Carbon-water flux datasets of Eurasian meteorological stations

Mining carbon-water flux information from more widely distributed meteorological stations has great potential to deal with the challenge of insufficient observational data on these fluxes. We constructed a new framework to assess the transferability of Eurasian carbon-water flux simulation models (random forest model, RFM) to meteorological stations. We used a combination of the determination coefficient (R2) and Euclidean distance to evaluate the match between these models and meteorological station data. Not all meteorological stations could be matched with suitable RFM. Results showed that 85.9% and 99.1% of the meteorological stations (N=4466) satisfying the transfer condition (R2 ≥ 0.5) could produce the carbon and water fluxes when using remote sensing (RS) variables in RFM construction, respectively. Without the use of RS variables, 66.8% and 98.7% of the meteorological stations (N=6849) satisfying the transfer condition (R2 ≥ 0.5) could produce the carbon and water fluxes, respectively. The 4 spatio-temporal carbon-water flux datasets with quasi-observational characteristics that we generated at meteorological stations have great potential to improve the accuracy of assessments of ecosystem carbon-water dynamics at regional and global scales.