Atmospheric and Oceanic Sciences

Farber's typological approach expands burnout models by identifying three burnout subtypes: frenetic, underchallenged, and worn-out. Despite its potential, research on this approach is limited, especially in the German working population. This study translated and validated the Burnout Questionnaire for Clinical Subtypes (BCSQ-12) into German and examined these burnout subtypes in the German working population. Using a non-probabilistic online quota sampling method, 616 employees were surveyed. The BCSQ-12 was translated and evaluated for its psychometric properties (EFA, Varimax rotation). Burnout subtypes were identified through hierarchical and K-means cluster analysis and analysed for symptomatologic, work-related and individual differences, particularly structural impairment (ANOVA). Additional questionnaires assessed burnout (MBI-GS-D), depression (PHQ-9), work engagement (UWES-9), job demands (COPSOQ), social support (MSPSS), and structural impairment (OPD-SQ). The BCSQ-12 demonstrated satisfactory psychometric properties and confirmed its three-factor structure. The cluster analyses revealed four profiles: non-burned-out, frenetic, underchallenged and worn-out, which differed in terms of the symptomatologic, work-related and individual factors. The pattern of resources and demands between the burnout subtypes indicates a deterioration from the frenetic to the underchallenged to the worn-out employee. The present results emphasize possible advantages of including burnout profiles into the general assessment of burnout and indicate the need for tailored interventions.

OBJECTIVES: Psoriatic arthritis (PsA) is a heterogeneous, chronic inflammatory disease with diverse musculoskeletal and extra-articular manifestations, including enthesitis and dactylitis, which contribute substantially to disease burden, disability, and poor quality of life. Therapeutic advances have mainly focused on polyarticular disease, with limited representation of other PsA domains and real-world complexity. The Observational Best Practices Research Initiative for Psoriatic Arthritis (OBRI-PsA) was established as a national registry to systematically capture diverse PsA phenotypes, assess real-world treatment patterns, and longitudinal clinical outcomes. This report describes the registry methodology and presents preliminary findings from the initial enrolled cohort. METHODS: OBRI-PsA is actively enrolling patients with active PsA initiating new disease-modifying therapy, irrespective of domain or burden. Baseline and follow-up data are systematically collected to evaluate treatment responses, patient-reported outcomes, productivity, and real-world strategies. Descriptive analyses of 18-month outcomes from the first 101 enrolled patients are presented as a proof-of-concept; the full study protocol is available in the supplementary material. RESULTS: As of September 2024, a total of 101 patients were enrolled (mean age 53.9 ± 12.7 years; 61.4% female). Symmetrical polyarthritis predominated (86.1%), with 91% having skin psoriasis, 49% enthesitis, and 42% dactylitis. At baseline, mean tender and swollen joint counts were 9.8 and 6.5. Over 18 months, patient- and physician-reported outcomes improved, yet only one-third achieved minimal disease activity (MDA). Response rates varied across domains, with the lowest rate observed for enthesitis (24%). Most patients (60-74%) remained on the same therapy at follow-up with no further modifications, despite ongoing disease activity. Patients with fibromyalgia reported higher disease activity, lower quality of life, and fewer treatment responses. CONCLUSION: Preliminary data from OBRI-PsA highlight persistent unmet needs and variable treatment responses across PsA phenotypes in real-world practice, with relatively few patients achieving treatment targets. These early signals underscore the importance of a comprehensive, longitudinal registry platform to better characterize disease heterogeneity and to inform future phenotype-driven, outcome-focused analyses.

Retinoblastoma (RB) is the most prevalent intraocular malignant tumor in infants and young children, predominantly driven by the biallelic inactivation of the RB1 gene. Although multimodal treatment strategies have markedly improved the survival rates of pediatric patients, chemotherapy resistance, particularly carboplatin (CBP) resistance, remains a major clinical hurdle. In recent years, ferroptosis, an iron-dependent and lipid peroxide-driven form of programmed cell death, has garnered significant attention for its role in tumor drug resistance. This study investigates the functional mechanisms of the transcription factor Spalt-Like Transcription Factor 1 (SALL1) and its downstream target gene Microsomal Glutathione S-Transferase 1 (MGST1) in RB. Bioinformatics analysis revealed a significant upregulation of SALL1 in RB, accompanied by enhanced activity of its regulatory network. Experimental evidence from ChIP-qPCR and luciferase reporter assays indicated that SALL1 binds to the promoter region of MGST1 and enhances its promoter transcriptional output. Functionally, knockdown of SALL1 suppressed cell proliferation and induced ferroptosis, as evidenced by increased levels of lipid peroxidation, elevated malondialdehyde (MDA), and decreased glutathione (GSH) levels. These effects were reversible by the ferroptosis inhibitor Fer-1 or overexpression of MGST1. In the CBP-resistant cell line Y79-R, knockdown of SALL1 notably reduced the IC50, enhanced chemosensitivity, and promoted cell death, a phenotype that could be rescued by overexpression of MGST1. Mechanistically, the SALL1-MGST1 axis promotes RB cell survival and drug resistance by inhibiting lipid peroxidation and ferroptosis. This study is the first to elucidate that the SALL1-MGST1 axis mediates CBP resistance in RB through the regulation of ferroptosis, providing a novel therapeutic target for reversing drug resistance.

