Search Results (433)

Polar-orbiting microwave radiometers provide indispensable all-weather measurements of the atmospheric state, yet revisit intervals of many hours leave critical gaps during rapidly evolving weather events. To address this limitation, we developed MIDAS (Microwave Inference via Diffusion Across Satellites), a probabilistic framework that estimates microwave brightness temperature (BT) fields across the geostationary full-disk domain from infrared observations at 10 min intervals. This study focuses on the five Microwave Humidity Sounder-2 (MWHS-2) humidity-sounding channels near 183 GHz, which provide vertically resolved water vapor information. MIDAS achieves relative errors below 0.5% for the majority of cases, with a channel-averaged mean absolute error of 1.15 K, outperforming a deterministic U-Net baseline (1.43 K). Beyond per-sample evaluation, MIDAS reproduces large-scale climatological patterns across the full-disk domain over a three-month summer period, consistent with Radiative Transfer for TOVS–Scattering (RTTOV-SCATT) simulations. In deep convective scenes where reconstruction is most difficult, the ensemble spread naturally tracks reconstruction difficulty, providing a built-in indicator of prediction confidence. Notably, MIDAS incorporates real-time polar-orbiting observations as physical constraints via a merge-sampling mechanism, reducing ensemble RMSE by over 20% and improving probabilistic calibration by more than 30%. Proof-of-concept assimilation experiments for two high-impact weather cases show that MIDAS-generated fields yield forecast improvements comparable to those from real satellite observations, reducing tropical cyclone track errors from approximately 110 km to 40 km and improving heavy precipitation forecasts at extreme rainfall thresholds where direct infrared assimilation shows no benefit. Overall, our framework demonstrates the potential of generative models to supplement sparse observational coverage and provide physically plausible microwave humidity fields for downstream applications. Full article

With global warming and rapid urbanization, short-duration summer rainstorms are becoming more intense and localized, posing growing challenges to urban flood resilience. However, their spatiotemporal characteristics, vertical structures, and environmental drivers remain poorly understood. Here, we combine multi-source remote sensing datasets and China’s new-generation satellite-borne dual-frequency precipitation radar observations to investigate summer rainstorms in Nanjing, China, during 2017–2024. Results reveal pronounced spatiotemporal heterogeneity, with higher rainfall intensities concentrated over urban and adjacent areas. During the study period, rainstorm intensity and duration increased by 7.44% and 38.63%, respectively, while the affected area decreased by 8.18%, indicating a transition toward more localized yet more intense rainfall events. Environmental analyses suggest that large-scale thermodynamic conditions and regional topographic forcing provide a favorable background for convection development, while local urban thermal effects may further modulate rainfall enhancement. Three-dimensional radar detection of an illustrative rainstorm event indicates an inverted-cone vertical structure, suggesting a mixed convective-stratiform precipitation structure involving both warm-rain and ice-phase processes. An Explainable Bayesian-Optimized XGBoost (EBOX) model further identifies near-surface air temperature and specific humidity as the primary environmental factors associated with rainstorm occurrence and development. Overall, this study highlights the value of integrating satellite remote sensing with explainable artificial intelligence to improve understanding of urban extreme rainfall and provide new insights into how climate change, topography, and urbanization jointly shape precipitation extremes in rapidly urbanizing monsoon regions. Full article

Understanding how interannual climatic variability shapes must composition is critical for predicting wine quality under warming conditions, particularly for acid-retaining cultivars such as Vitis vinifera L. cv. Furmint. This study—conducted as a continuation of a previous investigation on Furmint berry weight, total soluble solids and total dry extract—evaluated titratable acidity, pH, potassium, ammonia and tannin content across three contrasting vintages (2022–2024) in the Tokaj wine region. Using a high-resolution meteorological dataset and an extensive climatic parameter matrix, exploratory analysis was conducted to evaluate responses, and the most influential thermal, radiation-related and water-balance related climatic factors associated with each must parameter were identified. Total acidity and pH showed consistent sensitivity to climatic variability: acidity decreased with mid-season warm nights and abundant summer rainfall, while pH was inversely associated with extreme heat events but increased under higher early-season rainfall and post-véraison irradiation. Potassium content exhibited partly atypical responses, showing positive correlations with late-season warm nights and frequent summer precipitation, and negative with early heat. Ammonia displayed weak to moderate climatic dependence, while tannic acid consistently decreased with higher thermal and irradiation loads. Overall, these results imply cultivar-specific climatic responses in Furmint and suggest that temperature extremes, nighttime heat and rainfall timing are important factors shaping must composition, providing a foundation to better understand the expression of vintage effects under climate change. Full article

