Abstract
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.
Highlights
Over the years, extreme weather events have become increasingly frequent, causing dramatic economic and social damage
Our study focuses on a class of raster- or Digital Elevation Models (DEMs)-based approaches proposed in literature that has been recently shown to be very promising for the characterization of pluvial-flooding hazard in urban areas: the so-called Hierarchical Filling-&-Spilling Algorithm (HFSA) or Puddle-to-Puddle (P2P)
(a) identification of blue-spots, identification of blue-spots, spilling points and watersheds; identification flooded area resulting spilling points and watersheds; (b)(b) identification of of thethe flooded area resulting from therainfall rainfalldepth depth of 124 thethe urban flash-flood of the September 2017; pictures from the 124 mm mmrecorded recordedforfor urban flash-flood of 11 the
Summary
Extreme weather events have become increasingly frequent, causing dramatic economic and social damage. Water 2020, 12, 1514 reported economic losses caused by weather and climate-related extremes between 1980 and 2017 amounted to approximately EUR 426 billion (at 2017 values) accounting for about 83% of the monetary losses over that period [1]. As a matter of fact, the urban population growth, the high concentration of people and activities in urban areas and the development of cities occurred in the last two centuries represent a disruptive action for the natural system, resulting in excessive soil sealing, and alteration of the hydrological system [2,3]. According to the harmonized definition by Eurostat and the OECD (i.e., Organisation for Economic Co-operation and Development), urban areas—defined as cities, towns and suburbs—provide a home to over 70%
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