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Analysis of spatiotemporal evolution characteristics and driving factors of carbon storage in Dongting Lake Wetland, China
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Abstract: Lake wetlands play a crucial role as global carbon sinks, significantly contributing to carbon storage and ecological balance. This study estimates the quarterly carbon storage in the Dongting Lake wetland for the years 2010, 2015, and 2020, using MODIS remote sensing imagery and the InVEST model. A Structural Equation Model (SEM) was then employed to analyze the driving factors behind changes in carbon storage. Results show that intra-annual carbon storage increases and then decreases, with maximum level in the third quarter (average of 34.242 Tg) and a minimum one in the first quarter (average of 21.435 Tg). From 2010 to 2020, inter-annual carbon storage variations initially exhibited an increasing trend before decreasing, with the peak annual average carbon storage reaching 32.230 Tg in 2015. Notably, the coefficient of variation for intra-annual carbon storage increased from 8.5% in 2010 to 25.8% in 2020. Key driving factors that influence carbon storage changes include surface solar radiation, temperature, and water level, with carbon storage positively correlated with surface solar radiation and temperature, and negatively correlated with water level. These findings reveal the spatiotemporal evolution characteristics of carbon storage in the Dongting Lake wetland, offering scientific guidance for wetland conservation and regional climate adaptation policies.
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Key words:
- Lake wetland /
- Carbon storage /
- Dynamic evolution /
- Climate-hydrological drivers /
- Dongting Lake
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Figure 2. Flowchart for wetland carbon storage calculation and driving factor analysis
Figure 3. Spatial and temporal distribution of land cover in the research area
(a, b, c, and d correspond to the first, second, third, and fourth quarters of the corresponding year, respectively)
Figure 5. Quarterly and annual carbon storage in the research area
Notes：The carbon storage values of the bar chart correspond to the left y-axis, while the carbon storage values of the box plot correspond to the right y-axis.
Figure 7. SEM of driving factors and carbon storage
Notes: The arrows in (a) represent path coefficients, with * indicating P<0.1, ** indicating P<0.05, *** indicating P<0.001, and other paths having P>0.05; (b) illustrates the magnitude of influence of each independent variable on carbon storage.
Figure 8. Pearson correlation coefficient of driving factors and carbon storage
Notes: (a), (b), and (c) represent the first to second quarters, the second to third quarters, and the third to fourth quarters, respectively; (d) represents the 12 quarters of 2010, 2015, and 2020.
Table 1. Carbon storage sources of land use/cover types
Carbon storage types Sources of carbon storage Surface biomass carbon storage All living vegetation above the ground surface Underground biomass carbon storage Underground living root systems Soil organic carbon storage Organic carbon in mineral and organic soils Litter carbon storage Litter, standing or fallen deadwood 
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Table 2. The carbon density of various cover types in the research area (unit: Mg C/km2) (Cited from An et al. 2022)
Types $ {\mathit{C}}_{\mathit{a}\mathit{b}\mathit{o}\mathit{v}\mathit{e}} $ $ {\mathit{C}}_{\mathit{b}\mathit{e}\mathit{l}\mathit{o}\mathit{w}} $ $ {\mathit{C}}_{\mathit{s}\mathit{o}\mathit{i}\mathit{l}} $ $ {\mathit{C}}_{\mathit{d}\mathit{e}\mathit{a}\mathit{d}} $ Water 0 0 0 0 Mudflat 1 1 0.99 0 Sedge marsh 0.82 0.87 89 1 Reed marsh 6 6 20 0 Woodland 64.2 118 207.3 3.5
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