The southeastern Tibetan Plateau (SETP) lies at the intersection of extreme topography and the Indian summer monsoon, producing intense orographically driven precipitation. Resolving these extremes requires convection-permitting simulations (CPMs) due to the combined influence of complex terrain and vigorous convection. However, kilometer-scale simulations over SETP remain limited to short periods because of computational cost, preventing robust estimation of precipitation extremes. We analyze a decade of 1 km CPMs and apply the Simplified Metastatistical Extreme Value (SMEV) framework, which uses all wet-day precipitation rather than annual maxima, increasing the effective sample size by an order of magnitude. We apply the SMEV framework to estimate daily precipitation return levels up to 100 years. While SMEV increases the effective sample size, uncertainty remains non-negligible for long return periods when derived from a decadal record. Results show that CPMs’ estimates align with observations within 90% bootstrap confidence intervals (CIs). For instance, at a representative station (Obs: 51.6 mm/d), the 50-year return level is estimated at 57.1 mm/d (CI: 47.1–68.9 mm/d). In contrast, coarse-resolution products systematically overestimate these extremes by 50–100%, with their estimates often falling far outside the observed range beyond 20-year return periods. CPMs also reveal a model-derived, non-monotonic elevation dependence absent in coarse datasets. Instead of monotonic decline, three phrases emerge: a weak increase below 2700 m, a sharp decrease across mid-elevations, and a reversal above ~5300 m where orographic uplift enhances extremes, yielding a 2.3-fold increase. These results show that CPMs alter not only magnitude but also the spatial structure and elevation scaling of precipitation extremes, providing a physically constrained framework for extreme-value estimation in data-sparse mountains. Full article

As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationships, bypassing continuous crop physiological responses and limiting their capacity to capture fine-grained temporal impacts of meteorological anomalies. To address this, we propose a novel two-stage spatiotemporal functional framework that integrates high-resolution daily weather trajectories with satellite-derived indicators, utilizing the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to represent canopy structural vigor and hydraulic status, respectively. In the first stage, a Historical Functional Linear Model (HFLM) dynamically maps daily meteorological trajectories (temperature, precipitation, and solar radiation) onto continuous physiological curves under strict temporal causality constraints. This generates bivariate coefficient surfaces that reveal dynamic windows of vulnerability and capture divergent, lagged physiological responses to climate stress. In the second stage, a spatially heterogeneous functional additive model integrates these weather-shaped physiological trajectories alongside raw meteorological dynamics as joint predictors for county-level yields. By extracting functional principal components and modeling flexible non-linear biological responses while accounting for continuous spatial heterogeneity, this dual-channel frameworkcaptures key aspects of both chronic physiological stress and acute meteorological shocks. Validated across a 25-year (2000–2024) U.S. Corn Belt panel, the proposed DC-FAM achieves a mean weighted mean squared prediction error (WMSPE) of 242.33 (bu/acre)² and a median out-of-sample $R_{\mathrm{cv}}^2$ of 0.422, outperforming all benchmarks including a random forest. Attribution of the 2012 flash drought further demonstrates the framework’s capacity to mechanistically trace the complete disaster propagation chain from anomalous spring warming to mid-summer hydraulic failure. The proposed framework provides a transparent, biophysically grounded tool for decoding dynamic climate stress trajectories and disaster propagation chains, offering potential implications for adaptive farm management and precision agricultural insurance. Full article

