植物生态学报 ›› 2024, Vol. 48 ›› Issue (4): 428-444.DOI: 10.17521/cjpe.2022.0352 cstr: 32100.14.cjpe.2022.0352
所属专题: 全球变化与生态系统; 生态系统碳水能量通量; 碳储量
张计深1, 史新杰1, 刘宇诺1, 吴阳3,4, 彭守璋2,*()()
收稿日期:
2022-08-30
接受日期:
2023-05-15
出版日期:
2024-04-20
发布日期:
2024-05-11
通讯作者:
* (szp@nwafu.edu.cn)
基金资助:
ZHANG Ji-Shen1, SHI Xin-Jie1, LIU Yu-Nuo1, WU Yang3,4, PENG Shou-Zhang2,*()()
Received:
2022-08-30
Accepted:
2023-05-15
Online:
2024-04-20
Published:
2024-05-11
Contact:
* (szp@nwafu.edu.cn)
Supported by:
摘要:
陆地生态系统固碳是减缓大气CO2浓度升高的重要途径, 了解气候变化下潜在自然植被生态系统碳储量(ECS)有利于区域土地管理政策的制定。该研究基于遗传算法对LPJ-GUESS模型的敏感参数进行校准, 使用降尺度的气候数据驱动模型, 结合Mann-Kendall趋势检验、Sen’s斜率估计方法和偏相关分析法, 分析2001-2100年中国ECS的时空格局、趋势变化特征及气候主导因子。结果表明: 1)校准后的LPJ-GUESS模型模拟ECS的纳什效率系数和皮尔逊相关系数分别为0.751、0.901, 表明LPJ-GUESS模型可以较好地模拟中国ECS。2) 2001-2020年, 中国潜在自然植被ECS由东南向西北递减, 总量为156.06 Pg, 其中植被、凋落物、土壤碳储量分别占34.2%、1.9%、63.8%。相比于2001-2020年, 2081-2100年ECS具有相似的空间分布, ECS总量预计增加0.51-11.16 Pg; 2001-2020年和2021-2100年, 中国潜在自然植被ECS的增加速率分别为8.5 g·m-2·a-1、3.7-21.0 g·m-2·a-1。3) 2021-2100年中国东南部、内蒙古高原、青藏高原等地区ECS显著增加(37-44 g·m-2·a-1), 云贵高原南部、两广丘陵等地区ECS显著减少(45-72 g·m-2·a-1)。4)不同区域的气候主导因子不同: 气温在中国西北地区主导ECS变化; 受区域干旱程度影响, ECS与降水量的相关性由东南向西北递增; 辐射在高纬度、高海拔地区主导ECS变化; 在中国47.9%-56.1%的区域CO2浓度主导ECS变化。
张计深, 史新杰, 刘宇诺, 吴阳, 彭守璋. 气候变化下中国潜在自然植被生态系统碳储量动态. 植物生态学报, 2024, 48(4): 428-444. DOI: 10.17521/cjpe.2022.0352
ZHANG Ji-Shen, SHI Xin-Jie, LIU Yu-Nuo, WU Yang, PENG Shou-Zhang. Dynamics of ecosystem carbon storage of potential natural vegetation in China under climate change. Chinese Journal of Plant Ecology, 2024, 48(4): 428-444. DOI: 10.17521/cjpe.2022.0352
植物功能型 Plant functional type | min_surv (℃) | max_est (℃) | twmin_est (℃) | gdd5min_est (℃) |
---|---|---|---|---|
热带阔叶常绿树种 Tropical broadleaf evergreen trees | 4.99 | 1 000 | -1 000 | 3 500.00 |
热带阔叶雨绿树种 Tropical broadleaf raingreen trees | 8.91 | 1 000 | -1 000 | 3 000.00 |
亚热带常绿阔叶树种 Sub-tropical evergreen broadleaf trees | -3.07 | 17.72 | 15.40 | 2 887.70 |
温带落叶阔叶树种 Temperate deciduous broadleaf trees | -32.77 | -0.93 | 13.99 | 973.54 |
温带常绿针叶树种 Temperate evergreen needleleaf trees | -32.18 | 9.49 | 3.14 | 0.34 |
北方落叶针叶树种 Boreal deciduous needleleaf trees | -1 000 | -17.92 | 8.61 | 231.12 |
山地寒温性针叶树种 Mountain cold needleleaf trees | -16.20 | 17.34 | 1.62 | 1 000 |
温带落叶灌木 Temperate deciduous shrubs | -28.47 | 4.67 | 14.31 | 1 141.32 |
温带荒漠灌木 Temperate desert shrubs | -19.16 | -3.54 | 15.22 | 1 816.72 |
