Research papersHistorical floods in flood frequency analysis: Is this game worth the candle?

- One or two largest 'pre-instrumental' floods are added into the FFA.
- The theoretical maximum possible gain in flood quantile estimators is analysed.
- Both true and false modelling distribution functions are assumed.
- Except bias and square error the probability of overestimation is introduced.
- The addition historical flood does not always betters the results.

In flood frequency analysis (FFA) the profit from inclusion of historical information on the largest historical pre-instrumental floods depends primarily on reliability of the information, i.e. the accuracy of magnitude and return period of floods. This study is focused on possible theoretical maximum gain in accuracy of estimates of upper quantiles, that can be obtained by incorporating the largest historical floods of known return periods into the FFA. We assumed a simple case: N years of systematic records of annual maximum flows and either one largest (XM1) or two largest (XM1 and XM2) flood peak flows in a historical M-year long period. The problem is explored by Monte Carlo simulations with the maximum likelihood (ML) method. Both correct and false distributional assumptions are considered. In the first case the two-parameter extreme value models (Gumbel, log-Gumbel, Weibull) with various coefficients of variation serve as parent distributions. In the case of unknown parent distribution, the Weibull distribution was assumed as estimating model and the truncated Gumbel as parent distribution. The return periods of XM1 and XM2 are determined from the parent distribution. The results are then compared with the case, when return periods of XM1 and XM2 are defined by their plotting positions. The results are presented in terms of bias, root mean square error and the probability of overestimation of the quantile with 100-year return period. The results of the research indicate that the maximal profit of inclusion of pre-instrumental foods in the FFA may prove smaller than the cost of reconstruction of historical hydrological information.