Spatio-temporal Evolution and Scenario Prediction of Carbon Storage in Typical Wetlands in Poyang Lake Region

Wei Zezhu; Dong Bin; Xu Haifeng; Xu Zhili; Lu Zhipeng; Liu Xiao

doi:10.13961/j.cnki.stbctb.20230526.003

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Spatio-temporal Evolution and Scenario Prediction of Carbon Storage in Typical Wetlands in Poyang Lake Region

- Spatio-temporal Evolution and Scenario Prediction of Carbon Storage in Typical Wetlands in Poyang Lake Region
- Bulletin of Soiland Water Conservation Vol. 43, Issue 3, Pages: 290-300(2023)
- 作者机构：
  
  1. 安徽农业大学理学院,安徽,合肥,230036
  2. 安徽农业大学资源与环境学院,安徽,合肥,230036
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.20230526.003
  CLC： X36;S157.4;X171.1
- Published：2023
- 稿件说明：
移动端阅览
Wei Zezhu, Dong Bin, Xu Haifeng, et al. Spatio-temporal Evolution and Scenario Prediction of Carbon Storage in Typical Wetlands in Poyang Lake Region[J]. Bulletin of Soiland Water Conservation, 2023, 43(3): 290-300.
DOI：

Wei Zezhu, Dong Bin, Xu Haifeng, et al. Spatio-temporal Evolution and Scenario Prediction of Carbon Storage in Typical Wetlands in Poyang Lake Region[J]. Bulletin of Soiland Water Conservation, 2023, 43(3): 290-300. DOI： 10.13961/j.cnki.stbctb.20230526.003.

摘要

[目的

]

分析鄱阳湖湿地生态系统碳储量时空特征，为下一步鄱阳湖地区湿地保护，实现区域“碳达峰、碳中和”提供科学依据。[方法

]

结合InVEST和GeoSoS-FLUS模型，计算2000—2020年鄱阳湖地区典型湿地碳储量，并预测自然发展情景与生态保护情景下2030年碳储量变化。借助地理探测器模型，探究碳储量变化的驱动因素。[结果

]

①2000

2010

2020年鄱阳湖地区典型湿地碳储量分别为2.42×10

，2.48×10

，2.46×10

t。②高碳储量集中于中西部林地，低碳储量集中于中东部、西部和北部的湖泊水域。③土地利用是碳储量转移的主导因素，其中沼泽草地、沼泽地、林地、耕地对碳储量转移的解释力依次降低。④相较于自然发展情景，生态保护情景2020—2030年碳储量变化速率由-17.81%变化为-1.09%。[结论

]

合理的生态保护政策可以有效地保障湿地的固碳能力，应强化国土用途管制

落实生态保护措施

为提升区域碳储能力提供保障。

Abstract

[Objective] The temporal and spatial characteristics of carbon storage in the Poyang Lake wetland ecosystem were analyzed in order to provide scientifically based recommendations to protect Poyang Lake wetlands in the future and to produce regional "carbon peak and carbon neutrality".[Methods] InVEST and GeoSoS-FLUS models were combined to calculate carbon storage of typical wetlands in the Poyang Lake region from 2000 to 2020

and to predict carbon storage changes in 2030 under natural development scenarios and ecological protection scenarios. The factors driving carbon storage changes were determined by means of the geographic detector model.[Results] ① The carbon reserves of typical wetlands in the Poyang Lake region in 2000

2010 and 2020 were 2.42×106 t 2.48×106 t and 2.46×106 t

respectively. ② The high carbon reserves were concentrated in the central and western forests

while the low carbon reserves were concentrated in the east

central

western and northern lakes. ③ Land use was the dominant factor affecting carbon storage transfer. The explanatory power of vegetative cover type with respect to carbon storage transfer followed the order of marsh grassland> marshland>forest land>cultivated land. ④ Compared with the natural development scenario

the change rate of carbon storage for the ecological protection scenario changed from -17.81% to -1.09% during the period from 2020 to 2030.[Conclusion] Reasonable ecological protection policies can effectively guarantee the carbon sequestration capacity of wetlands. Land use control practices should be strengthened and ecological protection measures should be implemented as as to guarantee improvement in regional carbon storage capacity.

关键词

Keywords

references

Rubio-Loyola J, Paul-Fils W R S. Applied machine learning in industry 4.0:Case-study research in predictive models for black carbon emissions[J]. Sensors (Basel, Switzerland), 2022,22(10):3947.

刘泽淼,黄贤金,卢学鹤,等.共享社会经济路径下中国碳中和路径预测[J].地理学报,2022,77(9):2189-2201.

Meena S K, Dwivedi B S, Meena M C, et al. Effect of nutrient management on soil carbon quantities, qualities, and stock under rice-wheat production system[J]. Agriculture, 2022,12(11):1822.

Tai Haoyu, Xia Yonghua, Yan Min, et al. Construction of artificial forest point clouds by laser SLAM technology and estimation of carbon storage[J]. Applied Sciences, 2022,12(21):10838.

Walter K, Don A, Tiemeyer B, et al. Determining soil bulk density for carbon stock calculations:A systematic method comparison[J]. Soil Science Society of America Journal, 2016,80(3):579-591.

戴子熠,廖丽蓉,梁嘉慧,等.1988-2018年广西北海红树林蓝碳储量变化分析[J].海洋环境科学,2022,41(1):8-15.