热带-亚热带灌木 Tropical-subtropical shrubs | -5.99 | 1 000 | -1 000 | 1 950.99 |
高山常绿灌木 Alpine evergreen shrubs | -16.44 | 3.76 | 5.60 | 0.04 |
高寒落叶灌木 Alpine deciduous shrubs | -1 000 | 0.14 | 4.43 | 0.03 |
高寒荒漠灌木 Alpine desert shrubs | -22.09 | -13.66 | 2.50 | 0.00 |
温带草甸草本 Temperate meadow grasses | -32.18 | 20.14 | 9.50 | 0.23 |
温带草原草本 Temperate steppe grasses | -27.92 | -0.36 | 10.76 | 836.63 |
稀树草原草本 Savannah grasses | -6.11 | 1 000 | -1 000 | 2 274.44 |
高寒草甸草本 Alpine meadow grasses | -1 000 | -4.19 | 2.33 | 0.00 |
高寒草原草本 Alpine steppe grasses | -1 000 | -6.29 | 0.66 | 0.00 |
表1 植物功能型的生物气候学参数
Table 1 Bioclimatic parameters of plant functional types
植物功能型 Plant functional type | min_surv (℃) | max_est (℃) | twmin_est (℃) | gdd5min_est (℃) |
---|---|---|---|---|
热带阔叶常绿树种 Tropical broadleaf evergreen trees | 4.99 | 1 000 | -1 000 | 3 500.00 |
热带阔叶雨绿树种 Tropical broadleaf raingreen trees | 8.91 | 1 000 | -1 000 | 3 000.00 |
亚热带常绿阔叶树种 Sub-tropical evergreen broadleaf trees | -3.07 | 17.72 | 15.40 | 2 887.70 |
温带落叶阔叶树种 Temperate deciduous broadleaf trees | -32.77 | -0.93 | 13.99 | 973.54 |
温带常绿针叶树种 Temperate evergreen needleleaf trees | -32.18 | 9.49 | 3.14 | 0.34 |
北方落叶针叶树种 Boreal deciduous needleleaf trees | -1 000 | -17.92 | 8.61 | 231.12 |
山地寒温性针叶树种 Mountain cold needleleaf trees | -16.20 | 17.34 | 1.62 | 1 000 |
温带落叶灌木 Temperate deciduous shrubs | -28.47 | 4.67 | 14.31 | 1 141.32 |
温带荒漠灌木 Temperate desert shrubs | -19.16 | -3.54 | 15.22 | 1 816.72 |
热带-亚热带灌木 Tropical-subtropical shrubs | -5.99 | 1 000 | -1 000 | 1 950.99 |
高山常绿灌木 Alpine evergreen shrubs | -16.44 | 3.76 | 5.60 | 0.04 |
高寒落叶灌木 Alpine deciduous shrubs | -1 000 | 0.14 | 4.43 | 0.03 |
高寒荒漠灌木 Alpine desert shrubs | -22.09 | -13.66 | 2.50 | 0.00 |
温带草甸草本 Temperate meadow grasses | -32.18 | 20.14 | 9.50 | 0.23 |
温带草原草本 Temperate steppe grasses | -27.92 | -0.36 | 10.76 | 836.63 |
稀树草原草本 Savannah grasses | -6.11 | 1 000 | -1 000 | 2 274.44 |
高寒草甸草本 Alpine meadow grasses | -1 000 | -4.19 | 2.33 | 0.00 |
高寒草原草本 Alpine steppe grasses | -1 000 | -6.29 | 0.66 | 0.00 |
图2 LPJ-GUESS模型参数校准评价。A, 总初级生产力(GPP)评价。B, 蒸散量(ET)评价。C, 叶面积指数(LAI)评价。nse, 纳什效率系数; r, 皮尔逊相关系数。
Fig. 2 Parameter calibration evaluation of LPJ-GUESS model. A, Evaluation of gross primary productivity (GPP). B, Evaluation of evapotranspiration (ET). C, Evaluation of leaf area index (LAI). nse, Nash-Sutcliffe efficiency coefficient; r, Pearson correlation coefficient.
