Search Results (1,126)

Karst terrains hold vital global groundwater reserves, underpinning regional water security and ecological stability. To elucidate groundwater hydrochemical patterns and formation mechanisms in Wuhan’s karst zone, this study adopted the Gibbs model, correlation analysis, principal component analysis and positive matrix factorization to explore water–rock interactions, hydrochemical origins, element migration, hydrogeochemical facies and genetic processes. The results show that water in both confined porous loose rock aquifers (CPLRAs) and karst fissure carbonate rock aquifers (KFCRAs) is mainly of HCO₃–Ca and HCO₃·SO₄–Ca types. Carbonate dissolution dominates hydrochemical evolution, with Ca²⁺, Mg²⁺, and HCO₃⁻ as major ions. Natural water–rock interactions control the ionic characteristics of both groundwater types. Silicate weathering exerts a greater influence on water in the KFCRA, while water in the CPLRA has more complex ion sources. Anthropogenic activities contribute 17.52% and 17.61% to their hydrochemical variations, suggesting moderate human influence. Water in the CPLRA is mainly affected by domestic sewage and soil organic nitrogen, locally superimposed with industrial and mining disturbances. Water in the KFCRA is primarily influenced by agricultural pollution, with minor domestic sewage input. These findings provide a scientific basis for sustainable development, protection, and targeted pollution control of groundwater resources in the Wuhan karst area, and offer a reference for hydrochemical studies in comparable karst regions. Full article

Urban heat stress is intensifying under climate change, particularly in outdoor public spaces where conventional mechanical cooling is impractical. This study develops a climate-driven, system-level numerical framework to evaluate the pre-deployment feasibility of modular, solar-powered spray cooling canopies across 110 cities in Türkiye. Hourly Typical Meteorological Year (TMYx) weather files, representing a single typical year constructed from 2009 to 2023 source data, are used to estimate photovoltaic (PV) energy yield, electrical load, feasible misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. The misting operation is governed by a rule-based adaptive control strategy based on air temperature, relative humidity, and plane-of-array irradiance. To support transferable comparison, the cities are classified into six summer climate-archetype zones using k-means clustering of standardized climate variables, including temperature, humidity, irradiance, wind speed, and summer precipitation. Results show that evaporative cooling feasibility is governed primarily by humidity rather than temperature alone. Hot–Dry Inland cities exhibit the longest mean misting duration (501.90 h) and highest water demand (30,152 L per module), but the lowest PV-to-load autonomy ratio (1.55) because of high pump-driven electrical demand. In contrast, Humid Black Sea cities show minimal misting duration (11.43 h) and water use (465 L per module), but the highest autonomy ratio (39.68) due to very limited system activation. Thus, high autonomy does not necessarily indicate high cooling usefulness. The proposed framework provides a reproducible screening tool for identifying where PV-powered spray cooling canopies are climatically suitable, where water and PV sizing become limiting, and where alternative outdoor heat-mitigation strategies may be more appropriate. Full article

In Norway, agroclimatic zones (ACZs) are a valuable tool for national analyses in subject areas concerning the optimized management of agricultural land resources. However, current Norwegian ACZs have been criticized for having an outdated standard climate normal (1931–1960), a limited representation of the local climatic variation, a lack of important model parameters, and weak methodological documentation. Therefore, this paper presents new ACZs for Norway that address these weaknesses. The most significant methodological updates are the use of the standard climate normal of 1991–2020, additional weather data variables, the downscaling of weather data to 250 m hexagons, and the incorporation of phenological crop models for spring wheat, spring barley, and forage grass. The grass model was calibrated with the number of grass harvests at research stations, while the grain models were calibrated with subsidy claim data. The modeled zones for the three crops were combined into the general ACZs. Example maps of the crop zones and new ACZs for the selected regions and the whole country are presented. The new ACZs are more robust, agronomically relevant, and better aligned with the current climatic conditions in Norway. The deliberate exclusion of factors other than climate ensures the new ACZs’ national comparability and their applicability in policy development, land-use planning, climate adaptation, and agronomic assessments at the national scale. Full article

