Probable Maximum Flood estimation using upper bounded statistical models and its effect on high return period quantiles

2011 ◽  
pp. 323-328
Author(s):  
F Francés ◽  
B Botero
Keyword(s):  
Return Period ◽  
Statistical Models ◽  
High Return ◽  
Flood Estimation ◽  
Probable Maximum Flood

scholarly journals Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models

2010 ◽  
Vol 14 (12) ◽  
pp. 2617-2628 ◽  
Author(s):  
B. A. Botero ◽  
F. Francés
Keyword(s):  
Return Period ◽  
Robustness Analysis ◽  
Distribution Functions ◽  
Systematic Information ◽  
Estimation Uncertainty ◽  
Upper Limit ◽  
High Return ◽  
Probable Maximum Flood ◽  
Quantile Estimates

Abstract. This paper proposes the estimation of high return period quantiles using upper bounded distribution functions with Systematic and additional Non-Systematic information. The aim of the developed methodology is to reduce the estimation uncertainty of these quantiles, assuming the upper bound parameter of these distribution functions as a statistical estimator of the Probable Maximum Flood (PMF). Three upper bounded distribution functions, firstly used in Hydrology in the 90's (referred to in this work as TDF, LN4 and EV4), were applied at the Jucar River in Spain. Different methods to estimate the upper limit of these distribution functions have been merged with the Maximum Likelihood (ML) method. Results show that it is possible to obtain a statistical estimate of the PMF value and to establish its associated uncertainty. The behaviour for high return period quantiles is different for the three evaluated distributions and, for the case study, the EV4 gave better descriptive results. With enough information, the associated estimation uncertainty for very high return period quantiles is considered acceptable, even for the PMF estimate. From the robustness analysis, the EV4 distribution function appears to be more robust than the GEV and TCEV unbounded distribution functions in a typical Mediterranean river and Non-Systematic information availability scenario. In this scenario and if there is an upper limit, the GEV quantile estimates are clearly unacceptable.

scholarly journals Estimation of high return period flood quantiles using additional non-systematic information with upper bounded statistical models

2010 ◽  
Vol 7 (4) ◽  
pp. 5413-5440 ◽  
Author(s):  
B. A. Botero ◽  
F. Francés
Keyword(s):  
Return Period ◽  
Robustness Analysis ◽  
Distribution Functions ◽  
Statistical Estimator ◽  
Systematic Information ◽  
Estimation Uncertainty ◽  
High Return ◽  
Probable Maximum Flood ◽  
Flood Quantiles

Abstract. This paper proposes the estimation of high return period quantiles using upper bounded distribution functions with Systematic and additional Non-Systematic information. The aim of the developed methodology is to reduce the estimation uncertainty of these quantiles, assuming the upper bound parameter of these distribution functions as a statistical estimator of the Probable Maximum Flood (PMF). Three upper bounded distribution functions, firstly used in Hydrology in the 90's (referred to in this work as TDF, LN4 and EV4), were applied at the Jucar River in Spain. Different methods to estimate the upper limit of these distribution functions have been merged with the Maximum Likelihood (ML) method. Results show that it is possible to obtain a statistical estimate of the PMF value and to establish its associated uncertainty. The behaviour for high return period quantiles is different for the three evaluated distributions and, for the case study, the EV4 gave better descriptive results. With enough information, the associated estimation uncertainty is considered acceptable, even for the PMF estimate. From the robustness analysis, EV4 distribution function appears to be more robust than the GEV and TCEV unbounded distribution functions in a typical Mediterranean river and Non-Systematic information availability scenario.

