Search Results (289)

Based on the panel data of daily meteorological stations and winter wheat yield in Henan Province from 2000 to 2023, this study comprehensively used the Mann–Kendall trend test, wavelet coherence analysis (WTC), and other methods to reveal the temporal and spatial evolution of extreme precipitation and its multi-scale stress mechanism on grain yield. The results showed the following: (1) Extreme precipitation showed the characteristics of ‘frequent fluctuation-gentle trend-strong spatial heterogeneity’, and the maximum daily precipitation in spring (RX1DAY) showed a significant uplift. The increase in rainstorm events (R95p/R99p) in the southern region during the summer is particularly prominent; at the same time, the number of consecutive drought days (CDDs > 15 d) in the middle of autumn was significantly prolonged. It was also found that 2010 is a significant mutation node. Since then, the synergistic effect of ‘increasing drought days–increasing rainstorm frequency’ has begun to appear, and the short-period coherence of super-strong precipitation (R99p) has risen to more than 0.8. (2) The spatial pattern of winter wheat in Henan is characterized by the three-level differentiation of ‘stable core area, sensitive transition zone and shrinking suburban area’, and the stability of winter wheat has improved but there are still local risks. (3) There is a multi-scale stress mechanism of extreme precipitation on winter wheat yield. The long-period (4–8 years) drought and flood events drive the system risk through a 1–2-year lag effect (short-period (0.5–2 years) medium rainstorm intensity directly impacted the production system). This study proposes a ‘sub-scale governance’ strategy, using a 1–2-year lag window to establish a rainstorm warning mechanism, and optimizing drainage facilities for high-risk areas of floods in the south to improve the climate resilience of the agricultural system against the background of climate change. Full article

By utilizing daily precipitation data from 400 meteorological stations in the Yellow River Basin (YRB) of China, atmospheric and oceanic reanalysis data, this study investigates the climatological characteristics, leading modes, and relationships with atmospheric circulation and sea surface temperature (SST) of summer extreme precipitation in the YRB from 1981 to 2020 through the extreme precipitation metrics and Empirical Orthogonal Function (EOF) analysis. The results indicate that both the frequency and intensity of extreme precipitation exhibit an eastward and southward increasing pattern in terms of climate state, with regions of higher precipitation showing greater interannual variability. When precipitation in the YRB exhibits a spatially coherent enhancement pattern, high latitudes exhibits an Eurasian teleconnection wave train that facilitates the southward movement of cold air. Concurrently, the northward extension of the Western Pacific subtropical high (WPSH) enhances moisture transport from low latitudes to the YRB, against the backdrop of a transitioning SST pattern from El Niño to La Niña. When precipitation in the YRB shows a “south-increase, north-decrease” dipole pattern, the southward-shifted Ural high and westward-extended WPSH converge cold air and moist in the southern YRB region, with no dominant SST drivers identified. Full article

Climate change intensifies hydrological cycles, leading to an increased variability in terrestrial water storage anomalies (TWSAs) and a heightened drought risk. Understanding the spatiotemporal dynamics of TWSAs and their driving factors is crucial for sustainable water management. While previous studies have primarily attributed TWSAs to regional factors, this study employs wavelet coherence, partial correlation analysis, and multiple linear regression to comprehensively analyze TWSA dynamics and their drivers in the Hengduan Mountains (HDM) region from 2003 to 2022, incorporating both regional and global influences. Additionally, dry–wet variations were quantified using the GRACE-based Drought Severity Index (GRACE-DSI). Key findings include the following: The annual mean TWSA showed a non-significant decreasing trend (−2.83 mm/y, p > 0.05), accompanied by increased interannual variability. Notably, approximately 36.22% of the pixels in the western HDM region exhibited a significantly decreasing trend. The Nujiang River Basin (NRB) (−17.17 mm/y, p < 0.01) and the Lancang (−17.17 mm/y, p < 0.01) River Basin experienced the most pronounced declines. Regional factors—particularly precipitation (PRE)—drove TWSA in 59% of the HDM region, followed by potential evapotranspiration (PET, 28%) and vegetation dynamics (13%). Among global factors, the North Atlantic Oscillation showed a weak correlation with TWSAs (r = −0.19), indirectly affecting it via winter PET (r = −0.56, p < 0.05). The decline in TWSAs corresponds to an elevated drought risk, notably in the NRB, which recorded the largest GRACE-DSI decline (slope = −0.011, p < 0.05). This study links TWSAs to climate drivers and drought risk, offering a framework for improving water resource management and drought preparedness in climate-sensitive mountain regions. Full article