This study established a refined, distributed SWAT modeling framework that integrates elevation-band and snowmelt modules to reconstruct the alpine hydrological and sediment cycles of the Koshi River Basin (KRB) over the period 1990–2024, with climate scenarios constructed using the delta change approach. The KRB, a major transboundary watershed traversing China, Nepal, and India, was selected owing to its critical hydro-climatic role under the destabilizing “Asian Water Tower”; it generates substantial sediment yield, hosts the densest concentration of hydropower potential within the Ganges system, and spans an extreme vertical gradient from Mount Everest to the southern alluvial plains. Results reveal accelerated warming at a rate of 0.21 °C per decade and an overall warming–wetting trend, punctuated by an abrupt interdecadal shift around 2015. Precipitation dominated interannual streamflow variability, with enhanced rainfall triggering basin-wide sediment surges that overwhelmed the natural buffering capacity of the land surface. Conversely, rising temperatures intensified actual evapotranspiration, markedly depleting soil water and reducing total water yield and monsoon runoff, although sustained snow and glacier melt effectively elevated the dry-season low-flow baseline. The integrated climate forcing reshaped the disparity between hydrological extremes, imposing severe seasonal eco-hydrological stress that manifested as a pre-monsoon deficit in terrestrial green water and acute summer sediment outbursts for aquatic habitats. Furthermore, the flood regime exhibited an altered distribution, with mid-to-high frequency floods enhanced while low-frequency extreme flood peaks declined. The hydro-sedimentological regime consequently exhibits pronounced nonlinear responses to climate change, providing a critical, threshold-based scientific foundation for adaptive transboundary water resource management. Full article

The El Niño–Southern Oscillation (ENSO) is the main driver of interannual climate variability, strongly influencing precipitation, temperature, and extreme events worldwide. In South America, its impacts are well documented. However, studies examining different ENSO types—Eastern Pacific (EP), Central Pacific (CP), and Mixed (MX), defined according to the location of sea surface temperature (SST) anomalies in the tropical Pacific—remain limited, particularly for the Brazilian subtropical climate. This study investigates rainfall variability in the Brazilian subtropical region associated with different ENSO types. Composite analyses of precipitation, wind, and SST anomalies were performed, and monthly rainfall data from 703 stations were used to identify homogeneous regions. The results show the intensity and spatial coherence of rainfall signals vary according to El Niño type, with EP events favoring widespread wet conditions and CP events producing more heterogeneous or locally negative anomalies. For La Niña, the intensity and seasonal distribution of negative rainfall anomalies vary by ENSO type: stronger impacts occur in summer (EP), spring (MX), and autumn (CP). These findings improve the understanding of ENSO-related rainfall variability in the Brazilian subtropical region and provide valuable insights for the management of climate-related risks in an area frequently affected by rainfall extremes. Full article

Conventional spring sowing of sugar beet in Europe faces increasing constraints from summer drought, high temperatures, and Cercospora beticola pressure due to climate change. This two-year field trial (2021–2022 and 2022–2023) evaluated the feasibility of autumn-sown sugar beet at a northern Mediterranean site in Legnaro (Padua, NE Italy, 45°21′ N). Nine varieties were assessed across four sowing dates in 2021 (late September to early November) and two sowing dates in 2022 (late September and late October). Measurements included overwinter survival, bolting incidence at two reproductive stages, fresh root and above-ground biomass yield at sequential harvest dates, and root soluble solids (°Brix). Post-winter mortality was negligible following September and early-October sowings, moderate after late-October sowings (17.3% in 2021; 13.2% in 2022), and extremely high after early-November sowing (81.5%). These patterns indicated that winter survival was more strongly determined by crop developmental stage before winter than by seasonal minimal temperatures recorded in two seasons (−3.6 °C and −6.3 °C, respectively). Bolting incidence showed the opposite trend, reaching near-complete or complete expression in the earliest sowings regardless of variety choice. In late-October sowings, clear varietal differences emerged. Some varieties combined low bolting incidence with high root yields, reaching 81.1–84.4 t ha⁻¹ at the final harvest (early August 2023). Root juice soluble solids were higher in the drier 2021–2022 season (exceeding 20 °Brix in several cases) than in the wetter 2022–2023 season (consistently below 17.5 °Brix), reflecting dilution effects associated with the different seasonal precipitation. In the sequential harvest series of late-October 2022 sowing, later harvest dates were generally associated with lower soluble solids. These results indicate that, within the autumn sowing window evaluated, late-October sowing combined with appropriate varietal selection provided the best balance between overwinter survival, bolting incidence, and root yield at this northern Mediterranean latitude. Future multi-site studies, including spring-sown controls, could enable direct comparison with conventional sowing practice. Full article

