干旱区研究 ›› 2024, Vol. 41 ›› Issue (3): 499-508.doi: 10.13866/j.azr.2024.03.14 cstr: 32277.14.AZR.20240314
收稿日期:
2023-09-11
修回日期:
2024-01-14
出版日期:
2024-03-15
发布日期:
2024-04-01
通讯作者:
邵战林. E-mail: 944872210@qq.com作者简介:
李佳珂(1998-),男,硕士研究生,主要从事土地资源管理方面的研究. E-mail: jakelee2022@163.com
基金资助:
Received:
2023-09-11
Revised:
2024-01-14
Published:
2024-03-15
Online:
2024-04-01
摘要:
土地利用变化对陆地生态系统的碳储量变化有着重要影响,研究不同发展情景下陆地生态系用碳储量变化情况,有利于优化空间布局,协调土地利用与生态环境保护的关系。本研究结合PLUS和InVEST模型,通过多种驱动因素数据分析了2000—2020年乌鲁木齐市土地利用的变化特征,以此预测模拟2030年自然发展情景、生态保护优先情景和耕地保护优先情景下土地碳储量。结果表明:(1) 2000—2020年乌鲁木齐市林地、水域、建设面积、未利用地数量增加,耕地、草地面积减少。(2) 2030年,自然发展情景延续了以往发展模式,建设用地面积增幅为18.29%。生态保护优先情景下,建设用地的扩张速度得到有效控制,增幅已经减缓,为4.73%。耕地保护优先情景下耕地面积比自然发展情景下多了171 km2,耕地保护效果显著。(3) 2000—2020年,碳储量呈下降趋势,共计减少8.5×106 t。2030年自然发展情景下碳储量总量相较于2020年减少了4.065×106 t,生态保护优先情景下比自然增长情景高7.519×105 t,耕地保护优先情景比自然增长情景低1.979×106 t。因此,在未来乌鲁木齐市发展规划中,应当落实耕地保护责任,控制建设用地向林地、草地等高碳密度用地的扩张,优化用地布局,提高区域碳储量水平。
李佳珂, 邵战林. 基于PLUS和InVEST模型的乌鲁木齐市碳储量时空演变与预测[J]. 干旱区研究, 2024, 41(3): 499-508.
LI Jiake, SHAO Zhanlin. Spatiotemporal evolution and prediction of carbon stock in Urumqi City based on PLUS and InVEST models[J]. Arid Zone Research, 2024, 41(3): 499-508.
表4
乌鲁木齐市2000—2020年土地利用转移矩阵"
土地利用类型 | 2020年 | |||||||
---|---|---|---|---|---|---|---|---|
耕地 | 林地 | 草地 | 水域 | 建设用地 | 未利用地 | 总和 | ||
2000年 | 耕地 | 518 | 0.4 | 288.8 | 9.4 | 68.3 | 9.4 | 894.3 |
林地 | 0.3 | 495.2 | 0.0 | 0.0 | 0.0 | 0.0 | 495.5 | |
草地 | 116.3 | 72.0 | 6191.8 | 17.8 | 113.2 | 739.2 | 7250.3 | |
水域 | 7.1 | 0.5 | 9.7 | 52.8 | 1.1 | 12.0 | 83.2 | |
建设用地 | 0.1 | 0.0 | 0.1 | 0.3 | 238.4 | 0.0 | 238.9 | |
未利用地 | 16.0 | 0.1 | 183.3 | 13.1 | 12.4 | 4584.1 | 4809 | |
总和 | 657.8 | 568.2 | 6673.7 | 93.4 | 433.4 | 5344.7 | 13771.2 |
[1] |
Udara Willhelm Abeydeera L H, Wadu Mesthrige J, Samarasinghalage T I. Global research on carbon emissions: A scientometric review[J]. Sustainability, 2019, 11(14): 3972.
doi: 10.3390/su11143972 |
[2] |
Willcock S, Phillips O L, Platts P J, et al. Land cover change and carbon emissions over 100 years in an A frican biodiversity hotspot[J]. Global Change Biology, 2016, 22(8): 2787-2800.
