Predicting the impacts of climate change on hydroclimatic processes in small mountainous catchments requires long and realistic high-temporal-resolution simulations of key environmental variables, particularly precipitation, under future scenarios. Stochastic models provide an effective way to generate multi-decadal projections, but existing approaches struggle to reproduce the alternation of weather systems and sub-hourly extremes. We propose a stochastic framework that accurately describes both ordinary and extreme precipitation events, explicitly links intermittency with event inter-arrival characteristics, and represents different storm types (e.g., convective and stratiform). Our approach combines CoSMoS, which generates stochastic time series preserving probability distributions and correlation structures, with concepts from TENAX, which relates the occurrence frequency and the probability distribution of extreme precipitation to near-surface temperature. Climate change impacts are incorporated through projected changes in temperature distributions and large-scale weather patterns from regional climate models. The method is tested on the Rio Valfredda, a small Alpine catchment in the eastern Italian Alps. The sub-hourly resolution of the framework allows explicit representation of convective precipitation, a key driver of extreme events in Alpine environments.

Climate change has increased the frequency and intensity of extreme weather events, leading to greater risks for vulnerable urban areas. Inadequate infrastructure often exacerbates vulnerability of many areas, resulting in significant socioeconomic losses from climate-related hazards and in particular flooding. Satellite services, smart technologies such as GIS-based Digital Twin help to simulate flooding scenarios to support urban planning and decision-making and provide monitoring and short-term forecasting of floods thus contributing to enhance climate resilience and to strengthen financial risk strategies.To ensure that these systems operate effectively, the validation of their predicted outputs in terms of flooding maps is crucial. This task is usually possibly carried out using the satellite-based data available and particularly those from Synthetic Aperture Radar (SAR), which are effective in various meteorological conditions. In urban areas, the application of state-of-the-art SAR-based methods for flood detection is challenging due to the complexity of effects caused by the radar backscattering from built environments.This study focuses on validating flood maps for urbanized environments based on a consolidated approach that reprocesses the clustering result with fuzzy logic approach (Pulvirenti et al. 2023, DOI: 10.3390/w15071353) and here improved to better estimate flooding in urban areas. The method was applied to a severe precipitation event in November 2023 in Tuscany, Italy, which caused multiple flood episodes. Our focus was on the Florence-Prato-Pistoia plain, the most densely populated area in Tuscany. On November 2, heavy rainfall began in the early afternoon, accumulating 130-170 mm within 5-6 hours. This led to the first flood episodes after 19:00 local time, resulting in several casualties.Copernicus Rapid Mapper was activated on 03/11/2023, 04:21 (Local time = UTC+1). It produced an estimate of flooded area mainly using one COSMO-SkyMed image, collected on November 6, after a second storm occurred in the night between 4 and 5 November. In our analysis we used two images. For the common image, good spatial correspondence was obtained. However, due to the late availability of satellite images, critical early floods were missing.This work takes this case study to address the opportunity and challenges of flood detection in urban areas using satellite data. While highlighting the importance of having a satellite flood mapping service, some drawbacks are also pointed out, such as the lack of revising time that can imply missing early stages of floods to early implement search and rescue operations. Projects to improve revisiting time are related to the emergence of next generation constellations, such the ASI/ESA IRIDE multisatellite and multiservice constellation. In case of fast evolving phenomena, such as the one considered in this study, a higher time resolution of flood mapping would increase the chance to obtain data even in the first floods. In practice, resorting to modelling and sensor data coupled in digital twins eventually integrated with obtained from citizens science will be still unavoidable. This is demonstrated within the SCORE project (https://score-eu-project.eu/), a four-year EU-funded project aiming to increase climate resilience in European coastal cities (Coastal City Living Labs - CCLLs).

1. Increasing resilience to climate change requires innovative approaches and synergy among different actions. To overcome this challenge, the European Union has been stimulating innovative approac...