Impacts of Future Climate Changes on Spatio-Temporal Distribution of Terrestrial Ecosystems over China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Methods
2.3.1. The Potential Natural Vegetation Model
2.3.2. The Holdridge Life Zone Model
2.3.3. The CSCS Model Validation
2.3.4. The Kappa Statistic
3. Results and Discussion
3.1. Accuracy Verification Based on the CSCS Model
3.2. Spatial Distribution of PNV in China under the Current Scenario
3.3. Spatial Variations of PNV in China under Future Climate Scenarios
3.4. Geometrical Center Shift of Super-Classes for PNV in China
3.5. Spatio-Temporal Pattern Evolution of Super-Classes for PNV in China
3.6. Analysis of Sensitivity of PNV in China to Climate Changes
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Classification System | Class Name and Code | |||
---|---|---|---|---|
CSCS | IA | Frigid–extra-arid frigid desert, alpine desert | ID | Frigid subhumid moist tundra, alpine meadow steppe |
IIA | Cold temperate–extra-arid montane desert | IID | Cold temperate subhumid montane meadow steppe | |
IIIA | Cool temperate–extra-arid temperate zone desert | IIID | Cool temperate–subhumid meadow steppe | |
IVA | Warm temperate-extra arid warm temperate zone desert | IVD | Warm temperate subhumid forest steppe | |
VA | Warm–extra-arid subtropical desert | VD | Warm–subhumid deciduous broad-leaved forest | |
VIA | Subtropical–extra-arid subtropical desert | VID | Subtropical–subhumid sclerophyllous forest | |
VIIA | Tropical–extra-arid tropical desert | VIID | Tropical–subhumid tropical xerophytic forest | |
IB | Frigid–arid frigid zone semidesert, alpine semidesert | IE | Frigid–humid tundra, alpine meadow | |
IIB | Cold temperate–arid montane semidesert | IIE | Cold temperate–humid montane meadow | |
IIIB | Cool temperate–arid temperate zone semidesert | IIIE | Cool temperate–humid forest steppe, deciduous broad-leaved forest | |
IVB | Warm temperate–arid warm temperate zone semidesert | IVE | Warm temperate–humid deciduous broad-leaved forest | |
VB | Warm–arid warm subtropical semidesert | VE | Warm–humid evergreen broad-leaved forest | |
IB | Frigid–arid frigid zone semidesert, alpine semidesert | VIE | Subtropical–humid evergreen broad-leaved forest | |
IIB | Cold temperate–arid montane semidesert | VIIE | Tropical–humid seasonal rainforest | |
IC | Frigid–semiarid dry tundra, alpine steppe | IF | Frigid–perhumid rain tundra, alpine meadow | |
IIC | Cold temperate semiarid montane steppe | IIF | Cold temperate perhumid taiga forest | |
IIIC | Cool temperate–semiarid temperate typical steppe | IIIF | Cold temperate perhumid mixed coniferous broad-leaved forest | |
IVC | Warm temperate–semiarid warm temperate typical steppe | IVF | Warm temperate perhumid deciduous broad-leaved forest | |
VC | Warm–semiarid subtropical grass–fruticose steppe | VF | Warm–humid deciduous–evergreen broad-leaved forest | |
VIC | Subtropical–semiarid subtropical brush steppe | VIF | Subtropical perhumid evergreen broad-leaved forest | |
VIIC | Tropical–semiarid savanna | VIIF | Tropical–humid rainforest | |
HLZ | 1 | polar desert | 20 | warm temperate dry forest |
2 | subpolar dry tundra | 21 | warm temperate moist forest | |
3 | subpolar moist tundra | 22 | warm temperate wet forest | |
4 | subpolar wet tundra | 23 | warm temperate rainforest | |
5 | subpolar rain tundra | 24 | subtropical desert | |
6 | boreal desert | 25 | subtropical desert scrub | |
7 | boreal dry scrub | 26 | subtropical thorn woodland | |
8 | boreal moist forest | 27 | subtropical dry forest | |
9 | boreal wet forest | 28 | subtropical moist forest | |
10 | boreal rain forest | 29 | subtropical wet forest | |
11 | cool temperate desert | 30 | subtropical rainforest | |
12 | cool temperate desert scrub | 31 | tropical desert | |
13 | cool temperate steppe | 32 | tropical desert scrub | |
14 | cool temperate moist forest | 33 | tropical thorn woodland | |
15 | cool temperate wet forest | 34 | tropical very dry forest | |
16 | cool temperate rain forest | 35 | tropical dry forest | |
17 | warm temperate desert | 36 | tropical moist forest | |
17 | warm temperate desert scrub | 37 | tropical wet forest | |
19 | warm temperate thorn scrub | 38 | tropical rainforest | |
1:1,000,000 vegetation distribution map | 1 | cold temperate and temperate mountain coniferous forest | 23 | subalpine hard-leaved evergreen broad-leaved thicket |
