Search Results (852)

Global warming is reshaping terrestrial water cycling and near-surface climate risks through atmospheric moistening, enhanced precipitation variability, rising evaporative demand, and more frequent compound extremes. This narrative review synthesizes recent advances in land–atmosphere interactions and atmospheric water cycle feedbacks, and its distinctive contribution is to connect physical feedback chains with human land surface perturbations, compound risk, and observation model machine learning evidence. We reviewed the literature from Web of Science, Scopus, Google Scholar, publisher databases, and Crossref metadata, prioritizing peer-reviewed studies published mainly during 2010–2026 while retaining foundational work on soil moisture feedbacks, moisture recycling, irrigation, aerosols, and boundary-layer processes. The synthesis emphasizes where evidence is robust, where feedback signs are regime dependent, and where uncertainty still propagates from evapotranspiration partitioning, boundary-layer diagnosis, aerosol–cloud interactions, human water management, and nonstationary climate conditions. The review concludes that the same land surface perturbation may cool locally, increase humid heat exposure, alter downwind precipitation, or intensify water depletion, depending on the climate regime, season, scale, and management. Future research should therefore move beyond single-variable correlation analyses toward causal, cross-scale, and risk-oriented attribution frameworks that integrate multi-source observations, process models, moisture tracking, and physically constrained machine learning. Full article

Vegetation phenology is a sensitive indicator of climate change in China’s drylands (aridity index, AI < 0.65). However, accurate phenological monitoring remains challenging due to low signal-to-noise ratios, persistent soil background interference, and the scarcity of ground phenological sites. Existing global phenology products also perform poorly in hyper-arid and arid regions. This study developed an optimal phenology detection framework for China’s drylands by systematically evaluating various vegetation indices, noise-reduction techniques, fitting functions, and dynamic thresholds against ground observations, generating a dataset at 500-m resolution spanning 2001–2024. Specifically, we determined vegetation index thresholds to distinguish vegetated from non-vegetated pixels based on 453 field survey sites. Our results indicate that the Normalized Difference Phenology Index (NDPI) coupled with a 10% threshold and polynomial fitting provided the highest accuracy for Start of Season (SOS) (RMSE = 12.02 days). For End of Season (EOS), EVI2 combined with a 70% threshold and self-weighted double-logistic fitting yielded superior performance (RMSE = 19.89 days). Compared to the MODIS global phenology product (MCD12Q2), our dataset demonstrates significantly higher accuracy (higher R and lower RMSE) and broader spatial coverage, particularly in hyper-arid and arid regions. Spatiotemporal analysis reveals that SOS was earlier while EOS was later in more arid areas, potentially reflecting the opportunistic life strategies of ephemeral plants. Notably, a trend of delayed SOS was observed in these regions, which we potentially linked to the shifts in precipitation regimes under global change. This optimized framework and the resulting Chinese dryland phenology dataset provide a robust foundation for assessing ecosystem resilience and carbon cycle dynamics in water-limited environments. 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

Climate change is expected to significantly alter hydrological regimes in high-altitude tropical basins, where water availability strongly depends on precipitation variability and groundwater processes. The Ramis River basin, a major tributary of Lake Titicaca in the Peruvian Altiplano, is particularly vulnerable to hydroclimatic variability due to its dependence on seasonal water resources. This study evaluates the impacts of climate change on runoff, groundwater recharge, percolation, and renewable water resources using the SWAT hydrological model calibrated and validated for the period 1981–2024. Future projections were developed using the MPI-ESM-MR and ACCESS1-0 global climate models under RCP 4.5 and RCP 8.5 scenarios for the period 2025–2100, applying bias correction through CMhyd. The results indicate a strong sensitivity of basin hydrology to climate forcing. Under the MPI-ESM-MR model, runoff decreases by up to 68% under RCP 4.5, while extreme increases exceeding 130% are projected under RCP 8.5. In contrast, ACCESS1-0 shows moderate reductions in most scenarios. Renewable water resources exhibit a general declining trend (−23% to −41%), suggesting increasing water scarcity conditions. Additionally, the Standardized Precipitation Index (SPI) reveals a higher frequency and persistence of drought events toward the end of the century, particularly under high-emission scenarios. Overall, the findings indicate that the Ramis River basin may face a dual hydroclimatic risk characterized by reduced water availability and increased hydrological extremes. These results highlight the need to integrate climate projections into water resource management and to implement adaptive strategies to reduce future water vulnerability in high-Andean basins. Full article

