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This paper illustrates how historical information on floods can be retrieved in the a priori unfavourable case of small and sparsely populated catchments, and how this information can improve the estimation of flood discharge quantiles. It is based on the analysis of four small gauged rivers located in the south of France. Using various sources of archives, the flood discharges were estimated for past historical periods ranging from 100 to 200 years. The corresponding historical discharge series were then used in combination with the available systematic measurement series to evaluate peak discharge quantiles. The selected Bayesian Monte Carlo Markov Chain statistical inference approach provides credibility intervals for the estimated quantiles, the width of which reflects the information content of the data set used for the inference. The use of the historical data in the inference procedure leads to significant reductions of these credibility intervals, even if limitations in the content of historical inventories are introduced. A sensitivity analysis is conducted in the second part of the paper to evaluate to what extent these positive conclusions on the added value of historical data for flood frequency analysis can be extrapolated to other case studies. The influence of the number of historical floods documented (perception threshold) and of the level of knowledge of historical flood peak discharges is studied. The results are consistent with the previous works on the same issue and explain why these works led to apparently contradictory conclusions concerning the possible usefulness of historical data in flood frequency studies.
Payrastre et al. (Mon,) studied this question.