Articles | Volume 14, issue 12
https://doi.org/10.5194/hess-14-2465-2010
© Author(s) 2010. 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-14-2465-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
09 Dec 2010
Research article |
| 09 Dec 2010
Introducing empirical and probabilistic regional envelope curves into a mixed bounded distribution function
B. Guse
Deutsches GeoForschungsZentrum Potsdam GFZ, Section 5.4 – Hydrology, Telegrafenberg, 14473 Potsdam, Germany
Center for Disaster Management and Risk Reduction Technology (CEDIM), 76187 Karlsruhe, Germany
now at: Department of Hydrology and Water Resources Management, Christian-Albrechts-Universität zu Kiel, Olshausenstrasse 40, 24098 Kiel, Germany
Th. Hofherr
Center for Disaster Management and Risk Reduction Technology (CEDIM), 76187 Karlsruhe, Germany
Institute for Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), 76128 Karlsruhe, Germany
Geo Risks Research, Münchener Rückversicherungs-Gesellschaft, 80791 Munich, Germany
B. Merz
Deutsches GeoForschungsZentrum Potsdam GFZ, Section 5.4 – Hydrology, Telegrafenberg, 14473 Potsdam, Germany
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Stochastic approaches
FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms
Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system
Characteristics and process controls of statistical flood moments in Europe – a data-based analysis
Objective functions for information-theoretical monitoring network design: what is “optimal”?
Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach
Numerical investigation on the power of parametric and nonparametric tests for trend detection in annual maximum series
Spatially dependent flood probabilities to support the design of civil infrastructure systems
Technical note: Stochastic simulation of streamflow time series using phase randomization
Multivariate hydrologic design methods under nonstationary conditions and application to engineering practice
Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability distribution – Part 1: theoretical development
Ensemble modeling of stochastic unsteady open-channel flow in terms of its time–space evolutionary probability distribution – Part 2: numerical application
Characterizing the spatial variations and correlations of large rainstorms for landslide study
Assessment of extreme flood events in a changing climate for a long-term planning of socio-economic infrastructure in the Russian Arctic
Dealing with uncertainty in the probability of overtopping of a flood mitigation dam
Flood frequency analysis of historical flood data under stationary and non-stationary modelling
Selection of intense rainfall events based on intensity thresholds and lightning data in Switzerland
Towards modelling flood protection investment as a coupled human and natural system
A bivariate return period based on copulas for hydrologic dam design: accounting for reservoir routing in risk estimation
Examination of homogeneity of selected Irish pooling groups
Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models
Design flood hydrographs from the relationship between flood peak and volume
HESS Opinions "A random walk on water"
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022, https://doi.org/10.5194/hess-26-5373-2022, 2022
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A farm-scale hydroclimatic machine learning framework to advise farmers was developed. FarmCan uses remote sensing data and farmers' input to forecast crop water deficits. The 8 d composite variables are better than daily ones for forecasting water deficit. Evapotranspiration (ET) and potential ET are more effective than soil moisture at predicting crop water deficit. FarmCan uses a crop-specific schedule to use surface or root zone soil moisture.
Andrew J. Newman, Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 5603–5621, https://doi.org/10.5194/hess-25-5603-2021, https://doi.org/10.5194/hess-25-5603-2021, 2021
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This study assesses methods that estimate flood return periods to identify when we would obtain a large flood return estimate change if the method or input data were changed (sensitivities). We include an examination of multiple flood-generating models, which is a novel addition to the flood estimation literature. We highlight the need to select appropriate flood models for the study watershed. These results will help operational water agencies develop more robust risk assessments.
David Lun, Alberto Viglione, Miriam Bertola, Jürgen Komma, Juraj Parajka, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 5535–5560, https://doi.org/10.5194/hess-25-5535-2021, https://doi.org/10.5194/hess-25-5535-2021, 2021
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We investigate statistical properties of observed flood series on a European scale. There are pronounced regional patterns, for instance: regions with strong Atlantic influence show less year-to-year variability in the magnitude of observed floods when compared with more arid regions of Europe. The hydrological controls on the patterns are quantified and discussed. On the European scale, climate seems to be the dominant driver for the observed patterns.
