Safer_RAIN: a Fast-Processing DEM-Based Algorithm for Pluvial Flood
Hazard Assessment Across Large Urban Areas
Abstract
Urban areas (i.e. cities, towns and suburbs) provide a home to over 70%
of the EU‑population, and this number is expected to exceed 80% by 2050
(Tapia et al., ECOL INDIC, 2017). 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 in
urban areas makes them particularly vulnerable to pluvial flooding,
hence, assessing their vulnerability under current and future climate
scenarios is of paramount importance. Detailed hydrologic-hydraulic
numerical modelling is resource intensive and therefore scarcely
suitable for a consistent hazard assessment 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 for consistent pluvial flood
hazard characterization of fast-processing DEM-based methods, such as
the Hierarchical Filling and Spilling or Puddle-to-Puddle Dynamic
Filling and Spilling (see e.g. Zhang et al., J HYDROL, 2014; Chu et al.,
WATER RESOUR RES, 2013). As part of the activities of the EIT
Climate-KIC Demonstrator project SAFERPLACES (https://saferplaces.co/),
we developed a fast-processing algorithm, named Safer_RAIN, that
enables one to map pluvial flooding in large urban areas by implementing
a filling and spilling procedure that accounts for spatially distributed
rainfall input and infiltration processes (Green Ampt method). We
present the first applications of the algorithm to model recent urban
inundations occurred in Northern Italy. These preliminary applications,
compared against ground evidence and detailed output from a
two-dimensional hydrologic and hydraulic numerical model, highlight
limitations and potential of Safer_RAIN for identifying pluvial-hazard
hotspots across large urban environments.