RESUMO
Robust estimates of wetland soil organic carbon (SOC) pools are critical to understanding wetland carbon dynamics in the global carbon cycle. However, previous estimates were highly variable and uncertain, due likely to the data sources and method used. Here we used machine learning method to estimate SOC storage and their changes over time in China's wetlands based on wetland SOC density database, associated geospatial environmental data, and recently published wetland maps. We built a database of wetland SOC density in China that contains 809 samples from 181 published studies collected over the last 20 years as presented in the published literature. All samples were extended and standardized to a 1-m depth, on the basis of the relationship between SOC density data from soil profiles of different depths. We used three different machine learning methods to evaluate their robustness in estimating wetland SOC storage and changes in China. The results indicated that random forest model achieved accurate wetland SOC estimation with R2 being .65. The results showed that average SOC density of top 1 m in China's wetlands was 25.03 ± 3.11 kg C m-2 in 2000 and 26.57 ± 3.73 kg C m-2 in 2020, an increase of 6.15%. SOC storage change from 4.73 ± 0.58 Pg in 2000 to 4.35 ± 0.61 Pg in 2020, a decrease of 8.03%, due to 13.6% decreased in wetland area from 189.12 × 103 to 162.8 × 103 km2 in 2020, despite the increase in SOC density during the same time period. The carbon accumulation rate was 107.5 ± 12.4 g C m-2 year-1 since 2000 in wetlands with no area changes. Climate change caused variations in wetland SOC density, and a future warming and drying climate would lead to decreases in wetland SOC storage. Estimates under Shared Socioeconomic Pathway 1-2.6 (low-carbon emissions) suggested that wetland SOC storage in China would not change significantly by 2100, but under Shared Socioeconomic Pathway 5-8.5 (high-carbon emissions), it would decrease significantly by approximately 5.77%. In this study, estimates of wetland SOC storage were optimized from three aspects, including sample database, wetland extent, and estimation method. Our study indicates the importance of using consistent SOC density and extent data in estimating and projecting wetland SOC storage.
RESUMO
China implemented the National Wetland Conservation Program (NWCP) from 2002 to protect and rehabilitate wetlands. Under the background of sustainable development, assessment on the effectiveness of the NWCP is important to ecosystem management, especially in the Sanjiang Plain, the largest marsh distribution area and hotspot area with wetland loss. To achieve this aim, this study examined the changes in land cover and ecosystem services (ESs) from 1990 to 2000 and from 2000 to 2015 in the Sanjiang Plain as well as the nine national nature reserves for wetlands (NNRWs) by means of Landsat series images and the InVEST model. Results reveal that the NWCP played critical roles in reducing wetland loss and improving regional ESs. The shrinkage rate of wetlands in the Sanjiang Plain has been decreased remarkably, with a declined rate of wetland loss from 750 km2 yr-1 to 189 km2 yr-1. The reduction rate of habitat area in good suitable grade and ecosystem carbon stock declined notably during the period 2000-2015 compared to the period 1990-2000. The amount of water retention increased by 5.4%, while the grain production capacity was enhanced by nine times from 1990 to 2015. Specifically, since 2000, the reduction rate of wetland area in NNRWs (33 km2 yr-1) was obviously lower than that in the entire Sanjiang Plain, whilst various ESs in NNRWs were better than that in the whole Sanjiang Plain. This study is expected to provide an example for evaluating the effectiveness of the NWCP at other regions and support regional wetland conservation management.