Hossein Foroozand and Steven V. Weijs
Hydrol. Earth Syst. Sci., 25, 831–850, https://doi.org/10.5194/hess-25-831-2021, https://doi.org/10.5194/hess-25-831-2021, 2021
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In monitoring network design, we have to decide what to measure, where to measure, and when to measure. In this paper, we focus on the question of where to measure. Past literature has used the concept of information to choose a selection of locations that provide maximally informative data. In this paper, we look in detail at the proper mathematical formulation of the information concept as an objective. We argue that previous proposals for this formulation have been needlessly complicated.
Manuela I. Brunner and Eric Gilleland
Hydrol. Earth Syst. Sci., 24, 3967–3982, https://doi.org/10.5194/hess-24-3967-2020, https://doi.org/10.5194/hess-24-3967-2020, 2020
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Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but yet unobserved streamflow time series with the same characteristics as the observed data. We propose a stochastic simulation approach in the frequency domain instead of the time domain. Our evaluation results suggest that the flexible, continuous simulation approach is valuable for a diverse range of water management applications.
Vincenzo Totaro, Andrea Gioia, and Vito Iacobellis
Hydrol. Earth Syst. Sci., 24, 473–488, https://doi.org/10.5194/hess-24-473-2020, https://doi.org/10.5194/hess-24-473-2020, 2020
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We highlight the need for power evaluation in the application of null hypothesis significance tests for trend detection in extreme event analysis. In a wide range of conditions, depending on the underlying distribution of data, the test power may reach unacceptably low values. We propose the use of a parametric approach, based on model selection criteria, that allows one to choose the null hypothesis, to select the level of significance, and to check the test power using Monte Carlo experiments.
Phuong Dong Le, Michael Leonard, and Seth Westra
Hydrol. Earth Syst. Sci., 23, 4851–4867, https://doi.org/10.5194/hess-23-4851-2019, https://doi.org/10.5194/hess-23-4851-2019, 2019
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While conventional approaches focus on flood designs at individual locations, there are many situations requiring an understanding of spatial dependence of floods at multiple locations. This research describes a new framework for analyzing flood characteristics across civil infrastructure systems, including conditional and joint probabilities of floods. This work leads to a new flood estimation paradigm, which focuses on the risk of the entire system rather than each system element in isolation.
Manuela I. Brunner, András Bárdossy, and Reinhard Furrer
Hydrol. Earth Syst. Sci., 23, 3175–3187, https://doi.org/10.5194/hess-23-3175-2019, https://doi.org/10.5194/hess-23-3175-2019, 2019
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This study proposes a procedure for the generation of daily discharge data which considers temporal dependence both within short timescales and across different years. The simulation procedure can be applied to individual and multiple sites. It can be used for various applications such as the design of hydropower reservoirs, the assessment of flood risk or the assessment of drought persistence, and the estimation of the risk of multi-year droughts.
Cong Jiang, Lihua Xiong, Lei Yan, Jianfan Dong, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 23, 1683–1704, https://doi.org/10.5194/hess-23-1683-2019, https://doi.org/10.5194/hess-23-1683-2019, 2019
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We present the methods addressing the multivariate hydrologic design applied to the engineering practice under nonstationary conditions. A dynamic C-vine copula allowing for both time-varying marginal distributions and a time-varying dependence structure is developed to capture the nonstationarities of multivariate flood distribution. Then, the multivariate hydrologic design under nonstationary conditions is estimated through specifying the design criterion by average annual reliability.
Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 1993–2005, https://doi.org/10.5194/hess-22-1993-2018, https://doi.org/10.5194/hess-22-1993-2018, 2018
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A new method is proposed to solve the stochastic unsteady open-channel flow system in only one single simulation, as opposed to the many simulations usually done in the popular Monte Carlo approach. The derivation of this new method gave a deterministic and linear Fokker–Planck equation whose solution provided a powerful and effective approach for quantifying the ensemble behavior and variability of such a stochastic system, regardless of the number of parameters causing its uncertainty.
Alain Dib and M. Levent Kavvas
Hydrol. Earth Syst. Sci., 22, 2007–2021, https://doi.org/10.5194/hess-22-2007-2018, https://doi.org/10.5194/hess-22-2007-2018, 2018
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A newly proposed method is applied to solve a stochastic unsteady open-channel flow system (with an uncertain roughness coefficient) in only one simulation. After comparing its results to those of the Monte Carlo simulations, the new method was found to adequately predict the temporal and spatial evolution of the probability density of the flow variables of the system. This revealed the effectiveness, strength, and time efficiency of this new method as compared to other popular approaches.