The reliance on imported seeds for grassland rehabilitation in Mexico has led to increased costs and other difficulties in implementing grassland rehabilitation programs. Varieties need to be generated from local ecotypes that are outstanding in forage production and their response to rehabilitation programs. However, the scarcity of occurrence records is often a deterrent to niche and distribution modeling, hence the need for an approach that overcomes such limitations. The objectives of this study were (1) to develop a geographic information system (GIS)-based approach to determining the population distribution of a promising ecotype of Bouteloua curtipendula (Michx.) Torr. for grassland rehabilitation in the Chihuahuan Desert, Mexico; (2) to identify the edaphoclimatic variables that define the ecotype’s distribution; and (3) to develop models to determine the potential area for the use of the ecotype in grassland rehabilitation. The challenge for the present study was that only one georeferenced collection site of the ecotype in Chihuahua was available for use in the construction and calibration of the models. GIS software 10.3 was used to develop two potential distribution models: Model A, with variables obtained directly from a vector climate dataset, and Model B, with derived variables. A field work methodology was developed for the validation process using a georeferenced digital mesh and the nested sampling method modified by Whittaker. The information was analyzed with 10 non-parametric statistical tests. The two models had an overall accuracy and sensitivity level greater than 70% and a positive predictive power greater than 80%. The predicted population distribution areas in Chihuahua (18,158 ha) in the form of discontinuous patches cohered with those in previous reports on the distribution form of B. curtipendula. The edaphoclimatic variables influencing ecotype distribution were soil type, average minimum and maximum temperature in January, average maximum temperature in June, average minimum temperature in July, and average precipitation in August. The sensitivity analysis showed soil type as an important variable in defining the ecotype’s distribution. Considering soil as the main predictor variable, the potential rehabilitation area where the ecotype may be used was estimated at 7,181,735 ha in the Chihuahuan Desert region. The study developed and validated an approach to modeling the ecological niche of an ecotype of commercial interest, despite severe limitations in the number of georeferenced sites available for modeling. Further study is needed to explore its applicability to grassland rehabilitation in the Chihuahuan Desert and the study of rare and understudied ecotypes or species in other settings. Full article

Aluminum alloys based on the eutectic Al–Ni system are a promising class of lightweight materials for applications at elevated temperatures owing to the thermal stability of the eutectic Al₃Ni phase. In this study, the eutectic Al–Ni alloy was modified by the addition of 0.6 wt.% Zr to enhance the α_Al matrix by precipitation strengthening. The alloys were cast and subjected to T5 heat treatment followed by long-term isothermal aging at 350 °C. A comprehensive study was carried out to evaluate the evolution of microstructure, microhardness and mechanical performance over time. The formation of fine, coherent L1₂-Al₃Zr precipitates contributed to significant strengthening, as reflected by a ~60% increase in microhardness and an approximately twofold improvement in room temperature (RT) yield strength. A TEM analysis of the L1₂-Al₃Zr precipitates showed relatively good thermal stability after 30 days. Despite the improved mechanical properties at room temperature, the alloy did not retain this improvement when tested at 300 °C. Nevertheless, these results provide a comprehensive insight into the aging and thermal stability of Al–Ni–Zr alloys. Full article

