Exploring frequency analysis alternatives on instantaneous peak flow, in the context of flood plain delineation in Southern Québec, Canada.

2021 ◽  
Author(s):  
Simon Ricard ◽  
Alexis Bédard-Therrien ◽  
Annie-Claude ACP Parent ◽  
Brian Morse ◽  
François Anctil
Keyword(s):  
Frequency Analysis ◽  
Peak Flow ◽  
Flood Plain ◽  
Flood Frequency ◽  
Regional Frequency Analysis ◽  
Flood Frequency Analysis ◽  
Flow Data ◽  
Specific Context ◽  
Pros And Cons ◽  
Regional Frequency

A flood frequency analysis is conducted using instantaneous peak flow data over a hydrologic sub-region of southern Québec following three distinct methodological frameworks. First, the analysis is conducted locally using available instantaneous peak flow data. Second, the analysis is conducted locally using daily peak flow data processed in order to consider the peak flow effect. Third, a regional frequency analysis is conducted pooling all available instantaneous peak flow data over the study area. Results reveal a notable diversity in the resulting recurrence peak flow estimates and related uncertainties from one analysis to another. Expert judgement appears essential to arbitrate which alternative should be operated considering a specific context of application for flood plain delineation. Pros and cons for each approach are discussed. We finally encourage the use of a diversity of approaches in order to provide a robust assessment of uncertainty affecting peak flow estimates.

scholarly journals Region-of-influence approach to a frequency analysis of heavy precipitation in Slovakia

2007 ◽  
Vol 4 (4) ◽  
pp. 2361-2401 ◽  
Author(s):  
L. Gaál ◽  
J. Kyselý ◽  
J. Szolgay
Keyword(s):  
Frequency Analysis ◽  
Growth Curves ◽  
Heavy Precipitation ◽  
Flood Frequency ◽  
Regional Frequency Analysis ◽  
Flood Frequency Analysis ◽  
Design Values ◽  
Fixed Structure ◽  
Regional Frequency ◽  
Precipitation Events

Abstract. The L-moment-based regionalization approach developed by Hosking and Wallis (1997) is a frequently used tool in regional frequency modeling of heavy precipitation events. The method consists of the delineation of homogeneous pooling groups with a fixed structure, which may, however, lead to undesirable step-like changes in growth curves and design value estimates in the case of a transition from one pooling group to another. Unlike the standard methodology, the region-of-influence (ROI) approach does not make use of groups of sites (regions) with a fixed structure; instead, each site has its own "region", i.e. a group of sites that are sufficiently similar to the site of interest. The aim of the study is to develop a version of the ROI approach, which was originally proposed in order to overcome inconsistencies involved in flood frequency analysis, for the modeling of probabilities of heavy precipitation amounts. Various settings of the distance metric and pooled weighting factors are evaluated, and a comparison with the standard regional frequency analysis over the area of Slovakia is performed. The advantages of the ROI approach are assessed by means of simulation studies. It is demonstrated that almost any setting of parameters of the ROI method yields estimates of growth curves and design values at individual sites that are superior to the standard regional and at-site estimates.

scholarly journals Confidence intervals for the coefficient of L-variation in hydrological applications

2010 ◽  
Vol 14 (11) ◽  
pp. 2229-2242 ◽  
Author(s):  
A. Viglione
Keyword(s):  
Confidence Intervals ◽  
Frequency Analysis ◽  
Bootstrap Method ◽  
Flood Frequency ◽  
Point Estimation ◽  
Regional Frequency Analysis ◽  
Flood Frequency Analysis ◽  
Hydrological Variable ◽  
Regional Flood Frequency Analysis ◽  
T Distribution

Abstract. The coefficient of L-variation (L-CV) is commonly used in statistical hydrology, in particular in regional frequency analysis, as a measure of steepness for the frequency curve of the hydrological variable of interest. As opposed to the point estimation of the L-CV, in this work we are interested in the estimation of the interval of values (confidence interval) in which the L-CV is included at a given level of probability (confidence level). Several candidate distributions are compared in terms of their suitability to provide valid estimators of confidence intervals for the population L-CV. Monte-Carlo simulations of synthetic samples from distributions frequently used in hydrology are used as a basis for the comparison. The best estimator proves to be provided by the log-Student t distribution whose parameters are estimated without any assumption on the underlying parent distribution of the hydrological variable of interest. This estimator is shown to also outperform the non parametric bias-corrected and accelerated bootstrap method. An illustrative example of how this result can be used in hydrology is presented, namely in the comparison of methods for regional flood frequency analysis. In particular, it is shown that the confidence intervals for the L-CV can be used to assess the amount of spatial heterogeneity of flood data not explained by regionalization models.

scholarly journals Probabilistic Flood Hazard Maps from Monte Carlo Derived Peak Flow Values—An Application to Flood Risk Management in Zamora City (Spain)

2021 ◽  
Vol 11 (14) ◽  
pp. 6629
Author(s):  
Julio Garrote ◽  
Evelyng Peña ◽  
Andrés Díez-Herrero
Keyword(s):  
Risk Management ◽  
Monte Carlo ◽  
Frequency Analysis ◽  
Peak Flow ◽  
Flood Hazard ◽  
Flood Risk Management ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Hazard Maps ◽  
Flood Hazard Maps

