A process-based flood frequency analysis within a trivariate statistical framework. Application to a semi-arid Mediterranean case study

This paper proposes a trivariate methodology for flood frequency estimation. It combines the flood peak, storm magnitude, and initial soil moisture condition (ISMC) as the main flood-related statistical variables to be considered. The semi-arid Mediterranean Rambla del Poyo catchment has been used as a representative case study where the influence of the spatio-temporal variability of the storms and the ISMC on floods can lead to differences of up to two orders of magnitude in quantiles when the most commonly used methods are applied. In order to incorporate the main flood-generating mechanisms, the integrated use of a multidimensional storm generator with distributed hydrological modelling is proposed. Flood quantiles are then estimated by combining the maximum flows with the storm magnitude and ISMC in a trivariate probability distribution function through the application of Bayes' theorem and Lagrange's Mean Value theorem. Although the methodology proposed in this paper has been applied and tested in only one case study, it can be extended to other case studies due to its process-based orientation.

Automated summary

The paper proposes a trivariate methodology for flood frequency estimation, considering flood peak, storm magnitude, and initial soil moisture condition. By integrating a multidimensional storm generator with distributed hydrological modeling and applying Bayes' theorem and Lagrange's Mean Value theorem, flood quantiles can be estimated by taking into account the main flood-generating mechanisms. Although tested in only one case study, the methodology is process-based and can potentially be extended to other case studies.