Liang Gao, Limin Zhang, and Mengqian Lu
Hydrol. Earth Syst. Sci., 21, 4573–4589, https://doi.org/10.5194/hess-21-4573-2017, https://doi.org/10.5194/hess-21-4573-2017, 2017
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Rainfall is the primary trigger of landslides. However, the rainfall intensity is not uniform in space, which causes more landslides in the area of intense rainfall. The primary objective of this paper is to quantify spatial correlation characteristics of three landslide-triggering large storms in Hong Kong. The spatial maximum rolling rainfall is represented by a trend surface and a random field of residuals. The scales of fluctuation of the residuals are found between 5 km and 30 km.
Elena Shevnina, Ekaterina Kourzeneva, Viktor Kovalenko, and Timo Vihma
Hydrol. Earth Syst. Sci., 21, 2559–2578, https://doi.org/10.5194/hess-21-2559-2017, https://doi.org/10.5194/hess-21-2559-2017, 2017
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This paper presents the probabilistic approach to evaluate design floods in a changing climate, adapted in this case to the northern territories. For the Russian Arctic, the regions are delineated, where it is suggested to correct engineering hydrological calculations to account for climate change. An example of the calculation of a maximal discharge of 1 % exceedance probability for the Nadym River at Nadym is provided.
Eleni Maria Michailidi and Baldassare Bacchi
Hydrol. Earth Syst. Sci., 21, 2497–2507, https://doi.org/10.5194/hess-21-2497-2017, https://doi.org/10.5194/hess-21-2497-2017, 2017
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In this research, we explored how the sampling uncertainty of flood variables (flood peak, volume, etc.) can reflect on a structural variable, which in our case was the maximum water level (MWL) of a reservoir controlled by a dam. Next, we incorporated additional information from different sources for a better estimation of the uncertainty in the probability of exceedance of the MWL. Results showed the importance of providing confidence intervals in the risk assessment of a structure.
M. J. Machado, B. A. Botero, J. López, F. Francés, A. Díez-Herrero, and G. Benito
Hydrol. Earth Syst. Sci., 19, 2561–2576, https://doi.org/10.5194/hess-19-2561-2015, https://doi.org/10.5194/hess-19-2561-2015, 2015
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A flood frequency analysis using a 400-year historical flood record was carried out using a stationary model (based on maximum likelihood estimators) and a non-stationary model that incorporates external covariates (climatic and environmental). The stationary model was successful in providing an average discharge around which value flood quantiles estimated by non-stationary models fluctuate through time.
L. Gaál, P. Molnar, and J. Szolgay
Hydrol. Earth Syst. Sci., 18, 1561–1573, https://doi.org/10.5194/hess-18-1561-2014, https://doi.org/10.5194/hess-18-1561-2014, 2014
P. E. O'Connell and G. O'Donnell
Hydrol. Earth Syst. Sci., 18, 155–171, https://doi.org/10.5194/hess-18-155-2014, https://doi.org/10.5194/hess-18-155-2014, 2014
A. I. Requena, L. Mediero, and L. Garrote
Hydrol. Earth Syst. Sci., 17, 3023–3038, https://doi.org/10.5194/hess-17-3023-2013, https://doi.org/10.5194/hess-17-3023-2013, 2013
S. Das and C. Cunnane
Hydrol. Earth Syst. Sci., 15, 819–830, https://doi.org/10.5194/hess-15-819-2011, https://doi.org/10.5194/hess-15-819-2011, 2011
B. A. Botero and F. Francés
Hydrol. Earth Syst. Sci., 14, 2617–2628, https://doi.org/10.5194/hess-14-2617-2010, https://doi.org/10.5194/hess-14-2617-2010, 2010
L. Mediero, A. Jiménez-Álvarez, and L. Garrote
Hydrol. Earth Syst. Sci., 14, 2495–2505, https://doi.org/10.5194/hess-14-2495-2010, https://doi.org/10.5194/hess-14-2495-2010, 2010
D. Koutsoyiannis
Hydrol. Earth Syst. Sci., 14, 585–601, https://doi.org/10.5194/hess-14-585-2010, https://doi.org/10.5194/hess-14-585-2010, 2010
Cited articles
Benito, G., Lang, M., Barriendos, M., Llasat, M. C., Francés, F., Ouarda, T. B. M. J., Thorndycraft, V. R., Enzel, Y., Bárdossy, A., Coeur, D., and Bobée, B.: Use of Systematic, Palaeoflood and Historical Data for the Improvement of Flood Risk Estimation, Review of Scientific Methods, Nat. Hazards, 31(3), 623–643, 2004.