图3 潜在自然植被生态系统碳储量(ECS)模拟结果评价。nse, 纳什效率系数; r, 皮尔逊相关系数。
Fig. 3 evaluation of simulation results of potential natural vegetation ecosystem carbon storage (ECS). nse, Nash-sutcliffe efficiency coefficient; r, Pearson correlation coefficient.
文献来源; 研究时期(年) Literature source; research period (year) | 类型 Type | PNVCS (Pg) | LCS (Pg) | SCS (Pg) | ECS (Pg) |
---|---|---|---|---|---|
Tang et al., | 中国实际碳储量, 植被 Actual carbon storage of China, vegetation | 12.55 ± 3.07 | 0.46 ± 0.48 | 49.92 ± 4.98 | 62.93 ± 3.39 |
中国实际碳储量, 作物 Actual carbon storage of China, crops | 0.55 ± 0.02 | - | 15.77 ± 0.57 | 16.32 ± 0.41 | |
中国实际碳储量, 所有 Actual carbon storage of China, all | 14.29 ± 0.74 | 0.46 ± 0.48 | 74.98 ± 1.28 | 89.27 ± 1.05 | |
本研究 This study; 2011-2015 | 中国潜在碳储量, 植被 Potential carbon storage of China, vegetation | 29.86 | 1.74 | 64.66 | 96.26 |
中国潜在碳储量, 作物 Potential carbon storage of China, crops | 21.20 | 1.03 | 25.78 | 48.00 | |
中国潜在碳储量, 所有 Potential carbon storage of China, all | 53.43 | 3.00 | 99.62 | 156.06 | |
Bloom et al., | 中国实际碳储量, 所有 Actual carbon storage of China, all | - | - | - | 128.47 |
本研究 This study; 2001-2010 | 中国潜在碳储量, 所有 Potential carbon storage of China, all | 52.34 | 2.92 | 98.12 | 155.65 |
Walker et al., | 中国实际碳储量, 限制 Actual carbon storage of China, restriction | 20.90 | - | 165.15 | 186.56 |
中国实际碳储量, 不限制 Actual carbon storage of China, no restriction | 25.65 | - | 234.98 | 260.64 | |
中国潜在碳储量, 限制 Potential carbon storage of China, restriction | 27.65 | - | 173.25 | 201.85 | |
中国潜在碳储量, 不限制 Potential carbon storage of China, no restriction | 40.08 | - | 255.07 | 296.43 | |
本研究 This study; 2016 | 中国潜在碳储量, 限制 Potential carbon storage of China, restriction | 30.49 | 1.77 | 61.17 | 93.42 |
中国潜在碳储量, 不限制 Potential carbon storage of China, no restriction | 53.24 | 3.00 | 98.32 | 154.56 | |
Walker et al., | 中国潜在碳储量, RCP85 Potential carbon storage of China, RCP85 | 39.07 | - | - | - |
本研究 This study; 2050 | 中国潜在碳储量, SSP119 Potential carbon storage of China, SSP119 | 53.54 | 3.04 | 96.66 | 153.24 |
SSP245 | 51.87 | 3.07 | 95.50 | 150.44 | |
SSP585 | 54.22 | 3.23 | 95.80 | 153.26 |
表2 中国陆地碳储量估算比较(平均值±标准差)
Table 2 Comparison of terrestrial carbon storage estimates over China (mean ± SD)
文献来源; 研究时期(年) Literature source; research period (year) | 类型 Type | PNVCS (Pg) | LCS (Pg) | SCS (Pg) | ECS (Pg) |
---|---|---|---|---|---|
Tang et al., | 中国实际碳储量, 植被 Actual carbon storage of China, vegetation | 12.55 ± 3.07 | 0.46 ± 0.48 | 49.92 ± 4.98 | 62.93 ± 3.39 |
中国实际碳储量, 作物 Actual carbon storage of China, crops | 0.55 ± 0.02 | - | 15.77 ± 0.57 | 16.32 ± 0.41 | |
中国实际碳储量, 所有 Actual carbon storage of China, all | 14.29 ± 0.74 | 0.46 ± 0.48 | 74.98 ± 1.28 | 89.27 ± 1.05 | |
