Earth and Environmental Sciences

This study proposes an innovative framework that combines hydroclimatic and agro-environmental predictors, including nitrate concentration and NDVI, with climatic factors such as Rainfall, temperature, and evapotranspiration to forecast piezometric level variations in the Saïss Basin, Morocco. Eight Machine Learning (ML) models were benchmarked, and feature importance was assessed using Shapley Additive exPlanations (SHAP) to ensure model transparency and interpretability. In parallel, the PELT algorithm was applied to detect structural change points, while Sen’s slope estimator and the Mann–Kendall test were used to quantify long-term trends. The Extra Trees (ET) model achieved the best performance (R2 = 0.92), with Rainfall emerging as the most influential predictor, followed by nitrate concentration, confirming the added value of hydrochemical indicators for groundwater forecasting. Change-point analysis revealed significant declines during the 1980s and 1990s, followed by lower-amplitude fluctuations since the late 2000s. Projections toward 2050 suggest partial stabilization in the central part of the basin under favorable recharge conditions, whereas persistent declines are expected to continue in peripheral areas subjected to sustained groundwater abstraction pressure. These findings provide a robust and transferable decision-support tool for the sustainable management of groundwater resources in semi-arid agricultural area.

Abstract Machine learning (ML) has become a promising tool for image analysis, particularly for datasets with instrument-specific characteristics and variable image quality. The Balloon-borne Ice Cloud particle Imager (B-ICI) collects and images ice particles on a film strip during ascent through the troposphere. While the instrument provides high optical and pixel resolution, the background features of the film and oil coating introduce noise that complicates image analysis. To process these raw data obtained from B-ICI, a set of tools, BICI-NET, has been created. It consists of two convolutional neural network (CNN) models: BICI-NET-segmentation, which identifies ice particles in the images, and BICI-NET-classification, which categorises the particles by shape. A visual inspection step is included to ensure the quality of the segmentation results, allowing users to accept, correct, or reject individual mask images. These masks are then used to extract particle properties such as maximum dimension, cross-sectional area, and aspect ratio. The segmentation model was initially trained on data from 2013–2018, and its performance improves iteratively as new, inspected data are added to the training set. Using this model, the duration of the segmentation process is reduced by approximately 90%. Currently particles are classified into four different categories: columns; compacts; plates; and rosettes. These shape categories have been chosen based on current needs and because they had been used in previous studies with B-ICI data. We present the BICI-NET processing pipeline, its validation, and performance results. Comparisons with a manually analysed test dataset show good agreement, especially for particles larger than ≳ 100 μm. Minor biases affect smaller particles, and most misclassification occur in the plates category, which is under-represented in the training data.

Abstract. This paper describes the data quality of the first weather radar with a solid state power amplifier (SSPA) in use at the German Meteorological Service. The new transmitter has been integrated into the existing C-Band radar at the Observatory Hohenpeissenberg in October 2023. The resulting setup is unique: most of the radar hardware (wave guides, pedestal, antenna, radome) is shared between the magnetron and solid state transmitters. The same weather situation can therefore be observed with both transmitter types with a small time difference of around five minutes, while most elements of uncertainty from the hardware can be disregarded for their comparison. A two pulse scheme is investigated with an un-modulated short pulse and a long pulse with non-linear frequency modulation. The scheme provides similar spatial resolution compared to the magnetron system. We show the results of the comparison of the data from both transmitters, focusing on reflectivity, Doppler moments and dual-polarization data. Magnetron and SSPA transmitters provide comparable data quality in areas with a signal-to-noise ratio (SNR) >20 dB. For lower SNR, the SSPA outperforms the magnetron transmitter. This is especially noticeable in ranges above 130 km from the radar. Data at the transition between the modulated long pulse and the un-modulated gap filler short pulse are investigated in detail. It is shown that the matching works well and a simple approach with fixed offsets is sufficient to provide a smooth transition. Range sidelobes are investigated with examples originating from strong clutter targets and an intense convective cell. For targets stronger than 55 dBZ, range sidelobes reach levels in many radar moments (including dual-polarization moments) that resemble meteorological echoes. They influence the whole length of the pulse (30 km in the presented case). The effect on radar products and possible mitigation approaches still have to be investigated. In general, SSPA transmitters for weather radars are assessed as viable in terms of data quality and are considered as an option to replace magnetron transmitters in the DWD weather radar network.

