Advances in high water modelling to improve climate change adaptation, flood risk reduction and stormwater management

Abstract

Probabilistic modelling of extreme events has a crucial role in both reducing flood risks, improving our adaptation to climate change, and managing local stormwater. Empirical flow records are suitable for it just since recently. The alternative hydrological modelling with rainfall input data has been the Gumbel distribution. This prevails since 1941, despite Gumbel?s obvious warn about that no changes in both climate and the basin should occur within the observation nor forecasting periods. Thus, advances in flood modelling are today needed because: i) criticism against Gumbel distribution on both theoretical, empirical and even ethical grounds, ii) gauge data are increasingly available at even 15? interval, iii) the dynamics of great weather types and large atmospheric circulation systems are better know, allowing downscaling to hydrological processes, and iv) forecasting has to take into account climate change and anthopogenic impact on river systems. We validate here a new approach to analyse time series of river flow and reveal flood recurrence and magnitude. Classical autocorrelation analysis (AA) is used, but considering a wider range of return periods than usual; thus, results are applicable to more time scales, including climate change adaptation, flood risk reduction and stormwater management. Further, so calculated return periods are validated against the occurrence of objective great weather events (GWL)