Articles | Volume 17, issue 8
Hydrol. Earth Syst. Sci., 17, 3189–3203, 2013

Special issue: Statistical methods for hydrological applications

Hydrol. Earth Syst. Sci., 17, 3189–3203, 2013

Research article 12 Aug 2013

Research article | 12 Aug 2013

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

J. López and F. Francés J. López and F. Francés
  • Research Institute of Water and Environmental Engineering, Universitat Politècnica de València, Spain

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 modelled 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 flood distributions are modelled 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 it is possible to properly introduce the impact on the flood regime of intensified reservoir regulation strategies. The inclusion of external covariates permits the use of these models as predictive tools. Finally, the application of non-stationary analysis shows that the differences between the non-stationary quantiles and their stationary equivalents may be important over long periods of time.