The Manning’s Roughness Coefficient Calibration Method to Improve Flood Hazard Analysis in the Absence of River Bathymetric Data: Application to the Urban Historical Zamora City Centre in Spain

Abstract

The accurate estimation of flood risk depends on, among other factors, a correct delineation of the floodable area and its associated hydrodynamic parameters. This characterization becomes fundamental in the flood hazard analyses that are carried out in urban areas. To achieve this objective, it is necessary to have a correct characterization of the topography, both inside the riverbed (bathymetry) and outside it. Outside the riverbed, the LiDAR data led to an important improvement, but not so inside the riverbed. To overcome these deficiencies, different models with simplified bathymetry or modified inflow hydrographs were used. Here, we present a model that is based upon the calibration of the Manning’s n value inside the riverbed. The use of abnormally low Manning’s n values made it possible to reproduce both the extent of the flooded area and the flow depth value within it (outside the riverbed) in an acceptable manner. The reduction in the average error in the flow depth value from 50–75 cm (models without bathymetry and “natural” Manning’s n values) to only about 10 cm (models without bathymetry and “calibrated” Manning’s n values), was propagated towards a reduction in the estimation of direct flood damage, which fell from 25–30% to about 5%.

Acclimatize experimental approach to adjudicate hydraulic coefficients under different bed material configurations and slopes with and without weir

“…In both weir and no-weir scenarios, higher Manning's roughness coefficients are related with lower discharges. Increased roughness coefficients cause slower flow velocities and higher energy losses, resulting in lower discharge values.This relationship holds true for various fixed bed materials (Aqeel Al-Adill et al, 2015; [ 34 , 36 , 42 ]).…”

“…Under the bed slope of 1:200, the average values of the Manning roughness coefficient and discharge differ from those under the bed slopes of 1:500 and 1:300. The variation in average Manning roughness coefficient and discharge values between different bed slopes indicates the channel bed slope has a substantial influence on flow properties[ 42 , 46 ]. These findings illustrate the necessity of considering bed slope into account when evaluating and constructing channels, since it has an essential effect on the overall flow behaviors and performance of the system.…”

Remote Sensing Methodology for Roughness Estimation in Ungauged Streams for Different Hydraulic/Hydrodynamic Modeling Approaches

“…Few studies in flood inundation modeling, such as Dimitriadis et al [40], Sharma and Regonda [91], and Papaioannou et al, [36,42], have examined the usage of variable roughness coefficient values, but the channel roughness values were assumed spatially homogeneous. Even fewer works in flood inundation modeling have examined the usage of variable spatial distributed roughness coefficient values for the river bed (e.g., [92]). It should be mentioned that based on F1 and F2 skill scores, the high roughness coefficient scenario provided slightly higher values than the fifth roughness coefficient scenario.…”

An Application of 1-D Model for Assessing Saline Intrusion in the Coastal Areas of the Vietnamese Mekong Delta

“…Some study has suggested that a parameter of main importance is the roughness of the river channel and the banks. Here, the Manning coefficient is used as roughness parameter (Doncker et al, 2009;Garrote et al, 2021;Hameed & Ali, 2013). Therefore, in this study, we selected the Manning's roughness coefficients as the most sensitive parameter in the model, and then, should be used for the calibration.…”