Study on Carbon Fixation Capacity and Its Influencing Factors Based on InVEST Model in Wuhu
Affiliation:

Anhui University

  • Article
  • | |
  • Metrics
  • |
  • Reference [34]
  • |
  • Related
  • |
  • Cited by
  • | |
  • Comments
    Abstract:

    [Objective] Regional carbon storage can be improved by optimizing and adjusting management. Investigating the service function of carbon storage is a crucial guarantee for maintaining ecosystem stability and has a significant influence on ecological environment conservation. [Methods] The Carbon storage module of the InVEST model was used to quantitatively evaluate and study the spatial distribution of carbon storage, explore the effects of land use degree, topography, meteorology, soil erosion, and other factors, and calculate the hot spots of carbon storage based on correlation analysis superposition, using land use data from 2010, 2015, and 2021 in Wuhu. [Results] (1) Carbon storage in Wuhu City has declined by 4.15105 t in recent years due to land use change, with an annual decrease trend. The carbon sequestration capacity of grassland was lower than that of cultivated land, and the carbon storage capacity of cultivated land was 7.41×106 t, while that of forest was 5489.01t/km2. (2) Among natural factors, land use type, elevation, slope, and land use degree were the most important in determining the spatial distribution of carbon stocks, which increased gradually step by step with altitude and slope, and the overall distribution of carbon stocks was "low in the north and high in the south." (3) Carbon storage and soil conservation are highly positively associated, mutually reinforcing, and synergistic among ecological and environmental variables; yet, there is a trade-off with soil erosion. (4) The ratio of "high-high accumulation" in southern China was 18.77%, whereas it was just 2.73% in northern China. The hotspots of carbon storage declined year by year as a result of the effect of resource development and usage, with 11.95% of the excellent regions concentrated in the southern mountain forest, and certain places being vulnerable and needing to be conserved and optimized. [Conclusion] The study of carbon storage change and its influencing elements is critical for improving carbon sequestration capacity for carbon neutrality and urban sustainable development, as well as providing scientific references for arable land conservation and green agricultural development.