Against the backdrop of global warming and the ‘warming and wetting’ trend in north-western China, changes in seasonal snowpack and glacial ice in high-altitude cold regions directly impact water security in inland river basins. At present, there is a paucity of systematic research concerning the long-term evolution of snow and ice cover, multi-scale climate responses and future trends in the source region of the Keriya River on the northern slope of the Kunlun Mountains. To address this, this study utilised Landsat remote sensing imagery and meteorological station data from 2005 to 2024. Employing a multi-model fusion framework that integrates various machine learning and time-series models—including random forests, gradient boosting trees and ARIMA—the research incorporated trend factors, climate cycle identification and probabilistic modelling of extreme events to systematically analyse the spatiotemporal variability of snow/ice coverage and its multiscale coupling relationships with air temperature and precipitation. Given the inherent limitations of optical remote sensing methods in distinguishing between seasonal snow and glacial ice, this study defines the extracted coverage type as snow/ice coverage. Given the inherent limitations of optical remote sensing methods in distinguishing between seasonal snow and glacial ice, this study defines the extracted coverage type as snow/ice coverage. The results indicate that: (1) the annual average snow/ice cover percentage in the study area shows a non-significant decreasing trend (−0.69%/year, p > 0.1); within the year, it exhibits a pattern of accumulation in winter and melting in summer, with a peak in January (average 63.2%) and a trough in August (average 11.6%); (2) snow/ice cover percentage increases significantly with altitude; the annual average SICP in the <2000 m elevation zone is 5.2%; in the 2000–3000 m and 3000–4000 m altitude ranges, this rises to 5.7% and 8.3%, respectively, representing the primary seasonal snow/ice distribution zones; in areas above 6000 m, the annual average reaches 70.3%, constituting a zone of perennial stable snow/ice cover; (3) the relationship between snow/ice and temperature and precipitation exhibits significant time-scale dependence: correlations are weak on an annual scale (temperature R = −0.25, precipitation R = −0.14), but significantly strengthen on a monthly scale and exhibit seasonal differentiation; during the melting season, temperature exerts a dominant negative influence (August R = −0.35), whilst during the accumulation season, solid precipitation provides a positive supplement (February R = 0.34), with the strongest correlation with temperature occurring in September (R = −0.50); (4) it is projected that between 2025 and 2044, snow and ice cover will follow a fluctuating downward trend (averaging an annual decrease of roughly −0.12%), falling to approximately 29% by 2044; at the same time, temperatures are expected to continue rising (+0.035 °C per year), whilst precipitation will increase slightly (+0.4% per year). The results of this study provide a sound scientific basis for formulating sustainable water resource management strategies for the northern flank of the Kunlun Mountains and optimising measures to regulate snowmelt runoff. They are of great importance for safeguarding the stability of the oasis ecological systems in the Keriya River basin and ensuring the sustainable development and utilisation of water resources. Full article

This study analyzes changes in temperature, precipitation, and drought conditions on three small islands in the southern Adriatic (Vis, Lastovo, and Mljet) over the period 1981–2024, to identify the spatial and seasonal heterogeneity of the climate signal and its relationship with drought occurrence. The analysis reveals a statistically significant and consistent increase in mean annual air temperature at all analyzed stations, with warming being strongly seasonally asymmetric and most pronounced during the summer months. In contrast, precipitation trends are weak, spatially heterogeneous, and statistically insignificant in most cases, with a locally pronounced increase in precipitation in the interior and more orographically complex areas of Mljet. Drought conditions were assessed using the Standardized Precipitation Index (SPI) and the New Drought Index (NDI). The annual SPI exhibits strong interannual variability without a clear long-term trend, and in some cases an apparent increase driven by episodic extremely wet years. In contrast, the NDI clearly detects a systematic increase in aridity, particularly during the warm part of the year, reflecting the combined effect of rising temperatures and unfavourable precipitation distribution. June emerges as a key transitional month with a regionally coherent and statistically significant drying signal, whereas October shows weak and inconsistent trends due to the dominance of episodic precipitation extremes. The results confirm that drought assessment on small Mediterranean islands based solely on precipitation may be misleading, and that integrated indices incorporating the energy aspect of climate provide a more realistic representation of changes in aridity. Full article