doi: 10.1111/gcb.13218 pmid: 26748590 |
[3] |
Wang Z, Zeng J, Chen W. Impact of urban expansion on carbon storage under multi-scenario simulations in Wuhan, China[J]. Environmental Science and Pollution Research, 2022, 29(30): 45507-45526.
doi: 10.1007/s11356-022-19146-6 |
[4] | 朱志强, 马晓双, 胡洪. 基于耦合FLUS-InVEST模型的广州市生态系统碳储量时空演变与预测[J]. 水土保持通报, 2021, 41(2): 222-229, 239. |
[Zhu Zhiqiang, Ma Xiaoshuang, Hu Hong. Spatio-temporal evolution and prediction of ecosystem carbon stocks in Guangzhou City by coupling FLUS-InVEST models[J]. Bulletin of Soil and Water Conservation, 2021, 41(2): 222-229, 239.] | |
[5] | 杨潋威, 赵娟, 朱家田, 等. 基于PLUS和InVEST模型的西安市生态系统碳储量时空变化与预测[J]. 自然资源遥感, 2022, 34(4): 175-182. |
[Yang Nuanwei, Zhao Juan, Zhu Jiatian, et al. Spatial-temporal change and prediction of carbon stock in the ecosystem of Xi’an based on PLUS and InVEST models[J]. Remote Sensing of Natural Resources, 2022, 34(4): 175-182.] | |
[6] | 雒舒琪, 胡晓萌, 孙媛, 等. 耦合PLUS-InVEST模型的多情景土地利用变化及其对碳储量影响[J]. 中国生态农业学报, 2023, 31(2): 300-314. |
[Luo Shuqi, Hu Xiaomeng, Sun Yuan, et al. Multi-scenario land use change and its impact on carbon storage based on coupled PLUS-InVEST model[J]. Chinese Journal of Eco-Agriculture, 2023, 31(2): 300-314.] | |
[7] |
Anindita S, Sleutel S, Vandenberghe D, et al. Land use impacts on weathering, soil properties, and carbon storage in wet Andosols, Indonesia[J]. Geoderma, 2022, 423: 115963.
doi: 10.1016/j.geoderma.2022.115963 |
[8] |
Islam I, Cui S, Hoque M Z, et al. Dynamics of tree outside forest land cover development and ecosystem carbon storage change in Eastern Coastal Zone, Bangladesh[J]. Land, 2022, 11(1): 76.
doi: 10.3390/land11010076 |
[9] | 李忠佩, 王效举. 红壤丘陵区土地利用方式变更后土壤有机碳动态变化的模拟[J]. 应用生态学报, 1998, 9(4): 30-35. |
[Li Zhongpei, Wang Xiaoju. Simulation of soil organic carbon dynamic after changing land use pattern in hilly red soil region[J]. Chinese Journal of Applied Ecology, 1998, 9(4): 30-35.] | |
[10] | 杨玉姣, 陈云明, 曹扬. 黄土丘陵区油松人工林生态系统碳密度及其分配[J]. 生态学报, 2014, 34(8): 2128-2136. |
[Yang Yujiao, Chen Yunming, Cao Yang. Carbon density and distribution of Pinus tabulaeformis plantation ecosystem in Hilly Loess Plateau[J]. Acta Ecologica Sinica, 2014, 34(8): 2128-2136.] | |
[11] |
Tang X, Zhao X, Bai Y, et al. Carbon pools in China’s terrestrial ecosystems: New estimates based on an intensive field survey[J]. Proceedings of the National Academy of Sciences, 2018, 115(16): 4021-4026.
