it’s really dark down there
TRANSCRIPT
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It’s really dark down there: Uncertainty in groundwater hydrology
Larry Winter
University of Arizona
QUIET 2017
SISSA
July 21, 2017
NSF: 1707658-001
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Spatial scales and typical dynamics
Individual pore: 10 m – 10 mm radii, 0.1 – 10 cm length
Dynamics: Poiseuille Eqn, Navier-Stokes Eqns
Explicit porous microstructures: 1 cm – 1 m sample lengths
Dynamics: Navier-Stokes Eqns
Laboratory: 1 – 10 m3 blocks
Dynamics: Stokes Flows / Darcy’s Law
Field: 10m – 1 km
Dynamics: Darcy’s Law
Local aquifer: 1 – 10 km Dynamics: Diffusion (Darcy’s Law)
Basin-scale: 1 – 104 km Dynamics: Diffusion (Darcy’s Law)
} TypicalScales of
Measurement/Observation
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Flow through porous media: alternate representations
m-mm
cm-km
Pore Space
Porous Continuum
(r x )
Porous microstructure• Void and solid phases• Navier-Stokes equations• Detailed pore geometry
(x) =1 if x pore space
0 otherwise
Continuum• Darcy’s Law, advection-diffusion• Effective parameters• Hydraulic conductivity [L/t] • Head [L], velocity, concentration
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Highly heterogeneous media
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Model (theory)Domain of Application
Scales
Assumptions in addition to IBCs & forcing functions
NSEPore-Pore network - cms
(1) Newton's 2nd Law
(2) Conservation of mass
Darcy's lawElementary volume of a
porous mediumcm-m
Continuum representation of porous medium. Uniform material
Continuity eqn
Eqn of state
Flow eqn
2D Transmissivity
with Sy
Unconfined aquifer
kmT doesn't vary with head
2D Transmissivity
with SConfined aquifer km
(1) Confining beds are plane and parallel, (2) One principal direction of K perpendicular to confining beds, (3) head gradient independent of z, (4) h/t doesn't depend on z
Diffusion eqn cm-km Known K(x)
Models, their applications, scales, and assumptions
The … equations for the circulation of a fluid in a porous medium [relating to Darcy’s law] are significant only for [small] volumes of a porous medium
-- Marsily
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Measurement scales: REV
Measurementscale
1
0
Porosity
REVs
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REV?
Photo 0.15 cm tip 0.31 cm tip
0.63cm tip 1.27 cm tip
Berea sandstone
Puff permeameter images. Tidwell et al., 1999
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Pore microstructures
Berea Sandstone (Courtesy Ming Zang)
Simulation
Berea Sandstone
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Biological Porous Media: Human Pancreas
Murakami et al., “Microcirculatory Patterns in Human Pancreas,” (1994)
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Flow through porous media: Lab scale
Center for experimental study of subsurface environmental processesColorado School of Mines
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Flow through porous media: Field scale
Capillary rise
InfiltrationPlant uptake
(transpiration)Evaporation
usgs
Recharge
Interflow
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System of aquifers
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Aquifer systems
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High Plains Aquifer
450,000 km2
Elevation: 2400m – 355m Few streams
The Great Plains produce about 25% of US crops and livestock.
Great reliance on ground water for agriculture
30% of all ground water pumped for irrigation in the United States.
