title: Flood probability quantification for road infrastructure: Data-driven spatial-statistical approach and case study applications.
abstract: Climate-driven increase in the frequency of extreme hydrological events is expected to impose greater strain on the built environment and major transport infrastructure, such as roads and railways. This study develops a data-driven spatial-statistical approach to quantifying and mapping the probability of flooding at critical road-stream intersection locations, where water flow and sediment transport may accumulate and cause serious road damage. The approach is based on novel integration of key watershed and road characteristics, including also measures of sediment connectivity. The approach is concretely applied to and quantified for two specific study case examples in southwest Sweden, with documented road flooding effects of recorded extreme rainfall. The novel contributions of this study in combining a sediment connectivity account with that of soil type, land use, spatial precipitation-runoff variability and road drainage in catchments, and in extending the connectivity measure use for different types of catchments, improve the accuracy of model results for road flood probability.
authors.affiliation: Stockholm University, Department of Physical Geography and Bolin Centre for Climate Research, SE-106 91 Stockholm, Sweden. Electronic address: zahra.kalantari@natgeo.su.se.
authors.forename: Zahra
authors.initials: Z
authors.lastname: Kalantari
authors.affiliation: Research Institute for Geo-Hydrological Protection, National Research Council, Padova, Italy. Electronic address: marco.cavalli@irpi.cnr.it.
authors.forename: Marco
authors.initials: M
authors.lastname: Cavalli
authors.affiliation: Swedish Meteorological and Hydrological Institute (SMHI), SE-601 76 Norrköping, Sweden. Electronic address: carolina.cantone@smhi.se.
authors.forename: Carolina
authors.initials: C
authors.lastname: Cantone
authors.affiliation: Research Institute for Geo-Hydrological Protection, National Research Council, Padova, Italy. Electronic address: stefano.crema@irpi.cnr.it.
authors.forename: Stefano
authors.initials: S
authors.lastname: Crema
authors.affiliation: Stockholm University, Department of Physical Geography and Bolin Centre for Climate Research, SE-106 91 Stockholm, Sweden. Electronic address: georgia.destouni@natgeo.su.se.
authors.forename: Georgia
authors.initials: G
authors.lastname: Destouni
keywords: Climate change adaptation; Decision making; GIS; Multivariate statistical model; Sediment connectivity