cresp iii rnl 03: quantifying and reducing uncertainties in characterization, flow-transport...
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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
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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
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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
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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
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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
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Pumping Test Inference of Deep Vadose Zone Properties
Borden Test Layout:
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Pumping Test Inference of Deep Vadose Zone Properties
Borden Best-Fit Solution:
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Pumping Test Inference of Deep Vadose Zone Properties
Borden Best-Fit Parameter Estimates:
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Pumping Test Inference of Deep Vadose Zone Properties
Borden Vadose Zone Characteristic Estimates:
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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
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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
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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 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?
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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 variogram
Exp0
Sph0
Pow 0
0 5 10 15 20 250
0.2
0.4
0.6
0.8
1
Separation distance (m)
Var
iogr
am
(c)
Sample variogram
Exp0
Sph0
Pow 0
0 5 10 15 20 250
0.2
0.4
0.6
0.8
1
Separation distance (m)
Var
iogr
am
(e)
Sample variogram
Exp0
Sph0
Pow 0
0 5 10 15 20 250
0.2
0.4
0.6
0.8
Separation distance (m)
Var
iogr
am
(b)
Sample variogram
Exp0
Sph0
Pow 0
0 5 10 15 20 250
0.2
0.4
0.6
0.8
Separation distance (m)
Var
iogr
am
(d)
Sample variogram
Exp0
Sph0
Pow 0
0 5 10 15 20 250
0.2
0.4
0.6
0.8
Separation distance (m)
Var
iogr
am
(f)
Sample variogram
Exp0
Sph0
Pow 0
CV IICV I
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Multimodel Bayesian Method to Assess the Worth of Data
ALRS prior & preposterior uncertainty measures:
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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
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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
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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
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Scaling Properties of Space – Time Variables
ALRS log k data are close to Gaussian
Their increments show heavy tails
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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
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Scaling Properties of Space – Time Variables
Model generated signal:
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Scaling Properties of Space – Time Variables
Model generated signal:
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Scaling Properties of Space – Time Variables
ALRS log k signal (highly irregular, not
unlike synthetic signal):
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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
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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)
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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.
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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.