Search Results (161)

The increasing frequency of disasters caused by landslides, mainly due to climate change leading to more intense extreme events, requires reliable predictive models for risk mitigation. Italy, in particular, is a country at high risk of landslides, but the lack of an updated catalogue of landslide activation dates poses a significant challenge for defining reliable activation thresholds. This study develops a methodology for mapping landslide susceptibility based on events in a pilot area of central Italy, integrating a database of landslides with known activation dates with predisposing and triggering parameters. Two statistical techniques were compared to assess their predictive performance in discriminating landslide from non-landslide conditions during extreme precipitation events. A comparison between binary logistic regression (BLR) and decision trees (QUEST) revealed the clear superiority of the BLR model, which achieved excellent predictive accuracy (AUC = 0.913). The model identified clay-rich lithology, gentle slopes (0–16°) and maximum daily precipitation as the most significant controlling factors. This result led to the generation of three derivative products: a susceptibility map, a hazard map for an extreme precipitation scenario with a 100-year return period, and a spatially distributed map of activation thresholds. This threshold map quantifies the intensity of precipitation required to exceed a critical probability of landslide initiation (p > 0.7) at any point in the territory. The susceptibility map highlights critical areas within the study area, while the hazard map also includes the return period of the event. The threshold map is a direct and operational tool for early warning systems, transforming a statistical model into a guide for real-time risk management. The study area serves as a pilot area that could allow this methodology to be replicated. With the integration of real-time meteorological data, it could function as a real-time warning system. The proposed framework therefore provides a directly actionable tool for civil protection agencies, land-use planning authorities, and emergency managers, enabling location-specific rainfall alert thresholds to be issued rather than a single regional value, with the potential to reduce both false alarms and missed warnings. Full article

With the increasing number of failures in high-voltage cross-linked polyethylene cables caused by buffer layer ablation, it is of great significance to investigate the electro–thermal coupling characteristics and ablation driving mechanisms under different defect conditions. Based on a multiphysics coupling model, an electro–thermal coupled simulation of the cable buffer layer and corrugated aluminum sheath was carried out, considering three typical defect types: air-gap barrier, moisture ingress, and white-powder barrier. The distributions of air-gap electric field, interfacial current density, temperature, and heat source were systematically analyzed. From the perspective of ablation mechanisms, the maximum air-gap electric field and its spatial location, as well as the maximum temperature of the buffer layer and its corresponding region, were investigated under different defect conditions. Meanwhile, the probabilities of electrical ablation and thermal ablation, together with their corresponding threshold parameters, were quantitatively evaluated. The results show that when an air-gap barrier exists between the buffer layer and the aluminum sheath, air breakdown may occur when the air-gap thickness is approximately 0.01–0.05 mm. When the buffer layer is moisture-contaminated and the defect length exceeds approximately 2 m, the buffer layer temperature may exceed 165 °C. When white-powder precipitates in the buffer layer, partial discharge may be initiated at the early stage. With the increase in powder barrier proportion, the buffer layer temperature may exceed approximately 220 °C. It should be noted that these critical characteristics are obtained under the simulation conditions of this study. The specific values depend on material parameters and operating conditions and can provide theoretical support for cable operation condition assessment. Full article

Research on the evolutionary patterns and propagation mechanisms of different drought types is of great significance for regional water resources management and the prevention and control of agricultural drought risks. Taking the arid region in the western Chinese Loess Plateau as the study area, this paper systematically revealed the spatiotemporal variation characteristics, propagation lag time and conditional probability of meteorological and agricultural droughts based on the monthly Standardized Precipitation Evapotranspiration Index (SPEI) and self-calibrating Palmer Drought Severity Index (scPDSI) during 1985–2022 by comprehensively adopting the Mann–Kendall trend test, Sen’s slope estimation, run theory, drought frequency analysis, as well as the Copula function and event-matching method. The results showed that during the study period, meteorological drought (characterized by SPEI) exhibited an insignificant intensification overall, while agricultural drought (characterized by scPDSI) presented a significant mitigation at the monthly scale. The maximum occurrence frequency of agricultural drought reached 70.39%, which was significantly higher than that of meteorological drought (38.82%); in addition, agricultural drought featured a longer average duration and greater severity, with a spatial pattern of higher in the northwest and lower in the southeast in the study area. The average propagation lag time of drought derived from the Copula function was 1.41 months, versus 2.19 months obtained by the event-matching method. When meteorological drought reached the moderate level (SPEI < −1.0), it was likely to trigger agricultural drought of mild or higher severity. The research findings can provide a scientific reference for formulating differentiated drought prevention strategies in the arid region of the western Loess Plateau, China. Full article

