Articles | Volume 19, issue 7
https://doi.org/10.5194/nhess-19-1445-2019
© 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-19-1445-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Jul 2019
Research article |
| 18 Jul 2019
| 18 Jul 2019
Reducing uncertainties in flood inundation outputs of a two-dimensional hydrodynamic model by constraining roughness
Chair of Hydrology and River Basin Management, Department of Civil, Geo and
Environmental Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
Jorge Leandro
Chair of Hydrology and River Basin Management, Department of Civil, Geo and
Environmental Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
Markus Disse
Chair of Hydrology and River Basin Management, Department of Civil, Geo and
Environmental Engineering, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
Viewed
Total article views: 2,937 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Feb 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,976 | 881 | 80 | 2,937 | 62 | 78 |
- HTML: 1,976
- PDF: 881
- XML: 80
- Total: 2,937
- BibTeX: 62
- EndNote: 78
Total article views: 2,249 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 Jul 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,646 | 538 | 65 | 2,249 | 51 | 68 |
- HTML: 1,646
- PDF: 538
- XML: 65
- Total: 2,249
- BibTeX: 51
- EndNote: 68
Total article views: 688 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Feb 2019)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 330 | 343 | 15 | 688 | 11 | 10 |
- HTML: 330
- PDF: 343
- XML: 15
- Total: 688
- BibTeX: 11
- EndNote: 10
Viewed (geographical distribution)
Total article views: 2,937 (including HTML, PDF, and XML)
Thereof 2,440 with geography defined
and 497 with unknown origin.
Total article views: 2,249 (including HTML, PDF, and XML)
Thereof 1,939 with geography defined
and 310 with unknown origin.
Total article views: 688 (including HTML, PDF, and XML)
Thereof 501 with geography defined
and 187 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
15 citations as recorded by crossref.
- Comparison of flood hydrograph prediction between synthetic unit hydrograph methods and rain-on-grid model for Katulampa watershed, Indonesia B. Ginting et al. 10.15292/acta.hydro.2023.05
- A 2D Real-Time Flood Forecast Framework Based on a Hybrid Historical and Synthetic Runoff Database G. Crotti et al. 10.3390/w12010114
- Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps B. Sanders et al. 10.1029/2019EF001391
- Patterns of Difference between Physical and 1-D Calibrated Effective Roughness Parameters in Mountain Rivers S. Cedillo et al. 10.3390/w13223202
- Evaluating the satellite-derived DEM accuracy with rain-on-grid modeling for flood hydrograph prediction of Katulampa Watershed, Indonesia B. Ginting et al. 10.1080/15715124.2024.2312857
- An overview of approaches for reducing uncertainties in hydrological forecasting: Progress and challenges A. Panchanathan et al. 10.1016/j.earscirev.2024.104956
- Effect of the Likelihood Function on the Calibration of the Effective Manning Roughness Factor S. Cedillo et al. 10.3390/w16202879
- Physics-Informed Neural Network water surface predictability for 1D steady-state open channel cases with different flow types and complex bed profile shapes S. Cedillo et al. 10.1186/s40323-022-00226-8
- Rapid prediction of urban flooding at street-scale using physics-informed machine learning-based surrogate modeling Y. Bhattarai et al. 10.1016/j.teadva.2024.200116
- Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting P. Abbaszadeh et al. 10.1016/j.isci.2022.105201
- What is the actual composition of specific land cover? An evaluation of the accuracy at a national scale – Remote sensing in comparison to topographic land cover J. Bihałowicz et al. 10.1016/j.rsase.2024.101319
- Data‐driven model for river flood forecasting based on a Bayesian network approach B. Boutkhamouine et al. 10.1111/1468-5973.12316
- Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models D. Muñoz et al. 10.5194/hess-28-2531-2024
- Efecto del refinamiento de la descripción de la rugosidad en una aproximación 2D para un río de montaña: un caso de estudio J. Cedillo Galarza et al. 10.17163/lgr.n33.2021.08
- Analysis of the Likelihood Function and Cutoff Threshold in the GLUE Procedure for Calibration of the Resistance Parameters of Mountain Rivers S. Cedillo et al. 10.1007/s11269-024-03869-x
13 citations as recorded by crossref.
- Comparison of flood hydrograph prediction between synthetic unit hydrograph methods and rain-on-grid model for Katulampa watershed, Indonesia B. Ginting et al. 10.15292/acta.hydro.2023.05
- A 2D Real-Time Flood Forecast Framework Based on a Hybrid Historical and Synthetic Runoff Database G. Crotti et al. 10.3390/w12010114
- Collaborative Modeling With Fine‐Resolution Data Enhances Flood Awareness, Minimizes Differences in Flood Perception, and Produces Actionable Flood Maps B. Sanders et al. 10.1029/2019EF001391
- Patterns of Difference between Physical and 1-D Calibrated Effective Roughness Parameters in Mountain Rivers S. Cedillo et al. 10.3390/w13223202
- Evaluating the satellite-derived DEM accuracy with rain-on-grid modeling for flood hydrograph prediction of Katulampa Watershed, Indonesia B. Ginting et al. 10.1080/15715124.2024.2312857
- An overview of approaches for reducing uncertainties in hydrological forecasting: Progress and challenges A. Panchanathan et al. 10.1016/j.earscirev.2024.104956
- Effect of the Likelihood Function on the Calibration of the Effective Manning Roughness Factor S. Cedillo et al. 10.3390/w16202879
- Physics-Informed Neural Network water surface predictability for 1D steady-state open channel cases with different flow types and complex bed profile shapes S. Cedillo et al. 10.1186/s40323-022-00226-8
- Rapid prediction of urban flooding at street-scale using physics-informed machine learning-based surrogate modeling Y. Bhattarai et al. 10.1016/j.teadva.2024.200116
- Perspective on uncertainty quantification and reduction in compound flood modeling and forecasting P. Abbaszadeh et al. 10.1016/j.isci.2022.105201
- What is the actual composition of specific land cover? An evaluation of the accuracy at a national scale – Remote sensing in comparison to topographic land cover J. Bihałowicz et al. 10.1016/j.rsase.2024.101319
- Data‐driven model for river flood forecasting based on a Bayesian network approach B. Boutkhamouine et al. 10.1111/1468-5973.12316
- Quantifying cascading uncertainty in compound flood modeling with linked process-based and machine learning models D. Muñoz et al. 10.5194/hess-28-2531-2024
2 citations as recorded by crossref.
- Efecto del refinamiento de la descripción de la rugosidad en una aproximación 2D para un río de montaña: un caso de estudio J. Cedillo Galarza et al. 10.17163/lgr.n33.2021.08
- Analysis of the Likelihood Function and Cutoff Threshold in the GLUE Procedure for Calibration of the Resistance Parameters of Mountain Rivers S. Cedillo et al. 10.1007/s11269-024-03869-x
Latest update: 23 Nov 2024
Short summary
This study investigates the use of measured water levels to reduce uncertainty bounds of two-dimensional hydrodynamic model output. Uncertainty assessment is generally not reported in practice due to the lack of best practices and too wide uncertainty bounds. Hence, a novel method to reduce the bounds by constraining the model parameter, mainly roughness, is presented. The operational practitioners as well as researchers benefit from the study in the field of flood risk management.
This study investigates the use of measured water levels to reduce uncertainty bounds of...
Altmetrics
Final-revised paper
Preprint