BKG GeoDataCentre (Federal Agency for Cartography and Geodesy): Digital Landscape Model ATKIS Basis DLM, Frankfurt/Main, 2005.
Burn, D. H.: Evaluation of Regional Flood Frequency Analysis with a Region of Influence Approach, Water Resour. Res., 26(8), 2257–2265, 1990.
Castellarin, A.: Probabilistic envelope curves for design flood estimation at ungauged sites, Water Resour. Res., 43(4), W04406, https://doi.org/04410.01029/02005WR004384, 2007.
Castellarin, A., Vogel, R. M., and Matalas, N. C.: Probabilistic behaviour of a regional envelope curve, Water Resour. Res., 41, W06018, https://doi.org/06010.01029/02004WR003042, 2005.
Castellarin, A., Vogel, R. M., and Matalas, N. C.: Multivariate probabilistic regional envelopes of extreme floods, J. Hydrol., 336(3–4), 376–390, 2007.
Chbab, E. H., Buiteveld, H., and Diermanse, F.: Estimating exceedance frequencies of extreme river discharges using statistical methods and physically based approach, Österr. Wasser- und Abfallwirtschaft, 58(3–4), 35–43, 2006.
Cohn, T. A. and Stedinger, J. R.: Use of Historical Information in a Maximum Likelihood Framework, J. Hydrol., 96(1–4), 215–233, 1987.
Condie, R. and Lee, K. A.: Flood frequency analysis with historic information, J. Hydrol., 58(1–2), 47–61, 1982.
Costa, J. E.: A comparison of the largest rainfall-runoff floods in the United States with those of the People's Republic of China and the world, J. Hydrol., 96(1–4), 101–115, 1987.
Crippen, J. R.: Envelopes Curves for Extreme Flood Events, J. Hydraul. Eng.-ASCE, 108(8), 1208–1212, 1982.
Dalrymple, T.: Flood frequency analyses, US Geol. Surv. Water Supply Pap., 1543-A, 1960.
El Adlouni, S., Bobée, B., and Ouarda, T. B. M. J.: On the tails of extreme event distributions in hydrology, J. Hydrol., 355(1–4), 16–33, 2008.
England Jr., J. F., Jarrett, R. D., and Salas, J. D.: Data-based comparisons of moments estimators using historical and paleoflood data, J. Hydrol., 278(1–4), 172–196, 2003a.
England Jr., J. F., Salas, J. D., and Jarrett, R. D.: Comparisons of two moments-based estimators that utilize historical and paleoflood data for the log Pearson type III distribution, Water Resour. Res., 39(7), 1243, https://doi.org/1210.1029/2002WR001791, 2003b.
Enzel, Y., Ely, L. L., House, P. K., Baker, V. R., and Webb, R. H.: Paleoflood evidence for a natural upper bound to flood magnitudes in the Colorado River basin, Water Resour. Res., 29(5), 2287–2298, 1993.
Fernandes, W. and Naghettini, M.: Integrated frequency analysis of extreme flood peaks and flood volumes using the regionalized quantiles of rainfall depths as auxiliary variables, J. Hydrol. Eng.-ASCE, 13(3), 171–179, 2008.
Fernandes, W., Naghettini, M., and Loschi, R.: A Bayesian approach for estimating extreme flood probabilities with upper-bounded distribution functions, Stoch. Env. Res. Risk A., 24(8), 1127–1143, https://doi.org/10.1007/s00477-010-0365-4, 2010.
Francés, F.: Using the TCEV distribution function with systematic and non-systematic data in a regional flood frequency analysis, Stoch. Hydrol. Hydraul., 12(4), 267–283, 1998.
