不同土地利用情景下四平市生态系统碳储量时空演变

邹桃红; 陈鹏; 刘家福; 杜会石

PDF(3010 KB)

中国环境科学 ›› 2023, Vol. 43 ›› Issue (10) : 5508-5518.

环境生态

不同土地利用情景下四平市生态系统碳储量时空演变

邹桃红, 陈鹏, 刘家福, 杜会石

作者信息 +

Spatio-temporal dynamics of territorial ecosystem carbon storage under different land use scenarios in Siping City

ZOU Tao-hong, CHEN Peng, LIU Jia-fu, DU Hui-shi

Author information +

文章历史 +

摘要

基于1990~2020年四平市土地利用类型数据，运用GeoSOS-FLUS模型，设定自然发展、耕地保护和生态优先三种情景，模拟不同情景下2030年四平市土地利用空间格局，同时结合InVEST模型定量分析研究区1990~2020年碳储量的时空分异特征，并探讨不同情境下土地利用变化对碳储量的影响，评估未来碳储量的潜力.结果表明：1990~2020年四平市耕地和林地分别减少了951.55km²和357.54km²，且以1990~2000年间的降幅最大.草地和建设用地呈增加趋势，分别增加了702.97km²和587.64km².2030年在生态优先情景下，林地呈扩张态势，耕地有少量增加，在耕地保护情景下，耕地数量得到有效保障，而林地和草地有不同程度缩减.建设用地在三种情景下都呈现扩张的趋势，在自然发展情景下增长幅度最大.1990~2020年，四平市陆地生态系统的总碳储量及平均碳密度呈连续减少的势态，以1990~2000年的降幅最大，主要原因是该时段内土地利用变化以耕地的减少和建设用地的增加为主.四平市碳储量的高值区位于东部伊通满族自治县和铁东区，该区土地利用主要以林地为主.在研究时段内，总碳储量呈现减少趋势，且在1990~2000年内下降幅度最大，减少了约0.703×10⁷t.在三种不同情景下，2030年研究区碳储量和平均碳密度呈现不同的发展趋势，生态优先情景能够有效的提高陆地生态系统的总碳储量和平均碳密度，而在自然发展和耕地保护情景下，两者都呈现下降趋势，耕地保护情景下的降低幅度相比自然发展情景明显减少.因此，研究区在未来土地资源规划中，应基于生态优先情景，保障耕地资源，优化土地利用结构，既保障耕地质量，又增加生态系统碳汇.

Abstract

Based on the land use data of Siping City from 1990 to 2020, the Geo-FLUS model was employed to simulate the spatial patterns of land use for 2030 across three different scenarios:natural development, cultivated land protection and ecological priority. Additionally, the InVEST model was coupled to quantitively analyze the spatial and temporal variation of carbon storage from 1990 to 2020, as well the impact of land use change on carbon storage under different scenarios. The results showed that:(1) From 1990 to 2020, cultivated land and forest land in Siping City decreased by 951.55km² and 357.54km², respectively, with the sharpest decline between 1990 to 2000. Conversely, the grassland and built-up land use exhilited an increasing trend, expanding by 702.97km² and 587.64km², respectively. Under the ecological priority scenario in 2030, the forest land use was projected to expand, while the cultivated land would gradually increase. Cultivated land protection scenario ensured cultivated land availability but led to reductions in forest land and grassland. Built-up land use expanded across all three scenarios, most notably under natural development scenario. (2) Ecosystem carbon storage and average density in Siping City showed a continuous decline tendency from 1990 to 2020, with the steepest drop between 1990 and 2000. This decline was mainly due to decreased cultivated land and increased built-up land during that period. (3) Higher carbon storage value were observed in the eastern Yitong Manchu Automous and Tiedong area, characterized by forest land. Total carbon storage expressed a declining trend over the study period, decreasing greatly by about 0.703×10⁷tons between 1990 and 2000. (4) Carbon storage and average carbon density in 2030 exhibited distinct trends across the three different scenarios. Ecological priority scenario led to an effective increase in both metrics for terrestrial ecosystem. Conversely natural development and cultivated land protection scenario yields declines, though the reduction under the cultivated land protection scenario was significantly lower than that under natural development scenario. Therefore, prioritizing ecological priority scenario in future land resource planning can ensure cultivated land resources, optimize land use structure, cultivated land quality, and amplify ecosystem carbon sequestration.