王祖华,刘红梅,关庆伟,等.南京城市森林生态系统的碳储量和碳密度[J].南京林业大学学报(自然科学版),2011,35(4):18-22.

邓喆,丁文广,蒲晓婷,等.基于InVEST模型的祁连山国家公园碳储量时空分布研究[J].水土保持通报,2022,42(3):324-334.

Nave L E, Domke G M, Hofmeister K L, et al. Reforestation can sequester two petagrams of carbon in US topsoils in a century[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018,115(11):2776-2781.

纪昌品,张晓平.鄱阳湖不同湿地植物群落光合碳储量及分配[J].水土保持研究,2022,29(3):121-127.

Gemitzi A, Albarakat R, Kratouna F, et al. Land cover and vegetation carbon stock changes in Greece:A 29-year assessment based on CORINE and Landsat land cover data[J]. Science of the Total Environment, 2021,786:147408.

Aguilar F X, Sudekum H, McGarvey R, et al. Impacts of the US southeast wood pellet industry on local forest carbon stocks[J]. Scientific Reports, 2022,12(1):19449.

李甜甜.鄱阳湖生态经济区农田植被碳储量与碳密度分析[J].时代农机,2016,43(3):145-146.

张琛悦,赵霞,辛玉春,等.青海省草地生态系统碳储量及其分布特征[J].北京师范大学学报(自然科学版),2022,58(2):286-292.

Hu Bisong, Zou Lin, Qi Shuhua, et al. Evaluating the vulnerability of Siberian crane habitats and the influences of water level intervals in Poyang Lake wetland, China[J]. Remote Sensing, 2022,14(12):2774.

Xia Yang, Fang Chaoyang, Lin Hui, et al. Spatiotemporal evolution of wetland eco-hydrological connectivity in the Poyang Lake area based on long time-series remote sensing images[J]. Remote Sensing,2021,13(23):4812.

He Linqian, Liu Qian, Wang Decai, et al. Estimation of soil organic carbon storage based on digital soil mapping technique[J]. Journal of Applied Ecology, 2021,32(2):591-600.

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.

李克让,王绍强,曹明奎.中国植被和土壤碳贮量[J].中国科学(D辑:地球科学),2003,33(1):72-80.

揣小伟,黄贤金,郑泽庆,等.江苏省土地利用变化对陆地生态系统碳储量的影响[J].资源科学,2011,33(10):1932-1939.

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.

Kumar A, Kumar M. Estimation of biomass and soil carbon stock in the hydroelectric catchment of India and its implementation to climate change[J]. Journal of Sustainable Forestry, 2022,41(7):642-657.

吴振华,俞钦平,王亚蓓,等.景区扩容影响下的桂林市土地利用情景模拟与预测[J].水土保持通报,2022,42(5):131-139.

Das Milon, Chandra Nath Panna, Reang Demsai, et al. Tree diversity and the improved estimate of carbon storage for traditional agroforestry systems in North East India[J]. Applied Ecology and Environmental Sciences, 2020,8(4):154-159.

Dai Erfu, Wang Yahui. Attribution analysis for water yield service based on the geographical detector method:A case study of the Hengduan Mountain region[J]. Journal of Geographical Sciences, 2020,30(6):1005-1020.

Taran M, Ahirwal J, Deb S, et al. Variability of carbon stored in inland freshwater wetland in Northeast India[J]. Science of the Total Environment, 2023,859:160384.

Griffiths L N, Mitsch W J. Estimating the effects of a hurricane on carbon storage in mangrove wetlands in Southwest Florida[J]. Plants (Basel, Switzerland), 2021,10(8):1749.

Aitali R, Snoussi M, Kolker A S, et al. Effects of land use/land cover changes on carbon storage in North African coastal wetlands[J]. Journal of Marine Science and Engineering, 2022,10(3):364.

Slessarev E W, Chadwick O A, Sokol N W, et al. Rock weathering controls the potential for soil carbon storage at a continental scale[J]. Biogeochemistry, 2022,157(1):1-13.

Bacar T S, Cheng Yiben, Wang Yunqi, et al. The effect of vegetation restoration in soil organic carbon storage[J]. Open Journal of Soil Science, 2022,12(9):427-445.

Schweizer S A, Mueller C W, Höschen C, et al. The role of clay content and mineral surface area for soil organic carbon storage in an arable toposequence[J]. Biogeochemistry, 2021,156(3):401-420.

申霞,王鹏,王为攀,等.滨海盐沼净碳汇能力研究方法综述[J].生态学杂志,2022,41(4):792-803.

熊彩云,张学玲,肖复明,等.鄱阳湖湿地生态系统服务功能变化及其对策分析[J].人民长江,2011,42(9):28-32.

李若玮,叶冲冲,王毅,等.基于InVEST模型的青藏高原碳储量估算及其驱动力分析[J].草地学报,2021,29(S1):43-51.

徐凯健,谢锦升,曾宏达,等.生态恢复条件下典型红壤侵蚀区马尾松林碳储量时空变化与驱动力分析[J].中国水土保持科学,2016,14(1):89-96.

林彤,杨木壮,吴大放,等.基于InVEST-PLUS模型的碳储量空间关联性及预测:以广东省为例[J].中国环境科学,2022,42(10):4827-4839.

邵壮,陈然,赵晶,等.基于FLUS与InVEST模型的北京市生态系统碳储量时空演变与预测[J].生态学报,2022,42(23):9456-9469.

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