Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates

Abstract

Recent evidences of the impact of persistent modes of regional climate variability, coupled with the intensification of human activities, have led hydrologists to study flood regime without applying the hypothesis of stationarity. In this study, a framework for flood frequency analysis is developed on the basis of a tool that enables us to address the modelling of non-stationary time series, namely, the "generalized additive models for location, scale and shape" (GAMLSS). Two approaches to non-stationary modelling in GAMLSS were applied to the annual maximum flood records of 20 continental Spanish rivers. The results of the first approach, in which the parameters of the selected distributions were modeled as a function of time only, show the presence of clear non-stationarities in the flood regime. In a second approach, the parameters of the distributions are modeled as functions of climate indices (Arctic Oscillation, North Atlantic Oscillation, Mediterranean Oscillation and the Western Mediterranean Oscillation) and a reservoir index that is proposed in this paper. The results when incorporating external covariates in the study highlight the important role of interannual variability in low-frequency climate forcings when modelling the flood regime in continental Spanish rivers. Also, with this approach is possible to properly introduce the impact on the flood regime of intensified reservoir regulation strategies and to be used as predictive tools. Application of non-stationary analysis shows that the differences between the quantiles obtained and their stationary equivalents may be important over long periods of time

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

“…urbanisation, regulation of rivers, construction of dikes, deforestation, etc.) (Coles, 2001;Lopez and Frances, 2013;Merz et al, 2012;Prosdocimi et al, 2015;Salas and Obeysekera, 2014;Strupczewski et al, 2001;Villarini et al, 2009;Vogel et al, 2011). Recently, more attention has been paid to non-stationary frequency estimation of flood extremes.…”

Around and about an application of the GAMLSS package to non-stationary flood frequency analysis

“…Some GAMLSS applications in hydrological/climatological time-series are presented by Villarini et al (2009aVillarini et al ( , b, 2010aVillarini et al ( , b, 2012, Machado et al (2015), López and Francés (2013), Osorio and Galiano (2012), Hudson et al (2008). GAMLSS have been used to model seasonal rainfall and temperature in Rome by Villarini et al (2010a); they showed that the GAMLSS models could represent the magnitude and spread in the seasonal time series with parameters being a smooth function of time or teleconnection indices.…”

“…GAMLSS have been used to model seasonal rainfall and temperature in Rome by Villarini et al (2010a); they showed that the GAMLSS models could represent the magnitude and spread in the seasonal time series with parameters being a smooth function of time or teleconnection indices. GAMLSS models have been used for flood frequency analysis in Villarini et al (2009a, b) and López and Francés (2013). The study by Machado et al (2015) also applied GAMLSS to model flood data using historical information.…”

“…A very few hydrological studies have applied the threeparameter GAMLSS distributions, e.g., López and Francés (2013) and Zhang et al (2015). In both studies, the Generalized Gamma (GG) distribution was applied to develop a framework for flood frequency analysis for annual maximum daily flows and maximum daily precipitation.…”

Application of the L-moments approach to the analysis of regional flood frequency in Northern Algeria

“…In studying flow series, Bernier (1959) showed that the Frechet distribution is better than the Gumbel distribution at fitting extreme values. Other distributions are best suited to the modelling of maximum flow rates, such as the generalized extreme value (GEV) distribution in Great Britain, the log-normal distribution in China, the log-Pearson type III distribution in the USA (Bobée, 1999), the gamma distribution in central and Eastern Algeria (Hebal et al, 2011a(Hebal et al, , 2011b) and the log-normal distribution in the Chellif Basin of Algeria (Achite and Meddi, 2004), the gamma distribution in Morocco (Riad et al, 2006) and Spain (López and Francés, 2012), the generalized normal (GNO) distribution for Northern Tunisia and both the GEV and GNO distributions for Central and Southern Tunisia (Ellouze and Abida, 2008).…”