Evaluation of 1D hydraulic models for the simulation of mountain fluvial floods: a case study of the Santa Bárbara River in Ecuador

Abstract

Abstract River flooding is a key topic for water managers because of the social and economic losses it can cause. The complex topography and dynamics of mountain rivers has limited the analysis of their behavior during flood events (e.g., sediment transport, flooding). This study aims to test the performance of three hydraulic 1D models (HEC-RAS, MIKE 11, and Flood Modeller) to estimate inundation water levels for a mountain river. The evaluation of these models was performed considering steady state conditions through 10 scenarios, i.e. five discharge return periods, and two types of cross sections data: (a) type I, a detailed field survey complemented with information extracted from DEM, derived from LiDAR; and (b) type II, cross sections exclusively derived from the DEM. The research was conducted for a reach of 5 km of the Santa Bárbara River, with an average slope of 0.25%. HEC-RAS model results for cross sections type I, were previously validated and therefore used as reference for comparison between other models and scenarios. The goodness-of-fit between models was measured based on the Nash-Sutcliffe model efficiency coefficient (EF). The main goal of the current study was to determine the variability of inundation level results compared with a validated model as reference, using the same input data for the three modeling packages. Our analysis shows that, when using cross section type I, the evaluated modeling packages yield similar results (EF were between 0.94 and 0.99). On the other hand, the goodness of fit decreased when using type II data, with an average EF of 0.98 (HEC-RAS), 0.88 (Flood Modeller) and 0.85 (MIKE 11) when compared to the reference model. The authors conclude that it is highly recommend for practitioners to use geometric data type I instead of type II in order to obtain similar performance in the tested models. Only HEC-RAS type II has the same performance as type I models (average EF of 0.98).