Polycyclic aromatic hydrocarbons (PAHs) are toxic, carcinogenic organic contaminants widely distributed throughout the marine environment, directly affecting marine life and human health. Surveying these compounds in sentinel organisms, such as seabirds, is an effective way to assess environmental quality. In this sense, this research aimed to (i) develop and validate a high-performance liquid chromatography (HPLC) analytical method for the quantification of the 16 priority PAHs in blood plasma; (ii) establish an easy and quick protocol to reduce sample handling and potential contamination; and (iii) apply the method to Brown booby ( Sula leucogaster ) blood samples from specimens collected along the coast of the state of Rio de Janeiro in 2025. The performance parameters of the developed method met well-established criteria and the Limits of Quantification (LOQ) achieved herein (0.06 - 4.48 ng mL -1 ) are consistent with the main goal and comparable to recent literature. The analyzed samples (n=13) presented low PAH concentrations (from -1 ), consistent with chronic background exposure and no evidence of acute events. The contamination profile suggests exposure to low molecular weight PAH, possibly due to Brown booby trophic position and habitat. This research delivers an initial database of an ongoing monitoring project for future assistance in the event of oil spills or other acute contamination sources.

Synthetic Aperture Radar (SAR) possesses the capacity for all-weather imaging and is widely applied in target detection. However, robust SAR target detection remains challenging due to the limited availability of task-relevant labeled samples that jointly cover target categories, depression angles, and complex target–background contexts. In this paper, we propose a Data-Augmented Gather-and-Distribute Network (DA-GDNet) for SAR image target detection. By jointly optimizing at both the data and architectural levels, the proposed approach enhances the model’s capacity for target detection in complex backgrounds. Specifically, we design a SAR image data augmentation strategy that integrates three-dimensional modeling with deep learning. Meanwhile, we incorporate a Gather–Distribute (GD) mechanism and a Spatial Feature Enhancement Module (SFEM) to achieve efficient multi-scale feature fusion and enhance the saliency of target regions. Experimental results on the MSTAR dataset and ATRNet-STAR dataset demonstrate that DA-GDNet not only improves detection accuracy and robustness, but also significantly strengthens the model’s adaptability to variations in depression angles and complex backgrounds.

Region-level weakly supervised hyperspectral target detection (HTD) using multiple-instance learning (MIL) reduces annotation costs but encounters challenges such as bag label ambiguity, boundary over-smoothing, and test-time computational latency. To address these issues, we propose Hyper-VMIL, a spatial–spectral topology-regularized variational hypergraph network. Hyper-VMIL formulates latent target localization as variational inference over dual-path hypergraphs: a boundary-aware spatial hypergraph modeling geometric patch continuity and a dynamic spectral-manifold hypergraph capturing non-local material similarity. Node-adaptive gating dynamically balances spatial and spectral evidence to mitigate over-smoothing near target boundaries. Furthermore, a confidence-aware continuous posterior refinement (CTPR) mechanism reduces the confirmation bias associated with conventional hard pseudo-label binarization. Finally, a teacher–student distillation strategy transfers contextual topology into a lightweight single-spectrum student detector. Benchmark experiments on simulated ASTER and airborne MUUFL Gulfport and Avon datasets show that Hyper-VMIL achieves competitive performance against 15 baseline methods. Notably, Hyper-VMIL supports dual inference modes: Context Mode provides improved detection accuracy (+4.6% average NAUC over VMIL-ECM on MUUFL), while Pixel Mode enables single-spectrum inference (1.25μs single-instance latency and an amortized streaming throughput of 0.015μs per pixel) suitable for onboard real-time deployment.

