52 citations as recorded by crossref.

1. Improving Hydrological Simulations with a Dynamic Vegetation Parameter Framework H. Gu et al. 10.3390/w16223335
2. A fast physically-guided emulator of MATSIRO land surface model R. Olson et al. 10.1016/j.jhydrol.2024.131093
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4. High-resolution European daily soil moisture derived with machine learning (2003–2020) S. O et al. 10.1038/s41597-022-01785-6
5. Contrasting Drought Propagation Into the Terrestrial Water Cycle Between Dry and Wet Regions W. Li et al. 10.1029/2022EF003441
6. Optimizing Soil Moisture Station Networks for Future Climates V. Bessenbacher et al. 10.1029/2022GL101667
7. Identifying Structural Priors in a Hybrid Differentiable Model for Stream Water Temperature Modeling F. Rahmani et al. 10.1029/2023WR034420
8. Differentiable modelling to unify machine learning and physical models for geosciences C. Shen et al. 10.1038/s43017-023-00450-9
9. Inundation–Desiccation State Prediction for Salt Pans in the Western Pannonian Basin Using Remote Sensing, Groundwater, and Meteorological Data H. Schauer et al. 10.3390/rs15194659
10. Interpretable machine learning guided by physical mechanisms reveals drivers of runoff under dynamic land use changes S. Wang et al. 10.1016/j.jenvman.2024.121978
11. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
12. Climate Change and Hydrological Extremes J. Xiong & Y. Yang 10.1007/s40641-024-00198-4
13. Long-term drought effects on landscape water storage and recovery under contrasting landuses S. Luo et al. 10.1016/j.jhydrol.2024.131339
14. Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
15. Diagnosing modeling errors in global terrestrial water storage interannual variability H. Lee et al. 10.5194/hess-27-1531-2023
16. Improving global gross primary productivity estimation using two-leaf light use efficiency model by considering various environmental factors via machine learning Z. Li et al. 10.1016/j.scitotenv.2024.176673
17. Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL) D. Feng et al. 10.5194/gmd-17-7181-2024
18. Widespread increasing vegetation sensitivity to soil moisture W. Li et al. 10.1038/s41467-022-31667-9
19. A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations L. Brocca et al. 10.3389/fsci.2023.1190191
20. Global streamflow modelling using process-informed machine learning M. Magni et al. 10.2166/hydro.2023.217
21. Enhancing streamflow simulation in large and human-regulated basins: Long short-term memory with multiscale attributes A. Tursun et al. 10.1016/j.jhydrol.2024.130771
22. Hybrid modeling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning R. ElGhawi et al. 10.1088/1748-9326/acbbe0
23. DHI-GHM: Real-time and forecasted hydrology for the entire planet A. Murray et al. 10.1016/j.jhydrol.2023.129431
24. Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning Z. Wei et al. 10.1016/j.rse.2024.114371
25. Multimodel ensemble estimation of Landsat-like global terrestrial latent heat flux using a generalized deep CNN-LSTM integration algorithm X. Guo et al. 10.1016/j.agrformet.2024.109962
26. Enhancing streamflow simulation using hybridized machine learning models in a semi-arid basin of the Chinese loess Plateau Q. Yu et al. 10.1016/j.jhydrol.2023.129115
27. 地球表层特征参量反演与模拟的机理<bold>-</bold>学习耦合范式 焕. 沈 & 良. 张 10.1360/SSTe-2022-0089
28. Commentary on ‘Physics-informed deep learning parameterization of ocean vertical mixing improves climate simulations’ by Zhu et al. G. Camps-Valls 10.1093/nsr/nwac092
29. Iterative integration of deep learning in hybrid Earth surface system modelling M. Chen et al. 10.1038/s43017-023-00452-7
30. Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al. 10.1002/wcc.914
31. Exploring the performance and interpretability of hybrid hydrologic model coupling physical mechanisms and deep learning M. He et al. 10.1016/j.jhydrol.2024.132440
32. Benchmarking multimodel terrestrial water storage seasonal cycle against Gravity Recovery and Climate Experiment (GRACE) observations over major global river basins S. Bibi et al. 10.5194/hess-28-1725-2024
33. Assessing the time variability of GIEMS-2 satellite-derived surface water extent over 30 years J. Bernard et al. 10.3389/frsen.2024.1399234
34. To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization E. Acuña Espinoza et al. 10.5194/hess-28-2705-2024
35. Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems L. Liu et al. 10.1038/s41467-023-43860-5
36. Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for hydrological processes P. Bhasme et al. 10.1016/j.jhydrol.2022.128618
37. Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review M. Zaresefat & R. Derakhshani 10.3390/w15091750
38. Improving hydrologic models for predictions and process understanding using neural ODEs M. Höge et al. 10.5194/hess-26-5085-2022
39. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al. 10.3390/w14142211
40. KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments L. Liu et al. 10.5194/gmd-15-2839-2022
41. DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology G. Liu et al. 10.5194/gmd-17-6683-2024
42. The evolution of Arctic permafrost over the last 3 centuries from ensemble simulations with the CryoGridLite permafrost model M. Langer et al. 10.5194/tc-18-363-2024
43. A hybrid framework for short-term irrigation demand forecasting L. Forouhar et al. 10.1016/j.agwat.2022.107861
44. Widespread and complex drought effects on vegetation physiology inferred from space W. Li et al. 10.1038/s41467-023-40226-9
45. Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning T. Nguyen et al. 10.1016/j.rse.2024.114109
46. Mechanism-learning coupling paradigms for parameter inversion and simulation in earth surface systems H. Shen & L. Zhang 10.1007/s11430-022-9999-9
47. Identifying impacts of global climate teleconnection patterns on land water storage using machine learning I. Kalu et al. 10.1016/j.ejrh.2023.101346
48. Pushing the frontiers in climate modelling and analysis with machine learning V. Eyring et al. 10.1038/s41558-024-02095-y
49. Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering R. Muñoz-Carpena et al. 10.1371/journal.pwat.0000059
50. Using microwave-assisted extraction with advanced artificial intelligence models for predicting tannins in black pepper (Piper nigrum L.) M. Khajeh et al. 10.1016/j.jarmap.2024.100594
51. The importance of vegetation in understanding terrestrial water storage variations T. Trautmann et al. 10.5194/hess-26-1089-2022
52. Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments L. Zhong et al. 10.1029/2022WR034118