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

Sadeh, Yuval; Cohen, Hai; Maman, Shimrit; Blumberg, Dan G.

doi:10.3390/rs10101505

Cited by 24 publications

(21 citation statements)

References 51 publications

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“…The RMSh value obtained from the plot‐scale DEM is 0.01 m. This agrees with the RMSh of desert pavement in a nearby arid environment with identical lithology (Sadeh et al ., ). Accordingly, we set a Manning roughness coefficient ( n ) of 0.024 (in s m ‐ 0 .33 ) which is the suggested value for fine gravel (Chow, ) and is similar to the value in Sadeh e t al .…”

Section: Resultsmentioning

confidence: 97%

“…Accordingly, we set a Manning roughness coefficient ( n ) of 0.024 (in s m ‐ 0 .33 ) which is the suggested value for fine gravel (Chow, ) and is similar to the value in Sadeh e t al . (). To account for uncertainty in the Manning coefficient we considered a range of n values (0.02−0.028), consisting of the commonly used values for similar geomorphic units (Arcement and Schneider, ; Woolhiser et al, ; Sadeh et al ., , and references therein).…”

Section: Resultsmentioning

confidence: 97%

“…(). To account for uncertainty in the Manning coefficient we considered a range of n values (0.02−0.028), consisting of the commonly used values for similar geomorphic units (Arcement and Schneider, ; Woolhiser et al, ; Sadeh et al ., , and references therein).…”

Section: Resultsmentioning

confidence: 99%

“…The relation between RMSh and the Manning roughness coefficient was discussed by Sadeh e t al . () in a similar nearby watershed. We further verified the range of Manning roughness values by comparison to published values across equivalent geomorphic units (e.g.…”

Section: Methodsmentioning

confidence: 99%

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Linking frequency of rainstorms, runoff generation and sediment transport across hyperarid talus‐pediment slopes

Shmilovitz

Morin

Rinat

et al. 2020

Earth Surf Processes Landf

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Documenting hillslope response to hydroclimatic forcing is crucial to our understanding of landscape evolution. The evolution of talus‐pediment sequences (talus flatirons) in arid areas was often linked to climatic cycles, although the physical processes that may account for such a link remain obscure. Our approach is to integrate field measurements, remote sensing of rainfall and modeling to link between storm frequency, runoff, erosion and sediment transport. We present a quantitative hydrometeorological analysis of rainstorms, their geomorphic impact and their potential role in the evolution of hyperarid talus‐pediment slopes in the Negev desert, Israel. Rainstorm properties were defined based on intensity–duration–frequency curves and using a rainfall simulator, artificial rainstorms were executed in the field. Then, the obtained measured experimental results were up‐scaled to the entire slope length using a fully distributed hydrological model. In addition, natural storms and their hydro‐geomorphic impacts were monitored using X‐band radar and time‐lapse cameras. These integrated analyses constrain the rainfall threshold for local runoff generation at rain intensity of 14 to 22 mm h‐1 for a duration of five minutes and provide a high‐resolution characterization of small‐scale runoff‐generating rain cells. The current frequency of such runoff‐producing rainstorms is ~1–3 per year. However, extending this local value into the full extent of hillslope runoff indicates that it occurs only under rainstorms with ≥ 100‐years return interval, or 1% annual exceedance probability. Sheetwash efficiency rises with downslope distance; beyond a threshold distance of ~100 m, runoff during rainstorms with such annual exceedance probability are capable of transporting surface clasts. The erosion efficiency of these discrete rare events highlights their potential importance in shaping the landscape of arid regions. Our results support the hypothesis that a shift in the properties and frequency of extreme events can trigger significant geomorphic transitions in areas that remained hyperarid during the entire Quaternary. © 2020 John Wiley & Sons, Ltd.

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Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Linking frequency of rainstorms, runoff generation and sediment transport across hyperarid talus‐pediment slopes

Shmilovitz

Morin

Rinat

et al. 2020

Earth Surf Processes Landf

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show abstract

“…In contrast to general literature including only flood modeling (Tallat et al, 2011;Chinnarasri and Phothiwijit, 2016;Dimitriadis et al, 2016;Khattak et al, 2016;Patel et al, 2017;Afshari et al, 2018) and only Manning value from satellite image (Straatsma and Baptist, 2008;Forzieri et al, 2011;Bates, 2012;Sadeh et al, 2018) or only Manning value from sampling of terrain (Cowan, 1956;Jarsjö et al, 2015), Manning roughness coefficients was obtained by being used remote sensing and modified Cowan method to enhance the modelling accuracy and these data were also analyzed using photos showing flood level and computational model FLO 2D. The calibration of this model was made with Hydrologic Engineering Center-River Analysis System (HEC-RAS), which has 2D flow simulation functionality.…”

