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but also with some difference, and the land use structure simulated with GTAP-
AEZ is more close to the real conditions in some AEZs than that obtained with
GCAM. For example, the consistence between the forest land area simulated with
GCAM and the real one reached more than 80 %, while that with GTAP-AEZ
reached only 37 %. Overall, GCAM involves the driving factors of the rapid
economic development, which makes the simulation more close to the reality.
However, neither of the two models takes account of the impacts of policies on
socioeconomic development, which also has great influence on the land use change.
Therefore, it is necessary to calibrate the models through optimizing the model
input parameters. When the models are calibrated through adjusting these socio-
economic parameters according to the specific conditions, the overall simulation
accuracy of GCAM reached 82 % and that of GTAP-AEZ also reached 60 %. So
that it is possible and necessary to improve the simulation accuracy through cali-
brating input parameters of the models according to the specific conditions.
In recent decades, more and more land use simulation models have been
developed, but it is still a hard task to implement the calibration of input
parameters for these models. In the study, the land use structure of China in 2010
is simulated with GCAM and GTAP-AEZ under the RCP 4.5 scenario, both of
which were further calibrated through adjusting the input parameters, focusing on
comparing the accuracy of the results simulated by two models. The result indi-
cates the simulated areas of cropland and forest land with both two models are
higher than the real one, while the simulated areas of grassland and built-up land
were lower than the real values, and the accuracy is greatly improved after the
calibration.
2.4 Summary
The framework of LUCD model compatible with RCMs was introduced, which
has been divided into three sub-modules. The modeling approaches of three
modules of the LUCD model should be accordant with specific RCM, so that we
make the LUCC classification flexible in the LUCD model. However, due to the
uncertainties of climate change, economic development, and other factors, it is
very difficult to accurately simulate the long-term land use change in the future.
Therefore, it is necessary to study more deeply on how to optimize the parameters
according to the specific conditions in the future.
Finally, we introduced the Global Change Assessment Model (GCAM) and the
GTAP-AEZ model which can take account of the influence of social economy and
climate change at the global scale. We simulated the land use structure of China in
2010 with the two models and compared the results with the real one. Also, we
calibrated these parameters of models according to the China's national conditions
and implemented the simulation again. The result indicates that the calibrated
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