摘要
土壤水分是表达全球气候变化效应的关键陆面要素。为研发质量可靠的全球未来多情景地表土壤水分融合数据集,本研究首先利用改进的三重搭配(Enhanced Triple Collocation)对22套CMIP6(Coupled Model Intercomparison Project Phase 6)土壤水分数据开展精度评价,获取随机误差标准差(Random Error Standard Deviation,RESD)和相关系数(Correlation Coefficient,CC),选出参与融合的地球系统模式;其次,基于RESD和CC的归一化加权对筛选后的9套地球系统模式数据进行融合;最后,通过站点实测数据评价验证融合数据的精度。数据集内容包括:(1)基于SSP126、SSP245、SSP585情景预测的2015–2100年每月的土壤水分空间分布数据,空间分辨率为0.5°;(2)NAQU、REMEDHUS、SMOSMANIA、TWENTE四个站点的实测数据。数据集存储为.tif、.shp和.csv格式,由3,124个数据文件组成,数据量为829 MB(压缩为4个文件,770 MB)。
Soil moisture is a key land surface element to express the effects of global climate change.In order to develop a reliable global future multi-scenario surface soil moisture fusion dataset,this study firstly utilized the Enhanced Triple Collocation(ETC)to evaluate the accuracy of 22 CMIP6(Coupled Model Intercomparison Project Phase 6)soil moisture datasets,and obtained the random error standard deviation(RESD)and correlation coefficient(CC)to select the qualified Earth System Model datasets.Secondly,9 of the Earth System Model datasets were fused based on the normalized weighting of RESD and CC.Finally,the accuracy of the fused data was verified by the evaluation of the measured data at the stations,and the results showed that the fused soil moisture data could effectively describe the pattern of global surface soil moisture.The dataset includes:(1)global monthly 0.5°resolution soil moisture data of SSP1-2.6,SSP2-4.5,and SSP5-8.5.(2)in situ measurements from 4 networks,which are NAQU,REMEDHUS,SMOSMANIA,and TWENTE.The dataset is archived in.tif,.shp and.csv formats,and consists of 3,124 data files with data size of 829 MB(compressed into 4 files with data size of 770 MB).
作者
杨芬
刘杨晓月
Yang,F;Liu,Y.X.Y(Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China)
出处
《全球变化数据学报(中英文)》
2025年第2期155-162,V0155-V0162,共16页
Journal of Global Change Data & Discovery
基金
中华人民共和国科学技术部(2022YFF0711603)
国家自然科学基金(42101475)。
关键词
地表土壤水分
未来多情景
全球
融合
surface soil moisture
future multi-scenario
global
fusion
