Interpreting the Manning Roughness Coefficient in Overland Flow Simulations with Coupled Hydrological-Hydraulic Distributed Models

Abstract

There is still little experience on the effect of the Manning roughness coefficient in coupled hydrological-hydraulic distributed models based on the solution of the Shallow Water Equations (SWE), where the Manning coefficient affects not only channel flow on the basin hydrographic network but also rainfall-runoff processes on the hillslopes. In this kind of model, roughness takes the role of the concentration time in classic conceptual or aggregated modelling methods, as is the case of the unit hydrograph method. Three different approaches were used to adjust the Manning roughness coefficient in order to fit the results with other methodologies or field observations—by comparing the resulting time of concentration with classic formulas, by comparing the runoff hydrographs obtained with aggregated models, and by comparing the runoff water volumes with observations. A wide dispersion of the roughness coefficients was observed to be generally much higher than the common values used in open channel flow hydraulics.

Near surface roughness estimation: A parameterization derived from artificial rainfall experiments and two-dimensional hydrodynamic modelling for multiple vegetation coverages

“…The obstructive effects of roughness determine the velocity of the runoff on the surface and therefore the formation speed of a flood wave. In general, a smooth surface leads to a more rapidly rising flood wave, while peak discharge can be significantly lower on rough surfaces (Sanz-Ramos et al, 2021).…”

Implementación de embalses en cálculos hidrológicos con Iber

“…El empleo de Iber para la modelización de procesos de lluvia-escorrentía ya ha sido validado extensamente en trabajos anteriores a diferentes escalas que van desde los ensayos de laboratorio hasta cuencas de pequeño y mediano tamaño (Cea et al, 2014;Fraga et al, 2016Fraga et al, , 2019García-Alén et al, 2022;Marcos et al, 2020;Sanz-Ramos et al, 2021).…”

“…Para la modelización numérica de este caso de estudio se han definido los usos de suelo en la cuenca de acuerdo al mapa de usos de suelo del CORINE Land Cover 2018 (European Union Copernicus Land Monitoring Service, 2018) y las recomendaciones de la Guía Metodológica para el desarrollo del Sistema Nacional de Cartografía de Zonas Inundables (Sánchez y Lastra, 2011), la cual propone un valor de coeficiente de Manning para cada uno de los usos de suelo del CORINE (Figura 6a). Aunque para este caso de estudio se han asumido estos valores de rugosidad, se ha observado que en modelos de flujo superficial basados en las ecuaciones de aguas someras bidimensionales los valores de rugosidad son comúnmente más altos que los utilizados para los cauces de ríos (Cea et al, 2010;Fraga et al, 2013;Sanz-Ramos et al, 2021). La topografía se ha establecido a partir del Modelo Digital del Terreno (MDT) con resolución espacial de 25 m proporcionado por el Instituto Geográfico Nacional (IGN-CNIG, 2021).…”

A Machine Learning Based Method for Fast Estimation of Damages Due to Failure of Off-Stream Reservoirs

“…Iber is a 2D numerical tool for modelling hydrodynamic and sediment transport , which currently includes additional calculation modules for simulating hydrological processes (Cea & Bladé Castellet, 2015;Sanz-Ramos et al, 2021), pollutant propagation (Cea et al, 2016), large-wood transport (Ruiz-Villanueva et al, 2014) and physical habitat suitability assessment . Iber includes the Breach tool to simulate a gap in the dyke by entering parameters.…”