Search Results (1,867)

Internal waves play a critical role in material transport, vertical mixing, and energy dissipation within stratified aquatic systems. Their dynamics are strongly modulated by thermal stratification and surface meteorological forcing. This study examines the influence of interannual meteorological variability from 1980 to 2010 on internal wave behavior using a series of numerical simulations in Lake Biwa in Japan. In each simulation, air temperature, wind speed, or precipitation was perturbed by ±2 standard deviations relative to the climatological mean. Power spectral analysis of simulated velocity fields was conducted for the surface, thermocline, and bottom layers, focusing on super-inertial (6–16 h), near-inertial (~16–30 h), and sub-inertial (>30 h) frequency bands. The results show that higher air temperatures intensify stratification and enhance near-inertial internal waves, particularly within the thermocline, whereas cooler conditions favor sub-inertial wave dominance. Increased wind speeds amplify internal wave energy across all layers, with the strongest effect occurring in the high-frequency band due to intensified wind stress and vertical shear, while weaker winds suppress wave activity. Precipitation variability primarily affects surface stratification, exerting more localized and weaker impacts. These findings highlight the non-linear, depth-dependent responses of internal waves to atmospheric drivers and improve understanding of the coupling between climate variability and internal wave energetics. The insights gained provide a basis for more accurate predictions and sustainable management of stratified aquatic ecosystems under future climate scenarios. Full article

Research on Mesoscale Convective Systems (MCSs) in Ecuador has focused on regional studies. However, it lacks a thorough and general examination of their relationship with the nation’s diverse orography and large-scale phenomena. This study conducts a climatological analysis of MCS occurrence throughout Ecuador’s natural regions. We perform this study using Sen’s Slope and the Mann–Kendall test. Teleconnections from the Pacific and Atlantic Oceans are studied through wavelet decomposition between time series and Pacific and Atlantic oceanic indices. The main factors that control MCS formation depend on the region. The Intertropical Convergence Zone (ITCZ) at the large scale affects the entire territory. In western Ecuador, MCS formation is mostly related to the El Niño current and the Chocó Low-Level Jet (CLLJ). The Orinoco Low-Level Jet (OLLJ) and evapotranspiration and nocturnal convection display the largest roles in the east. A progressive intensification of activity from Highlands-North in SON is detected (0.143 MCSs per year). MCSs contribute 26% of total precipitation on average, with regional variations from Coast-South (16.41%) to Amazon-North (44.13%). The research confirms existing knowledge about El Niño’s strong relationship (ρ = 0.7) with MCS occurrence in coastal areas while uncovering new complex patterns. The Trans-Nino Index (TNI) functions as a critical two-sided modulator that conventional analysis methods fail to detect. It produces null correlations over conventional time series of MCS occurrence yet emerges as a primary driver of low-frequency variability in the proposed six natural zones of Ecuador. Wavelet decomposition reveals contrasting TNI responses: Amazon-North shows positive correlation (0.73) while Amazon-South exhibits negative correlation (−0.70) at low frequencies. This affects Walker circulations dynamics over the Pacific Ocean. This research establishes fundamental knowledge about MCSs in Ecuador. It builds on a database with strong methodology as a backbone. The research provides essential information about the factors leading to convection in the country. This will help improve seasonal forecast accuracy and risk management effectiveness. Full article

This paper presents the development of a high-resolution composite index to monitor and quantify climate-related risks across Italy. The country’s complex climatic variability, extensive coastline, and low insurance penetration highlight the urgent need for robust, locally calibrated tools to bridge the climate protection gap. Building on the methodological framework of existing actuarial climate indices, previously adapted for France and the Iberian Peninsula, the index integrates six standardised indicators capturing warm and cool temperature extremes, heavy precipitation intensity, dry spell duration, high wind frequency, and sea level change. It leverages hourly ERA5-Land reanalysis data and monthly sea level observations from tide gauges. Results show a clear upward trend in climate anomalies, with regional and seasonal differentiation. Among all components, sea level is most strongly correlated with the composite index, underscoring Italy’s vulnerability to marine-related risks. Comparative analysis with European indices confirms both the robustness and specificity of the Italian exposure profile, reinforcing the need for tailored risk metrics. The index can support innovative risk transfer mechanisms, including climate-related insurance, regulatory stress testing, and resilience planning. Combining scientific rigour with operational relevance, it offers a consistent, transparent, and policy-relevant tool for managing climate risk in Italy and contributing to harmonised European frameworks. Full article

