Atmospheric and Oceanic Sciences

ABSTRACT This study used the observed daily minimum temperature from 1995 to 2014 in China, combined with simulations from 21 models from CMIP6, to define and extract characteristic indicators of cold spells from a process‐oriented perspective. A method using probability distribution (Cold Spell Model, CSM) is constructed based on extreme value theory, which can effectively characterise the intensity, frequency and duration of cold spells. Results demonstrate that the intensity of extreme cold spells decreases from north to south across China. Northern China typically experiences low‐frequency, longer‐duration and extreme cold spells, whereas southern China is characterised by shorter‐duration events. A cumulative probability distribution transformation method is applied to correct biases in model simulations, showing that bias correction simulation has better performance over the raw simulations. Based on the corrected projections, the extreme cold spells in China are expected to become shorter, less frequent and generally less intense under future warming, with more pronounced changes under the 2.0°C warming compared to 1.5°C warming. However, notable spatial variations exist: the intensity of cold spells decreases in most eastern regions but increases in western regions. The average annual frequency of cold spells generally declines, with more significant changes observed on the Qinghai‐Tibet Plateau. Furthermore, the duration of cold spells shortens significantly across most regions. Future cold spell risks in China will generally decrease across most return periods. However, risks are projected to increase for low‐frequency (once per year) and short‐duration (1‐day) extreme events in certain regions, while risks for high‐frequency and long‐duration events significantly decline. An additional 0.5°C of warming further reduces the probability of cold spells.

ABSTRACT A comprehensive understanding of drought evolution in the Yellow River Basin (YRB) is essential for effective drought mitigation and water resource management. Based on the daily‐scale Modified Comprehensive Meteorological Drought Index (MCI), this study analyzes the spatiotemporal characteristics of drought in the YRB over the past 60 years. The main findings are: The average annual drought days exceed 60, with high‐frequency areas (> 120 days) concentrated in the upper‐middle reaches junction, the eastern middle reaches, and the western lower reaches. Most areas show a decreasing trend in drought days, with the most significant reduction in the source region (20 days/decade). Drought regimes vary regionally: the source region experiences infrequent, short, and mild droughts; the middle‐lower section of the upper reaches has infrequent but prolonged and intense droughts; while most middle‐lower reaches face frequent, short‐duration, yet relatively high‐intensity droughts. Spring drought dominates (25%–40%), followed by summer drought (15%–30%), with spring–summer consecutive drought being the most common cross‐seasonal pattern. During drought events, precipitation deficits are more pronounced in the middle‐lower section of the upper reaches and the lower reaches. Evapotranspiration anomalies are generally higher in the middle‐lower reaches. Precipitation‐dominated droughts (20%–35%) mainly occur in the middle‐lower section of the upper reaches. Evapotranspiration‐aggravated droughts (25%–35%) are concentrated in the source region. Synergistic droughts (25%–30%) are mainly found in the eastern middle reaches, the lower reaches, and localized areas. Since 1961, precipitation‐dominated droughts have decreased, while evapotranspiration‐aggravated and synergistic droughts have increased in most parts of the YRB. Daily‐scale monitoring enables a more accurate characterization of drought processes, providing critical insights for regional drought management and supporting ecological conservation and high‐quality development in the basin.

The roughness coefficient is a vital parameter in river dynamics calculations, and its accuracy is crucial for simulating water flow. Various factors contribute to channel roughness, and the underlying mechanisms are quite complex. There is a strong spatiotemporal correlation, which complicates the calculations, particularly when hydrological data is lacking or insufficient. In this study, we solved the two-dimensional shallow-water equations using the Population Simplex Evolution (PSE) with the Finite Volume Method (FVM). This approach allowed us to obtain samples for calibrating channel roughness coefficients. To enhance the analysis, we introduced a Convolutional Neural Network (CNN) to reduce the dimensionality of input parameters and extract the temporal characteristics of the flow series. Notably, we integrated a Transformer to capture the spatial characteristics of the time series. By combining the PSE-FVM with the CNN-Transformer, we effectively calibrated the roughness coefficients. Our findings indicated that the integrated PSE-FVM and CNN-Transformer model achieved high accuracy and efficiency in this calibration process. Specifically, the cross-correlation coefficients exceeded 0.90 for calibration results from September to December 2020. We recorded an average absolute deviation of 7 cm between the calculated and measured maximum water levels, and the average calibration runtime ratio was approximately 0.19% when comparing the CNN-Transformer to the PSE-FVM. Importantly, this approach could be used for rivers with incomplete hydrological data. Our work highlighted spatiotemporal correlations between roughness coefficients and their influencing factors, thereby facilitating the integration of river dynamics models with intelligent algorithms. Therefore, these findings may serve as a valuable reference for river numerical analysis, flood impact assessment, and the development of digital twins and information systems for water-related engineering projects.

