Abstract
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.
Highlights
One of the challenges facing the field of hydrology is gaining a better understanding of flood regimes
It has been observed that the main patterns of low-frequency atmospheric variability affecting Europe are correlated in their principal variability components; in order to address the question of parsimony in the models, we propose using empirical orthogonal functions analysis (EOFs) to identify the principal components (PCs) that contain the greatest variance of climate indices
(annual maximum peak discharges in this paper) has a parametric cumulative distribution function and its parameters can be modelled as function of selected covariates, in this case time or climate indices (AOi, MOi, NAOi and WeMOi) and reservoir index (RIi)
Summary
One of the challenges facing the field of hydrology is gaining a better understanding of flood regimes. In recent decades the evidence of natural variation in the climatic system, as well as the potential influence of human activity on climate change or in directly changing the hydrologic cycle (NRC, 1998), have made the hypothesis of stationarity widely questionable With this point in mind, several researchers have begun exploring the validity of this hypothesis in flood regimes in many regions around the world, considering the effect of natural climate variability (Douglas et al, 2000; Franks, 2002; Mudelsee et al, 2003; Milly et al, 2005; Villarini et al, 2009a; Wilson et al, 2010) or land use changes (Hejazi and Markus, 2009; Villarini et al, 2009b; Vogel et al, 2011). These studies have revealed clear violations of the assumption of stationarity, which is consistent with studies that indicate an acceleration in the hydrologic cycle (Allen and Smith, 1996; Held and Soden, 2006)
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