Global warming poses a growing threat to winter wheat production in Henan Province, a critical region for China’s food security, necessitating a quantitative assessment of climate impacts. This study aimed to quantify the dominant climatic drivers of winter wheat yield and assess its spatiotemporal evolution and future risks under climate change, thereby providing a scientific basis for targeted adaptation strategies. Thus, the APSIM model in combination with the Geodetector method was applied to quantify the spatiotemporal response of winter wheat yield to climate change in Henan Province under historical (1957–2020) and SSP245 scenarios. The study results demonstrated significant trends in climatic factors during the winter wheat growing season: precipitation decreased by an average of 3.09 mm/decade, sunshine hours declined by 36 h/decade, wind speed reduced by 0.447 m/(s·decade), and evaporation decreased by 14.7 mm/decade. In contrast, the accumulated temperature ≥ 0 °C significantly increased by 70.9 °C·d/decade. Geodetector analysis further identified accumulated temperature as the dominant climatic driver (q = 0.548), followed by precipitation (q = 0.340) and sunshine hours (q = 0.261). Yield simulations from 1960 to 2018 indicated that most regions maintained stable or slightly increasing yields (<50 kg·ha⁻¹·decade⁻¹), though some areas experienced fluctuating declines. Under future scenarios, major production regions in Henan Province (Zhengzhou, Xinxiang, Luoyang) are projected to see substantial yield increases, with growth rates of 147.2–148.9 kg·ha⁻¹·decade⁻¹. Specifically, Xinxiang is expected to achieve yields of 6200 kg·ha⁻¹. The frequency of climate-induced negative yield years decreased by approximately 35% after 2003, highlighting the role of improved agricultural technologies in enhancing climate resilience. This study clarifies how multiple climatic factors jointly affect winter wheat yield, identifying rising accumulated temperature and water stress as key future constraints. It recommends optimizing varietal selection and cultivation practices according to regional climate patterns to improve policy relevance and local applicability. Full article

The increasing frequency of extreme climate events (ECEs) is expected to significantly affect crop yields in the future, threatening regional and global food security. However, uncertainties in yield projections persist due to regional variability, model differences, and scenario assumptions. Leveraging historical agricultural disaster and meteorological data from China (1995–2014), this study employs the vulnerability curve assessment to determine the most appropriate models for assessing crop yields affected by different ECEs (drought, extreme precipitation, extreme low temperature, and extreme wind) across six regions. By integrating multi-model and multi-scenario (SSP1-2.6, SSP3-7.0, SSP5-8.5) future climate data from Coupled Model Intercomparison Project Phase 6 (CMIP6), we conducted pooled prediction of the individual and combined impacts of different ECEs on crop yields for the near-term (2020–2040) and mid-term (2041–2060). The median of multi-model prediction of crop yield reductions in China was −16.0% (range: −32.5% to −2.6%), with more severe losses in Northeast, Northwest, and North China, particularly under higher radiative forcing scenarios. Drought is the most destructive of the four types of ECEs. These results will aid decision-makers in identifying high-risk zones for crop yields affected by ECEs and provide a scientific basis for the developing targeted adaptation strategies in various regions. Full article

Southwestern China, characterized by highly undulating terrain and mountainous areas, faces frequent landslide disasters. However, previous studies in this region mostly neglected the role of extreme rainfall in landslide susceptibility assessment and the socio-economic risks threatened by landslides. To address these gaps, this study integrated 688 recorded landslides for Tengchong City in the southwest of China and 10 influencing factors (topography, lithology, climate, vegetation, and human activities), particularly two extreme precipitation indices of maximum consecutive 5 day precipitation (Rx5day) and maximum length of wet spell (CWD). These influencing factors were selected after ensuring variable independence via multicollinearity analysis. Four machine learning models were then built for landslide susceptibility assessment. The Random Forest model performed the best with an Area Under Curve (AUC) of 0.88 and identified elevation, normalized difference vegetation index (NDVI), lithology, and CWD as the four most important influencing factors. Landslides in Tengchong are concentrated in areas with low NDVI (<0.57), indicating increased vegetation cover might reduce landslide frequency. Landslide risk was further quantified via the Analytic Hierarchy Process (AHP) by integrating multiple socio-economic factors. High-risk zones were pinpointed in central-southern Tengchong (e.g., Heshun and Tuantian townships) due to their high social exposure and vulnerability. Overall, this study highlights extreme rainfall and vegetation as key modifiers of landslide susceptibility and identifies the regions with high landslide risk, which provides targeted scientific support for regional early-warning systems and risk management. Full article

