Contents lists available at ScienceDirect Computers, Environment and Urban Systems journal homepage: www.elsevier.com/locate/ceus A GIS tool for cost-effective delineation of flood-prone areas Caterina Samela, Raffaele Albano, Aurelia Sole, Salvatore Manfreda ⁎ Università degli Studi della Basilicata, Potenza 85100, Italy ARTICLE INFO Keywords: Flood susceptibility Digital Elevation Model (DEM) Geomorphic Flood Index Linear binary classification Data scarce environments Geographic Information System (GIS) ABSTRACT Delineation of flood hazard and flood risk areas is a critical issue, but practical difficulties regularly make complete achievement of the task a challenge. In data-scarce environments (e.g. ungauged basins, large-scale analyses), useful information about flood hazard exposure can be obtained using geomorphic methods. In order to advance this field of research, we implemented in the QGIS environment an automated DEM-based procedure that exhibited high accuracy and reliability in identifying the flood-prone areas in several test sites located in Europe, the United States and Africa. This tool, named Geomorphic Flood Area tool (GFA tool), enables rapid and cost-effective flood mapping by performing a linear binary classification based on the recently proposed Geomorphic Flood Index (GFI). The GFA tool provides a user-friendly strategy to map flood exposure over large areas. A demonstrative application of the GFA tool is presented in which a detailed flood map was derived for Romania. 1. Introduction Floods are the most frequently occurring and costliest natural ha- zard throughout the world, and flood damages constitute about a third of the economic losses inflicted by natural hazards (Munich, 2005). In the period 1975–2001, a total of 1816 flood events killed over 175,000 people and affected > 2.2 billion worldwide (Jonkman, 2005). More- over, the United Nations (UNISDR and CRED, 2015) has estimated that one third of the world's population (around 2.3 billion people) has been effected by flood in the last 20 years. Flood inundation maps are at the base of flood risk management, informing the public and city planners about flood-prone areas in a region. Most flood inundation maps are developed by computer mod- elling, involving hydrologic analyses to estimate the peak flow dis- charge for assigned return periods, hydraulic simulations to estimate water surface elevations, and terrain analysis to estimate the inundation area (Alfieri et al., 2014; Bradley, Cooper, Potter, & Price, 1996; Knebl, Yang, Hutchison, & Maidment, 2005; Sole et al., 2013; Whiteaker, Robayo, Maidment, & Obenour, 2006). Despite recent advancements in computational techniques and availability of high-resolution topographic data, flood hazard maps are still lacking in many countries. The main difficulty in using a specific method or model is primarily correlated to the significant amount of data and parameters required by these models. Thus, their calibration and validation is a rather challenging task, especially considering that gauging stations are heterogeneously and unevenly distributed (Di Baldassarre, Schumann, & Bates, 2009). This is especially relevant in developing countries, which suffer from weak coping strategies and inefficient mechanisms for disaster management due to limited re- sources for flood protection. Traditional modelling approaches are costly, making them unaffordable not only for developing countries, but also for more developed ones. For instance, in the U.S., many rural counties and several minor tributaries do not have any associated flood inundation information. FEMA (Federal Emergency Management Agency) (2006) estimated that flood inundation mapping could cost from $3000 to $6000/km of river reach in the U.S. Therefore, there is a need to look for efficient and inexpensive ways to derive flood in- undation maps. In this scenario, several studies have demonstrated that flood-prone areas can be delineated using methods which rely on geomorphologic characterization of a river basin (Clubb et al., 2017; De Risi, Jalayer, & De Paola, 2015; Degiorgis et al., 2012; Dodov & Foufoula-Georgiou, 2006; Gallant & Dowling, 2003; Jafarzadegan & Merwade, 2017; McGlynn & Seibert, 2003; Nardi, Vivoni, & Grimaldi, 2006; Noman, Nelson, & Zundel, 2001; Wolman, 1971). A mutual causal relationship exists between flooding and the shape and extension of floodplains, since fluvial geomorphology is essentially shaped by flood-driven phenomena (Arnaud-Fassetta et al., 2009; Nardi, Biscarini, Di Francesco, Manciola, & Ubertini, 2013). Given this assumption, we have developed a practical and cost-ef- fective procedure (proposed by Samela, Troy, & Manfreda, 2017) to preliminarily delineate flood-prone areas in poor data environments and for large-scale analyses based on easily available information. https://doi.org/10.1016/j.compenvurbsys.2018.01.013 Received 29 July 2017; Received in revised form 29 January 2018; Accepted 30 January 2018 ⁎ Corresponding author. E-mail address: salvatore.manfreda@unibas.it (S. Manfreda). Computers, Environment and Urban Systems xxx (xxxx) xxx–xxx 0198-9715/ © 2018 Elsevier Ltd. All rights reserved. Please cite this article as: Samela, C., Computers, Environment and Urban Systems (2018), https://doi.org/10.1016/j.compenvurbsys.2018.01.013