Abstract. Land surface energy, water and carbon fluxes are key for understanding Earth's climate system, yet global continuous high resolution fluxes datasets remain scarce. In this study, we present a global long-term (2000–2020) hourly 9 km dataset of terrestrial water-energy-carbon fluxes, generated by integrating model simulations, in-situ measurements, and machine learning with remote sensing and meteorological data. First, the integrated STEMMUS-SCOPE model was deployed to simulate land surface fluxes over 170 sites with in-situ measurements. The selected model-output variables include net radiation (Rn), latent heat flux (LE), sensible heat flux (H), soil heat flux (G), gross primary productivity (GPP), solar-induced fluorescence at 685 and 740 nm (SIF685, SIF740). Next, optimal interpolation was applied to merge Rn, LE, and H from STEMMUS-SCOPE simulations with eddy covariance observations. The optimal estimate of Rn, LE, H alongside STEMMUS-SCOPE simulated G, GPP, SIF685, SIF740 were then used as training data-pairs to develop the emulator using a multivariate Random Forest (RF) regression algorithm, referred to as Random Forest with Optimal Interpolation (RFOI) to predict terrestrial water-energy-carbon fluxes. The results demonstrate that RFOI can estimate land surface fluxes with Pearson Correlation Coefficient score (r-score) values higher than 0. 88 except for GPP (Rn 0. 99, LE 0. 88, H 0. 92, G 0. 92, GPP 0. 8, SIF685 0. 99, SIF740 0. 99). The testing results on independent stations (which were not included for developing the emulator) show r-score values higher than 0. 8. The feature importance indicates that incoming shortwave radiation, surface soil moisture, and leaf area index are top predictor variables that determine the prediction performance. FluxHourly enables analysis of ecosystem responses to climate extremes at unprecedented spatiotemporal scales. FluxHourly is available at https: //doi. org/10. 11888/Terre. tpdc. 302319 (Han et al. , 2025a).
Han et al. (2025) studied this question.