.2

0.4

0.6

0.8

Separation distance (m)

Var

iogr

am

(f)

Sample variogramExp0Sph0Pow 0

CV IICV I

Multimodel Bayesian Method to Assess the Worth of Data

ALRS prior & preposterior uncertainty measures:

Multimodel Bayesian Method to Assess the Worth of Data

ALRS posterior & preposterior uncertainty reduction measures:

Though preposterior and posterior measuresdiffer, both select borehole X2 in CV I and V2in CV II

Scaling Properties of Space – Time Variables

The Problem: Earth and environmental variables span

multiple space – time scales Their multiscale statistics remain poorly

understoodThe 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

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

Scaling Properties of Space – Time Variables

ALRS log k data are close to Gaussian Their increments show heavy tails

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

Scaling Properties of Space – Time Variables

Model generated signal:

Scaling Properties of Space – Time Variables

Model generated signal:

Scaling Properties of Space – Time Variables

ALRS log k signal (highly irregular, notunlike synthetic signal):

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

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)

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.

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.