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

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%.

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Urban Flood Modeling Using 2D Shallow-Water Equations in Ouagadougou, Burkina Faso

Water ◽

10.3390/w12082120 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2120

Author(s):

Gnenakantanhan Coulibaly ◽

Babacar Leye ◽

Fowe Tazen ◽

Lawani Adjadi Mounirou ◽

Harouna Karambiri

Keyword(s):

Shallow Water ◽

Shallow Water Equations ◽

Urban Areas ◽

Flood Hazard ◽

Scientific Information ◽

Roughness Coefficient ◽

Flood Events ◽

African Countries ◽

Numerical Tool ◽

2D Shallow Water Equations

Appropriate methods and tools accessibility for bi-dimensional flow simulation leads to their weak use for floods assessment and forecasting in West African countries, particularly in urban areas where huge losses of life and property are recorded. To mitigate flood risks or to elaborate flood adaptation strategies, there is a need for scientific information on flood events. This paper focuses on a numerical tool developed for urban inundation extent simulation due to extreme tropical rainfall in Ouagadougou city. Two-dimensional (2D) shallow-water equations are solved using a finite volume method with a Harten, Lax, Van Leer (HLL) numerical fluxes approach. The Digital Elevation Model provided by NASA’s Shuttle Radar Topography Mission (SRTM) was used as the main input of the model. The results have shown the capability of the numerical tool developed to simulate flow depths in natural watercourses. The sensitivity of the model to rainfall intensity and soil roughness coefficient was highlighted through flood spatial extent and water depth at the outlet of the watershed. The performance of the model was assessed through the simulation of two flood events, with satisfactory values of the Nash–Sutcliffe criterion of 0.61 and 0.69. The study is expected to be useful for flood managers and decision makers in assessing flood hazard and vulnerability.

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Manning’s N Value of Bridge-in-Backpack

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.638-640.965 ◽

2014 ◽

Vol 638-640 ◽

pp. 965-968

Author(s):

Jing Ma ◽

Ling Qiang Yang

Keyword(s):

Water Flow ◽

Roughness Coefficient ◽

N Value ◽

New Type ◽

Manning’S Roughness Coefficient ◽

Manning’S N ◽

Manning's N

Bridge-in-a-Backpack is a new type bridge. this study will investigate the interaction of flow under the bridge with the tubes and decking, and recommend Manning’s roughness coefficient for water flow under the composite backbridge system.

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Evaluation of Manning’s n Roughness Coefficient in Arid Environments by Using SAR Backscatter

Remote Sensing ◽

10.3390/rs10101505 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1505 ◽

Cited By ~ 5

Author(s):

Yuval Sadeh ◽

Hai Cohen ◽

Shimrit Maman ◽

Dan Blumberg

Keyword(s):

Surface Roughness ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Roughness Coefficient ◽

Arid Environments ◽

Hydrological Models ◽

Radar Backscatter ◽

Manning’S Roughness Coefficient ◽

Manning’S N ◽

Manning's N

The prediction of arid region flash floods (magnitude and frequency) is essential to ensure the safety of human life and infrastructures and is commonly based on hydrological models. Traditionally, catchment characteristics are extracted using point-based measurements. A considerable improvement of point-based observations is offered by remote sensing technologies, which enables the determination of continuous spatial hydrological parameters and variables, such as surface roughness, which significantly influence runoff velocity and depth. Hydrological models commonly express the surface roughness using Manning’s roughness coefficient (n) as a key variable. The objectives were thus to determine surface roughness by exploiting a new high spatial resolution spaceborne synthetic aperture radar (SAR) technology and to examine the correlation between radar backscatter and Manning’s roughness coefficient in an arid environment. A very strong correlation (R2 = 0.97) was found between the constellation of small satellites for Mediterranean basin observation (COSMO)-SkyMed SAR backscatter and surface roughness. The results of this research demonstrate the feasibility of using an X-band spaceborne sensor with high spatial resolution for the evaluation of surface roughness in flat arid environments. The innovative method proposed to evaluate Manning’s n roughness coefficient in arid environments with sparse vegetation cover using radar backscatter may lead to improvements in the performance of hydrological models.

