Articles | Volume 17, issue 1
https://doi.org/10.5194/hess-17-177-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-17-177-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
18 Jan 2013
Research article |
| 18 Jan 2013
Application of data-based mechanistic modelling for flood forecasting at multiple locations in the Eden catchment in the National Flood Forecasting System (England and Wales)
D. Leedal
Lancaster Environment Centre, Lancaster University, Lancaster, UK
A. H. Weerts
Deltares, P.O. Box 177, 2600 MH, Delft, The Netherlands
Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands
P. J. Smith
Lancaster Environment Centre, Lancaster University, Lancaster, UK
K. J. Beven
Lancaster Environment Centre, Lancaster University, Lancaster, UK
Geocentrum, Uppsala University, Uppsala, Sweden
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Cited
19 citations as recorded by crossref.
- The Great Catchment Hysteresis Challenge K. Beven et al. https://doi.org/10.1002/hyp.70438
- Development of roughness updating based on artificial neural network in a river hydraulic model for flash flood forecasting J. Fu et al. https://doi.org/10.1007/s12040-015-0644-z
- Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data M. Ockenden et al. https://doi.org/10.5194/hess-21-6425-2017
- A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven https://doi.org/10.1002/wat2.1761
- Use of point scale models to improve conceptual understanding in complex aquifers: An example from a sandstone aquifer in the Eden valley, Cumbria, UK A. Lafare et al. https://doi.org/10.1002/hyp.14143
- A Monte Carlo simulation and sensitivity analysis framework demonstrating the advantages of probabilistic forecasting over deterministic forecasting in terms of flood warning reliability L. Duque et al. https://doi.org/10.1016/j.jhydrol.2023.129340
- Exploratory studies into seasonal flow forecasting potential for large lakes K. Sene et al. https://doi.org/10.5194/hess-22-127-2018
- Deep learning of flood forecasting by considering interpretability and physical constraints T. Zhang et al. https://doi.org/10.5194/hess-29-5955-2025
- Real-time hydrograph modelling in the upper Nysa Kłodzka river basin (SW Poland): a two-model hydrologic ensemble prediction approach T. Niedzielski & B. Miziński https://doi.org/10.1007/s00477-016-1251-5
- Testing probabilistic adaptive real‐time flood forecasting models P. Smith et al. https://doi.org/10.1111/jfr3.12055
- Real-time streamflow forecasting: AI vs. Hydrologic insights W. Krajewski et al. https://doi.org/10.1016/j.hydroa.2021.100110
- Nierji reservoir flood forecasting based on a Data-Based Mechanistic methodology G. Wei et al. https://doi.org/10.1016/j.jhydrol.2018.10.026
- How groundwater time series and aquifer property data explain heterogeneity in the Permo-Triassic sandstone aquifers of the Eden Valley, Cumbria, UK A. Colyer et al. https://doi.org/10.1007/s10040-021-02437-6
- Development of a Joint Probabilistic Rainfall‐Runoff Model for High‐to‐Extreme Flow Projections Under Changing Climatic Conditions K. Li et al. https://doi.org/10.1029/2021WR031557
- Validation of a national hydrological model H. McMillan et al. https://doi.org/10.1016/j.jhydrol.2016.07.043
- A Streamflow and Water Level Forecasting Model for the Ganges, Brahmaputra, and Meghna Rivers with Requisite Simplicity W. Palash et al. https://doi.org/10.1175/JHM-D-16-0202.1
- Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts T. Charoensuk et al. https://doi.org/10.1016/j.ejrh.2024.101737
- Use of seasonal trend decomposition to understand groundwater behaviour in the Permo-Triassic Sandstone aquifer, Eden Valley, UK A. Lafare et al. https://doi.org/10.1007/s10040-015-1309-3
- Community-based early warning systems for flood risk mitigation in Nepal P. Smith et al. https://doi.org/10.5194/nhess-17-423-2017
19 citations as recorded by crossref.
- The Great Catchment Hysteresis Challenge K. Beven et al. https://doi.org/10.1002/hyp.70438
- Development of roughness updating based on artificial neural network in a river hydraulic model for flash flood forecasting J. Fu et al. https://doi.org/10.1007/s12040-015-0644-z
- Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data M. Ockenden et al. https://doi.org/10.5194/hess-21-6425-2017
- A short history of philosophies of hydrological model evaluation and hypothesis testing K. Beven https://doi.org/10.1002/wat2.1761
- Use of point scale models to improve conceptual understanding in complex aquifers: An example from a sandstone aquifer in the Eden valley, Cumbria, UK A. Lafare et al. https://doi.org/10.1002/hyp.14143
- A Monte Carlo simulation and sensitivity analysis framework demonstrating the advantages of probabilistic forecasting over deterministic forecasting in terms of flood warning reliability L. Duque et al. https://doi.org/10.1016/j.jhydrol.2023.129340
- Exploratory studies into seasonal flow forecasting potential for large lakes K. Sene et al. https://doi.org/10.5194/hess-22-127-2018
- Deep learning of flood forecasting by considering interpretability and physical constraints T. Zhang et al. https://doi.org/10.5194/hess-29-5955-2025
- Real-time hydrograph modelling in the upper Nysa Kłodzka river basin (SW Poland): a two-model hydrologic ensemble prediction approach T. Niedzielski & B. Miziński https://doi.org/10.1007/s00477-016-1251-5
- Testing probabilistic adaptive real‐time flood forecasting models P. Smith et al. https://doi.org/10.1111/jfr3.12055
- Real-time streamflow forecasting: AI vs. Hydrologic insights W. Krajewski et al. https://doi.org/10.1016/j.hydroa.2021.100110
- Nierji reservoir flood forecasting based on a Data-Based Mechanistic methodology G. Wei et al. https://doi.org/10.1016/j.jhydrol.2018.10.026
- How groundwater time series and aquifer property data explain heterogeneity in the Permo-Triassic sandstone aquifers of the Eden Valley, Cumbria, UK A. Colyer et al. https://doi.org/10.1007/s10040-021-02437-6
- Development of a Joint Probabilistic Rainfall‐Runoff Model for High‐to‐Extreme Flow Projections Under Changing Climatic Conditions K. Li et al. https://doi.org/10.1029/2021WR031557
- Validation of a national hydrological model H. McMillan et al. https://doi.org/10.1016/j.jhydrol.2016.07.043
- A Streamflow and Water Level Forecasting Model for the Ganges, Brahmaputra, and Meghna Rivers with Requisite Simplicity W. Palash et al. https://doi.org/10.1175/JHM-D-16-0202.1
- Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts T. Charoensuk et al. https://doi.org/10.1016/j.ejrh.2024.101737
- Use of seasonal trend decomposition to understand groundwater behaviour in the Permo-Triassic Sandstone aquifer, Eden Valley, UK A. Lafare et al. https://doi.org/10.1007/s10040-015-1309-3
- Community-based early warning systems for flood risk mitigation in Nepal P. Smith et al. https://doi.org/10.5194/nhess-17-423-2017
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