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Abstract
Understanding the mechanisms of the coastal wetland ecosystem carbon sink function is crucial for adapting to, mitigating, and predicting global climate change. Landscape metrics and Carnegie Ames Stanford Approach (CASA) model were used to quantify the spatiotemporal patterns of soil organic carbon (SOC) and vegetation carbon sequestration (VCS) in the Yellow River Delta (YRD). GeoDetector model was used to explore the effects of various factors influencing VCS. The results showed that: (1) From 1999 to 2020, the area of natural wetlands and non-wetlands decreased, while the area of artificial wetlands increased. (2) The SOC stock in the 0 ~ 100 cm depth in 1999 and 2020 were about 7.8871 Tg C and 7.0521 Tg C, respectively. The amount of VCS increased from 0.2309 Tg C in 2000 to 0.3681 Tg C in 2020, with a significant annual increase of 6532 Mg C. In the past 21 years, the total carbon sink in the YRD was 6.0952 Tg C. The amount of VCS was equivalent to 2.93% of SOC stock in 1999, rising to 5.22% by 2020. (3) Vegetation cover has the greatest influence on the carbon sink function in the YRD, followed by precipitation and biodiversity. From 2000 to 2020, the effect of biodiversity on the carbon sinks of natural wetlands and artificial wetlands increased. The synergistic effect of wetland type and vegetation cover on carbon sink effect was replaced by the synergistic effect of biodiversity and vegetation cover. The identification of the primary factors that influence VCS in the YRD is significant to the understanding of the spatiotemporal evolution of carbon sink effect in coastal wetland ecosystems, and to the guidance of the rational development and protection of coastal wetlands and promote sustainable ecosystem development.
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Details
1 Chinese Academy of Sciences, Yantai Institute of Coastal Zone Research, Yantai, China (GRID:grid.9227.e) (ISNI:0000000119573309); Xi’an University of Science and Technology, College of Geomatics, Xi’an, China (GRID:grid.440720.5) (ISNI:0000 0004 1759 0801)
2 Chinese Academy of Sciences, Yantai Institute of Coastal Zone Research, Yantai, China (GRID:grid.9227.e) (ISNI:0000000119573309)
3 Xi’an University of Science and Technology, College of Geomatics, Xi’an, China (GRID:grid.440720.5) (ISNI:0000 0004 1759 0801)
4 The University of Hong Kong, Department of Geography, Hong Kong, China (GRID:grid.194645.b) (ISNI:0000 0001 2174 2757)