Wildfires can significantly affect mountain hillslopes through the combustion of trees and shrubs and changes in soil properties. The type and magnitude of the associated post-fire effects depend on several factors, including fire severity and soil physical–mechanical-hydraulic features that, coupled with climate and topographic conditions, may cause increased runoff, erosion, and slope instability as consequence of intense rainfall. The post-fire response of slopes is highly site-specific. Therefore, in situ surveys and laboratory tests are needed to quantify changes in key soil parameters. The present study documents the post-fire physical and hydromechanical properties of pyroclastic topsoil collected from three test sites that suffered wildfires and rainfall-induced post-fire events in 2019 and 2020 in the Sarno Mountains (Campania Region, southern Italy). The tested pyroclastic soils in burned conditions show (i) no significant changes in grain size distribution, soil organic matter, and specific gravity; (ii) a deterioration in shear strength in terms of decreased soil cohesion caused by the fire-induced weakening of root systems; and (iii) a decrease in hydraulic conductivity. Accordingly, it can be argued that the documented post-fire erosion responses were mainly caused by the reduced cohesion and hydraulic conductivity of the burned topsoil layer, as well as by the loss of vegetation cover and the deposition of fire residues. Although deserving further deepening, this study can represent the necessary background for understanding the initiation mechanism of post-fire erosion processes in the analyzed area and on several natural slopes under similar conditions.
Every summer, wildfires affect thousands of steep watersheds in Italy, causing the partial or complete destruction of vegetation, and changes in soil hydraulic properties. Such effects alter the hydrologic response of watersheds, increasing post-fire debris and sediment-laden flow hazard. This study characterizes the most relevant predisposing and triggering factors for a sequence of four post-fire flooding events, which, in the late summer-autumn of 2017, affected Montoro village in southern Italy. This research work consists of a fire severity assessment based on multispectral satellite images, characterization of meteorological systems and related flood-triggering rainfall, and provides an overview of the damage that occurred in the repeatedly affected urban area using crowdsourced data. The research findings demonstrate that the analyzed area burned with moderate-high (64.4%) and low severity (35.6%) levels. All the flooding events were triggered by rainfall evaluated as non-extreme, but with relevant peak intensities (I10 and I30), associated with the first convective storms impacting the burned watersheds. The crowdsourced data highlight the fact that roads and buildings on footslopes were inundated by mud and debris transported by rapid flows. The study identifies a clear relationship between wildfires and flooding processes and provides useful information for hazard assessment and emergency management operations.
Because of the ongoing changing climate, extreme rainfall events’ frequency at the global scale is expected to increase, thus resulting in high social and economic impacts. A Meteo/Hydro/Hydraulic forecasting chain combining heterogeneous observational data sources is a crucial component for an Early Warning System and is a fundamental asset for Civil Protection Authorities to correctly predict these events, their effects, and put in place anticipatory actions. During the last week of October 2021 an intense Mediterranean hurricane (Apollo) affected many Mediterranean countries (Tunisia, Algeria, Malta, and Italy) with a death toll of seven people. The CIMA Meteo/Hydro/Hydraulic forecasting chain, including the WRF model, the hydrological model Continuum, the automatic system for water detection (AUTOWADE), and the hydraulic model TELEMAC-2D, was operated in real-time to predict the Apollo weather evolution as well as its hydrological and hydraulic impacts, in support of the early warning activities of the Italian Civil Protection Department. The WRF model assimilating radar data and in situ weather stations showed very good predictive capability for rainfall timing and location over eastern Sicily, thus supporting accurate river flow peak forecasting with the hydrological model Continuum. Based on WRF predictions, the daily automatic system for water detection (AUTOWADE) run using Sentinel 1 data was anticipated with respect to the scheduled timing to quickly produce a flood monitoring map. Ad hoc tasking of the COSMO-SkyMed satellite constellation was also performed to overcome the S1 data latency in eastern Sicily. The resulting automated operational mapping of floods and inland waters was integrated with the subsequent execution of the hydraulic model TELEMAC-2D to have a complete representation of the flooded area with water depth and water velocity.