The extreme storm of November 2023 caused unprecedented damage across the Black Sea coastal zone. This paper examines the principal drivers of the subsequent coastal flooding and provides an assessment of the economic losses incurred by infrastructure damage in the Sochi region. The study employs the WAVEWATCH III spectral wave model alongside empirical run-up formulations. Simulation results indicate that the significant wave height peaked at 8.9 m in the central basin during the event of 26–27 November 2023, wavelengths exceeded 200.0 m and peak periods were 10.0–12.0 s. While the maximum significant wave height in the Sochi area reached 5.5 m—ranking among the highest recorded events for this locality—the corresponding average wavelength reached an absolute 45-year maximum of 234 m. This exceptional wavelength proved critical to the generation of extreme wave run-up. Media source analysis confirms that the primary mechanisms of destruction were severe wave run-up and the overtopping of seawall defence structures. Empirical calculations demonstrate that the combination of extreme wave conditions and severely narrow beach profiles conributed to a run-up exceeding 5.2 m with an additional water level rise due to storm surge, which led to widespread overtopping and flooding. Finally, the direct economic impact was quantified for the Imeretinskaya Lowland (Sirius Federal Territory). Based on official cost-estimation documentation, direct infrastructure damage is valued at approximately 2 million (at 2024 prices).
Advancing scientific capacity to track and predict spatiotemporal drought evolution is fundamental to anticipatory action that can save lives and minimize property loss across drought-prone Sudan. This study systematically characterizes drought events across Sudan during 1991–2023 using the Standardized Precipitation Evapotranspiration Index at 3-month (SPEI3) and 12-month (SPEI12) accumulation periods. It uses PERSIANN-CDR precipitation and ERA5 reanalysis temperature data, both at a spatial resolution of 0.25° × 0.25°. Employing an integrated analytical framework combining drought event identification, spatiotemporal clustering, centroid tracking, and propagation pathway analysis, we quantified drought characteristics including duration, severity, extent, and migration patterns across Sudan’s climatic zones. Results show that SPEI3 exhibits high spatial mobility with centroid displacements ranging from 441 to 4,763 km, predominantly migrating westward and northwestward in response to intra-seasonal rainfall variability and Intertropical Convergence Zone dynamics. Meanwhile, SPEI12 demonstrates extended persistence (up to 27 months), substantially higher cumulative severity (> 30 × 106 km2·month), and more directional propagation pathways, with centroids stabilizing over central and northern Sudan due to land-atmosphere feedback. These persistence and severity values are descriptive measures of drought. The 2009–2011 event represents the most severe long-term drought, exhibiting coherent south-to-north migration consistent with sustained monsoonal weakening. The central Sudan plains are hotspots where drought severity intensifies despite moderate duration. Scale-dependent propagation patterns indicate that drought evolution is controlled by complex interactions among atmospheric circulation variability, land-surface processes, and spatial domain geometry rather than duration alone, with spatial domain geometry reflecting geometric constraints on centroid concentration. These findings provide pivotal insights for developing multi-timescale drought-monitoring systems, enhancing early-warning capacity, and informing climate-adaptation strategies in Sudan and analogous dryland systems globally.
Whether landslide size, mobility, and spatial distribution patterns exhibit consistency or variability under different rainfall events within the same region remains insufficiently explored through systematic comparative studies. In this study, northern Guangdong, China, was selected as the study area, and three typical rainfall events that occurred in 2021, 2022, and 2024 were investigated. Multi-temporal rainfall-induced landslide inventories were constructed based on high-resolution optical remote sensing imagery, and a systematic comparative analysis was conducted to examine landslide size, mobility relationships, and spatial distribution patterns. The results indicate that the cumulative area–frequency distributions of the three inventories show similar decreasing patterns, with small- and medium-scale landslides dominating the inventories, while the corresponding probability density distributions exhibit distinct event-specific characteristics. The relationships among travel distance, landslide height, and landslide area exhibit empirical power-law scaling relationships, although the scaling exponents vary among rainfall events. The exponent of the height–area relationship decreases across the three events, whereas the travel-distance–area relationship exhibits event-specific variations, indicating differences in the scaling characteristics of landslide mobility among the inventories. These variations may be associated with differences in rainfall characteristics, antecedent environmental conditions, and previous rainfall disturbances, although their relative contributions cannot be directly separated based on the available inventories. Landslides are mainly concentrated at moderate elevations and slope gradients and frequently occur in areas characterized by specific geological, soil, and fluvial conditions. The spatial distribution of landslides is associated with accumulated rainfall, topographic conditions, geological settings, and geomorphological factors, although these spatial associations do not establish direct causal relationships. The multi-temporal landslide database established in this study provides a basis for comparative analysis of landslide scaling characteristics and spatial patterns under multiple rainfall events.
Flood relief teams must route vehicles and allocate scarce inspections using road reports that may be stale, misplaced or manipulated. We link vehicle-specific passability, likelihood-robust report fusion and inspection targeting to a dispatch loss that counts unserved demand and capacity sent onto impassable corridors. A stylised model describes when source inspection can become preferable to road inspection; a candidate rule combines state-based road priority with a source branch. Across 100 paired synthetic instances, bounded aggregation (shrink-and-reject) reduces regret by 117–337 passenger-equivalents relative to eight comparators under concentrated injections at the tested shares of 0.2, 0.3 and 0.5. With shrink-and-reject, under mislocated, stale and shallow-water reports, inspecting the most decision-critical road reduces regret by 48–71 passenger-equivalents relative to inspecting the most-reported unit. Honest bursts reveal the cost of rejecting repeated reports. A replay of the 2021 Zhengzhou flood uses 36 human-reviewed road reports, 276 road units and independent police closure lists. The estimated dispatch gain over the prior falls from 584.8 to 1.8 passenger-equivalents when analysis is restricted to five reports describing events before the decision; their posting times are unavailable. Fusion rankings also vary with credibility inputs and the assumed states of unlisted roads. These results support targeting inspections by their dispatch consequences.
Gravity-driven mass flows represent a significant near-field hazard in volcanic environments and, increasingly, in coastal regions affected by climate-driven slope instability. Despite their destructive potential, quantitative assessments of real-time detection systems operating under natural conditions remain scarce. Here we present the performance and sensitivity of the tsunami early-warning system currently operating at Stromboli Volcano (Italy), based on offshore pressure gauges and a real-time detection algorithm. Sixteen years of sea-level records (2010–2026), including 63 tsunamis events ranging from 0.02 to 2 m height, show that the system issues alert within less than 22 s from the tsunami onset and tens of seconds before its maximum amplitude, with no false alarms. A sensitivity analysis, based on Peak-Over-Threshold statistics and extreme wave modeling, demonstrates that the detection capability remains effective even under extreme sea state conditions also for tsunami with height as low as 4 m. To support regional tsunami warning and reduce the alerts for tsunami with no inundation effects on a distant (> 10 km) coasts, we apply real-time Ensemble Empirical Mode Decomposition (EEMD), which successfully extracts tsunami signals and preserves waveform characteristics. EEMD also isolates longer period sea level oscillations associated with atmospheric pressure waves within the meteotsunami frequency band, demonstrating the applicability of the method to multiple classes of non-seismic tsunamis. This work provides the first long-term quantitative evaluation of an operational near-field early-warning system for volcanogenic tsunamis and proposes a transferable multi-tier framework for landslide-tsunami and meteotsunami hazard mitigation for coastal environments worldwide.
