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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. [ABSTRACT FROM AUTHOR]

Among the various factors that contribute to flood risk, heavy storms, inadequate storm drainage systems, and the concentration of population and assets have usually been considered to be fundamental factors affecting flood damage. Climate change is also a real threat, bringing heavier and more frequent storms. This study presents a methodology for comparatively evaluating the impact of the flood risk factors using a GIS-based flood damage prediction model (FDPM). The FDPM calculates flood inundation depths using the XP-SWMM routine and monetary flood damages using a flood damage estimation model for various storms and catchment conditions. The concept of flood risk in this context is defined as the product of flood damage and the probability of its occurrence. This study produces a flood risk structure in a risk assessment framework. The method is applied to the Kiba drainage area in Tokyo, Japan. The study gives a quantitative evaluation of the changes in flood risk due to risk factors such as increase in asset values, flood control, and climate change using a flood risk impact factor (FRIF). The FRIF is introduced as an index to evaluate the impact of various sources of increased or reduced flood risk to society. [ABSTRACT FROM AUTHOR]

Most flood inundation models do not come with an uncertainty analysis component chiefly because of the complexity associated with model calibration. Additionally, the fact that the models are both data- and compute-intensive, and since uncertainty results from multiple sources, adds another layer of complexity for model use. In the present study, flood inundation modeling was performed in the Godavari River Basin using the Hydrologic Engineering Center—River Analysis System 2D (HEC-RAS 2D) model. The model simulations were generated for six different scenarios that resulted from combinations of different geometric, hydraulic and hydrologic conditions. Thus, the resulted simulations account for multiple sources of uncertainty. The SRTM-30 m and MERIT-90 m Digital elevation Model (DEM), two sets of Manning's roughness coefficient (Manning's n) and observed and estimated boundary conditions, were used to reflect geometric, hydraulic and hydrologic uncertainties, respectively. The HEC-RAS 2D model ran in an unsteady state mode for the abovementioned six scenarios for the selected three flood events that were observed in three different years, i.e., 1986, 2005 and 2015. The water surface elevation (H) was compared in all scenarios as well as with the observed values at selected locations. In addition, 'H' values were analyzed for two different structures of the computational model. The average correlation coefficient (r) between the observed and simulated H values is greater than 0.85, and the highest r, i.e., 0.95, was observed for the combination of MERIT-90 m DEM and optimized (obtained via trial and error) Manning's n. The analysis shows uncertainty in the river geometry information, and the results highlight the varying role of geometric, hydraulic and hydrologic conditions in the water surface elevation estimates. In addition to the role of the abovementioned, the study recommends a systematic model calibration and river junction modeling to understand the hydrodynamics upstream and downstream of the junction. [ABSTRACT FROM AUTHOR]