Articles | Volume 17, issue 8
https://doi.org/10.5194/hess-17-3039-2013
© 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-17-3039-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
01 Aug 2013
Research article |
| 01 Aug 2013
Sedimentation monitoring including uncertainty analysis in complex floodplains: a case study in the Mekong Delta
N. V. Manh
GFZ – German Research Center for Geoscience, Section 5.4 Hydrology, Potsdam, Germany
GFZ – German Research Center for Geoscience, Section 5.4 Hydrology, Potsdam, Germany
GFZ – German Research Center for Geoscience, Section 5.4 Hydrology, Potsdam, Germany
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J. Hall, B. Arheimer, G. T. Aronica, A. Bilibashi, M. Boháč, O. Bonacci, M. Borga, P. Burlando, A. Castellarin, G. B. Chirico, P. Claps, K. Fiala, L. Gaál, L. Gorbachova, A. Gül, J. Hannaford, A. Kiss, T. Kjeldsen, S. Kohnová, J. J. Koskela, N. Macdonald, M. Mavrova-Guirguinova, O. Ledvinka, L. Mediero, B. Merz, R. Merz, P. Molnar, A. Montanari, M. Osuch, J. Parajka, R. A. P. Perdigão, I. Radevski, B. Renard, M. Rogger, J. L. Salinas, E. Sauquet, M. Šraj, J. Szolgay, A. Viglione, E. Volpi, D. Wilson, K. Zaimi, and G. Blöschl
Proc. IAHS, 370, 89–95, https://doi.org/10.5194/piahs-370-89-2015, https://doi.org/10.5194/piahs-370-89-2015, 2015
A. Gafurov, S. Vorogushyn, D. Farinotti, D. Duethmann, A. Merkushkin, and B. Merz
The Cryosphere, 9, 451–463, https://doi.org/10.5194/tc-9-451-2015, https://doi.org/10.5194/tc-9-451-2015, 2015
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Spatially distributed snow-cover data are available only for the recent past from remote sensing. Sometimes we need snow-cover data over a longer period for climate impact analysis for the calibration/validation of hydrological models. In this study we present a methodology to reconstruct snow cover in the past using available long-term in situ data and recently available remote sensing snow-cover data. The results show about 85% accuracy although only a limited number of stations (7) were used.
K. Schröter, M. Kunz, F. Elmer, B. Mühr, and B. Merz
Hydrol. Earth Syst. Sci., 19, 309–327, https://doi.org/10.5194/hess-19-309-2015, https://doi.org/10.5194/hess-19-309-2015, 2015
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Extreme antecedent precipitation, increased initial hydraulic load in the river network and strong but not extraordinary event precipitation were key drivers for the flood in June 2013 in Germany. Our results are based on extreme value statistics and aggregated severity indices which we evaluated for a set of 74 historic large-scale floods. This flood database and the methodological framework enable the rapid assessment of future floods using precipitation and discharge observations.
N. V. Manh, N. V. Dung, N. N. Hung, B. Merz, and H. Apel
Hydrol. Earth Syst. Sci., 18, 3033–3053, https://doi.org/10.5194/hess-18-3033-2014, https://doi.org/10.5194/hess-18-3033-2014, 2014
J. Hall, B. Arheimer, M. Borga, R. Brázdil, P. Claps, A. Kiss, T. R. Kjeldsen, J. Kriaučiūnienė, Z. W. Kundzewicz, M. Lang, M. C. Llasat, N. Macdonald, N. McIntyre, L. Mediero, B. Merz, R. Merz, P. Molnar, A. Montanari, C. Neuhold, J. Parajka, R. A. P. Perdigão, L. Plavcová, M. Rogger, J. L. Salinas, E. Sauquet, C. Schär, J. Szolgay, A. Viglione, and G. Blöschl
Hydrol. Earth Syst. Sci., 18, 2735–2772, https://doi.org/10.5194/hess-18-2735-2014, https://doi.org/10.5194/hess-18-2735-2014, 2014
B. Merz, J. Aerts, K. Arnbjerg-Nielsen, M. Baldi, A. Becker, A. Bichet, G. Blöschl, L. M. Bouwer, A. Brauer, F. Cioffi, J. M. Delgado, M. Gocht, F. Guzzetti, S. Harrigan, K. Hirschboeck, C. Kilsby, W. Kron, H.-H. Kwon, U. Lall, R. Merz, K. Nissen, P. Salvatti, T. Swierczynski, U. Ulbrich, A. Viglione, P. J. Ward, M. Weiler, B. Wilhelm, and M. Nied
Nat. Hazards Earth Syst. Sci., 14, 1921–1942, https://doi.org/10.5194/nhess-14-1921-2014, https://doi.org/10.5194/nhess-14-1921-2014, 2014
J. M. Delgado, B. Merz, and H. Apel
Nat. Hazards Earth Syst. Sci., 14, 1579–1589, https://doi.org/10.5194/nhess-14-1579-2014, https://doi.org/10.5194/nhess-14-1579-2014, 2014
S. Uhlemann, A. H. Thieken, and B. Merz
Nat. Hazards Earth Syst. Sci., 14, 189–208, https://doi.org/10.5194/nhess-14-189-2014, https://doi.org/10.5194/nhess-14-189-2014, 2014
S. Vorogushyn and B. Merz