Introduction This study provides the first comprehensive bibliometric analysis of research on climate change and children’s psychological wellbeing (2001–2025), examining whose children’s experiences are studied and which voices shape this rapidly expanding field through a climate justice lens. Methods We analyzed 1,217 documents from Web of Science and Scopus using co-occurrence, citation, and co-authorship analyses. Results The field exhibited explosive growth (22.52% annually), surging from fewer than 10 publications before 2010 to 262 in 2025. However, profound geographic inequalities revealed epistemic injustice: the United States (740 publications, 60.8%), United Kingdom (396, 32.5%), and Australia (351, 28.8%) dominated production while Small Island Developing States, Sub-Saharan Africa, and climate-vulnerable regions remained severely underrepresented despite experiencing disproportionate impacts. Nine thematic clusters emerged, with eco-anxiety, climate education, and pro-environmental behavior—reflecting Western individualistic frameworks—dominating discourse, while structural themes addressing colonialism, displacement, and systemic violence remained underdeveloped. Collaboration networks exhibited US–Australia–Europe centrality with limited Global South engagement. Discussion This concentration constitutes epistemic injustice wherein nations bearing greatest historical responsibility for emissions while experiencing buffered impacts dominate knowledge production, risking universalization of Western experiences while marginalizing the majority of the world’s climate-affected children. Addressing these inequalities requires systemic transformation: redirecting funding to Global South institutions, diversifying editorial boards, valuing diverse epistemologies, ensuring equitable partnerships, and decolonizing theoretical frameworks. Such transformation represents not merely academic fairness but a climate justice imperative essential for developing knowledge systems that center marginalized voices and inform interventions responsive to realities shaping climate vulnerability globally.

Marine Functional Connectivity (MFC) science – the study of marine organism movements and their ecological consequences across scales – is emerging as a critical framework for understanding how marine biodiversity underpins spatial linkages and interdependencies across ocean basins, depth gradients and land–sea systems. Despite major advances in seabed mapping, ocean observation and hydrological modeling, knowledge of functional connectivity at sea remains fragmented and insufficient to effectively inform management. Marine species transport energy, nutrients, biomass and matter across ecosystems and political boundaries, making MFC essential for conservation, climate resilience and sustainable ocean governance. Recent decades have produced substantial progress in observing and quantifying MFC through genetics, telemetry, natural tags, dispersal modeling and remote sensing. However, major gaps persist across taxa, habitats, depths, regions and temporal scales. Global change further complicates MFC assessment by altering ocean conditions, species distributions and ecosystem dynamics. Addressing these gaps requires harmonized global observations, open-access data sharing, standardized metrics and integration of biodiversity data into global ocean models and Digital Twins of the Ocean. Advances in predictive modeling, ecological network analysis, food-web theory and socio-ecological frameworks are improving MFC integration into marine spatial planning and ecosystem management. Yet connectivity processes remain insufficiently incorporated into governance and decision-making frameworks, particularly across jurisdictions and in Areas Beyond National Jurisdiction. Future progress will depend on stronger international collaboration, transdisciplinary research, technological innovation and closer integration between science, policy and management.