All flood hazard and risk assessment suffer from a certain degree of uncertainty due to multiple factors, such as flood frequency analysis, hydrodynamic model calibration, or flood damage (magnitude–damage functions) models. The uncertainty linked to the flood frequency analysis is one of the most important factors (previous and present estimation point to 40%). Flood frequency analysis uncertainty has been approached from different points of view, such as the application of complex statistical models, the regionalization processes of peak flows, or the inclusion of non-systematic data. Here, we present an achievable approach to defining the uncertainty linked to flood frequency analysis by using the Monte Carlo method. Using the city of Zamora as the study site, the uncertainty is delimited by confidence intervals of a peak flow quantile of a 500-year return period. Probabilistic maps are derived from hydrodynamic results, and further analysis include flood hazard maps for human loss of stability and vehicle damage. Although the effect of this uncertainty is conditioned by the shape of the terrain, the results obtained may allow managers to achieve more consistent land-use planning. All those Zamora city results point out the probable underestimation of flood hazard (the higher hazard areas increase around 20%) and risk when the uncertainty analysis is not considered, thus limiting the efficiency of flood risk management tasks.

scholarly journals On the sampling distribution of the coefficient of L-variation for hydrological applications

2010 ◽  
Vol 7 (4) ◽  
pp. 5467-5496
Author(s):  
A. Viglione
Keyword(s):  
Frequency Analysis ◽  
Parametric Method ◽  
Flood Frequency ◽  
Regional Frequency Analysis ◽  
Flood Frequency Analysis ◽  
Small Samples ◽  
Hydrological Variable ◽  
Wide Range ◽  
Regional Flood Frequency Analysis ◽  
T Distribution

Abstract. The coefficient of L-variation (L-CV) is commonly used in statistical hydrology, in particular in regional frequency analysis, as a measure of steepness of the frequency curve. The aim of this work is to infer the full frequency distribution of the sample L-CV (and, consequently, its confidence intervals) for small samples and without making assumptions on the underlying parent distribution of the hydrological variable of interest. Several two-parameters candidate distributions are compared for a wide range of cases using Monte-Carlo simulations. A distribution-free method, recently proposed to estimate the variance structure of sample L-moments, is used to provide the parameters for the candidate distributions. It is shown that the log-Student t distribution approximates best, in most of the cases, the distribution of the sample L-CV and that a simple correction of the bias for the sample L-CV and its variance improves the fit. Also, the parametric method proposed here is demonstrated to perform better than the non-parametric bootstrap. An example of how this result could be used in hydrology is presented, namely in the comparison of methods for regional flood frequency analysis.

Identification of suitable copulas for bivariate frequency analysis of flood peak and flood volume data

2011 ◽  
Vol 42 (2-3) ◽  
pp. 193-216 ◽  
Author(s):  
Hemant Chowdhary ◽  
Luis A. Escobar ◽  
Vijay P. Singh
Keyword(s):  
Frequency Analysis ◽  
Goodness Of Fit ◽  
Peak Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Dependence Structure ◽  
Volume Data ◽  
Data Set ◽  
Flood Peak ◽  
Goodness Of Fit Tests

Multivariate flood frequency analysis, involving flood peak flow, volume and duration, has been traditionally accomplished by employing available functional bivariate and multivariate frequency distributions that have a restriction on the marginals to be from the same family of distributions. The copula concept overcomes this restriction by allowing a combination of arbitrarily chosen marginal types. It also provides a wider choice of admissible dependence structure as compared to the conventional approach. The availability of a vast variety of copula types makes the selection of an appropriate copula family for different hydrological applications a non-trivial task. Graphical and analytic goodness-of-fit tests for testing the suitability of copulas are beginning to evolve and are being developed; there is limited experience of their usage at present, especially in the hydrological field. This paper provides a step-wise procedure for copula selection and illustrates its application to bivariate flood frequency analysis, involving flood peak flow and volume data. Several graphical procedures, tail dependence characteristics, and formal goodness-of-fit tests involving a parametric bootstrap-based technique are considered while investigating the relative applicability of six copula families. The Clayton copula has been identified as a valid model for the particular flood peak flow and volume data set considered in the study.

scholarly journals Estimating the flood frequency distribution at seasonal and annual time scales

2012 ◽  
Vol 16 (12) ◽  
pp. 4651-4660 ◽  
Author(s):  
E. Baratti ◽  
A. Montanari ◽  
A. Castellarin ◽  
J. L. Salinas ◽  
A. Viglione ◽  
...  
Keyword(s):  
Objective Function ◽  
Frequency Distribution ◽  
Frequency Analysis ◽  
Peak Flow ◽  
Flood Frequency ◽  
Cumulative Distribution ◽  
Flood Frequency Analysis ◽  
Frequency Regime ◽  
Seasonal Flood ◽  
Annual Time

Abstract. We propose an original approach to infer the flood frequency distribution at seasonal and annual time scale. Our purpose is to estimate the peak flow that is expected for an assigned return period T, independently of the season in which it occurs (i.e. annual flood frequency regime), as well as in different selected sub-yearly periods (i.e. seasonal flood frequency regime). While a huge literature exists on annual flood frequency analysis, few studies have focused on the estimation of seasonal flood frequencies despite the relevance of the issue, for instance when scheduling along the months of the year the construction phases of river engineering works directly interacting with the active river bed, like for instance dams. An approximate method for joint frequency analysis is presented here that guarantees consistency between fitted annual and seasonal distributions, i.e. the annual cumulative distribution is the product of the seasonal cumulative distribution functions, under the assumption of independence among floods in different seasons. In our method the parameters of the seasonal frequency distributions are fitted by maximising an objective function that accounts for the likelihoods of both seasonal and annual peaks. In contrast to previous studies, our procedure is conceived to allow the users to introduce subjective weights to the components of the objective function in order to emphasize the fitting of specific seasons or of the annual peak flow distribution. An application to the time series of the Blue Nile daily flows at the Sudan–Ethiopia border is presented.

Sign in / Sign up

Export Citation Format

Share Document