Courtesy USGS
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Karst systems
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Sources of uncertainty
Winter and Tartakovsky, 2013
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Flow through porous structures: experiments
Moroni et al., 2001
Computational
Eulag CFD Simulator (Prussa et al., 2006). Immersed boundary method for pore spaces
(Smolarkiewicz and Winter, 2010)
Physical
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Computational experiments
Hyman et al. (2012)
Particle trajectories – Yellow is fast
Synthetic Beads Volcanic Tuf
Smolarkiewicz and Winter (2010)
Synthetic medium
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Heterogeneous velocities
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Expanding (left) and Contracting Regions (right)
(Hyman and Winter, Phys Rev E, 2013)
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Field scale and larger
https://www.swstechnology.com/Kansas Geological Survey
USGS Modflowhttps://water.usgs.gov/ogw/modflow/
ComputationalPhysical
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System dynamics: Continuum representation
ParametersConductivity: K(x), [K] = m/sPermeability: k(x) = (/ g m
Transmissivity: T(x), [T] = m2/sStorativity: S, [S] = 1Dispersion coefficient: D, [D] = m2/s
State variablesHydraulic head: h(x, t), [h] = mDarcy flux: q(x, t), [q] = m/sFlow rate: Q(x, t), [Q] = m3/sConcentration: c(x, t), [c] = M/m3
K∇h
S∂h∂t
+ F=
Flow Mass Transport
K(x)∇hq =
Darcy’s Law Continuity
q + F) = 0
(S∂h∂t
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Groundwater Flow: Some Foundational Problems
Inverse problem. Estimate basic parameters (hydraulic conductivity) at a given scale of analysis (porous microstructures -- aquifers) from data.
Most are highly heterogeneous, e.g., K(x) = Ki(x) if x material i
1st Forward Problem (Heterogeneities). Determine effects of material heterogeneities on flow/transport at a given scale.
Scale-up. Scale observations of heterogeneous parameters up to effective parameters at a larger scale.
Scale-Down. Scale parameters averaged at a larger scale down to realistic distribution of heterogeneities at a smaller scale.
2nd Forward Problem (Prediction). Quantify uncertainties about system states arising from incomplete knowledge of parameters and model structure for a specific aquifer.
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Scale-upEffective parameters
Statistically uniform
• Stationary and ergodic. Glimm and Kim, 1998
• Single hydro-geological material produced at more or less the same time by more or less the same process.
• Asymptotic expansions. Gelhar and Axness, 1983, Winter et al., 1984; Fannjiang and Papanicolaou, 1997
Statistically heterogeneous media
• Separable scales. Winter and Tartakovsky, 2001. Clark et al., in prep.
• Self-similarity. Neuman, 1994. Molz, 2004
Yeh et al, 2009
Neuman, 1994
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Scale-down and Inverse Problem
Realizations of pore spaces with specified correlations (Adler, 1992) or physical properties, e.g., Minkowski functionals of integral geometry (Hyman and Winter, 2014) can be produced by thresholding Gaussian random fields.
Statistical interpolation
• Spatial covariance, structure function, Kriging:
(x) = E[ ||K(x + ) – K(x)||2]
• Monte Carlo simulation
Sequential estimation
Thresholded fields
Thresholded surface Simulated pore space
Thresholded Gaussian Fields
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1st Forward problem: Effect of heterogeneities
Zhu et al, 201512 realizations
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1st Forward problem: Effect of heterogeneities
Zhu et al, 2015
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2nd Forward Problem: Prediction
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Bayesian Hydrogeology
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Predictions
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Models of reduced complexity
Reduced physicsLattice Boltzmann. Chen and Doolen,
Continuous time random walkhttps://www.weizmann.ac.il/EPS/People/Brian/CTRW/Berkowitz, 2006.
State transition diagrams. Winter and Tartakovsky (2009)
Jump processes
Reduced dimensionalityOrthogonal polynomials. Xiu and Karniadakis, 2003; Zhang and Lu, 2004; Xiu and Tartakovsky, 2006
Wavelet transforms. Foufoula-Georgieu
LBM
CTRW
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RCM for particcle breakthrough
105 particles~ slow and fast statesLx = Ly = 1.28 cm, Lz = 2,56 cm~ residence times per statevv~ constant velocitiesv>> v
Vertical velocities
C. Clark – UAJ. Hyman – LANLA. Guadagnini -- Politecnico
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Transitions and break through
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Results
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Continuous time Markov chain model