2 | temperate coniferous forest | 24 | evergreen coniferous thicket in subalpine mountains | |
3 | subtropical coniferous forest | 25 | temperate dwarf semiarbor desert | |
4 | tropical coniferous forest | 26 | temperate shrub desert | |
5 | subtropical and tropical montane coniferous forest | 27 | temperate steppe shrub desert | |
6 | temperate coniferous and deciduous broad-leaved mixed forest | 28 | temperate semishrub, dwarf semishrub desert | |
7 | mixed forest of coniferous, evergreen broad-leaved and deciduous broad-leaved forest in subtropical mountain areas | 29 | temperate succulent halophyte dwarf shrub desert | |
8 | temperate deciduous broad-leaved forest, temperate deciduous leaflet forest | 30 | temperate desert of annual herbs | |
9 | temperate deciduous leaflet forest | 31 | alpine cushion-like dwarf semishrub desert | |
10 | subtropical deciduous broad-leaved forest | 32 | temperate grass and miscellaneous grass meadow grassland | |
11 | subtropical evergreen and deciduous broad-leaved mixed forest | 33 | typical grassland of temperate tufted grass | |
12 | subtropical evergreen broad-leaved forest | 34 | temperate fascicled dwarf grass and dwarf semishrub desert grassland | |
13 | subtropical monsoon evergreen broad-leaved forest | 35 | alpine grass and moss grassland | |
14 | subtropical hardleaf evergreen broad-leaved forest and copse | 36 | temperate grass | |
15 | monsoon rain forest | 37 | subtropical and tropical grass | |
16 | tropical rain forest | 38 | temperate grass and miscellaneous grass meadow | |
17 | subtropical and tropical bamboo forest and clusters | 39 | temperate grass, liverwort, and miscellaneous grass bog meadow | |
18 | deciduous thicket in temperate zone | 40 | temperate grass and miscellaneous grass salt meadow | |
19 | subtropical, tropical evergreen broad-leaved, deciduous broad-leaved scrub (often containing rare trees) | 41 | alpine hyssop and miscellaneous grass meadow | |
20 | tropical coral limestone fleshy evergreen broad-leaved thicket and copse | 46 | alpine tundra | |
21 | subtropical and tropical xerophytic evergreen succulent prickly thicket | 47 | alpine cushioned vegetation | |
22 | deciduous broad-leaf thicket in subalpine mountains | 48 | alpine sparse vegetation |
ID Class_Code Class_Name | Area Percentage (%) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
T0 | T1 | T2 | T3 | T4 | |||||||||||
RCP2.6 | RCP4.5 | RCP8.5 | RCP2.6 | RCP4.5 | RCP8.5 | RCP2.6 | RCP4.5 | RC8.5 | RCP2.6 | RCP4.5 | RCP8.5 | ||||
1 | IA | Frigid–extra-arid frigid desert, alpine desert | 0.03 | - | - | - | - | - | - | - | - | - | 0.01 | - | - |
2 | IIA | Cold temperate–extra-arid montane desert | 0.65 | 0.21 | 0.12 | 0.13 | 0.13 | 0.08 | 0.04 | 0.11 | 0.06 | 0.01 | 0.08 | 0.05 | 0.01 |
3 | IIIA | Cool temperate–extra arid temperate zone desert | 3.19 | 2.25 | 1.91 | 1.89 | 1.97 | 1.68 | 1.32 | 1.95 | 1.41 | 0.95 | 1.86 | 1.33 | 0.73 |
4 | IVA | Warm temperate–extra arid warm temperate zone desert | 9.39 | 10.29 | 10.43 | 10.44 | 10.38 | 9.84 | 6.60 | 10.59 | 7.69 | 5.51 | 10.42 | 6.94 | 4.78 |
5 | VA | Warm–extra-arid subtropical desert | 0.10 | 0.16 | 0.26 | 0.54 | 0.56 | 1.07 | 4.89 | 0.43 | 3.35 | 6.26 | 0.42 | 4.11 | 5.77 |
6 | VIA | Subtropical–extra-arid subtropical desert | - | - | - | 0.01 | - | 0.01 | 0.05 | 0.01 | 0.02 | 0.28 | - | 0.03 | 1.48 |
7 | VIIA | Tropical–extra-arid tropical desert | - | - | - | - | - | - | - | - | - | - | - | - | - |
8 | IB | Frigid–arid frigid zone semidesert, alpine semidesert | 0.46 | 0.34 | 0.18 | 0.19 | 0.19 | 0.16 | 0.09 | 0.40 | 0.13 | 0.03 | 0.51 | 0.11 | 0.03 |
9 | IIB | Cold temperate–arid montane semidesert | 0.66 | 1.06 | 1.10 | 1.10 | 1.08 | 1.07 | 0.93 | 1.12 | 0.95 | 0.71 | 1.08 | 0.89 | 0.64 |
10 | IIIB | Cool temperate–arid temperate zone semidesert | 3.46 | 5.27 | 5.09 | 4.84 | 4.93 | 4.34 | 3.89 | 4.80 | 4.02 | 2.60 | 4.88 | 3.97 | 2.16 |
11 | IVB | Warm temperate–arid warm temperate zone semidesert | 1.86 | 3.02 | 3.59 | 3.76 | 3.46 | 4.16 | 5.49 | 3.64 | 4.56 | 6.88 | 3.68 | 4.80 | 7.23 |
12 | VB | Warm–arid warm subtropical semidesert | - | 0.01 | 0.02 | 0.11 | 0.23 | 0.17 | 0.29 | 0.10 | 0.25 | 0.58 | 0.24 | 0.16 | 0.54 |