Understanding the climate-driven range dynamics of the oriental hornet (Vespa orientalis) is essential for ecological risk assessment and biodiversity management. This study utilized Maximum Entropy (MaxEnt) modeling to estimate current and future (2050) habitat suitability across the Western Palearctic. The model demonstrated strong predictive performance, yielding a mean cross-validation AUC of 0.95 ± 0.01 and a TSS of 0.78 ± 0.02, indicating high stability and discriminatory capacity. Jackknife analysis and response curves identified temperature annual range (bio7) and annual precipitation (bio12) as the primary environmental drivers. The species exhibits a distinct preference for moderate thermal variability and balanced moisture regimes, while extreme summer heat (bio5) and warm winter conditions (bio11) impose significant constraints. Current projections identify a high-suitability core concentrated within the Mediterranean basin. By mid-century, projections indicate a spatial reorganization marked by localized gains mainly in the eastern part of the study region alongside suitability losses across North Africa and parts of southern Europe. Multivariate Environmental Similarity Surface (MESS) analysis confirmed high model transferability across most expansion zones, despite increased uncertainty in hyper-arid and high-altitude regions. These findings underscore the dynamic nature of the V. orientalis climatic niche and provide a critical baseline for proactive biosecurity and monitoring in emerging high-risk regions. Given the global decline in Hymenoptera diversity, this study provides timely insights into species-specific responses to climate change, supporting broader efforts in biodiversity conservation and ecological risk assessment. Full article

The Tibetan Plateau (TP) atmospheric heat source crucially modulates East Asian summer monsoon precipitation, yet its synergy with upstream oceanic signals remains elusive. Using observations (1971–2020) and CMIP6 simulations, we investigate mechanisms coupling the summer TP heating and precipitation over the Middle and Lower Yangtze River (MLYR). SVD analysis reveals a robust positive coupling between them. Mechanistically, TP heating triggers a quasi-stationary Rossby wave train, inducing a “saddle-like” circulation that drives intense MLYR moisture convergence (contributing >90% to precipitation changes). Crucially, we re-examine the upstream oceanic precursor to propose a “dual-pathway superposition” framework. Contrary to the assumed linear causal chain, four-quadrant analysis reveals the spring Indian Ocean Basin Warming (IOBW) and summer TP heating are largely independent drivers (R = 0.24). While IOBW thermodynamically excites an Anomalous Anticyclone supplying abundant MLYR moisture, it lacks robust control over TP heating, which is dominated by internal atmospheric dynamics. However, our findings reveal a critical non-linear synergy: extreme MLYR rainfall strictly requires the coincidental phase overlap of these independent pathways (strong dynamic lifting coupled with oceanic moisture). CMIP6 simulations corroborate this independence, further emphasizing that extreme MLYR rainfall results from phase superposition rather than a single causal chain. Full article