Abstract Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with substantial mortality. While fine particulate matter (PM2.5) is a recognized risk factor for multiple infectious diseases, its role in the prognosis of SFTS remains unexplored. In a cohort of 3583 hospitalized SFTS patients, we found that long-term pre-admission exposure to ambient PM2.5 was significantly associated with an increased risk of SFTS-attributable death, with a corresponding hazard ratio of 1.16 (95% CI: 1.04 and 1.30) per 10 μg/m3 increment. Exploratory mediation analysis suggested that viral load partially explains this association. In a model-based counterfactual burden assessment, we estimated that 137 SFTS-attributable deaths in Xinyang during 2010–2022 might have been potentially avoidable under a hypothetical scenario in which annual PM2.5 concentrations complied with the Chinese National Ambient Air Quality Standards. Complementary experimental studies showed that PM2.5 pre-exposure exacerbated SFTSV infection, increasing viral replication and tissue injury. Transcriptomic analyses implicated mitochondrial redox metabolism and reactive-oxygen-species-related pathways, while ROS/MDA measurements and N-acetylcysteine intervention supported PM2.5-induced oxidative priming as a plausible upstream contributing mechanism for enhanced SFTSV replication and disease severity. In summary, this study provides epidemiological evidence linking long-term PM2.5 exposure to poorer SFTS prognosis and suggests a biologically plausible role of oxidative stress-related redox imbalance. These findings highlight the potential relevance of air-quality improvement for reducing severe outcomes of SFTS in endemic regions.

Although semantic 3D city models are internationally available and becoming increasingly detailed, the incorporation of material information remains largely untapped. However, a structured representation of materials and their physical properties could substantially broaden the application spectrum and analytical capabilities for urban digital twins. At the same time, the growing number of repeated mobile laser scans of cities and their street spaces yields a wealth of observations influenced by the material characteristics of the corresponding surfaces. To leverage this information, we propose radiometric fingerprints of object surfaces by grouping LiDAR observations reflected from the same semantic object under varying distances, incident angles, environmental conditions, sensors, and scanning campaigns. Our study demonstrates how 312.4 million individual beams acquired across four campaigns using five LiDAR sensors on the Audi Autonomous Driving Dataset (A2D2) vehicle can be automatically associated with 6368 individual objects of the semantic 3D city model. The model comprises a comprehensive and semantic representation of four inner-city streets at Level of Detail (LOD) 3 with centimeter-level accuracy. It is based on the CityGML 3.0 standard and enables fine-grained sub-differentiation of objects. The extracted radiometric fingerprints for object surfaces reveal recurring intra-class patterns that indicate class-dominant materials. The semantic model, the method implementations, and the developed geodatabase solution 3DSensorDB are released under: https://github.com/tum-gis/sensordb

Object detection in remote sensing imagery remains challenging due to vast scale variations, complex backgrounds, and the prevalence of small, densely packed targets. Existing CNN-based detectors are limited by restricted receptive fields, while Transformer-based methods incur prohibitive computational overhead for high-resolution inputs. In this paper, we propose SFSMamba-DETR, a detection framework that integrates state space models with Dual-Scale Window Attention for efficient and accurate remote sensing object detection. Specifically, we design a Selective Feature Scanning (SFS) module that uses the Mamba-based 2D Selective Scan mechanism to model long-range spatial dependencies with linear computational complexity. To capture both fine-grained local patterns and broader contextual cues simultaneously, we introduce a Dual-Scale Window Attention (DSWA) mechanism that operates at two complementary window scales with multi-kernel convolution bridging. These modules are orchestrated within a Cross-scale Feature Aggregation Module (CFAM) that performs hierarchical multi-scale fusion in a hybrid encoder. Extensive experiments on three primary benchmarks (MAR20, UCAS-AOD, and the Jilin-1 Satellite Aircraft Detection Dataset), together with supplementary results on DOTA and DIOR, demonstrate that SFSMamba-DETR achieves strong detection accuracy while maintaining competitive inference speed.