本研究 This study; 2011-2015 | 中国潜在碳储量, 植被 Potential carbon storage of China, vegetation | 29.86 | 1.74 | 64.66 | 96.26 |
中国潜在碳储量, 作物 Potential carbon storage of China, crops | 21.20 | 1.03 | 25.78 | 48.00 | |
中国潜在碳储量, 所有 Potential carbon storage of China, all | 53.43 | 3.00 | 99.62 | 156.06 | |
Bloom et al., | 中国实际碳储量, 所有 Actual carbon storage of China, all | - | - | - | 128.47 |
本研究 This study; 2001-2010 | 中国潜在碳储量, 所有 Potential carbon storage of China, all | 52.34 | 2.92 | 98.12 | 155.65 |
Walker et al., | 中国实际碳储量, 限制 Actual carbon storage of China, restriction | 20.90 | - | 165.15 | 186.56 |
中国实际碳储量, 不限制 Actual carbon storage of China, no restriction | 25.65 | - | 234.98 | 260.64 | |
中国潜在碳储量, 限制 Potential carbon storage of China, restriction | 27.65 | - | 173.25 | 201.85 | |
中国潜在碳储量, 不限制 Potential carbon storage of China, no restriction | 40.08 | - | 255.07 | 296.43 | |
本研究 This study; 2016 | 中国潜在碳储量, 限制 Potential carbon storage of China, restriction | 30.49 | 1.77 | 61.17 | 93.42 |
中国潜在碳储量, 不限制 Potential carbon storage of China, no restriction | 53.24 | 3.00 | 98.32 | 154.56 | |
Walker et al., | 中国潜在碳储量, RCP85 Potential carbon storage of China, RCP85 | 39.07 | - | - | - |
本研究 This study; 2050 | 中国潜在碳储量, SSP119 Potential carbon storage of China, SSP119 | 53.54 | 3.04 | 96.66 | 153.24 |
SSP245 | 51.87 | 3.07 | 95.50 | 150.44 | |
SSP585 | 54.22 | 3.23 | 95.80 | 153.26 |
图4 2001-2020年中国潜在自然植被碳储量(A)、凋落物碳储量(B)、土壤碳储量(C)和生态系统碳储量(D)的空间分布。
Fig. 4 Spatial distribution of potential natural vegetation carbon storage (A), litter carbon storage (B), soil carbon storage (C) and potential natural vegetation ecosystem carbon storage (D) over China during 2001-2100.
图5 2081-2100年中国潜在自然植被碳储量(A)、凋落物碳储量(B)、土壤碳储量(C)和生态系统碳储量(D)的空间变化(相比于2001-2020年)。1, SSP119情景; 2, SSP245情景; 3, SSP585情景; 4, 相对于2001-2020年各个碳储量变化的面积比例。
Fig. 5 Spatial distribution of potential natural vegetation carbon storage (A), litter carbon storage (B), soil carbon storage (C) and ecosystem carbon storage (D) over China during 2081-2100 relative to 2001-2020. 1, SSP119 scenario; 2, SSP245 scenario; 3, SSP585 scenario; 4, area ratio of each carbon storage changes relative to 2001-2020.
图6 2001-2100中国地区潜在自然植被碳储量(A)、凋落物碳储量(B)、土壤碳储量(C)和生态系统碳储量(D)变化。
Fig. 6 Change of potential natural vegetation carbon storage (A), litter carbon storage (B), soil carbon storage (C) and ecosystem carbon storage (D) over entire China during 2001-2100.
图7 2001-2020年与2021-2100年中国潜在自然植被生态系统碳储量变化趋势空间分布。A, 显著上升区。B, 显著下降区。1, 2001-2020年; 2, SSP119情景; 3, SSP245情景; 4, SSP585情景。
Fig. 7 Spatial distribution of trend magnitude in potential natural vegetation ecosystem carbon storage over China during 2001-2020 and 2021-2100 periods. A, Significant rise area. B, Significant drop area. 1, 2001-2020; 2, SSP119 scenario; 3, SSP245 scenario; 4, SSP585 scenario.