INTRODUCTION: Early identification of patients at high risk of mortality remains a clinical challenge in acute pancreatitis. Existing prognostic tools are often complex or require repeated assessments, limiting their routine use. We aimed to develop and internally validate an early admission-phase risk score for predicting in-hospital mortality in patients with acute pancreatitis. METHODS: We conducted a retrospective cohort study of consecutive adult patients admitted with acute pancreatitis between 2020 and 2024. Acute pancreatitis was defined according to the revised Atlanta criteria. Multivariable logistic regression was used to develop a prediction model for in-hospital mortality using routinely available demographic, clinical, and laboratory variables obtained within the first 24 hours of hospitalization. Missing data were handled using multiple imputation. Model performance was assessed by discrimination and calibration, with internal validation performed using bootstrap resampling and temporal validation conducted in patients admitted during 2023-2024. RESULTS: A total of 1,041 patients were included, of whom 53 (5.1%) died during hospitalization. The final model incorporated age, early multi-organ dysfunction within 24 hours of admission, C-reactive protein, and urea levels. The model showed good discrimination (area under the receiver operating characteristic curve, 0.81) and good agreement between predicted and observed in-hospital mortality probabilities. Bootstrap internal validation showed minimal optimism, and temporal validation in patients admitted during 2023-2024 confirmed stable model performance. A simplified risk score derived from the model stratified patients into low-to-intermediate-risk and high-risk categories, with substantially higher observed in-hospital mortality in the high-risk group. CONCLUSIONS: We developed and internally validated an admission-phase risk score for predicting in-hospital mortality in patients with acute pancreatitis using readily available clinical variables. The proposed score may support early inpatient risk stratification within the first 24 hours of hospitalization. However, external validation in independent cohorts across diverse healthcare settings, patient populations, and pancreatitis etiologies is required before broader clinical application.

INTRODUCTION: The burden of mental health problems remains largely unexplored among vulnerable young people, especially those seeking peer support. Accessing peer support is often a first form of help-seeking, allowing early identification of signs of distress. The lost (mental) health and expenses of these young people upon presenting for peer support can be revealed through monitoring of health-related quality of life (HRQoL) and costs of mental healthcare and productivity loss, examined in this study among young people visiting the @ease peer-to-peer walk-in centres in the Netherlands. METHODS: From @ease's inception in January 2018 to mid-2024, a total of 940 answered questionnaires gathered through consecutive sampling contained minimally one required item. This bottom-up prevalence-based study focused on youth aged 12-30 who sought peer counselling at @ease. Burden of disease was estimated by: (1) HRQoL (EQ-5D-5L), and (2) Cost-of-illness through school absenteeism and mental healthcare use. Multiple imputation was used before conducting regression analyses, followed by non-parametric bootstrapping. This study expands upon an earlier publication that analysed data up to May 2019. RESULTS: HRQoL was impaired (M = 0.64, SD = 0.24) and significantly lower if living alone or having parents with mental health problems, and higher if having a higher level of social and occupational functioning. In the three months before presenting, 35.2% of young people had been absent from school (3 days on average, costing €358 per individual) and 33.4% had visited mental healthcare (2 visits on average, costing €234 per individual). Total cost-of-illness was €1,501,743 annually, and €2,318 per individual. Mental healthcare costs were higher for those born in the Netherlands and without occupation, and school absenteeism costs were higher outside the COVID-19 pandemic and if not born in the Netherlands. CONCLUSIONS: Found impairments and costs underscore the importance of investing in early-stage low-threshold services where substantial burden is already detectable, and of strengthening their capacity and links to stepped-care pathways to ensure timely support. Initiatives that help improve functioning and aid with challenging contexts may be advantageous in lowering the burden. Prospective (cost-)effectiveness studies are needed.

