Post on 30-Jul-2020
Using water storage variations derived from GRACEto calibrate a global hydrology model
S. Werth, A. Güntner, S. Petrovic, R. Schmidt, J. Kusche
GeoForschungsZentrum (GFZ) Potsdam, Germany
Workshop Hydrology from Space, Geneva 11/2007
Background
Calibrationof the global hydrological model WGHM
Water balance of a river basin:
P = E + R + ΔS
P: PrecipitationE: EvapotranspirationR: RunoffΔS: Water storage change
Model input
Calibration variable
(measured time series of river discharge)
Objectives
Multi-criterial calibrationof the global hydrological model WGHM
Water balance of a river basin :
P = E + R + ΔS
P: PrecipitationE: EvapotranspirationR: Runoff (measured time series of river discharge)ΔS: Water storage change (basin-average values from GRACE)
Calibration variables
Model input
The WaterGAP Global Hydrology Model (WGHM)
• Conceptual waterbalance model
• 0.5° spatial resolution
• Daily time-step
• Human water use accounted for
• Climate forcing data from CRU, GPCC, ECMWF
• Calibration for river dischargeat 1200 stations worldwide
Total continental storage change:
ΔS = ΔScanopy + ΔSsnow + ΔSsoil + ΔSgw + ΔSlakes + ΔSwetl + ΔSriver
1) Analyse model properties with respect to storage change
- Identify sensitive model parameters- Uncertainty assessment
2) Select adequate GRACE data and filter tools
3) Perform multi-objective model calibration
5
Work steps
Hydrological model calibration with GRACE data
Step 1.1) Selection of calibration parameters
Hydrological model calibration with GRACE data
Parameter sensitivity varies regionally with the dominant water storage processes / components
Step 1.2) Model uncertainty analysis
Hydrological model calibration with GRACE data
Structural model errors may exist if model uncertainty range does not enclose GRACE data
1) Analyse model properties with respect to storage change
- Identify sensitive model parameters- Uncertainty assessment
2) Select adequate GRACE data and filter tools
3) Perform multi-objective model calibration
5
Work steps
Hydrological model calibration with GRACE data
Step 2) Selection of adequate GRACE filter tools to derivebasin-average water mass variations
Hydrological model calibration with GRACE data
Filter type Parameter for filter intensity Source
Gaussian filter (GF) filter width Jekeli, 1981
Filter optimized for basin shape (OF) max. satellite error Swenson and Wahr, 2002
Filter 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
Step 2) Selection of adequate GRACE filter tools: Amazon basin
Hydrological model calibration with GRACE data
0.0
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1.0Pe
rfom
ance
valu
e
500 1000 1500filterwidth (km)
5101520max. satellite error (mm)
DDK decorrelated
w =10, n =30a e
w=30, n =30
a
e
w=20, n =30
a
ew =10, n =30
a
e
w =20, n =30a e
w =30, n =30a e
GFCEF decorrelated
OFCEF decorrelated
0
w =30, n =30a
ew =20, n =30
a
e
w =10, n =30a
e
SFCEF decorrelated
5 10 15 20error factor
50
100
150
2004006008001000corrlation length (km)
= 250 mmσs
w =30, n =30a e
w =20, n =30a e
w =10, n =30a e 20
MFCEF decorrelated
Gaussian filter (GF)
Filter optimized for basin shape (OF)
Filter optimized for exp. signal model (MF)
GRACE signal-noise-ratio optimized (SF)
Correlation Error Filter (CEF)
Decorrelation Filter (DDK)
Step 2) Selection of adequate GRACE filter tools: Lena basin
Hydrological model calibration with GRACE data
Gaussian filter (GF)
Filter optimized for basin shape(OF)
Filter optimized for exp. signal model (MF)
GRACE signal-noise-ratio optimized (SF)
Correlation Error Filter (CEF)
Decorrelation Filter (DDK)
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500 1000 1500 5101520
w=10, n
=30
a
e
w=30, n
=30
a
e
w=20, n =30
a
e
w =30, n =30a e
w =20 , n =30
a
ew =10, n =30a
e
CEF decorrelated CEF decorrelated
DDK decorrelated
CEF decorrelated
0 5 10 15 20
w =20, n =30a e
w =10, n =30a e
w =30, n =30a e
CEF decorrelated
2004006008001000
w =30, n =30a e
50
250= 300 mm
200
150
100
20
Perf
oman
ceva
lue
filterwidth (km) max. satellite error (mm)
GF OF SF
error factorcorrlation length (km)
MF
Step 2) Selection of adequate GRACE filter tools
Hydrological model calibration with GRACE data
1014, 4
30, 3, 2, 30
250, 200
0.4
13
200
Parameter Value
a, p
wa, we, na, ne
σs [mm], cl [km]
error factor
εmax [mm]
rg [km]
Parameter
0.74
0.63
0.78
0.77
0.78
0.75
Performance valueExample: Amazon basin
FilterGaussian filter (GF)
Filter optimized for basin shape (OF)
Filter optimized for exp. signal model (MF)
GRACE signal-noise-ratio optimized (SF)
Correlation Error Filter (CEF)
Decorrelation Filter (DDK)
DDKGFMississippiSFSFVolgaMFCEFMFYukon
MFMFGangesOF,MFOF, MFAmazonGLDASWGHMBasin
Optimal filter for 5 example river basins and two global hydrological models
1) Analyse model properties with respect to storage change
- Identify sensitive model parameters- Uncertainty assessment
2) Select adequate GRACE data and filter tools
3) Perform multi-objective model calibration
5
Work steps
Hydrological model calibration with GRACE data
Ob basin
1.0 0.5 0.0 -0.51.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
Nash-Sutcliffe coefficient for water storage change
Nas
h-S
utcl
iffe
coef
ficie
nt
for r
iver
dis
char
ge
6
perfect model
simulation
Hydrological model calibration with GRACE data
Step 3.1) Test for single-objective calibration
WGHM Monte-Carlo run
Standard WGHM calibrated for river discharge
WGHM single-objective calibration for GRACE storage change
Evaluationof error
stop ?
parameter set ranking
Optimalsolution
yes
no
initial parameter
sets
Model simulation
GRACE total storage
variation
RunoffMeasurement
data
7
0
erro
rdi
scha
rge
Pareto Frontier
0
single model simulation
error total storage change0
Hydrological model calibration with GRACE data
Step 3.2) Multi-objective calibration approach
Parameter variations
Model simulation
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
Hydrological model calibration with GRACE data
Step 3.3) Implementation of multi-objective calibration algorithms into WGHM
14
Hydrological model calibration with GRACE data
Conclusions
• Model parameter sensitivity for water storage change varies regionally with the dominant storage components
• GRACE data help to identify structural model errors during model uncertainty assessment
• Adequate filter tools to derive basin-average storage change from GRACE vary with location and river basin characteristics
• GRACE data are highly valuable to constrain large-scale hydrological models in a multi-objective calibration framework
Using water storage variations derived from GRACEto calibrate a global hydrology model
S. Werth, A. Güntner, S. Petrovic, R. Schmidt, J. Kusche
GeoForschungsZentrum (GFZ) Potsdam, Germany
Workshop Hydrology from Space, Geneva 11/2007