图8 2001-2100年中国潜在自然植被生态系统碳储量与年降水量(A)、年平均气温(B)、年辐射量(C)、大气CO2浓度(D)之间相关性的空间分布。1, SSP119情景; 2, SSP245情景; 3, SSP585情景。
Fig. 8 Spatial distribution of the correlation of potential natural vegetation ecosystem carbon storage with annual precipitation (A), annual mean air temperature (B), annual radiation (C) and atmospheric CO2 concentration (D) over China during 2001-2100. 1, SSP119 scenario; 2, SSP245 scenario; 3, SSP585 scenario.
图9 2001-2100年中国年降水量(A)、年平均气温(B)、年辐射量(C)变化趋势空间分布。1, SSP119情景; 2, SSP245情景; 3, SSP585情景。
Fig. 9 Spatial distribution of trend of annual precipitation (A), annual mean air temperature (B), annual radiation (C) over China during 2001-2100. 1, SSP119 scenario; 2, SSP245 scenario; 3, SSP585 scenario.
图10 2001-2100年中国潜在自然植被生态系统碳储量气候主导因子的空间分布(A)和面积比例(B)。1, SSP119情景; 2, SSP245情景; 3, SSP585情景。
Fig. 10 Spatial distribution (A) and area ratio (B) of climate dominant factors of potential natural vegetation ecosystem carbon storage over China during 2001-2100. 1, SSP119 scenario; 2, SSP245 scenario; 3, SSP585 scenario.
[1] |
Bloom AA, Exbrayat JF, van der Velde IR, Feng L, Williams M (2016). The decadal state of the terrestrial carbon cycle: global retrievals of terrestrial carbon allocation, pools, and residence times. Proceedings of the National Academy of Sciences of the United States of America, 113, 1285-1290.
DOI PMID |
[2] | Che ML, Chen BZ, Wang Y, Guo XY (2014). Review of dynamic global vegetation models (DGVMs). Chinese Journal of Applied Ecology, 25, 263-271. |
[车明亮, 陈报章, 王瑛, 郭祥云 (2014). 全球植被动力学模型研究综述. 应用生态学报, 25, 263-271.] | |
[3] | Chen ST, Guo B, Yang F, Han BM, Fan YW, Yang X, He TL, Liu Y, Yang WN (2020). Spatial and temporal patterns of NPP and its response to climate change in the Qinghai- Tibet Plateau from 2000 to 2015. Journal of Natural Resources, 35, 2511-2527. |
[陈舒婷, 郭兵, 杨飞, 韩保民, 范业稳, 杨潇, 何田莉, 刘悦, 杨雯娜 (2020). 2000-2015年青藏高原植被NPP时空变化格局及其对气候变化的响应. 自然资源学报, 35, 2511-2527.]
DOI |
|
[4] | Chen ZP, Xu Q (2016). Analysis of precipitation characteristics in Jinhua by Mann-Kendall test method. Bulletin of Science and Technology, 32(6), 47-50. |
[陈中平, 徐强 (2016). Mann-Kendall检验法分析降水量时程变化特征. 科技通报, 32(6), 47-50.] | |
[5] | Cheng RM, Feng XH, Xiao WF, Wang RL, Wang XR, Du HT (2011). Response of net productivity of masson pine plantation to climate change in north subtropical Region. Acta Ecologica Sinica, 31, 2086-2095. |
[程瑞梅, 封晓辉, 肖文发, 王瑞丽, 王晓荣, 杜化堂 (2011). 北亚热带马尾松净生产力对气候变化的响应. 生态学报, 31, 2086-2095.] | |
[6] | Chi H, Sun GQ, Huang JL, Guo ZF, Ni WJ, Fu AM (2015). National forest aboveground biomass mapping from ICESat/GLAS data and MODIS imagery in China. Remote Sensing, 7, 5534-5564. |
[7] | del Arco Aguilar MJ, González-González R, Garzón-Machado V, Pizarro-Hernández B (2010). Actual and potential natural vegetation on the Canary Islands and its conservation status. Biodiversity and Conservation, 19, 3089-3140. |
[8] | Ding YX, Liang SQ, Peng SZ (2019). Climate change affects forest productivity in a typical climate transition region of China. Sustainability, 11, 2856. DOI: 10.3390/su11102856. |
[9] |
Fang J, Kato T, Guo Z, Yang Y, Hu H, Shen H, Zhao X, Kishimoto-Mo AW, Tang Y, Houghton RA (2014). Evidence for environmentally enhanced forest growth. Proceedings of the National Academy of Sciences of the United States of America, 111, 9527-9532.