Abstract Discovering functional crystalline materials entails navigating an immense combinatorial design space. Although recent advances in generative artificial intelligence have enabled the sampling of chemically plausible compositions and structures, a fundamental challenge remains: the objective misalignment between the likelihood-based sampling in generative modelling and the targeted focus on underexplored regions where novel compounds reside. Here we introduce a reinforcement learning framework that guides latent denoising diffusion models in finding diverse and novel, yet thermodynamically viable, crystalline compounds. Our approach integrates group-relative policy optimization with verifiable, multi-objective rewards that jointly balance creativity, stability and diversity. Beyond de novo generation, we demonstrate enhanced property-guided design that preserves chemical validity while targeting desired functional properties. This approach establishes a modular foundation for controllable AI-driven inverse design that addresses the novelty–validity trade-off across the scientific discovery applications of generative models.

Maritime visual surveillance is increasingly used as a complementary sensing modality to radar/AIS in coastal VTS and onboard situational awareness, yet its reliability degrades under sea clutter and adverse weather. This paper presents a practical tracking-by-detection framework that couples an enhanced YOLOv11 detector with lightweight data association. The detector adopts a GELAN backbone and a weighted bi-directional feature pyramid network (BiFPN) to strengthen multi-scale representation, and replaces the regression objective with Unified IoU (UIoU) to improve box tightness for IoU-based association. To stabilize detections across domains and mitigate fine-grained label jitter, we construct a cross-domain single-class training set (Ship-1C) by fusing SeaShips images with frames sampled from SMD. For adverse-weather evaluation, we build SeaShips-Complex via physically motivated fog/rain/snow synthesis and further conduct external validation on real rainy and snowy WaterScenes images. On two representative maritime video sequences, including a self-collected low-visibility hazy video and a public waterway/port video, a maritime-tuned ByteTrack configuration (high detection threshold + low IoU matching threshold) attains 89.74% MOTA with low identity switches. The proposed detector runs at 344.83 FPS at the detector level on an RTX 4060 GPU. The results support the feasibility of deploying the proposed framework for shore-based monitoring and engineering-grade maritime perception.

As the Middle Route of the South-to-North Water Diversion Project is an artificial riverine ecosystem, a scientific evaluation of its stability and the identification of its key influencing factors are required to continuously ensure water quality safety and water supply capacity along the route. From March to April 2025, two seasonal surveys were conducted at 19 monitoring sites established in the Henan province of the main channel. These surveys included the collection of multi-trophic organisms (phytoplankton, benthic algae, zooplankton, macroinvertebrates, and fish) and measurements of water environmental factors. Based on the survey data, a Water Ecosystem Stability Index (SI) was constructed by integrating multi-trophic and multi-dimensional indicators. The evaluation results from this index indicated that the overall ecosystem stability in April was superior to that in March within the study area. Principal Component Analysis (PCA) of the environmental factors identified water temperature (WT), flow velocity (V), permanganate index (COD Mn ), total nitrogen (TN), silicate (Silicate), and total organic carbon (TOC) as the main environmental drivers. Optimal fitting analysis between these environmental factors and the SI revealed that V and TN were significantly negatively correlated with ecosystem stability. Conversely, TOC was found to significantly promote the enhancement of ecosystem stability when its concentration was below 6.475 mg·L -1 . Biodiversity analysis indicated that the community structure of macroinvertebrates was simplified and their diversity was low, a pattern attributed to the high discharge, habitat homogeneity, and highly channelized nature of the main channel. Variation partitioning analysis (VPA) showed that joint explanation rate of predator diversity and environmental factors for stability variation was 56.3%, significantly higher than that of other variables, indicating that under the synergy of environmental factors and predator diversity, a key role in maintaining long-term stability of this ecosystem is played by their synergy. A significant negative correlation was observed between stability and benthic algal biomass ( p < 0.05), further revealing that predators maintain system stability through efficient top-down control, thereby inhibiting the excessive expansion of primary producers. This study provides a methodological reference for research on the stability of the Middle Route Project of the South-to-North Water Diversion and similar artificial ecosystems.

