Alfonso Senatore

Wildfires in the Mediterranean are expected to become increasingly frequent and intense under climate change and increasing drought conditions. Owing to the growing computational capabilities of modern GPUs, deep learning approaches are increasingly being adopted for wildfire modeling and often outperform conventional methods. This study presents a deep feed-forward neural network, named the Wildfire Neural Network (WNN), designed to generate daily wildfire susceptibility maps. The model integrates meteorological variables, FWI-derived indicators, geomorphological attributes, land-use information, and anthropogenic proxies. WNN was trained and validated using multi-year data from Calabria, southern Italy, a Mediterranean wildfire hotspot. Compared with the operational Fire Weather Index baseline, WNN achieved higher AUROC values and greater spatial information content, with AUROC improvements of approximately 7% to 21% considering the historical configurations.

Understanding the evolving patterns of intense rainfall in the Mediterranean under climate change is an urgent challenge. Focusing on southern Italy, a representative sub-region of the Mediterranean basin, we examine in detail the role of sea-atmosphere-orography interactions, particularly the impact of increasing sea surface temperatures (SSTs), in enhancing heavy precipitation despite overall drying. Twenty consecutive precipitation events, identified during a particularly intense rainy season (September–December 2019), are reproduced at convection-permitting resolution (2 km) using the Weather Research and Forecasting (WRF) model forced by ERA5 reanalysis boundary conditions. Two additional scenarios are then tested: one with past SST levels approximating those of 1980, and another with SST increases consistent with end-of-century Shared Socioeconomic Pathways (SSPs), such as SSP 3-7.0 and SSP 5-8.5. The WRF simulations accurately reproduce cyclone tracks and precipitation patterns, indicating that, with all other factors held constant, increased SSTs could boost the frequency of heavy rainfall over land by intensifying otherwise weaker events. However, for the most intense events analyzed, spatially averaged accumulated precipitation over land remains largely unchanged because the heaviest precipitation occurs over the sea. The study highlights the value of high-resolution, convection-permitting simulations for capturing complex processes in orographically challenging regions and helps shed light on the seemingly contradictory coexistence of increasing daily precipitation extremes and declining annual precipitation totals in Southern Europe.

One of the main obstacles to natural risk reduction is the effective communication of risks to stakeholders and the public. Although numerous instruments, models, and platforms can be integrated, the lack of common protocols and standards often requires ad hoc adjustments. This contribution presents PAER (Piattaforma Acquisizione ed Elaborazione Rischi - Risk Acquisition and Processing Platform), a newly developed web platform within the Tech4You project - Technologies for climate change adaptation and quality of life improvement, funded under the Next Gen EU framework. PAER is characterized by an extremely flexible structure that can collect data from multiple sources, including variable-time-series data, rasters, and images.The PAER framework is a service dedicated to risk acquisition and processing, designed to store and manage heterogeneous types of data, both temporal and spatial. The platform can integrate information from ground-based monitoring networks and outputs generated by complex computational models. The service is essential for visualizing and storing environmental variables and for monitoring critical areas, thereby supporting advanced analyses and timely decision-making.PAER targets a wide range of stakeholders, both public and private. Owing to its ease of use and flexibility in customizing data-entry methods, the service is accessible to a range of users, each with different needs and levels of expertise. Users can configure a comprehensive data-validation workflow, including automated alerts, to ensure that all collected information is accurate and reliable. The optimized database structure enables fast and efficient queries, even when managing large datasets, ensuring smooth performance. Additionally, the system supports automatic data acquisition through APIs, simplifying integration with external sources and streamlining data flow management.The experimental validation involved multiple pilot projects across different domains. PAER successfully integrated datasets from hydrological monitoring and flood-risk assessment, including cosmic-ray neutron sensors for soil moisture estimation, pedestrian flood instability maps, and intelligent camera data from urban areas. The platform also managed in situ observations from piezometers, soil moisture probes, and meteorological stations, combined with weather forecasts produced by numerical models at regional and seasonal scales. Furthermore, PAER integrated wildfire-monitoring data, including intelligent camera imagery, regional risk maps, and automatically collected CO2 sensor measurements from forest mountainous areas. Additionally, the platform incorporates a Decision Support System (DSS) for agriculture based on the AquaCrop model, which automatically leverages other information already present in the platform and can be highly customized by users to meet specific agricultural management needs.The experimental results confirm that PAER provides a robust, unified environment for integrating, storing, and analyzing different environmental datasets, demonstrating its suitability for multi-domain, multi-scale environmental risk monitoring applications. The platform's flexibility and scalability make it an ideal candidate for broader adoption in environmental risk management worldwide. Future developments will focus on expanding the range of integrated data sources and applying PAER to additional natural-hazard scenarios across diverse geographical contexts, thereby fostering international collaboration and knowledge sharing in risk assessment and mitigation.

