56 citations as recorded by crossref.

1. Flood Modeling Using HEC-RAS Based on Extreme Rainfall Data G. San-Deo https://doi.org/10.51903/3by9tc70
2. Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method X. Xiang et al. https://doi.org/10.1016/j.jhydrol.2024.131867
3. Feature Importance in Machine Learning with Explainable Artificial Intelligence (XAI) for Rainfall Prediction M. Patel et al. https://doi.org/10.1051/itmconf/20246503007
4. Classification and mechanism of spring and summer floods in northern Xinjiang from 2006 to 2011 P. Chen et al. https://doi.org/10.1002/asl.1193
5. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang https://doi.org/10.3390/w16152199
6. Warming and vegetation greening drive recent surge in flash droughts J. Li et al. https://doi.org/10.1126/sciadv.aea8452
7. Model-based assessment of flood generation mechanisms over Poland: The roles of precipitation, snowmelt, and soil moisture excess N. Venegas-Cordero et al. https://doi.org/10.1016/j.scitotenv.2023.164626
8. Enhancing Multi-Step Reservoir Inflow Forecasting: A Time-Variant Encoder–Decoder Approach M. Fan et al. https://doi.org/10.3390/geosciences15080279
9. Shifting cold regions streamflow regimes in North America affect flood frequency analysis D. Burn & P. Whitfield https://doi.org/10.1080/02626667.2024.2422531
10. Extreme and compound ocean events are key drivers of projected low pelagic fish biomass N. Le Grix et al. https://doi.org/10.1111/gcb.16968
11. Research on river velocity and flow estimation based on scalar transport equation J. Wang et al. https://doi.org/10.2166/hydro.2025.315
12. Using explainable artificial intelligence (XAI) methods to understand the nonlinear relationship between the Three Gorges Dam and downstream flood X. Wei et al. https://doi.org/10.1016/j.ejrh.2024.101776
13. Diverging trends in large floods across Europe in a warming climate B. Fang et al. https://doi.org/10.1038/s43247-025-02734-y
14. Interpretable feature incorporation machine-learning framework for flood magnitude estimation E. Ford et al. https://doi.org/10.5194/hess-30-2135-2026
15. Evolution of flood generating processes under climate change in France Y. Tramblay et al. https://doi.org/10.5194/hess-29-7023-2025
16. Changes in Mediterranean flood processes and seasonality Y. Tramblay et al. https://doi.org/10.5194/hess-27-2973-2023
17. Winter flood significantly changes salinity and nutrient export from land to sea I. Bussmann et al. https://doi.org/10.3389/fmars.2025.1599007
18. Innovative daily runoff prediction model integrating black-winged kite algorithm and Mamba2–Transformer architecture D. Xu et al. https://doi.org/10.1016/j.ecoinf.2025.103565
19. Drivers of high-frequency extreme sea levels around northern Europe – synergies between recurrent neural networks and random forest C. Heuzé et al. https://doi.org/10.5194/os-21-1813-2025
20. Winter climate preconditioning of summer vegetation extremes in the Northern Hemisphere M. Anand et al. https://doi.org/10.1088/1748-9326/ad627d
21. An Explainable AI Model for Anomaly Detection in Water Quality Data X. Rigueira et al. https://doi.org/10.1007/s11269-026-04676-2
22. Transformed-stationary EVA 2.0: a generalized framework for non-stationary multivariate extremes analysis M. Bahmanpour et al. https://doi.org/10.5194/hess-30-2301-2026
23. Associated influence of moisture sources and catchment drivers on the flood mechanism of a tropical basin R. Choudhary & C. Dhanya https://doi.org/10.1016/j.jhydrol.2026.135363
24. Unveiling summer marine heatwave onset mechanisms in the South China sea using an explainable deep learning method M. Guo et al. https://doi.org/10.1007/s00382-025-07817-2
25. Enhancing flood prediction in the Lower Mekong River Basin by a scale-independent interpretable deep learning model Y. Qiu et al. https://doi.org/10.1016/j.eiar.2025.108130
26. River-lake connectivity modulates lake droughts: Comparative insights from China’s two largest river-connected lakes C. Xue et al. https://doi.org/10.1016/j.ejrh.2026.103282
27. Clustering of causal graphs to explore drivers of river discharge W. Günther et al. https://doi.org/10.1017/eds.2023.17
28. Bridging the gap: Explainable AI for advancing Earth System Science F. Huang et al. https://doi.org/10.1007/s11430-025-1898-3
