Matt Rodell Hydrological Sciences Branch, NASA GSFC Enhancing the Value of GRACE for Hydrology Matt...
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Transcript of Matt Rodell Hydrological Sciences Branch, NASA GSFC Enhancing the Value of GRACE for Hydrology Matt...
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
Enhancing the Value of GRACEEnhancing the Value of GRACEfor Hydrologyfor Hydrology
Matt Rodell1, Jay Famiglietti2, and Ben Zaitchik1,3
1 Hydrological Sciences Branch, NASA Goddard Space Flight Center2 Earth System Science, University of California, Irvine
3 Earth System Science Interdisciplinary Center, University of Maryland
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
• Demonstrate that the value of GRACE data can be enhanced by synthesizing them with other observations and models
• Describe a few hydrological applications
MotivationMotivation
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
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Observed Groundwater
GRACE GroundwaterMississippi River basin
Illinois
Soil moisture from a land surface model (top) or in situ observations (bottom) can be used to isolate groundwater from GRACE derived TWS variations
GRACE groundwater estimate
Groundwater well observations
GRACE groundwater estimates (smoothed)
Rodell et al., Hydrogeology, 2006
Yeh et al., WRR, 2006
Matt RodellMatt RodellNASA GSFCNASA GSFC
Isolating Groundwater from GRACE TWSIsolating Groundwater from GRACE TWS
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
Evapotranspiration (ET) estimated using a terrestrial water budget:
SQPET
Observation based precipitation product
River runoff observations
From GRACE
ET as a Water Balance ResidualET as a Water Balance Residual
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
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Catchment LSM GRACE/3B42 Time Period
Comparison of ET Estimates Over the Comparison of ET Estimates Over the Mississippi River BasinMississippi River Basin
Updated from Rodell et al., GRL, 2004
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
Comparison of ET Estimates Over the Comparison of ET Estimates Over the Mississippi River BasinMississippi River Basin
[mm/day] GRACE/3B42
GRACE/CMAP
NOAA/GDAS
ECMWF AFWA GLDAS/Noah
GLDAS/CLM2
GLDAS/Mosaic
NLDAS/Noah
Catchment LSM
Mean 1.53 1.40 2.53 1.99* 1.98 1.64 1.34 1.75 2.22* 1.84
Bias -0.13 1.00 0.47 0.46 0.12 -0.19 0.22 0.44 0.32
Corr. Coef. 0.99 0.90 0.91 0.91 0.92 0.92 0.92 0.97 0.89
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Catchment LSM GRACE/3B42 Time Period
• (P-Q) determines long term average ET; GRACE ΔS enables generation of ET time series
• High bias in modeled ET is a known issue
• Potential application is improvement of land surface and atmospheric models
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
• Offline simulations of the Catchment LSM using GLDAS forcing data
• 10 year spin-up under 2002 forcing• 20-member ensemble simulations for
open loop (OL) and data assimilation (DA)
• Monthly GRACE anomalies: CSR/GFZ/JPL mean, Jan 2003 - May 2006
• Ensemble Kalman smoother DA
Assimilation of GRACE TWS DataAssimilation of GRACE TWS Data
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
Results have higher resolution than GRACE alone, better accuracy than model alone.
GRACE TWS anomalyJanuary 2003 – June 2006
GRACE Assimilating Catchment LSM TWS anomaly, mm
January 2003 – June 2006
From scales useful for water cycle and climate studies…
To scales needed for water resources and agricultural
applications
Assimilation of GRACE TWS DataAssimilation of GRACE TWS Data
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
Upper Mississippi
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Ohio-Tennessee
colu
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Missouri
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Lower Miss-Red-Arkansascolu
mn
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Modeled Water Storage
Model-GRACE Assimilation
GRACE Water Storage
Models produce continuous time series.
Mississippi River sub-basins
Daily estimates are critical for
operational applications
Monthly GRACE data anchor
model results in reality
Assimilation of GRACE TWS DataAssimilation of GRACE TWS Data
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
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Models separate snow, soil moisture, and groundwater; GRACE ensures accuracy.
Mississippi River basin
Assimilation of GRACE TWS DataAssimilation of GRACE TWS Data
Catchment LSM TWS
GRACE-Assimilation TWS
From a global, integrated observationTo application-specific water storage components
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Snow Water Equivalent
Soil Moisture
Groundwater
Observed Groundwater
GRACE Total Water
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
Assimilation of GRACE TWS DataAssimilation of GRACE TWS Data
Statistically significant improvement of groundwater estimates
r RMSE r RMSE skillMississippi 0.59 23.5 0.69 18.7 0.20
Ohio-TN 0.78 62.8 0.82 41.1 0.35Upper Miss. 0.29 42.6 0.29 40.1 0.06
Red-Ark. / L.M. 0.69 30.9 0.72 26.5 0.14Missouri 0.41 24.5 0.66 19.7 0.20
OL GRACE DA
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
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River Discharge OL GRACE DA OL GRACE DA
Kanawha 537 0.41 0.42 0.52 0.52Wabash 1,001 0.55 0.62 0.18 0.18
Illinois 527 0.68 0.72 0.30 0.29
Minnesota 160 0.61 0.69 0.35 0.36
Arkansas 240 0.19 0.29 0.20 0.22
Ouachita 83 0.37 0.35 0.04 0.04
Yellowstone 212 0.24 0.26 0.35 0.42Kansas 107 0.4 0.49 0.55 0.59
rTWS rR
Assimilation of GRACE TWS DataAssimilation of GRACE TWS Data
Some improvement of runoff estimates
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
• More sophisticated error estimates
• Evaluation in other large basins
• Implement Routing Model
• Application: Drought monitoring
• Application: Seasonal prediction systemsApril 2005
-2.5 2.5-0.7 0.7 -14 14-3.8 3.8
A B
-2.5 2.5-0.7 0.7 -14 14-3.8 3.8
A BSoil Moisture Latent Heat Flux
% W m-2
GRACE data assimilation influences other modeled variables as well
Assimilation of GRACE DataAssimilation of GRACE Data
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
Application to Drought MonitoringApplication to Drought Monitoring
June 2005
April 2006
GRACE Obs GRACE Assimilation US Drought Monitor
Matt RodellMatt Rodell
Hydrological Sciences Branch, NASA GSFCHydrological Sciences Branch, NASA GSFC
• GRACE data have enabled many innovative scientific studies, but we must also begin to apply GRACE for socially relevant applications
• The value of GRACE data can be enhanced by merging them with information from other sources
- auxiliary observations
- data assimilating models
• Creativity is the key
☆ GLDAS output are now available from: http://disc.gsfc.nasa.gov/hydrology/index.shtml
SummarySummary