This study aims to identify the optimal aging regime that balances strength and intergranular corrosion (IGC) resistance in a 2060 Al–Li alloy under T8 temper. The evolution of microstructure, mechanical properties, and IGC behavior was systematically investigated across various aging conditions. The most relevant results show that the optimal regime for the 3% pre-stretched alloy is 150 °C for 32–48 h. At the peak-aged state (150 °C/48 h), the alloy achieves a yield strength (YS) of 521 MPa and ultimate tensile strength (UTS) of 541 MPa in the longitudinal (L) direction, and 486 MPa and 548 MPa in the long-transverse (LT) direction, with elongations of 11.1% and 12.2%, respectively. Under this condition, the corrosion mode shifts from IGC to pitting, with a maximum pitting depth of 98.6 μm. Microstructural analyses confirm that the T₁ (Al₂CuLi) phase is the primary strengthening precipitate. Critically, as aging temperature and time increase, T₁ plates extensively nucleate and grow within grain interiors, while their distribution at grain boundaries (GBs) becomes discontinuous. This discontinuous GB precipitate morphology interrupts continuous anodic dissolution channels, thereby significantly enhancing localized corrosion resistance. Notably, these findings can offer practical guidance for industrial heat treatments of third-generation Al–Li alloys, particularly for safety-critical aerospace components where both strength and corrosion resistance are mandatory. Full article

Nickel ferrite (NiFe₂O₄) is one of the most studied inverse-spinel ferrites because it combines moderate saturation magnetization, comparatively high electrical resistivity, chemical stability, and broad synthesis flexibility. Yet the literature shows that the measured structure and magnetism of NiFe₂O₄ are not intrinsic constants; they evolve strongly with dimensionality, size, thickness, strain state, cation distribution, surface spin disorder, and synthesis pathway. This review develops a unified theoretical and literature-based interpretation of how dimensionality reshapes the structural and magnetic behavior of NiFe₂O₄ across bulk ceramics, nanoparticles, one-dimensional nanostructures, polycrystalline thin films, and ultrathin epitaxial films. The review is anchored in the two uploaded nickel ferrite attachments and expanded using internet-sourced journal literature on spinel inversion, surface effects, mechanochemical synthesis, sputtered and pulsed laser deposited thin films, and epitaxial ultrathin-film anomalies. The central novelty of this article is the formulation of a dimensionality-dependent framework in which the observed magnetic response is governed by a competition among three coupled factors: (i) the cation-distribution function, which controls the A–B superexchange balance and therefore the net ferrimagnetic moment; (ii) the microstructural coherence function, which measures how crystallinity, strain, defects, and anti-phase boundaries preserve or degrade exchange continuity; and (iii) the surface/interface spin-order parameter, which quantifies the loss or reconfiguration of magnetic order at free surfaces and buried interfaces. Within this framework, bulk NiFe₂O₄ behaves as a near-equilibrium inverse spinel with relatively stable magnetization, whereas nanoscale NiFe₂O₄ experiences strong spin canting and finite-size suppression due to the growing fraction of disordered surface spins. Thin films introduce a distinct regime in which strain, texture, anti-phase boundaries, substrate mismatch, and growth kinetics determine both anisotropy and magnetization. In ultrathin epitaxial films, off-equilibrium cation redistribution and interface-controlled electronic reconstruction may even generate magnetization values far above bulk expectations. The review also compares major synthesis routes—solid-state reaction, sol–gel, co-precipitation, hydrothermal growth, reactive milling, combustion, pulsed laser deposition, and radio-frequency sputtering—and explains why each route biases the final dimensionality-dependent properties differently. A set of word-style equations is provided to formalize spinel inversion, finite-size suppression, anisotropy scaling, coercivity trends, and superparamagnetic crossover. Beyond summarizing the field, the review proposes a regime map linking dimensionality to characteristic structural defects and magnetic signatures, and it identifies unresolved questions concerning the true origin of enhanced magnetization in ultrathin NiFe₂O₄, the interplay between anti-phase boundaries and strain, and the distinction between intrinsic inversion changes and extrinsic substrate artifacts. The resulting article offers a submission-ready, originality-focused review that positions dimensionality as the master variable governing structure–magnetism correlations in nickel ferrite. Full article

