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Variations and errors estimates of TWS from GRACE for hydrological
applications
Liangjing Zhang, Henryk Dobslaw, Maik Thomas
German Research Centre for Geosciences (GFZ)
Department 1: Geodesy and Remote Sensing
Section 1.3: Earth System Modelling
jzhang@gfz-potsdam.de
GRACE
Motivation
Spherical harmonics Clm, Slm
Post-processing
1
http://www.essc.psu.edu/
Terrestrial water storage (TWS)
Grids
(Rodell etc,2004)
Validation
Spherical harmonics Clm, Slm (d/o 90)
Level 2 RL05a from GFZ
GRACE Data
WGHM, LSDM, JSBACH, MPI-HM, GLDAS
Time span:2003.01—2012.12
2
Post-processing method
GRACE Clm, Slm
Rescaled TWS estimates
-Degree 1 added -C20 replaced -Mean reduced -DDK2 Filtering
Filtered GRACE
TWS
Hydrological Model
Filtered Model TWS
Least square fit
Spectral domain Spatial domain
Rescaling factor k
Spectral/Spatial domain
3
GLDAS
4
Post-processing method -scaling factors
LSDM WGHM
JSBACH MPI-HM
Median
Contributions from each model to the median
Median: "middle" of a sorted list of numbers
5
Post-processing method -scaling factors
Median of the rescaling factors
5
Post-processing method -scaling factors
The variation coefficient of rescaling factors Correlation between filtered and original TWS (GLDAS)
Landerer&Swenson(2012)
5
Post-processing method -scaling factors
Landerer&Swenson(2012)
The effect of rescaling
6
Post-processing method -scaling factors
Error estimates
• Measurement error
Filtered TWS errors propagated from “calibrated errors” multiplied by scaling factors
• Leakage error
• Rescaling error
Multiplying by the RMS of the differences between each scaling factor and the median value
7
Error estimates -gridded Measurement error Leakage error
Rescaling error Total error
Gridded error estimates serve as a start point for deriving basin-averaged TWS errors
The gridded TWS errors are spatially correlated
Method from Landerer&Swenson(2012) to consider covariance between different grids
d0 represent the de-correlation length scale in the Gaussian window
Error estimates
9
10
Measurement error Leakage error
Rescaling error Total error
Error estimates –basin-averaged
Validation of hydrological models
GRACE Amplitude GRACE Phase
Mod
els
Am
plitu
de
Mod
els
Pha
se
Annual cycle
11
Normalized RMS of TWS differences from GRACE and models
12
LSDM
GLDAS WGHM
Validation of hydrological models
LSDM-ECMWF
LSDM with different forcings
LSDM-WFDEI
Conclusions • Globally gridded TWS variations and basin-scaled errors have
been obtained
• A median value of rescaling factors from five hydrological models make the rescaling more robust against particular weakness of a certain model
• Validation from GRACE TWS can be used to identify the deficiencies in the models which can help to improve the models (LSDM)
• Forcing data can have large effect on the model simulated TWS
14
Thank you very much.
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