doi: 10.1073/pnas.1700291115 |
[12] | 凌思源, 高子滢, 马闯, 等. 基于CASA模型的天津地区植被净初级生产力及植被碳汇量估测[J]. 天津农业科学, 2022, 28(12): 69-75, 81. |
[Ling Siyuan, Gao Ziying, Ma Chuang, et al. Estimation of net primary productivity of vegetation and vegetation carbon sink in Tianjin area based on CASA model[J]. Tianjin Agricultural Science, 2022, 28(12): 69-75, 81.] | |
[13] |
Zhao J, Xie H, Ma J, et al. Integrated remote sensing and model approach for impact assessment of future climate change on the carbon budget of global forest ecosystems[J]. Global and Planetary Change, 2021, 203: 103542.
doi: 10.1016/j.gloplacha.2021.103542 |
[14] |
Lindeskog M, Smith B, Lagergren F, et al. Accounting for forest management in the estimation of forest carbon balance using the dynamic vegetation model LPJ-GUESS: Implementation and evaluation of simulations for Europe[J]. Geoscientific Model Development, 2021, 14(10): 6071-6112.
doi: 10.5194/gmd-14-6071-2021 |
[15] |
Singh P, Benbi D K. Modeling soil organic carbon with DNDC and RothC models in different wheat-based cropping systems in north-western India[J]. Communications in Soil Science and Plant Analysis, 2020, 51(9): 1184-1203.
doi: 10.1080/00103624.2020.1751850 |
[16] |
Nelson E, Sander H, Hawthorne P, et al. Projecting global land-use change and its effect on ecosystem service provision and biodiversity with simple models[J]. PLoS One, 2010, 5(12): 14327.
doi: 10.1371/journal.pone.0014327 pmid: 21179509 |
[17] |
Polasky S, Nelson E, Pennington D, et al. The impact of land-use change on ecosystem services, biodiversity and returns to landowners: A case study in the state of minnesota[J]. Environmental and Resource Economics, 2011, 48(2): 219-242.
doi: 10.1007/s10640-010-9407-0 |
[18] |
Zhang F, Zhan J, Zhang Q, et al. Impacts of land use/cover change on terrestrial carbon stocks in Uganda[J]. Physics and Chemistry of the Earth, Parts A/B/C, 2017, 101: 195-203.
doi: 10.1016/j.pce.2017.03.005 |
[19] |
Piyathilake I, Udayakumara E P N, Ranaweera L V, et al. Modeling predictive assessment of carbon storage using InVEST model in Uva Province, Sri Lanka[J]. Modeling Earth Systems and Environment, 2022, 8(2): 2213-2223.
doi: 10.1007/s40808-021-01207-3 |
[20] | 朱丽亚, 胡克, 孙爽, 等. 基于InVEST模型的辽宁省海岸带碳储量时空变化研究[J]. 现代地质, 2022, 36(1): 96-104. |
[Zhu Liya, Hu Ke, Sun Shuang, et al. Research on the spatiotemporal variation of carbon storage in the coastal zone of Liaoning Province based on InVEST model[J]. Geoscienc, 2022, 36(1): 96-104.] | |
[21] | 卢雅焱, 徐晓亮, 李基才, 等. 基于InVEST模型的新疆天山碳储量时空演变研究[J]. 干旱区研究, 2022, 39(6): 1896-1906. |
[Lu Yayan, Xu Xiaoliang, Li Jicai, et al. Research on the spatio-temporal variation of carbon storage in the Xinjiang Tianshan Mountains based on the InVEST model[J]. Arid Zone Research, 2022, 39(6): 1896-1906.] | |
[22] | 陆汝成, 黄贤金, 左天惠, 等. 基于CLUE-S和Markov复合模型的土地利用情景模拟研究——以江苏省环太湖地区为例[J]. 地理科学, 2009, 29(4): 577-581. |
[Lu Rucheng, Huang Xianjin, Zuo Tianhui, et al. Land use scenarios simulation based on CLUE-S and Markov composite model—a case study of Taihu Lake Rim in Jiangsu Province[J]. Scientia Geographica Sinica, 2009, 29(4): 577-581.]
doi: 10.13249/j.cnki.sgs.2009.04.577 |
|
[23] |
Li J, Gong J, Guldmann J M, et al. Carbon dynamics in the northeastern qinghai-tibetan plateau from 1990 to 2030 using landsat land use/cover change data[J]. Remote Sensing, 2020, 12(3): 528.
