22 citations as recorded by crossref.

1. Projections of streamflow intermittence under climate change in European drying river networks L. Mimeau et al. https://doi.org/10.5194/hess-29-1615-2025
2. The Duration of Dry Events Promotes PVC Film Fragmentation in Intermittent Rivers N. Barthelemy et al. https://doi.org/10.1021/acs.est.4c00528
3. A review of dynamic monitoring methods for intermittent rivers: Integrating remote sensing and machine learning C. Xie & A. Lv https://doi.org/10.1007/s11442-026-2469-x
4. Exploring the spatio-temporal dynamics of disturbed metacommunities: A mechanistic modeling approach to species resistance and resilience strategies in drying river networks L. Journiac et al. https://doi.org/10.1016/j.ecolmodel.2025.111136
5. Are regional groundwater models suitable for simulating wetlands, rivers and intermittence? The example of the French AquiFR platform L. Guillaumot et al. https://doi.org/10.1016/j.jhydrol.2024.132019
6. Enhancing precipitation intensity estimation using ERA5-land reanalysis with statistical and machine learning approaches A. Abdolmanafi et al. https://doi.org/10.1016/j.rineng.2025.104928
7. Investigating streamflow dynamics in heterogeneous catchments through sensitivity function analysis S. Rusjan et al. https://doi.org/10.1016/j.jhydrol.2026.136409
8. Streamflow generation in a nested system of intermittent and perennial tropical streams under changing land use G. Mosquera et al. https://doi.org/10.5194/hess-29-7073-2025
9. River intermittency: mapping and upscaling of water occurrence using unmanned aerial vehicle, Random Forest and remote sensing landscape attributes N. Soares et al. https://doi.org/10.5194/hess-30-849-2026
10. Streamflow Forecasting Based on PatchTST, LSTM, and Ensemble Learning Approaches H. Feizi & M. Sattari https://doi.org/10.1007/s11269-025-04397-y
11. Variations in Present and Future Hourly Extreme Rainfall: Insights from High-Resolution Data and Novel Temporal Disaggregation Model Y. Dai et al. https://doi.org/10.3390/w16233463
12. Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang https://doi.org/10.3390/w16152199
13. Zero-flow and hydrotype estimation with karst-SWAT and Sentinel-2 data in the Keritis Basin, Crete G. Manfreda et al. https://doi.org/10.1016/j.ejrh.2026.103431
14. Predicting salinity and alkalinity fluxes of U.S. freshwater in a changing climate: Integrating anthropogenic and natural influences using data-driven models B. E et al. https://doi.org/10.1016/j.apgeochem.2025.106285
15. Hydrospatial variability and sustainability in intermittent rivers and ephemeral streams: a global review of seasonal dynamics and environmental pressures A. Kumar et al. https://doi.org/10.1080/15715124.2026.2660299
16. Environmental factors affecting streambed microplastics in a non-perennial river catchment N. Barthelemy et al. https://doi.org/10.1016/j.envres.2026.124753
17. Changes in the flowing drainage network and stream chemistry during rainfall events for two pre-Alpine catchments I. Bujak-Ozga et al. https://doi.org/10.5194/hess-29-2339-2025
18. CNN-LSTM-RF integration for predicting Mississippi River discharge dynamics F. Kaleybar & A. Molavi https://doi.org/10.1007/s11600-025-01719-x
19. A hybrid machine learning and physics-based approach to model high-resolution inundation A. Peeples et al. https://doi.org/10.1016/j.jhydrol.2026.136246
20. Hydroclimatic variability and socio-hydrological adaptation in the Yiluo River Basin (2013–2023) of China Y. Fang et al. https://doi.org/10.1007/s43832-026-00442-y
21. Improving calibration of groundwater flow models using headwater streamflow intermittence R. Abhervé et al. https://doi.org/10.1002/hyp.15167
22. Carbon emissions from inland waters may be underestimated: Evidence from European river networks fragmented by drying N. López‐Rojo et al. https://doi.org/10.1002/lol2.10408