In this study, we aimed to provide a detailed microanatomical description of the female reproductive system of Octopus vulgaris, an internally fertilized cephalopod, and to identify its functional characteristics. The female reproductive system of O. vulgaris consists of the ovary, common oviduct, proximal oviduct, oviducal gland, and distal oviduct, and histologically, the structure is similar to that reported in other octopods. The presence of spermatozoa and spermatophore remnants in the distal oviduct suggests that spermatophoric reaction may occur in this region. The released sperm then migrate to the spermathecae of the oviducal gland, where they are anchored to the epithelial layer via a helical acrosome and stored long-term with the support of secretory cells. During the spent and degenerative stages, sperm detach from the epithelial layer of the spermathecae in the oviducal gland and are released into the central cavity of the oviducal gland, where fertilization is presumed to occur. The fertilized eggs are then coated with secretory substances from the central and peripheral glands and subsequently pass through the acidic distal oviduct before being released. These findings demonstrate that the female reproductive system of O. vulgaris exhibits structural differentiation necessary for spermatophoric reaction, sperm storage, and fertilization. This study provides fundamental insights into the reproductive process of internally fertilized cephalopods and serves as a valuable reference for future research on cephalopod reproductive physiology.

The Middle Ordovician Yijianfang Formation in the Shuntuoguole area of the Tarim Basin is an important ultra-deep carbonate exploration target, but favorable reservoir prediction remains difficult because effective storage space is controlled jointly by depositional architecture, diagenetic modification and strike-slip faulting. In this study, formability refers to the capacity of depositional fabrics to create initial pore space, modifiability refers to the potential for diagenetic processes to preserve or enlarge pores, and connectivity refers to the linkage of pores, vugs and fractures into an effective flow network. To clarify these controls, we integrate seismic interpretation, well-log correlation, core and thin-section observations, carbon–oxygen isotopes, elemental logging data and Dionisos-based 3D forward stratigraphic modeling. The Yijianfang Formation is divided into four third-order sequences (SQ1–SQ4), bounded by sequence boundaries (SB1–SB4) and containing maximum flooding surfaces (MFS1–MFS4). MFS4 is correlated with the regional T 7 4 seismic marker. Depositional systems are dominated by restricted and open carbonate platforms, with intraplatform shoals, intershoal areas and lagoonal settings forming the main facies associations. Thickness differentiation and shoal development were strongest during SQ2–SQ3, indicating that these intervals provided the most favorable depositional basis for reservoir formation. Diagenetic modification was facies dependent. Grain-supported shoal facies commonly developed initial intergranular pores but were prone to cement occlusion, whereas micritic intershoal and lagoonal facies had poorer primary porosity but could be enlarged by dissolution along stylolites and microfractures. Exposure-related dissolution and fracture enhancement were preferentially developed near SB3–SB4 and in adjacent rapid facies-transition zones. Late strike-slip fault activity and related fluids further improved reservoir connectivity within damage zones. Therefore, favorable reservoirs are most likely where SQ2–SQ3 shoal complexes, SB3–SB4-adjacent dissolution-prone intervals and strike-slip fault damage zones overlap. This framework supports reservoir prediction by using facies to locate favorable belts, sequence surfaces to select intervals and faults to evaluate connectivity.

Urbanization and greening reshape the surface thermal environment, with opposing thermal effects on urban sustainability and population health. Here, we integrate multidimensional analysis and LightGBM models to quantify how urbanization and greening alter surface temperature and thereby modulate surface urban heat island (SUHI) dynamics across 325 Chinese cities from 2000 to 2025. Our study reveals that urbanization-induced warming is consistently strongest in neighboring rural areas, whereas vegetation-driven cooling intensifies sharply in urban areas during summer. These opposing thermal effects exhibit clear seasonal and diurnal asymmetries, with the strongest responses occurring in summer and during the daytime. Climatic factors emerge as the dominant drivers of surface thermal variability across the urban-rural gradient. Urbanization-induced warming was the dominant contributor to the mean SUHI intensity of 0.39 °C, whereas urban greening represented a cooling contribution share of approximately 31.83% during summer. This contrast between persistent urbanization-induced warming and seasonally concentrated vegetation-driven cooling reveals the constrained timing and spatial dependence of effective biophysical mitigation. Our findings clarify the dual and asymmetric roles of urbanization and greening in urban thermal dynamics and provide quantitative benchmarks for differentiated, climate-resilient planning.

