The influence of non-stationarity in extreme hydrological events on flood frequency estimation

Abstract

Substantial evidence shows that the frequency of hydrological extremes has been changing and is likely to continue to change in the near future. Non-stationary models for flood frequency analyses are one method of accounting for these changes in estimating design values. The objective of the present study is to compare four models in terms of goodness of fit, their uncertainties, the parameter estimation methods and the implications for estimating flood quantiles. Stationary and non-stationary models using the GEV distribution were considered, with parameters dependent on time and on annual precipitation. Furthermore, in order to study the influence of the parameter estimation approach on the results, the maximum likelihood (MLE) and Bayesian Monte Carlo Markov chain (MCMC) methods were compared. The methods were tested for two gauging stations in Slovenia that exhibit significantly increasing trends in annual maximum (AM) discharge series. The comparison of the models suggests that the stationary model tends to underestimate flood quantiles relative to the non-stationary models in recent years. The model with annual precipitation as a covariate exhibits the best goodness-of-fit performance. For a 10% increase in annual precipitation, the 10-year flood increases by 8%. Use of the model for design purposes requires scenarios of future annual precipitation. It is argued that these may be obtained more reliably than scenarios of extreme event precipitation which makes the proposed model more practically useful than alternative models.

Flood type classification and assessment of their past changes across Europe

“…In a classical statistical framework of 10 attribution, it could be difficult to separate the effects of the different potential drivers on the change in flooding . However, some studies have attempted to assess the relative importance of different drivers through investigation of the significance of covariates related to the different drivers used in statistical distributions of flood flows (Prosdocimi et al, 2015;Šraj et al, 2016Šraj et al, , Viglione et al 2016. Employing a simulation based assessment would enable identifying the relative importance of the different drivers in explaining the detected changes in flooding if enough information were available on 15 the temporal evolution of the different drivers.…”

A 60-Minute Design Rainstorm for the Urban Area of Yangpu District, Shanghai, China

“…The inevitable trend of global climate change has brought about considerably frequent occurrences of extreme climate events [1][2][3][4]. Recently, under the influence of typhoons, urban floods caused by rainstorms have exerted great harm on the safety of society.…”

“…The main objectives of this study are: (1) to derive the design hyetographs for a 60-min rainstorm with different return periods in Yangpu District in order to serve as the input to an urban runoff model; (2) to explore the influences of separating single-peak and double-peak rainstorms in deriving hyetographs; (3) to compare the methodology of combining mode hyetographs and the P&C method with the classic design hyetograph method. The methodology and procedure used to derive hyetographs in this study can also be a reference for other small cities and regions.…”

Nonstationarity in maximum annual daily streamflow series from Southern Brazil

“…Traditional methods developed for that purpose are based on the assumption of stationarity, which implies that the variable under analysis has a time invariant probability density function with fixed parameters (PETROW; READ;VOGEL, , 2016SRAJ et al, 2016;VOGEL et al, 2015;YAINDL;WALTER, 2011). However, the stationarity hypothesis might be rendered invalid due to several factors that influence streamflow, e.g., hydroclimatic changes (MILLY et al, 2008), urbanization (VOGEL; YAINDL; WALTER, 2011), agricultural management practices (FOUFOULA-GEORGIOU et al, 2015) operation of hydropower plants (RÄSÄNEN et al, 2017) and reservoirs (ZAJAC et al, 2017).…”