doi: 10.3390/rs12030528 |
[24] | 史名杰, 武红旗, 贾宏涛, 等. 基于MCE-CA-Markov和InVEST模型的伊犁谷地碳储量时空演变及预测[J]. 农业资源与环境学报, 2021, 38(6): 1010-1019. |
[Shi Mingjie, Wu Hongqi, Jia Hongtao, et al. Temporal and spatial evolution and prediction of carbon stocks in Yili Valley based on MCE-CA-Markov and InVEST models[J]. Journal of Agricultural Resources and Environment, 2021, 38(6): 1010-1019.] | |
[25] | 李俊, 杨德宏, 吴锋振, 等. 基于PLUS与InVEST模型的昆明市土地利用变化动态模拟与碳储量评估[J]. 水土保持通报, 2023, 43(1): 378-387. |
[Li Jun, Yang Dehong, Wu Fengzhen, et al. Dynamic simulation of land use changes and assessment of carbon storage in Kunming City based on PLUS and InVEST models[J]. Bulletin of Soil and Water Conservation, 2023, 43(1): 378-387.] | |
[26] | 胡丰, 张艳, 郭宇, 等. 基于PLUS和InVEST模型的渭河流域土地利用与生境质量时空变化及预测[J]. 干旱区地理, 2022, 45(4): 1125-1136. |
[Hu Feng, Zhang Yan, Guo Yu, et al. Spatial and temporal changes in land use and habitat quality in the Weihe River Basin based on the PLUS and InVEST models and predictions[J]. Arid Land Geography, 2022, 45(4): 1125-1136.] | |
[27] | 杨洁, 谢保鹏, 张德罡. 基于InVEST和CA-Markov模型的黄河流域碳储量时空变化研究[J]. 中国生态农业学报, 2021, 29(6): 1018-1029. |
[Yang Jie, Xie Baopeng, Zhang Degang. Spatio-temporal evolution of carbon stocks in the Yellow River Basin based on InVEST and CA-Markov models[J]. Chinese Journal of Eco-Agriculture, 2021, 29(6): 1018-1029.] | |
[28] | 韩敏, 徐长春, 隆云霞, 等. 西北干旱区不同土地利用情景下的碳储量及碳源/汇变化模拟与预估[J]. 水土保持通报, 2022, 42(3): 335-344. |
[Han Min, Xu Changchun, Long Yunxia, et al. Simulation and prediction of changes in carbon storage and carbon source/sink under different land use scenarios in arid region of Northwest China[J]. Bulletin of Soil and Water Conservation, 2022, 42(3): 335-344.] | |
[29] | 陈宁, 辛存林, 唐道斌, 等. 中国西北地区多情景土地利用优化与碳储量评估[J]. 环境科学, 2023, 44(8): 1-16. |
[Chen Ning, Xin Cunlin, Tang Daobin, et al. Multi-scenario land use optimization and carbon storage assessment in Northwest China[J]. Environmental Science, 2023, 44(8): 1-16.]
doi: 10.1021/es9036176 |
|
[30] | 丁岳, 王柳柱, 桂峰, 等. 基于InVEST模型和PLUS模型的环杭州湾生态系统碳储量[J]. 环境科学, 2023, 44(6): 1-12. |
[Ding Yue, Wang Liuzhu, Gui Feng, et al. Ecosystem carbon storage in Hangzhou Bay Area based on InVEST and PLUS models[J]. Environmental Science, 2023, 44(6): 1-12.]
doi: 10.1021/es9036176 |
|
[31] | 刘洋, 张军, 周冬梅, 等. 基于InVEST模型的疏勒河流域碳储量时空变化研究[J]. 生态学报, 2021, 41(10): 4052-4065. |
[Liu Yang, Zhang Jun, Zhou Dongmei, et al. Temporal and spatial variation of carbon storage in the Shule River Basin based on InVEST model[J]. Acta Ecologica Sinica, 2021, 41(10): 4052-4065.] |
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