Typhoon-induced landslides in coastal mountainous regions are controlled by the coupled effects of rainfall, wind, topography, and storm-track geometry. However, conventional static susceptibility models have limited ability to represent event-scale forcing under extreme weather conditions. This study develops a physics-guided dynamic landslide susceptibility framework and retrospectively applies it to the 2017 Tropical Cyclone Cempaka event in Pacitan Regency, Indonesia, where 743 landslides were identified. The framework integrates static terrain factors, antecedent wetness, event-scale rainfall accumulation and intensity, maximum wind speed, and a typhoon geometric exposure index derived from IBTrACS best-track information that represents track proximity, topographic shielding, rainfall-favored quadrant effects, and storm-motion effects. Under spatial block cross-validation, model performance improved progressively from the static baseline to the full-factor model, with the receiver operating characteristic area under the curve (ROC-AUC) increasing from 0.648 to 0.751, the precision–recall area under the curve (PR-AUC) reaching 0.826, and the F1-score reaching 0.744. The full-factor model also reduced missed landslide cases from 328 to 205 and concentrated predicted high-susceptibility zones along the typhoon exposure corridor. Additional parameter-sensitivity analyses further indicate that the event-based E_geo setting produced positive performance increments under the event-consistent quadrant convention. These results indicate that physically meaningful typhoon-exposure information can improve the spatial discrimination and interpretability of event-scale landslide susceptibility assessment. Full article

Greenhouse microclimate regulation is challenging due to nonlinear coupling among temperature, humidity, soil moisture, and light intensity, which limits the effectiveness of conventional threshold-based and PID control strategies under time-varying environmental disturbances. This paper presents a forecast-augmented ensemble control framework that combines Random Forest, Gradient Boosting, and Support Vector Machine classifiers with one-hour-ahead weather forecasts for closed-loop greenhouse microclimate regulation. The proposed system was deployed and validated in a working greenhouse cultivating cucumber (cv. ‘Madora F1’) over 28 consecutive days. Sensor measurements and forecast inputs were processed through a unified preprocessing pipeline, while control actions were generated through majority voting and executed on Raspberry Pi 4B edge hardware with a worst-case inference latency below 18 ms. The proposed framework achieved a temperature RMSE of 0.83 °C during field deployment. For reference, RMSE values of 3.21 °C and 1.94 °C were obtained for the threshold-based and PID baseline controllers, respectively, under the adopted disturbance-consistent evaluation protocol. Compliance rates reached 96.4% for temperature, 94.1% for relative humidity, and 97.2% for soil moisture across 40,320 resampled observation intervals (60 s analysis grid) derived from the original 10 s acquisition stream. Integration of short-term weather forecasts enabled anticipatory irrigation management, reducing irrigation pump operation by 18% without compromising soil-moisture compliance and yielding an estimated annual energy saving of 158 kWh per greenhouse zone. Unlike prediction-oriented greenhouse artificial-intelligence studies, the proposed approach implements a deployable forecast-augmented closed-loop control architecture validated under continuous real-world greenhouse operation. Full article

Despite the large amount of successful existing methods and frameworks for planning sets of multiple unmanned aerial systems (UASs), there is still a lack of coordination frameworks that are capable of coping with real-world operational conditions. This paper presents U-Plan, an integrated management framework for the coordination of multi-UAS missions. U-Plan is designed to plan, schedule, monitor, and replan a heterogeneous set of UASs to complete point of interest (PoI) visiting missions while ensuring that all the generated trajectories are safe, feasible, and compliant with the required PoIs’ arrival times, UAS kinematics and energy constraints, and the existing 3D no-fly zones (NFZs). U-Plan is designed as a practical tool for strongly dynamic missions and is built upon three core components: (1) an NFZ-aware route computation method that explicitly accounts for NFZs prior to vehicle routing problem (VRP) optimization, resulting in shorter NFZ-safe routes; (2) a trajectory smoothing module that ensures the generation of kinematically feasible trajectories for fixed-wing UASs; and (3) a mission supervision module for real-time monitoring and replanning in case of changes in the UAS, mission, wind speed, or airspace restrictions. To validate the proposed architecture, we conducted rigorous experiments utilizing the VECTOR-SIL autopilot and Visionair Ground Control Station to realistically replicate the behavior of certified fixed-wing autopilots under various weather conditions using the exact same hardware and flight control software that runs onboard the physical drones. The validation shows U-Plan’s capacity to efficiently satisfy complex mission requirements with strong scalability. Due to its high computational efficiency, U-Plan enables online mission replanning, allowing UAS fleets to seamlessly adapt to changes that are typical of real-world operational scenarios. Full article

