A benchmark dataset for half-hourly evapotranspiration estimation in China from 2000 to 2024

Abstract. Latent heat flux (LE) provides a direct representation of terrestrial evapotranspiration (ET) and plays a critical role in hydrological cycle studies, land surface model development, and the evaluation of remotely sensed evapotranspiration products. Although flux observations based on the eddy covariance technique are widely regarded as essential benchmark data for evapotranspiration estimation, existing ChinaFlux observations are generally limited by short observation periods and extensive data gaps, which substantially constrain their applicability in long-term change analyses and multi-scale studies. To address these limitations, we developed a gap-filling and temporal prolongation framework specifically designed for half-hourly LE and established a continuous ground-based benchmark dataset covering China for the period 2000–2024 based on observations from 50 ChinaFlux sites. The framework is built upon an automated machine learning approach (AutoML-H2O) and integrates ERA5-Land reanalysis data with MODIS vegetation indices, enabling accurate gap-filling within observation periods and reliable prolongation beyond observation intervals. Comprehensive evaluations demonstrate that the AutoML framework achieves high accuracy at the half-hourly scale across different gap-length scenarios, with an overall correlation coefficient (CC) of 0.862 and a root mean square error (RMSE) of 33.75 W m^-2, and it substantially outperforms conventional methods under long-gap conditions of 7 d and 30 d. The forward and backward prolongation results show high consistency (CC values of 0.902 and 0.896, respectively) and exhibit robust temporal stability under varying training data lengths. Multi-timescale validations further indicate that the prolonged LE data reasonably reproduce diurnal variations, seasonal cycles, and interannual variability from half-hourly to daily and monthly scales. Comparisons with ChinaFlux observations under strict quality control reveal good consistency across different temporal scales, underlying surface types, and climate zones. SHAP-based interpretability analysis indicates that energy supply consistently dominates LE variability, while vegetation state and water availability modulate their relative importance under different environmental conditions. Overall, we present the first continuous half-hourly ground-based LE benchmark dataset covering China for the period 2000–2024. This dataset provides essential data support for the evaluation of remotely sensed ET products, land surface model validation, and studies of regional water–energy cycles and climate change, and it is freely available via the following repository: https://doi.org/10.5281/zenodo.18194590 (Qian et al., 2026).