44 citations as recorded by crossref.

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2. CAMELE: Collocation-Analyzed Multi-source Ensembled Land Evapotranspiration Data C. Li et al. 10.5194/essd-16-1811-2024
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4. Analysis of Detailed Lake Variations and Associated Hydrologic Driving Factors in a Semi-Arid Ungauged Closed Watershed N. Wang et al. 10.3390/su15086535
5. Temporal and Spatial Variations of Potential and Actual Evapotranspiration and the Driving Mechanism over Equatorial Africa Using Satellite and Reanalysis-Based Observation I. Nooni et al. 10.3390/rs15123201
6. Progress of evapotranspiration research based on VOSviewer: A review Z. Yang et al. 10.2166/wst.2024.036
7. Surface‐driven amplification of Madden–Julian oscillation circulation anomalies across East Africa and its influence on the Turkana jet J. Talib et al. 10.1002/qj.4487
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9. Rising rainfall intensity induces spatially divergent hydrological changes within a large river basin Y. Wu et al. 10.1038/s41467-023-44562-8
10. Precipitation sensitivity of vegetation growth in southern China depends on geological settings L. Wang et al. 10.1016/j.jhydrol.2024.131916
11. Soil Moisture and Atmospheric Aridity Impact Spatio‐Temporal Changes in Evapotranspiration at a Global Scale W. Zhang et al. 10.1029/2022JD038046
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14. The Influence of ENSO on the Long‐Term Water Storage Anomalies in the Middle‐Lower Reaches of the Yangtze River Basin: Evaluation and Analysis X. Li et al. 10.1029/2023EA003007
15. Uncertainty Analysis and Data Fusion of Multi-Source Land Evapotranspiration Products Based on the TCH Method Z. Cui et al. 10.3390/rs16010028
16. A gap filling method for daily evapotranspiration of global flux data sets based on deep learning L. Qian et al. 10.1016/j.jhydrol.2024.131787
17. Representation of land–atmosphere coupling processes over Africa in coupled model intercomparison project Phase 6 A. Mwanthi et al. 10.1007/s00382-023-06710-0
18. Varying performance of eight evapotranspiration products with aridity and vegetation greenness across the globe H. Wang et al. 10.3389/fenvs.2023.1079520
19. Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets C. Deng et al. 10.1016/j.jhydrol.2023.130574
20. Main influencing factors of terrestrial evapotranspiration for different land cover types over the Tibetan Plateau in 1982–2014 X. Li et al. 10.3389/fenvs.2024.1346469
21. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. 10.5194/essd-16-1559-2024
22. A new temperature–photoperiod coupled phenology module in LPJ-GUESS model v4.1: optimizing estimation of terrestrial carbon and water processes S. Chen et al. 10.5194/gmd-17-2509-2024
23. Upland Hillslope Groundwater Subsidy Affects Low‐Flow Storage–Discharge Relationship H. Li & A. Ameli 10.1029/2022WR034155
24. Land Surface Modeling in the Himalayas: On the Importance of Evaporative Fluxes for the Water Balance of a High‐Elevation Catchment P. Buri et al. 10.1029/2022WR033841
25. Significant Disparity in Spatiotemporal Changes of Terrestrial Evapotranspiration across Reanalysis Datasets in China from 1982 to 2020 J. Bai et al. 10.3390/rs15184522
26. Evaluation of seven satellite-based and two reanalysis global terrestrial evapotranspiration products Z. Xie et al. 10.1016/j.jhydrol.2024.130649
27. Embedding a novel phenology model into the Common Land Model for improving the modeling of land-atmosphere fluxes H. Gui et al. 10.1016/j.ecolmodel.2024.110782
28. Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification H. Chen et al. 10.5194/gmd-16-5685-2023
29. Differences in soil water storage, consumption, and use efficiency of typical vegetation types and their responses to precipitation in the Loess Plateau, China J. Chen et al. 10.1016/j.scitotenv.2023.161710
30. Tibetan Plateau Runoff and Evapotranspiration Dataset by an observation-constrained cryosphere-hydrology model X. Fan et al. 10.1038/s41597-024-03623-3
31. Deep learning for daily potential evapotranspiration using a HS-LSTM approach X. Yan et al. 10.1016/j.atmosres.2023.106856
32. Reconstructing monthly 0.25° terrestrial evapotranspiration data in a remote arid region using Bayesian-driven ensemble learning method F. Ochege et al. 10.1016/j.jhydrol.2024.131115
33. Comprehensive evaluation of terrestrial evapotranspiration from different models under extreme condition over conterminous United States X. Yu et al. 10.1016/j.agwat.2023.108555
34. Learning Global Evapotranspiration Dataset Corrections from a Water Cycle Closure Supervision T. Hascoet et al. 10.3390/rs16010170
35. Prediction of global water use efficiency and its response to vapor pressure deficit and soil moisture coupling in the 21st century T. Chen et al. 10.1016/j.jhydrol.2024.131203
36. Spatiotemporal Change in Evapotranspiration across the Indus River Basin Detected by Combining GRACE/GRACE-FO and Swarm Observations L. Cui et al. 10.3390/rs15184469
37. The Future Change in Evaporation Based on the CMIP6 Merged Data Generated by Deep-Learning Method in China X. Niu et al. 10.3390/w14182800
38. Loss of green landscapes due to urban expansion in China Y. He et al. 10.1016/j.resconrec.2023.107228
39. High uncertainty of evapotranspiration products under extreme climatic conditions L. Qian et al. 10.1016/j.jhydrol.2023.130332
40. Revealing accuracy in climate dynamics: enhancing evapotranspiration estimation using advanced quantile regression and machine learning models S. Sharafi & M. Mohammadi Ghaleni 10.1007/s13201-024-02211-5
41. A harmonized global land evaporation dataset from model-based products covering 1980–2017 J. Lu et al. 10.5194/essd-13-5879-2021
42. Hydroclimatic Vulnerability of Wetlands to Upwind Land Use Changes S. Fahrländer et al. 10.1029/2023EF003837
43. A new temperature–photoperiod coupled phenology module in LPJ-GUESS model v4.1: optimizing estimation of terrestrial carbon and water processes S. Chen et al. 10.5194/gmd-17-2509-2024
44. Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data Y. Mao et al. 10.1029/2023JD039387