13 | VIB | Subtropical arid subtropical desert brush | - | 0.01 | 0.01 | 0.02 | <0.01 | 0.01 | 0.03 | 0.01 | 0.01 | 0.21 | 0.01 | 0.01 | 0.35 |
14 | VIIB | Tropical arid tropical desert brush | - | 0.02 | 0.03 | 0.04 | 0.03 | 0.03 | 0.04 | 0.03 | 0.02 | 0.09 | 0.02 | 0.02 | 0.08 |
15 | IC | Frigid–semiarid dry tundra, alpine steppe | 0.86 | 0.56 | 0.40 | 0.43 | 0.44 | 0.40 | 0.23 | 0.59 | 0.32 | 0.14 | 0.19 | 0.26 | 0.10 |
16 | IIC | Cold temperate semiarid montane steppe | 0.45 | 0.44 | 0.52 | 0.49 | 0.40 | 0.47 | 0.65 | 0.38 | 0.51 | 0.62 | 0.13 | 0.52 | 0.69 |
17 | IIIC | Cool temperate–semiarid temperate typical steppe | 2.82 | 3.83 | 4.08 | 3.97 | 4.10 | 3.02 | 3.15 | 3.18 | 2.43 | 2.49 | 0.67 | 2.33 | 2.19 |
18 | IVC | Warm temperate–semiarid warm temperate typical steppe | 0.22 | 1.60 | 2.49 | 2.58 | 2.27 | 2.22 | 3.76 | 2.41 | 2.52 | 3.85 | 0.55 | 2.61 | 3.91 |
19 | VC | Warm–semiarid subtropical grass–fruticose steppe | - | 0.64 | 1.01 | 1.35 | 1.28 | 1.36 | 1.93 | 1.26 | 1.56 | 1.32 | 0.59 | 1.70 | 1.11 |
20 | VIC | Subtropical–semiarid subtropical brush steppe | 0.01 | 0.19 | 0.26 | 0.32 | 0.26 | 0.20 | 0.60 | 0.23 | 0.19 | 1.63 | 0.02 | 0.21 | 1.73 |
21 | VIIC | Tropical–semiarid savanna | 0.01 | 0.07 | 0.10 | 0.08 | 0.07 | 0.11 | 0.12 | 0.07 | 0.09 | 0.21 | 0.03 | 0.11 | 0.22 |
22 | ID | Frigid subhumid moist tundra, alpine meadow steppe | 0.99 | 0.75 | 0.67 | 0.68 | 0.69 | 0.68 | 0.57 | 0.72 | 0.63 | 0.29 | 0.44 | 0.59 | 0.23 |
23 | IID | Cold temperate subhumid montane meadow steppe | 0.42 | 0.69 | 0.51 | 0.60 | 0.45 | 0.59 | 0.90 | 0.52 | 0.81 | 0.92 | 0.30 | 0.92 | 1.18 |
24 | IIID | Cool temperate–subhumid meadow steppe | 3.77 | 4.40 | 4.08 | 3.81 | 3.73 | 4.09 | 3.03 | 3.99 | 4.04 | 2.42 | 2.72 | 3.78 | 2.24 |
25 | IVD | Warm temperate sub humid forest steppe | 2.03 | 2.88 | 2.93 | 3.00 | 2.62 | 2.97 | 3.43 | 2.67 | 3.22 | 3.66 | 2.01 | 3.35 | 3.71 |
26 | VD | Warm–subhumid deciduous broad-leaved forest | 0.06 | 2.06 | 2.07 | 2.27 | 2.31 | 1.83 | 1.47 | 1.96 | 1.71 | 1.20 | 0.91 | 1.68 | 1.16 |
27 | VID | Subtropical–subhumid sclerophyllous forest | 0.09 | 0.64 | 1.29 | 1.63 | 1.08 | 1.71 | 3.51 | 1.23 | 2.01 | 4.37 | 0.18 | 2.20 | 4.34 |
28 | VIID | Tropical–subhumid tropical xerophytic forest | 0.01 | 0.21 | 0.27 | 0.26 | 0.23 | 0.31 | 0.37 | 0.24 | 0.30 | 0.60 | 0.05 | 0.32 | 0.79 |
29 | IE | Frigid–humid tundra, alpine meadow | 2.07 | 1.83 | 1.28 | 1.57 | 1.47 | 1.34 | 1.43 | 1.53 | 1.52 | 0.89 | 2.57 | 1.52 | 0.73 |
30 | IIE | Cold temperate–humid montane meadow | 1.56 | 1.20 | 1.12 | 1.34 | 1.25 | 1.28 | 1.44 | 1.20 | 1.28 | 1.75 | 1.84 | 1.22 | 1.89 |
31 | IIIE | Cool temperate–humid forest steppe, deciduous broad-leaved forest | 9.61 | 5.90 | 5.25 | 4.95 | 5.14 | 5.41 | 4.76 | 5.00 | 5.35 | 4.04 | 8.73 | 4.83 | 3.49 |
32 | IVE | Warm temperate–humid deciduous broad-leaved forest | 5.43 | 2.72 | 2.69 | 2.62 | 2.40 | 2.55 | 2.18 | 2.37 | 2.31 | 2.30 | 5.09 | 2.26 | 3.06 |
33 | VE | Warm–humid evergreen broad-leaved forest | 4.97 | 3.23 | 2.91 | 2.55 | 2.77 | 2.40 | 1.65 | 2.34 | 1.93 | 1.18 | 4.07 | 1.98 | 1.26 |
34 | VIE | Subtropical–humid evergreen broad-leaved forest | 2.58 | 4.29 | 4.75 | 4.52 | 4.05 | 6.23 | 5.79 | 4.25 | 5.49 | 5.75 | 5.48 | 5.37 | 6.33 |
35 | VIIE | Tropical–humid seasonal rain forest | 0.55 | 1.37 | 1.69 | 1.61 | 1.54 | 1.92 | 2.16 | 1.69 | 1.70 | 2.68 | 1.85 | 1.59 | 2.93 |
36 | IF | Frigid–perhumid rain tundra, alpine meadow | 15.56 | 14.37 | 14.97 | 13.91 | 14.96 | 13.38 | 11.76 | 14.25 | 12.85 | 9.69 | 13.48 | 12.66 | 8.16 |
37 | IIF | Cold temperate perhumid taiga forest | 6.47 | 6.61 | 6.54 | 6.65 | 6.46 | 7.10 | 6.72 | 6.74 | 7.10 | 7.39 | 6.86 | 7.20 | 7.49 |
38 | IIIF | Cold temperate perhumid mixed coniferous broad-leaved forest | 5.12 | 4.66 | 4.48 | 4.92 | 4.80 | 5.60 | 5.14 | 5.62 | 5.78 | 6.76 | 5.38 | 6.32 | 7.35 |
39 | IVF | Warm temperate perhumid deciduous broad-leaved forest | 2.91 | 2.30 | 1.85 | 1.77 | 1.82 | 1.96 | 1.53 | 1.99 | 1.94 | 1.56 | 1.87 | 1.87 | 1.81 |
40 | VF | Warm–humid deciduous–evergreen broad-leaved forest | 4.68 | 2.76 | 2.25 | 2.02 | 2.67 | 1.86 | 1.36 | 2.49 | 1.75 | 0.81 | 2.36 | 1.55 | 0.71 |
41 | VIF | Subtropical perhumid evergreen broad-leaved forest | 6.24 | 6.10 | 5.81 | 5.87 | 6.55 | 5.36 | 5.39 | 6.69 | 6.51 | 5.63 | 7.09 | 6.72 | 5.00 |
