Articles | Volume 20, issue 12
https://doi.org/10.5194/nhess-20-3521-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-3521-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Downsizing parameter ensembles for simulations of rare floods
Anna E. Sikorska-Senoner
CORRESPONDING AUTHOR
University of Zurich, Department of Geography, Zurich, Switzerland
Bettina Schaefli
University of Lausanne, Institute of Earth Surface Dynamics, Lausanne, Switzerland
University of Bern, Institute of Geography, Bern, Switzerland
Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Jan Seibert
University of Zurich, Department of Geography, Zurich, Switzerland
Swedish University of Agricultural Sciences, Department of Aquatic Sciences and Assessment, Uppsala, Sweden
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Cited
14 citations as recorded by crossref.
- Evaluation of a stochastic weather generator for long-term ensemble streamflow forecasts S. Sohrabi & F. Brissette 10.1080/02626667.2021.1873343
- A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
- User-tailored sub-selection of climate model ensemble members for impact studies A. Sikorska-Senoner et al. 10.1016/j.scitotenv.2024.175769
- Delineating modelling uncertainty in river flow indicators with representative parameter sets A. Sikorska-Senoner 10.1016/j.advwatres.2021.104024
- A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations A. Sikorska-Senoner & J. Quilty 10.1016/j.envsoft.2021.105094
- Why do we have so many different hydrological models? A review based on the case of Switzerland P. Horton et al. 10.1002/wat2.1574
- Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool D. Hah et al. 10.1016/j.envsoft.2022.105474
- Comprehensive space–time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin D. Viviroli et al. 10.5194/nhess-22-2891-2022
- Clustering model responses in the frequency space for improved simulation‐based flood risk studies: The role of a cluster number A. Sikorska‐Senoner 10.1111/jfr3.12772
- Impact of different weather generator scenarios on extreme flood estimates in Switzerland E. Kritidou et al. 10.1007/s00477-024-02843-8
- Can continuous simulation be used as an alternative for flood regionalisation? A large sample example from Chile P. Acuña & A. Pizarro 10.1016/j.jhydrol.2023.130118
- The role of antecedent conditions in translating precipitation events into extreme floods at the catchment scale and in a large-basin context M. Staudinger et al. 10.5194/nhess-25-247-2025
- Uncertainty in the Number of Calibration Repetitions of a Hydrologic Model in Varying Climatic Conditions P. Sleziak et al. 10.3390/w12092362
- Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models M. Brunner et al. 10.5194/hess-25-105-2021
12 citations as recorded by crossref.
- Evaluation of a stochastic weather generator for long-term ensemble streamflow forecasts S. Sohrabi & F. Brissette 10.1080/02626667.2021.1873343
- A stochastic conceptual-data-driven approach for improved hydrological simulations J. Quilty et al. 10.1016/j.envsoft.2022.105326
- User-tailored sub-selection of climate model ensemble members for impact studies A. Sikorska-Senoner et al. 10.1016/j.scitotenv.2024.175769
- Delineating modelling uncertainty in river flow indicators with representative parameter sets A. Sikorska-Senoner 10.1016/j.advwatres.2021.104024
- A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations A. Sikorska-Senoner & J. Quilty 10.1016/j.envsoft.2021.105094
- Why do we have so many different hydrological models? A review based on the case of Switzerland P. Horton et al. 10.1002/wat2.1574
- Ensemble and stochastic conceptual data-driven approaches for improving streamflow simulations: Exploring different hydrological and data-driven models and a diagnostic tool D. Hah et al. 10.1016/j.envsoft.2022.105474
- Comprehensive space–time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin D. Viviroli et al. 10.5194/nhess-22-2891-2022
- Clustering model responses in the frequency space for improved simulation‐based flood risk studies: The role of a cluster number A. Sikorska‐Senoner 10.1111/jfr3.12772
- Impact of different weather generator scenarios on extreme flood estimates in Switzerland E. Kritidou et al. 10.1007/s00477-024-02843-8
- Can continuous simulation be used as an alternative for flood regionalisation? A large sample example from Chile P. Acuña & A. Pizarro 10.1016/j.jhydrol.2023.130118
- The role of antecedent conditions in translating precipitation events into extreme floods at the catchment scale and in a large-basin context M. Staudinger et al. 10.5194/nhess-25-247-2025
2 citations as recorded by crossref.
- Uncertainty in the Number of Calibration Repetitions of a Hydrologic Model in Varying Climatic Conditions P. Sleziak et al. 10.3390/w12092362
- Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models M. Brunner et al. 10.5194/hess-25-105-2021
Latest update: 21 Feb 2025
Short summary
This work proposes methods for reducing the computational requirements of hydrological simulations for the estimation of very rare floods that occur on average less than once in 1000 years. These methods enable the analysis of long streamflow time series (here for example 10 000 years) at low computational costs and with modelling uncertainty. They are to be used within continuous simulation frameworks with long input time series and are readily transferable to similar simulation tasks.
This work proposes methods for reducing the computational requirements of hydrological...
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