The accuracy of the prediction of floods of high return period

1966 ◽  
Vol 2 (2) ◽  
pp. 191-198 ◽  
Author(s):  
J. E. Nash ◽  
J. Amorocho
Keyword(s):  
Return Period ◽  
High Return

Évaluation des méthodes utilisées pour l'estimation de la crue maximale probable en régions nordiques

1999 ◽  
Vol 26 (3) ◽  
pp. 355-367 ◽  
Author(s):  
I Debs ◽  
D Sparks ◽  
J Rousselle ◽  
S Birikundavyi
Keyword(s):  
Return Period ◽  
Extreme Event ◽  
Snow Accumulation ◽  
Model Estimation ◽  
Hydrologic Response ◽  
Return Periods ◽  
Design Flood ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Probable Maximum Flood

Among all existing methods for estimating extreme floods, the probable maximum flood method is the safest, since it is a flood with a probability of excedance that is theoretically zero. In the early 1970s, this flood was calculated as the combination of the probable maximum precipitation (PMP) and the probable maximum snow accumulation (PMSA). In the 1990s, this combination has been considered to be highly improbable. Experts advise against combining two maximized events and, instead, recommend combining one maximized event with a relatively typical extreme event. This article presents a sensitivity analysis on the return period to be used for the typical extreme event to be combined with the maximized event to obtain a "more realistic" PMF. To achieve this, all the steps of a PMF study were reviewed and applied to the Sainte-Marguerite watershed, i.e., calibration and (or) validation of SSARR model, estimation of the PMP, the PMSA, and the temperature sequence. Different flood scenarios have been simulated including accumulated snowfall corresponding to return periods of 50, 100, and 500 years, followed by PMP and PMSA, followed by precipitation corresponding to return periods of 50, 100, and 500 years. It has been noticed that the use of a return period of 50, 100, or 500 years, to represent the unmaximized extreme event, has little effect on the hydrologic response of the basin. Based on the results of this work the use of a return period of 100 years is recommended.Key words: probable maximum flood, probable maximum precipitation, probable maximum snow accumulation, design flood, SSARR model.

Assessment of techniques to include historical information in flood frequency distributions for hydrological dam safety assessment

2021 ◽  
Author(s):  
Enrique Soriano Martín ◽  
Antonio Jiménez ◽  
Luis Mediero
Keyword(s):  
Return Period ◽  
Uncertainty Reduction ◽  
Flood Frequency ◽  
Dam Safety ◽  
Return Periods ◽  
Historical Information ◽  
Probability Weighted Moments ◽  
High Return ◽  
Partial Probability ◽  
Quantile Estimates

<p>Flood peak quantiles for return periods up to 10 000 years are required for dam design and safety assessment, though flood series usually have a record length of around 20-40 years that leads to a high uncertainty. The utility of historical data of flooding is generally recognised for estimating the magnitude of extreme events with return periods in excess of 100 years. Therefore, historical information can be incorporated in flood frequency analyses to reduce uncertainties in high return period flood quantile estimates that are used in hydrological dam safety analyses.</p><p>This study assesses a set of existing techniques to incorporate historical information of flooding in extreme frequency analyses, focusing on their reliability and uncertainty reduction for high return periods that are used for dam safety analysis. Monte Carlo simulations are used to assess both the reliability and uncertainty in high return period quantile estimates. Varying lengths in the historical (Nh = 100 and 200 years) and systematic (Ns = 20, 40 and 60 years) periods are considered. In addition, a varying number of known flood magnitudes that exceed a given perception threshold in the historical period are also considered (k = 1-2). The values of Nh, Ns and k used in the study are the most usual in practice.</p><p>The reliability and uncertainty reduction in flood quantile estimates for each technique depend on the statistical properties of flood series. Therefore, a set of feasible combinations of L-coefficient of variation (L-CV) and skewness (L-CS) values should be considered. The analysis aims to understand how each technique behaves in terms of flood quantile reliability and uncertainty reduction depending on the L-moment statistics of flood series. In this study, L-CV and L-CS regional values in the 29 homogeneous regions identified in Spain for developing the national map of flood quantiles by the Centre for Hydrographic Studies of CEDEX are considered.</p><p>The results show that the maximum likelihood estimator (MLE) and weighted moments (WM) techniques show the best results in the regions with small L-CS values. However, the biased partial probability weighted moments (BPPWM) technique shows the best results in the regions with high L-CS values. While the expected moments algorithm (EMA) tends to underestimate flood quantiles for high return periods, the unbiased partial probability weighted moments (UPPWM) technique tends to overestimate them. In addition, including historical information of flooding in flood frequency analyses improves flood quantile estimates in most cases regardless the technique that is used. Uncertainty reduction in high return period flood quantile estimates are higher for short systematic time series, regions with high L-CS values and long historical periods.</p><p><strong>Acknowledgments:</strong> This research has been supported by the project SAFERDAMS (PID2019-107027RB-I00) funded by the Spanish Ministry of Science and Innovation.</p>

