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

Abstract

This study investigates the generation of spatially distributed roughness coefficient maps based on image analysis and the extent to which those roughness coefficient values affect the flood inundation modeling using different hydraulic/hydrodynamic modeling approaches ungauged streams. Unmanned Aerial Vehicle (UAV) images were used for the generation of high-resolution Orthophoto mosaic (1.34 cm/px) and Digital Elevation Model (DEM). Among various pixel-based and object-based image analyses (OBIA), a Grey-Level Co-occurrence Matrix (GLCM) was eventually selected to examine several texture parameters. The combination of local entropy values (OBIA method) with Maximum Likelihood Classifier (MLC; pixel-based analysis) was highlighted as a satisfactory approach (65% accuracy) to determine dominant grain classes along a stream with inhomogeneous bed composition. Spatially distributed roughness coefficient maps were generated based on the riverbed image analysis (grain size classification), the size-frequency distributions of river bed materials derived from field works (grid sampling), detailed land use data, and the usage of several empirical formulas that used for the estimation of Manning’s n values. One-dimensional (1D), two-dimensional (2D), and coupled (1D/2D) hydraulic modeling approaches were used for flood inundation modeling using specific Manning’s n roughness coefficient map scenarios. The validation of the simulated flooded area was accomplished using historical flood extent data, the Critical Success Index (CSI), and CSI penalization. The methodology was applied and demonstrated at the ungauged Xerias stream reach, Greece, and indicated that it might be applied to other Mediterranean streams with similar characteristics and flow conditions.

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

“…Flow resistance, turbulence, and channel form all contribute to these variances([ 33 ]; Sanz-Ramos et al, 2021). Understanding these variations is essential for accurate hydraulic analysis and channel design in real-world situations[ 44 , 45 ].…”

“…These variables influence variations in flow resistance, turbulence, and channel shape, resulting in changes in roughness and discharge across the channel. It stresses the need to take these elements into account while conducting hydraulic analysis and channel design in order to properly forecast and optimize flow behaviors [ 45 , 47 ]. When the bed configuration was adjusted from random to perpendicular to the flow bed pattern with and without weir, both the roughness coefficient and discharge dropped [ 26 ].…”

Hydrological simulation of ungauged basins via forcing by large‐scale hydrology models

“…Driven by the need for comprehensive water volume information at river basin scale to articulate secure and safe water resources management plans, as mandated for instance by the WFD at the European scale, the research introduces a methodological approach for streamflow simulation at ungauged basins. The lack of observed discharges typically used for tuning models parameters (Wagener & Montanari, 2011) is currently cured by various approaches as outlined in the introduction (e.g., Blöschl et al, 2013; Grimaldi et al, 2021; Hrachowitz et al, 2013; Kratzert et al, 2019; Papaioannou et al, 2022; Sivapalan et al, 2003; Teutschbein et al, 2018; Yu et al, 2023). Our methodology, on the one hand adopts well‐defined outputs from the PUB initiative, such as the (a) use of semi‐distributed or conceptual models rather than physical based models (Razavi & Coulibaly, 2013; Yadav et al, 2007), where in our case we implement the MIKE NAM rainfall‐runoff lumped and conceptual model, and (b) transfer of knowledge gained in partially ungauged basin to those with similar topographic and climatic characteristics but lacking water‐related measurements.…”

“…The lack of hydrometric data resulted on the Predictions in Ungauged Basins (PUB) initiative (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) (Sivapalan et al, 2003) that fostered a major paradigm shift from data and calibration focused methods to methods based on theoretical understanding of the physical processes within a hydrosystem (Blöschl et al, 2013;Hrachowitz et al, 2013). As a follow-up to the initiative, current researches on ungauged basins propose the use of near-infrared satellite images for runoff estimation (Yu et al, 2023), or the use of drones to define the hydraulic model roughness coefficient and evaluate the runoff (Papaioannou et al, 2022), or the linkage of landscape with runoff signatures (Teutschbein et al, 2018). Grimaldi et al (2021) proposed the use of continuous hydrologic modelling as an advanced alternative in estimating the design hydrograph in small and ungauged basins, while Kratzert et al (2019) make use of machine learning techniques, namely Long short-term memory (LSTM) networks, to predict the streamflows in 531 basins.…”

Developing Internal and External Proportional Integral Derivative Water Surface Controller in HEC-RAS

“…Additionally, integration with other models or simulation techniques is possible with HEC-RAS [16,17]. HEC-RAS is utilized for the calibration and selection of the most appropriate Manning's roughness coefficient using common boundary conditions [18].…”