The Gebel Shalman-Wadi Biarn (GSh-WB) area in Egypt’s South Eastern Desert hosts NYF-type rare-metal pegmatites with significant U, Th, Nb-Ta, and REEs mineralization. This study integrates field observations, petrography, mineralogy, whole-rock geochemistry, and gamma-ray spectrometry to characterize these pegmatites and evaluate their economic potential. The pegmatites occur as veins, dykes, and zoned pockets hosted entirely within syenogranites. Petrography, pegmatites, and syenogranites are primarily composed of K-feldspar, albite, and quartz with trace amounts of biotite and muscovite. The environmental scanning electron microscope (ESEM) revealed the presence of the following minerals: autunite, kasolite, thorite, monazite-(Ce), parisite, xenotime-(Y), ferrocolumbite, hydroxyplumbobrtafite, aeschynite-(Y), and zircon, which are the major U-Th, Nb-Ta, and REE-bearing minerals. Additionally, gold, cassiterite, wolframite, pyrrhotite, chalcopyrite, and brass alloy were identified as sources of precious and base metals. Both groups’ chondrite-normalized REE patterns, which display slightly elevated LREE patterns and negative Eu anomalies, point to fractional crystallization involving plagioclase fractionation. Consequently, pegmatite and syenogranites are believed to have mostly formed from the partial melting of a reconstituted juvenile crust and its weathered sediments associated with Neoproterozoic magmatism. The marginally positive Ce anomaly in the (GSh-WB) pegmatites (1.02–0.98) may be associated with monazite crystallization resulting from enhanced fractionation. The Th and U levels range from 101 to 28.6 ppm and from 51 to 5.8 ppm, respectively. The magnitude of the tetrad effect in the rare earth elements of the analyzed rocks exceeds one (T1 = 1.12–1.02, T3 = 0.92–1.08, and T1,3 = 1.01–1.05), suggesting an M-type tetrad effect. The presence of this tetrad effect is indicative of granite that has been significantly altered by hydrothermal processes and is extensively fractionated. Chondrite-normalized REE patterns of the pegmatites (average ΣREE = 439 ppm) and their host syenogranites (average ΣREE = 192 ppm) show similar trends characterized by enrichment of light rare earth elements (LREEs) relative to heavy rare earth elements (HREEs) and pronounced negative Eu anomalies (Eu/Eu* = 0.09–0.22). These features, together with negative Sr and Ba anomalies, likely reflect extensive fractional crystallization of feldspars and feature anorogenic rocks. Spectrometric analysis reveals eU values of 2.0–288 ppm and eTh values of 7.0–455 ppm in pegmatite samples, with eU/eTh ratios (0.49–0.39) exceeding the typical continental crust value of 0.25, indicating uranium enrichment. Both magmatic and hydrothermal processes contributed to the observed radioactivity. The spatial distribution of uranium shows lithological and structural controls. The GSh-WB pegmatites represent a potential target for uranium exploration. Full article