Modelling and prediction of mean monthly flow are of particular importance for long-term planning in hydrology and water resources management. Therefore, a simplified and robust modelling procedure, derived from clearly and concisely established methodology, can benefit both researchers and practitioners. The main objective of this study is to develop a robust yet simple model, capable of producing predictions of satisfactory accuracy on previously unseen data. Two chronological data allocation strategies (C1 and C2), differing in the proportions of training, calibration, and verification subsets, were evaluated to analyze their influence on model accuracy and reliability. Chain and ensemble modelling techniques were applied, resulting in several stacking regressors with different combinations of base models and final estimators. The best-performing ensemble (C2) consisted of a support vector machine, histogram gradient boosting regressor, elastic net, and two dummy regressors as base models, with an artificial neural network as the final estimator. Within the ensemble structure, dummy regressors and histogram gradient boosting regressor were used to extend the predictive range, while elastic net and support vector machine captured the overall flow bias and fundamental flow dynamics. The artificial neural network final estimator was used to integrate these components into the final flow prediction. Compared to C1, the C2 allocations strategy achieved improved generalization capability and narrower confidence intervals due to the larger training subset, indicating higher model reliability for long-term monthly flow forecasting. The study additionally emphasizes the importance of appropriate methodological workflow, careful dataset treatment, and comprehensive model evaluation using complementary statistical and hydrological analysis tools.

Urban combined sewer systems are increasingly challenged by climate-intensified rainfall, combined sewer overflows, and receiving-water degradation. This study presents a retrospective evaluation of a hybrid green-gray retrofit program implemented in the Zhenjiang Sponge City pilot watershed, China, where green stormwater infrastructure, drainage network upgrades, and a centralized deep tunnel system were integrated within a densely developed watershed constrained by limited space, low native-soil permeability, shallow groundwater, and aging infrastructure. System performance was evaluated using long-term operational observations, representative hydraulic and water-quality monitoring, municipal operational records, and supporting engineering analyses at both facility and watershed scales. The results demonstrated sustained hydraulic functionality after 7–10 years of operation, with approximately 90% of the monitored bioretention systems maintaining effective infiltration rates greater than 80 mm h−1. Event-based monitoring indicated substantial reductions in runoff volume and pollutant loads, including TSS, COD, NH3–N, and TP. Following implementation, annual combined sewer overflow occurrence at major outfalls decreased from 318 to 24 events, representing a 92.5% reduction. Supporting engineering analyses indicated that green stormwater infrastructure retrofits alone reduced overflow frequency by approximately 41.8% and overflow volume by approximately 61.1%, while integration with deep tunnels increased reductions to approximately 58.8% and 85.3%, respectively. Official receiving-water monitoring records further indicated that Class III or better water-quality conditions were maintained during approximately 74.7% of the monitored days between 2021 and 2026. These findings provide long-term watershed-scale evidence that hybrid green-gray retrofit strategies can integrate green stormwater infrastructure with centralized overflow regulation to achieve sustained overflow reduction and receiving-water improvement in highly constrained urban watersheds.

Slope stability has consistently been a critical concern in mountainous road sections, with precipitation being the most significant factor precipitating slope instability. This study aims to elucidate the mechanism of slope instability under precipitation conditions and the extent of the impact of internal disaster-causing factors. To achieve this objective, a numerical simulation analysis method combining GeoStudio2018R2 and FLAC3D7.0 software was employed to conduct a comprehensive analysis of an unstable slope in Xinjiang. Regarding research methodology, cyclic precipitation and seasonal snowmelt were considered as external influencing factors. Initially, a two-dimensional model was constructed using GeoStudio software to analyze the spatial and temporal variations in pore water pressure and moisture content within the slope, elucidating their dynamic characteristics at different temporal and spatial scales. Subsequently, a three-dimensional numerical model was established using FLAC3D software to conduct a detailed analysis of the stress–strain state of the slope under various conditions, thereby obtaining disaster parameters such as displacement and sliding velocity in different directions. Through further comparison and verification of the overall stability analysis results of the slope obtained from both software packages, it was observed that they exhibited a consistent trend. The research findings indicate that under conditions of high-intensity short-term precipitation, the safety factor of the slope decreases to the lowest level, potentially leading to shallow landslides with smaller displacement but faster sliding velocity. Conversely, seasonal snowmelt and long-term localized precipitation have a more profound impact on the internal structure of the slope, with the sliding zone potentially penetrating into the deep bedrock. Although the occurrence frequency is low, the impact range is extensive. By combining two-dimensional and three-dimensional analyses, a comprehensive assessment of the different disaster-causing factors of the slope was conducted, enhancing the accuracy of the analysis results. The research findings provide a scientific basis and reference value for the formulation of subsequent slope protection and monitoring plans.

