Articles | Volume 25, issue 1
https://doi.org/10.5194/hess-25-105-2021
© Author(s) 2021. 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-25-105-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jan 2021
Research article |
| 06 Jan 2021
| 06 Jan 2021
Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Lieke A. Melsen
Hydrology and Quantitative Water Management, Wageningen University, Wageningen, the Netherlands
Andrew W. Wood
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Oldrich Rakovec
Department Computational Hydrosystems, Helmholtz Centre for Environmental Research, Leipzig, Germany
Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague – Suchdol, Czech Republic
Naoki Mizukami
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Wouter J. M. Knoben
University of Saskatchewan Coldwater Laboratory, Canmore, Alberta, Canada
Martyn P. Clark
University of Saskatchewan Coldwater Laboratory, Canmore, Alberta, Canada
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- Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
- Extreme floods in Europe: going beyond observations using reforecast ensemble pooling M. Brunner & L. Slater 10.5194/hess-26-469-2022
- Floods and droughts: a multivariate perspective M. Brunner 10.5194/hess-27-2479-2023
- Spatio-temporal clustering of extreme floods in Great Britain G. Formetta et al. 10.1080/02626667.2024.2367167
- A systematic review on machine learning algorithms used for forecasting lake‐water level fluctuations S. Sannasi Chakravarthy et al. 10.1002/cpe.7231
- Lead-time-dependent calibration of a flood forecasting model P. Astagneau et al. 10.1016/j.jhydrol.2024.132119
- Evaluation of Multiple Satellite, Reanalysis, and Merged Precipitation Products for Hydrological Modeling in the Data-Scarce Tributaries of the Pearl River Basin, China Z. Gao et al. 10.3390/rs15225349
- Complex High‐ and Low‐Flow Networks Differ in Their Spatial Correlation Characteristics, Drivers, and Changes M. Brunner & E. Gilleland 10.1029/2021WR030049
- Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al. 10.1080/02626667.2022.2104646
- Relative Impact of Assimilation of Multi-Source Observations using 3D-Var on Simulation of Extreme Rainfall Events over Karnataka, India A. Bankar et al. 10.1016/j.atmosres.2024.107777
- Model-based assessment of flood generation mechanisms over Poland: The roles of precipitation, snowmelt, and soil moisture excess N. Venegas-Cordero et al. 10.1016/j.scitotenv.2023.164626
- Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate F. Tootoonchi et al. 10.1016/j.jhydrol.2023.129807
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- An extremeness threshold determines the regional response of floods to changes in rainfall extremes M. Brunner et al. 10.1038/s43247-021-00248-x
- Future Change Projections of Extreme Floods at Catchment Scale and Hydrodynamic Response of Its Downstream Lake Based on Catchment‐Waterbody Relationship Simulation R. Hua et al. 10.1029/2022JD037972
- A new diagram for performance evaluation of complex models A. Izzaddin et al. 10.1007/s00477-024-02678-3
- On the evaluation of climate change impact models T. Wagener et al. 10.1002/wcc.772
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- Hydrological models weighting for hydrological projections: The impacts on future peak flows M. Castaneda-Gonzalez et al. 10.1016/j.jhydrol.2023.130098
20 citations as recorded by crossref.
- Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
- On the links between sub-seasonal clustering of extreme precipitation and high discharge in Switzerland and Europe A. Tuel et al. 10.5194/hess-26-2649-2022
- Toward Improved Regional Hydrological Model Performance Using State‐Of‐The‐Science Data‐Informed Soil Parameters C. Li et al. 10.1029/2023WR034431
- Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
- Extreme floods in Europe: going beyond observations using reforecast ensemble pooling M. Brunner & L. Slater 10.5194/hess-26-469-2022
- Floods and droughts: a multivariate perspective M. Brunner 10.5194/hess-27-2479-2023
- Spatio-temporal clustering of extreme floods in Great Britain G. Formetta et al. 10.1080/02626667.2024.2367167
- A systematic review on machine learning algorithms used for forecasting lake‐water level fluctuations S. Sannasi Chakravarthy et al. 10.1002/cpe.7231
- Lead-time-dependent calibration of a flood forecasting model P. Astagneau et al. 10.1016/j.jhydrol.2024.132119
- Evaluation of Multiple Satellite, Reanalysis, and Merged Precipitation Products for Hydrological Modeling in the Data-Scarce Tributaries of the Pearl River Basin, China Z. Gao et al. 10.3390/rs15225349
- Complex High‐ and Low‐Flow Networks Differ in Their Spatial Correlation Characteristics, Drivers, and Changes M. Brunner & E. Gilleland 10.1029/2021WR030049
- Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al. 10.1080/02626667.2022.2104646
- Relative Impact of Assimilation of Multi-Source Observations using 3D-Var on Simulation of Extreme Rainfall Events over Karnataka, India A. Bankar et al. 10.1016/j.atmosres.2024.107777
- Model-based assessment of flood generation mechanisms over Poland: The roles of precipitation, snowmelt, and soil moisture excess N. Venegas-Cordero et al. 10.1016/j.scitotenv.2023.164626
- Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate F. Tootoonchi et al. 10.1016/j.jhydrol.2023.129807
- Моделирование геопространства зон затопления и подтопления реки Луга Т. Балтыжакова et al. 10.33764/2411-1759-2024-29-4-71-82
- An extremeness threshold determines the regional response of floods to changes in rainfall extremes M. Brunner et al. 10.1038/s43247-021-00248-x
- Future Change Projections of Extreme Floods at Catchment Scale and Hydrodynamic Response of Its Downstream Lake Based on Catchment‐Waterbody Relationship Simulation R. Hua et al. 10.1029/2022JD037972
- A new diagram for performance evaluation of complex models A. Izzaddin et al. 10.1007/s00477-024-02678-3
- On the evaluation of climate change impact models T. Wagener et al. 10.1002/wcc.772
2 citations as recorded by crossref.
- When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias P. Astagneau et al. 10.1080/02626667.2021.1923720
- Hydrological models weighting for hydrological projections: The impacts on future peak flows M. Castaneda-Gonzalez et al. 10.1016/j.jhydrol.2023.130098
Latest update: 20 Nov 2024
Short summary
Assessments of current, local, and regional flood hazards and their future changes often involve the use of hydrologic models. A reliable model ideally reproduces both local flood characteristics and regional aspects of flooding. In this paper we investigate how such characteristics are represented by hydrologic models. Our results show that both the modeling of local and regional flood characteristics are challenging, especially under changing climate conditions.
Assessments of current, local, and regional flood hazards and their future changes often involve...
