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Data-driven modeling on the global annual soil nitrous oxide emissions: Spatial pattern and attributes

文献类型：期刊论文

作者Liao, Jiaqiang4,5; Huang, Yuanyuan5; Li, Zhaolei2,3,5; Niu, Shuli1,4,5
刊名SCIENCE OF THE TOTAL ENVIRONMENT
出版日期2023-12-10
卷号903页码:9
关键词Soil nitrous oxide Spatial pattern Data-driven model Forest Grassland Cropland
ISSN号0048-9697
DOI10.1016/j.scitotenv.2023.166472
通讯作者Niu, Shuli(sniu@igsnrr.ac.cn)
英文摘要Previous assessments generated divergent estimates of global terrestrial soil nitrous oxide (N2O) emission and its spatial distributions, which did not match the observed data well. The objectives of this study were to generate a global map of terrestrial soil N2O emissions based on field observations (n = 5549) and quantify the contribution of different variables for predicting the global variation of N2O emissions. We provided spatially explicit maps of annual soil N2O emission rates across forest, grassland and cropland using the random forest approach. The global mean soil N2O emission rate in our data-driven model was 0.059 & PLUSMN; 0.006 g N m-2 year-, 1 which was lower than the estimates from previous model ensembles. Soil N2O emissions were higher in the northern than southern hemisphere. The average annual soil N2O emission rate of cropland (0.094 & PLUSMN; 0.009 g N m-2 year- 1) was higher than that of forest (0.039 & PLUSMN; 0.004 g N m-2 year- 1) and grassland (0.045 & PLUSMN; 0.007 g N m-2 year- 1). In addition, we found that soil nitrogen substrates dominated the changes in soil N2O emissions and the relative importance of nitrate, ammonium, and fertilizer in predicting soil N2O emissions was greater than that of mean annual temperature and precipitation. Our data-driven model results implied that previous process-based model may overestimate the global soil N2O emission rates due to limited validation data and incomplete assumptions on related-mechanisms. This study highlights the importance of global field observations in N2O emission estima-tion, which can provide an independent dataset to constrain previous process-based models for better prediction.
WOS关键词GREENHOUSE-GAS EMISSIONS ; N2O EMISSIONS ; PH ; METAANALYSIS ; TEMPERATURE ; METHANE ; FLUXES
资助项目National Natural Science Founda- tion of China[31988102] ; National Natural Science Founda- tion of China[31625006] ; International Collabora- tion Program of Chinese Academy of Sciences[131A11KYSB20180010]
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001072642200001
出版者ELSEVIER
资助机构National Natural Science Founda- tion of China ; International Collabora- tion Program of Chinese Academy of Sciences
源URL[http://ir.igsnrr.ac.cn/handle/311030/198186]  
专题中国科学院地理科学与资源研究所
通讯作者Niu, Shuli
作者单位1.11A,Datun Rd, Beijing 100101, Peoples R China
2.Southwest Univ, Acad Agr Sci, Chongqing 400715, Peoples R China
3.Southwest Univ, Coll Resources & Environm, Interdisciplinary Res Ctr Agr Green Dev Yangtze Ri, Chongqing 400715, Peoples R China
4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
5.Chinese Acad Sci, Key Lab Ecosyst Network Observat & Modeling, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Liao, Jiaqiang,Huang, Yuanyuan,Li, Zhaolei,et al. Data-driven modeling on the global annual soil nitrous oxide emissions: Spatial pattern and attributes[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2023,903:9.
APA Liao, Jiaqiang,Huang, Yuanyuan,Li, Zhaolei,&Niu, Shuli.(2023).Data-driven modeling on the global annual soil nitrous oxide emissions: Spatial pattern and attributes.SCIENCE OF THE TOTAL ENVIRONMENT,903,9.
MLA Liao, Jiaqiang,et al."Data-driven modeling on the global annual soil nitrous oxide emissions: Spatial pattern and attributes".SCIENCE OF THE TOTAL ENVIRONMENT 903(2023):9.

入库方式： OAI收割

来源：地理科学与资源研究所

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