In this paper, we analyse drought variability in Spain (1950–2024) using the Standardised Precipitation–Evapotranspiration Index (SPEI) at 6-, 12-, and 24-month scales. Using 43 long-record meteorological observatories (AEMET), we compute SPEI from quality-controlled (QC), homogenised series, and derive coherent drought regions via clustering and assess trends in the frequency, duration, and intensity of dry episodes (SPEI ≤ −1.5), including seasonality and statistical significance (p < 0.05). Short-term behaviour (SPEI-6) has become more complex in recent decades, with the emergence of a “Catalonia” type and stronger June–October deficits across the northern interior; Mediterranean coasts show smaller or non-significant changes. Long-term behaviour (SPEI-24) is more structural, with increasing persistence and duration over the north-eastern interior and Andalusia–La Mancha, consistent with multi-year drought. Overall, short and long scales converge on rising drought severity and persistence across interior Spain, supporting multi-scale monitoring and region-specific adaptation in agriculture, water resources, and forest management. Key figures are as follows: at 6 months—frequency 0.09/0.08 per decade (Centre–León/Catalonia), duration 0.59/0.50 months per decade, intensity −0.12 to −0.10 SPEI per decade; at 24 months—frequency 0.5 per decade (Cantabrian/NE interior), duration 0.8/0.7/0.4 months per decade (Andalusia–La Mancha/NE interior/Cabo de Gata–Almería), intensity −0.06 SPEI per decade; Mediterranean changes are smaller or non-significant. Full article

Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R² and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate. Full article

This study presents an integrated assessment of four decades (1985–2023) of environmental and climate alterations in the principal metropolitan conurbation of the eastern Brazilian Amazon, encompassing Belém and its adjacent municipalities. By combining high-resolution land use/land cover (LULC) dynamics with in situ meteorological data, including understudied elements, such as relative humidity (RH) and wind speed, and satellite-derived precipitation estimates (CHIRPS v3), we advance the scientific understanding of regional climate trends. Our results document significant climate shifts, including pronounced dry-season warming (+1.5 °C), atmospheric drying (−4% in RH), attenuated wind patterns (−0.4 m s⁻¹), and altered precipitation regimes, which exhibit strong spatiotemporal coupling with extensive forest loss (−20%) and rapid urban expansion (+84%) between 1985 and 2023. Multivariate analyses reveal that these land–climate interactions are strongest during the dry regime, underscoring the role of surface–atmosphere feedbacks in amplifying regional changes. Comparative analysis of past (1980–1999) and present (2005–2024) decades demonstrates a marked intensification in the frequency and magnitude of extreme seasonal climate events. These findings elucidate a critical feedback mechanism that exacerbates climate risks in tropical urban areas. Consequently, we argue that mitigation public policies must prioritize the strict conservation of peri-urban forest fragments (vital for moisture recycling and local climate regulation) and the strategic implementation of green infrastructure aligned with prevailing wind patterns to enhance thermal comfort and resilience to hydrological extremes. Full article

Extreme precipitation events, increasingly driven by climate change, are becoming more frequent and pose significant challenges to both the ecological environment and human society. Using the MSWEP data, this study constructed eight event-based extreme precipitation indicators so as to systematically analyze the spatiotemporal characteristics and dominant types of extreme precipitation across Central Asia and its three sub-regions from 1979 to 2023. The results revealed the following: (1) Extreme precipitation events exhibit a pronounced spatial preference for high-altitude areas, with the total number of events reaching up to 698 in these regions. (2) From 1979 to 1991, the frequency of extreme precipitation events has decreased in Central Asia (by 1.742 events per 13 years), while their duration has however increased (by 0.52 days per 13 years). The period from 1992 to 2009 experienced the most significant and widespread decline in the magnitude of extreme precipitation indicators. In contrast, from 2010 to 2023, all indicators—except for the event frequency (EF) and event intensity (EI)—have shown rising tendencies across the region. (3) Regarding the dominant event types, based on the proportion of extreme precipitation frequency across areas, the Southwestern Desert (SD) and northern Kazakhstan (NK) regions are characterized by a more prominent combination of rear-peak (TDP2) and front-peak (TDP1) events, whereas the southeastern mountains (SM) region is rather dominated by a combination of rear-peak (TDP2) and balanced-type (TDP3) events. (4) The EF and event duration (ED) are strongly associated with the Digital Elevation Model (DEM) and Aridity Index (AI). The spatial patterns of EF and ED are closely linked, with the sub-humid and mountainous regions demonstrating the highest frequency and longest duration of extreme precipitation events. Full article