Section: Each Component Of the Equation Is Explained As Followsmentioning

confidence: 93%

Obtaining the Manning roughness with terrestrialremote sensing technique and flood modeling using FLO-2D

Demir

KESKİN

2020

Geofizika (Online)

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Determining the Manning roughness coefficients is one of the most important steps in flood modeling. The roughness coefficients cause differences in flood areas, water levels, and velocities in the process of modeling. This study aims to determine both the Manning roughness coefficient in the river sections and outside of the river regions by using the Cowan method and remote sensing technique in the flood modeling. In the flood modeling, FLO-2D Pro program which can simulate flood propagation in two dimensions was utilized. Mert River in Samsun province located in the northern part of Turkey was chosen as the study area. Samples taken from the river were subjected to sieve analysis, the types of constituent material were determined according to the median diameters and the roughness coefficients were obtained using the Cowan method. For regions outside of the river were applied the maximum likelihood method being one of the controlled classification methods. Manning roughness values were assigned the classified image sections. Remote sensing techniques were meticulously employed to achieve time management in areas outside the river and a new approach was proposed in the Manning assessment of flood areas to ensure uniformity in the study area. In the classification made using the maximum likelihood method, the overall classification accuracy was 92.9% and the kappa ratio “κ” was 90.64%. The results were calibrated with the last hazardous flood images in 2012 and HEC-RAS 2D program, another flood modeling program.

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Flood hazard mapping for data-scarce and ungauged coastal river basins using advanced hydrodynamic models, high temporal-spatial resolution remote sensing precipitation data, and satellite imageries

Trinh

Molkenthin

2021

Nat Hazards

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This paper presents an integrated approach to simulate flooding and inundation for small- and medium-sized coastal river basins where measured data are not available or scarce. By coupling the rainfall–runoff model, the one-dimensional and two-dimensional models, and the integration of these with global tide model, satellite precipitation products, and synthetic aperture radar imageries, a comprehensive flood modeling system for Tra Bong river basin selected as a case study was set up and operated. Particularly, in this study, the lumped conceptual model was transformed into the semi-distributed model to increase the parameter sets of donor basins for applying the physical similarity approach. The temporal downscaling technique was applied to disaggregate daily rainfall data using satellite-based precipitation products. To select an appropriate satellite-derived rainfall product, two high temporal-spatial resolution products (0.1 × 0.1 degrees and 1 h) including GSMaP_GNRT6 and CMORPH_CRT were examined at 1-day and 1-h resolutions by comparing with ground-measured rainfall. The CMORPH_CRT product showed better performance in terms of statistical errors such as Correlation Coefficient, Probability of Detection, False Alarm Ratio, and Critical Success Index. Land cover/land use, flood extent, and flood depths derived from Sentinel-1A imageries and a digital elevation model were employed to determine the surface roughness and validate the flood modeling. The results obtained from the modeling system were found to be in good agreement with collected data in terms of NSE (0.3–0.8), RMSE (0.19–0.94), RPE (− 213 to 0.7%), F1 (0.55), and F2 (0.37). Subsequently, various scenarios of flood frequency with 10-, 20-, 50-, and 100-year return periods under the probability analysis of extreme values were developed to create the flood hazard maps for the study area. The flood hazards were then investigated based on the flood intensity classification of depth, duration, and velocity. These hazard maps are significantly important for flood hazard assessments or flood risk assessments. This study demonstrated that applying advanced hydrodynamic models on computing flood inundation and flood hazard analysis in data-scarce and ungauged coastal river basins is completely feasible. This study provides an approach that can be used also for other ungauged river basins to better understand flooding and inundation through flood hazard mapping.

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

Cited by 24 publications

References 51 publications

Linking frequency of rainstorms, runoff generation and sediment transport across hyperarid talus‐pediment slopes

Linking frequency of rainstorms, runoff generation and sediment transport across hyperarid talus‐pediment slopes

Obtaining the Manning roughness with terrestrialremote sensing technique and flood modeling using FLO-2D

Flood hazard mapping for data-scarce and ungauged coastal river basins using advanced hydrodynamic models, high temporal-spatial resolution remote sensing precipitation data, and satellite imageries

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