Wetlands and surface-water features regulate the thermal environment of cities through evaporative cooling, yet in arid metropolitan regions these hydrological buffers are scarce and rarely quantified against urban heat. Here, we link satellite-derived surface-water wetness to land surface temperature (LST) and urban heat island (UHI) intensity in Riyadh, Saudi Arabia, using an explainable Geospatial Artificial Intelligence (GeoAI) framework. We assembled 2000 cloud-masked Landsat 8/9 sample points for July 2014 and 2024 in Google Earth Engine and derived the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and two surface-water indices, the Modified Normalized Difference Water Index (MNDWI) and the Normalized Difference Water Index (NDWI), together with LST, UHI, terrain and population. Surface-water wetness was the strongest cool-side correlate of thermal stress: MNDWI related negatively to LST (r = −0.48) and to UHI intensity (r = −0.53), stronger than either vegetation or built-up density (both p < 0.001). Each 0.1 increase in MNDWI corresponded to a 2.2 °C reduction in LST. Five machine-learning algorithms predicted LST with test R2 of 0.71–0.76 and UHI with R2 of 0.68–0.72, and SHapley Additive exPlanations (SHAPs) identified MNDWI as the single most important thermal driver, ahead of elevation and vegetation. Point-level LST rose by 1.99 °C between 2014 and 2024 (p < 0.001), while open surface water was absent from all 2000 samples, indicating a hydrological deficit in the city’s thermal regulation. These findings suggest that protecting and expanding blue–green features along corridors such as Wadi Hanifah offers a measurable cooling lever for arid-city climate adaptation.

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.

Spatial and temporal patterns of terrestrial water storage (TWS), and their relationship with groundwater levels, were investigated with the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Data Assimilation System (GLDAS) land surface model results, and climate observations for the Murray–Darling Basin (MDB). The results show that: (1) TWS displays a clear temporal variability: a negative TWS anomaly with a declining trend during 2002–2009, a positive TWS anomaly with a decreasing trend during 2010–2017, and a period of mixed positive and negative TWS anomalies being accompanied by an increasing trend from 2018 to 2025; (2) five dominant cluster patterns were identified that explain the spatial variability of temporal TWS across the MDB; (3) overall, TWS temporal variability is strongly correlated with rainfall, although it is weak at certain locations; (4) TWS is also influenced by evaporation (both actual and potential evapotranspiration, AET and PET) and runoff, and a combined model significantly improves the overall performance in explaining TWS temporal variability; and (5) TWS-derived groundwater storage changes show both similarities and differences in comparison with groundwater level observation changes, reflecting complex hydrogeological processes and the influence of human activities such as groundwater extraction. These findings provide valuable insights to support improved groundwater resource management with GRACE satellite information and land surface models.

Carbon storage dynamics in dryland and semi-arid ecosystems remain a major uncertainty in global carbon cycle assessments, particularly in regions like the Yellow River Basin (YRB). Using the Arid Ecosystem Model (AEM), we simulated the spatiotemporal evolution of four major carbon pools—total carbon (TOTC), vegetation carbon (VEGC), soil organic carbon (SOC), and litter carbon (LTRC)—from 1981 to 2060 under factorial climate scenarios. During 1981–2020, TOTC increased by 0.09 Pg C (+3.54%), driven by gains in VEGC (+0.03 Pg C, +21.43%) and SOC (+0.06 Pg C, +2.78%). LTRC showed minimal net change but was highly sensitive to interannual variability. From 2021 to 2060, under the high-emission SSP5 scenario, TOTC is projected to increase by 0.114 Pg C (+4.81%), with VEGC contributing most of the gain (+23.87%). CO2_only simulations showed similar increases, underscoring the dominant role of CO2 fertilization. In contrast, warming and precipitation alone produced weaker and more variable effects. Spatially, upper YRB regions are expected to maintain strong sink capacity, while the Loess Plateau and central-western subregions remain vulnerable to warming and moisture decline. LTRC exhibited the highest variability across scenarios (−18% to +22%), highlighting its role as a sensitive indicator of sink stability. These findings emphasize the need to account for nonlinear climate–carbon interactions and regional heterogeneity. Region-specific, adaptive strategies that integrate ecological restoration and climate adaptation will be critical to enhancing carbon sinks and supporting China’s carbon neutrality targets in the Yellow River Basin.