This study investigated the influence of long-term climatic phenomena on the hydroclimatic dynamics of the Grey River Basin in Chilean Patagonia. By analyzing hydroclimatological datasets from the last four decades (1980 to 2020), including precipitation, temperature, wind speed, potential evapotranspiration, and streamflow, we identified key trends and correlations with three large-scale climate indices: the Antarctic Oscillation (AAO), El Niño—Southern Oscillation (ENSO), and Pacific Decadal Oscillation (PDO). Statistical methods such as the Mann–Kendall test, Sen’s slope, PCA, and wavelet coherence were applied. The results indicate a significant upward trend in streamflow, with Sen’s slope of 0.710 m³/s/year (p-value = 0.020), particularly since 2002, while other variables showed limited or no significant trends. AAO exhibited the strongest correlations with streamflow and wind speed, while ENSO 3.4 was the most influential ENSO index, especially during the two extreme El Niño events in 1982, 1997, and 2014. PDO showed weaker relationships overall. Wavelet analysis revealed coherent periodicities at 1- and 2-year frequencies between AAO and flow (wavelet coherence = 0.44), and at 2- to 4-year intervals between ENSO and precipitation (wavelet coherence = 0.63). These findings highlight the sensitivity of the Grey River basin to climatic variability and reinforce the need for integrated water resource management in the face of ongoing climate change. Full article

Wildfires pose a significant threat to the natural and built environment and may alter the hydrologic cycle in burned areas increasing the risk of flooding, erosion, debris flows, and shallow landslides. In this paper, we investigate the feasibility of using differential interferometric synthetic aperture radar (DInSAR) to interpret changes in ground surface elevation following the 2017 Eagle Creek Wildfire in Oregon, USA. We show that DInSAR is capable of measuring ground surface displacements in burned areas not obscured by vegetation cover and that interferometric coherence can differentiate between areas that experienced different burn severities. The distribution of projected vertical displacement was analyzed, suggesting that different areas experience variable rates of change, with some showing little to no change for up to four years after the fire. Comparison of the projected vertical displacements with cumulative precipitation and soil moisture suggests that increases in precipitation and soil moisture are related to periods of increased vertical displacement. The findings of this study suggest that DInSAR may have value where in situ instrumentation is infeasible and may assist in prioritizing areas at high-risk of erosion or other changes over large geographical extents and measurement locations for deployment of instrumentation. Full article

The semiconductor industry currently lacks a robust, holistic method for detecting parameter drifts in wide-bandgap (WBG) manufacturing, where conventional fault detection and classification (FDC) practices often rely on static thresholds or isolated data modalities. Such legacy approaches cannot fully capture the intricate, multi-modal shifts that either gradually erode product quality or trigger abrupt process disruptions. To surmount these challenges, we present M2D2 (Multi-Machine and Multi-Modal Drift Detection), an end-to-end framework that integrates data preprocessing, baseline modeling, short- and long-term drift detection, interpretability, and a drift-aware federated paradigm. By leveraging self-supervised or unsupervised learning, M2D2 constructs a resilient baseline of nominal behavior across numeric, textual, and categorical features, thereby facilitating the early detection of both rapid spikes and slow-onset deviations. An interpretability layer—using attention visualization or SHAP/LIME—delineates which sensors, logs, or batch identifiers precipitate each drift alert, accelerating root-cause analysis. An active learning loop dynamically refines threshold settings and model parameters in response to real-time feedback, reducing false positives while adapting to evolving production conditions. Crucially, M2D2’s drift-aware federated learning mechanism reweights local updates based on each site’s drift severity, preserving global model integrity at scale. The key scientific breakthrough of this work lies in combining advanced multi-modal processing, short- and long-term anomaly detection, transparent model explainability, and an adaptive federated infrastructure—all within a single, coherent framework. Evaluations of real WBG fabrication data confirm that M2D2 substantially improves drift detection accuracy, broadens anomaly coverage, and offers a transparent, scalable solution for next-generation semiconductor manufacturing. Full article