Climate change can alter both the amount and timing of inflows to water supply reservoirs while also increasing heat-driven demand and the likelihood of stressful warm-season conditions. Climate-driven changes in inflow to Eagle Mountain Lake Reservoir (Texas, USA) were quantified by integrating (i) a calibrated SWAT model evaluated at four USGS stream gauges, (ii) statistically downscaled CMIP6 daily precipitation and minimum/maximum temperature at seven stations/grid points for a historical baseline (2003–2022) and two future windows (2031–2050 and 2081–2100) under SSP1-2.6, SSP2-4.5, and SSP5-8.5, and (iii) observed reservoir operations (lake level, water supply releases, and flood discharge; 1990–2022). A standard watershed climate workflow is reframed through an operations-focused lens, wherein projected inflow changes are translated into decision-relevant indicators via the utilization of observed thresholds and operating mode signals. Included within this framework are spring refill-season inflow shifts, a hot–dry month metric, and storage threshold performance measures, which are coupled with screening-level probabilities linked to multi-year inflow deficits. Across models and stations, mean annual temperature increases by 0.7–1.9 °C in the 2030s and by 0.7–6.1 °C in the 2080s, while annual precipitation changes remain uncertain (−24% to +55%). Daily projections show a strong increase in extreme heat days (daily Tmax above the historical 95th percentile), from about 18 days yr⁻¹ historically to about 30–33 days yr⁻¹ in the 2030s and about 34–82 days yr⁻¹ by the 2080s. Hot–dry months (monthly mean Tmax above the historical 90th percentile and monthly precipitation below the historical median) increase modestly by mid-century and rise to about 1.5 months yr⁻¹ on average by the 2080s under SSP5-8.5. SWAT simulations indicate that the mean annual inflow declines by 17–20% across scenarios, with the largest reductions during the spring refill period (March–June). Historical operations show that hot–dry months are associated with approximately double the mean water supply release (7.2 vs. 3.5 m³/s) and a lower monthly minimum lake level (about 0.30 m; about 1.0 ft lower on average). Flood discharges occur almost exclusively when lake elevation is at or above about 197.8 m and follow multi-day rainfall clusters (cross-validated AUC = 0.99). Together, these results indicate that earlier-season inflow reductions and more frequent hot–dry stress will tighten the operational margin between refill, summer demand, and flood management, underscoring the need for adaptive drought response triggers and integrated drought–flood planning for the Dallas–Fort Worth region. Full article

Supercooled liquid water (SLW) in mixed-phase clouds significantly influences precipitation efficiency and aviation safety. However, a comprehensive understanding of its vertical structure has been hampered by a lack of sustained, vertically resolved observations over the North China Plain. This study presents the first systematic analysis of SLW vertical distribution and microphysics in this region, utilizing a year-long dataset (2022) from synergistic ground-based instruments in Beijing. Our retrieval approach integrates Ka-band cloud radar, microwave radiometer, ceilometer, and radiosonde data, combining fuzzy-logic phase classification with a liquid water content inversion constrained by column liquid water path. Key findings reveal a distinct bimodal seasonality: SLW primarily occurs at mid-to-upper levels (4–7.5 km) during spring and summer, driven by convective lofting, while winter SLW is confined to lower altitudes (1–2 km) under stable atmospheric conditions. The temperature-dependent occurrence probability of SLW clouds has an annual maximum at −12 °C. The diurnal variation in SLW in summer shows peaks in the afternoon and at night, corresponding to convective cloud activity. Spring, autumn, and winter do not exhibit strong diurnal variations. Retrieved microphysical properties, including liquid water content and droplet effective radius, are consistent with in situ aircraft measurements, validating our methodology. This analysis provides a critical observational benchmark and offers actionable insights for improving cloud microphysics parameterizations in models and optimizing weather modification strategies, such as seeding altitude and timing, in this water-stressed region. Full article

