Meeting challenges on the calibration of the global hydrological model WGHM with GRACE data input - PowerPoint PPT Presentation

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Meeting challenges on the calibration of the global hydrological model WGHM with GRACE data input

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Amazon. GLDAS. WGHM. Basin. Optimal filter for 5 basin ... Amazon wNSC values and filter parameter for different filter types. Filter. Gaussian filter (GF) ... – PowerPoint PPT presentation

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Title: Meeting challenges on the calibration of the global hydrological model WGHM with GRACE data input


1
Meeting challenges on the calibrationof the
global hydrological modelWGHM with GRACE data
input
  • S. Werth
  • A. Güntner
  • with input from R. Schmidt and J. Kusche

2
Introduction
Time-Variable Gravity and Surface Mass Processes
Validation, Processing and First Application of
New Satellite Gravity Data (TIVAGAM)
Terrestrial water balance
?S P - R - E
?S Water storage changeP Precipitation E
Evaporation R Runoff
2
3
The WaterGAP Global Hydrology Model (WGHM)
  • Conceptual waterbalance model
  • 0.5 spatial resolution
  • Daily time-step
  • Climate forcing data from CRU, GPCC, ECMWF
  • Human water use accounted for
  • Calibration for river dischargeat 1200 stations
    worldwide

Total continental storage change
?S ?Scanop ?Ssnow ?Ssoil ?Sgw ?Slakes
?Swetl ?Sriver
3
4
Correspondence between GRACE and WGHM
Mean maximum annual storage change (Gaussian
filtering, 500 km)
GRACE
WGHM
Aim Improve WGHM model results by a new
calibration with GRACE data.
4
5
Work plan for model calibration
  • Analyze model properties
  • Identification sensitive parameters
  • Model uncertainty
  • Calibration test runs
  • 2) Select adequate GRACE data and filter tools
  • 3) Perform multi-objective model calibration

5
6
Work plan for model calibration
  • Analyze model properties
  • Identification sensitive parameters
  • Model uncertainty
  • Calibration test runs
  • 2) Select adequate GRACE data and filter tools
  • 3) Perform multi-objective model calibration

5
7
1c) Single-objective calibration
perfect modelsimulation
6
8
Calibration approach
initial parameter sets
GRACE total storage variation
RunoffMeasurementdata
current parametersets
Model simulation
Evaluationof error
Parameter-variation
Optimalsolution
no
parameter set ranking
yes
stop ?
7
9
Work plan for model calibration
  • Analyze model properties
  • Identification sensitive parameters
  • Model uncertainty
  • Calibration test runs
  • 2) Select adequate GRACE data and filter tools
  • 3) Perform multi-objective model calibration

8
10
2) GRACE filter tool evaluation
worldwide 22 largest WGHM river basins
Filter type Parameter Source Gaussian filter
(GF) filter width Jekeli, 1981 Optimized for
basin shape (OF) max. satellite error Swenson and
Wahr, 2002 Optimized for exp. signal model
(MF) correlation length, signal variance Swenson
and Wahr, 2002 GRACE signal-noise-ratio optimized
(SF) factor of formal errors Seo et al,
2005 Correlation Error Filter (CEF) filter
window properties Swenson and Wahr,
2006 Decorrelation Filter (DDK) covariance matrix
parameter Kusche, 2007
9
11
2) GRACE filter tool evaluation Amazon
10
12
2) GRACE filter tool evaluation Lena
11
13
2) GRACE filter tool evaluation
Filter
Amazon wNSC values and filter parameter for
different filter types
Optimal filter for 5 basin examples
12
14
Work plan for model calibration
  • Analyze model properties
  • Identification sensitive parameters
  • Model uncertainty
  • Calibration test runs
  • 2) Select adequate GRACE data and filter tools
  • 3) Perform multi-objective model calibration

13
15
Work plan for model calibration
  • 3) Calibration Realization

Implementation of Multi-objective calibration
algorithms into WGHM DDS Dynamically Dimension
Search ? single-objective calibration algorithm
extended for mutli-objective problems NSGA-II Non-
dominated Sorting Genetic Algorithm ?
evolutionary multi objective calibration algorithm
14
16
Summary and Outlook
  • fulfilled steps
  • Model studies for selected river basins
  • Analyses of GRACE filter tools
  • Implementation of calibration algorithm
  • next steps
  • Multi-objective calibration runs
  • Use of differently processed GRACE data,
  • e.g. signal proportions from analysis of
    Schmidt et. al. 2007

15
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