Reducing uncertainty in flood frequency analyses: A comparison of local and regional approaches involving information on extreme historical floods

Abstract

Summary This paper proposes a detailed comparison of local and regional approaches for flood frequency analyses, with a special emphasis on the effects of (a) the information on extreme floods used in the analysis (historical data or recent extreme floods observed at ungauged sites), and (b) the assumptions associated with regional approaches (statistical homogeneity of considered series, independence of observations). The results presented are based on two case studies: the Ard e che and Argens rivers regions in south-east of France. Four approaches are compared: 1 – local analysis based on continuous measured series, 2 – local analysis with historical information, 3 – regional index-flood analysis based on continuous series, 4 – regional analysis involving information on extremes (including both historical floods and recent floods observed at ungauged sites). The inference approach used is based on a GEV distribution and a Bayesian Monte Carlo Markov Chain approach for parameters estimation. The comparison relies both on (1) available observed datasets and (2) Monte Carlo simulations in order to evaluate the effects of sampling variability and to analyze the possible influence of regional heterogeneities. The results indicate that a relatively limited level of regional heterogeneity, which may not be detected through homogeneity tests, may significantly affect the performances of regional approaches. These results also illustrate the added value of information on extreme floods, historical floods or recent floods observed at ungauged sites, in both local and regional approaches. As far as possible, gathering such information and incorporating it into flood frequency studies should be promoted. Finally, the presented Monte Carlo simulations appear as an interesting analysis tool for adapting the estimation strategy to the available data for each specific case study.