Oriented object detection (OOD) in remote sensing images (RSIs) suffers from insufficient feature representation caused by arbitrary rotation angles and small spatial resolutions. Existing spatial–frequency fusion paradigms merely implement single-granularity feature interaction, either global image-level frequency compensation or local instance-level feature refinement, and fail to simultaneously capture global scene semantic consistency and local object fine-grained discriminability. In this paper, we propose a unified dual-level spatial–frequency collaborative detector (DSCDet) for remote sensing OOD tasks. Different from previous decoupled designs, the proposed DSCDet constructs a complete spatial–frequency collaborative fusion paradigm that shares a generic wavelet-based frequency extraction mechanism and cross-feature fusion module, which is adaptively deployed at both image-level and instance-level granularities. Specifically, our method introduces Haar wavelet transform to extract multi-scale frequency mutation features. On this basis, a generic cross-domain attention fusion (GCDAF) is constructed with granularity-dependent positional encoding constraints. The core difference between dual granularity fusion lies in geometric positional encoding, where image-level fusion adopts global scene positional embedding to maintain overall semantic stability, and instance-level fusion leverages local pairwise instance positional embedding to optimize fine-grained target feature interaction. The unified dual-level fusion architecture comprehensively integrates global semantic integrity and local target specificity, forming a robust and universal spatial–frequency feature representation system. Extensive experiments on three public remote sensing datasets, including DOTA-v1.0, DOTA-v1.5 and DIOR-R, demonstrate that the proposed DSCDet achieves competitive and superior performance against state-of-the-art OOD detectors.

Abstract Black carbon (BC) in marine sediments provides a unique archive of combustion history and a reservoir for long-term carbon storage. However, the distinct roles of char BC (CBC) and soot BC (SBC) in combustion-source tracing and carbon-cycle assessment remain poorly resolved in marginal seas. Here, we analyze four sediment cores from the Beibu Gulf, South China Sea, using benzene poly(carboxylic acid) (BPCA) and chemothermal oxidation at 375 °C (CTO-375) methods to resolve subtype-specific BC records. CBC accounted for 52–57% of the estimated total BC burial across all settings, consistent with sustained biomass-burning contributions. SBC exhibited higher spatiotemporal variability and sensitivity to fossil fuel expansion, particularly near industrial zones. Estimated BC burial fluxes varied spatially, largely reflecting differences in representative sedimentation rates rather than BC content. Notably, nearshore sites exhibited higher burial despite lower BC content. Centennial-scale profiles revealed that SBC effectively traced regional transitions from biomass to fossil fuel combustion, while CBC preserved the long-term fire legacy. These findings highlight the complementary roles of CBC and SBC in reconstructing combustion histories and assessing BC burial in marginal seas. Burial of biomass-derived BC can contribute to net atmospheric CO2 sequestration, whereas fossil-derived BC burial represents the transfer of fossil carbon into long-term sedimentary storage.

High-resolution range profile (HRRP) reconstruction is essential for extracting range-direction scattering characteristics in wideband radar remote sensing, particularly in synthetic aperture radar (SAR) and inverse synthetic aperture radar (ISAR) imaging. Coded interrupted sampling (CIS) can improve radar low probability of intercept (LPI) performance by controlling signal transmission with a binary sequence. However, the reduced number of valid echo samples may degrade HRRP reconstruction, especially under low-duty-ratio and low signal-to-noise ratio (SNR) conditions. Conventional orthogonal matching pursuit (OMP) processes each frame independently and ignores the inter-frame continuity of scattering-center positions, which may lead to false selections and missed detections. To address this problem, this paper proposes a candidate-interval-assisted orthogonal matching pursuit (CI-OMP) algorithm based on multi-frame sequential priors. Stable scattering-center positions are extracted from historical reconstruction results and expanded into candidate intervals to guide atom matching in the current frame. Simulation results show that CI-OMP outperforms standard OMP in terms of normalized mean squared error (NMSE), tolerant support recovery rate (Tol-SRR), and peak-to-sidelobe ratio (PSLR). At a duty ratio of 0.20, CI-OMP reduces the NMSE by 1.71 dB and improves the PSLR by 7.56 dB compared with OMP. In addition, the candidate-interval strategy reduces the atom-search range by approximately 54–75% under different duty ratios and by approximately 50–83% under different SNRs, demonstrating improved search efficiency. These results demonstrate that CI-OMP improves the accuracy, robustness, and search efficiency of HRRP reconstruction for CIS radar echoes, particularly under low-duty-ratio and low-to-medium-SNR conditions.