Dry climate regions face heightened risks of flooding and infrastructure damage even with minimal rainfall. Climate change is intensifying this vulnerability by increasing the duration, frequency, and intensity of precipitation events in areas that have historically experienced arid conditions. As a result, accurate precipitation estimation in these regions is critical for effective planning, risk mitigation, and infrastructure resilience. This study evaluates the performance of five satellite- and model-based precipitation products by comparing them against in situ rain gauge observations in a dry-climate region: The fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) (analyzing maximum and minimum precipitation rates separately), the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), the Western Land Data Assimilation System (WLDAS), and the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG). The analysis focuses on both average daily rainfall and extreme precipitation events, with particular attention to precipitation magnitude and the accuracy of event detection, using a combination of statistical metrics—including bias ratio, mean error, and correlation coefficient—as well as contingency statistics such as probability of detection, false alarm rate, missed precipitation fraction, and false precipitation fraction. The study area is Palm Desert, a mountainous, arid, and urban region in Southern California, which exemplifies the challenges faced by dry regions under changing climate conditions. Among the products assessed, WLDAS ranked highest in measuring total precipitation and extreme rainfall amounts but performed the worst in detecting the occurrence of both average and extreme rainfall events. In contrast, IMERG and ERA5-MIN demonstrated the strongest ability to detect the timing of precipitation, though they were less accurate in estimating the magnitude of rainfall per event. Overall, this study provides valuable insights into the reliability and limitations of different precipitation estimation products in dry regions, where even small amounts of rainfall can have disproportionately large impacts on infrastructure and public safety. Full article

Lightning is the primary natural cause of wildfires in mid- to high-latitude forests, and it is increasing in frequency under climate change. Traditional fire danger forecasts, reliant on standard meteorological data, often fail to capture extreme events and future risk. To address this issue, we integrate extreme climate indices with meteorological, vegetation, soil, and topographic data, and apply four machine learning methods to build probabilistic models for lightning fire occurrence. The results show that incorporating extreme climate indices significantly improves model performance. Among the models, XGBoost achieved the highest accuracy (87.4%) and AUC (0.903), clearly outperforming traditional fire weather indices (accuracy 60%–71%). Model interpretation with SHapley Additive exPlanations (SHAP) further revealed the driving mechanisms and interaction effects of extreme factors. Extreme temperature and precipitation indices contributed nearly 60% to fire occurrence, with growing season length (GSL), minimum of daily maximum temperature (TXn), diurnal temperature range (DTR), and warm spell duration index (WSDI) identified as key drivers. In contrast, heavy precipitation indices exerted a suppressing effect. Compound hot and dry conditions amplified fuel aridity and markedly increased ignition probability. This interpretable framework improves short-term lightning fire prediction and offers quantitative support for risk warning and resource allocation in a warming climate. Full article

Flooding is one of Africa’s most impactful natural disasters, significantly affecting human lives, infrastructure, and economies. This study examines the spatial and temporal distribution of historical flood events across the continent from 1927 to 2020, with a focus on fatalities, affected populations, and economic damage. Data from the Emergency Events Database (EM-DAT), the fifth generation of bias-corrected European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) observational datasets were used to calculate extreme precipitation indices—Consecutive Wet Days (CWD), annual precipitation on very wet days (R95PTOT), and Annual Maximum Precipitation (AMP). Spatial analysis tools and the Mann–Kendall test were used to assess trends in flood occurrences, while Pearson correlation analysis identified key meteorological drivers across 16 African capital cities for 1981–2019. A flood frequency analysis was conducted using Weibull, Gamma, Lognormal, Gumbel, and Logistic probability distribution models to compute flood return periods for up to 100 years. Results reveal a significant upward trend with a slope above 0.50 floods per year in flood frequency and impact over the period, particularly in regions such as West Africa (Nigeria, Ghana), East Africa (Ethiopia, Kenya, Tanzania), North Africa (Algeria, Morocco), Central Africa (Angola, Democratic Republic of Congo), and Southern Africa (Mozambique, Malawi, South Africa). Positive trends (at 99% significance level with slopes ranging between 0.50 and 0.60 floods per year) were observed in flood-related fatalities, affected populations, and economic damage across Regional Economic Communities (RECs), individual countries, and cities of Africa. The CWD, R95PTOT, and AMP indices emerged as reliable predictors of flood events, while non-stationary return periods exhibited low uncertainties for events within 20 years. These findings underscore the urgency of implementing robust flood disaster management strategies, enhancing flood forecasting systems, and designing resilient infrastructure to mitigate growing flood risks in Africa’s rapidly changing climate. Full article

