中国湿地碳储量分布特征及其影响因素
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摘要
湿地巨大的碳储存能力使其在稳定全球气候变化中占有重要地位,并对全球土壤碳储量做出重要贡献.本文在阐明湿地碳储量估算方法的基础上,分析我国主要湿地区碳储量并讨论气候、植被、土壤性质、土地利用等因素对湿地碳储量的影响.结果表明:东北湿地区和青藏高原湿地区是八大湿地区中碳储量最高的两大区域;泥炭湿地的高稳定性、低分解率及酚氧化酶的作用使其成为内陆地区碳储量最高的湿地类型;单一因素的双向干扰及多重因素的交互作用使得湿地碳储量的影响因素和作用机理更加复杂.注重多重因素的交互作用,并结合数据同化技术,有利于湿地碳储量及湿地生态系统价值预测与评估.
Wetland plays an important role in stabilizing climate change and makes a significant contribution to global soil carbon storage due to its huge carbon storage capacity. Based on a summary of estimation methods of carbon storage, this study analyzed carbon storage and its influencing factors of typical wetlands in China, inclusing climate, vegetation, soil property and land use. The results showed that wetlands in Northeast China and the Tibetan Plateau had the highest carbon sto-rage among the eight wetland areas. Peat wetland had the highest carbon storage in inland area due to its higher stability, lower decomposition rate, and the impact of phenol oxidase. The bidirectional interference of single factor and combined effects of multiple factors made the influencing factors and mechanisms more complicated. Our results would contribute to the prediction and evaluation of wetland carbon storage and the value of ecosystem services through laying emphasis on the combined effects of multiple factors and applying the data assimilation technology.
Wetland plays an important role in stabilizing climate change and makes a significant contribution to global soil carbon storage due to its huge carbon storage capacity. Based on a summary of estimation methods of carbon storage, this study analyzed carbon storage and its influencing factors of typical wetlands in China, inclusing climate, vegetation, soil property and land use. The results showed that wetlands in Northeast China and the Tibetan Plateau had the highest carbon sto-rage among the eight wetland areas. Peat wetland had the highest carbon storage in inland area due to its higher stability, lower decomposition rate, and the impact of phenol oxidase. The bidirectional interference of single factor and combined effects of multiple factors made the influencing factors and mechanisms more complicated. Our results would contribute to the prediction and evaluation of wetland carbon storage and the value of ecosystem services through laying emphasis on the combined effects of multiple factors and applying the data assimilation technology.
引文
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