This study systematically investigates the influence of different heat treatment processes on the microstructural evolution and mechanical properties of Cu-15Ni-3Al alloys, with particular emphasis on the synergistic strengthening mechanisms of spinodal decomposition and precipitation hardening. Two distinct aging routes—solution aging and direct aging—were designed to facilitate a comparative assessment of microstructural characteristics and their correlation with mechanical performance. Comprehensive characterization was conducted using scanning electron microscopy (SEM), X-ray diffraction (XRD), transmission electron microscopy (TEM), and room-temperature tensile testing to elucidate the structure–property relationships. The results reveal that direct aging promotes the formation of fine, coherent L1₂-type Ni₃Al precipitates and the evolution of Ni-enriched regions initially generated through spinodal decomposition into stable Ni₃Al precipitates. These microstructural features act as effective barriers to dislocation motion, thereby significantly enhancing both strength and ductility. The findings provide valuable insights into optimizing heat treatment strategies to improve the performance of Cu-Ni-Al alloys. Full article

The objective of this study was to analyse the influence of two large-scale climate indices on precipitation in the Danube basin, both separately and in combination. The evolution of the hydroclimatic regime in this area is of particular importance but has received limited attention. One of the indices for these data is the well-known the North Atlantic Oscillation (NAOI) climate index, which has been used in numerous investigations; the aim of using this index is to determine its influence on various hydroclimatic variables in many regions of the globe. The other index, the Greenland–Balkan Oscillation index (GBOI), has been demonstrated to have a greater influence on various hydroclimatic variables in Southeastern Europe compared to the NAOI. First, through different bivariate methods, such as estimating wavelet total coherence (WTC) in the time–frequency domain and applying partial wavelet coherence (PWC), the performance of the GBOI contributing to precipitation in the Danube basin was compared with that of the NAOI in the winter season. Then, by using relatively simple multivariate methods such as multiple linear regression (MLR) and a variant thereof called ridge regression (RR), notable results were obtained regarding the prediction of overall precipitation in the Danube basin in the winter season. The training period was 90 years (1901–1990), and the testing period was 30 years (1991–2020). The used Nash–Sutcliffe (NS) performance criterion varied between 0.65 and 0.94, depending on the preprocessing approach applied for the input data, proving that statistical modelling for the winter season is both simple and powerful compared to modern deep learning methods. Full article

In this work, a crystal plasticity (CP)-based continuum modeling approach is employed to investigate the interaction between dislocations and coherent ${\gamma }^{″}$ precipitates in the Inconel 718 (IN718) superalloy. A finite element (FE) model is developed to accurately represent realistic microstructures in IN718, specifically incorporating a disk-shaped precipitate embedded within a matrix phase. A length-scale-dependent CP modeling simulation informed by molecular dynamics (MD) findings is conducted. The results indicate that the three ${\gamma }^{″}$ variants behave differently under uniaxial loading conditions, altering the deformation process in the $\gamma$ phase and leading to significant strain and stress heterogeneities. The presence of dislocation shearing in the ${\gamma }^{″}$ variants reduces the localization of strain and dislocation densities in the adjacent $\gamma$ phase. The strain gradient-governed geometrically necessary dislocation (GND) density plays a dominant role in influencing strain hardening behavior. The length scale effect is further quantified by considering four different precipitate sizes, with the major axis ranging from 12.5 nm to 100 nm. The findings show that smaller precipitate sizes result in stronger strain hardening, and the size of ${\gamma }^{″}$ precipitates significantly alters GND density evolution. Full article

Snow cover variations significantly affect the stability of regional water supply and terrestrial ecosystems in arid northwest China. This study comprehensively evaluates snow resource changes since 2000 by integrating multisource remote sensing datasets and analyzing four key indicators: snow cover area (SCA), snow phenology (SP), snow depth (SD), and snow water equivalent (SWE). The results reveal a slight downtrend in SCA over the past two decades, with an annual decline rate of 7.13 × 10³ km². The maximum SCA (1.28 × 10⁶ km²) occurred in 2010, while the minimum (7.25 × 10⁵ km²) was recorded in 2014. Spatially, SCA peaked in December in the north and January in the south, with high-altitude subregions (Ili River Basin (IRB), Tarim River Region (TRR), North Kunlun Mountains (NKM), and Qaidam Basin (QDB)) maintaining stable summer snow cover due to low temperatures and high precipitation. Analysis of snow phenology indicates a significant shortening of snow cover duration (SCD), with 62.40% of the study area showing a declining trend, primarily driven by earlier snowmelt. Both SD and SWE exhibited widespread declines, affecting 75.09% and 84.85% of the study area, respectively. The most pronounced SD reductions occurred in TRR (94.44%), while SWE losses were particularly severe in North Tianshan Mountains (NTM, 94.61%). The total snow mass in northwest China was estimated at 108.95 million tons, with northern Xinjiang accounting for 66.24 million tons (60.8%), followed by southern Xinjiang (37.44 million tons) and the Hexi Inland Region (5.27 million tons). Consistency analysis revealed coherent declines across all indicators in 55.56% of the study area. Significant SD and SCD reductions occurred in TRR and Tuha Basin (THB), while SWE declines were widespread in NTM and IRB, driven by rising temperatures and decreased snowfall. The findings underscore the urgent need for adaptive strategies to address emerging challenges for water security and ecological stability in the region. Full article

