1. 安徽大学 资源与环境工程学院,安徽,合肥,230031
2. 安徽省地勘局第二水文工程地质勘查院,安徽,芜湖,241100
3. 安徽省地质测绘技术院空间信息应用技术中心,安徽,合肥,230000
4. 安徽省公共气象服务中心,安徽,合肥,230000
5. 安徽省地质科学研究所,安徽,合肥,230000
6. 安徽省地质测绘技术院测绘航测分院,安徽,合肥,230000
7. 安徽省环境监测中心站,安徽,合肥,230000
8. 天立泰科技股份有限公司,安徽,合肥,230000
纸质出版:2023
移动端阅览
夏全升, 洪欣, 桂翔, 等. 基于InVEST模型的芜湖市固碳能力及影响因子研究[J]. 水土保持通报, 2023,43(5):385-394.
Xia Quansheng, Hong Xin, Gui Xiang, et al. A Study on Carbon Fixation Capacity and Its Influencing Factors Based on InVEST Model at Wuhu City[J]. Bulletin of Soiland Water Conservation, 2023, 43(5): 385-394.
夏全升, 洪欣, 桂翔, 等. 基于InVEST模型的芜湖市固碳能力及影响因子研究[J]. 水土保持通报, 2023,43(5):385-394. DOI: 10.13961/j.cnki.stbctb.20230526.001.
Xia Quansheng, Hong Xin, Gui Xiang, et al. A Study on Carbon Fixation Capacity and Its Influencing Factors Based on InVEST Model at Wuhu City[J]. Bulletin of Soiland Water Conservation, 2023, 43(5): 385-394. DOI: 10.13961/j.cnki.stbctb.20230526.001.
[目的
]
研究安徽省芜湖市2011—2021年碳储量时空分布格局,同时探究生态环境因子、地形因子、气象因子和土地利用程度对其固碳能力的影响变化,为芜湖市土地资源管理及绿色农业发展提供参考依据。[方法
]
以芜湖市2011,2015,2021年土地利用数据,利用InVEST模型Carbon storage模块定量评估研究碳储量空间分布,探究土地利用程度、地形、气象、土壤侵蚀等因子影响,并根据相关性分析叠加计算碳储量热点区域。[结果
]
①近年,芜湖市因土地利用变化碳储量减少了4.14×10
5
t,呈逐年减少趋势;固碳能力:草地<耕地<林地,林地为5 488.97 t/km
2
且耕地碳储量高达7.39×10
6
t。②在自然因素中,用地类型、海拔、坡度及土地利用程度是影响碳储量空间分布主要原因,随海拔、坡度升高而逐级缓慢增加,碳储量整体呈“北低南高”分布情况。③在生态环境因素中,碳储量与土壤保持为显著正相关,相辅相成互为协同关系;相反,与土壤侵蚀互为权衡关系。④南部碳储量呈现“高—高集聚”占18.77%,北部为“低—低集聚”仅为2.73%;碳储量热点区域因资源开发利用影响呈逐年减少趋势,优良区域占11.95%,集中于南部山林地带,局部较弱需重点保护管理优化。[结论
]
2011—2021年芜湖市固碳总量逐年减少,固碳速率呈现逐年减弱趋势
固碳能力较稳定。芜湖市北部固碳能力相对较弱,可通过土地管理优化以提升其固碳能力。
[Objective] The spatial and temporal distribution pattern of carbon storage at Wuhu City
Anhui Province from 2011 to 2021 were analyzed
and the influence of ecological environmental factors
topographic factors
meteorological factors
and land use degree on carbon sequestration capacity were determined in order to provide a reference for land resource management and green agricultural development at Wuhu City. [Methods] The carbon storage module of the InVEST model was used to quantitatively determine the spatial distribution of carbon storage
to explore the effects of land use degree
topography
meteorology
soil erosion
and other factors
and to calculate the hot spots of carbon storage based on correlation analysis superposition using land use data from 2011
2015
and 2021 at Wuhu City. [Results] ① Carbon storage at Wuhu City has declined by 4.15×105 t in recent years due to land use changes
with an annually decreasing trend. The carbon sequestration capacity of grassland was lower than that of cultivated land. The carbon storage capacity of cultivated land was 7.41×106 t
while that of forest land was 5 489.01 t/km2. ② Land use type
elevation
slope
and land use degree were the most important natural factors determining the spatial distribution of carbon stocks
which increased gradually step by step with altitude and slope. The overall distribution of carbon stocks was “lower in the north and higher in the south.” ③ Carbon storage and soil conservation were significantly and positively associated
mutually reinforcing
and synergistic among ecological and environmental variables; yet
there was a trade-off with soil erosion. ④ Carbon storage in the south showed a pattern of “high-high accumulation”
accounting for 18.77% of the total carbon accumulation
whereas carbon storage in the north showed a pattern of low-low accumulation
accounting for just 2.73% of the total carbon accumulation. The hotspots of carbon storage declined over time as a result of the effect of resource development and usage
with 11.95% of the area classified as excellent concentrated in the southern mountain forest. Certain areas were found to be vulnerable and will need to be conserved and optimized. [Conclusion] From 2011 to 2021
the total amount of carbon sequestration at Wuhu City decreased year by year
and the carbon sequestration rate showed a trend of weakening over time
while carbon sequestration capacity was relatively stable. Carbon sequestration capacity in the northern part of Wuhu City was relatively weak
and could be increased through land management optimization.
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(6):1526-1538.
赵其国,黄国勤,钱海燕.低碳农业[J].土壤,2011,43(1):1-5.
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,2006,38(6):745-749.
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(2):253-259.
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.
Zhao Gang, 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.
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.
Tang Huajun, Qiu Jianjun, 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.
Xu Shengxiang, Shi Xuezheng, Zhao Yongcun, et al. Carbon sequestration potential of recommended management practices for paddy soils of China,1980—2050[J]. Geoderma, 2011,166(1):206-213.
Yang Jie, Huang Xin. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth System Science Data, 2021,13(8):3907-3925.
Tallis H T, Ricketts T, Guerry A D, et al. InVEST 2.5.3 User's Guide [M]. The Natural Capital Project, Stanford, 2013.
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(5):1015-1023.
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(5):1117-1127.
吴素业.安徽大别山区降雨侵蚀力简化算法与时空分布规律[J].中国水土保持,1994(4):12-13.
梁音,史学正.长江以南东部丘陵山区土壤可蚀性
K
值研究[J].水土保持研究,1999,6(2):47-52.
张群.巢湖流域土壤侵蚀与水土保持环境效益评价[D].安徽芜湖:安徽师范大学,2013.
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,1997,12(2):105-111.
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(8):25-31.
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(2):538-545.
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(3):800-807.
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(1):267-272.
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.
Bai Yang, Zhuang Changwei, Ouyang Zhiyun, et al. Spatial characteristics between biodiversity and ecosystem services in a human-dominated watershed [J]. Ecological Complexity, 2011,8(2):177-183.
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.
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.
钟亮,林媚珍,周汝波.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(5):16-25.
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.
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(5):381-388.
0
浏览量
583
下载量
5
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621