ABSTRACT Frequent drought‐flood abrupt alternation (DFAA) events in the Yangtze River Basin (YRB), characterized by complex formation mechanisms and limited predictability, pose a prominent challenge in climate research. A daily‐scale DFAA index is developed to enhance event identification accuracy by coupling dual signals from precipitation and soil moisture in this study. The selected 86 DFAA events from 1961–2023 are classified into two distinct spatial patterns: an Eastern type affecting the lower reaches and a Northern type influencing the northern middle reaches of YRB. The multi‐scale analysis demonstrates that DFAA alternation periods are primarily driven by 10–30‐day intraseasonal oscillations (ISOs), while the maintenance of drought and flood phases relies on 30–90‐day ISOs. However, during the alternation phase, Eastern‐type events are governed by upper‐troposphere dynamical processes, manifested through energy dispersion from the high‐latitude Rossby wave trains that drive rapid reversal of the 10–30‐day geopotential height over East Asia, accompanied by eastward‐propagating upstream westerly anomalies promoting westward extension of the jet stream over Japan, ultimately establishing strong upper‐level divergence over the key region and triggering the alternation to flood conditions. In contrast, Northern‐type events are predominantly controlled by low‐level processes, where 10–30‐day scale cyclonic circulation intensifies rapidly through lee‐wave forcing from the Qinling–Daba Mountains during its southward migration, triggering low‐level convergence and ascent that facilitate precipitation development. During the drought or flood phase, both types exhibit baroclinic structures over the key region, and the Eastern type primarily relies on eastward‐propagating 30–90‐day Rossby wave energy over the subtropics, whereas the Northern type is influenced by converging wave energy transported from both mid‐high latitudes and the subtropical region. This study provides a scientific basis for understanding the mechanisms of extreme hydrological events in the basin and improving extended‐range forecasting capabilities.

Planktothrix species are among the most widespread bloom-forming cyanobacteria in freshwater ecosystems and are of particular concern because of their ability to produce cyanotoxins and form persistent harmful algal blooms (HABs). Among them, Planktothrix agardhii and Planktothrix rubescens are the most extensively studied species and are responsible for a large proportion of bloom events reported in European lakes. This review synthesizes current knowledge on the taxonomy, ecophysiology, toxin production, environmental drivers, species interactions, and management of Planktothrix blooms, with a particular focus on European freshwater ecosystems. The available evidence highlights marked ecological differences between the two dominant species. P. agardhii is primarily associated with shallow, eutrophic, and well-mixed lakes, whereas P. rubescens is typically found in deep, stratified, and relatively transparent water bodies, where it forms persistent metalimnetic populations. These contrasting ecological strategies influence bloom development, toxin dynamics, detection, and management. Nutrient availability, light climate, temperature, water column stability, and biological interactions all contribute to bloom establishment and persistence, while climate change is expected to further modify bloom frequency, duration, and geographic distribution. The review also examines current monitoring and mitigation approaches, highlighting the limitations of conventional surface-based surveys for detecting deep P. rubescens populations and emphasizing the need for integrated monitoring strategies combining depth-resolved sampling, molecular tools, and toxin analyses. Overall, understanding the ecological and physiological diversity of Planktothrix species is essential for improving risk assessment, developing effective management measures, and mitigating the impacts of cyanobacterial blooms in European freshwaters.

Accurate field-scale surface soil moisture (SSM) measures are important for agricultural water management. Conventional satellite SSM products remain too coarse for within-field applications. Here, we developed sequence-aware deep learning models for growing-season SSM estimation by fusing data from Sentinel-1 C-band SAR, harmonized Landsat-8/Sentinel-2 (HLS), and local ancillary datasets. We assembled a multi-source dataset on Sentinel-1 overpass time for 2016–2024 (May–September), yielding 1469 samples and 65 features per sample, including SAR and optical features, meteorological data, soil texture and bulk density, topography, crop labels, irrigation-likelihood flag, and irregular-time-step encoding. We compared long short-term memory (LSTM) and temporal convolutional neural network (TCN) architectures together with attention-augmented variants, including feature attention (FA), temporal attention (TA), and the combined feature–temporal attention (FTA). Models were trained and tested on seven years of data and were validated based on a temporal generalization using combined data of a wet year (2016) and a dry year (2023). The best model, FTA-TCN, achieved R2 = 0.851, RMSE = 0.024 m3.m−3, and MAE = 0.020 m3.m−3 on the withheld validation years, outperforming the base LSTM (R2 = 0.422; RMSE = 0.053 m3.m−3; MAE = 0.043 m3.m−3) and the base TCN (R2 = 0.746; RMSE = 0.034 m3.m−3; MAE = 0.022 m3.m−3). Shapley additive explanations (SHAP) analysis indicated that antecedent precipitation and short-term rainfall accumulations were dominant forcings, while soil texture, elevation, incidence angle, and vegetation indices modulated SSM variability. Satellite-derived features accounted for ~28.5% of aggregated SHAP importance. Overall, the results show that dual-attention temporal convolution can capture field-scale SSM dynamics across wet and dry seasons when satellite signals are coupled with local soil-meteorological-management context.

