A mechanistic approach to include climate change and unplanned urban sprawl in landslide susceptibility maps.

Empirical evidence shows that climate, deforestation and informal housing (i.e. unregulated construction practices typical of fast-growing developing countries) can increase landslide occurrence. However, these environmental changes have not been considered jointly and in a dynamic way in regional or national landslide susceptibility assessments. This gap might be due to a lack of models that can represent large areas (>100km2) in a computationally efficient way, while simultaneously considering the effect of rainfall infiltration, vegetation and housing. We therefore suggest a new method that uses a hillslope-scale mechanistic model to generate regional susceptibility maps under changing climate and informal urbanisation, which also accounts for existing uncertainties. An application in the Caribbean shows that the landslide susceptibility estimated with the new method and associated with a past rainfall-intensive hurricane identifies ~67.5 % of the landslides observed after that event. We subsequently demonstrate that the hypothetical expansion of informal housing (including deforestation) increases landslide susceptibility more (+20 %) than intensified rainstorms due to climate change (+6 %). However, their combined effect leads to a much greater landslide occurrence (up to +40 %) than if the two drivers were considered independently. Results demonstrate the importance of including both land cover and climate change in landslide susceptibility assessments. Furthermore, by modelling mechanistically the overlooked dynamics between urban growth and climate change, our methodology can provide quantitative information of the main landslide drivers (e.g. quantifying the relative impact of deforestation vs informal urbanisation) and locations where these drivers are or might become most detrimental for slope stability. Such information is often missing in data-scarce developing countries but is key for supporting national long-term environmental planning, for targeting financial efforts, as well as for fostering national or international investments for landslide mitigation.

Data-driven flood hazard zonation of Italy.

We present Flood-SHE, a data-driven, statistically-based procedure for the delineation of areas expected to be inundated by river floods. We applied Flood-SHE in the 23 River Basin Authorities (RBAs) in Italy using information on the presence or absence of inundations obtained from existing flood zonings as the dependent variable, and six hydro-morphometric variables computed from a 10 m × 10 m DEM as covariates. We trained 96 models for each RBA using 32 combinations of the hydro-morphometric covariates for the three return periods, for a total of 2208 models, which we validated using 32 model sets for each of the covariate combinations and return periods, for a total of 3072 validation models. In all the RBAs, Flood-SHE delineated accurately potentially inundated areas that matched closely the corresponding flood zonings defined by physically-based hydro-dynamic flood routing and inundation models. Flood-SHE delineated larger to much larger areas as potentially subject of being inundated than the physically-based models, depending on the quality of the flood information. Analysis of the sites with flood human consequences revealed that the new data-driven inundation zones are good predictors of flood risk to the population of Italy. Our experiment confirmed that a small number of hydro-morphometric terrain variables is sufficient to delineate accurate inundation zonings in a variety of physiographical settings, opening to the possibility of using Flood-SHE in other areas. We expect the new data-driven inundation zonings to be useful where flood zonings built on hydrological modelling are not available, and to decide where improved flood hazard zoning is needed.

LAND-deFeND - An innovative database structure for landslides and floods and their consequences.

Information on historical landslides and floods - collectively called "geo-hydrological hazards - is key to understand the complex dynamics of the events, to estimate the temporal and spatial frequency of damaging events, and to quantify their impact. A number of databases on geo-hydrological hazards and their consequences have been developed worldwide at different geographical and temporal scales. Of the few available database structures that can handle information on both landslides and floods some are outdated and others were not designed to store, organize, and manage information on single phenomena or on the type and monetary value of the damages and the remediation actions. Here, we present the LANDslides and Floods National Database (LAND-deFeND), a new database structure able to store, organize, and manage in a single digital structure spatial information collected from various sources with different accuracy. In designing LAND-deFeND, we defined four groups of entities, namely: nature-related, human-related, geospatial-related, and information-source-related entities that collectively can describe fully the geo-hydrological hazards and their consequences. In LAND-deFeND, the main entities are the nature-related entities, encompassing: (i) the "phenomenon", a single landslide or local inundation, (ii) the "event", which represent the ensemble of the inundations and/or landslides occurred in a conventional geographical area in a limited period, and (iii) the "trigger", which is the meteo-climatic or seismic cause (trigger) of the geo-hydrological hazards. LAND-deFeND maintains the relations between the nature-related entities and the human-related entities even where the information is missing partially. The physical model of the LAND-deFeND contains 32 tables, including nine input tables, 21 dictionary tables, and two association tables, and ten views, including specific views that make the database structure compliant with the EC INSPIRE and the Floods Directives. The LAND-deFeND database structure is open, and freely available from http://geomorphology.irpi.cnr.it/tools.