Typhoon-induced hazards in South Korea exhibit strong spatial heterogeneity, requiring localized assessments to support impact-based early warning. This study develops a district-level typhoon hazard framework by integrating high-resolution meteorological fields with structural and hydrological vulnerability indicators. Two impact-oriented indices were formulated: the Strong Wind Risk Index (SWI), based on 3 s gust wind intensity and building-age fragility, and the Heavy Rainfall Risk Index (HRI), combining probable maximum precipitation with permeability and river-network density. Hazard levels were classified into four categories, Attention, Caution, Warning, and Danger, using district-specific percentile thresholds consistent with the THIRA methodology. Nationwide analysis across 250 districts revealed a pronounced coastal–inland gradient: mean SWI and HRI values in Busan were approximately 1.9 and 6.3 times higher than those in Seoul, respectively. Sub-district mapping further identified localized hotspots driven by topographic exposure and structural vulnerability. By establishing statistically derived, region-specific thresholds, this framework provides an operational foundation for integrating localized hazard interpretation into Korea’s Typhoon Ready System (TRS). The results strengthen the scientific basis for adaptive, evidence-based early warning and climate-resilient disaster-risk governance. Full article

China’s Yellow River basin encounters widespread risks of reduced runoff and intensified hydrological drought. This study focuses on the middle and upper reaches of the Dahei River, the Yellow River’s primary tributary. In this region, the Soil & Water Assessment Tool (SWAT) hydrological model was employed to simulate hydrological processes, identify runoff changes and hydrological drought characteristics, and conduct attribution analysis from 1983 to 2022, as well as to project trends over the next 40 years. The results indicate that total runoff during the impact period (1999–2022) decreased by 55.26% compared to the baseline period (1983–1998). Climate change accounted for a contribution rate of 38.6% to this decline, while human activities accounted for 61.4%. Additionally, climate primarily altered surface runoff (SURQ) and lateral groundwater flow (LATQ) through precipitation changes, while land use had a predominant influence on total runoff volume by modifying SURQ. Both factors exhibited relatively minor effects on LATQ. Moreover, human activities contribute to hydrological drought at a rate of 36.11% to 94.25%. Drought probability is significantly influenced by climate through precipitation and temperature changes, while land use primarily mitigates hydrological drought by impacting the three runoff components. It is predicted that over the next 40 years, total runoff will decrease by 2.08% to 60.16%, along with hydrological droughts that are more frequent, longer in average duration, and more intense; however, the Maximum Drought Duration is anticipated to shorten. In the east and northeast, hydrological drought presents a trend of intensification, with central and western regions exhibiting weaker or declining changes. Full article

Hydroclimatic and geomorphological prerequisites for mudflow hazard were studied using data on several of the largest flood events in the Khamar-Daban mountain area (Lake Baikal, East Siberia) for the period from 1966 to 2022. The data include flood-forming precipitation and atmospheric circulation patterns, the amount of related suspended sediment discharge in the years of high floods, as well as terrain features favorable for the formation of catastrophic floods and mudflows. Floods and mudflows in the area can arise under conditions of extremely high daily precipitation (up to 200 mm or more) after the territory becomes moistened by prolonged rainfall under meridional air transport. The maximum water discharge correlates with a multifold increase in the suspended sediment discharge and turbidity. The increase in sediment discharge associated with maximum water discharge (floods) of ≤10% probability is apparently due to 4–9 times higher flow rates. On the other hand, the formation of the solid runoff component in the area is controlled geomorphologically by slope processes depending on slope steepness, elevation contrasts, and the thickness of soft sediments subject to denudation and transport. The geomorphological conditions are most favorable for the development of mudflows and catastrophic floods in the catchments of the Bezymyannaya, Slyudyanka, Khara-Murin, and Utulik rivers. Floods and mudflows are especially hazardous on the southern shore of Lake Baikal, encircled by the Khamar-Daban Range, where active mudflow processes pose risks to the towns of Slyudyanka and Baikalsk, as well as to the sludge storage facilities of the abandoned Baikal Pulp and Paper Mill. Full article

