The flood warning system can be effectively used to reduce the potential property damages and loss of lives. Therefore, a reliable flood warning system is required for the evacuation of people from probable inundation area in sufficient lead time. Hence, this study was commenced to predict the travel time and generate inundation maps along the Grand River, Ohio for various flood stages. A widely
... [Show full abstract] accepted hydraulic tool, Hydraulic Engineering Center River Analysis System (HEC-RAS), was used to perform the hydraulic simulation. HEC-GeoRAS, an ArcGIS extension tool, was used to prepare geospatial data and generate flood inundation maps for various flood stages. A topographic survey was conducted to obtain the accurate elevation of river channels. The hydraulic simulations were carried out using six different elevation datasets and various ranges of Manning's roughness to quantify the uncertainties in travel time and inundation area prediction due to the resolutions of the elevation datasets and Manning's roughness. The study showed that the coarse elevation dataset, which was 30m Digital Elevation Model (DEM) without integration of survey data, provided higher travel time and inundation area. It over predicted (11.03%-15.01%) in travel time and inundation area (32.56%-44.52%) for various return period floods when compared with the results of Light Detection and Ranging (LiDAR) integrated with survey data. Moreover, Manning's roughness was found to be more sensitive in channel sections than that of floodplains. The decrease in travel time and inundation area was observed with the decrease in Manning's roughness. The highest decrement of 21.38% and 8.97% in travel time and inundation area was observed when roughness value was decreased in channel sections, while the decrement in travel time and inundation area was 3.45% and 1.49% when roughness value was decreased in floodplains. The difference in predicted travel time and inundation area, while using LiDAR integrated with survey data, was not considerably different from 10m DEM integrated with survey data. However, LiDAR with survey data predicted conservative travel time which would be safe to consider for the evacuation planning from probable inundation areas. Therefore, LiDAR integrated with survey data was used for the calculation of travel time and generation of flood inundation maps for 12 different selected flood stages. The estimated travel time can be used for the evacuation of the people. Similarly, the rating curve and the flood inundation maps can be used to issue flood warning. More than 100 houses, many roads, bridges and parks along the Grand River are susceptible to 500 year return period flood. Therefore, it is suggested to install the siren system in various locations of the river.
In addition, winter flooding due to ice jams is one of the major problems as it has caused severe damages along the Grand River and nearby bridge structures frequently. Therefore, the effects of ice cover and ice jams on the river level near bridges were investigated. The increase in river stage and inundation area was observed, when ice cover and ice jam was considered in the simulation. The average increase in river stage was approximately 2 ft for maximum winter discharge. Likewise, the increase in inundation area varied from 24% to 52% for various winter flows resulting in the highest increment for the lowest winter discharge. In addition, the increase in river stage was noticed at the upstream section of bridges during winter when the model was simulated considering bridges. The effects of resolution of elevation datasets and ice jam/ice cover in flood travel time and inundations maps would be valuable assets for decision makers and planners for flood management and rescue operation in future.