In many orographically complex monsoon basins, rain gauge networks are sparse and lack the long-term continuous records required for reliable precipitation probability analysis. Traditional regional frequency analysis assumes spatially uniform precipitation across the analysis zone, which is inadequate for basins with steep rainfall gradients and strong seasonal variability. Gridded precipitation products (GPPs) provide spatially continuous, long-term records that enable grid-cell-level probability distribution fitting. However, GPPs may exhibit local biases and errors, and statistical evaluation against gauge observations is necessary before application. This study was conducted in the Phetchaburi–Prachuap Khiri Khan River Basin, western Thailand, a region with steep orographic and coastal rainfall gradients. Four GPPs, namely CHIRPS, CHELSA, WorldClim, and PERSIANN-CCS-CDR, were evaluated against gauge observations. The best-performing product, after monthly bias correction, was then used to generate spatially continuous monthly exceedance probability maps using grid-cell gamma distribution fitting. CHELSA showed the best overall performance across all evaluation metrics (correlation coefficient (r) = 0.908, percent bias (PBIAS) = 7.0%, root mean square error (RMSE) = 48.3 mm), passing the Kolmogorov–Smirnov (KS) goodness-of-fit test at all 96 station-months. CHIRPS and WorldClim showed satisfactory overall performance but exhibited localized biases in complex terrain, whereas PERSIANN-CCS-CDR substantially overestimated wet-season rainfall, limiting its suitability for this basin. Spatial precipitation patterns varied markedly between monsoon regimes, shifting from a dominant west-to-east orographic gradient during the southwest monsoon to a less differentiated advective pattern during the northeast monsoon. Furthermore, analysis at the 75% exceedance probability level showed that mean-based effective rainfall overestimated reliable water supply in high-variance months, leading to underestimation of supplemental irrigation demand. The generated maps provide spatially explicit dependable rainfall estimates across the basin, supporting probabilistic agricultural water management at multiple planning scales in orographically complex monsoon basins. Full article

Machine learning approaches to wildfire spread prediction are constrained by the lack of standardized, multi-source, spatiotemporal datasets that fuse terrain, weather, and fire-state information into a single ML-ready format. We present WildfireCube, a reproducible event-centric pipeline and methodology for constructing dense fourth-order spatiotemporal tensors of shape (T, C, H, W) at 30 m spatial and 3 h temporal resolution. Following the analysis-ready data convention established in the Earth Observation community, the pipeline fuses four open data sources: the Copernicus GLO-30 Digital Elevation Model for static terrain derivatives, ERA5-Land reanalysis for hourly weather forcing, Sentinel-2 Level-2A imagery for spectral vegetation and burn-severity indices, and NASA FIRMS active-fire hotspot detections for fire-state reconstruction via ordinary kriging. The resulting 13-channel normalized tensor separates causal drivers into three physically motivated groups: static landscape controls (elevation, slope, aspect, fuel load), dynamic atmospheric forcings (wind components, temperature, precipitation), and evolving fire state (fire-front mask, burn severity, fractional burn, observation confidence). A physics-informed normalization framework maps all channels to bounded ranges using fixed physical constants rather than sample statistics, ensuring cross-event comparability and exact invertibility. We demonstrate the pipeline on 13 wildfire events across the United States, Canada, and Greece (2017–2023), producing a processed catalog exceeding 300 GB compressed and spanning a 14-fold range in burned area, a 27 °C range in mean temperature, and different fire regimes. Event tensors are stored in chunked Zarr archives with Zstandard compression, achieving a 2.58× compression ratio. As future work, the pipeline will be applied to a 40-event target catalog projected to exceed 2 TB of raw data, providing the multi-regime diversity and scale required for training robust deep learning models for spatiotemporal wildfire prediction. Full article

This study investigates the hydroclimatic impacts of climate change on the Göynük Stream Basin, a snow-fed tributary within the Euphrates River Basin, utilizing flow, precipitation, and temperature data from 1975 to 2022. The Göynük Stream Basin is characterized by high-altitude, harsh continental conditions, with its flow regime heavily influenced by snowmelt, rendering it particularly sensitive to climate change. Employing a suite of trend analysis methods, including Mann–Kendall, Spearman Rho, Theil–Sen, Şen-Innovative Trend Analysis (ITA), and Innovative Polygon Trend Analysis (IPTA), the research evaluated annual and seasonal data from one stream and four meteorological stations across multiple significance levels (90%, 95%, 99%). Unlike conventional hydroclimatic studies based solely on monotonic trend detection, this study integrates classical trend tests, innovative trend approaches, temporal regime-based analysis (RAPS), and machine learning techniques within a unified assessment framework to evaluate both hydroclimatic variability and runoff predictability under climate change conditions. Key findings indicate a significant decline in annual flow rates by approximately 9.37%, with a notable decrease in maximum flow rates evidenced by a negative trend slope of −0.2726 m³/s/year. While precipitation trends were generally decreasing, temperature data exhibited significant increases, especially during winter and spring. Seasonal analysis revealed substantial flow reductions in summer and autumn, coupled with an earlier timing of the annual maximum flow, shifting from mid-May to late March/early April, suggesting earlier snowmelt. The study concludes that the Göynük Stream Basin is experiencing increasing hydroclimatic pressures attributable to climate change. These insights are crucial for water resource management and serve as a guideline for similar snow-fed sub-basins within the broader Euphrates River Basin. Furthermore, the integration of a machine learning approach, utilizing meteorological and seasonal data, demonstrated strong monthly runoff prediction capabilities with NRMSE of 4.11% and R² equal to 0.951. Feature importance analysis highlighted seasonality and temperature as primary predictive factors. However, a marked decline in model accuracy after 2011 was observed, indicating a non-stationarity in the hydroclimatic system, likely driven by climate change impacts and underscoring the need for adaptive management strategies. Full article

