j. huang and j. halpenny geodetic survey division, ess 615 booth st., ottawa, on
DESCRIPTION
Joint International GSTM and DFG SPP Symposium, October 15-17, 2007 at GFZ Potsdam, Germany. Estimating variation of groundwater storage within the Great Lakes Water Basin from GRACE, soil moisture and lake levels. J. Huang and J. Halpenny Geodetic Survey Division, ESS - PowerPoint PPT PresentationTRANSCRIPT
Estimating variation of groundwater storage within the
Great Lakes Water Basin from GRACE, soil moisture
and lake levels
Joint International GSTM and DFG SPP Symposium,
October 15-17, 2007 at GFZ Potsdam, Germany
J. Huang and J. Halpenny
Geodetic Survey Division, ESS
615 Booth St., Ottawa, ON
Canada’s Natural Resources – Now and for the Future 2
Outline
1. Introduction
2. Method
3. Analysis of monthly GRACE models
4. Estimation of groundwater variation
5. Conclusions
3
1. Introduction
Quebec
L. Superior:82,000 km2
L. Michigan:57,800 km2
L. Huron:59,600 km2
L. Ontario:18,960 km2
L. Erie:25,700 km2
Area of the Great Lakes Water Basin: 766,000 km2
4
2. Method (1/2)
Cnm(ti), Snm(ti)
Least-Squares Fitting
TrendSeasonalSignals
Residuals
GaussianFilter
HarmonicSynthesis
GW VariationEstimation
GL StorageVariation
Snow,Ice, SM
GWVariation
Processing flowchart:
GL: Great Lakes
GW: Groundwater
SM: Soil Moisture
Spherical Harmonic Coefficients
5
2. Method (2/2)
n
m
nminminm
L
n
n
eei PmtSmtC
r
a
r
GMtN
0
*
20
)(sinsin)(cos)()(
Time-Variable (TV) geoid from GRACE:
nirttBttA
ttatttvtCtC
Cnm
CSi
Cnm
CAi
Cnm
iCnmi
Cnmnminm
,...2,14cos2cos
)(2
1))(()()(
00
20000
The model for Least-squares fitting of harmonic time-variable coefficients:
)()()( 00 ttatvtv iCnm
Cnmi
Cnm
Velocity at epoch ti: Signal-to-Noise Ratio (SNR):
Cnm
Cnm
Cnm
Cnm
x
BAavxx
SNR ,,,,ˆ
6
3. Analysis of monthly GRACE models (1/5)
S8,1
Spherical harmonic coefficient time series (red dots) and their LS fitting (blue dot line):
S8,5
S12,1
S12,7
S16,1
S16,9 S20,11
S20,1
7
3. Analysis of monthly GRACE models (2/5)
n
n
m
mCnm
Snm
n
n
m
mCnm
Snm
n
n
m
mCnm
Snm
n
n
m
mCnm
Snm
Linear: Quadratic:
Annual: Semi-annual:
8
3. Analysis of monthly GRACE models (3/5)
Linear: Quadratic:
Annual: Semi-annual:
RMS signal per degree vs. a posteriori standard deviation:
n=14
9
3. Analysis of monthly GRACE models (4/5)Trend (RMS = 15 mm/a): Annual (RMS = 37 mm):
Semi-annual (RMS = 5 mm): Residual (RMS = 27 mm):
10
3. Analysis of monthly GRACE models (5/5)
Method Min Max Mean StdDev RMS
A: Gaussian - 185 262 - 4 46 46
B: Least-Squares + Gaussian - 204 325 - 4 51 51
B - A - 88 103 1 24 24
A: Gaussian Filtering B: Least-Squares Fitting + Gaussian Filtering
Unit: mm
11
4. Estimation of groundwater variation (1/4)
w(,) =
)()()()()( iriannsemiiannitrendiGLB tHtHtHtHtH
dtHwA
tH ixix )(),(1
)(
The mean water-thickness-equivalent over the GLB by:
Each component by:
Simulation:
Global WTE Gridof 15' by 15'
SphericalHarmonic Model
WTE over theGLB
12
CSRRL04:(60 months)
4. Estimation of groundwater variation (2/4)
GFZRL04:(53 months)
13
4. Estimation of groundwater variation (3/4)
Lake Levels:
GLDAS SM&SW:
14
4. Estimation of groundwater variation (4/4)
GroundwaterEstimation fromCSRRL04:
GroundwaterEstimation fromGFZRL04:
15
5. Conclusions
1. The combination of the least-squares fitting and Gaussian filtering enhances the extracted GRACE signal by about 10% over the Gaussian filtering alone.
2. The total water storage variation (RMS=3.5 cm) from GRACE demonstrates close agreement (magnitude and phase) to the soil moisture and snow variation (RMS=3.7 cm) from GLDAS over the Great Lakes Water Basin.
3. The mean lake level variation (RMS=4.1 cm) over the basin demonstrates a comparable magnitude to the GRACE estimate but a phase lag of about 3 months.
4. The estimated groundwater variation (RMS=4.1 cm) implies that groundwater plays a key role in replenishing the Great Lakes.