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Climate change and land use are major drivers of environmental and socioeconomic transformations in landscapes and in coastal areas. The objective of this study was to present an interdisciplinary and participatory research methodology for analysing the evolutionary process of a coastal case study, the Trabocchi Coast in the Abruzzo Region (Italy), from multiple perspectives, including climate change, technological history, conservation protocols, and social function. The goal was to assess the resilience of these coastal structures, i.e., their ability to cope and adapt to changes while maintaining their identity and recognition. The adopted approach combined qualitative and quantitative data from the meteorological analyses, literature review, and field investigations involving a participatory workshop, from which a significant portion of the analysed results presented here emerged. The results indicate that the Trabocchi Coast has undergone significant changes in recent decades, posing a serious threat to cultural heritage due to climate change (sea-level rise, coastal erosion, storms, flooding and salinisation), changes in use and mass tourism. However, these changes have also presented challenges and opportunities for coastal development, stimulating diverse resilient responses from local communities, ranging from resistance to innovation. The paper discusses the factors that may make the Trabocchi Coast a model of resilience considering these changes. This is supported by the role of local institutions as guarantors of the cultural heritage value of the trabocchi in that specific landscape context, as evidenced by the approval of the "Costa dei Trabocchi Special Territorial Project" by the Abruzzo Region in 2023. [ABSTRACT FROM AUTHOR]

The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model's precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms. [ABSTRACT FROM AUTHOR]