A Grand Challenge Problem in Hydrology and Water...
Transcript of A Grand Challenge Problem in Hydrology and Water...
A Grand Challenge Problem in Hydrology and Water Resources: Integration of Process
Understanding and Observational DATA from Different Platforms and Scales
Binayak P. Mohanty Texas A&M University
February 13, 2015
http://vadosezone.tamu.edu/
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Big Data Analytics – What & Why? • “Big” data
§ Ever increasing volume, variety and velocity (3V problem)
§ 103-106 rows (data records) ; 102-103 columns (variables)
• Data analytics § Process of examining data to uncover hidden patterns, unknown
correlations and other useful information that can be used to make better decisions (SAS Institute)
§ Key aspects – (a) collecting and managing data, (b) applying statistics and machine learning, and (c) interpretation, communication and visualization
Courtsey Srikanta Mishra
BIG Data in Water Resource Analysis Water Sustainability for the 21st Century
Climate Change and Extremes!
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DROUGHT FLOOD
STREAMS in USA
* Climate Change * Land Use Change/ Urbanization/ Agriculture / Mining/ … * Population Growth/ Overuse * Limited Conservation Measures * Lack of Social Awareness/ Education
Can our Societies have Sustainable Water Resources in the 21st century?!
Example - NASA GRACE SATELLITES SHOW SEVERE GROUNDWATER DEPLETITION in NORTHWEST INDIA (2002-2008)
Stress on our Water Resources…
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Water Cycle and Hydrologic Processes
First Principle: Need to Close the Water Budget at Any Scale!
The Earth in 2D
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Most Earth Sciences Static and Dynamic Data are Collected at Different Extents, Resolutions, and Support!
• Process Scale • Observation/Measurement Scale • Modeling (Working Scale)
Dominant Hydrologic Processes at Different Scales
Regional
Watershed
Field
Pore
SCALE
Process Scales
• Space Scale – Example: Unsaturated flow in soil at the cm-scale to flood in river systems of million of square kilometers
• Time Scale – Example: From flash floods of several minutes duration to flow in aquifers over hundreds of years
Preferential Flow at Different Space Scales
Runoff Pattern at Different Time Scales
Process Scales
Measurement Scales
Bottom- Up Approach Top- Down Approach
Recent Study Traditional Hydrology
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Texas Water
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Land Surface Hydrologic Processes at Local Scale…
Run-on Run-off
Upward flux
Percolation
Soil moisture observations
Ground Water -100 cm
0-5 cm
Free drainage
GW -150 cm
GW -200 cm
Evapotranspiration
Heterogeneity of soil profile
Infiltration
Day
T∇
H∇
Pg∇
Thermal Vapor
& Thermal Liquid
Isothermal Liquid
& Isothermal
Vapor
Advective Vapor
& Advective
Liquid
Vadose Zone Research Group
Coupled water and heat transport background
Night
T∇
H∇
Pg∇
Thermal Vapor
& Thermal Liquid
Isothermal Liquid
& Isothermal
Vapor
Advective Vapor
& Advective
Liquid
Evaporation Condensation
Pg∇
H∇
T∇
Coupled
Pg∇
T∇
H∇
Coupled
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Day
T∇
H∇
Pg∇
Vadose Zone Research Group
Coupled water and heat transport background
Night
T∇
H∇
Pg∇
Water
energy
Phase change (Evaporation
/condensation)
Evaporation Condensation
Pg∇
H∇
T∇
Coupled
Pg∇
T∇
H∇
Coupled
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1 Equilibrium model (phase change finished instantaneously)
( )( )
( ) ( ) ( )⎪⎭
⎪⎬⎫
⎪⎩
⎪⎨⎧
+∇+∇∂
∂++∇
∂
∂+∇+
⎪⎭
⎪⎬⎫
⎪⎩
⎪⎨⎧
⎥⎦
⎤⎢⎣
⎡∇++⎟⎟
⎠
⎞⎜⎜⎝
⎛++∇∇=
∂
+∂
gzPkk
TT
DDDD
TDKP
zKt
ggg
rgsv
vdpdf
vdpdf
TDlTw
gll
gvll
ρµ
ρρ
ψψρ
γψρ
θρθρψ
Water-Gas-Heat balance: Equation system (1)-(2)-(3)
Water-Heat balance: Equation system (1)-(2) [e.g., PdV, 1957 and Milly 1982 model]
( )( )( )[ ]
( ) ( )[ ] ( ){ }raarvvrllT
llgvrgvvdadalllsss
TTcqLTTcqTTcqzT
tWLTTccc
t
−++−+−∇−⎟⎠
⎞⎜⎝
⎛∂
∂∇
=∂
∂−+−+++
∂
∂
0
0
λ
θρθρθρθρθρθρ
(1)
[ ] ( )⎥⎥⎦
⎤
⎢⎢⎣
⎡+∇∇=
∂
∂ gzPkk
t ggg
rgsggg ρµ
ρθρ
The mechanistic formulation-balance of mass and energy equation
1.3 Gas balance
1.2 Heat balance
1.1 Water (liquid + vapor) balance
Vadose Zone Research Group
Coupled water and heat transport Mathematical model development
(2)
(3)
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Local/Point Sampling
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U.S. Watershed Soil Moisture Validation Sites
• All sites include 5 cm
• LW, LR, WG, RC since 2002
• WC, FC are new
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Soil Moisture Remote Sensing Space-Borne NASA: AMSR-E on AQUA SMAP
Air-Borne NOAA: PSR
Spatio-Temporal Data in Iowa
!
!
!
!!!
!
AMSR-E (JAN 15 2004)Soil Moisture cm3/cm3
0 - 0.0750.075 - 0.1050.105 - 0.1350.135 - 0.1650.165 - 0.2300.230 - 0.500
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4
3
5
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Global Coverage every 1.5 days 18
BNN Based Pedo-‐Topo-‐Vegetation-‐Transfer Function
% Sand% Silt% ClayBulk DensityNDVI or LAIDEMINPUTS
NEURAL NETWORKTraining
Coarse Scale Data
Bootstrapping
θ0bar
θ15barθ0.3bar
TARGETS
Fine-scale Soil Data
Fine-scaleθ0barθ15barθ0.3bar
OUTPUTS
Training
% Sand% Silt% ClayBulk DensityNDVI or LAIDEMINPUTS
Non-linear bias correction
Bayesian NN
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θr
θs
α
β
n
h
Multivariate distribution
in space
Prior probability
Rel
axed
PD
F ch
osen
from
dom
inan
t soi
ls
type
with
in A
MSR
-E p
ixel
AMSR-E measurements time series
θ={θ1, θ2 ,…, θT}
MCMC
Markov random process
Posterior distribution of up-scaled hydraulic
parameters at AMSR-E pixel scale
Schematic of parameter estimation
θ
MCMC for Scaling Up from Local Scale to Footprint Scale
0 1 2 3 4 5x 104
0.6
0.8
1Upscaling Parameter
iterationsB
eta
0.4 0.6 0.8 10
0.02
0.04MeanPosterior Plot
Mean
p(m
ean)
Likelihood
Scaling law: Xβ
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http://vadosezone.tamu.edu/
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