DOI PMID |
[10] | Feng XH, Cheng RM, Xiao WF, Wang RL, Wang XR, Liu ZB (2013). Productivity and carbon dynamic of the masson pine stands in Jigongshan region based on LPJ-GUESS model. Scientia Silvae Sinicae, 49(4), 7-15. |
[封晓辉, 程瑞梅, 肖文发, 王瑞丽, 王晓荣, 刘泽彬 (2013). 基于LPJ-GUESS模型的鸡公山马尾松林生产力和碳动态. 林业科学, 49(4), 7-15.] | |
[11] | Guo DX (2021). Growth Simulation and Regional Irrigation Schedule Optimization for Winter Wheat and Summer Maize Based on Aquacrop Model in the Fenwei Plain. PhD dissertation, Northwest A&F University, Yangling, Shaanxi, |
[郭大辛 (2021). 基于AquaCrop模型的汾渭平原冬小麦-夏玉米生长模拟与区域灌溉制度优化. 博士学位论文, 西北农林科技大学, 陕西杨凌.] | |
[12] | He HL, Wang SQ, Zhang L, Wang JB, Ren XL, Zhou L, Piao SL, Yan H, Ju WM, Gu FX, Yu SY, Yang YH, Wang MM, Niu ZE, Ge R, et al. (2019). Altered trends in carbon uptake in China’s terrestrial ecosystems under the enhanced summer monsoon and warming hiatus. National Science Review, 6, 505-514. |
[13] | Hickler T, Vohland K, Feehan J, Miller PA, Smith B, Costa L, Giesecke T, Fronzek S, Carter TR, Cramer W, Kühn I, Sykes MT (2012). Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model. Global Ecology and Biogeography, 21, 50-63. |
[14] | Jia JH, Liu HY, Lin ZS (2019). Multi-time scale changes of vegetation NPP in six provinces of northwest China and their responses to climate change. Acta Ecologica Sinica, 39, 5058-5069. |
[贾俊鹤, 刘会玉, 林振山 (2019). 中国西北地区植被NPP多时间尺度变化及其对气候变化的响应. 生态学报, 39, 5058-5069.] | |
[15] | Ju WM, Chen JM (2008). Simulating the effects of past changes in climate, atmospheric composition, and fire disturbance on soil carbon in Canada’s forests and wetlands. Global Biogeochemical Cycles, 22, GB3010. DOI: 10.1029/2007GB002935. |
[16] | Li HY, Zhang CG, Wang SZ, Ma WD, Liu FG, Chen Q, Zhou Q, Xia XS, Niu BC (2022). Response of vegetation dynamics to hydrothermal conditions on the Qinghai-Tibet Plateau in the last 40 years. Acta Ecologica Sinica, 42, 4770-4783. |
[李红英, 张存桂, 汪生珍, 马伟东, 刘峰贵, 陈琼, 周强, 夏兴生, 牛百成 (2022). 近40年青藏高原植被动态变化对水热条件的响应. 生态学报, 42, 4770-4783.] | |
[17] | Li KR, Wang SQ, Cao MK (2003). Carbon storage of vegetation and soil in China. Science in China (Series D), 33, 72-80. |
[李克让, 王绍强, 曹明奎 (2003). 中国植被和土壤碳贮量. 中国科学(D辑), 33, 72-80.] | |
[18] | Li Y, Yuan HY, Yu JQ, Zhang GW, Liu KP (2019). Application of genetic algorithm in optimization problems. Journal of Shandong Industrial Technology, (12), 242-243. |
[李岩, 袁弘宇, 于佳乔, 张更伟, 刘克平 (2019). 遗传算法在优化问题中的应用综述. 山东工业技术, (12), 242-243.] | |
[19] | Liang SQ, Peng SZ, Chen YM (2019). Carbon cycles of forest ecosystems in a typical climate transition zone under future climate change: a case study of Shaanxi Province, China. Forests, 10, 1150. DOI: 10.3390/f10121150. |
[20] |
Liu RG, Li N, Su HX, Sang WG (2009). Simulation and analysis on future carbon balance of three deciduous forests in Beijing mountain area, warm temperate zone of China. Chinese Journal of Plant Ecology, 33, 516-534.
