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Abstract
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Flood modelling is among the most challenging scientific task because it covers a wide area of complex physical phenomena associated with highly uncertain and non-linear processes where the development of physically interpretive solutions usually suffers from the lack of recorded data.
The objective of the Special Issue, titled "Modern Developments in Flood Modelling", is to define and discuss several related topics, aiming to provide new insights within the geoscientific domain on the use of new remote sensing datasets in the service of flood modelling, on new methodologies addressing complex problems such as joint probability theory and rainfall maximum modelling at different temporal scales, and on strategies for reproducing catastrophic events in data-scarce areas and modelling flood risk with new tools in coastal areas.
This Special Issue comprises thirteen contributions tackling the above-mentioned goals. Our issue received a high number of diverse submissions, with an 82% acceptance rate.
The articles in this Special Issue address a wide variety of topics reflecting the challenges mentioned above. Their details are briefly presented below.
The paper "Regional Ombrian Curves: Design Rainfall Estimation for a Spatially Diverse Rainfall Regime" [1] by Theano Iliopoulou, Nikolaos Malamos and Demetris Koutsoyiannis demonstrates new insight in modelling regional ombrian curves (I.D.F curves) by providing a new parsimonious model of the extreme rainfall properties at any point in a given area. The curves were constructed following a newly revisited mathematical formulation of single-site curves coupled with a new regionalization approach. The results showed that the model efficiently captures the spatial variability of extreme rainfall in the area, covering scales from 5 min to 48 h.
The paper "Forensic Hydrology: A Complete Reconstruction of an Extreme Flood Event in Data-Scarce Area" [2] by Aristoteles Tegos, Alexandros Ziogas, Vasilis Bellos and Apostolos Tzimas presents a state-of-the-art approach to reconstructing catastrophic flooding events in data-scarce areas. The study focused on the recent catastrophic flooding event, namely medicane Ianos, which substantially affected the town of Karditsa, Greece. A rainfall-runoff CN-unit hydrograph model was combined with a hydrodynamic model based on a 2D shallow water equations model. Having used numerous remote sensing rainfall datasets along with satellite flooding footage and videos posted to social media sites such as Facebook, the catastrophic event was reconstructed efficiently in a high-complexity area associated with low-lying flooding fluvial and pluvial water paths.
The paper "Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network" [3] by Raphaël A. H. Kilsdonk, Anouk Bomers and Kathelijne Wijnberg presents a long short-term memory (LSTM) neural network model to predict flood time series at 230 manhole locations present in the sewer system of the city of Amersfoort. According to the authors, it is the first time that an LSTM was applied to such a large sewer system in addition to a wide variety of synthetic precipitation events in terms of precipitation intensity. It was concluded that the LSTM could accurately predict the timing and volume of flooding for the large number of manholes for historic precipitation events and that the LSTM was able to reduce forecasting times, demonstrating the applicability of using this methodology as an early flood-warning system in urban areas.
The paper "Flood Exposure of Residential Areas and Infrastructure in Greece" [4] by Stefanos Stefanidis, Vasileios Alexandridis and Theodora Theodoridou exhibits the first nationwide spatial assessment of flood exposure in residential areas and infrastructures in Greece. Spatial analysis and open access data were used to illustrate the variations in flood exposure. The ratio of the urban fabric, transportation and social, industrial and commercial infrastructures in 100-year flood zones was evaluated, as well as the spatial pattern of the exposure. Based on the authors' view, the proposed methodology could serve as a roadmap for integrated flood risk assessment, as the results can be easily overlaid with other spatial data for further analysis.
The paper "Identifying Modelling Issues through the Use of an Open Real-World Flood Dataset" [5] by Vasilis Bellos, Ioannis Kourtis, Eirini Raptaki, Spyros Handrinos, John Kalogiros, Ioannis Sibetheros and Vassilios Tsihrintzis deals with the reconstruction of the flood wave that hit the town of Mandra (Athens, Greece) on 15 November 2017. The flash flood event was caused by a huge storm which was part of the Medicane Numa-Zeno. The works used in the reconstruction were associated with (a) the post-event collection of 44 maximum water depths and (b) hydrodynamic simulation employing the HEC-RAS and MIKE FLOOD software. Calibration strategies in computationally demanding cases were considered, and whether the calibrated parameters can be blindly transferred to another simulator (informed modeling) was tested.
Language: en |