Scale-dependence of observational and modelling uncertainties in forensic flash flood analysis

The mismatch between the space-time scales of flash flood occurrence and those of the typical hydro meteorological monitoring networks has stimulated the development of forensic flash flood analysis, which involves post-flood indirect peak discharge estimation in ungauged channels and flood response modelling driven by weather-radar rainfall estimates. However, both approaches are potentially affected by significant uncertainties. Assessment of scale dependence of such uncertainties is important to identify how uncertainty affecting forensic flash flood analysis increases with decreasing basin size. In this work, we apply the forensic methodology to the flash flood of November 18, 2013 in Sardinia (Italy). We introduce the 'flash flood forensic consistency index' as a tool to compare the probability distribution of peak discharge uncertainties from observational and model estimates, and to determine the scale effects of the forensic analysis concept. Uncertainties in field-based peak flow estimates are evaluated through a first-order error analysis of the Taylor series approximation of the slope-conveyance method, whereas uncertainties in flash flood modelling are based on the Generalized Likelihood Uncertainty Estimation methodology using a distributed hydrologic model. Results show no significant relationship between observational and modelling uncertainties, considered independently, and basin area and channel bed slope. Conversely, when considering the interaction between the two uncertainty distributions, a relationship arises between their degree of overlap and basin size. In particular, with decreasing basin area or increasing channel bed slope, the absolute relative bias between the estimated peak flow values increases more than their relative uncertainties, decreasing the consistency index. This calls for more robust approaches for the analysis of flash flood response in small-sized rugged-relief mountain basins, which are of high interest for flood risk management.