Impact of river confluences on return periods of large floods

2020 ◽  
Author(s):  
Björn Guse ◽  
Luzie Wietzke ◽  
Sophie Ullrich ◽  
Bruno Merz ◽  
Sergiy Vorogushyn
Keyword(s):  
River Basin ◽  
Triple Point ◽  
Return Period ◽  
Return Periods ◽  
Flood Wave ◽  
Main River ◽  
High Return ◽  
Flood Magnitude ◽  
River Confluences ◽  
The Impact

<p>The severity of floods is not only affected by the physiogeographic characteristics and the meteorological conditions of the catchment, but also by the river network. If a flood occurs at the same time in tributary and main river, the tributary flood wave can amplify the flood wave in the main river. To investigate the impact of flood wave superposition, the 6-10 largest floods in the four main German river basins (Danube, Elbe, Rhine, Weser) are analyzed. The flood waves are tracked along the river course. Flood magnitude and flood timing are analyzed at each triple point. A triple point consists of the hydrological stations in the tributary and in the main river (upstream and downstream of the confluence). The return periods are calculated separately at each triple point for all three hydrological stations. In addition, changes in the return periods along a river course are analyzed for each flood event. The flood magnitudes and their return periods are compared with the spatiotemporal precipitation distributions and other influencing factors. The results show that the contribution of the different confluences to the flood severity at the main river is event-specific. Partly, the return period is only high at the lower parts of the river basin, partly a high return period in the upper parts of the river basin does not lead to a high return period downstream.</p>

scholarly journals Return period assessment of urban pluvial floods through modelling of rainfall–flood response

2018 ◽  
Vol 20 (4) ◽  
pp. 829-845 ◽  
Author(s):  
Damian Murla Tuyls ◽  
Søren Thorndahl ◽  
Michael R. Rasmussen
Keyword(s):  
Return Period ◽  
Urban Areas ◽  
Urban Drainage ◽  
Return Periods ◽  
Drainage Systems ◽  
Flood Impacts ◽  
Urban Drainage Systems ◽  
Flood Estimation ◽  
Flood Response ◽  
The Impact

Abstract Intense rainfall in urban areas can often generate severe flood impacts. Consequently, it is crucial to design systems to minimize potential flood damages. Traditional, simple design of urban drainage systems assumes agreement between rainfall return period and its consequent flood return period; however, this does not always apply. Hydraulic infrastructures found in urban drainage systems can increase system heterogeneity and perturb the impact of severe rainfall response. In this study, a surface flood return period assessment was carried out at Lystrup (Denmark), which has received the impact of flooding in recent years. A 35 years' rainfall dataset together with a coupled 1D/2D surface and network model was used to analyse and assess flood return period response. Results show an ambiguous relation between rainfall and flood return periods indicating that linear rainfall–runoff relationships will, for the analysed case study, be insufficient for flood estimation. Simulation-based mapping of return periods for flood area and volume has been suggested, and moreover, a novel approach has been developed to map local flood response time and relate this to rainfall characteristics. This approach allows to carefully analyse rainfall impacts and flooding response for a correct flood return period assessment in urban areas.

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