DOI |
[刘瑞刚, 李娜, 苏宏新, 桑卫国 (2009). 北京山区3种暖温带森林生态系统未来碳平衡的模拟与分析. 植物生态学报, 33, 516-534.]
DOI |
|
[21] | Peng SS, Piao SL, Wang T, Sun JY, Shen ZH (2009). Temperature sensitivity of soil respiration in different ecosystems in China. Soil Biology & Biochemistry, 41, 1008-1014. |
[22] | Peng SZ, Ding YX, Liu WZ, Li Z (2019). 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth System Science Data, 11, 1931-1946. |
[23] | Peng SZ, Li Z (2018a). Incorporation of potential natural vegetation into revegetation programmes for sustainable land management. Land Degradation & Development, 29, 3503-3511. |
[24] | Peng SZ, Li Z (2018b). Potential land use adjustment for future climate change adaptation in revegetated regions. Science of the Total Environment, 639, 476-484. |
[25] | Peng SZ, Yu KL, Li Z, Wen ZM, Zhang C (2019). Integrating potential natural vegetation and habitat suitability into revegetation programs for sustainable ecosystems under future climate change. Agricultural and Forest Meteorology, 269-270, 270-284. |
[26] | Piao SL, He Y, Wang XH, Chen FH (2022). Estimation of China’s terrestrial ecosystem carbon sink—Methods, progress and prospects. Scientia Sinica (Terrae), 52, 1010-1020. |
[朴世龙, 何悦, 王旭辉, 陈发虎 (2022). 中国陆地生态系统碳汇估算: 方法、进展、展望. 中国科学: 地球科学, 52, 1010-1020.] | |
[27] | Saltelli A (2002). Making best use of model evaluations to compute sensitivity indices. Computer Physics Communications, 145, 280-297. |
[28] | Santhi C, Arnold JG, Williams JR, Dugas WA, Srinivasan R, Hauck LM (2001). Validation of the swat model on a large rwer basin with point and nonpoint sources. Journal of the American Water Resources Association, 37, 1169-1188. |
[29] |
Shu C, Geng BX, Fang WW, Xiu P (2020). Parameter analysis and optimization using genetic algorithm in a marine ecosystem model of the northern South China Sea. Journal of Tropical Oceanography, 39(2), 98-106.
DOI |
[舒婵, 耿兵绪, 房巍巍, 修鹏 (2020). 南海北部海洋生态模型的参数分析及遗传算法优化. 热带海洋学报, 39(2), 98-106.]
DOI |
|
[30] | Smith B, Wårlind D, Arneth A, Hickler T, Leadley P, Siltberg J, Zaehle S (2014). Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences, 11, 2027-2054. |
[31] | Su YL, Wu X, Guo CY, Chen XD, Liu L, Peng SZ (2022). The spatiotemporal pattern and trend of annual precipitation over China from 2001 to 2100. Journal of Lanzhou University (Natural Sciences), 58, 641-649. |
[苏艳丽, 吴璿, 郭辰昱, 陈笑蝶, 刘力, 彭守璋 (2022). 2001-2100年中国降水时空格局及其趋势分析. 兰州大学学报(自然科学版), 58, 641-649.] | |
[32] | Su YL, Zhang JL, Peng SZ, Ding YX (2022). Simulating ecological functions of vegetation using a dynamic vegetation model. Forests, 13, 1464. DOI: 10.3390/f13091464. |
[33] | Tang GP, Beckage B, Smith B (2014). Potential future dynamics of carbon fluxes and pools in New England forests and their climatic sensitivities: a model-based study. Global Biogeochemical Cycles, 28, 286-299. |
[34] | Tang XL, Zhao X, Bai YF, Tang ZY, Wang WT, Zhao YC, Wan HW, Xie ZQ, Shi XZ, Wu BF, Wang GX, Yan JH, Ma KP, Du S, Li SG, et al. (2018). Carbon pools in China’s terrestrial ecosystems: new estimates based on an intensive field survey. Proceedings of the National Academy of Sciences of the United States of America, 115, 4021-4026. |
[35] | The Editorial Committee of Vegetation of China, Chinese Academy of Sciences (2007). Vegetation Map of China and Its Geographical Pattern—Illustration of the Vegetation Map of the People’s Republic of China (1:1 000 000). Geological Publishing House, Beijing. |