Accurate power load forecasting is essential for efficient electricity grid management, yet capturing cross-variate dependencies in multivariate time series remains a persistent challenge. Recent channel-independent methods based on Transformer and recurrent architectures have achieved strong forecasting performance, but they discard potentially useful information from correlated variates such as weather conditions and neighboring consumption zones. In this paper, we propose RWKV-CVM, a lightweight extension of the RWKV-TS architecture that introduces a trainable Cross-Variate Mixing (CVM) module to selectively incorporate inter-variate information while preserving the linear time complexity of the backbone. The CVM module is a gated, row-stochastic mixing matrix—initialized from the training set absolute Pearson correlations and modulated by a single learned scalar gate that is applied to the normalized input series before patching, adding only 65 trainable parameters to the backbone. We evaluate the method under a single unified harness (three random seeds, consistent normalization, and re-executed DLinear, iTransformer and RWKV-TS baselines) on three settings: the Tetouan city power consumption dataset forecast jointly for all three zones at horizons up to 72 h (including the operationally relevant 24 h day-ahead and 48 h two-day-ahead horizons) and the ETTh1 and Weather benchmarks under a $10\%$ few-shot protocol. Averaged over horizons, RWKV-CVM attains the lowest mean MSE on all three datasets (Tetouan all-zone $0.0427$, ETTh1 $0.640$, Weather $0.250$), narrowly ahead of the strongly-tuned baselines and its own RWKV-TS backbone. The advantage is modest, is concentrated at longer horizons, and is selective across target zones; on several individual horizons and in the full-data regime, a baseline is preferable, and we report these cases explicitly. These results indicate that a controlled, lightweight injection of cross-variate information can improve multivariate load forecasting on average without sacrificing computational efficiency. Full article

In summary, near-surface icing risk in complex alpine terrain is jointly controlled by freezing conditions, moisture supply, freeze–thaw transitions, and topographic energy processes. Traditional approaches relying on sparse station data or single temperature thresholds fail to capture spatial heterogeneity, and frequent cloud cover together with topographic errors severely limit the application of thermal infrared remote sensing. Taking the area along the Duku Highway in the Tianshan Mountains as the study region, a daily icing risk assessment framework at 250 m resolution was constructed using multi-source remote sensing, ERA5-Land reanalysis data, topographic correction, and an energy–moisture dual-constrained model. A diurnal temperature cycle model, the CAP index, and physics-constrained machine learning were integrated to reconstruct the daily minimum land surface temperature ($T_{s,\mathrm{m}\mathrm{i}\mathrm{n}}$) at 250 m resolution under all weather conditions. A probabilistic two-tier risk assessment model was then established by incorporating moisture, topography, and freeze–thaw transitions. The results show that high-risk zones occur primarily in valleys and topographically constrained corridors rather than the coldest elevations. Validation against Landsat LST (r = 0.886) and the Bayanbulak station (bias −0.76 °C, RMSE 5.62 °C, r = 0.91) confirms spatial and seasonal accuracy. Sensitivity and Monte Carlo analyses indicate the RiskScore is mainly controlled by the low-temperature weight, while upstream parameters are less influential. The framework is best applied as a screening and early-warning product to identify sub-kilometer potential icing corridors, complementing point measurements and short-range forecasts. Full article

This research was conducted on mountain summits in South Yakutia, Russia. The findings indicate that within the goltsy altitudinal belt (comparable to the alpine zone), the weathering intensity of rocks above 1400 m depends on the development of snow-ice formations, particularly snowpatches. Snowpatches promote physical rock weathering along their edges by up to 1.5–4 times more intensely compared to baseline levels. The ground temperature at the edges was examined in relation to air temperatures. The conditions that facilitate rock weathering at the snowpatch edge during the summer months are characterized by diurnal air temperatures below 10–15 °C, with minimum temperatures below 5 °C. Nivation processes in the goltsy altitudinal belt of South Yakutia are considered as one of the dominant geomorphic agents. However, future changes are expected in the existing nival-glacial belts, as snowpatches respond rapidly to climate change, with the mean annual air temperature in South Yakutia exhibiting a rising trend. Full article