Seasonal hydro-meteorological forecasts are increasingly crucial for water resource management and risk mitigation in Mediterranean coastal regions, where climate variability and anthropogenic pressures create complex challenges. However, the application of global seasonal prediction systems at local scales remains limited by systematic biases and coarse spatial resolution, which hinder their operational use in decision-making processes. This contribution presents a two-level BCSD (Bias Correction Spatial Disaggregation) seasonal forecasting system applied to a complex orographic Mediterranean area.The system is based on the application of EQM (Empirical Quantile Mapping), as described by Lorenz et al. (2021), and focuses on precipitation and 2m air temperature variables from 1981 to 2024. The original product is the SEAS5 ensemble forecast released by ECMWF, with a horizontal resolution of approximately 36 km. The first level covers all of southern Italy and uses ERA5-Land as the reference dataset, yielding a horizontal resolution of approximately 9 km, whereas the second level, which can be seen as a refinement, focuses on the Calabria region and uses an observed, high-quality dataset, achieving a horizontal resolution of 5 km. Finally, a water balance model, calibrated over the Crati catchment, the main Calabrian river (southern Italy), has been intensively tested, focusing on the streamflow prediction. The performance has been evaluated using bias, spatial correlation, and the CRPSS (Continuous Ranked Probability Skill Score) metrics.The results reveal systematic biases in raw SEAS5 predictions, with precipitation consistently underestimated by up to 20 mm/month, particularly during transitional months (May, June, and September), whereas 2m air temperature exhibits a persistent warm bias of approximately +1°C. The first-level BCSD correction substantially reduces these errors across southern Italy, yielding positive CRPSS values for precipitation, especially during the summer season (JJA), and marked improvements in temperature predictions (CRPSS > 0.40). The spatial correlation increases, with precipitation average increasing by 19% and temperature by 16%. However, compared with observation, residual biases persist at the catchment scale, with winter precipitation (NDJ) remaining underestimated by more than 60 mm/month over the Crati, and autumn temperatures (SON) slightly overestimated. The implementation of the second-level BCSD effectively addresses these local-scale discrepancies, enhancing the spatial correlation and CRPSS skill scores and ensuring that the hydrological model receives minimally biased forcings, predicting realistic streamflow.This two-stage correction framework demonstrates the system's capability to preserve probabilistic forecast skill while enabling reliable impact assessments through the hydrological modeling chain, thereby bridging the gap between global seasonal predictions and local water resource management applications. As a further step, the first-level BCSD has been operationally implemented and is freely available at https://cesmma.unical.it/cwfv2/seasonal.html, as requested by several local stakeholders. In the future, the second-level BCSD and the hydrological impacts evaluation will be operationally implemented.Reference: Lorenz, C., et al. Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions, Earth Syst. Sci. Data, 13, 2701–2722, https://doi.org/10.5194/essd-13-2701-2021, 2021.

Understanding the evolving characteristics of extreme precipitation in the Mediterranean Basin is a critical scientific priority amid ongoing climate change. In particular, the observational analysis reveals an apparent paradox in several areas of this region: while total annual precipitation declines, the frequency and magnitude of concentrated heavy rainfall events do not decline, and in some cases even increase. To elucidate the physical mechanisms underlying this seemingly contradictory behavior, we examine the interplay among marine, atmospheric, and topographic factors, and quantify the contribution of rising sea surface temperatures (SSTs) to the amplification of extreme precipitation events.The methodological framework is based on the numerical reconstruction of twenty precipitation events that occurred during an active wet season extending from September to December 2019. These events are simulated using the Weather Research and Forecasting (WRF) model, configured at a convection-permitting resolution (2 km as horizontal grid spacing). The simulations are initialized and constrained using ERA5 reanalysis data. To isolate and quantify the specific influence of SST evolution on precipitation dynamics, we implement a PGW (Pseudo Global Warning) approach incorporating three distinct SST scenarios: a baseline configuration utilizing observed 2019 SST values, a retrospective scenario employing SST conditions representative of approximately 1980 (-1 °C), and a prospective scenario incorporating SST increases (+3 °C) consistent with end-of-century projections under various Shared Socioeconomic Pathways (SSPs).The high-resolution WRF simulations demonstrate robust skill in reproducing atmospheric circulation features and the spatial distribution of precipitation across the complex orographic terrain. Comparative analysis across SST scenarios reveals that elevated SSTs increase the frequency of intense precipitation over terrestrial areas, primarily by increasing atmospheric moisture availability. However, the absolute magnitude of peak rainfall accumulations overland exhibits relatively modest sensitivity to SST variations, as the highest precipitation predominantly occurs offshore.This investigation underscores the added value of convection-permitting atmospheric modeling approaches in capturing the physical processes governing precipitation extremes in topographically complex Mediterranean coastal environments. The findings contribute substantively to reconciling the apparently paradoxical coexistence of declining annual precipitation totals with intensifying daily precipitation extremes, a pattern with profound implications for water resource management, flood risk assessment, and climate adaptation strategies in vulnerable Mediterranean communities.