导出引用

邹桃红, 陈鹏, 刘家福, 杜会石. 不同土地利用情景下四平市生态系统碳储量时空演变[J]. 中国环境科学. 2023, 43(10): 5508-5518

ZOU Tao-hong, CHEN Peng, LIU Jia-fu, DU Hui-shi. Spatio-temporal dynamics of territorial ecosystem carbon storage under different land use scenarios in Siping City[J]. China Environmental Science. 2023, 43(10): 5508-5518

中图分类号： X142

参考文献

[1] 张燕,师学义,唐倩.不同土地利用情景下汾河上游地区碳储量评估[J]. 生态学报, 2021,41(1):360-373. Zhang Y, Shi X Y, Tang Q. Carbon storage assessment in the upper reaches of the Fenhe River under different land use scenarios[J]. Acta Ecologica Sinica, 2021,41(1):360-373.
[2] Baumann M, Gasparri I, Piquer-Rodriguez M, et al. Carbon emissions from agricultural expansion and intensification in the Chaco[J]. Glob Chang Biol, 2017,23(5):1902-1916.
[3] 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(中英文), 2021, 29(6):1018-1029. Yang J, Xie B P, Zhang D G. 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.
[4] 王晓东,蒙吉军.土地利用变化的环境生态效应研究进展[J]. 北京大学学报(自然科学版), 2014,50(6):1133-1140. Wang X D, Meng J J. Research progress on the environmental-ecological impacts of land use change[J]. Acta Scientiarum Naturalium University Pekinensis, 2014,50(6):1133-1140.
[5] 曹周亮,张欣蓉,员学锋,等.基于土地利用变化的陕西省县域碳排放时空变化及影响因素研究[J]. 水土保持通报, 2022,42(5):376-385. Chao Z L, Zhang X R, Yuan X F, et al. Spatio-temporal variation and influencing factors of CO₂ emission at county scale in Shaanxi Province based on land use change[J]. Bulletin of Soil and Water Conservation. 2022,42(5):376-385.
[6] 马晓哲,王铮.土地利用变化对区域碳源汇的影响研究进展[J]. 生态学报, 2015,35(17):5898-5907. Ma X Z, Wang Z. Progress in the study on the impact of land-use change on regional carbon sources and sinks[J]. Acta Ecologica Sinica, 2015,35(17):5898-5907.
[7] 方精云,陈安平,赵淑清,等.中国森林生物量的估算:对Fang等Science一文(Science, 2001,291:2320-2322)的若干说明[J]. 植物生态学报, 2002,26(2):243-249. Fang J Y, Chen A P, Zhao S Q, et al. Estimating biomass carbon of China's forest:supplementary notes on report published in Science (291:2320-2322) by Fang et al. (2001)[J]. Acta Phytoecologica Sinica, 2002,26(2):243-249.
[8] Wu S N, Li J, Zhou W M, et al. A statistical analysis of spatiotemporal variations and determinant factors of forest carbon storage under China's Natural Forest Protection Program[J]. Journal of Forestry Research, 2017,29(2):415-424.
[9] 林子奇,王培俊,刘旗,等.2005~2020年淮海经济区耕地碳储量时空演变特征及碳汇区识别[J]. 农业工程学报, 2022,38(19):259-268. Lin Z Q, Wang P J, Liu Q, et al. Spatio-temporal evolution characteristics of cultivated land carbon storage and identification of carbon sink areas in Huaihai Economic Zone from 2005 to 2020[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022,38(19):259-268.
[10] Jiang W G, Deng Y, Tang Z H, et al. Modelling the potential impacts of urban ecosystem changes on carbon storage under different scenarios by linking the CLUE-S and the InVEST models[J]. Ecological Modelling, 2017,(345):30-40.
[11] Liang Y J, Liu L J, Huang J J. Integrating the SD-CLUE-S and InVEST models into assessment of oasis carbon storage in northwestern China[J]. PLoS One, 2017,12(2):e0172494.
[12] Sharp R, Tallis H T, Ricketts T, et al. InVEST 3.2.0 User's Guide[R]. Stanford University, University of Minnesota, 2015.
[13] 王蓓,赵军,仲俊涛.2005~2015年石羊河流域生态系统服务时空分异[J]. 干旱区研究, 2019,36(2):478-485. Wang B, Zhao J, Zhong J T. Spatiotemporal differentiation of ecosystem services in the Shiyang River basin from 2005 to 2015[J]. Arid Zone Research, 2019,36(2):474-485.
