•We consider atmospheric, catchment and river drivers of flood change.•Alternative hypotheses on driver/flood change relationship are compared.•We couple flood and driver data with information from ...the literature.•In Upper Austria flood dynamics are associated to extreme rainfall fluctuations.
Flood changes may be attributed to drivers of change that belong to three main classes: atmospheric, catchment and river system drivers. In this work, we propose a data-based attribution approach for selecting which driver best relates to variations in time of the flood frequency curve. The flood peaks are assumed to follow a Gumbel distribution, whose location parameter changes in time as a function of the decadal variations of one of the following alternative covariates: annual and extreme precipitation for different durations, an agricultural land-use intensification index, and reservoir construction in the catchment, quantified by an index. The parameters of this attribution model are estimated by Bayesian inference. Prior information on one of these parameters, the elasticity of flood peaks to the respective driver, is taken from the existing literature to increase the robustness of the method to spurious correlations between flood and covariate time series. Therefore, the attribution model is informed in two ways: by the use of covariates, representing the drivers of change, and by the priors, representing the hydrological understanding of how these covariates influence floods. The Watanabe-Akaike information criterion is used to compare models involving alternative covariates. We apply the approach to 96 catchments in Upper Austria, where positive flood peak trends have been observed in the past 50 years. Results show that, in Upper Austria, one or seven day extreme precipitation is usually a better covariate for variations of the flood frequency curve than precipitation at longer time scales. Agricultural land-use intensification rarely is the best covariate, and the reservoir index never is, suggesting that catchment and river drivers are less important than atmospheric ones. Not all the positive flood trends correspond to a significant correlation between floods and the covariates, suggesting that other drivers or other flood-driver relations should be considered to attribute flood trends in Upper Austria.
•Time trend- and precipitation-informed models are tested.•31 gauging stations with 55 years of data were used.•Precipitation-informed models outperform time trend-informed models.•Tested models ...yielded significantly different flood quantiles.•The best-fitting model is to some extent related to the flow regime.
Estimation of reliable design discharges under variable climate is a key challenge for today’s engineers. Therefore, researchers are intensively exploring different alternative approaches in order to improve standard methods for design discharge estimation. Paper investigates the performance of time-invariant, time trend- and precipitation-informed models based on generalized extreme value (GEV) distribution for 31 Slovenian discharge gauging stations with data availability from 1961 until 2015. Different rainfall durations are used as covariates in the case of precipitation-informed models. The selected catchments are located in different climate regions and characterized by five flow regimes. The results indicate that in most cases precipitation-informed models gave better fit to the measured data comparing to time-invariant and time trend-informed models. Relative differences in the design discharge estimations associated with 10- and 100-year return periods using time trend- and precipitation-informed models compared to time-invariant model were up to 60%. Additionally, the results indicate that identified best-fitting model of individual gauging station can to some extent be related to its flow regime.
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