蒸散发(Evapotranspiration，ET)是陆地水、碳和能量交换的重要组成部分。基于不同模型和不同遥感数据估算的ET，存在不同程度的不确定性。贝叶斯模型平均(Bayesian model averaging，BMA)提供了降低不确定性的一种途径。本研究采用中国三江源地区水热通量观测数据，以ARTS、PT-JPL、MOD 16和SSEBop遥感蒸散发产品为基础，进行了 BMA集成研究，生成了三江源地区2003-2015年250 m空间分辨率的年均地表蒸散发数据集。通过验证各输入模型和BMA集成模型结果，发现基于BMA的ET与通量观测数据相关性达0。94，能够解释观测数据季节变化的89％，优于单个模型的性能。说明BMA模型集成能够整合不同模型内在优势，降低结果估算的不确定性，从而获得更可靠的估算结果。本数据集可为三江源区域水热变化研究和生态系统调节功能评估提供更精确的数据支持。
A dataset of surface evapotranspiration in the Three-River Headwaters Region of Qinghai Province based on Bayesian model averaging from 2003 to 2015
Evapotranspiration(ET)plays an important in the exchange of water,carbon and energy on land.The ET estimates based on different models and remote sensing data have different degrees of uncertainty.Bayesian model averaging(BMA)provides a way to reduce the uncertainty.Based on water and heat flux observation data in the Three-River Headwaters Region of China,evapotranspiration simulated by ARTS and PT-JPL models,and internationally shared MOD 16 and SSEBop remote sensing evapotranspiration products,we conducted a BMA integration study,resulting in a dataset of surface evapotranspiration in the Three-River Headwaters Region of Qinghai Province based on Bayesian model averaging from 2003 to 2015.By verifying the results of each input model and BMA integrated model,it is found that the correlation between ET based on BMA and flux observation data is 0.94,which can explain 89％of the seasonal variation data,outperforming the performance of a single model.The results show that BMA model integration can integrate the inherent advantages of different models and reduce the uncertainty of result estimation for more reliable estimation results.This dataset can provide more accurate data for studying hydrothermal changes and evaluating ecosystem regulation functions in the Three-River Headwaters Region.
evaporationthe Three-River Headwaters Region of ChinaBayesian model averaging(BMA)flux dataremote sensing product
万方数据