[14] 任涵,张静静,朱文博,等.太行山淇河流域土地利用变化对生境的影响[J]. 地理科学进展, 2018,37(12):1693-1704. Ren H, Zhang J J, Zhu W B, et al. Impact of land use change on habitat in the Qihe River basin of Taihang Mountains[J]. Progress in Geography, 2018,37(12):1693-1704.
[15] 张晓瑶,张潇,李冬花,等.城市土地利用变化对生态系统服务价值影响的多情景模拟——以深圳市为例[J]. 生态学报, 2022,42(6):2086-2097. Zhang X Y, Zhang X, Li D H, et al. Multi-Scenario simulation of the impact of urban land use change on ecosystem service value in Shenzhen[J]. Acta Ecologica Sinica, 2022,42(6):2086-2097.
[16] 雷军成,刘纪新,雍凡,等.基于CLUE-S和InVEST模型的五马河流域生态系统服务多情景评估[J]. 生态与农村环境学报, 2017,33(12):1084-1093. Lei J C, Liu J X, Yong F, et al. Multi-scenario ecosystem service assessment of Wuma River valley based on CLUE-S and InVEST models[J]. Journal of Ecology and Rural Environment, 2017,33(12):1084-1093.
[17] 王蓓,赵军,胡秀芳.基于InVEST模型的黑河流域生态系统服务空间格局分析[J]. 生态学杂志, 2016,35(10):2783-2792. Wang B, Zhao J, Hu X F. Spatial pattern analysis of ecosystem services based on InVEST in Heihe River Basin[J]. Chinese Journal of Ecology, 2016,35(10):2783-2792.
[18] 赫晓慧,徐雅婷,范学峰,等.中原城市群区域碳储量的时空变化和预测研究[J]. 中国环境科学, 2022,42(6):2965-2976. He X H, Xu Y T, Fan X F, et al. Temporal and spatial variation and prediction of regional carbon storage in Zhongyuan Urban Agglomeration[J]. China Environmental Science, 2022,42(6):2965-2976.
[19] Zhu G F, Qiu D D, Zhang Z X, et al. Land-use changes lead to a decrease in carbon storage in arid region, China[J]. Ecological Indicators, 2021,127.
[20] Zhao M M, He Z B, Du J, et al. Assessing the effects of ecological engineering on carbon storage by linking the CA-Markov and InVEST models[J]. Ecological Indicators, 2019,9829-38.
[21] 史名杰,武红旗,贾宏涛,等.基于MCE-CA-Markov和InVEST模型的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 M J, Wu H Q, Jia H T, 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 Resource and Environment, 2021,38(6):1010-1019.
[22] 朱文博,张静静,崔耀平,等.基于土地利用变化情景的生态系统碳储量评估——以太行山淇河流域为例[J]. 地理学报, 2019,74(3):446-459. Zhu W B, Zhang J J, Cui Y P, et al. Assessment of territorial ecosystem carbon storage based on land use change scenario:A case study in Qihe River Basin[J]. Acta Geographica Sinica, 2019,74(3):446-459.
[23] Liu X P, Liang X, Li X, et al. A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects[J]. Landscape and Urban Planning, 2017,16894-116.
[24] 邓元杰,姚顺波,侯孟阳,等.退耕还林还草工程对生态系统碳储存服务的影响-以黄土高原丘陵沟壑区子长县为例[J]. 自然资源学报, 2020,35(4):826-844. Deng Y J, Yao S B, Hou M Y, et al. Assessing the effects of the Green for Grain program on ecosystem carbon storage service by linking the InVEST and FLUS models:A case study of Zichang county in hilly and gully region of Loess Plateau[J]. Journal of Natural Resources, 2020,35(4):826-844.
[25] 张凯琪,陈建军,侯建坤,等.耦合InVEST与GeoSOS-FLUS模型的桂林市碳储量可持续发展研究[J]. 中国环境科学, 2022;42(6):2799-2809. Zhang K Q, Chen J J, Hou J K, et al. Study on sustainable development of carbon storage in Guilin coupled with InVEST and GeoSOS-FLUS model[J]. China Environment Science, 2022,42(6):2799-2809.
[26] 赵林峰,刘小平,刘鹏华,等.基于地理分区与FLUS模型的城市扩张模拟与预警[J]. 地球信息科学学报, 2020,22(3):517-530. Zhao L F, Liu X P, Liu P H, et al. Urban expansion simulation and early warning based on geospatial partition and FLUS model[J]. Journal of Geo-Information Science, 2020,22(3):517-530.
[27] 赵胤程,覃盟琳,庞雅月,等.基于FLUS-InVEST模型的北部湾城市群生态空间碳汇演变模拟及驱动因素研究[J]. 水土保持通报, 2022,42(3):345-355. Zhao Y C, Qin M L, Pang Y Y, et al. Evolution simulation and driving factors of eco-spatial carbon sinks in Beibu Gulf urban agglomeration based on FLUS-InVEST model[J]. Bulletin of Soil and Water Conservation, 2022,42(3):345-355.