Assessing the impact of climate change on water availability requires robust and spatially consistent modelling frameworks, particularly in Mediterranean regions characterized by strong hydroclimatic variability. The Mediterranean basin is widely recognized as a climate-change hotspot, where increasing temperatures and changes in precipitation regimes are expected to exacerbate water scarcity and hydrological extremes, especially in southern areas (Lazoglou et al., 2024). This study investigates future water availability across Southern Italy, with a specific focus on the Southern Apennines River Basin District, by coupling high-resolution climate projections with a large-scale gridded hydrological model.Future climate forcing was derived from the Global Climate Model MPI-ESM-1-2-HR under the SSP5-8.5 scenario and dynamically downscaled at a convection-permitting scale (~4 km) using the WRF model. Bias-corrected precipitation and air temperature fields were subsequently re-gridded to 1-km resolution and used to force HYGRID-M (an acronym for HYdrological GRIDed – Monthly), a distributed water balance model operating at monthly time scale. HYGRID-M simulates actual evapotranspiration and runoff by integrating climatic inputs with spatially distributed information on land use, soil properties, and topography. The modelling framework was applied to simulate water balance components for the future period 2025–2044 and compared with a observed baseline (2000–2023), analyzing changes in precipitation seasonality and temperature, together with their impacts on evapotranspiration and runoff at both temporal and spatial scales.Results indicate a marked reduction in summer precipitation and a consistent increase in air temperature across all months, with warming reaching approximately +2°C. Despite higher temperatures, both actual evapotranspiration and runoff exhibited predominantly negative anomalies relative to the observed period, reflecting increased water limitations rather than a persistent long-term decreasing trend. Actual evapotranspiration (AET) exhibited yearly variations ranging from -29% to +16% with a mean of -2% and a standard deviation of 10.2% while runoff (Q) ranged from –39% to +46%, with a mean of –1.3% and a standard deviation of 24%, indicating the strong interannual variability with alternating dry and wetter years. The negligible contribution of snow accumulation and melting under future climatic conditions further alters seasonal runoff dynamics. Spatially, evapotranspiration responses were heterogeneous, with localized increases in Puglia and parts of Basilicata, whereas runoff showed mixed signals, with widespread reductions across Campania, Molise, Abruzzo and parts of Calabria.Overall, the results highlight a shift toward drier and more variable hydroclimatic conditions in Southern Italy, emphasizing the importance of integrated high-resolution climate–hydrological modelling for supporting climate adaptation and sustainable water resource planning at river basin scale.

Accurately quantifying inorganic carbon dynamics in stream networks is essential for constraining estimates of catchment-scale carbon dioxide (CO₂) outgassing, identifying its sources (autochthonous vs. allochthonous), and assessing sensitivity to hydrological and geomorphological controls. However, most existing approaches rely on single-station measurements or simplified reach-scale models, neglecting transport processes, upstream–downstream connectivity, network topology, and spatial heterogeneity of lateral inputs. Here, we present a network-based sampling design coupled with a Bayesian modelling framework to estimate network-scale balances of dissolved inorganic carbon (DIC), alkalinity, pH, and dissolved oxygen (O₂).We applied this framework to two headwater catchments with contrasting geology, climate, land cover, and hydrological regimes: Valfredda (5.3 km², north-eastern Italian Alps, Alpine climate) and Turbolo (6.9 km², southern Italy, Mediterranean climate). In both catchments, we conducted spatially distributed sampling of approximately 15 sites per network within a single day, measuring total alkalinity, dissolved CO₂, O₂, water temperature, pH, and discharge. Sampling was timed during periods of negligible gross primary production to minimize diel variability. This assumption was verified using continuous monitoring stations providing diel cycles of O₂, CO₂, and pH.The framework models pixel-scale mass balances of DIC, O₂, and alkalinity by accounting for upstream and lateral inputs, in-stream production, atmospheric exchange, and downstream export. Instantaneous carbonate equilibrium is assumed to derive pH and CO₂ from alkalinity and DIC, allowing direct comparison with observations. Reaeration coefficients are estimated using empirical relationships based on hydraulic properties. The model enables the mapping of longitudinal biogeochemical patterns across entire stream networks and the derivation of network-scale carbon budgets.The two catchments exhibit contrasting spatial dynamics. In the alpine Valfredda network, high headwater alkalinity associated with dolomitic lithology produces elevated initial DIC concentrations, while high turbulence promotes intense degassing, driving dissolved CO₂ concentrations toward atmospheric equilibrium downstream. In contrast, the Turbolo network is characterized by heterogeneous lithology and dense vegetation, generating diffuse, carbon-rich lateral inputs that sustain elevated CO₂ concentrations even in downstream reaches. In both systems, the model successfully reproduces observed spatial patterns, highlighting the importance of lateral inflows and network structure in shaping catchment-scale CO₂ dynamics.At the network scale, carbon flux magnitudes and dominant pathways differ markedly between catchments. Despite similar wetted channel areas (~7000 m² in Turbolo and ~8300 m² in Valfredda), Valfredda releases more than four times more CO₂ per unit stream surface area (~1.45 vs. ~0.36 mol m⁻² d⁻¹), corresponding to total fluxes of ~12 000 and ~2500 mol d⁻¹, respectively. In Valfredda, CO₂ evasion is sustained almost equally by lateral inputs and in-stream respiration, whereas in Turbolo it is dominated by lateral fluxes. Higher gas exchange rates and lower alkalinity in Valfredda favor rapid atmospheric CO₂ release, while lower turbulence and higher alkalinity in Turbolo promote downstream DIC transport.Overall, our results demonstrate that reliable estimates of riverine CO₂ emissions require explicit representation of spatial structure, hydrological connectivity, and coupled biogeochemical processes across stream networks, providing a generalizable approach for scaling carbon fluxes from point measurements to entire catchments.

