Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China
文献类型：期刊论文
| 作者 | Wang, Shuai2,3; Roland, Bol3,4; Adhikari, Kabindra5; Zhuang, Qianlai6; Jin, Xinxin; Qian, Fengkui |
| 刊名 | FOREST ECOSYSTEMS
 |
| 出版日期 | 2023-04-01 |
| 卷号 | 10页码:100101 |
| 关键词 | Soil organic carbon stocks Forest ecosystem Spatial -temporal variation Carbon sink Digital soil mapping |
| ISSN号 | 2095-6355 |
| DOI | 10.1016/j.fecs.2023.100101 |
| 文献子类 | Article |
| 英文摘要 | Forest soil carbon is a major carbon pool of terrestrial ecosystems, and accurate estimation of soil organic carbon (SOC) stocks in forest ecosystems is rather challenging. This study compared the prediction performance of three empirical model approaches namely, regression kriging (RK), multiple stepwise regression (MSR), random forest (RF), and boosted regression trees (BRT) to predict SOC stocks in Northeast China for 1990 and 2015. Furthermore, the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified. A total of 82 (in 1990) and 157 (in 2015) topsoil (0-20 cm) samples with 12 environmental factors (soil property, climate, topography and biology) were selected for model construction. Randomly selected 80% of the soil sample data were used to train the models and the other 20% data for model verification using mean absolute error, root mean square error, coefficient of determination and Lin's consistency correlation coefficient indices. We found BRT model as the best prediction model and it could explain 67% and 60% spatial variation of SOC stocks, in 1990, and 2015, respectively. Predicted maps of all models in both periods showed similar spatial distribution characteristics, with the lower SOC in northeast and higher SOC in southwest. Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods. SOC stocks were mainly stored under Cambosols, Gleyosols and Isohumosols, accounting for 95.6% (1990) and 95.9% (2015). Overall, SOC stocks increased by 471 Tg C during the past 25 years. Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories. The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks. Overall, our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale. |
| WOS关键词 | LAND-USE ; CLIMATE-CHANGE ; STOCKS ; REGRESSION ; GRADIENT ; PREDICTION ; VARIABLES ; MODELS ; MATTER ; AREA |
| WOS研究方向 | Forestry |
| WOS记录号 | WOS:000955034700001 |
| 出版者 | KEAI PUBLISHING LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/190545]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Purdue Univ, Dept Earth Atmospher & Planetary Sci, W Lafayette, IN 47907 USA 2.Shenyang Agr Univ, Coll Land & Environm, Shenyang 110866, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 4.Forschungszentrum Julich, Inst Bio & Geosci, Agrosphere IBG 3, Wilhelm Johnen Str, D-52428 Julich, Germany 5.Bangor Univ, Environm Ctr Wales, Sch Nat Sci, Bangor LL57 2UW, Wales 6.USDA ARS, Grassland Soil & Water Res Lab, Temple, TX 76502 USA |
| 推荐引用方式 GB/T 7714 | Wang, Shuai,Roland, Bol,Adhikari, Kabindra,et al. Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China[J]. FOREST ECOSYSTEMS,2023,10:100101. |
| APA | Wang, Shuai,Roland, Bol,Adhikari, Kabindra,Zhuang, Qianlai,Jin, Xinxin,&Qian, Fengkui.(2023).Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China.FOREST ECOSYSTEMS,10,100101. |
| MLA | Wang, Shuai,et al."Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China".FOREST ECOSYSTEMS 10(2023):100101. |
入库方式： OAI收割
来源：地理科学与资源研究所
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