Abstract
Imperviousness has increased due to urbanization, as has the frequency of extreme rainfall events by climate change. Various countermeasures, such as structural and nonstructural measures, are required to prepare for these effects. Flood forecasting is a representative nonstructural measure. Flood forecasting techniques have been developed for the prevention of repetitive flood damage in urban areas. It is difficult to apply some flood forecasting techniques using training processes because training needs to be applied at every usage. The other flood forecasting techniques that use rainfall data predicted by radar are not appropriate for small areas, such as single drainage basins. In this study, a new flood forecasting technique is suggested to reduce flood damage in urban areas. The flood nomograph consists of the first flooding nodes in rainfall runoff simulations with synthetic rainfall data at each duration. When selecting the first flooding node, the initial amount of synthetic rainfall is 1 mm, which increases in 1 mm increments until flooding occurs. The advantage of this flood forecasting technique is its simple application using real-time rainfall data. This technique can be used to prepare a preemptive response in the process of urban flood management.
Highlights
Climate change has had various effects, including an increase in the frequency of extreme rainfall events
Synthetic rainfall is used as input data for rainfall runoff simulations, and it is essential for the generation of a flood nomograph
Various flood forecasting techniques have been developed and suggested. This simple new flood forecasting technique was needed because training time is essential for applying current flood forecasting techniques
Summary
Climate change has had various effects, including an increase in the frequency of extreme rainfall events. Water 2018, 10, 53 in real-time flood forecasting has been conducted for the 622 km Kamp catchment in Austria [6] These methods are difficult to use with small watersheds because the time interval of rainfall data in previous studies has been long. Flood forecasting techniques using a neural network require training and are time-consuming, and the application process is complex In these previous studies, the applied range in flood forecasting using the long interval rainfall data is not appropriate for a single urban drainage area requiring a short interval of time. Flood forecasting techniques in previous studies have been suggested for rivers, i.e., large, watershed applications, and the application processes of such techniques are complex They are not suitable for small urban drainage areas, since the time of concentration is less than one hour. A historical rainfall event was applied to the flood nomograph, and the results of this application were analyzed
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