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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