42 | VIIF | Tropical–humid rainforest | 0.75 | 1.08 | 1.00 | 1.11 | 1.22 | 1.08 | 1.28 | 1.20 | 1.67 | 1.74 | 1.36 | 1.93 | 2.40 |
ID | Evolution Type | RCP2.6 | RCP4.5 | RCP8.5 | |||
---|---|---|---|---|---|---|---|
Area (104 km2) | Percentage (%) | Area (104 km2) | Percentage (%) | Area (104 km2) | Percentage (%) | ||
0 | 1→2 | 0.02 | <0.01 | <0.01 | <0.01 | <0.01 | <0.01 |
1 | 1→3 | 2.26 | 0.24 | 2.61 | 0.27 | 4.87 | 0.51 |
2 | 1→4 | 0.26 | 0.03 | 1.99 | 0.21 | 5.31 | 0.55 |
3 | 1→5 | 6.00 | 0.63 | 10.25 | 1.07 | 25.31 | 2.64 |
4 | 1→6 | 21.19 | 2.21 | 32.91 | 3.43 | 69.23 | 7.21 |
5 | 1→7 | - | - | <0.01 | <0.01 | <0.01 | <0.01 |
6 | 2→1 | 0.02 | <0.01 | 0.01 | <0.01 | <0.01 | <0.01 |
7 | 2→3 | 10.93 | 1.14 | 12.56 | 1.31 | 11.49 | 1.20 |
8 | 2→4 | 0.01 | <0.01 | 0.12 | 0.01 | 0.09 | 0.01 |
9 | 2→5 | 0.01 | <0.01 | 0.01 | <0.01 | 0.01 | 0.00 |
10 | 2→9 | 3.07 | 0.32 | 38.87 | 4.05 | 67.99 | 7.09 |
11 | 3→1 | 0.12 | 0.01 | <0.01 | <0.01 | - | - |
12 | 3→2 | 5.84 | 0.61 | 4.45 | 0.46 | 5.45 | 0.57 |
13 | 3→4 | 0.33 | 0.03 | 1.02 | 0.11 | 1.03 | 0.11 |
14 | 3→5 | 0.75 | 0.08 | 0.40 | 0.04 | 0.07 | 0.01 |
15 | 3→6 | 0.02 | <0.01 | <0.01 | <0.01 | 0.00 | 0.00 |
16 | 3→9 | - | - | 0.05 | 0.01 | 0.74 | 0.08 |
17 | 4→1 | 0.07 | 0.01 | <0.01 | <0.01 | - | - |
18 | 4→2 | <0.01 | <0.01 | 0.00 | <0.01 | <0.01 | <0.01 |
19 | 4→3 | 25.53 | 2.66 | 24.06 | 2.51 | 26.54 | 2.77 |
20 | 4→5 | 3.92 | 0.41 | 1.21 | 0.13 | 0.69 | 0.07 |
21 | 4→6 | 0.27 | 0.03 | 0.06 | 0.01 | 0.03 | <0.01 |
22 | 4→10 | - | - | - | - | 1.20 | 0.12 |
23 | 5→1 | 0.53 | 0.06 | <0.01 | <0.01 | - | - |
24 | 5→3 | 4.07 | 0.42 | 3.20 | 0.33 | 7.91 | 0.82 |
25 | 5→4 | 6.25 | 0.65 | 27.32 | 2.85 | 32.68 | 3.41 |
26 | 5→6 | 17.93 | 1.87 | 8.62 | 0.90 | 7.19 | 0.75 |
27 | 6→1 | 0.95 | 0.10 | 0.03 | <0.01 | <0.01 | <0.01 |
28 | 6→3 | 1.83 | 0.19 | 0.50 | 0.05 | 0.67 | 0.07 |
29 | 6→4 | 7.87 | 0.82 | 29.63 | 3.09 | 31.77 | 3.31 |
30 | 6→5 | 9.66 | 1.01 | 29.08 | 3.03 | 17.48 | 1.82 |
31 | 6→7 | 34.36 | 3.58 | 36.59 | 3.81 | 55.58 | 5.79 |
32 | 6→8 | - | - | - | - | <0.01 | <0.01 |
33 | 6→10 | 0.05 | <0.01 | 0.56 | 0.06 | 14.80 | 1.54 |
34 | 7→4 | 0.03 | <0.01 | 0.44 | 0.05 | <0.01 | <0.01 |
35 | 7→6 | 0.12 | 0.01 | 0.02 | <0.01 | <0.01 | <0.01 |
36 | 7→8 | 19.06 | 16.55 | 24.97 | 2.60 | 46.82 | 4.88 |
37 | 7→10 | 0.43 | 0.04 | 2.20 | 0.23 | 6.52 | 0.68 |
38 | 8→7 | <0.01 | <0.01 | <0.01 | <0.01 | - | - |
39 | 8→10 | 0.13 | 0.01 | 0.46 | 0.05 | 0.32 | 0.03 |
40 | 9→2 | 0.04 | <0.01 | <0.01 | <0.01 | - | - |
41 | 10→7 | <0.01 | <0.01 | - | - | - | - |
References
- Liang, T.G.; Feng, Q.S.; Cao, J.J.; Xie, H.J.; Lin, H.L.; Zhao, J.; Ren, J.Z. Changes in global potential vegetation distributions from 1911 to 2000 as simulated by the Comprehensive Sequential Classification System approach. Chin. Sci. Bull. 2012, 57, 1298–1310. [Google Scholar] [CrossRef] [Green Version]
- Zhao, C.Y. Potential vegetation modelling with variable leaf area index in semi-arid grassland of Loess plateau, China. IEEE Cat. 2003, 5, 3311–3313. [Google Scholar] [CrossRef]
- Che, Y.J.; Zhao, J.; Zhang, M.J.; Wang, S.J.; Qi, Y. Potential vegetation and its sensitivity under different climate change scenarios from 2070 to 2099 in China. Acta Ecol. Sin. 2016, 36, 2885–2895. [Google Scholar] [CrossRef]
- Fang, J.Y.; Zhu, J.L.; Shi, Y. The responses of ecosystems to global warming. Chin. Sci. Bull 2018, 63, 136–140. [Google Scholar] [CrossRef] [Green Version]
- Tang, G.P.; Shafer, S.L.; Bartlein, P.J.; Holman, J.O. Effects of experimental protocol on global vegetation model accuracy: A comparison of simulated and observed vegetation patterns for Asia. Ecol. Model. 2009, 220, 1481–1491. [Google Scholar] [CrossRef]
- Lee, E.; He, Y.Q.; Zhou, M.; Liang, J.J. Potential feedback of recent vegetation changes on summer rainfall in the Sahel. PhGeo 2015, 36, 449–470. [Google Scholar] [CrossRef]
- Mahmood, R.; Pielke, R., Sr.; Hubbard, K.; Niyogi, D.; Dirmeyer, P.; Mcalpine, C.; Carleton, A.M.; Hale, R.; Gameda, S.; Beltrán-Przekurat, A.; et al. Land cover changes and their biogeophysical effects on climate. IJCli 2014, 34, 929–953. [Google Scholar] [CrossRef] [Green Version]
- Tapiador, F.J.; Moreno, R.; Navarro, A.; Luis Sanchez, J.; Garcia-Ortega, E. Climate classifications from regional and global climate models: Performances for present climate estimates and expected changes in the future at high spatial resolution. AtmRe 2019, 228, 107–121. [Google Scholar] [CrossRef]
