Regional climate models (RCMs) are commonly used for high-resolution assessment of climate change impacts. This research assesses the reliability of several RCMs in a Mediterranean area (southern Italy), comparing historic climate and drought characteristics with high-density and high-quality ground-based observational datasets. We propose a general methodology and identify the more skilful models able to reproduce precipitation and temperature variability as well as drought characteristics.

This study proposes a new drought index that combines meteorological and agricultural drought aspects. The index is scalable, transferable all over the globe, can be updated in near real time and is a remote-sensing product, since only satellite-based datasets were employed. A set of rules to objectively identify drought events is also implemented. We found that the set of rules, applied together with the new index, outperformed conventional drought indices in identifying droughts in Haiti.

The paper presents the flood and landslide early warning system HEWS developed by the University of Messina for the Integrated Multi-Risk Decentralised Functional Centre of Sicily (Italy). HEWS implements a methodology based on the combined use of rainfall thresholds, soil moisture modelling and quantitative precipitation forecast (QPF) to issue alert bulletins both for floods and landslide. The software Delft-FEWS has been adopted as operation platform to support the implementation of HEWS.

Flood risk estimates are characterised by significant uncertainties; accordingly, evaluating the reliability of such estimates (i.e. validating flood risk models) is crucial. Here, we discuss the state of art of flood risk models validation with the aim of identifying policy and research recommendations towards promoting more common practice of validation. The main conclusions from this review can be summarised as the need of higher quality data to perform validation and of benchmark solutions.