Pedotransfer函数
地球系统科学
计算机科学
土壤图
水准点(测量)
环境科学
土壤科学
财产(哲学)
可扩展性
数字土壤制图
土壤水分
数据库
生态学
地理
地图学
哲学
认识论
生物
导水率
作者
Yongjiu Dai,Wei ShangGuan,Nan Wei,Qinchuan Xin,Hua Yuan,Shupeng Zhang,Shaofeng Liu,Xingjie Lu,Dagang Wang,Fapeng Yan
标识
DOI:10.5194/soil-5-137-2019
摘要
Abstract. Soil is an important regulator of Earth system processes, but remains one of the least well-described data layers in Earth system models (ESMs). We reviewed global soil property maps from the perspective of ESMs, including soil physical and chemical and biological properties, which can also offer insights to soil data developers and users. These soil datasets provide model inputs, initial variables, and benchmark datasets. For modelling use, the dataset should be geographically continuous and scalable and have uncertainty estimates. The popular soil datasets used in ESMs are often based on limited soil profiles and coarse-resolution soil-type maps with various uncertainty sources. Updated and comprehensive soil information needs to be incorporated into ESMs. New generation soil datasets derived through digital soil mapping with abundant, harmonized, and quality-controlled soil observations and environmental covariates are preferred to those derived through the linkage method (i.e. taxotransfer rule-based method) for ESMs. SoilGrids has the highest accuracy and resolution among the global soil datasets, while other recently developed datasets offer useful compensation. Because there is no universal pedotransfer function, an ensemble of them may be more suitable for providing derived soil properties to ESMs. Aggregation and upscaling of soil data are needed for model use, but can be avoided by using a subgrid method in ESMs at the expense of increases in model complexity. Producing soil property maps in a time series still remains challenging. The uncertainties in soil data need to be estimated and incorporated into ESMs.
科研通智能强力驱动
Strongly Powered by AbleSci AI