Articles | Volume 19, issue 1
https://doi.org/10.5194/nhess-19-19-2019
© Author(s) 2019. 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-19-19-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Jan 2019
Research article |
| 07 Jan 2019
| 07 Jan 2019
Ensemble flood forecasting considering dominant runoff processes – Part 1: Set-up and application to nested basins (Emme, Switzerland)
Manuel Antonetti
Swiss Federal Institute for Forest, Snow and Landscape Research,
Birmensdorf, Switzerland
University of Zurich, Department of
Geography, Zurich, Switzerland
Christoph Horat
Swiss Federal Institute for Forest, Snow and Landscape Research,
Birmensdorf, Switzerland
ETH, Institute for Atmospheric and
Climate Science, Zurich, Switzerland
Ioannis V. Sideris
MeteoSwiss, Swiss Federal
Office of Meteorology and Climatology, Locarno, Switzerland
Massimiliano Zappa
CORRESPONDING AUTHOR
Swiss Federal Institute for Forest, Snow and Landscape Research,
Birmensdorf, Switzerland
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Cited
20 citations as recorded by crossref.
- Delineation of Flood Susceptibility Zone Using Analytical Hierarchy Process and Frequency Ratio Methods: A Case Study of Dakshin Dinajpur District, India D. Sarkar et al. 10.1007/s12524-023-01777-y
- Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts M. Poletti et al. 10.5194/hess-23-3823-2019
- Ensemble flood forecasting: Current status and future opportunities W. Wu et al. 10.1002/wat2.1432
- Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets J. Liu et al. 10.3390/rs13234945
- IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling B. Mohammadi et al. 10.1038/s41598-022-16215-1
- Ensemble flash flood predictions using a high-resolution nationwide distributed rainfall-runoff model: case study of the heavy rain event of July 2018 and Typhoon Hagibis in 2019 T. Sayama et al. 10.1186/s40645-020-00391-7
- 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
- Flash-flood propagation susceptibility estimation using weights of evidence and their novel ensembles with multicriteria decision making and machine learning R. Costache et al. 10.1080/10106049.2021.2001580
- Integrated Framework for Detecting the Areas Prone to Flooding Generated by Flash-Floods in Small River Catchments R. Costache et al. 10.3390/w13060758
- Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method F. Hosseini et al. 10.1016/j.scitotenv.2019.135161
- Evaluation of two hydrometeorological ensemble strategies for flash-flood forecasting over a catchment of the eastern Pyrenees H. Roux et al. 10.5194/nhess-20-425-2020
- Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles R. Costache & D. Tien Bui 10.1016/j.scitotenv.2019.136492
- Multi-lead-time short-term runoff forecasting based on Ensemble Attention Temporal Convolutional Network C. Zhang et al. 10.1016/j.eswa.2023.122935
- Two decades of ensemble flood forecasting: a state-of-the-art on past developments, present applications and future opportunities J. Das et al. 10.1080/02626667.2021.2023157
- A new hybrid equilibrium optimized SysFor based geospatial data mining for tropical storm-induced flash flood susceptible mapping P. Ngo et al. 10.1016/j.jenvman.2020.111858
- A new avenue to improve the performance of integrated modeling for flash flood susceptibility assessment: Applying cluster algorithms J. Liu et al. 10.1016/j.ecolind.2022.109785
- OpenForecast v2: Development and Benchmarking of the First National-Scale Operational Runoff Forecasting System in Russia G. Ayzel 10.3390/hydrology8010003
- A Geomorphic Approach for Identifying Flash Flood Potential Areas in the East Rapti River Basin of Nepal T. Pangali Sharma et al. 10.3390/ijgi10040247
- Flood hazard risk forecasting index (FHRFI) for urban areas: The Hurricane Harvey case study T. Jurlina et al. 10.1002/met.1845
- Identification of Hydrological Models for Enhanced Ensemble Reservoir Inflow Forecasting in a Large Complex Prairie Watershed . Awol et al. 10.3390/w11112201
19 citations as recorded by crossref.
- Delineation of Flood Susceptibility Zone Using Analytical Hierarchy Process and Frequency Ratio Methods: A Case Study of Dakshin Dinajpur District, India D. Sarkar et al. 10.1007/s12524-023-01777-y
- Using nowcasting technique and data assimilation in a meteorological model to improve very short range hydrological forecasts M. Poletti et al. 10.5194/hess-23-3823-2019
- Ensemble flood forecasting: Current status and future opportunities W. Wu et al. 10.1002/wat2.1432
- Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets J. Liu et al. 10.3390/rs13234945
- IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling B. Mohammadi et al. 10.1038/s41598-022-16215-1
- Ensemble flash flood predictions using a high-resolution nationwide distributed rainfall-runoff model: case study of the heavy rain event of July 2018 and Typhoon Hagibis in 2019 T. Sayama et al. 10.1186/s40645-020-00391-7
- 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
- Flash-flood propagation susceptibility estimation using weights of evidence and their novel ensembles with multicriteria decision making and machine learning R. Costache et al. 10.1080/10106049.2021.2001580
- Integrated Framework for Detecting the Areas Prone to Flooding Generated by Flash-Floods in Small River Catchments R. Costache et al. 10.3390/w13060758
- Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method F. Hosseini et al. 10.1016/j.scitotenv.2019.135161
- Evaluation of two hydrometeorological ensemble strategies for flash-flood forecasting over a catchment of the eastern Pyrenees H. Roux et al. 10.5194/nhess-20-425-2020
- Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles R. Costache & D. Tien Bui 10.1016/j.scitotenv.2019.136492
- Multi-lead-time short-term runoff forecasting based on Ensemble Attention Temporal Convolutional Network C. Zhang et al. 10.1016/j.eswa.2023.122935
- Two decades of ensemble flood forecasting: a state-of-the-art on past developments, present applications and future opportunities J. Das et al. 10.1080/02626667.2021.2023157
- A new hybrid equilibrium optimized SysFor based geospatial data mining for tropical storm-induced flash flood susceptible mapping P. Ngo et al. 10.1016/j.jenvman.2020.111858
- A new avenue to improve the performance of integrated modeling for flash flood susceptibility assessment: Applying cluster algorithms J. Liu et al. 10.1016/j.ecolind.2022.109785
- OpenForecast v2: Development and Benchmarking of the First National-Scale Operational Runoff Forecasting System in Russia G. Ayzel 10.3390/hydrology8010003
- A Geomorphic Approach for Identifying Flash Flood Potential Areas in the East Rapti River Basin of Nepal T. Pangali Sharma et al. 10.3390/ijgi10040247
- Flood hazard risk forecasting index (FHRFI) for urban areas: The Hurricane Harvey case study T. Jurlina et al. 10.1002/met.1845
Latest update: 10 Sep 2024
Short summary
To predict timing and magnitude peak run-off, meteorological and calibrated hydrological models are commonly coupled. A flash-flood forecasting chain is presented based on a process-based run-off generation module with no need for calibration. This chain has been evaluated using data for the Emme catchment (Switzerland). The outcomes of this study show that operational flash predictions in ungauged basins can benefit from the use of information on run-off processes.
To predict timing and magnitude peak run-off, meteorological and calibrated hydrological models...
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