Francés, F. and Botero, B. A.: Probable maximum flood estimation using systematic and non-systematic information, in: Paleofloods, Historical Floods and Climatic Variability: Applications in Flood Risk Assessment, Proceedings of the PHEFRA workshop, edited by: Thorndycraft, V. R., Benito, G., Barriendos, M., and Llasat, M. C., Barcelona/Spain, 223–229, 2003.
Gaume, E., Bain, V., Bernardara, P., Newinger, O., Barbuc, M., Bateman, A., Blaskovicova, L., Blöschl, G., Borga, M., Dumitrescu, A., Daliakopoulos, I., Garcia, J., Irimescu, A., Kohnová, S., Koutroulis, A., Marchi, L., Matreata, S., Medina, V., Precisco, E., Sempere-Torres, D., Stancalie, G., Szolgay, J., Tsanis, I., Velasco, D., and Viglione, A.: A compilation of data on European flash floods, J. Hydrol., 367(1–2), 70–78, 2009.
Guillot, P. and Duband, D.: La méthode du gradex pour le calcul de la probabilité des crues à partir des pluies, Floods and Their Computation – Proceedings of the Leningrad Symposium, IAHS-AISH P., 84, 560–569, 1967.
Guse, B., Castellarin, A., Thieken, A. H., and Merz, B.: Effects of intersite dependence of nested catchment structures on probabilistic regional envelope curves, Hydrol. Earth Syst. Sci., 13, 1699–1712, https://doi.org/10.5194/hess-13-1699-2009, 2009.
Guse, B., Thieken, A. H., Castellarin, A., and Merz, B.: Deriving probabilistic regional envelope curves with two pooling methods, J. Hydrol., 380(1–2), 14–26, 2010.
Gutknecht, D., Blöschl, G., Reszler, C., and Heindl, H.: A "Multi-Pillar"-Approach to the Estimation of Low Probability Design Floods, Österr. Wasser- und Abfallwirtschaft, 58(3–4), 44–50, 2006.
Herschy, R.: The world's maximum observed floods, Flow Meas. Instrum., 13, 231–235, 2002.
Hofherr, T., Kottmeier, C., Heneka, P., and Ruck, B.: Wintersturm Risiko Modell und Echtzeit-Schadensprognose, in: CEDIM Entwicklungsbericht Dezember 2008, edited by: CEDIM – Center for Disaster Management and Risk Reduction Technology, unpublished work, 17–19, 2008.
Hosking, J. R. M. and Wallis, J. R.: Paleoflood hydrology and flood frequency analysis, Water Resour. Res., 22(4), 543–550, 1986a.
Hosking, J. R. M. and Wallis, J. R.: The value of historical data in flood frequency analysis, Water Resour. Res., 22(4), 1606–1612, 1986b.
Hosking, J. R. M. and Wallis, J. R.: Some statistics useful in regional frequency analysis, J. Hydrol., 29(2), 271–281, 1993.
Hosking, J. R. M. and Wallis, J. R.: Regional frequency analysis: an approach based on L-moments, Cambridge University Press, Cambridge, UK, 1997.
Houghton-Carr, H.: Flood Estimation Handbook 4: Restatement and application of the Flood Studies Report rainfall-runoff method, Institute of Hydrology, Wallingford, UK, 1999.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90 m Database, http://srtm.csi.cgiar.org, last access: 19 May, 2008.
Kanda, J. A.: A New Extreme Value Distribution With Lower and Upper Limits For Earthquake Motion and Wind Speeds, Theor. Appl., 31, 351–360, 1981.
Martins, E. S. and Stedinger, J. R.: Historical information in a generalized maximum likelihood framework with partial duration and annual maximum series, Water Resour. Res., 37(8), 2559–2567, 2001.
Merz, R. and Blöschl, G.: Flood frequency hydrology: 1. Temporal, spatial, and causal expansion of information, Water Resour. Res., 44, W08432, https://doi.org/08410.01029/02007WR006744, 2008a.
Merz, R. and Blöschl, G.: Flood frequency hydrology: 2. Combining data evidence, Water Resour. Res., 44, W08433, https://doi.org/08410.01029/02007WR006745, 2008b.
Merz, B. and Thieken, A. H.: Separating Natural and Epistemic Uncertainty in Flood Frequency Analysis, J. Hydrol., 309(1–4), 114–132, 2005.