Arctic sea ice variations exhibit relatively strong statistical associations with precipitation variability over northeastern and southern China. Using Arctic Ocean reanalysis data from the EU Copernicus Project, this study examines the time-lagged statistical relationships between winter Arctic sea ice conditions and subsequent spring precipitation variability over China through wavelet analysis and Granger causality tests. Singular value decomposition (SVD) identifies the Barents, Kara, East Siberian, and Chukchi Seas as key regions exhibiting strong associations with spring precipitation anomalies. Increased winter sea ice in the East Siberian and Chukchi Seas generates positive geopotential height anomalies over the Arctic and negative anomalies over Northeast Asia, adjusting upper-level jet streams and influencing precipitation patterns in Northeast China. Conversely, increased sea ice in the Barents–Kara Seas leads to persistent negative geopotential height anomalies simultaneously occurring over both the Arctic and South China regions, enhancing southern jet stream activity and intensifying warm-moist airflow at the 850 hPa level, thus favoring precipitation in southern China. Compared to considering only climate factors such as the Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), and Arctic Oscillation (AO), the inclusion of Arctic sea ice significantly enhances the influence of multiple climate factors on precipitation variability in China. Full article

This pioneering study examined the complex interplay between climate changes and landscape ecological dynamics through a spatiotemporal analysis (1980–2023) of China’s climatically vulnerable Qinling Mountains. The results revealed significant trends in landscape indices, indicating the ecosystem sensitivity of the Qinling Mountains to climate change. The analysis revealed temperature and precipitation as the primary climatic drivers differentially affecting land cover systems. Qinling’s thermal regime has undergone progressive intensification under anthropogenic warming, contrasting with precipitation’s nonlinear variability marked by decadal oscillations. Persistent warming trajectories align with observed vegetation shifts toward higher elevations and latitudes. Landscape metrics demonstrated scale-dependent climate synchronization, achieving full coherence at the macroscale and partial alignment across ecosystem-specific configurations. These multiscale interactions delineate a dual mechanism where climate directly reshapes landscape ecological patterns while modulating human–environment feedback loops. Full article

Optimizing the design of low-tungsten-content alloys represents an effective approach to address the insufficient strength and toughness of conventional tungsten alloys. This study focuses on the design and fabrication of low-tungsten-content alloys, specifically investigating the effects of Nb addition on the low-temperature sintering microstructure and mechanical properties of 50W–Ni–Fe alloy. The results demonstrate that Nb significantly lowers the liquid phase formation temperature, shifting the densification mechanism from solid phase sintering to liquid phase sintering. Nb primarily dissolves in the γ-(Ni,Fe) matrix phase and forms nanoscale γ″-Ni₃Nb precipitates. These γ″-Ni₃Nb precipitates maintain coherent interfaces with the γ-(Ni,Fe) matrix phase, exhibiting excellent interfacial bonding, which markedly enhances the hardness and modulus of the matrix phase. Through the strengthening effects of solid solution strengthening and precipitation strengthening, the tensile strength of the alloy increases to 1259 MPa while maintaining a total elongation of 23.1%. The fracture mode of the 50W-Ni-Fe-Nb alloy transitions to a mixed mechanism involving cleavage fracture of W and ductile rupture of the matrix phase. Full article