Fourth Study Conference on BALTEX Scala Cinema Gudhjem

- 100 -
Parameter Estimation of the SVAT Schemes TERRA/LM and
REMO/ECHAM Using a Multi-Criteria Method
K.-P. Johnsen, S.Huneke, J.Geyer and H.-T.Mengelkamp
GKSS Research Center, D-21502 Geesthacht, Germany, (johnsen@gkss.de)
1. Introduction
The major task of the project EVA-GRIPS funded by
the German Ministry of Research and Education is the
development of a concept to calculate area averaged
evaporation and sensible heat fluxes over heterogeneous
land surfaces [1] for one pixel of different NWP models.
Known concepts like the MOSAIC approach, the tile
approach, the flux coupling approach and the application
of effective parameters were implemented into standalone
versions of the SVAT schemes TERRA/LM and
REMO/ECHAM.
The latent and sensible heat fluxes derived from the
SVAT schemes are currently compared with
measurements of the heat fluxes taken around the
Meteorological Observatory Lindenberg of the Deutscher
Wetterdienst (DWD) during the three LITFASS
campaigns 1998, 2002 and 2003.
Here we show results obtained with the effective
parameter approach. In order to simulate the heat fluxes
with the SVAT schemes it is necessary to estimate
appropriate values for all model parameters. Because not
all parameters necessary to know for the input can be
measured this approach allows to obtain the unknown
parameters by minimizing objective functions describing
the disagreement between the SVAT schemes and the
measurements.
2. Some Results
2.1. Calibration
To calibrate the SVAT schemes we applied the multiobjective
shuffled complex evolution algorithm
MOSCEM-UA [2] to obtain global minima of
independent objective functions.
Sensible Heat
Objective Function
0.14
0.13
0.12
0.11
0.1
0.09
0.08
Objective Space
Pareto Rank 1
Pareto Rank 2
0.16 0.17 0.18 0.19 0.2
Latent Heat Objective Function
Figure 1: Objective space for the parameter sets of the
REMO model with pareto rank 1 and 2 (calibration
period).
Latent Heat Flux [W/m²]
300
200
100
Latent Heat - Calibration
Min. Pareto Rank 1
Max. Pareto Rank 1
observed
0
19.5.03 24.5.03 29.5.03 3.6.03
Time
-100
Figure 2: Latent heat flux measurements above barley
taken during LITFASS 2003 and compared with
minimum and maximum REMO model results for
parameter sets with pareto rank 1 (calibration period).
As independent objective functions we used a modified
Nash-Sutcliff measure (optimum at 0) applied to the
differences between the measured and calculated heat
fluxes. The algorithm allows to optimize N independent
objective functions parallel. Here is N=2 for the latent
and sensible heat fluxes.
Sensible Heat Flux [W/m²]
400
300
200
100
Sensible Heat - Calibration
Min. Pareto Rank 1
Max. Pareto Rank 1
observed
0
19.5.03 24.5.03 29.5.03 3.6.03
Time
-100
Figure 3: Sensible heat flux measurements above barley
taken during LITFASS 2003 and compared with
minimum and maximum REMO model results for all
parameter sets with pareto rank 1 (calibration period).
The algorithm determines the set of pareto-optimal
objective function vectors of rank R. A vector x of
objective functions is said to dominate (i.e. has a lower
rank than) another objective function vector y if an i exist
for all xi with x i