Articles | Volume 28, issue 4
https://doi.org/10.5194/hess-28-851-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-851-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Feb 2024
Research article |
| 23 Feb 2024
| 23 Feb 2024
Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model
UR RiverLy, INRAE, Villeurbanne, France
Annika Künne
Institute of Geography, Friedrich Schiller University Jena, Jena, Germany
Flora Branger
UR RiverLy, INRAE, Villeurbanne, France
Sven Kralisch
Institute of Geography, Friedrich Schiller University Jena, Jena, Germany
Alexandre Devers
UR RiverLy, INRAE, Villeurbanne, France
Jean-Philippe Vidal
UR RiverLy, INRAE, Villeurbanne, France
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Cited
19 citations as recorded by crossref.
- 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
- The Duration of Dry Events Promotes PVC Film Fragmentation in Intermittent Rivers N. Barthelemy et al. https://doi.org/10.1021/acs.est.4c00528
- 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
- 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
- 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
- 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
- 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
- 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
- Streamflow Forecasting Based on PatchTST, LSTM, and Ensemble Learning Approaches H. Feizi & M. Sattari https://doi.org/10.1007/s11269-025-04397-y
- 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
- Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang https://doi.org/10.3390/w16152199
- 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
- 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
- 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
- Environmental factors affecting streambed microplastics in a non-perennial river catchment N. Barthelemy et al. https://doi.org/10.1016/j.envres.2026.124753
- 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
- CNN-LSTM-RF integration for predicting Mississippi River discharge dynamics F. Kaleybar & A. Molavi https://doi.org/10.1007/s11600-025-01719-x
- Improving calibration of groundwater flow models using headwater streamflow intermittence R. Abhervé et al. https://doi.org/10.1002/hyp.15167
- 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
19 citations as recorded by crossref.
- 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
- The Duration of Dry Events Promotes PVC Film Fragmentation in Intermittent Rivers N. Barthelemy et al. https://doi.org/10.1021/acs.est.4c00528
- 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
- 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
- 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
- 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
- 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
- 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
- Streamflow Forecasting Based on PatchTST, LSTM, and Ensemble Learning Approaches H. Feizi & M. Sattari https://doi.org/10.1007/s11269-025-04397-y
- 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
- Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins H. Yu & Q. Yang https://doi.org/10.3390/w16152199
- 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
- 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
- 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
- Environmental factors affecting streambed microplastics in a non-perennial river catchment N. Barthelemy et al. https://doi.org/10.1016/j.envres.2026.124753
- 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
- CNN-LSTM-RF integration for predicting Mississippi River discharge dynamics F. Kaleybar & A. Molavi https://doi.org/10.1007/s11600-025-01719-x
- Improving calibration of groundwater flow models using headwater streamflow intermittence R. Abhervé et al. https://doi.org/10.1002/hyp.15167
- 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
Saved (final revised paper)
Latest update: 14 Jul 2026
Short summary
Modelling flow intermittence is essential for predicting the future evolution of drying in river networks and better understanding the ecological and socio-economic impacts. However, modelling flow intermittence is challenging, and observed data on temporary rivers are scarce. This study presents a new modelling approach for predicting flow intermittence in river networks and shows that combining different sources of observed data reduces the model uncertainty.
Modelling flow intermittence is essential for predicting the future evolution of drying in river...
