77 citations as recorded by crossref.

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11. Characteristics of Winter Wheat Evapotranspiration in Eastern China and Comparative Evaluation of Applicability of Different Reference Evapotranspiration Models H. He et al. https://doi.org/10.1007/s42729-022-00795-y
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18. A hybrid machine learning and optimal stomatal behavior model to reveal the role of vegetation dynamics in potential evapotranspiration and drought W. Liu et al. https://doi.org/10.1016/j.jhydrol.2026.135446
19. A comparative study of ecohydrologies of a tropical mangrove and a broadleaf deciduous forest using eddy covariance measurement P. Deb Burman et al. https://doi.org/10.1007/s00703-021-00840-y
20. High resolution (1 km-daily) potential evapotranspiration dataset over Europe and the Mediterranean region S. Bouabdelli et al. https://doi.org/10.1016/j.jhydrol.2025.133839
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24. Improving the Representation of Long‐Term Storage Variations With Conceptual Hydrological Models in Data‐Scarce Regions P. Hulsman et al. https://doi.org/10.1029/2020WR028837
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26. Seasonality of energy and water fluxes in a tropical moist forest in Vietnam O. Kuricheva et al. https://doi.org/10.1016/j.agrformet.2020.108268
27. A 0.1° monthly potential evapotranspiration dataset based on the optimal models over global vegetation zones Z. Bi et al. https://doi.org/10.1038/s41597-026-06956-3
28. A deep learning-based hybrid model of global terrestrial evaporation A. Koppa et al. https://doi.org/10.1038/s41467-022-29543-7
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31. A physically-based potential evapotranspiration model for global water availability projections Z. Liu et al. https://doi.org/10.1016/j.jhydrol.2023.129767
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33. New measures of deep soil water recharge during the vegetation restoration process in semi-arid regions of northern China Y. Cheng et al. https://doi.org/10.5194/hess-24-5875-2020
34. Increases in the temperature seasonal cycle indicate long-term drying trends in Amazonia P. Ritchie et al. https://doi.org/10.1038/s43247-022-00528-0
35. Hourly potential evapotranspiration at 0.1° resolution for the global land surface from 1981-present M. Singer et al. https://doi.org/10.1038/s41597-021-01003-9
36. Exploring the Potential of Sub-daily Microwave Remote Sensing Observations for Estimating Evaporation in Forests E. Tronquo et al. https://doi.org/10.1109/JSTARS.2026.3679245
37. Evaluation of 18 models for calculating potential evapotranspiration in different climatic zones of China Y. Yang et al. https://doi.org/10.1016/j.agwat.2020.106545
38. Global Coordination in Plant Physiological and Rooting Strategies in Response to Water Stress Y. Liu et al. https://doi.org/10.1029/2020GB006758
39. Global changes in potential evapotranspiration (1992–2020) and associated drivers: Shuttleworth-Wallace model-based analysis Z. Bi et al. https://doi.org/10.1016/j.jhydrol.2025.133435
40. Estimating land evapotranspiration from potential evapotranspiration constrained by soil water at daily scale Z. Liu https://doi.org/10.1016/j.scitotenv.2022.155327
41. Exploring the stationarity of Australian temperature, precipitation and pan evaporation records over the last century A. Ukkola et al. https://doi.org/10.1088/1748-9326/ab545c
42. Optimized empirical model based on whale optimization algorithm for simulate daily reference crop evapotranspiration in different climatic regions of China Z. Wu et al. https://doi.org/10.1016/j.jhydrol.2022.128084
43. Modeling gross primary production and transpiration from sun-induced chlorophyll fluorescence using a mechanistic light-response approach Q. Beauclaire et al. https://doi.org/10.1016/j.rse.2024.114150
44. Reviews and syntheses: Turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities P. Stoy et al. https://doi.org/10.5194/bg-16-3747-2019
45. Comparison of different empirical methods and data-driven models for estimating reference evapotranspiration in semi-arid Central Anatolian Region of Turkey I. Yurtseven & Y. Serengil https://doi.org/10.1007/s12517-021-08150-8
46. Necessity of incorporating realistic land surface parameters for trend analyses of potential evapotranspiration and drought S. Sun et al. https://doi.org/10.1016/j.jhydrol.2025.132938
47. Sun-induced fluorescence closely linked to ecosystem transpiration as evidenced by satellite data and radiative transfer models W. Maes et al. https://doi.org/10.1016/j.rse.2020.112030
48. Climatology and trends of reference evapotranspiration in Spain M. Tomas‐Burguera et al. https://doi.org/10.1002/joc.6817
