Flood4castrtf: a Real-Time Urban Flood Forecasting Model

sustainability Article Flood4castRTF: A Real-Time Urban Flood Forecasting Model Michel Craninx 1,* , Koen Hilgersom 2 , Jef Dams 1 , Guido Vaes 2, Thomas Danckaert 1 and Jan Bronders 1 1 Environmental Modelling Unit, Flemish Institute for Technological Research (VITO), 2400 Mol, Belgium; [email protected] (J.D.); [email protected] (T.D.); [email protected] (J.B.) 2 Hydroscan NV, 3010 Leuven, Belgium; [email protected] (K.H.); [email protected] (G.V.) * Correspondence: [email protected] Abstract: Worldwide, climate change increases the frequency and intensity of heavy rainstorms. The increasing severity of consequent floods has major socio-economic impacts, especially in urban environments. Urban flood modelling supports the assessment of these impacts, both in current climate conditions and for forecasted climate change scenarios. Over the past decade, model frameworks that allow flood modelling in real-time have been gaining widespread popularity. Flood4castRTF is a novel urban flood model that applies a grid-based approach at a modelling scale coarser than most recent detailed physically based models. Automatic model set-up based on commonly available GIS data facilitates quick model building in contrast with detailed physically based models. The coarser grid scale applied in Flood4castRTF pursues a better agreement with the resolution of the forcing rainfall data and allows speeding up of the calculations. The modelling approach conceptualises cell-to-cell interactions while at the same time maintaining relevant and interpretable physical descriptions of flow drivers and resistances. A case study comparison of Flood4castRTF results with flood results from two detailed models shows that detailed models do not necessarily outperform the accuracy of Flood4castRTF with flooded areas in-between the two detailed models. A successful model application for a high climate change scenario is demonstrated. The reduced Citation: Craninx, M.; Hilgersom, K.; data need, consisting mainly of widely available data, makes the presented modelling approach Dams, J.; Vaes, G.; Danckaert, T.; applicable in data scarce regions with no terrain inventories. Moreover, the method is cost effective Bronders, J. Flood4castRTF: A for applications which do not require detailed physically based modelling. Real-Time Urban Flood Forecasting Model. Sustainability 2021, 13, 5651. Keywords: flood modelling; urban flooding; climate change; fast model set-up; grid modelling; https://doi.org/10.3390/su13105651 data scarcity Academic Editor: Giuseppe Barbaro Received: 22 March 2021 1. Introduction Accepted: 12 May 2021 1.1. Urban Flood Modelling Published: 18 May 2021 Due to the devastating effect of floods, understanding the drivers of floods has been an Publisher’s Note: MDPI stays neutral important research topic for decades. Since the 1970s the development and application of with regard to jurisdictional claims in flood inundation models has been an important part of research on floods [1]. Especially in published maps and institutional affil- urban environments, floods can have a very high economic and social cost. These impacts iations. will certainly further increase in future due to climate change and further urbanisation. Due to the specific characteristics of urban environments and the high socio-economic impact of floods in urban areas, urban flood models have been developed. These urban flood models take into account the urban drainage system which has an important influence on the occurrence of floods in urban environments. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. Urban flood models are currently extensively used for flood risk mapping, urban This article is an open access article drainage planning and engineering, water resource management, and real-time flood distributed under the terms and forecasting [1]. To cover the requirements of the different uses of urban flood models, a conditions of the Creative Commons range of different model systems were developed [2,3]. Worldwide, there is a tendency Attribution (CC BY) license (https:// towards the application of (semi-)distributed physically based models for urban run-off creativecommons.org/licenses/by/ modelling. This tendency is understandable given the complex physical interactions in 4.0/). urban catchments [4]. Additionally, the increase in computational power facilitates a higher Sustainability 2021, 13, 5651. https://doi.org/10.3390/su13105651 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 5651 2 of 25 differentiation and complexity of such models, further explaining the tendency for 1D- 2D detailed physically based models [5]. These full 1D–2D models represent the urban drainage network in one dimension (1D) and couple a 2D free surface model for overland flow. The Saint-Venant equations are used to simulate the flow dynamics for both the urban drainage and flow at the surface. Costabile et al. [6] demonstrated the necessity to apply such fully dynamic modelling when the goal of the urban flood inundation mapping activity includes the local estimation of flood hazard and vulnerability based on a combination of computed water depths and velocities. Flow velocities are particularly relevant to assess safety issues concerning people, for example, pedestrians and drivers’ vulnerability in a certain flood scenario because walking and driving in floodwaters are identified as the main danger for people during floods [7]. Despite the advantage with respect to accuracy and a superior local estimation of flood hazard and vulnerability, the detailed physically based modelling approach also has disad- vantages. Distributed physically based models are known to exhibit over-parameterisation and over-complexity [8]. Especially the high computation demand and extensive data requirements complicate setting up detailed physically based models for larger areas or in regions where less data are available [9]. On top of that, the resolution of their discretisation often largely exceeds the resolution of the forcing rainfall data [10]. Especially, the high computation demand complicates the use of detailed physically based models for real-time flood forecasting [1]. In the past, methods have been introduced to partly overcome the high computational demand of fine mesh discretisations. For example, flexible meshing avoids the need to apply a fine resolution throughout the complete model domain [11–13]. Ferraro et al. [12] present a method to derive the mesh size limits applied to the unstructured flexible mesh from a spectral analysis to the terrain. Alternatively, subgrid techniques can be applied to speed up the calculations [14]. Section 2.1.3 presents a more complete literature study on subgrid applications. Teng et al. [1] reviewed state-of-the-art methods for flood inundation modelling and conclude that there is no such thing as a “perfect model” and the aim of developing and using models that are ‘as realistic as possible’ should be balanced against computational demand, investment in data collection and model set-up, and the requirements of the end user. 1.2. Real-Time Flood Forecasting Real-time flood forecasting tools, however, can provide lifesaving information to local inhabitants or emergency services and are therefore value tools to mitigate the impact of flash floods. Spatial understanding of high-risk areas which enables emergency responders to prioritise evacuations and other actions are ideally implemented at the onset of a predicted extreme event [7]. Driven by the advances in high-resolution numerical weather predictors and the increasing frequency of high intensity rainfall events, due to climate change, there is a growing demand for flood models that are suited to real-time applications. To allow using detailed physically based flood models for real-time flood forecasting, various approaches have been proposed to decrease the computational speed [15]. Graphical processing units (GPU) have been successfully applied to decrease simulation time for both 1D [16] and 2D models [17]. Additionally, parallel computing (e.g., on cloud servers) and code parallelisation have been applied to reduce simulation time of detailed physically based models [15]. Using a lower spatial resolution is another option to reduce the computational demand. However, using lower spatial resolutions compromises model accuracy. Despite the developments in computer capability and advances in computation efficiency of hydrodynamic models, the use of these kind of models for real-time flood forecasting is still difficult [1]. To overcome the difficulties of the detailed physically based models for real-time flood forecasting, alternative modelling approaches have been proposed. Different approaches can be used to reduce the computational demand of the flood Sustainability 2021, 13, 5651 3 of 25 models: simplifying the 2D shallow water equations by for example omitting the inertia terms (e.g., [18]), using cellular automata approaches [19], using simplified, non-physical- based methods [1], or by applying empirical/data driven surrogate models [20] or a hybrid approach using a series of lumped models in combination with logistic regression [21]. Despite these recent advantages in simplifying and reducing the computational demand of urban flood models, several difficulties remain. The high data requirements and associated model set-up costs for 1D–2D detailed physically based models constitute another difficulty in setting up urban flood models. Required data such as properties of the sewer

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