The increasing contribution of non-dispatchable renewable energy sources has changed the operating patterns of hydropower plants, especially in run-of-river schemes with limited storage capacity. Under intermittent dispatch, turbine shutdowns may occur while environmental-flow requirements must still be maintained. In these situations, spillway releases become more frequent, creating hydraulic conditions that can lead to total dissolved gas (TDG) supersaturation downstream. This study evaluates TDG supersaturation as an operational constraint in an Amazonian run-of-river hydropower plant. This assessment combines field measurements with controlled laboratory exposure tests using representative native fish species. Total gas pressure (TGP) was measured in both field and laboratory settings and converted into TDG saturation percentages. A laboratory-scale TDG generation system was developed to reproduce supersaturation conditions associated with spillway operation. Field measurements recorded TDG levels above 130% during spillway-activation events associated with dispatch decisions. In the laboratory, exposure tests showed rapid loss of viability in native Amazonian fish at TDG levels of 115% and above. These results indicate operational warning ranges for sustained spillway use. The findings suggest that TDG supersaturation should not be treated solely as an environmental issue. Rather, it represents a technical constraint on hydropower dispatch in systems with increasing renewable penetration. Incorporating TDG-related limits into operational planning and spillway management may help preserve hydropower flexibility while supporting environmental compliance.

Abstract We propose a simple model of Rossby-Kelvin instability based on a flow with zero potential vorticity gradients except for a single discontinuity in potential vorticity at the jet latitude. The basic state has constant angular momentum equatorward of the jet latitude and uniform relative vorticity on its poleward side. With no potential vorticity gradients equatoward of the jet, Rossby waves are trapped and the rotational circulation in the tropics is entirely determined by the vorticity perturbation at the vorticity front. The simple model produces unstable modes in good agreement with previous studies of Rossby-Kelvin instability and with the modes found to drive superrotation in some idealized simulations. The simplified framework facilitates an interpretation of the instability in terms of the interaction between the divergent Kelvin-wave circulation and the rotational Rossby-wave circulation. It is shown that the growth of the modes is associated with spatial correlation between equatorial divergence and anticyclonic subtropical vorticity, and requires an eastward tilt with latitude of the zonal wind perturbation.

Abstract. Flushing and dilution are major phenomena of solute export dynamics during precipitation events in headwater catchments but are hard to predict, even if catchment properties are well known. Normalized cumulative load (NCL) functions have been used to visualize and classify event-based discharge–load relationships, distinguishing between dilution, flushing, and constant export behavior. This study presents an enhanced version of the classical NCL function approach by combining it with hydrograph separation. Over an 18 month period, discharge and solute concentrations were monitored in an agriculturally influenced headwater catchment in the German low mountain ranges, with a focus on nitrate (NO3-) and total phosphorus, and a complementary dataset of major ions. Discharge was separated using stable water isotope signals into event water and total discharge. Both discharge components were then analyzed for solute loads (NO3-, total phosphorus, and major ions). The results reveal significant differences in solute export dynamics between event water and total discharge, including unexpected similarities in the export patterns of nitrate and total phosphorus. The proposed method also highlights a shift from predominantly constant export behavior in the total discharge (coefficient of variation: 0.13) to more pronounced flushing or dilution patterns in the event water (coefficient of variation: 0.36). These findings indicate a fundamental difference between the hydrological processes governing the solute export dynamics of the catchment. While the signal of total event discharge indicates constant behavior, the separated event water exhibits strong flushing or dilution tendencies. The observed shifts in the export patterns, which are likely linked to the activation of drainage systems and depletion of NO3- legacy storages, raise the question if the event water fraction should be monitored more closely in terms of its potential for dynamic pollutant transport. The proposed method is straightforward to implement, yields statistically robust results for the dataset and provides new insights into solute input pathways in headwater catchments.