Hydrol. Earth Syst. Sci., 17, 3871–3884, https://doi.org/10.5194/hess-17-3871-2013, https://doi.org/10.5194/hess-17-3871-2013, 2013
A. Domeneghetti, S. Vorogushyn, A. Castellarin, B. Merz, and A. Brath
Hydrol. Earth Syst. Sci., 17, 3127–3140, https://doi.org/10.5194/hess-17-3127-2013, https://doi.org/10.5194/hess-17-3127-2013, 2013
D. Duethmann, J. Zimmer, A. Gafurov, A. Güntner, D. Kriegel, B. Merz, and S. Vorogushyn
Hydrol. Earth Syst. Sci., 17, 2415–2434, https://doi.org/10.5194/hess-17-2415-2013, https://doi.org/10.5194/hess-17-2415-2013, 2013
M. Nied, Y. Hundecha, and B. Merz
Hydrol. Earth Syst. Sci., 17, 1401–1414, https://doi.org/10.5194/hess-17-1401-2013, https://doi.org/10.5194/hess-17-1401-2013, 2013
S. Uhlemann, R. Bertelmann, and B. Merz
Hydrol. Earth Syst. Sci., 17, 895–911, https://doi.org/10.5194/hess-17-895-2013, https://doi.org/10.5194/hess-17-895-2013, 2013
N. V. Dung, B. Merz, A. Bárdossy, and H. Apel
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhessd-1-275-2013, https://doi.org/10.5194/nhessd-1-275-2013, 2013
Revised manuscript not accepted
B. Merz, H. Kreibich, and U. Lall
Nat. Hazards Earth Syst. Sci., 13, 53–64, https://doi.org/10.5194/nhess-13-53-2013, https://doi.org/10.5194/nhess-13-53-2013, 2013
B. Jongman, H. Kreibich, H. Apel, J. I. Barredo, P. D. Bates, L. Feyen, A. Gericke, J. Neal, J. C. J. H. Aerts, and P. J. Ward
Nat. Hazards Earth Syst. Sci., 12, 3733–3752, https://doi.org/10.5194/nhess-12-3733-2012, https://doi.org/10.5194/nhess-12-3733-2012, 2012
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Uncertainty analysis
A comprehensive uncertainty framework for historical flood frequency analysis: a 500-year-long case study
Bayesian calibration of a flood simulator using binary flood extent observations
Intercomparison of global reanalysis precipitation for flood risk modelling
Seamless streamflow forecasting at daily to monthly scales: MuTHRE lets you have your cake and eat it too
An uncertainty partition approach for inferring interactive hydrologic risks
Predicting discharge capacity of vegetated compound channels: uncertainty and identifiability of one-dimensional process-based models
Uncertainty quantification of floodplain friction in hydrodynamic models
Developing a drought-monitoring index for the contiguous US using SMAP
Technical note: Assessment of observation quality for data assimilation in flood models
Assessing the impact of uncertainty on flood risk estimates with reliability analysis using 1-D and 2-D hydraulic models
The transferability of hydrological models under nonstationary climatic conditions
Why hydrological predictions should be evaluated using information theory
Spatial uncertainty assessment in modelling reference evapotranspiration at regional scale
Confidence intervals for the coefficient of L-variation in hydrological applications
Possibilistic uncertainty analysis of a conceptual model of snowmelt runoff
Mathieu Lucas, Michel Lang, Benjamin Renard, and Jérôme Le Coz
Hydrol. Earth Syst. Sci., 28, 5031–5047, https://doi.org/10.5194/hess-28-5031-2024, https://doi.org/10.5194/hess-28-5031-2024, 2024
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The proposed flood frequency model accounts for uncertainty in the perception threshold S and the starting date of the historical period. Using a 500-year-long case study, inclusion of historical floods reduces the uncertainty in flood quantiles, even when only the number of exceedances of S is known. Ignoring threshold uncertainty leads to underestimated flood quantile uncertainty. This underlines the value of using a comprehensive framework for uncertainty estimation.
Mariano Balbi and David Charles Bonaventure Lallemant
Hydrol. Earth Syst. Sci., 27, 1089–1108, https://doi.org/10.5194/hess-27-1089-2023, https://doi.org/10.5194/hess-27-1089-2023, 2023
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We proposed a methodology to obtain useful and robust probabilistic predictions from computational flood simulators using satellite-borne flood extent observations. We developed a Bayesian framework to obtain the uncertainty in roughness parameters, in observations errors, and in simulator structural deficiencies. We found that it can yield improvements in predictions relative to current methodologies and can potentially lead to consistent ways of combining data from different sources.
