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Open AccessArticle

Safer_RAIN: A DEM-Based Hierarchical Filling-&-Spilling Algorithm for Pluvial Flood Hazard Assessment and Mapping across Large Urban Areas

1
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, Italy
2
GECOsistema Srl, 47521 Cesena, Italy
3
Dipl.-Ing. Günter Humer GmbH (IBH), 4682 Geboltskirchen, Austria
4
CMCC Euro-Mediterranean Center on Climate Change, Ca’ Foscari University of Venice, 30175 Venice, Italy
*
Author to whom correspondence should be addressed.
Water 2020, 12(6), 1514; https://doi.org/10.3390/w12061514
Received: 28 April 2020 / Revised: 22 May 2020 / Accepted: 24 May 2020 / Published: 26 May 2020
(This article belongs to the Section Hydrology and Hydrogeology)
The increase in frequency and intensity of extreme precipitation events caused by the changing climate (e.g., cloudbursts, rainstorms, heavy rainfall, hail, heavy snow), combined with the high population density and concentration of assets, makes urban areas particularly vulnerable to pluvial flooding. Hence, assessing their vulnerability under current and future climate scenarios is of paramount importance. Detailed hydrologic-hydraulic numerical modeling is resource intensive and therefore scarcely suitable for performing consistent hazard assessments across large urban settlements. Given the steadily increasing availability of LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models), several studies highlighted the potential of fast-processing DEM-based methods, such as the Hierarchical Filling-&-Spilling or Puddle-to-Puddle Dynamic Filling-&-Spilling Algorithms (abbreviated herein as HFSAs). We develop a fast-processing HFSA, named Safer_RAIN, that enables mapping of pluvial flooding in large urban areas by accounting for spatially distributed rainfall input and infiltration processes through a pixel-based Green-Ampt model. We present the first applications of the algorithm to two case studies in Northern Italy. Safer_RAIN output is compared against ground evidence and detailed output from a two-dimensional (2D) hydrologic and hydraulic numerical model (overall index of agreement between Safer_RAIN and 2D benchmark model: sensitivity and specificity up to 71% and 99%, respectively), highlighting potential and limitations of the proposed algorithm for identifying pluvial flood-hazard hotspots across large urban environments. View Full-Text
Keywords: extreme rainfall events; flood hazard; Digital Elevation Model (DEM); fast-processing HFSA; Lignano Sabbiadoro; Rimini; nature-based solutions extreme rainfall events; flood hazard; Digital Elevation Model (DEM); fast-processing HFSA; Lignano Sabbiadoro; Rimini; nature-based solutions
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MDPI and ACS Style

Samela, C.; Persiano, S.; Bagli, S.; Luzzi, V.; Mazzoli, P.; Humer, G.; Reithofer, A.; Essenfelder, A.; Amadio, M.; Mysiak, J.; Castellarin, A. Safer_RAIN: A DEM-Based Hierarchical Filling-&-Spilling Algorithm for Pluvial Flood Hazard Assessment and Mapping across Large Urban Areas. Water 2020, 12, 1514. https://doi.org/10.3390/w12061514

AMA Style

Samela C, Persiano S, Bagli S, Luzzi V, Mazzoli P, Humer G, Reithofer A, Essenfelder A, Amadio M, Mysiak J, Castellarin A. Safer_RAIN: A DEM-Based Hierarchical Filling-&-Spilling Algorithm for Pluvial Flood Hazard Assessment and Mapping across Large Urban Areas. Water. 2020; 12(6):1514. https://doi.org/10.3390/w12061514

Chicago/Turabian Style

Samela, Caterina; Persiano, Simone; Bagli, Stefano; Luzzi, Valerio; Mazzoli, Paolo; Humer, Günter; Reithofer, Andreas; Essenfelder, Arthur; Amadio, Mattia; Mysiak, Jaroslav; Castellarin, Attilio. 2020. "Safer_RAIN: A DEM-Based Hierarchical Filling-&-Spilling Algorithm for Pluvial Flood Hazard Assessment and Mapping across Large Urban Areas" Water 12, no. 6: 1514. https://doi.org/10.3390/w12061514

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