Accurate rice yield estimation is essential for food security. Two key factors affecting estimation accuracy are the long-term upward trend in yield over time and regional heterogeneity across space. Current studies predominantly employ statistical detrending methods (e.g., moving averages, linear regression) to isolate temporal trends. However, such methods rely on prior assumptions about the time–yield relationship and may introduce systematic bias when these assumptions break down. Meanwhile, the individual contributions of temporal and spatial information, and their interactive effects, have not been systematically evaluated within a unified framework. We selected 112 rice-growing counties across six U.S. states (2000–2021), using vegetation index (Normalized Difference Vegetation Index), meteorological indicators (growing degree days, killing degree days, and cumulative precipitation), and spatiotemporal variables (year, longitude, and latitude). We designed six input configurations to compare conventional detrending against direct temporal variable inclusion, testing across four model architectures (Long Short-Term Memory, Random Forest, XGBoost, and Transformer). Results showed that: (1) directly inputting year significantly outperformed detrending across all models, with the combined spatiotemporal configuration achieving the best performance (LSTM R2 = 0.61 vs. 0.54 for detrending); (2) year was the most important predictor in SHAP analysis, with spatiotemporal variables ranking higher than most meteorological and remote sensing variables; (3) spatial information consistently improved accuracy and mitigated systematic bias for extreme yield regions; (4) the combined configuration performed best across different states, years (including extreme climate events), and yield levels, achieving near-end-of-season accuracy at the grain-filling stage (1.5–2 months before harvest). This study demonstrates that integrating raw spatiotemporal data directly into deep learning models is more effective than statistical detrending, offering a simpler and more robust approach for large-scale crop yield estimation.

Accurate forecasting of high-dimensional meteorological fields remains challenging due to the complex spatio-temporal dynamics of atmospheric systems and the presence of heterogeneous training difficulty across space and lead time. Existing deep forecasting approaches usually optimize all prediction units uniformly, which may overemphasize low-benefit or weakly generalizable supervision signals. To address this issue, we propose Spatio-Temporal Selective Learning (ST-SL), an online training framework that estimates the learnability of each prediction unit by comparing the main model with a frozen reference model and computes the loss only over selected high-benefit spatio-temporal units. To provide an effective forecasting backbone, we further introduce VASTFormer, a variable-aware spatio-temporal Transformer that models cross-variable dependencies, incorporates physics-enhanced Solar Positional Encoding, and captures atmospheric trajectories with an efficient temporal translator. Experiments on the ERA5 reanalysis dataset show that VASTFormer outperforms representative spatio-temporal baselines, while ST-SL further improves accuracy without adding inference-time parameters or computational cost. Compared with the strongest baseline, VASTFormer+ST-SL reduces MSE, MAE, and RMSE by 8.84%, 6.70%, and 4.54%, respectively. Meteorological skill evaluation further shows an average ACC of 0.9801 and RMSESS of 0.8104, and percentile-based extreme-condition evaluations confirm consistent improvements across standard and high-impact forecasting scenarios. These results indicate that selective supervision can improve generalization in dense meteorological forecasting.

ABSTRACT This study explores the climatic drivers influencing the monthly Burned Area (BA) during the winter fire season (November–April) in northern Italy from 2008 to 2022 at 0.11° spatial resolution, providing an example of the current climatic dynamics affecting mountains. The results of the analysis indicated that the Alps and northern Apennines are mainly characterised by a winter fire regime. In these mountain areas, the Western Alps experienced the largest wildfire events, with return periods lower than 6 years. Parallel to BA data, 150 daily precipitation and temperature ground series were collected, converted to monthly scale, quality controlled, and gridded at the 0.11° spatial resolution. Several climatic indices were computed for precipitation, temperature, and droughts. To find the best BA predictors, we checked the correlations of BA with different temporal aggregations of climatic indices. For each pixel of the grid, we performed multilinear regression models using all possible combinations of the significant drivers. The selection of the best regression models was based on an out‐of‐sample procedure, and the model performance was tested by comparing the predicted BA with the observed data, estimating explained variance and correlation. While rising temperatures are often assumed to be the main driver of BA under climate change, our study revealed that low precipitation and water balance deficit from December to March played the most significant role in influencing BA during the winter fire season.