- Zhang, J.H.; Fu, C.B.; Yan, X.D.; Emori, S.; Kanzawa, H. Global Respondence Analysis of LAI Versus Surface Air Temperature and Precipitation Variations. Chin. J. Geophys. 2002, 45, 631–637. [Google Scholar] [CrossRef]
- Zhang, J.H.; Yao, F.M.; Fu, C.B.; Yan, X.D. Study on response of ecosystem to the East Asian monsoon in eastern China using LAI data derived from remote sensing information. Prog. Nat. Sci. 2004, 14, 279–282. [Google Scholar] [CrossRef]
- Du, H.Y.; Zhao, J.; Shi, Y.F.; Che, Y.J. The succession of potential vegetation in China and its sensitivity under climate change. Chin. J. Ecol. 2018, 37, 1459–1466. [Google Scholar] [CrossRef]
- Gang, C.C.; Zhang, Y.Z.; Wang, Z.Q.; Chen, Y.Z.; Yue, Y.; Li, J.L.; Ji Mi, C.; Qi, J.G.; Inakwu, O. Modeling the dynamics of distribution, extent, and NPP of global terrestrial ecosystems in response to future climate change. Glob. Planet. Chang. 2017, 148, 153–165. [Google Scholar] [CrossRef]
- Gang, C.C.; Zhou, W.; Li, J.L.; Chen, Y.Z.; Mu, S.J.; Ren, J.Z.; Chen, J.M.; Zhao, J. Assessing the Spatiotemporal Variation in Distribution, Extent and NPP of Terrestrial Ecosystems in Response to Climate Change from 1911 to 2000. PLoS ONE 2013, 8, 1–12. [Google Scholar] [CrossRef] [Green Version]
- Nzabarinda, V.; Bao, A.; Xu, W.; Uwamahoro, S.; Jiang, L.; Duan, Y.; Nahayo, L.; Yu, T.; Wang, T.; Long, G. Assessment and Evaluation of the Response of Vegetation Dynamics to Climate Variability in Africa. Sustainability 2021, 13, 1234. [Google Scholar] [CrossRef]
- Cao, D.; Zhang, J.; Xun, L.; Yang, S.; Yao, F. Spatiotemporal variations of global terrestrial vegetation climate potential productivity under climate change. Sci. Total Environ. 2021, 770, 145320. [Google Scholar] [CrossRef]
- Horion, S.; Cornet, Y.; Erpicum, M.; Tychon, B. Studying interactions between climate variability and vegetation dynamic using a phenology based approach. Int. J. Appl. Earth Obs. 2013, 20, 20–32. [Google Scholar] [CrossRef]
- Igbawua, T.; Zhang, J.H.; Chang, Q.; Yao, F.M. Vegetation dynamics in relation with climate over Nigeria from 1982 to 2011. Environ. Earth Sci. 2016, 75, 518–533. [Google Scholar] [CrossRef]
- Chang, Q.; Zhang, J.H.; Jiao, W.Z.; Yao, F.M.; Wang, S.Y. Spatiotemporal dynamics of the climatic impacts on greenup date in the Tibetan Plateau. Environ. Earth Sci. 2016, 75, 1343–1356. [Google Scholar] [CrossRef]
- Yao, F.M.; Qin, P.C.; Zhang, J.H.; Lin, E.D.; Boken, V. Uncertainties in assessing the effect of climate change on agriculture using model simulation and uncertainty processing methods. Chin. Sci. Bull. 2011, 56, 729–737. [Google Scholar] [CrossRef] [Green Version]
- Chiarucci, A.; Arau´jo, M.B.; Decocq, G.; Beierkuhnlein, C.; Ferna´ndez-Palacios, J.M.A. The concept of potential natural vegetation: An epitaph? J. Veg. Sci. 2010, 21, 1172–1178. [Google Scholar] [CrossRef]
- Li, F.; Zhao, J.; Zhao, C.Y.; Hao, J.M.; Zheng, J.J. The potential vegetation spatial distributions and patterns in China. Acta Ecol. Sin. 2008, 28, 5347–5355. [Google Scholar] [CrossRef]
- Lin, H.L.; Feng, Q.S.; Liang, T.G.; Ren, J.Z. Modelling global-scale potential grassland changes in spatio-temporal patterns to global climate change. Int. J. Sustain. Dev. World Ecol. 2013, 20, 83–96. [Google Scholar] [CrossRef]
- Bolliger, J.; Kienast, F.; Bugmann, H. Comparing models for tree distributions: Concept, structures, and behavior. Ecol. Model. 2000, 134, 89–102. [Google Scholar] [CrossRef]
- Yue, T.X.; Fan, Z.M.; Chen, C.F.; Sun, X.F.; Li, B.L. Surface modelling of global terrestrial ecosystems under three climate change scenarios. Ecol. Model. 2011, 222, 2342–2361. [Google Scholar] [CrossRef]
- Hickler, T.; Vohland, K.; Feehan, J.; Miller, P.A.; Smith, B.; Costa, L.; Giesecke, T.; Fronzek, S.; Carter, T.R.; Cramer, W.; et al. Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model. Glob. Ecol. Biogeogr. 2015, 21, 50–63. [Google Scholar] [CrossRef]
- Kaplan, J.O.; Bigelow, N.H.; Prentice, I.C.; Harrison, S.P.; Bartlein, P.J.; Christensen, T.R.; Cramer, W.; Matveyeva, N.V.; Mcguire, A.D.; Murray, D.F. Climate change and Arctic ecosystems: 2. Modeling, paleodata-model comparisons, and future projections. J. Geophys. Res. Atmos. 2003, 108, 12–17. [Google Scholar] [CrossRef] [Green Version]