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Flood Modelling in Urban Areas

Acta hydrotechnica ◽

10.15292/acta.hydro.2018.02 ◽

2018 ◽

pp. 21-33

Author(s):

Gašper Rak ◽

Sara Grobljar ◽

Franci Steinman

Keyword(s):

Cell Size ◽

Urban Areas ◽

Flood Hazard ◽

Digital Terrain Model ◽

Water Current ◽

Roughness Coefficient ◽

Hydraulic Roughness ◽

Hydraulic Models ◽

Run Off ◽

The Impact

The impact of flooding is significantly greater in urban areas than in rural environments, as the exposure and value of property and the likelihood of endangering human lives is higher. There is therefore a great need for hydraulic models, which can predict the direction and extent of flooding. Buildings pose obstacles to water flow, considerably affecting its course, wherefore buildings should be taken into account in hydraulic models. This study compared two different ways of taking account of buildings in mathematical hydraulic models. The first approach models buildings by increasing the value of the hydraulic roughness coefficient for building footprints, while the second approach includes buildings in a digital terrain model at their locations. We also analysed the sensitivity of modelling results in respect of the cell size of the computational mesh, which can significantly affect the results of hydraulic model. Hydraulic analysis was carried out with 2D model for area of Gornja Radgona, which would be the flood of the Mura River in the event a part of flood protection wall collapsed. The impact of cell size and the approach of modelling buildings on the run-off regime and flood hazard within the analysed area was checked by indicators, such as water depth, velocity of the water current, extent of flooded areas, spatial distribution of flood hazard classes, etc. Changes in the duration of flood propagation along the urban area were also analysed.

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Gene expression programming to predict Manning’s n in meandering flows

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2016-0569 ◽

2018 ◽

Vol 45 (4) ◽

pp. 304-313 ◽

Cited By ~ 6

Author(s):

Arpan Pradhan ◽

Kishanjit K. Khatua

Keyword(s):

Gene Expression ◽

Field Study ◽

Discharge Capacity ◽

Gene Expression Programming ◽

Roughness Coefficient ◽

Compound Channel ◽

Factors Affecting ◽

Manning’S N ◽

Bed Material ◽

Manning's N

Accurate prediction of Manning’s roughness coefficient is essential for the computation of conveyance capacity in open channels. There are various factors affecting the roughness coefficient in a meandering compound channel and not just the bed material. The factors, geometric as well as hydraulic, are investigated and incorporated in the prediction of Manning’s n. In this study, a new and accurate technique, gene expression programming (GEP) is used to estimate Manning’s n. The estimated value of Manning’s n is used in the evaluation of the conveyance capacity of meandering compound channels. Existing methods on conveyance estimation are assessed to carry out a comparison between them and the proposed GEP model. Results show that the discharge capacity computed by the new model provides far better results than the traditional models. The developed GEP model is validated with three individual sections of a natural river, signifying that the model can be applied to field study of rivers, within the stated range of parameters.

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Understanding Spatial Variability of NO2 in Urban Areas Using Spatial Modelling and Data Fusion Approaches

Atmosphere ◽

10.3390/atmos12020179 ◽

2021 ◽

Vol 12 (2) ◽

pp. 179

Author(s):

Said Munir ◽

Martin Mayfield ◽

Daniel Coca

Keyword(s):