Abstract Focusing on the dynamics of internal gravity waves, we introduce a general framework for the vertical discretisation of the stratified linearised primitive equations. We derive a discrete formulation that preserves energy conservation and leads to a consistent discrete Sturm–Liouville problem. A ‐indexed family of discretisations is introduced and a perturbation analysis provides an explicit expression for the leading‐order eigenvalue errors. We show that an appropriate choice of parameter reduces the leading‐order eigenvalue error significantly and, for uniform grids, makes it vanish, yielding fourth‐order instead of second‐order convergence. An explicit optimal vertical grid is also derived for a single eigenmode, and the extension to grids optimised for multiple modes is discussed.

At 1 km resolution, NDVI projections for heterogeneous landscapes can appear spatially coherent in aggregate while concealing substantial class-level prediction weaknesses, a limitation that has received limited systematic attention in the NDVI projection literature. This study applies a four-component assessment workflow to Northeast China (NEC) for 2040 under SSP1-2.6, SSP2-4.5, and SSP5-8.5, integrating multi-stage model selection, land-cover-stratified validation, quantile-regression-based uncertainty characterization, and validation-priority ranking. Among three candidate tree-based models evaluated using spatial block cross-validation, temporal holdout validation, long-jump extrapolation, and climatic perturbation tests, LightGBM showed the most balanced and consistent performance, with spatial CV R2 = 0.654 ± 0.123, temporal holdout R2 = 0.710, and long-jump R2 = 0.671, and was therefore selected for the 2040 projection. Projected regional mean NDVI increased modestly from 0.393 in 2020 to 0.414–0.417 across scenarios, with limited divergence among SSP pathways at this near-term horizon. Class-stratified validation of the 2020 holdout prediction revealed that global model performance masked strong class-level heterogeneity, with R2 values ranging from 0.576 for Construction land to −0.886 for Unused land. Water bodies and Unused land exhibited negative R2 values, indicating weak class-level predictive support relative to a simple class-mean benchmark. Residual decomposition showed that Water bodies combined high random error with elevated systematic deviation, whereas Unused land was mainly characterized by systematic bias, suggesting different needs for class-specific model improvement. The Uncertainty Risk Index (URI), derived from 95% prediction intervals, was highest in Construction land and lowest in Cropland across all scenarios. Integrating historical residuals with future URI-identified Water bodies, Unused land, and Construction land as the highest-priority classes for future targeted validation. These priorities arise from both limited class representation and intrinsic NDVI-related complexity, including low vegetation signal, mixed-pixel effects, and heterogeneous land-surface composition. These results demonstrate that land-cover-stratified error decomposition and uncertainty-informed priority ranking reveal class-specific projection limitations that aggregate accuracy metrics can conceal.

Cloud contamination obscures ground objects, interferes with surface reflectance, and disrupts spatial continuity. In thick-cloud regions, surface structures and spectral information are often extensively missing. CNN-based cloud removal methods can recover local textures, but they are less effective at modeling global structures and color consistency over large cloud-covered areas. Transformer-based methods capture long-range dependencies; however, standard self-attention introduces high computational and memory costs for high-resolution remote sensing images. Efficient attention reduces this cost but may weaken edge and texture discriminability. SAR imagery can penetrate clouds and provide surface structural information, yet repeated SAR injections may propagate speckle noise, cross-modal misalignment, and imaging discrepancies through deep restoration layers. To address these issues, this paper proposes DST-SARNet, a dual-stage SAR structural guidance network for optical remote sensing image cloud removal. In this framework, dual-stage refers to two explicit SAR-guidance positions: early structural skeleton guidance at the input side and late high-frequency modulation near the output. The Texture-Aware Asymmetric Retrieval module is placed between these two stages as a bottleneck memory retrieval operation rather than as a third dense SAR injection stage. With this design, SAR provides structural skeletons, readable texture memory, and terminal detail compensation, while the optical branch remains responsible for color, semantics, and spectral appearance recovery. Experiments on the SMILE-CR and SEN12MS-CR datasets show that DST-SARNet effectively restores cloud-contaminated imagery with a compact model scale, demonstrating its potential for efficient SAR-assisted optical cloud removal.

