Investor Presentaiton
Model assessment
City
Lon
Lat
(a) NSE under RCP45
Dunhuang
(b) NSE under RCP85
94.71
40.13
Gaotai
99.84
Dunhuang 0.81 0.80 0.86 0.87 0.80 0.83
39.14
Dunhuang 0.81
0.80 0.83 0.83 0.80 0.84
0.9
Jiayuguan
98.28
39.78
0.9
Gaotai 0.86 0.84 0.87 0.90 0.85 0.87
Jiuquan
Gaotai 0.86 0.84 0.84 0.88
98.5
0.84 0.89
39.71
0.8
Lanzhou
103.73
0.8
36.03
Jiayuguan 0.30 0.10
0.40
0.34 0.25 0.22-
Tianshui
Jiayuguan
0.16 0.41 0.33
0.19 0.23-
105.69
34.6
0.7
0.7
Wuwei
102.61
37.94
Jiuquan 0775 0.75
0.80
0.80 0.81 0.80
Jiuquan
0.75
0.77
0.77
0.80 0.81
Yumen
97.58
39.81
0.6
0.6
Zhangye
100.46
38.93
Lanzhou -0.64
0.58
0.70
0.62 0.65-
Lanzhou 0.59
0.57
0.67
0.69
0.59 0.65-
Xian
108.95
34.27
0.5
0.5
Tianshui 0.55 0.54
0.63
0.63 0.57 0.58-
Wuwei 0.69 0274 0.77
Yumen 0.49 0.42 0.57
Under both RCP45 and RCP85, models
have best performances in Dunhuang
and Gaotai, and least in Jiayuguan and Zhangye 074 0.77
Xian.
Generally, IPSL performs the best for
all cities under both RCP45 and RCP85.
Models have different performance for
different cities.
Xian 0.29 02 0.34 0.37 0.30
ACCESS1
CNRM
Figure 5. The NSE between different modelled and satellite-based
NDVI in different cities under RCP4.5 and RCP8.5.
Tianshui 0.49 0.51
0.61 0.59 0.55 0.58
0.4
0.4
0.77
0.77
0.76-
Wuwei 0.70
0.77 0.78
0.3
0.3
0.59
0.49 0.50
Yumen 0.47 0.44
0.2
0.81
0.82 0.81 0.81
bcc
MPI
IPSL
GFDL
Zhangye 173
0.76
0.77
0.1
0.35
Xian 0.31 0.17 0.33
0
bcc
MPI
IPSL
GFDL
CNRM
ACCESS1
0.53 0.54 0.49 0.50
0.2
0.78 0.79 0.81
0.1
0.40
0.26
0.29-
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