The Sichuan Basin is a high-incidence area for China’s drought–flood abrupt alternation (DFAA) events. To reveal the spatiotemporal evolution characteristics and driving factors of drought–flood abrupt alternation (DFAA) compound disasters in the Sichuan Basin, this study identified drought-to-flood (DF) and flood-to-drought (FD) events using the Standardized Precipitation Evapotranspiration Index based on meteorological data and circulation factors from 1963 to 2022. By constructing a standardized drought–flood abrupt alternation magnitude index to classify event grades, combined with methods such as trend analysis, Morlet wavelet and Random Forest, the study explored the trend variation laws, spatial distribution patterns, and core driving factors of DFAA events in the basin. The results showed that on the interannual scale, the upward trend of FD events was more obvious than that of DF events, with a significant increase in the proportion of moderate and severe events; both the frequency and intensity of summer FD events increased significantly, and the intensity of winter FD events also exhibited a marked upward trend. Spatially, DF events occurred frequently in Guang’an and Chongqing, while FD events were concentrated in the western edge of the basin, as well as Yibin and Luzhou. Moderate and severe events were more prominent in the edge areas of the basin. The occurrence of DFAA events was generally jointly driven by the meteorological factors and regulation of large-scale sea surface temperature-circulation factors: the triggering factors of DF events showed a diversified and decentralized characteristic, while FD events were mainly driven by the subtropical high, and tropical sea surface temperature anomalies were the common precursor signal for both types of events. This study provides a scientific basis and technical support for the formulation of disaster prevention and mitigation strategies and the optimal management of water resources for compound extreme meteorological disasters in the Sichuan Basin. Full article

Drought is a complex natural hazard with severe impacts on ecosystems, agriculture, water resources, and socio-economic stability. Understanding its spatiotemporal evolution is critical for effective drought monitoring and prevention. This study analyzed drought characteristics in Henan province from 1961 to 2023 using the Standardized Precipitation Evapotranspiration Index (SPEI), calculated from daily meteorological data at 111 meteorological stations. Drought was examined at annual and seasonal scales across multiple time scales, including the 1-month time scale (SPEI1), 3-month time scale (SPEI3), and 12-month time scale (SPEI12), and future trends were assessed using Theil–Sen Median and Hurst exponent analyses. Key findings revealed the following: (1) Drought frequency showed a non-significant increasing trend overall, but drought intensity increased significantly, with severe and extreme droughts becoming more frequent. Most areas are projected to continue aridification. (2) Winter recorded the highest frequency and occurrence of droughts, followed by autumn and summer. Except for summer, moderate and severe droughts increased across all seasons. Extreme droughts increased significantly across all seasons, especially in spring and autumn. (3) High annual drought frequency was concentrated in the northwest, north, and east. Spatial patterns varied by drought severity: slight droughts were more common in the north, moderate droughts in the central–east, severe droughts in the west and south, and extreme droughts in the southwest and north. (4) Empirical Orthogonal Function (EOF) analysis revealed three main spatial modes: a uniform regional pattern, a southeast–northwest contrast, and a central–eastern opposition. Shorter time scales provided more detailed spatial patterns, while longer scales better reflected interannual characteristics of drought and flood variations. This study offers valuable insights for improving drought assessment and supporting risk management and policy decisions. Full article

Groundwater is a critical component of the terrestrial water system, and understanding the spatiotemporal evolution of national groundwater storage is essential for ensuring water security and ecological sustainability in China. Based on GRACE and GRACE-FO satellite gravity data from January 2005 to December 2024, combined with land surface water components derived from GLDAS, this study investigates the spatiotemporal variations and driving mechanisms of groundwater storage anomalies (GWSAs) across China using parametric additive decomposition, Theil–Sen trend estimation, and the Mann–Kendall significance test. The results indicate that: (1) groundwater storage in China has experienced a persistent decline at an average rate of −1.97 mm yr⁻¹, with a cumulative depletion of 38.55 mm. This decline exhibits distinct phases, characterized by a rapid decrease during 2005–2011, a moderated decline during 2012–2017, and a renewed acceleration with increased variability during 2018–2024; (2) spatially, GWSA shows a pronounced “increase in the south and decrease in the north” pattern, with extremely significant declines concentrated in the North China Plain and arid northwestern inland regions, while significant increases are mainly observed in southwestern China and southeastern coastal areas; (3) GWSA exhibits a stable seasonal cycle of “summer surplus and spring deficit,” with groundwater replenishment driven by summer precipitation and substantial depletion caused by agricultural water demand in spring; and (4) groundwater variations in northern China are primarily controlled by intensive agricultural irrigation, resulting in sustained depletion, whereas southern China is mainly influenced by natural precipitation, maintaining a relatively dynamic equilibrium. These findings provide valuable insights for region-specific groundwater management and the sustainable utilization of water resources in China. Full article