Small and medium-sized (SMS) lakes in cold–arid regions are highly sensitive to climate change and play critical roles in regional hydrological and ecological processes. However, their long-term dynamic evolution along aridity–humidity gradients remains insufficiently understood. This study aims to reveal the spatiotemporal variations in SMS lakes on the Inner Mongolia Plateau and clarify their climatic driving mechanisms. Based on Landsat imagery and meteorological data (1984–2021) on the Google Earth Engine (GEE) platform, this study quantified the spatiotemporal variations in SMS lakes and adopted an ecological–geographical zoning framework to characterize lake responses across aridity–humidity gradients. Results indicate that, from 1984 to 2021, the total area of SMS lakes showed an insignificant linear trend but a net increase of 117% (396.50–860.33 km²), while the lake number increased by 155%, with 59 new lakes. The dynamics followed four stages: expansion (1984–1993), fluctuation (1994–2002), low-level stability (2003–2011), and recovery (2012–2021). Notably, recovery levels remained below the pre-2003 peak, with 2003 identified as a critical turning point. Lake numbers responded to climatic stress earlier than area changes. Spatially, lake variations in arid regions were primarily controlled by energy-related factors (e.g., temperature and potential evapotranspiration), while lake changes in semi-humid regions were dominated by precipitation-regulated water availability. Semi-arid regions presented transitional characteristics constrained by both energy and water factors. Although extreme weather events did not dominate long-term lake evolution, they significantly exacerbated short-term lake fluctuations. Overall, the controlling mechanism of SMS lakes shifted from energy limitation to water regulation under ongoing climate warming, highlighting pronounced regional differences in climate–lake interactions. Full article

Adverse weather conditions, particularly thunderstorms, are the primary cause of flight delays and safety threats, accounting for approximately 58.7% of irregular flights in 2025. Traditional static rerouting methods often fail to adapt to the non-linear evolution of convective weather. This paper proposes a high-fidelity dynamic rerouting framework to enhance flight safety and efficiency. In the perception layer, a RainNet deep learning model is employed for short-term recursive nowcasting of radar reflectivity, which is subsequently transformed into Dynamic Avoidance Zones (DAZ) via clustering and convex hull algorithms. In the decision layer, a two-stage improved Genetic Algorithm (GA) is developed to solve the rerouting path. The first stage generates initial collaborative solutions under a receding-horizon framework, while the second stage applies a “path-straightening” module to reduce cumulative turning angles and curvature fluctuations. The comparative results in actual scenarios demonstrate a distinct dual-advantage over baseline methodologies. Compared to sampling-based strategies, the proposed model reduces the path length by 14.79%. Furthermore, when compared to heuristic algorithms, it actively trades a negligible 1% distance margin to achieve a massive 92.7% reduction in the cumulative turning angle. With a maximum single turn of only 32.51°, the trajectory completely eliminates sawtooth jitter and redundant detours. Ultimately, this research provides essential technical support for improving air traffic management efficiency and reducing controller workload during severe weather events. Full article

The ocean–atmosphere turbulent heat exchange plays a critical role in the energy and moisture budgets of the Tropical Atlantic Ocean (TAO) and in weather and climate forecasts. However, its estimation strongly depends on the choice of bulk parameterization, as direct in situ measurements are sparse. This study evaluates sensible (Hs) and latent (Hl) heat fluxes derived from three bulk parameterization schemes used operationally in models at the Brazilian Center for Weather Forecast and Climate Studies (CPTEC) of the National Institute for Space Research (INPE), Brazil: the Brazilian Atmospheric Model (BAM), the Modular Ocean Model version 6 (MOM6), and the Weather Research and Forecasting (WRF) model. Using daily in situ observations from seven Prediction and Research Moored Array in the Tropical Atlantic (PIRATA) buoys across the TAO during 1997–2023, we computed monthly mean fluxes and compared them against the Coupled Ocean–atmosphere Response Experiment (COARE) algorithm version 3.0b (COARE 3.0b) reference. COARE version 3.6 (COARE 3.6) and European Centre for Medium-Range Weather Forecast (ECMWF) Reanalysis 5th generation (ERA5) data were included as additional benchmarks. All offline schemes were forced with identical buoy data, isolating differences in internal physical assumptions. Hl is approximately one order of magnitude larger than Hs across all sites, and inter-scheme differences are substantially larger for Hl (±50 W∙m⁻²) than for Hs (±5 W∙m⁻²). All schemes reproduce the seasonal cycle linked to the Intertropical Convergence Zone (ITCZ) migration and trade-wind variability, with correlations generally exceeding 0.8 (p < 0.001) for most buoys. However, systematic magnitude biases remain. The Coordinated Ocean Research Experiments (CORE) bulk formulation implemented in MOM6 (MOM6-CORE) shows high temporal correlation (often r ≈ 1.0) but a persistent negative bias for both Hs and Hl (e.g., B1 Hl bias = −24.0 W∙m⁻²), indicating weaker turbulent exchange relative to COARE 3.0b. BAM overestimates Hs (by 1–3 W∙m⁻²) and underestimates Hl at most northern and southern sites, while the parametrization of the Yonsei University (YSU) implemented in the WRF model (WRF-YSU) amplifies Hs variability intermittently, particularly at the equator (B4). As expected, COARE 3.6 remains the closest to the reference (differences < 1 W∙m⁻² for Hs and <7 W∙m⁻² for Hl; r ≈ 0.99). ERA5 captures temporal variability well (r ≈ 0.7–0.9) but systematically overestimates Hl (positive bias up to +47.6 W∙m⁻² at B7), implying stronger evaporative cooling. Buoy-specific regimes modulate skill. The choice of bulk formulation thus remains a first-order source of uncertainty in turbulent heat flux estimates over the TAO, with direct implications for mixed-layer heat budgets, SST evolution, and coupled ocean–atmosphere variability. MOM6-CORE provides the most consistent performance relative to the COARE reference and emerges as the most robust option for operational applications at CPTEC/INPE. The findings also provide guidance for improving the representation of ocean–atmosphere turbulent exchanges in MONAN (Model for Ocean-Land-Atmosphere Prediction), the new Brazilian Earth System Model under development for weather and climate prediction. Full article

