Geography Reference
In-Depth Information
7.2.1 Data and Methodology
7.2.1.1 Data Processing
The data used in this study include land cover data, climate forcing data, and
meteorological observation data. In this study, the 1 km 9 1 km resolution land
cover data of the USGS classification system in year 2000 derived from USGS
Land Cover Institute, including 24 land cover types, were used as the baseline
data. The land cover data of year 2010 and 2100 were predicted with the data of
land conversion data, which were derived from the Asia-Pacific Integrated Model
with the global economy model (AIM/CGE) based on RCPs 6.0 scenario. In the
AIM/CGE model, land resource was treated as a production factor for agriculture,
livestock, forestry, and biomass energy production. Urban land area expanded due
to the population and economic growth, while the cropland area expanded for
meeting the increasing food demand. The land cover data and underlying land
surface change data during 1500-2100 can be obtained through data fusion at
0.5 9 0.5 resolution (Hurtt et al.
2011
).
As there is difference in the spatial resolution and classification system between
the land cover data of the USGS classification system and the RCP-based land
conversion data, it is necessary to project the future land use and land cover data
and upscale it to a higher resolution (1-10 km) as the requirement of WRF model.
As it is well acknowledged that regional climate models (RCMs) with spatial
resolution at or coarser than 30 km are unable to produce accurate climate fore-
casts (Jin and Miller
2007
). Higher resolution allowed the model to feature the
regional geophysical conditions and predict the regional climate with more
accuracy. For example, WRF simulations can be done at a resolution of 4 km,
which allows many small scale features, such as mountains and coastlines, for our
purposes (Mawalagedara and Oglesby
2012
). In this study, we set the resolution of
the land cover data to be 5 km 9 5 km in WRF model. Taking the processing
of land cover data of year 2100 as an example, first, the accumulated fraction of
different kinds of land conversion in each 0.5 9 0.5 grid from 2000 to 2100 was
calculated (Fig.
7.5
). Then the dominant conversion type (with maxima conversion
amount) of each grid cell could be identified, and thereafter whether the land cover
type of grids changed or not was identified through setting threshold value of the
conversion rate. The threshold values were mainly set to reveal the conversion
trend, as to each type of conversion, the threshold value was set to be the 50
th
percentile of the conversion rate. The land cover data in year 2010 and 2100 were
further obtained from the land cover data of the USGS classification in year 2000
and the land conversion data during 2000-2010 and 2000-2100. Finally the
underlying land surface data were transformed to grid data of 5 km 9 5km
through resampling.
Model output of RCP 6.0, such as air temperature, specific humidity, sea level
pressure, eastward wind, northward wind, and geopotential height from 2000 to
2100 were used as the atmospheric forcing dataset in the WRF model. In addition,