Urban flood damage is increasing globally, particularly in major cities. Factors contributing to flood risk include urban environmental changes, such as watershed development and precipitation variations caused by climate change. Rapid urbanization and weather anomalies further complicate flood management and damage mitigation. Additionally, detailed analyses at small spatial units (e.g., roads, buildings) remain insufficient. Hence, urban flood analysis considering such spatial variations is required. This study analyzed flood risk in Ulsan, Korea, under a severe flood scenario. Land cover changes from the 1980s to 2010s were examined in 10-year intervals, along with the frequency of heavy rainfall and high river water levels that trigger severe floods. Flood risk was structured as a matrix of likelihood and impact. The results revealed that land cover changes, influenced by development policies or regulations, had a minimal impact on urban flood risk, which is likely because effective drainage systems and stringent urban planning regulations mitigated their effects. However, the frequency and intensity of extreme precipitation events had a substantial effect. These findings were validated using a comparative analysis of an inundation damage trace map and flood range simulated by a physical model. The 10 m grid resolution and time-series likelihood-and-impact framework used in this study can inform budget allocation, resource mobilization, disaster prevention planning, and decision-making during disaster response efforts in major cities. Full article

The increasing droughts and climate change effects and their frequencies worldwide are a critical threat, especially to regions facing water scarcity and wars. Therefore, comprehensive drought evaluation and trend analysis are crucial for water resources management, climate change, and drought mitigation plans. Classical drought evaluation methods predominantly rely on in situ observations, often limited or unavailable in many regions, particularly in developing countries such as Palestine. This study investigates the temporal and spatial characteristics and trends of drought across Palestine between 1940 and 2025. To the best of our knowledge, for the first time in the literature, bias-corrected ERA5 precipitation data are employed alongside ground-based observations to assess drought using the Standardized Precipitation Index (SPI) at multiple timescales (1-, 6-, and 12-month). Trend detection was performed through conventional statistical approaches, including the Mann–Kendall test, Spearman’s Rho, and Sen’s slope (SS), as well as the Frequency-Innovative Trend Analysis (F-ITA) method. Furthermore, the performance of the original and bias-corrected ERA5 precipitation datasets was evaluated against observational data using statistical metrics. The main findings indicated that the bias correction significantly improves the accuracy of the ERA5 precipitation data. Also, droughts in SPI-1 and SPI-6 ranged from 4 to 5 months, the minimum at which a drought can be classified. In addition, the average drought duration at a 12-month timescale ranged between 14 and 16 months. At short (SPI-1) and medium (SPI-6) timescales, no significant trends were found, whereas at the long timescale (SPI-12) all stations showed a significant decreasing SPI trend, such as −5.611 in Jenin, reflecting intensifying drought conditions. For F-ITA, the frequencies of extreme drought classification increased from 0.4% in the first period to 2.18% in the second period. The findings of this research have important implications for drought management, water policy planning, and climate adaptation in Palestine. Full article

