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

Abstract

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.

Taşkın Modelleme Yöntemlerinin Gözden Geçirilmesi ve Karşılaştırılması

“…Geçmiş verileri dikkate alan modellerin genellikle doğru sonuç verdiği kabul edilir. Gözlem verileri uzaktan algılama ile taşkın derecesinin belirlenmesi, hidrodinamik modelleme ve model kalibrasyonu gibi diğer yöntemler için de bir girdi konumundadır (Beden, 2019;Demir & Ülke Keskin, 2020;Smith, 1997).…”

Use of the WinTR-55 Hydrologic Model on Determination of Flood Peak Discharge: The Case of Kirklareli Vize Stream and Samsun Minoz Stream Watersheds

“…Lastly, regional peak discharge maps covering a range of return periods from 2 to 1000 years were produced. Demir and Keskin (2020) aimed to determine Manning roughness coefficient and flood by using remote sensing. They used the HEC-RAS 2D and FLO-2D programs to flood modeling in Samsun-Turkey.…”

Application of convolutional neural network in fusion and classification of multi-source remote sensing data

“…As a depth detection technology, remote sensing is applied to space exploration, urban planning, rescue and disaster relief. It combines multi-disciplinary technologies such as earth science, space science, and computer, so it has different characteristics in terms of scope of use and technical tools (Demir and Ulke, 2020 ; Zhou et al, 2021c ; Du et al, 2022 ; Lu et al, 2022 ). However, facing different application scenarios, remote sensing image classification needs higher accuracy, and the accuracy and performance of image classification determine the quality of the application effect.…”