Data Fusion ◽

Spatial Variability ◽

Urban Areas ◽

Road Traffic ◽

Dispersion Model ◽

Low Cost ◽

Spatial Modelling ◽

City Centre ◽

Small Scale ◽

The City

Small-scale spatial variability in NO2 concentrations is analysed with the help of pollution maps. Maps of NO2 estimated by the Airviro dispersion model and land use regression (LUR) model are fused with measured NO2 concentrations from low-cost sensors (LCS), reference sensors and diffusion tubes. In this study, geostatistical universal kriging was employed for fusing (integrating) model estimations with measured NO2 concentrations. The results showed that the data fusion approach was capable of estimating realistic NO2 concentration maps that inherited spatial patterns of the pollutant from the model estimations and adjusted the modelled values using the measured concentrations. Maps produced by the fusion of NO2-LCS with NO2-LUR produced better results, with r-value 0.96 and RMSE 9.09. Data fusion adds value to both measured and estimated concentrations: the measured data are improved by predicting spatiotemporal gaps, whereas the modelled data are improved by constraining them with observed data. Hotspots of NO2 were shown in the city centre, eastern parts of the city towards the motorway (M1) and on some major roads. Air quality standards were exceeded at several locations in Sheffield, where annual mean NO2 levels were higher than 40 µg/m3. Road traffic was considered to be the dominant emission source of NO2 in Sheffield.

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A Study on Urban Inundation Using SWMM in Busan, Korea, Using Existing Dams and Artificial Underground Waterways

Water ◽

10.3390/w13121708 ◽

2021 ◽

Vol 13 (12) ◽

pp. 1708

Author(s):

Yeon-Moon Choo ◽

Sang-Bo Sim ◽

Yeon-Woong Choe

Keyword(s):

Climate Change ◽

Urban Areas ◽

Flood Damage ◽

Virtual Model ◽

Sustainable Cities ◽

Annual Average ◽

New Methods ◽

Average Rainfall ◽

Reduction Methods ◽

Urbanized Areas

The annual average rainfall in Busan area is increasing, causing frequent flooding of Busan’s Suyeong and Oncheon rivers. Due to the increase in urbanized areas and climate change, it is difficult to reduce flood damage. Therefore, new methods are needed to reduce urban inundation. This study models the effects of three flood reduction methods involving Oncheon River, Suyeong River, and the Hoedong Dam, which is situated on the Suyeong. Using EPA-SWMM, a virtual model of the dam and the rivers was created, then modified with changes to the dam’s height, the installation of a floodgate on the dam, and the creation of an underground waterway to carry excess flow from the Oncheon to the Hoedong Dam. The results of this study show that increasing the height of the dam by 3 m, 4 m, or 6 m led to a 27%, 37%, and 48% reduction in flooding, respectively, on the Suyeong River. It was also found that installing a floodgate of 10 × 4 m, 15 × 4 m, or 20 × 4 min the dam would result in a flood reduction of 2.7% and 2.9%, respectively. Furthermore, the construction of the underground waterway could lead to an expected 25% flood reduction in the Oncheon River. Measures such as these offer the potential to protect the lives and property of citizens in densely populated urban areas and develop sustainable cities and communities. Therefore, the modifications to the dam and the underground waterway proposed in this study are considered to be useful.

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Analysis of Occlusion Effects for Map-Based Self-Localization in Urban Areas

Sensors ◽

10.3390/s21155196 ◽

2021 ◽

Vol 21 (15) ◽

pp. 5196

Author(s):

Yuki Endo ◽

Ehsan Javanmardi ◽

Shunsuke Kamijo

Keyword(s):

Urban Areas ◽

Structural Information ◽

Average Error ◽

High Definition ◽

Geometrical Structures ◽

Real Environment ◽

Surrounding Environment ◽

Localization Errors ◽

Stable Estimation ◽

High Level

A high-definition (HD) map provides structural information for map-based self-localization, enabling stable estimation in real environments. In urban areas, there are many obstacles, such as buses, that occlude sensor observations, resulting in self-localization errors. However, most of the existing HD map-based self-localization evaluations do not consider sudden significant errors due to obstacles. Instead, they evaluate this in terms of average error over estimated trajectories in an environment with few occlusions. This study evaluated the effects of self-localization estimation on occlusion with synthetically generated obstacles in a real environment. Various patterns of synthetic occlusion enabled the analyses of the effects of self-localization error from various angles. Our experiments showed various characteristics that locations susceptible to obstacles have. For example, we found that occlusion in intersections tends to increase self-localization errors. In addition, we analyzed the geometrical structures of a surrounding environment in high-level error cases and low-level error cases with occlusions. As a result, we suggested the concept that the real environment should have to achieve robust self-localization under occlusion conditions.

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