Weather–yield relationships in arid agricultural regions are shaped jointly by temperature and precipitation exposure, irrigation conditions, crop choice, and management under water constraints. This study combines county-level cotton yield and maize grain-yield data for Xinjiang, China, from 2000 to 2020 with daily meteorological station records assigned to county-level weather exposures. We estimate two-way fixed-effects models that include temperature degree-day indicators and a quadratic precipitation term to examine crop-specific weather–yield associations in irrigation-intensive oasis agriculture. The baseline two-way fixed-effects estimates indicate that a 100 °C d increase in growing degree days is associated with a 2.85% increase in cotton yield and a 1.88% decrease in maize yield. For cotton, the baseline and common-trend specifications indicate a convex precipitation–yield association, with an estimated turning point of 141.07 mm (95% CI: 27.75–225.63 mm), while the pattern is less stable under prefecture-by-year fixed effects. Maize yield is more consistently negatively associated with growing-season heat accumulation. Post-2010 interaction terms indicate crop-differentiated changes in heat sensitivity, consistent with different temporal evolution of weather–yield associations across cotton and maize. Overall, the results show that climate-risk assessment in irrigation-intensive arid agriculture should distinguish between crop types, precipitation regimes, and the management conditions under which weather exposure is translated into yield outcomes. Full article

Additive manufacturing using laser powder bed fusion (LPBF) has been widely used to produce nickel-based superalloy components with complex shapes for high-temperature applications requiring creep resistance. In this research, the creep behavior of LPBF Inconel 718 under solution and double-aging heat treatments, performed at 590–650 °C under stresses of 450–550 MPa, is studied. The characterization included optical microscopy, scanning electron microscopy (SEM), porosity analysis, Vickers microhardness tests, and fracture surface examination. The findings revealed that even after heat treatment, the material maintained a mainly directional, columnar microstructure, with an average porosity below 1%, which was unevenly distributed and contained critical defects related to lack-of-fusion (LOF) and trapped powder. Fracture after creep presents regions of ductile failure alongside facets indicative of quasi-cleavage. Kinetic analysis revealed a high stress exponent (n = 18.26) and an activation energy (Qc = 410–538 kJ/mol), indicating that the deformation operates within the power-law breakdown (PLB) regime, where dislocation–precipitate interactions govern the creep rate in this precipitation-strengthened superalloy. Overall, the results highlight that the directional microstructure and residual defects typical of LPBF can reduce the creep resistance of Inconel 718, underscoring the importance of post-processing methods and internal defect control specifically tailored for additively manufactured materials. Full article

This work systematically investigates the effects of various heat treatment regimes, including solution treatment, solution treatment followed by aging at 623 K, 723 K and 823 K, and direct aging at the same temperatures, on the solidified microstructure, phase transformation behavior, and nanoindentation properties of thin NiTi samples fabricated by laser powder bed fusion (LPBF). The as-fabricated sample exhibits a strong {100}_B2<001>_B2 Cube texture (maximum texture index 25.77), a high dislocation density (2.70 × 10¹⁸ m⁻²), a single-step B19′↔B2 reversible transformation with A_f = 308.17 ± 3.08 K, and a recovery ratio of 0.46 ± 0.02. Subsequently, solution treatment homogenizes the microstructure, resulting in a lower dislocation density and a partial transformation from the Cube texture to the Goss texture. Further aging at 623 K after solution treatment achieves the highest recovery of 0.52 ± 0.03 by introducing fine and inferred-coherent Ni₄Ti₃ precipitates while maintaining a higher fraction of B2 phase at room temperature. However, aging at 723 K after solution treatment leads to a Goss-dominated texture, mixed austenite/martensite phases, and the lowest recovery (0.34 ± 0.01). In contrast, direct aging at 623 K or 723 K also yields lower recovery ratios (0.40 ± 0.06 and 0.35 ± 0.01, respectively), due to retained compositional inhomogeneity and higher dislocation densities. For direct aging at 823 K, however, the recovery ratio significantly increases to 0.49 ± 0.06. It is therefore suggested that the enhanced recovery performance can be achieved by combining solution treatment with low-temperature aging, which synergistically combines coherent precipitates, a fully austenitic matrix, and a favorable texture. Full article