Food production systems in Ecuador’s high Andean region are pivotal for food security, rural livelihoods, and agrobiodiversity, yet they are increasingly exposed to climate stress. We assessed four representative crops (tree tomato, quinoa, potato, and maize) across three Andean zones (North, Center, South) in 2015–2022 using monthly NASA POWER (MERRA-2) climate fields. After confirming non-normality, Spearman correlations and multiple linear regressions with leave-one-year-out validation were applied to quantify the influence of maximum/minimum temperature and precipitation on cultivated and harvested area, production, sales, and loss categories. To place monthly signals in a process context, daily extreme-event diagnostics (ETCCDI-style) were also computed: heat days ($\mathrm{TX}90$), ≥5-day dry spells, and the annual maximum consecutive dry days (${\mathrm{CDD}}_{max}$). Models explained a wide range of variability across crops and zones (approx. $R^2\sim 0.55$–0.99), with quinoa showing the most consistent fits (several outcomes $R^2>0.90$). Extremes provide an eye-catching, actionable picture: the Southern zone concentrated dryness hazards, with 1–5 dry spells $\ge 5$ days per year and ${\mathrm{CDD}}_{max}$ up to ∼8 days, while heat-day frequency showed non-significant declines across zones in 2015–2022. Reanalysis frost days were virtually zero—consistent with under-detection of local valley frosts at coarse resolution—so frost risk was interpreted via monthly signals and reported losses. Overall, the results show precipitation-driven vulnerabilities in the South and support quinoa’s role as a resilient option under increasing climate stress, offering concrete guidance for water management and climate-smart planning in mountain agroecosystems. Full article

Extreme drought events may become more frequent with climate change. Understanding the impact of extreme drought on grassland ecosystems is therefore crucial for the long-term sustainability of ecosystems. Here, we identified extreme drought events in the Inner Mongolia grasslands of China using long-term standardized precipitation evapotranspiration index (SPEI) data and evaluated drought resistance of the vegetation under extreme drought based on net primary production (NPP). The impact of consecutive extreme drought events and multiple discontinuous one-year extreme drought events on grasslands were further analyzed to investigate the response strategies of different grassland types to different drought conditions. We found that the frequency and area of extreme drought in 2000–2011 were significantly higher than those in 2012–2020, and the Xilingol League region showed the highest frequency of extreme drought events. Under extreme drought, vegetation resistance was positively correlated, where annual precipitation > 300 mm. The mean resistance of different grassland types followed the order: upland meadow (UM) > lowland meadow (LM) > temperate meadow steppe (TMS) > temperate desert (TD) > temperate steppe (TS) > temperate steppe desert (TSD) > temperate desert steppe (TDS). In the analysis of two cases of consecutive two-year extreme drought, all grassland types except TSD and TD showed obvious decreased resistance in the final drought year, with the highest reduction (0.16) in LM during 2010–2011, implying the widespread and significant inhibition of grassland growth by continuous drought. However, under the multiple discontinuous extreme drought events, the resistance of all grassland types showed a fluctuating but an overall increasing trend, suggesting the adaptability of grassland to drought. The results emphasize that management departments should pay more attention to regions with low resistance and enhance the stability of grassland production by increasing the proportion of drought-resistant plants in reaction to future extreme drought scenarios. Full article

Damaging mutations of the Angiotensin I-converting enzyme (ACE) that result in low ACE levels may increase the risk of developing late-onset Alzheimer’s disease (AD). We quantified blood ACE levels in EDTA-plasma from 147 subjects with 23 different heterozygous ACE mutations (and 70 controls) and estimated the effect of these mutations on ACE phenotype, using a set of monoclonal antibodies (mAbs) to ACE and two ACE substrates. We identified several mutations in both ACE domains (including the most frequent ACE mutation, Y215C), which led to decreased ACE levels in the blood, and thus could be considered as putative risk factors for late-onset AD. The precipitation of several ACE mutants (Q259R, A725P, C734Y) by specific mAbs changed significantly, and therefore, these mAbs could be markers of these mutations. Analysis of 50 of the most frequent ACE mutations demonstrates that more than 1.5% of the adult population may have mutations which lead to decreased ACE levels, and thus, the role of low ACE levels in the development of AD may be underappreciated. Intriguingly, statistical and cluster analyses of longevity patients revealed trends towards higher frequency of cognitive impairment among affected individuals with damaging ACE mutations. Systematic analysis of blood ACE levels in patients with various ACE mutations identifies individuals with low blood ACE levels who may be at increased risk for late-onset AD. Patients with transport-deficient ACE mutations theoretically could benefit from therapeutic treatment with a combination of chemical and pharmacological chaperones and proteasome inhibitors, as was demonstrated previously on a cell model of the transport-deficient ACE mutation Q1069R. Moreover, clinical association analysis suggests a trend linking damaging ACE mutations with increased risk of cognitive impairment. Full article