Nonlinear gradients alter the diffusion encoding in brain diffusion tensor imaging (DTI), leading to spatially varying diffusion weighting which bias quantitative measures if uncorrected. Although the overall effects of gradient nonlinearity correction in brain studies are typically minimal and often fall below the detection limits of traditional imaging resolutions and sensitivities, their cumulative impact on clinical outcomes requires further study. This study investigates the significance and effects of correcting gradient nonlinearity in DW-MRI, focusing on the microstructural and macrostructural changes in white matter (WM) and gray matter (GM) across a clinical cohort. Our primary aim is to clarify whether the observed nonlinearity significantly alters the interpretation of aging in clinical settings, particularly in studies comparing healthy individuals to those with neurological conditions. We assess the extent of nonlinear fields impact on individual scans, interscanner observations, and a tract-based analysis. Using data from the Vanderbilt Memory & Aging Project (n = 948 imaging sessions, 933 on Scanner B and 15 on Scanner A acquired with single-shell diffusion tensor imaging protocol), we find 1%, 3.3%, and 5-degree changes in microstructure measures, fractional anisotropy (FA), mean diffusivity (MD), and primary eigen vector (V1) respectively, affecting at least 20% of the brain. Across sessions, head positioning sampled typical clinical variability, with head offsets of approximately 0-10 mm and rotations of 0-10° relative to magnet isocenter. Subcortical regions in the superior regions, occipital lobules, and parietal lobules exhibit relatively higher impacts. Macrostructural measures show changes up to 12% after nonlinear field correction. GNL effects are 5% and 0.33% of FA and MD changes between mild cognitive impairment and controls. A simple power analysis indicates that these subtle effects of gradient nonlinearity correction can become statistically detectable in larger multi-site studies exceeding ~1000 subjects, suggesting that GNL should be considered and, where possible, corrected or at least quantified in such settings.

Stokes flow studies are fundamental to advancing medical and industrial technologies, particularly in areas such as drug targeting, cell studies, the optimization of drug carrier vehicles, high viscosity flows, and magnetic particle imaging. While previous research has focused on the motion of obliquely falling cylindrical rods and magnetic particle chains, a broader analytical framework is required to understand more complex particle-fluid migrations. In this paper, we first generalize the two-dimensional motion of an obliquely falling rod in a gravitational field to the three-dimensional motion of an object possessing three mutually perpendicular planes of symmetry falling through a viscous fluid in the Stokes limit. We derive a general formula for the three components of velocity-including both downward and sideways components-for objects of arbitrary orientation and uniform density. These analytical solutions are defined in terms of the object's orientation, specified via Euler angles, and the velocity of the object falling along each of its three principal axes, or the drag coefficient along each of those axes. We give a variety of examples of objects that satisfy this general formula. In addition, we apply the formula to a cuboid for which those velocity components along each of its principal axes have been measured experimentally by other researchers, thus giving both the downward and sideways components for arbitrary orientation. We then analyze the motion in a gradient magnetic field of elongated magnetic particles, such as nanorods and nanoellipsoids, for which the induced magnetic moment is along the long axis of the particle. We discuss the similarities and differences with the gravitational case. By providing a unified framework for predicting the trajectories of these symmetric bodies, this work enhances the understanding of the motion of inertial and magnetic particles under the influence of gravitational and gradient magnetic fields, respectively.

High Resolution Image Download MS PowerPoint Slide Regulators and voluntary corporate sustainability efforts are increasingly adopting time-matching requirements (TMRs) for clean electricity procurement for large loads, such as data centers, and electricity-intensive fuel production, such as hydrogen. We use a stochastic capacity expansion model (CEM) framework to assess how interannual weather variability affects the cost and emissions impact of procurement-driven infrastructure to meet annual and hourly TMRs using the case study of a grid-connected hydrogen producer in Texas. Our approach, which relies on co-optimizing investments and hourly operations over nine weather scenarios, reveals that hourly TMR comes at a higher cost premium compared to annual TMR than previously estimated by single-scenario deterministic modeling, while emissions outcomes remain directionally consistent. Demand flexibility and partial hourly TMR (80–90%) lowers the cost premium while preserving emissions benefits. We further examine how binding renewable portfolio standards (RPS) interact with TMR costs and emissions outcomes. When an RPS is applied to non-H 2 electricity demand, annual TMR reduces emissions comparably to hourly TMR at a lower cost. Incorporating H 2 -related electricity demand directly into the RPS constraint, rather than imposing a separate TMR, achieves similar emissions outcomes at still lower cost, suggesting that TMR-based clean electricity procurement─particularly hourly matching─offers limited additional value in regions with stringent grid decarbonization policies.