[28] 李艾玉,刘吉平,于佳,等.近30年四平市土地利用时空变化及生态环境效应[J]. 浙江农业科学, 2014,(7):1082-1087.
[29] Yang J, Huang X. The 30m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth System Science Data, 2021,13(8):3907-3925.
[30] 赵轩,彭建东,樊智宇,等."双评价"视角下基于FLUS模型的武汉大都市区土地利用模拟和城镇开发边界划定研究[J]. 地球信息科学学报, 2020,22(11):2212-2226. Zhao X, Peng J D, Fan Z Y, et al. Land use simulation and urban growth boundaries delineation in Wuhan metropolitan area based on FLUS model and "Dual Environment Evaluation"[J]. Journal of Geo-Information Science, 2020,22(11):2212-2226.
[31] 陈理庭,蔡海生,张婷,等.基于Markov-FLUS模型的饶河流域土地利用多情景模拟分析[J]. 生态学报, 2022,42(10):3947-3958. Chen L T, Cai H S, Zhang T, et al. Land use multi-scenario simulation analysis of Rao River Basin based on Markov-FLUS model[J]. Acta Ecologica Sinica, 2022,42(10):3947-3958.
[32] 王保盛,廖江福,祝薇,等.基于历史情景的FLUS模型邻域权重设置——以闽三角城市群2030年土地利用模拟为例[J]. 生态学报, 2019,39(12):4284-4298. Wang B S, Liao J F, Zhu W, et al. The weight of neighborhood setting of the FLUS model based on a historical scenario:A case study of land use simulation of urban agglomeration of the golden Triangle of Southern Fujian in 2030[J]. Acta Ecologica Sinica, 2019,39(12):4284-4298.
[33] 王旭,马伯文,李丹,等.基于FLUS模型的湖北省生态空间多情景模拟预测[J]. 自然资源学报, 2020,35(1):230-242. Wang X, Ma B W, Li D, et al. Multi-scenario simulation and prediction of ecological space in Hubei province based on FLUS model[J]. Journal of Natural Resources, 2020,35(1):230-242.
[34] 韩玉,丁素婷,杨太保.基于InVEST模型的甘肃白龙江流域生态系统碳储量及空间格局特征[J]. 中国环境科学, 2023,43(3):1298-1306. Han Y, Ding S T, Yang T B. Spatial and temporal distribution and driving factors of carbon storage in Zhongtiao Mountain ecosystem in southern Shanxi Province[J]. China Environmental Science, 2023, 43(3):1298-1306.
[35] 张春华,王莉媛,宋茜薇,等.1973~2013年黑龙江省森林碳储量及其动态变化[J]. 中国环境科学, 2018,38(12):4678-4686. Zhang C H, Wang L Y, Song Q W, et al. Biomass carbon stocks and dynamics of forests in Heilongjiang Province from 1973 to 2013[J]. China Environmental Science, 2018,38(12):4678-4686.
[36] 曲琛,李文,徐嘉,等.黑龙江黑土地保护利用对碳储量的潜在影响[J]. 西北林学院学报, 2023,(5):1-10. Qu C, Li W, Xu J, et al. Potential impact of black land conservation and utilization on carbon storage in Heilongjiang Province[J]. Journal of Northwest Forestry University, 2023,(5):1-10.
[37] 郭树平.黑龙江省碳储量及碳汇潜力分析[J]. 森林工程, 2011, 27(3):9-11. Guo S P. Analysis on carbon stock and potential carbon sequestration in Heilongjiang Province[J]. Forest Engineering, 2011,27(3):9-11,16.
[38] 陈光水,杨玉盛,谢锦升,等.中国森林的地下碳分配[J]. 生态学报, 2007,27(12):5148-5157. Chen G S, Yang Y S, Xie J S, et al. Total belowground carbon allocation in China's forest[J]. Acta Ecologica Sinica, 2007,27(12):5148-5157.
[39] Alam S A, Starr M, Clark B J F. Tree biomass and soil organic carbon densities across the Sudanese woodland savannah:A regional carbon sequestration study[J]. Journal of Arid Environments, 2013,(89):67-76.
[40] Zhou J J, Zhao Y R, Huang P, et al. Impacts of ecological restoration projects on the ecosystem carbon storage of inland river basin in arid area, China[J]. Ecological Indicators, 2020,118:106803.
[41] 刘冠,李国庆,李洁,等.基于InVEST模型的1999~2016年麻塔流域碳储量变化及空间格局研究[J]. 干旱区研究, 2021,38(1):267-274. Liu G, Li G Q, Li J, et al. Study on changes in carbon storage and its spatial pattern in Mata Watershed from 1999 to 2016 based on InVEST model[J]. Arid Zone Research, 2021,38(1):267-274.