SummaryDownscaling methods are used to derive stream flow at a high temporal resolution from a data series that has a coarser time resolution. These algorithms are useful for many applications, such as water management and statistical analysis, because in many cases stream flow time series are available with coarse temporal steps (monthly), especially when considering historical data; however, in many cases, data that have a finer temporal resolution are needed (daily).In this study, we considered a simple but efficient stochastic auto-regressive model that is able to downscale the available stream flow data from monthly to daily time resolution and applied it to a large dataset that covered the entire North and Central American continent. Basins with different drainage areas and different hydro-climatic characteristics were considered, and the results show the general good ability of the analysed model to downscale monthly stream flows to daily stream flows, especially regarding the reproduction of the annual maxima. If the performance in terms of the reproduction of hydrographs and duration curves is considered, better results are obtained for those cases in which the hydrologic regime is such that the annual maxima stream flow show low or medium variability, which means that they have a low or medium coefficient of variation; however, when the variability increases, the performance of the model decreases

The aim of this paper is to address the monitoring of the recovery phase in the aftermath of Hurricane Matthew (28 September–10 October 2016) in the town of Jérémie, southwestern Haiti. This is accomplished via a novel change detection method that has been formulated, in a data fusion perspective, in terms of multitemporal supervised classification. The availability of very high resolution images provided by last-generation satellite synthetic aperture radar (SAR) and optical sensors makes this analysis promising from an application perspective and simultaneously challenging from a processing viewpoint. Indeed, pursuing such a goal requires the development of novel methodologies able to exploit the large amount of detailed information provided by this type of data. To take advantage of the temporal and spatial information associated with such images, the proposed method integrates multisensor, multisource, and contextual information. Markov random field modeling is adopted here to integrate the spatial context and the temporal correlation associated with images acquired at different dates. Moreover, the adoption of a region-based approach allows for the characterization of the geometrical structures in the images through multiple segmentation maps at different scales and times. The performances of the proposed approach are evaluated on multisensor pairs of COSMO-SkyMed SAR and Pléiades optical images acquired over Jérémie, in the aftermath of and during the three years after Hurricane Matthew. The effectiveness of the change detection results is analyzed both quantitatively, through the computation of accuracy measures on a test set, and qualitatively, by visual inspection of the classification maps. The robustness of the proposed method with respect to different algorithmic choices is also assessed, and the detected changes are discussed in relation to the recovery endeavors in the area and ground-truth data collected in the field in April 2019.

This paper illustrates some applications of COSMO-SkyMed (CSK) observations for rapid mapping of flooded areas and damages in small to medium size catchments. The results presented here have been obtained within the framework of the project "OPERA Civil protection from floods" funded by the Italian Space Agency and run by a team of scientific research centres and private companies. The project aims to the systematic evaluation of the added value of the use of Earth Observation techniques into operational flood prediction chains. Due to the specific geomorphology of Italy, the focus is mainly on flash floods on small sized river catchments. Monitoring and modelling processes at proper space-time scales in this environment raise several issues to be solved, compared to applications in larger river basins. Here we address some related to the suitable use of CSK imagery.

The increasing frequency and severity of extreme natural events, along with their escalating impacts, highlight the urgent need for robust tools to assess and strengthen national capacities for disaster preparedness and recovery. In this context, this paper introduces one of these tools, called the Recovery Gap Index (RGI), a comprehensive composite index designed to quantify and evaluate the post-extreme natural event response and recovery capabilities at the country level. The tool addresses the need for a systematic approach to quantify resilience and evaluate the impacts of consecutive events on vulnerable areas. The RGI synthesizes data from three well-established indices: the World Risk Index, INFORM, and Global Infrastructure Risk Model and Index (GIRI), covering critical dimensions related to sociodemographic factors, infrastructure, governance, technology, and economic resilience. By extracting key parameters from these diverse indices and aggregating them, the RGI provides a detailed assessment of each nation’s ability to manage the aftermath of extreme natural events. The index findings reveal significant regional disparities in recovery capacities, with European countries exhibiting stronger resilience, whereas many African and Asian nations face substantial challenges. Furthermore, this study proposes several potential future enhancements, such as the incorporation of early warning systems and insurance coverage metrics, aimed at improving its accuracy and practical application. The RGI aims to be a valuable tool for policymakers, disaster management professionals, and stakeholders, enabling them to make informed decisions and implement targeted interventions to further enhance global disaster resilience.