Within the domain of Earth observation, tracking terrestrial transitions via high-resolution optical data plays a fundamental role. Nevertheless, current methods face critical challenges, including the difficulty in collaborative modeling of local details and global features and the singularity of bi-temporal difference representation, along with insufficient cross-scale feature communication, thereby constraining both the precision and resilience of models when applied to complicated environments. To solve these problems, we propose LADENet (Locally Adaptive Mamba and Multi-scale Feature Enhancement Network), an innovative framework that synergizes CNN, Transformer, and Mamba paradigms. By leveraging customized local contextual refinement alongside sophisticated hierarchical fusion, this integration delivers highly precise and resilient detection performance. LADENet adopts a weight-sharing multi-level Transformer encoder combined with a sequence reduction mechanism to generate multi-scale global features, achieving precise alignment of bi-temporal features and global context modeling while reducing computational complexity. To realize accurate localization and local enhancement of changed regions, we design a dual spatiotemporal adaptive local feature marking module based on State-Space Scanning (SSS). This module screens high-saliency changed regions through an adaptive scanning strategy, realizes pixel-aligned spatiotemporal feature fusion via cross-temporal state-space scanning, and introduces a sliding window boundary calibration mechanism to alleviate boundary information loss caused by window segmentation. To strengthen the feature representation of changed regions, a dual-branch difference enhancement module is constructed, which collaboratively captures global change trends and fine-grained local features through an attention-enhanced difference branch and a multi-scale convolution concatenation branch, effectively suppressing background interference. To address the semantic gap between cross-scale features, a global cross-scale spatial feature fusion decoder is proposed, which balances local detail preservation and global context perception through the synergy of spatial attention and two-dimensional selective scanning, completing refined multi-scale feature fusion and spatial resolution recovery. To rigorously validate the proposed LADENet, comprehensive experiments were conducted across four widely adopted bi-temporal benchmarks: LEVIR-CD, WHU-CD, CLCD-CD, and GVLM-CD. The presented architecture establishes substantial superiority over existing cutting-edge methodologies across primary evaluation criteria. Specifically, it yields an F1-measure of 91.06% alongside an IoU of 85.28% in the LEVIR-CD tests, while registering 90.51% (F1) and 82.45% (IoU) for WHU-CD. Similarly, robust outcomes are delivered on CLCD-CD (82.15% F1, 72.83% IoU) as well as GVLM-CD (89.12% F1, 77.78% IoU). These results demonstrate that LADENet possesses excellent detection accuracy, boundary delineation capability and generalization performance in diverse and intricate bi-temporal observation environments.

Abstract. Ultra–low-noise inertial sensors are a cornerstone of modern geoscientific instrumentation, enabling high-resolution observations across seismology, geodesy, gravimetry, and vibration isolation. Achieving and reliably predicting their performance requires a rigorous treatment of physical causality, noise propagation, and uncertainty, particularly in force-feedback architectures operating near fundamental limits. In this study, we introduce a causal and uncertainty-aware digital-twin framework for the design and metrological assessment of ultra–low-noise geoscientific inertial sensors. The proposed framework integrates mechanical dynamics, force-feedback control, transduction, and digital acquisition within a physically realisable model that explicitly enforces causality and stability constraints. Starting from a minimal equation-of-motion description, the digital twin is formulated in the frequency domain to construct causal transfer functions and a comprehensive noise-budget model. The framework enables the systematic separation of fundamental thermal noise limits from implementation-dependent noise sources, including readout, actuation, and digital acquisition effects. We introduce quantitative performance metrics based on self-noise spectra, dominant noise regimes, crossover frequencies, and near-plateau bandwidths, allowing complex spectral behaviour to be condensed into actionable design indicators. Parameter uncertainties are propagated through the digital twin to provide uncertainty-aware performance estimates and robustness diagnostics. Through a series of illustrative analyses, we demonstrate how the proposed digital twin supports informed design trade-offs, identifies performance bottlenecks, and prevents non-physical or overly optimistic sensitivity estimates arising from non-causal modelling assumptions. While focused on inertial sensors, the methodology is general and transferable to other classes of geoscientific instruments. The framework provides a transparent and extensible foundation for next-generation sensor design, virtual experimentation, and metrologically consistent performance prediction.