In many developing regions, the lack of on-site weather data impedes the estimation of rainfall-driven processes, such as soil erosion. Satellite-based precipitation products (SPPs) can support hydrological modelling in gauge-sparse regions by providing continuous rainfall estimates. Accurate rainfall estimation is crucial to soil erosion modelling, particularly in data-scarce regions such as the TRB. In this study, seven satellite-based precipitation products—CHIRPS, IMERG, TRMM, ERA5, GLDAS, and PERSIANN-CDR, along with the IMD gridded dataset—were evaluated for their ability to represent rainfall patterns and support R-factor estimation in the RUSLE framework. This is the first comprehensive evaluation of multiple SPPs for RUSLE-based soil erosion modelling in the Tungabhadra river basin (TRB), providing insights for ungauged watersheds in India. CHIRPS and IMERG displayed relatively smooth and continuous patterns, while PERSIANN-CDR and TRMM exhibited fragmented rainfall zones. ERA5 and GLDAS demonstrated consistent but moderate values across the basin. IMD data served as the reference product for comparison. The findings reveal that the choice of precipitation dataset directly affects the accuracy of erosion estimation. Therefore, multi-dataset evaluation is recommended for reliable assessment of soil loss and watershed planning in ungauged or partially gauged catchments. Full article

With the rapid growth of tourism in Dayi County over the past decade, this study develops a meteorological disaster risk assessment framework for major tourist attractions in this region. Drawing upon daily precipitation and temperature records from 25 meteorological stations (2014–2023) alongside multi-source geospatial data, we evaluate six primary attractions: Xiling Snow Mountain, Huashuiwan, Anren Ancient Town, Xinchang Ancient Town, Tianfu Huaxigu Valley, and Shujiu Cultural Park. The evaluation model integrates four core dimensions: hazard, environmental sensitivity, asset vulnerability, and disaster mitigation capacity. Indicator weights are determined through the Analytic Hierarchy Process, and GIS-based spatial analysis is employed for risk zonation. Additionally, the 45-year ChinaMet dataset provides independent validation for the long-term stability of the hazard assessment. Results reveal a distinct west-low, east-high composite risk gradient. High-altitude mountainous regions in the west exhibit a lower overall risk. Despite frequent extreme weather events, extensive vegetation coverage and low visitor density effectively buffer the negative impacts of physical hazards. Conversely, tourist attractions on the eastern plains fall within high-risk zones. Concentrated visitor populations, dense built environments, and low-lying terrain collectively amplify exposure to severe rainstorms and extreme heatwaves. These findings demonstrate that meteorological disaster risk in tourism destinations fundamentally arises from the deep coupling of natural and human systems. Thus, this study provides a scientific basis for implementing differentiated disaster prevention, mitigation, and localized emergency management strategies. Full article