[ 中国科学院中国植被图编辑委员会 (2007). 中国植被及其地理格局——中华人民共和国植被图(1: 1 000 000)说明书. 地质出版社, 北京.] | |
[36] | Thurner M, Beer C, Santoro M, Carvalhais N, Wutzler T, Schepaschenko D, Shvidenko A, Kompter E, Ahrens B, Levick SR, Schmullius C (2014). Carbon stock and density of northern boreal and temperate forests. Global Ecology and Biogeography, 23, 297-310. |
[37] | Venkataraman K, Tummuri S, Medina A, Perry J (2016). 21st century drought outlook for major climate divisions of Texas based on CMIP5 multimodel ensemble: implications for water resource management. Journal of Hydrology, 534, 300-316. |
[38] | Walker WS, Gorelik SR, Cook-Patton SC, Baccini A, Farina MK, Solvik KK, Ellis PW, Sanderman J, Houghton RA, Leavitt SM, Schwalm CR, Griscom BW (2022). The global potential for increased storage of carbon on land. Proceedings of the National Academy of Sciences of the United States of America, 119, e2111312119. DOI: 10.1073/pnas.2111312119. |
[39] | Wang QJ (1997). Using genetic algorithms to optimise model parameters. Environmental Modelling & Software, 12, 27-34. |
[40] | Weng ES, Zhou GS (2005). Defining plant functional types in China for global change studies. Acta Phytoecologica Sinica, 29, 81-97. |
[翁恩生, 周广胜 (2005). 用于全球变化研究的中国植物功能型划分. 植物生态学报, 29, 81-97.]
DOI |
|
[41] | Wolf A, Callaghan TV, Larson K (2008). Future changes in vegetation and ecosystem function of the Barents Region. Climatic Change, 87, 51-73. |
[42] | Wramneby A, Smith B, Zaehle S, Sykes MT (2008). Parameter uncertainties in the modelling of vegetation dynamics— Effects on tree community structure and ecosystem functioning in European forest biomes. Ecological Modelling, 216, 277-290. |
[43] | Ye XC, Yang XX, Liu FH, Wu J, Liu J (2021). Spatio-temporal variations of land vegetation gross primary production in the Yangtze River Basin and correlation with meteorological factors. Acta Ecologica Sinica, 41, 6949-6959. |
[叶许春, 杨晓霞, 刘福红, 吴娟, 刘佳 (2021). 长江流域陆地植被总初级生产力时空变化特征及其气候驱动因子. 生态学报, 41, 6949-6959.] | |
[44] |
Yi XS, Yin YY, Li GS, Peng JT (2011). Temperature variation in recent 50 years in the Three-River Headwaters Region of Qinghai Province. Acta Geographica Sinica, 66, 1451-1465.
DOI |
[易湘生, 尹衍雨, 李国胜, 彭景涛 (2011). 青海三江源地区近50年来的气温变化. 地理学报, 66, 1451-1465.] | |
[45] | Yu K, Ciais P, Seneviratne SI, Liu Z, Chen HYH, Barichivich J, Allen CD, Yang H, Huang Y, Ballantyne AP (2022). Field-based tree mortality constraint reduces estimates of model-projected forest carbon sinks. Nature Communications, 13, 2094. DOI: 10.1038/s41467-022-29619-4. |
[46] | Zhang JS, Chen XD, Peng SZ (2022). Trend and spatiotemporal differences of annual mean temperature in China from 2001 to 2100. Journal of Southwest University (Natural Science Edition), 44(12), 112-124. |
[张计深, 陈笑蝶, 彭守璋 (2022). 2001-2100年中国温度变化趋势及时空差异研究. 西南大学学报(自然科学版), 44(12), 112-124.] | |
[47] | Zhang XR, Cao Q, Ji SP, Chen H, Zhang TJ, Liu J (2022). Quantifying the contributions of climate change and human activities to vegetation dynamic changes in the Yellow River Delta. Acta Scientiae Circumstantia, 42(1), 56-69. |
[张心茹, 曹茜, 季舒平, 陈浩, 张廷靖, 刘建 (2022). 气候变化和人类活动对黄河三角洲植被动态变化的影响. 环境科学学报, 42(1), 56-69.] |
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