Estuaries are vital ecosystems that support biodiversity, human populations, and economic activities such as fisheries, aquaculture, and tourism. Managing estuarine systems often involves interventions to regulate sediment and nutrient inputs with the goal of improving water quality, and subsequently the economic values of the estuary. However, these management strategies can produce unintended consequences. The Chesapeake Bay is an estuary recovering from eutrophication which provides clear case studies for these patterns. In the Chesapeake Bay, decreased sediment inputs, resulting from shoreline stabilization and watershed modifications, can impair marsh resilience to sea level rise and disrupt sediment-light-algal interactions affecting water clarity. Likewise, reductions in riverine nutrient inputs, while beneficial for mitigating eutrophication, may contribute to surface water acidification. These outcomes complicate assessments of restoration success. Throughout other estuaries’ recovery from historical nutrient and sediment pollution, similar patterns have been observed in several other estuarine systems worldwide. Effective long-term estuarine management requires integrating diverse monitoring approaches, adaptive decision-making, and ecosystem-based strategies to balance environmental and economic objectives. Understanding both the intended and unintended effects of management actions is crucial to ensuring sustainable outcomes along the non-linear path of recovery from eutrophication, including both nutrient and sediment pollution. Future efforts should prioritize holistic assessments, stakeholder involvement, and targeted modeling to guide effective estuarine conservation and restoration.

Moderate-resolution leaf area index (LAI) retrieval over heterogeneous landscapes is affected not only by unresolved subpixel composition in coarse-resolution predictors, but also by structural bias in supervisory labels aggregated from higher-resolution products. To address this issue, we developed a reference-guided two-stage workflow to improve LAI retrieval from the Visible Infrared Imaging Radiometer Suite (VIIRS). In the first stage, aggregated Sentinel-2 LAI was calibrated against Ground-Based Observations for Validation (GBOV) LP3 reference LAI using subpixel plant functional type (PFT) fractions and forest-sensitive hinge terms to generate corrected 500 m labels. In the second stage, a random-forest model was trained using VIIRS spectral reflectance, viewing geometry, vegetation indices, texture, and subpixel compositional variables. Model development was based on 2020–2021 data from 11 U.S. GBOV sites. Performance was evaluated by same-site temporal transfer to 2019 and 2022 and by strict leave-one-site-out (LOSO) validation. Label calibration improved agreement with GBOV from a coefficient of determination (R2) of 0.752 and a root mean square error (RMSE) of 1.110 to an R2 of 0.908 and an RMSE of 0.676. Under LOSO validation, the final model achieved an R2 of 0.901 with an RMSE of 0.703. On the 2019/2022 overlap subset shared by the final VIIRS retrieval, the official VNP product, and the GBOV reference, the final model achieved an R2 of 0.905 and an RMSE of 0.609, compared with 0.755 and 0.978 for the official VNP product. These results show that reference-guided label correction, combined with explicit subpixel compositional information, can substantially improve VIIRS LAI retrieval over mixed pixels within the evaluated study domain.

Landslides are among the most common and destructive geological hazards and pose a significant threat to the long-term stability of infrastructure systems. In particular, long-distance power transmission corridors often traverse mountainous and forested regions, where landslides can endanger tower foundations and transmission line safety. Such landslides predominantly occur in sloped forested areas, where dense vegetation causes severe occlusion that blurs landslide boundaries and creates strong visual similarity with surrounding land covers. Consequently, accurate and efficient landslide identification from remote sensing imagery remains a significant challenge. To address these challenges, we propose a structural constrained contrastive learning network (SC-Net) for reliable landslide extraction from remote sensing images. First, a multi-structural feature extraction module is designed to capture landslide-specific geometric characteristics. These features are further enhanced by fusing multi-scale semantic representations extracted from a pretrained backbone network through an attention-based adaptive feature fusion module. Additionally, a mask-constrained object-level contrastive learning strategy is introduced to enforce global structural consistency at the landslide object-level, thereby improving the discriminability between landslide and non-landslide regions. Extensive experiments conducted on the publicly available CAS landslide dataset demonstrate the effectiveness of the proposed method. The proposed SC-Net achieves IoU scores of 89.89% and 79.76% on the CAS-UAV and CAS-SAT datasets, respectively, outperforming the best-performing baseline by 2.09% and 0.46%. The proposed method provides an effective solution for large-scale landslide monitoring and demonstrates potential for applications in power transmission corridor inspection and infrastructure safety assessment.