49. Enhanced understanding of warming and humidifying on ground heat flux in the Tibetan Plateau Hinterland J. He et al. https://doi.org/10.1016/j.atmosres.2024.107799
50. Exploring trends in wet-season precipitation and drought indices in wet, humid and dry regions C. Funk et al. https://doi.org/10.1088/1748-9326/ab4a6c
51. Tropical Peatland Hydrology Simulated With a Global Land Surface Model S. Apers et al. https://doi.org/10.1029/2021MS002784
52. A gridded dataset of a leaf-age-dependent leaf area index seasonality product over tropical and subtropical evergreen broadleaved forests X. Yang et al. https://doi.org/10.5194/essd-15-2601-2023
53. Applicability analysis of different evapotranspiration models for maize farmland in the lower Yellow River Plain based on eddy covariance measurements X. Ren et al. https://doi.org/10.1016/j.ecohyd.2024.10.005
54. Inter‐Basin Water Transfer Effectively Compensates for Regional Unsustainable Water Use J. Dong et al. https://doi.org/10.1029/2023WR035129
55. Assessing the ability of potential evaporation models to capture the sensitivity to temperature Z. Liu et al. https://doi.org/10.1016/j.agrformet.2022.108886
56. CABra: a novel large-sample dataset for Brazilian catchments A. Almagro et al. https://doi.org/10.5194/hess-25-3105-2021
57. Peruvian North Coast Climate Variability and Regional Ocean–Atmosphere Forcing M. Jury & L. Alfaro-Garcia https://doi.org/10.3390/coasts4030026
58. Evaporation From Six Water Bodies of Various Sizes in East Asia: An Analysis on Size Dependency S. Han & F. Guo https://doi.org/10.1029/2022WR032650
59. Error characterization of global land evapotranspiration products: Collocation-based approach C. Li et al. https://doi.org/10.1016/j.jhydrol.2022.128102
60. Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data H. Wu & W. Ying https://doi.org/10.3390/rs11212520
61. Simple Models Outperform More Complex Big‐Leaf Models of Daily Transpiration in Forested Biomes R. Bright et al. https://doi.org/10.1029/2022GL100100
62. On the Estimation of Potential Evaporation Under Wet and Dry Conditions Z. Tu & Y. Yang https://doi.org/10.1029/2021WR031486
63. Diagnosing evapotranspiration responses to water deficit across biomes using deep learning F. Giardina et al. https://doi.org/10.1111/nph.19197
64. The impact of nonlinear surface energy partitioning on potential evapotranspiration: A machine learning study based on FLUXNET data W. Liu et al. https://doi.org/10.1016/j.agrformet.2025.111005
65. Unraveling the influence of atmospheric evaporative demand on drought and its response to climate change S. Vicente‐Serrano et al. https://doi.org/10.1002/wcc.632
66. A global 5 km monthly potential evapotranspiration dataset (1982–2015) estimated by the Shuttleworth–Wallace model S. Sun et al. https://doi.org/10.5194/essd-15-4849-2023
67. Estimating the sensitivity of the Priestley–Taylor coefficient to air temperature and humidity Z. Liu et al. https://doi.org/10.5194/hess-28-4349-2024
68. Calibrating a remote sensing evapotranspiration model using the Budyko framework P. Bai & C. Cai https://doi.org/10.1016/j.agrformet.2023.109757
69. Comprehensive evaluation of nine evapotranspiration products from remote sensing, gauge upscaling and land surface model over China D. Wang et al. https://doi.org/10.1371/journal.pone.0313762
70. How well does the Evaporative Stress Index from ECOSTRESS capture site-based stresses? H. Thakur et al. https://doi.org/10.1080/07038992.2026.2645742
71. Land‐Atmosphere Coupling Constrains Increases to Potential Evaporation in a Warming Climate: Implications at Local and Global Scales Y. Kim et al. https://doi.org/10.1029/2022EF002886
72. Two-dimensional modeling of the impact of surface coverage on the evaporation and hydrodynamics of a shallow tropical reservoir I. Lima Neto https://doi.org/10.1016/j.jhydrol.2025.134217
73. Improvement in the Estimation of Daily Net Surface Radiation in China M. Xiao & D. Kong https://doi.org/10.1061/(ASCE)IR.1943-4774.0001539
74. Dynamic parameterization of Priestley–Taylor coefficient for a more accurate potential evapotranspiration estimation in ungauged regions P. Raghav & M. Kumar https://doi.org/10.1088/1748-9326/ae6464
75. Land Cover Control on the Drivers of Evaporation and Sensible Heat Fluxes: An Observation‐Based Synthesis for the Netherlands F. Jansen et al. https://doi.org/10.1029/2022WR034361
76. Testing a maximum evaporation theory over saturated land: implications for potential evaporation estimation Z. Tu et al. https://doi.org/10.5194/hess-26-1745-2022
77. Supporting decision-making in agricultural water management under data scarcity using global datasets – chances, limits and potential improvements B. Kayatz et al. https://doi.org/10.1016/j.agwat.2024.108803