29. Significant roles of snow and vegetation cover in modulating altitudinal gradients of land surface temperature over Asia high mountains H. Zhang et al. https://doi.org/10.1016/j.agrformet.2025.110406
30. Flash Flood Assessment using Geospatial data: A Case Study of Semel City, Kurdistan Region-Iraq. S. Abdi et al. https://doi.org/10.64362/zjse.87
31. Identification of time-varying parameters of a monthly Budyko function in US MOPEX catchments and its implications W. Liu et al. https://doi.org/10.1016/j.ejrh.2025.102348
32. Revealing long-term runoff mechanisms using the deep learning model interpretable framework driven by global and local information B. Yang et al. https://doi.org/10.1016/j.jhydrol.2025.134167
33. Event duration matters: spatial–temporal patterns of United States daily and multi-day precipitation extremes S. Eero et al. https://doi.org/10.3389/frwa.2026.1753501
34. Basin-informed flood frequency analysis using deep learning exhibits consistent projected regional patterns over CONUS R. Lazin et al. https://doi.org/10.1038/s41598-025-97610-2
35. Advancing subseasonal reservoir inflow forecasts using an explainable machine learning method M. Fan et al. https://doi.org/10.1016/j.ejrh.2023.101584
36. Explainable deep learning identifies patterns and drivers of freshwater harmful algal blooms S. Chen et al. https://doi.org/10.1016/j.ese.2024.100522
37. A method of applying deep learning based optical flow algorithm to river flow discharge measurement J. Wang et al. https://doi.org/10.1088/1361-6501/ad3183
38. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al. https://doi.org/10.1098/rsta.2024.0287
39. 加强融合：可解释人工智能推动地球系统科学发展 菲. 黄 et al. https://doi.org/10.1360/N072025-0316
40. Compounding effects in flood drivers challenge estimates of extreme river floods S. Jiang et al. https://doi.org/10.1126/sciadv.adl4005
41. Exploring the influence of hydrological indicators on flow regimes through a data-driven modeling approach in the Huai river basin, China Q. Wei et al. https://doi.org/10.1016/j.envres.2025.122605
42. Spatially resolved rainfall streamflow modeling in central Europe M. Vischer et al. https://doi.org/10.5194/hess-29-5233-2025
43. Shifted dominant flood drivers of an alpine glacierized catchment in the Tianshan region revealed through interpretable deep learning W. Liang et al. https://doi.org/10.1038/s41612-025-00918-z
44. Toward real-time high-resolution fluvial flood forecasting: A robust surrogate approach based on overland flow models G. Nguyen et al. https://doi.org/10.1016/j.envsoft.2025.106716
45. Direct and lagged climate change effects intensified the 2022 European drought E. Bevacqua et al. https://doi.org/10.1038/s41561-024-01559-2
46. Forecasting of compound ocean-fluvial floods using machine learning S. Moradian et al. https://doi.org/10.1016/j.jenvman.2024.121295
47. Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application X. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106350
48. Rhine flood stories: Spatio‐temporal analysis of historic and projected flood genesis in the Rhine River basin E. Rottler et al. https://doi.org/10.1002/hyp.14918
49. Research progresses and prospects of multi-sphere compound extremes from the Earth System perspective Z. Hao & Y. Chen https://doi.org/10.1007/s11430-023-1201-y
50. An increase in the spatial extent of European floods over the last 70 years B. Fang et al. https://doi.org/10.5194/hess-28-3755-2024
51. Space-time image velocity measurement method based on improved GhostNetV2 Y. Feng et al. https://doi.org/10.1088/2631-8695/adb8a3
52. Explainable daily warning of algal concentration exceedance from SHAP-guided temperature and light thresholds D. Xu et al. https://doi.org/10.1016/j.jece.2026.123014
53. Enhancing flood forecasting performance using effective and transparent explainable hybrid deep learning model M. Hasan et al. https://doi.org/10.1007/s12145-025-01930-w
54. Explainable Earth Surface Forecasting Under Extreme Events O. Pellicer‐Valero et al. https://doi.org/10.1029/2024EF005446
55. Runoff concentration decline for Tarim river due to a dramatic increasing of runoff in cold season and hydro-junction regulation: Past and future S. Qianjuan et al. https://doi.org/10.1016/j.ejrh.2024.101962
56. Rain-on-wet-soil compound floods in lowlands: the combined effect of large rain events and shallow groundwater on discharge peaks in a changing climate C. Brauer et al. https://doi.org/10.5194/hess-30-249-2026