Fergus McClean, Richard Dawson, and Chris Kilsby
Hydrol. Earth Syst. Sci., 27, 331–347, https://doi.org/10.5194/hess-27-331-2023, https://doi.org/10.5194/hess-27-331-2023, 2023
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Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis. We investigate the impact of using four reanalysis products on simulations of past flood events. All reanalysis products underestimated the number of buildings inundated, compared to a benchmark national dataset. These findings show that while global reanalyses provide a useful resource for flood modelling where no other data are available, they may underestimate impact in some cases.
David McInerney, Mark Thyer, Dmitri Kavetski, Richard Laugesen, Fitsum Woldemeskel, Narendra Tuteja, and George Kuczera
Hydrol. Earth Syst. Sci., 26, 5669–5683, https://doi.org/10.5194/hess-26-5669-2022, https://doi.org/10.5194/hess-26-5669-2022, 2022
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Streamflow forecasts a day to a month ahead are highly valuable for water resources management. Current practice often develops forecasts for specific lead times and aggregation timescales. In contrast, a single, seamless forecast can serve multiple lead times/timescales. This study shows seamless forecasts can match the performance of forecasts developed specifically at the monthly scale, while maintaining quality at other lead times. Hence, users need not sacrifice capability for performance.
Yurui Fan, Kai Huang, Guohe Huang, Yongping Li, and Feng Wang
Hydrol. Earth Syst. Sci., 24, 4601–4624, https://doi.org/10.5194/hess-24-4601-2020, https://doi.org/10.5194/hess-24-4601-2020, 2020
Adam Kiczko, Kaisa Västilä, Adam Kozioł, Janusz Kubrak, Elżbieta Kubrak, and Marcin Krukowski
Hydrol. Earth Syst. Sci., 24, 4135–4167, https://doi.org/10.5194/hess-24-4135-2020, https://doi.org/10.5194/hess-24-4135-2020, 2020
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The study compares the uncertainty of discharge curves for vegetated channels, calculated using several methods, including the simplest ones, based on the Manning formula and advanced approaches, providing a detailed physical representation of the channel flow processes. Parameters of each method were identified for the same data sets. The outcomes of the study include the widths of confidence intervals, showing which method was the most successful in explaining observations.
Guilherme Luiz Dalledonne, Rebekka Kopmann, and Thomas Brudy-Zippelius
Hydrol. Earth Syst. Sci., 23, 3373–3385, https://doi.org/10.5194/hess-23-3373-2019, https://doi.org/10.5194/hess-23-3373-2019, 2019
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This study presents how the concept of uncertainty quantification can be applied to river engineering problems and how important it is to understand the limitations of numerical models from a probabilistic point of view. We investigated floodplain friction formulations using different uncertainty quantification methods and estimated their contribution in the final results. The analysis of uncertainties is planned to be integrated in future projects and also extended to more complex scenarios.
Sara Sadri, Eric F. Wood, and Ming Pan
Hydrol. Earth Syst. Sci., 22, 6611–6626, https://doi.org/10.5194/hess-22-6611-2018, https://doi.org/10.5194/hess-22-6611-2018, 2018
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Of particular interest to NASA's SMAP-based agricultural applications is a monitoring product that assesses near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for the meaningful assessment of its indices. This study presents initial insights about using SMAP Level 3 and Level 4 for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS).
Joanne A. Waller, Javier García-Pintado, David C. Mason, Sarah L. Dance, and Nancy K. Nichols
Hydrol. Earth Syst. Sci., 22, 3983–3992, https://doi.org/10.5194/hess-22-3983-2018, https://doi.org/10.5194/hess-22-3983-2018, 2018
L. Altarejos-García, M. L. Martínez-Chenoll, I. Escuder-Bueno, and A. Serrano-Lombillo
Hydrol. Earth Syst. Sci., 16, 1895–1914, https://doi.org/10.5194/hess-16-1895-2012, https://doi.org/10.5194/hess-16-1895-2012, 2012
C. Z. Li, L. Zhang, H. Wang, Y. Q. Zhang, F. L. Yu, and D. H. Yan
Hydrol. Earth Syst. Sci., 16, 1239–1254, https://doi.org/10.5194/hess-16-1239-2012, https://doi.org/10.5194/hess-16-1239-2012, 2012
S. V. Weijs, G. Schoups, and N. van de Giesen
Hydrol. Earth Syst. Sci., 14, 2545–2558, https://doi.org/10.5194/hess-14-2545-2010, https://doi.org/10.5194/hess-14-2545-2010, 2010
G. Buttafuoco, T. Caloiero, and R. Coscarelli
Hydrol. Earth Syst. Sci., 14, 2319–2327, https://doi.org/10.5194/hess-14-2319-2010, https://doi.org/10.5194/hess-14-2319-2010, 2010
A. Viglione
Hydrol. Earth Syst. Sci., 14, 2229–2242, https://doi.org/10.5194/hess-14-2229-2010, https://doi.org/10.5194/hess-14-2229-2010, 2010
A. P. Jacquin
Hydrol. Earth Syst. Sci., 14, 1681–1695, https://doi.org/10.5194/hess-14-1681-2010, https://doi.org/10.5194/hess-14-1681-2010, 2010
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