Merz, B. and Thieken, A. H.: Flood risk curves and uncertainty bounds, Nat. Hazards, 51(3), 437–458, 2009.
Merz, R., Blöschl, G., and Humer, G.: National flood discharge mapping in Austria, Nat. Hazards, 46(1), 53–72, 2008.
Moon, Y.-I., Lall, U., and Boswarth, K.: A comparison of tail probability estimators for flood frequency analysis, J. Hydrol., 151(2–4), 343–363, 1993.
Naghettini, M., Potter, K. W., and Illangasekare, T.: Estimating the Upper Tail of Flood-Peak Frequency Distributions Using Hydrometeorological Information, Water Resour. Res., 32(4), 1729–1740, 1996.
Peel, M. C., Wang, Q. J., Vogel, R. M., and McMahon, T. A.: The Utility of L-moment ratio Diagrams for selecting a regional probability distribution, Hydrolog. Sci. J., 46(1), 147–156, 2001.
Petrow, Th., Thieken, A. H., Kreibich, H., Bahlburg, C. H., and Merz, B.: Improvements on flood alleviation in Germany: Lessons learned from the Elbe flood in August 2002, Environ. Manage., 38(3), 717–732, 2006.
Petrow, Th., Merz, B., Lindenschmidt, K.-E., and Thieken, A. H.: Aspects of seasonality and flood generating circulation patterns in a mountainous catchment in south-eastern Germany, Hydrol. Earth Syst. Sci., 11, 1455–1468, https://doi.org/10.5194/hess-11-1455-2007, 2007.
Pohl, R.: Historische Hochwasser aus dem Erzgebirge, Fakultät Bauingenieurwesen, Institut für Wasserbau und Technische Hydromechanik, Technische Universität Dresden, Dresden Wasserbauliche Mitteilungen, Heft 28, 2004.
Reis Jr., D. S. and Stedinger, J. R.: Bayesian MCMC flood frequency analysis with historical information, J. Hydrol., 313(1–2), 97–116, 2005.
Robson, A. and Reed, D.: Flood Estimation Handbook 3: Statistical procedures of flood frequency estimation, Institute of Hydrology, Wallingford, UK, 338 pp., 1999.
Rossi, F., Fiorentino, M., and Versace, P.: Two component extreme value distribution for flood frequency analysis, Water Resour. Res., 20(5), 847–856, 1984.
Schumann, A. H.: Das hydrologische Risiko bei der Bemessung und der Bewirtschaftungsplanung von Talsperren, Wasserbauliche Mitteilungen, Heft 27, 33–46, 2004.
Schumann, A. H.: Flood statistical assessment of the event from August 2002 in the Mulde river basin, based on seasonal statistics, in German: Hochwasserstatistische Bewertung des Augusthochwassers 2002 im Einzugsgebiet der Mulde unter Anwendung der saisonalen Statistik, Hydrol. Wasserbewirts., 49, 200–206, 2005.
Stanescu, V. A.: Outstanding floods in Europe, A regionalization and comparison, International Conference on Flood Estimation, Berne, Switzerland, 697–706, 2002.
Stedinger, J. R. and Cohn, T. A.: Flood frequency analysis with historical and paleoflood information, Water Resour. Res., 22(3), 785–793, 1986.
Stedinger, J. R., Vogel, R. M., and Foufoula-Georgiou, E.: Frequency Analysis of extreme events, in: Handbook of Hydrology, edited by: Maidment, D. A., McGraw-Hill, New York, 18.11–18.66, 1993.
Thieken, A. H., Müller, M., Kreibich, H., and Merz, B.: Flood damage and influencing factors: New insights from the August 2002 flood in Germany, Water Resour. Res., 41(12), W12430, https://doi.org/101029/102005WR004177, 2005.
Ulbrich, U., Brücher, T., Fink, A. H., Leckebusch, G. C., Krüger, A., and Pinto, J. G.: The central European floods of August 2002: part 1 – Rainfall periods and flood development, Weather, 58(8), 371–377, 2003.
Vogel, R. M. and Fennessey, N. M.: L-moments diagrams should replace product moment diagrams, Water Resour. Res., 29(4), 1745–1752, 1993.
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