    Reference
    [1] 彭文宏,牟长城,常怡慧,等. 东北寒温带永久冻土区森林沼泽湿地生态系统碳储量[J]. 土壤学报, 2020, 57(06): 1526-1538.
    [2] 赵其国,黄国勤,钱海燕. 低碳农业[J]. 土壤, 2011, 43(01): 1-5.
    [3] 尹云锋,蔡祖聪. 不同施肥措施对潮土有机碳平衡及固碳潜力的影响[J]. 土壤, 2006(06): 745-749.
    [4] 徐胜祥,史学正,赵永存,等. 不同耕作措施下江苏省稻田土壤固碳潜力的模拟研究[J]. 土壤, 2012, 44(02): 253-259.
    [5] 陈中星,张楠,张黎明,等. 福建省土壤有机碳储量估算的尺度效应研究[J]. 土壤学报, 2018, 55(3): 606-619.
    [6] Zhao G, Bryan B A, King D, et al. Impact of agricultural management practices on soil organic carbon: Simulation of Australian wheat systems[J].Global Change Biology, 2013, 19(5): 1585-1597.
    [7] 韩冰,王效科,欧阳志云. 中国农田生态系统土壤碳库的饱和水平及其固碳潜力[J]. 农村生态环境, 2005, 21(4): 6-11.
    [8] Tang H J, Qiu J J, van Ranst E, et al. Estimations of soil organic carbon storage in cropland of China based on DNDC model[J]. Geoderma, 2006,134(1/2): 200-206.
    [9] Xu S X, Shi X Z, Zhao Y C, et al. Carbon sequestration potential of recommended management practices for paddy soils of China, 1980-2050[J].Geoderma, 2011, 166(1): 206-213.
    [10] 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.
    [11] Tallis H T, Ricketts T, Guerry A D, et al. InVEST 2.5.3 user’s guide. The Natural Capital Project, Stanford, 2013.
    [12] 武慧君,姚有如,苗雨青,等. 芜湖市城市森林土壤理化性质及碳库研究[J]. 土壤通报, 2018, 49(05): 1015-1023.
    [13] 刘菊,傅斌,张成虎,等. 基于InVEST模型的岷江上游生态系统水源涵养量与价值评估[J]. 长江流域资源与环境, 2019, 28(03): 577-585.
    [14] Yu C Q, Huang X, Chen H, et al. Managing nitrogen to restore water quality in China[J]. Nature, 2019, 567(7749): 516-520.
    [15] 焦闪闪,张黎明,蒋威,等. 基于1:5万土壤数据库的福建省耕地全氮储量动态变化研究[J]. 土壤学报, 2016, 53(5): 1107-1119.
    [16] 杜佳衡,王锦. 基于InVEST模型的大理州永平县水生态系统服务功能时空变化分析[J]. 西部林业科学, 2021, 50(06): 91-102.
    [17] 史志华,刘前进,张含玉,等. 近十年土壤侵蚀与水土保持研究进展与展望[J]. 土壤学报, 2020, 57(05): 1117-1127.
    [18] 李锐. 中国主要水蚀区土壤侵蚀过程与调控研究[J]. 水土保持通报, 2011, 31(5): 1-6.
    [19] 马小玲,张宽地,杨帆,等. 坡面细沟侵蚀断面形态发育影响因素分析及动力特性试验[J]. 农业工程学报, 2017, 33(4): 209-216.
    [20] 张光辉. 对坡面径流挟沙力研究的几点认识[J]. 水科学进展, 2018, 29(2): 151-158.
    [21] 贾婉琳,吴赛男,陈昂. 基于InVEST模型的赤水河流域生态系统服务功能评估研究[J]. 中国水利水电科学研究院学报, 2020, 18(04): 313-320.
    [22] 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.
    [23] 潘韬,吴绍洪,戴尔阜,等. 基于InVEST模型的三江源区生态系统水源供给服务时空变化[J]. 应用生态学报, 2013, 24(01): 183-189.
    [24] 庄大方,刘纪远. 中国土地利用程度的区域分异模型研究[J]. 自然资源学报, 1997(02): 10-16.
    [25] 林海明,杜子芳. 主成分分析综合评价应该注意的问题[J]. 统计研究, 2013, 30(08): 25-31.
    [26] 陈姗姗,刘康,李婷,等. 基于InVEST模型的商洛市水土保持生态服务功能研究[J]. 土壤学报, 2016, 53(03): 800-807.
    [27] 卢开东,王健健,马燮铫,等. 基于DPSIR模型的芜湖市水生态承载力研究与建议[J]. 环境工程技术学报, 2022, 12(02): 538-545.
    [28] 王秀明,刘谞承,龙颖贤,等. 基于改进的InVEST模型的韶关市生态系统服务功能时空变化特征及影响因素[J]. 水土保持研究, 2020, 27(05): 381-388.
    [29] 杨君,周鹏全,袁淑君,等. 基于InVEST模型的洞庭湖生态经济区生态系统服务功能研究[J]. 水土保持通报, 2022, 42(01): 267-272+282.
    [30] 王大尚,李屹峰,郑华,等. 密云水库上游流域生态系统服务功能空间特征及其与居民福祉的关系[J]. 生态学报, 2014, 34(1): 70-81.
    [31] Bai Y, Zhuang C W, Ouyang Z Y, et al. Spatial characteristics between biodiversity and ecosystem services in a human-dominated watershed[J]. Ecological Complexity, 2011, 8(2): 177-183.
    [32] 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.
    [33] 张立伟,傅伯杰. 生态系统服务制图研究进展[J]. 生态学报, 2014, 34(2): 316-325.
    [34] 钟亮,林媚珍,周汝波,等. 基于InVEST模型的佛山市生态系统服务空间格局分析[J]. 生态科学, 2020, 39(05): 16-25.附图:图1 年均降雨图Fig. 1? Annual average rainfall map图2 气象及土地利用因子变化趋势示意图Fig. 2? Change trend of meteorological and land use factors图3 生态环境因子空间聚类图Fig. 3? Spatial cluster map of ecological environment factors图4 土地利用程度分析图Fig. 4? Analysis map of land use degree图5 生态环境因子冷热点图Fig. 5? Map of cold and hotspot of ecological environment factors附表:表1 土地利用程度分级赋值表等级未利用地级林、草、水用地级农业用地级城镇用地级土地利用类型未利用地林地、草地、水域耕地建设用地分级指数1234Table 1? Grading assignment table of land use degree表2 InVEST模型结果统计表Table 2? Statistics of InVEST model results碳储量(×104 t)生境质量氮输出(t/km2)氮输出(t)土壤保持(t/ha)土壤保持(×104 t)产水量(mm)产水量(×104 t)2010年耕地749.3710.5514.90916616.27554.7022342.074596.539253818.878林地472.2670.8310.06351.4161085.7189299.017222.46218569.345草地0.2360.3160.1470.205226.8244.020822.948145.810水域62.9500.1440.0000.00932.173158.5461.56675.376建设用地38.0150.1700.28088.16965.527240.073879.27231955.767未利用地0.0020.4080.0960.003164.4800.0641003.5633.884总计1322.83916756.07712043.795304569.0592015年耕地745.0580.5534.91916620.65861.5122618.229591.090249941.631林地414.9710.8280.06445.6241185.9488923.576217.32215917.701草地0.0540.4820.1570.057917.0193.717811.33132.981水域65.4380.1410.0000.00933.159169.9151.68584.404建设用地49.5090.1530.288116.50966.065315.252879.68041665.600未利用地0.0010.5170.0910.00079.9540.0141008.1061.815总计1275.03116782.85712030.703307644.1322021年耕地740.7820.5544.91916495.35162.6612651.725589.078247616.155林地426.2960.8270.06446.6531146.1208860.237214.51916158.918草地0.0270.5740.1580.0311066.0792.128725.21314.490水域59.5360.1430.0000.00832.859153.1811.63874.582建设用地54.7450.1500.290129.12968.420361.024879.77146074.128未利用地0.0000.3820.1100.001502.1340.0501011.9111.002总计1281.38716671.17312028.345309939.274
    Related
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation
Share
Article Metrics
  • Abstract:531
  • PDF: 533
  • HTML: 0
  • Cited by: 0
History
  • Received:November 28,2022
  • Revised:April 12,2023
  • Adopted:April 12,2023
  • Online: November 09,2023