Tectona grandis L.f. (teak) is a timber species of exceptional commercial value, widely cultivated in Ecuador for export to international markets. This study aimed to ecogeographically characterise current production and identify zones with high potential for exploitation, using tools from CAPFITOGEN v3.0 and the MaxEnt maximum entropy algorithm, based on data from 1023 plantations. The territory was classified into 26 ecogeographic categories, of which teak is present in 13. Categories 17, 19, and 21 were predominant, collectively accounting for 88.27% of the analysed plantations. Sixteen relevant variables (comprising four climatic, four edaphic, and eight geophysical factors) served as predictors in MaxEnt, with model validation demonstrating strong accuracy (AUC = 0.924). The most influential factors for teak suitability were precipitation seasonality, altitude, annual precipitation and September wind speed. Areas with elevated and high probabilities for teak exploitation were quantified at 6737.83 km² and 10,154.70 km², respectively, with Guayas, Los Ríos, and Manabí provinces showing the most favourable conditions. This integrative framework provides an evidence-based basis for land-use planning and resource management, supporting more sustainable and efficient development of Ecuador’s teak forestry sector. Full article

Increasing climate change leads to the variability of dependencies among meteorological factors. Currently, the investigation of the interdependence of meteorological variables primarily focuses on the bivariate relationships, such as precipitation and temperature or precipitation and wind speed. However, the high-dimensional dependencies among multiple meteorological factors have not been thoroughly explored. This paper proposes a statistical analysis framework that comprehensively analyzes the changes in dependencies among meteorological factors. This statistical analysis framework is based on multivariate joint distributions and enables the detection of dependency change points as well as the analysis of drivers using total probability formulations and orthogonal experiments. Taking the Huang-Huai-Hai region, a recipient area of the South-to-North Water Diversion project, as the study area, we constructed a vine copula-based multivariate joint distribution for precipitation (Pre) and six meteorological factors: temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), wind speed (Win), relative humidity (Rhu), and the Southern Oscillation Index (SOI). The results indicate that a change point exists in the dependence of the 7-dimensional variables (Pre and six meteorological factors) in the Huang-Huai-Hai region in 2013. Tmin, Win, and Tmax are the primary driving factors affecting the precipitation–meteorological dependency relationship. The cumulative distribution function (CDF) is used to describe the probability distribution of precipitation and related meteorological factors. The optimal CDF values of the multivariate joint distribution model were achieved with Rhu and Tmax at level 3, SOI and Tm at level 2, and Win and Tmin at level 1. The results can provide a theoretical method for testing the non-stationarity of high-dimensional meteorological variable dependencies and offer conditional probability support for constructing meteorological prediction machine learning models. Full article

Comprehending the effects of climate change on the range of endangered species is essential for formulating successful conservation strategies. This research examines two nationally protected dung beetle species (Heliocopris dominus and Heliocopris bucephalus) in China to forecast their probable habitat range under present and future climate scenarios. Employing MaxEnt modeling with validated occurrence records and environmental variables, we discerned critical factors affecting their distribution and anticipated changes in habitat suitability. Results reveal that isothermality, temperature seasonality, maximum temperature of the warmest month, and annual precipitation are the principal environmental drivers. Presently, appropriate habitats are primarily located in southern Yunnan and Hainan, with future forecasts indicating a northward extension into additional areas. These findings offer critical insights for choosing conservation zones for these vulnerable species amid shifting climate conditions. Full article

Arid mountainous regions are vulnerable to extreme hydrological events such as floods and droughts. Providing accurate and continuous rainfall records with no gaps is crucial for effective flood mitigation and water resource management in these and downstream areas. Satellite data and geospatial interpolation can be employed for this purpose and to provide continuous data series. However, it is essential to thoroughly assess these methods to avoid an increase in errors and uncertainties in the design of flood protection and water resource management systems. The current study focuses on the mountainous region in northern Oman, which covers approximately 50,000 square kilometers, accounting for 16% of Oman’s total area. The study utilizes data from 279 rain gauges spanning from 1975 to 2009, with varying annual data gaps. Due to the limited accuracy of satellite data in arid and mountainous regions, 51 geospatial interpolations were used to fill data gaps to yield maximum annual and total yearly precipitation data records. The root mean square error (RMSE) and correlation coefficient (R) were used to assess the most suitable geospatial interpolation technique. The selected geospatial interpolation technique was utilized to generate the spatial distribution of annual maxima and total yearly precipitation over the study area for the period from 1975 to 2009. Furthermore, gamma, normal, and extreme value families of probability density functions (PDFs) were evaluated to fit the rain gauge gap-filled datasets. Finally, maximum annual precipitation values for return periods of 2, 5, 10, 25, 50, and 100 years were generated for each rain gauge. The results show that the geostatistical interpolation techniques outperformed the deterministic interpolation techniques in generating the spatial distribution of maximum and total yearly records over the study area. Full article