4 Climate
Modeling
4.1 RCM Bias Correction
Linear regression was used to correct the RCM simulation results. The
climate correction sequence in the Xiangxi River Basin is from 1991 to
2005, in the Jinghe River Basin is from 1981 to 1987, and in the
Zhongzhou River Basin is 2009-2013. The variability of MAE in the three
basins is calculated (Fig. 5).
The climate simulation corrected for precipitation and temperature,
respectively. As can be seen from Fig. 5, the correction has different
effects in the two climatic factors. In general, this method has a
better correction on temperature. Among the three basins, Zhongzhou
River has the best correction effect, with an average of 83.66%; the
correction effect of Xiangxi River ranks the second, with an average of
78.05%; and the correction effect of Jinghe River is worse than the
others, with an average of 77.42%. The results on precipitation is not
ideal, and the correction effect are achieved just in Xiangxi River and
Zhongzhou River, i.e. 17.37%, 2.63%. This is because the RCM
simulation sequence and the measured sequence are arranged in the
ascending order firstly when the correction method is used. The climate
correction is performed on the basis of the climate sequence that
disturbs the one-to-one correspondence. Although this method may have
negative bias growth in precipitation correction, it has a more accurate
forecast effect on extreme weather during the whole forecast period for
long-term climate simulation.
At the same time, the results show that the climate simulation accuracy
by the four RCMs, i.e., RSM3, HadGEM3_RA, RegCM4 and WRF, has not much
different among the three basins. Therefore, in the subsequent part of
this paper, only the mean analysis results of the four RCMs are
presented.
4.2 Annual Climate Changes
Using RSM3, HadGEM3_RA, RegCM4 and WRF to forecast climate in the
period 2021-2050, the forecast is carried out in Xiangxi River, Jinghe
River and Zhongzhou River, respectively. And the forecast results are
corrected by the above climate bias-correction method. Since the four
RCMs have similar climate simulation accuracy, this section takes the
average forecast data of the four RCMs as forecast results. Taking the
mean historical observation data as baseline, the climate change trend
under two emission scenarios was analyzed as follows.
The annual analysis results are shown in Fig. 6. It can be seen that
annual average temperature in the three basins will increase in the next
30 years. Under the RCP8.5 emission scenario, the temperature rise trend
is more obvious. Temperature rise in Jinghe River is the strongest, and
the highest temperature under the RCP8.5 emission scenario appears in
2047, an increase of 43.39%. Compared with Jinghe River, the
temperature changes of the others are more gradual. The increase in
Xiangxi River does not exceed 30%, and in Zhongzhou River does not
exceed 10%. Under the RCP4.5 emission scenario, temperature in
Zhongzhou River has a negative growth in a few years.
Future average annual precipitation in the three river basins show
different trends. The value in Xiangxi River in the next 30 years shows
a significant upward trend. And there was no significant difference in
Xiangxi River under the two emission scenarios, all of which are around
16%. The annual average precipitation in Jinghe River shows a great
swing change in the next 30 years. The variability under RCP8.5 emission
scenario is higher than that under RCP4.5 emission scenario. Therefore,
it is more likely that extreme weather will occur in Jinghe River in the
next 30 years, and this situation is even more serious under the RCP8.5
emission scenario. Different from the other two basins, the annual
average precipitation in Zhongzhou River has a downward trend, with an
average decline of 16.27% under RCP4.5 emission scenario and 18.32%
under RCP8.5 emission scenario.
4.3 Monthly Climate Changes
Like annual climate changes
analysis, based on historical observation data, the climate monthly
change trend of the three basins in the next 30 years under two emission
scenarios was analyzed. The analysis results are shown in Fig. 7.
As can be seen from the figure, the monthly average temperature in the
three basins has an upward trend. The rise in Jinghe River is the most
obvious, especially in the summer (June-August) and winter
(December-February). And the growth rate is larger under RCP8.5 emission
scenario. Therefore, the summer temperature in Jinghe River will be
higher than the original, and the “warm winter” phenomenon may
continue to occur. Temperature rise in Xiangxi River mainly occurs in
the summer and autumn (September-November), while the temperature in
spring (March-May) and winter show a downward trend. Therefore, the
temperature difference among four seasons in Xiangxi River may be even
more different in the future. That means, the four seasons in Xiangxi
River may more distinct. The rise in Zhongzhou River mainly occurs in
the spring, while in the other three seasons have not obvious change.
Therefore, the temperature difference among four seasons in Zhongzhou
River may decrease in future.
There is a significant difference in the monthly average precipitation.
In the next 30 years, monthly average precipitation in Xiangxi River and
Zhongzhou River during the precipitation peak period (July) will
increase. Therefore, the probability of extreme weather in these two
basins may increase in the future. In particular, the precipitation
decreased significantly in April and May when precipitation was less in
Zhongzhou River, which would aggravate the occurrence of extreme
weather. However, Jinghe River did not show significant changes.
Significantly, it was analyzed that the annual average precipitation in
the Jinghe River has a swing change. Therefore, no significant change in
monthly average precipitation does not indicate that there is no change
in future precipitation.