- Ni, J. BIOME Models: Main Principles and Applications. Acta Phytoecol. Sin. 2002, 26, 481–488. [Google Scholar]
- Horidge, L.R. Determination of World Plant Formations From Simple Climatic Data. Science 1947, 105, 367. [Google Scholar] [CrossRef]
- Cramer, W.; Bondeau, A.; Woodward, F.I.; Prentice, I.C.; Betts, R.A.; Brovkin, V.; Cox, P.M.; Fisher, V.; Foley, J.A.; Friend, A.D. Global response of terrestrial ecosystem structure and function to CO2 and climate change: Results from six dynamic global vegetation models. Glob. Chang. Biol. 2001, 21, 50–63. [Google Scholar] [CrossRef] [Green Version]
- Salzmann, U.; Haywood, A.M.; Lunt, D.J.; Valdes, P.J.; Hill, D.J. A new global biome reconstruction and data-model comparison for the Middle Pliocene. Glob. Ecol. Biogeogr. 2008, 17, 432–447. [Google Scholar] [CrossRef]
- Haxeltine, A.; Prentice, I.C. BIOME3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types. GBioC 1996, 10, 693–709. [Google Scholar] [CrossRef]
- Zheng, Y.; Xie, Z.; Jiang, L.; Shimizu, H.; Drake, S. Changes in Holdridge Life Zone diversity in the Xinjiang Uygur Autonomous Region (XUAR) of China over the past 40 years. J. Arid Environ. 2006, 66, 113–126. [Google Scholar] [CrossRef]
- Ma, R.F.; Ma, X.Z. Using Holdridge life zone classification system to analyze the climate change characteristics of Inner Mongolia grassland in the past 50 years. Meteorol. J. Inn. Mong. 2008, 2, 24–28. [Google Scholar]
- Mulholland, P.J.; Fellows, C.; Tank, J.; Grimm, N.; Webster, J.; Hamilton, S.; Martí, E. Inter-Biome Comparison of Factors Controlling Stream Metabolism. Freshw. Biol. 2001, 46, 1503–1517. [Google Scholar] [CrossRef] [Green Version]
- Hao, J.M. Study on the Holdridge Life Zone and Potential Vegetation Spatial Paterns in China; Northwest Normal University: Lanzhou, China, 2009. [Google Scholar]
- Wang, M.; Venevsky, S.; Wu, C.; Berdnikov, S.; Sorokina, V.; Kulygin, V. Description of local carbon flux from large scale gridded climate data by a global dynamic vegetation model at variable time steps: Example of Euroflux sites. ScTEn 2020, 756, 143492. [Google Scholar]
- Ren, J.Z.; Hu, Z.Z.; Mu, X.D.; Zhang, J.J. Comprehensive and Sequential Classification System of Grassland and its generic meaning. Chin. J. Grassl. 1980, 29, 12–24. [Google Scholar]
- Ren, J.Z.; Hu, Z.Z.; Zhao, J.; Zhang, D.G.; Hou, F.J.; Lin, H.l.; Mu, X.D. A grassland classification system and its application in China. Rangel. J. 2008, 30, 199–209. [Google Scholar] [CrossRef]
- Lin, H.L.; Zhao, J.; Tian Gang, L.; Bogaer, J.; Li, Z.Q. A classification indices-based model for net primary productivity (NPP) and potential productivity of vegetation in China. Int. J. Biomath. 2012, 5, 1–23. [Google Scholar] [CrossRef]
- Fischlin, A.; Midgley, G.F.; Price, J.T.; Leemans, R.; Gopal, B.; Turley, C.; Rounsevell, M.; Dube, O.P.; Tarazona, J.; Velichko, A.A. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2007; pp. 211–272. [Google Scholar]
- Cao, D.; Zhang, J.-H.; Yan, H.; Xun, L.; Yang, S.; Yun, B.; Zhang, S.; Yao, F.; Zhou, W. Regional Assessment of Climate Potential Productivity of Terrestrial Ecosystems and Its Responses to Climate Change Over China From 1980-2018. IEEE Access 2020. [Google Scholar] [CrossRef]
- Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. IJCli 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
- Xiu, L.N. Spatio-Temporal Distribution Characteristics of the CSCS-Based Potential Natural Vegetation in China; Lanzhou University: Lanzhou, China, 2014. [Google Scholar]
- Ren, Z.C.; Zhu, H.Z.; Shi, H.; Liu, X.N. Spatio-temporal distribution pattern of potential natural vegetation and its response to climate change from Last Interglacial to future 2070s in China. J. Nat. Resour. 2020, 35, 1484–1498. [Google Scholar]
- Carlos, N.R.; Jaime, T.; Philip, T.; Andy, J.; Julian, R.V. High-resolution and bias-corrected CMIP5 projections for climate change impact assessments. Sci. Data 2020, 7, 1–14. [Google Scholar]
- Emori, S.; Taylor, K.; Hewitson, B.; Zermoglio, F.; Juckes, M.; Lautenschlager, M.; Stockhause, M. CMIP5 data provided at the IPCC Data Distribution Centre. Clim. Chang. 2016, 1–8. Available online: https://www.ipcc-data.org/docs/factsheets/TGICA_Fact_Sheet_CMIP5_data_provided_at_the_IPCC_DDC_Ver_1_2016.pdf (accessed on 7 March 2021).
