Geography Reference
In-Depth Information
The simulation scheme in this study is as follows. The land cover dataset of
year 2010 with the United States Geological Survey's (USGS) classification sys-
tem is used as the baseline underlying surface data in this study. Then, the
structural changes of land use in year 2010 and 2030 under different scenarios
were simulated with the module of GCAM and the future spatial pattern of land
cover is simulated with the DLS model. Furthermore, the land use data is used as
the input underlying surface data of the WRF model to simulate the impacts of
land cover change on the climate change.
4.2.1.4 Data and Processing
The input data of WRF model mainly include the underlying surface data and
climate forcing data. The 1 km resolution land cover data of the USGS classifi-
cation system in year 2010 were used as baseline data in this study. The baseline
data of land use/cover change were derived from the dataset of the National Key
Programme for Developing Basic Science in China (Grant No. 2010CB950900).
These data of the 1 km resolution were extracted from Landsat TM/ETM images
and classified according to the USGS classification system, the interpretation
accuracy of which exceeds 92 % (Liu et al. 2010 ). The land use/cover data in year
2020 and 2030 were predicted with the data of land conversion among land cover
types. Since different communities have different classification systems for the
land cover data, this study used the USGS classification, which includes 24 land
cover types. First, the 1 km resolution land cover data of USGS classification in
year 2010 were extracted from the USGS remote sensing images, and were used as
the land cover data in the baseline year. Then the land conversion data, which are
used to forecast the land use change (land conversion among different land cover
types) during 2010-2030, were simulated with the DLS model based on the dif-
ferent scenarios designed according to the land demand. Finally, the 1 km reso-
lution land cover data were converted into the 10 km resolution data according to
the requirement of WRF model.
The forcing data needed in WRF model include the wind field, surface air
temperature, long-wave radiation, and short-wave radiation. This study used the
climate forcing data from NCEP/FNL (Final) Operational Global Analysis data,
which is updated every 6 h. This dataset has been constructed and updated since
July of 1999 with the data assimilation of the remote sensing data and ground-
based observation data, etc. The NCEP/FNL dataset has higher accuracy and
spatial resolution, and it has the spatial resolution of 1 9 1 and the vertical height
of 27 layers. The static land surface data were the GEOG data provided by WRF
model, which were replaced with the land use/cover data under different scenarios
in the further simulation. This study has used the Noah land surface parameteri-
zation scheme, with which the simulation result is more stable and reasonable. The
data of the temperature field and precipitation field in this scheme were interpo-
lated with the large scale information.
Search WWH ::




Custom Search