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Flood frequency analysis under non-stationarity

100%

Strupczewski W. G. , Feluch W.

Geographia Polonica

1998

tom 71

Physics of flood frequency analysis. II. Convective diffusion model versus lognormal model

72%

Strupczewski W. , Singh V. , Węglarczyk S. , Mitosek H.

Acta Geophysica Polonica

2003

tom Vol. 51, nr 1

85-106

The similarity between the convective diffusion (CD) model and the lognormal (LN) distribution is shown by comparison of their moment estimates. Both models are tested using annual peak discharges observed at 39 gauging-sections of Polish rivers. The average value of the ration of the coefficient of skew ness to the coefficient of variation equals about 2.52, a value closer to the ration of the CD model than to the gamma or the lognormal model. The likelihood ratio indicates the preference of the CD over the LN model for 27 out of 39 cases. Applying the maximum likelihood (ML) method, one should take into account the consequence of the wrong distributional assumption on the estimate of moments. In the case of CD, the ML-estimate of the means is unbiased for any true distribution, which is not the case with the LN model, moreover the ML-estimate of the two fist moments of CD remains asymptotically unbiased if LN is true, while there is small bias in the opposite case. To check the objectivity of our inferences from empirical findings, a simulation experiment was carried out, which comprised generated CD- and LN- distributed samples and both the moment and likelihood criteria for the distribution choice. Its results clearly show that normal hydrological sample sizes are far too small for selecting the true distribution.

Physics of flood frequency analysis. Part I. Linear convective diffusion wave model

72%

Strupczewski W. , Węglarczyk S. , Singh V.

Acta Geophysica Polonica

2002

tom Vol. 50, nr 3

433-455

It is hypothesized that the impulse response of a linearized convective diffusion wave (CD) model is a probability distribution suitable for flood frequency analysis. This flood frequency model has two parameters, which are derived using the methods of moments and maximum likelihood. Also derived are errors in quantiles for these methods of parameter estimation. The distribution shows an equivalency of the two estimation methods with respect to the mean value - an important property in the case of unknown true distribution function. As the coefficient of variation tends to zero (with the mean fixed), the distribution tends to a normal one, similar to the lognormal and gamma distributions.

Flood hazard map of the Becho floodplain, Ethiopia, using nonstationary frequency model

72%

Tola S. Y. , Shetty A.

Acta Geophysica

2024

tom Vol. 72, no. 2

1079--1095

Flood estimates based on stationary flood frequency models are commonly used as inputs to flood hazard mapping. However, changing flood characteristics caused by climate change necessitate more accurate assessments of the probabilities of rare flood events. This study aims to develop a flood hazard map based on the nonstationary flood frequency using a generalized extreme value distribution model for the Becho floodplain in the upper Awash River basin. The distributional location parameter was modeled as a function of rainfall amount of different durations, annual total precipitation from wet days, yearly mean maximum temperature and time as covariates. The one-dimensional Hydrological Engineering Center River Analysis System (HEC-RAS) hydraulic model with steady flow analysis was used to generate flood hazard map input, depth and velocity, and inundation extent for different return periods. The result indicated that the model as a function of rainfall, such as monthly rainfall (August) and annual wet day precipitation, provided the best fit to the observed hydrological data. Rainfall as a covariate can explain the variation in the peak flood series. The developed hazard map based on depth alone and the combination of depth and velocity thresholds resulted in more than 70% of the floodplain area being classified as a high hazard zone under 2, 25, 50, and 100-years return periods. The current study assists water resource managers in considering changing environmental factors and an alternative flood frequency model for developing flood hazard management and mitigation strategies.