- Taylor, K.E.; Stouffer, R.J.; Meehl, G.A. An Overview of CMIP5 and the Experiment Design. Bull. Am. Meteorol. Soc. 2012, 93, 485–498. [Google Scholar] [CrossRef] [Green Version]
- Moss, R.H.; Edmonds, J.A.; Hibbard, K.A.; Manning, M.R.; Rose, S.K.; van Vuuren, D.P.; Carter, T.R.; Emori, S.; Kainuma, M.; Kram, T.; et al. The next generation of scenarios for climate change research and assessment. Nature 2010, 463, 747–756. [Google Scholar] [CrossRef] [PubMed]
- IPCC. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX); Intergovernmental Panel on Climate Change: Geneva, Switzerland, 2011. [Google Scholar]
- Liang, C.; Frauenfeld, O.W. Surface Air Temperature Changes over the Twentieth and Twenty-First Centuries in China Simulated by 20 CMIP5 Models. JCli 2014, 27, 3920–3937. [Google Scholar]
- Chen, A.F.; Feng, Q.; Zhang, J.K.; Li, Z.X.; Wang, G. A Review of Climate Change Scenario for Impacts Process Study. Sci. Geogr. Sin. 2015, 35, 84–90. [Google Scholar]
- Chen, X.C.; Xu, Y.; Xu, C.H.; Yao, Y. Assessment of Precipitation Simulations in China by CMIP5 Multi-models. Clim. Chang. Res. 2014, 10, 217–225. [Google Scholar]
- Julian, R.V.; Andy, J. Downscaling Global Circulation Model Outputs: The Delta Method Decision and Policy Analysis. Work. Pap. 2010, 1, 1–18. [Google Scholar]
- Hu, Z.Z.; Gao, C.X. Improvement of the Com prehensive and SequentialClassification System of Grasslands 1 Indices ofGrassland Classes and Index Chart. Acta Pratacult. Sin. 1995, 4, 1–7. [Google Scholar]
- Sheng, F.Q. Characteristics of Spatio-Temporal Distribution of Global and Regional Vegetable Types Based on CSCS Model; LanZhou Univeristy: Lanzhou, China, 2012. [Google Scholar]
- Prentice, I.C.; Cramer, W.; Harrison, S.P.; Leemans, R.; Monserud, R.A.; Solomon, A.M. A global biome model based on plant physiology and dominance, soil properties and climate. J. Biogeogr. 1992, 19, 117–134. [Google Scholar] [CrossRef]
- Cohen, J. A Coefficient of Agreement for Nominal Scales. Educ. Psychol. Meas. 1960, 20, 37–46. [Google Scholar] [CrossRef]
- Yates, D.N.; Kittel, T.G.F.; Cannon, R.F. Comparing the Correlative Holdridge Model to Mechanistic Biogeographical Models for Assessing Vegetation Distribution Response to Climatic Change. Clim. Chang. 2000, 44, 59–87. [Google Scholar] [CrossRef]
- Zhao, D.S.; Wu, S.H. Vulnerability of natural ecosystem in China under regional climate scenarios: An analysis based on eco-geographical regions. J. Geogr. Sci. 2014, 24, 237–248. [Google Scholar] [CrossRef]
- Aleman, J.C.; Blarquez, O.; Gourlet-Fleury, S.; Bremond, L.; Favier, C. Tree cover in Central Africa: Determinants and sensitivity under contrasted scenarios of global change. Sci. Rep. 2017, 7, 41393. [Google Scholar] [CrossRef] [Green Version]
Scenarios | Time (Year) | Source | Spatial Resolution | Temporal Resolution | URL | |
---|---|---|---|---|---|---|
The current scenario data | - | 1970–2000 | WorldClim 2.1 | 30 arc-s | Monthly | https://www.worldclim.org/ (accessed on 7 March 2021) |
The future climate scenarios data | RCP2.6 RCP4.5 RCP8.5 | 2050s (2040–2069) 2070s (2060–2089) 2070s (2060–2089) 2080s (2070–2099) | MRI-CGCM3 | 30 arc-s | Monthly | http://ccafs-climate.org/ (accessed on 7 March 2021) |
China administrative division map | - | - | National Natural Resources Standard Geographic Service Network | - | - | http://bzdt.ch.mnr.gov.cn (accessed on 7 March 2021) |
A vegetation map in China at a scale of 1:1,000,000 | - | - | Resource and Environment Science and Data Center | 1 km | - | http://www.resdc.cn/ (accessed on 7 March 2021) |
Code | Super-Classes | >0 °C Annual Cumulative Temperature (Σθ) | Humidity (K) | Corresponding Class Code |
---|---|---|---|---|
1 | Tundra and alpine steppe | 0–1300 | >0 | IA, IB, IC, ID, IE, IF |
2 | Cold desert | 1300–5300 | 0–0.3 | IIA, IIIA, IVA |
3 | Semidesert | 1300–6200 | 0.3–0.9 | IIB, IIIB, IVB, VB |
4 | Steppe | 1300–6200 | 0.9–1.2 | IIC, IIIC, IVC, VC |
5 | Temperate humid grassland | 1300–3700 | 1.2–2.0 | IID, IIID, IIE |
6 | Temperate forest | 1300–5300 | >1.2 | IVD, IIIE, IVE, IIF, IIIF, IVF |
7 | Subtropical forest | 5300–8000 | >1.2 | VD, VID, VE, VIE, VF, VIF |
8 | Tropical forest | >8000 | >1.5 | VIID, VIIE, VIIF |
9 | Warm desert | >5300 | 0–0.3 | VA, VIA, VIIA |
10 | Savanna | >6200 | 0.3–1.5 | VIB, VIIB, VIC, VIIC |
