Effect of input data in hydraulic modeling for flood warning systems

Abstract

The main objective of this study was to quantify the error associated with input data, including various resolutions of elevation datasets and Manning's roughness for travel time computation and floodplain mapping. This was accomplished on the test bed, the Grand River (Ohio, USA) using the HEC-RAS model. LiDAR data integrated with survey data provided conservative predictions, whereas coarser elevation datasets provided a positive difference in the travel time (11.03-15.01%) and inundation area (32.56-44.52%). The minimum differences in travel time and inundation area were 0.50-4.33% and 3.55-7.16%, respectively, when the result from LiDAR integrated with survey data was compared with a 10-m DEM integrated with survey data. The results suggest that a 10-m DEM in the channel and LiDAR data in the floodplain combined with survey data would be appropriate for a flood warning system. Additionally, Manning's roughness of the channel section was found to be more sensitive than that of the floodplain. The decrease in inundation area was highest (8.97%) for the lower value of Manning's roughness.

A Model-Based Flood Hazard Mapping on the Southern Slope of Himalaya

“…The HEC-GeoRAS pre-processing was then completed and the final output from the ArcGIS and HEC-GeoRAS was processed in the HEC-RAS 1D steady flow simulation. Manning's roughness coefficients (n = 0.04 or 0.02) were selected based on visual inspection of the river channel both upstream and downstream as suggested by [58,61] and were adjusted slightly to calibrate the model [56,62]. Similarly, Manning's roughness coefficients were specified for forest (0.08), agriculture (0.035), bare ground (0.03), shrubland (0.035), and grassland (0.032) according to visual inspection of the floodplain characteristics.…”

Empirical assessment of flood wave celerity–discharge relationships at local and reach scales

“…One of the key characteristics of a flood wave is its travel time between points of interest, or its speed. Flood wave speed, generally referred to as flood wave celerity, is an important parameter for flood alert and forecasting (Tang et al 2001, Reszler et al 2008, Saleh et al 2013, decision making and optimization of flood management structures (Seibert et al 2014, Skublics et al 2016, and it is also a key parameter in hydrological and hydraulic modelling efforts for both simplified and complex models (Wong and Laurenson 1983, Sriwongsitanon et al 1998, Perumal and Raju 2001, Tang et al 2001, Price 2009, David et al 2011, as an input parameter or even for validation (Bates and De Roo 2000, Horritt and Bates 2001, Lamichhane and Sharma 2018. More recently, Fleischmann et al (2016) and Collischonn et al (2017) highlighted that the relationship between flood wave celerity and discharge in rivers plays a substantial role in defining the hydrograph shape.…”

Performance of automated flood inundation mapping methods in a context of flash floods: a comparison of three methods based either on the Height Above Nearest Drainage (HAND) concept, or on 1D/2D shallow water equations

“…However, a large increase in resolution and accuracy of Digital Terrain Models (DTMs) has been observed in the last few years, particularly with the development of Lidar, and DTMs with a resolution of less than 10 m are now widely available even if their accuracy remains heterogeneous (Schumann and Bates, 2018). This evolution makes it possible to run hydraulic simulations on small rivers (Lamichhane and Sharma, 2018), even if information on bathymetry is still rarely available. Regionalized hydrological approaches also progressively help improve knowledge on flood regimes of upstream watercourses (Aubert et al, 2014).…”

“…Specific applications to flash floods have been proposed using Iber (García-Feal et al, 2018) and BreZo (Nguyen et al, 2016). Finally, in addition to high resolution 2D models, 1D SWE models may also be applied based on cross-sections extracted from high-resolution DTMs (Choi and Mantilla, 2015;Pons et al, 2014;Le Bihan et al, 2017;Lamichhane and Sharma, 2018), also showing interesting results in terms of accuracy and offering lower computation times.…”