Ground-level ozone (O3) has become a major air pollutant in China following PM2.5, particularly in the southeastern coastal region, where the frequent interaction of typhoons and the subtropical high complicates pollution control. In this paper, spatial autocorrelation and a multiscale geographically weighted regression (MGWR) model were employed to estimate the spatiotemporal heterogeneity and driving mechanisms of O3 in the Southeast Coastal urban agglomerations from 2015 to 2024. Temporally, the annual average O3 concentration exhibited a fluctuating trend of an initial increase, followed by a decrease and a subsequent rebound. A bimodal monthly pattern was observed, with peaks in May–June and August–September and minima in winter. Diurnally, the concentration showed a consistent pattern of being higher in the daytime and lower at night, peaking in the afternoon, driven by solar radiation and temperature. Spatially, O3 exhibited a distinct north–south gradient, with the highest in Jiangsu Province, followed by Shanghai, Zhejiang and Guangdong, and the lowest in Fujian. Significant spatial autocorrelation was detected, with hot spots in the Yangtze River Delta and cold spots in Fujian and adjacent areas. Seasonally, the most severe pollution with the greatest spatial heterogeneity, occurred in summer, contrasting with the uniformly low concentrations in winter. Compared with OLS and GWR, the MGWR demonstrated superior explanatory power. O3 was jointly influenced by precursors, natural factors, and socioeconomic factors, with the influence intensity ranked as follows: NO2 > average elevation > population density > annual precipitation> wind speed > built-up area > proportion of the secondary industry in GDP. Notably, the effects of NO2, annual precipitation, and the proportion of the secondary industry exhibited strong spatial heterogeneity, operating at finer spatial scales. These findings provide scientific support for sustainable air quality management and region-specific O3 control in southeastern coastal China.

Abstract Lake water surface elevation (WSE) data in Sweden are needed to understand water supply and ecosystem services provision. SWOT can complement WSE in situ measurements, but the influence of lake shape on its accuracy remains unclear. We explore the performance of the SWOT pixel‐cloud and this influence across 62 lakes from August 2023 to November 2025. We found that SWOT estimated WSE with a mean error of about 5 cm in lakes larger than 1 km 2 . Winter ice reduced the number of usable observations in northern lakes, but SWOT still captured major WSE changes when data were available. The influence of lake morphology on SWOT accuracy occurred mainly through shoreline effects reflected through the compactness of the lakes. These results show that SWOT provides useful WSE information in Sweden, but that lake morphology cannot be ignored.

In this study, we explored how early bark beetle attacks can be detected using weekly aggregated Sentinel-2 data in combination with static data, such as geo- and forestry data. We used an XGBoost classifier, known for its strength and reliability in classification, and compared three sets of data: static data only, satellite data only, and using both together. Having trained the models on cumulative weekly data, we were able to track changes in model performance and feature importance over time, identifying key weeks for the detection of bark beetle attacks. A systematic overview of feature importance identified the Red-edge 3 and blue Sentinel-2 bands as the most important when combined with static data; it also showed changes in feature importance compared to using satellite-only data, e.g., adding static features reduced the importance of red and red-edge 2 bands. Among the static features, land cover and landforms were the most important. Evaluating the temporal features for the combined model highlighted certain weeks as containing key information for detection: week 19, which was the main swarming week; week 25, which is 6 weeks after swarming and just before the second generation is completed; and weeks 31 and 33, more than 3 months after the tree was attacked and well after the new generation has swarmed. The study shows that combining static features with cumulative Sentinel-2, accumulated across weeks, are all important for improving the detection of bark beetle attacks, and that such ideas form an important part of early warning systems.