Because wood anatomical traits and tree-ring features vary with species and climatic regime, cellular-scale measurements complement ring-width chronologies and help with understanding how forests may respond to future environmental change. We developed anatomical chronologies spanning the 1900–2019 period from multi-century old yellow pines (Pinus jeffreyi Balf. and P. ponderosa P & C Laws.) at four sites surrounding the Tahoe Basin of the Sierra Nevada, at the border between Nevada and California, USA. Measurements of earlywood and latewood traits included lumen area, lumen length, lumen width, wall length, wall-to-lumen length ratio, and conductive area. Climate sensitivity was estimated by bootstrapped response functions with precipitation and temperature (monthly and seasonal) from the Global Historical Climate Network interpolated to the site locations. Moisture emerged as the primary control on anatomical trait expression, as significant coefficients involved precipitation rather than temperature. Earlywood lumen size and conductive capacity were associated with late winter through spring moisture, while cellular wall characteristics were connected with conditions during the growing season. Overall, our study provided new insights into the potential impacts of climatic changes on woody species of remarkable size and longevity in mountain forest ecosystems. Full article

Both variability in precipitation and rainfall extremes are key drivers of landslide activity, yet their combined influence with antecedent moisture conditions remains insufficiently quantified at regional or local scales. In this study, daily precipitation records over the past 25 years (2000–2024) were analyzed for five meteorological stations in Northern Croatia across multiple temporal scales. The aim was to investigate the impact of precipitation patterns and regime changes on landslide triggering in Hum na Sutli and the wider area. Statistical analyses (linear regression, Mann–Kendall trend assessment, and Pearson correlation) were applied, and antecedent wetness was quantified using the antecedent precipitation index (API). Results indicate weak, statistically insignificant positive trends in annual precipitation, accompanied by strong interannual variability and coherent regional behavior. Seasonal analysis reveals the dominance of warm-season precipitation with pronounced extremes, while short-duration and multi-day rainfall events exhibit high variability and clustering. The 2024 Hum na Sutli landslide coincided with elevated cumulative precipitation and sustained high API values, despite the absence of exceptionally extreme single-day rainfall events. These findings highlight the critical role of antecedent moisture accumulation combined with episodic high precipitation in slope failure. The study supports a conceptual model in which landslide triggering is governed by the interaction of preconditioning and short-term hydrometeorological factors, providing a basis for improved hazard and risk assessment. Additionally, preliminary rainfall threshold values are proposed as practical early-warning guidance for local communities in landslide-prone regions in Northern Croatia. Full article

Small decentralized water supply systems are often sensitive to local pollution and require a clear understanding of recharge conditions and the hydrodynamics within the water resource catchment. This study develops a conceptual hydrogeological model for the Šmarna Gora area based on geological mapping, long-term monitoring of chemical parameters, and stable isotope analyses (δ¹⁸O, δ²H) of precipitation and groundwater. The study was initiated in response to rising pollutant concentrations in the drinking water. Estimates of transit time (TT) and mean residence time (MRT) were used to characterize recharge, mixing processes, and differences between the SG and ZAVRH wells, the existing and alternative water supply wells. Isotope data show that the aquifer is predominantly recharged during colder periods and that Mediterranean air masses have become an increasingly important source of precipitation, suggesting a shift in precipitation patterns. The results indicate that SG has longer TT (6–8 months) and MRT (up to 1–2 years). In contrast, ZAVRH shows shorter TT and MRT (4–6 months), and lower pollutant concentrations. The hydrogeological regime in the catchment of the ZAVRH well is characterized by a dynamic, fast-flowing system with limited storage and more intensive dilution of contaminants by infiltrating water, whereas the catchment of the SG well functions as a deeper and more buffered aquifer with prolonged groundwater residence and a more direct hydraulic linkage to the contaminant source. The findings distinguish two hydrogeological regimes and provide a basis for planning water supply solutions and protection measures. Full article