Saharan Dust Outbreaks frequently hit the Mediterranean Basin, lasting for a few days. These phenomena have various implications for the ecosystem of the entire basin, affecting the atmosphere, lithosphere, biosphere, hydrosphere, and cryosphere. Moreover, they cause numerous hazards to human society, especially concerning the environment and health, and are particularly significant to people living in a “Dust Belt” around Sahara, including nearby areas such as the Mediterranean Basin. This study demonstrates that continuous dust intrusions from the Sahara, transported across distant geographic regions, cannot be considered random events; rather, they show long-range correlations for timescales shorter than 80 days. This behaviour generates a persistent and recurrent atmospheric pattern at inter-annual time scales and synoptic spatial scales, thus opening a new perspective for climate studies and evidencing a new kind of teleconnection between North Africa and the Mediterranean Basin.

Saharan Dust Outbreaks frequently hit the Mediterranean Basin, lasting for a few days. These phenomena have various implications for the ecosystem of the entire basin, affecting the atmosphere, lithosphere, biosphere, hydrosphere, and cryosphere. Moreover, they cause numerous hazards to human society, especially concerning the environment and health, and are particularly significant to people living in a “Dust Belt” around Sahara, including nearby areas such as the Mediterranean Basin. This study demonstrates that continuous dust intrusions from the Sahara, transported across distant geographic regions, cannot be considered random events; rather, they show long-range correlations for timescales shorter than 80 days. This behaviour generates a persistent and recurrent atmospheric pattern at inter-annual time scales and synoptic spatial scales, thus opening a new perspective for climate studies and evidencing a new kind of teleconnection between North Africa and the Mediterranean Basin.

Understanding the evolving patterns of intense rainfall in the Mediterranean under climate change is an urgent challenge. An analysis of the annual total and maximum one-day precipitation from 1955 to 2023 performed with the ERA5-Land dataset over the EURO-CORDEX domain reveals emerging patterns of contrasting trends along much of the northern Mediterranean coast, with heavy precipitation events increasing and total annual rainfall decreasing. An independent investigation on a ground-based dense monitoring network in southern Italy confirms the results. We focus on this representative sub-region of the study area to examine in detail the role of sea-atmosphere-orography interactions, particularly the impact of increasing sea surface temperature (SST), in enhancing heavy precipitation despite overall drying. Twenty consecutive precipitation events identified in a particularly intense rainy season (September–December 2019) are reproduced at a convection-permitting resolution (2 km) using the Weather Research and Forecasting (WRF) model with ERA5 reanalysis boundary conditions. Then, two scenarios are tested: one with past SST levels approximating 1980 and another with future SST increases in line with end-of-century Shared Socioeconomic Pathways (SSPs) projections. WRF simulations thoroughly describe cyclone tracks and precipitation patterns, showing that increased SST boosts the frequency of heavy rainfall events overland, though peak intensities remain mostly unchanged because the highest precipitations occur over the sea. The study demonstrates the unique capability of high-resolution, convection-permitting analyses to capture complex processes in orographically challenging regions and contributes to clarifying the seemingly contradictory trend of rising daily precipitation extremes despite falling annual precipitation totals in Southern Europe.