Code | Broad Vegetation Types | Super-Class Code (a) in the CSCS Model | Class Code (b) in the HLZ Model | Vegetation Code (b) in the Vegetation Map |
---|---|---|---|---|
1 | Tundra | 1 | 1, 2, 3, 4, 5 | 46, 47, 48 |
2 | Desert | 2, 3, 9 | 6, 7, 11, 12, 17, 18, 19, 24, 25, 31, 32 | 25, 26, 27, 28, 29, 30, 31 |
3 | Boreal and temperate forest | 6 | 8, 9, 10, 14, 15, 16 | 1, 2, 6, 7, 8, 9, 18, 22, 23, 24 |
4 | Subtropical and tropical forest | 7, 8 | 20, 21, 22, 23, 26, 27, 28, 29, 30, 33, 34, 35, 36, 37, 38 | 3, 4, 5, 7, 10, 11, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22 |
5 | Grassland | 4, 5, 10 | 13 | 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 |
RCP2.6 | RCP4.5 | RCP8.5 | ||
---|---|---|---|---|
Expansion pattern | IVB, VIIE | IID, IIF, IVB, IVD, VA, VC, VIA, VID, VIIF | IID, IIF, IVB, IVC, IVD, VIA, VIB, VIC, VIIC, VIID, VIIE, VIIF | |
Reduction pattern | IIA, IIC, IIIA, VF | IIA, IIC, IIIA, IVE, VF | IA, IB, IC, ID, IE, IF, IIA, IIIA, IIIE, VF | |
Fluctuation pattern | Expansion of fluctuation | IB, IE, IIB, IIE, IIF, IIIB, IIIF, IVA, IVC, VA, VB, VC, VD, VIB, VIC, VID, VIE, VIF, VIIB, VIIC, VIID, VIIF | IB, IE, IIB, IIE, IIF, IIIB, IIID, IIIF, IVC, VB, VD, VIC, VIE, VIF, VIIB, VIIC, VIID, VIIE | IIC, IIE, IIIF, VA, VB, VC, VID, VIE, VIIB |
Reduction of fluctuation | IA, IC, ID, IF, IID, IIIC, IIID, IIIE, IVD, IVE, IVF, VE | IA, IC, ID, IF, IIIC, IIIE, IVA, IVF, VE, VIB | IIB, IIIB, IIIC, IIID, VE |
Super-Class Code | RCP2.6 (km) | RCP4.5 (km) | RCP8.5 (km) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
T0–T1 | T1–T2 | T2–T3 | T3–T4 | T0–T1 | T1–T2 | T2–T3 | T3–T4 | T0–T1 | T1–T2 | T2–T3 | T3–T4 | ||
1 | Tundra and alpine steppe | 56.46 | 6.32 | 6.57 | 10.15 | 64.65 | 11.62 | 8.31 | 8.54 | 67.14 | 34.68 | 43.21 | 33.58 |
2 | Cold desert | 52.25 | 26.89 | 3.73 | 12.57 | 56.27 | 43.46 | 175.92 | 79.17 | 73.07 | 311.87 | 170.37 | 48.88 |
3 | Semidesert | 84.97 | 79.25 | 33.08 | 7.50 | 139.58 | 55.09 | 10.18 | 7.48 | 157.83 | 63.46 | 36.93 | 97.02 |
4 | Steppe | 786.43 | 123.43 | 152.58 | 201.30 | 859.23 | 140.45 | 55.85 | 44.20 | 863.88 | 42.80 | 140.36 | 110.38 |
5 | Temperate humid grassland | 178.35 | 80.04 | 25.41 | 401.74 | 194.27 | 213.04 | 109.35 | 85.01 | 102.37 | 432.26 | 401.46 | 190.11 |
6 | Temperate forest | 79.35 | 26.72 | 33.30 | 42.06 | 131.18 | 34.54 | 21.70 | 13.72 | 134.97 | 90.37 | 94.97 | 41.31 |
7 | Subtropical forest | 73.44 | 18.82 | 5.50 | 32.49 | 86.47 | 17.01 | 10.82 | 10.71 | 83.02 | 35.60 | 59.63 | 38.07 |
8 | Tropical forest | 94.84 | 15.28 | 4.75 | 10.06 | 110.81 | 16.48 | 19.12 | 8.11 | 110.40 | 36.78 | 47.83 | 59.16 |
9 | Warm desert | 78.88 | 535.59 | 4.15 | 10.47 | 226.56 | 334.95 | 54.16 | 5.94 | 529.66 | 57.78 | 77.48 | 78.02 |
10 | Savanna | 181.36 | 47.27 | 11.00 | 34.38 | 195.84 | 105.61 | 131.75 | 25.04 | 144.58 | 617.05 | 406.76 | 168.96 |
Sensitivity | RCP2.6 | RCP4.5 | RCP8.5 | ||||
---|---|---|---|---|---|---|---|
Area (104 km2) | Percentage (%) | Area (104 km2) | Percentage (%) | Area (104 km2) | Percentage (%) | ||
Insensitive area | Insensitive area | 463.09 | 48.26 | 361.51 | 37.67 | 177.14 | 18.46 |
Sensitive area | Low-sensitivity area | 242.41 | 25.26 | 412.70 | 43.01 | 460.00 | 47.94 |
Medium-sensitivity area | 182.56 | 19.02 | 141.38 | 14.73 | 247.31 | 25.77 | |
High-sensitivity area | 55.75 | 5.81 | 39.54 | 4.12 | 67.54 | 7.04 | |
Extremely high-sensitivity area | 15.81 | 1.65 | 4.49 | 0.47 | 7.63 | 0.79 | |
Sensitive area | 496.53 | 51.74 | 598.10 | 62.33 | 782.48 | 81.54 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, S.; Zhang, J.; Zhang, S.; Bai, Y.; Cao, D.; Cheng, T.; Sun, Z.; Liu, Q.; Sharma, T.P.P. Impacts of Future Climate Changes on Spatio-Temporal Distribution of Terrestrial Ecosystems over China. Sustainability 2021, 13, 3049. https://doi.org/10.3390/su13063049
Li S, Zhang J, Zhang S, Bai Y, Cao D, Cheng T, Sun Z, Liu Q, Sharma TPP. Impacts of Future Climate Changes on Spatio-Temporal Distribution of Terrestrial Ecosystems over China. Sustainability. 2021; 13(6):3049. https://doi.org/10.3390/su13063049
Chicago/Turabian StyleLi, Shuaishuai, Jiahua Zhang, Sha Zhang, Yun Bai, Dan Cao, Tiantian Cheng, Zhongtai Sun, Qi Liu, and Til Prasad Pangali Sharma. 2021. "Impacts of Future Climate Changes on Spatio-Temporal Distribution of Terrestrial Ecosystems over China" Sustainability 13, no. 6: 3049. https://doi.org/10.3390/su13063049