Hyperspectral image (HSI) classification remains a challenging task due to high spectral redundancy, complex spectral–spatial correlations, and the limited availability of labeled samples. To address these issues, this paper proposes a novel framework termed M3-Mamba, which integrates language-guided, multi-level, and Mamba-based spectral modeling for hyperspectral image classification. The proposed M3-Mamba leverages high-level semantic priors derived from multimodal representations to guide discriminative spectral modeling, enabling effective interaction between semantic information and fine-grained spectral features. In addition, a frequency-aware Mamba-based state space module is introduced to efficiently capture long-range spectral dependencies while avoiding the quadratic computational complexity of conventional attention mechanisms. Meanwhile, a text-guided modulation strategy is designed to adaptively reweight spectral responses under semantic guidance, suppressing redundant or noisy bands and enhancing class-relevant spectral responses without compromising spectral fidelity. This semantic-to-spectral modulation allows M3-Mamba to better cope with spectral variability and inter-class confusion. Extensive experiments conducted on four widely used benchmark datasets, including Indian Pines, Pavia University, Salinas, and Houston datasets, demonstrate thatM3-Mamba achieves competitive overall accuracy, average accuracy, and Kappa coefficient under the adopted benchmark settings. Ablation studies further validate the effectiveness of each key component, confirming that the proposed framework demonstrates promising effectiveness for hyperspectral image classification.

The growing urbanisation of India is a major contributor to the production of 72,368 million litres of wastewater daily. Unfortunately, not even 28 to 31% of the generated wastewater receives proper treatment before disposal, putting public health, water quality, and ecological conditions at risk. Traditional wastewater treatment technologies have been proven effective, but they cannot be applied in decentralised settings due to excessive initial investment costs, continuous power needs, and the need for expert supervision. Constructed wetlands (CWs) provide an efficient and environmentally friendly option for decentralised treatment, but these systems suffer from a gradual loss of effectiveness associated with the problem of media-clogging in traditional setups. This research investigates the functioning and efficiency of the Modified Towery Bio-rack Constructed Wetland (MTBRCW) technology designed specifically to mitigate media-clogging issues. The MTBRCW is tested on the basis of its performance under continuous operating conditions for thirteen months (January 2025 to January 2026), as well as on the effectiveness of the treatment at eight different hydraulic retention times (days 1 to 8). A pilot-scale MTBRCW system was monitored through two periodic sampling events (S1 and S2) conducted during each month of operation. The pilot-scale MTBRCW unit is made up of an inlet storage tank (volume 0.099 m3) followed by two wetland containers (volume 0.034 m3 each) planted with Typha angustifolia and Chrysopogon zizanioides (vetiver grass). In continuous testing mode, influent–effluent paired samples are collected for eight days at each HRT (totalling eighty samples), and samples are analysed according to APHA Standard Methods for pH, BOD, COD, TN, and TP. In continuous testing mode, the MTBRCW exhibits high removal efficiencies at the levels of 89.8% for BOD, 87.5% for COD, 78.2% for TN, and 74.4% for TP. The BOD/COD of the effluent was within the prescribed CPCB discharge limits for all thirteen months of the study, and the TN levels were adhered to in 12 out of 13 months, with one non-compliance event recorded only in July 2025 (effluent TN = 10.8 mg/L), coinciding with the peak monsoon hydraulic loading rate of 0.28 m3/m2·d. TP remained within CPCB limits in all thirteen months. In batch testing mode, removal efficiencies are 94.9% for BOD and 89.9% for COD by day 8. In addition, there were no indications of clogging or any reduction in hydraulic performance during the entire period of the tests through the use of visual inspections and measurement of the outlet flows, but this can only be seen as an observation in a field operation, and not as proof of the hydraulic performance of the system, since no tracer test or measurement of hydraulic conductivity was conducted.

In order to investigate possible ionospheric anomalies before a magnitude 7.6 earthquake (EQ) in the Japanese Noto Peninsula on 1 January 2024, we used oblique ionosonde data sounding at Wakkanai and Yamagawa with an assumed one-hop reflection point (ORP) only 169 km away from the EQ epicenter, which was not covered by vertical ionosonde observation. The NmF2 at ORP was analyzed. We found a long-lasting negative anomaly from the preceding day to the EQ occurrence, which was 3.37 standard deviations below the previous 30-day mean. We also found a positive anomaly 2 days before the EQ. In addition, we observed a new type of NmF2 anomaly around midday, which has not yet been reported in the literature. The midday NmF2 was anomalous for 10, 8 days, and a few hours before the EQ, implying possible pre-seismic modifications of the mesospheric meridional neutral wind. On the other hand, we also found NmF2 anomalies 3 and 4 days before a major aftershock on 9 January, revealing that the lithosphere–atmosphere–ionosphere coupling (LAIC) remained active, as seismic activity continued some days after the EQ mainshock. Since both positive and negative anomalies were observed before the EQ, pre-seismic atmospheric gravity wave (AGW) activity is the main agent driving LAIC processes. The present study not only reports pre-seismic anomalies before the 2024 Noto Peninsula EQ but also demonstrates the utility of oblique ionosondes on the ionospheric monitoring over Japan, especially for pre-seismic studies.