Abstract Causal inference plays a central role in sustainability studies, providing the foundation for evidence-based scientific discovery and policy evaluation. However, uncovering credible causal relationships in complex real-world settings often relies heavily on the judgment of domain experts and extensive contextual knowledge. With the emergence of large language models (LLMs), an open question arises as to whether these models can meaningfully support causal reasoning for sustainability research. This study systematically evaluates the capacity of LLM to infer causal hypotheses and methodological structures from contextual descriptions of empirical research. We construct a benchmark based on five high-quality, peer-reviewed sustainability studies and design structured prompts that replicate the informational context available to human researchers prior to estimation. LLM outputs are evaluated against expert-validated ground truth in terms of causal edge recovery, scope expansion, and alignment with identification strategies, allowing for a quantitative assessment of conceptual causal reasoning rather than numerical estimation. Our findings indicate that GPT-5 recovers core causal edges with moderate accuracy (0.33-0.85), and assigns causal direction with high reliability (0.87-1.00). However, the scope expansion rate achieves roughly 0.58-0.93 of the causal edges proposed by the model, indicating a strong tendency toward over-connection between variables. Overall, our findings contribute to a deeper understanding of the potential and limitations of LLMs as tools for causal reasoning and methodological support in empirical sustainability research.

Marine mammal mass stranding events (MSEs) are complex phenomena that require detailed investigation into their causes and behavioural precursors. In July 2023, Scotland experienced a large MSE involving 55 long-finned pilot whales ( Globicephala melas ), offering a rare opportunity to examine their pre-stranding feeding ecology and, by extension, habitat use using stable isotopes. We analysed carbon ( δ 13 C) and nitrogen ( δ 15 N) stable isotopes in matched samples of liver, skin, and muscle from 39 individuals to assess resource use patterns across different tissue integration timescales, as recent changes in feeding might indicate a change in habitat. Liver, representing the most recent dietary integration, showed lower δ 13 C values than skin and muscle. While δ 15 N values were higher in liver, TEF-corrected δ 15 N values did not differ across tissues, providing no detectable bulk-isotope evidence for major trophic level shifts across the sampled dietary integration windows represented by the different tissues. We applied the Stable Isotope Trajectory Analysis (SITA) framework in R for the first time to multi-tissue data from marine mammals. Trajectories revealed minimal net change across and between individuals, and the direction and magnitude of isotopic changes were consistent with shared temporal patterns in resource use. We found no consistent detectable bulk-isotope evidence of recent dietary or habitat shifts across the stranded group and no evidence for a coherent systematic directional shift in isotopic space over the time represented by the tissues analysed. Importantly, in a broader context, our findings highlight the potential of multi-tissue isotope analysis as a cost-effective tool for reconstructing short- to medium-term foraging history in stranded cetaceans, providing important insights into habitat and resource use of cryptic cetacean species.

Abstract

Heating and cooling of outdoor space steadily increased over the last years. The pull between economic growth and lessening the ecological footprint remains a major concern in the light of reaching sustainable development goals (SDGs). We looked at numbers and energy consumption of heating and outdoor cooling in gastronomic outdoor spaces during one heating period in two Vienna districts (Austria) and investigated citizens' perspectives on climate change, outdoor heating and cooling with an online and in-person questionnaire ( N = 72, N = 79). We found more outside heaters than officially registered in one district (59 visible heaters, 0 registered) which indicates a high number of heaters used in private gastronomic outdoor spaces. The calculated total ( N = 901) could supply up to 2500 two-person households with electricity for one year. Radiant heaters influenced the decision to visit a gastronomic establishment for 40% of participants and altered the choice of seating for 63%. Our results reflect a cognitive dissonance and a trade-off between environmental behaviour and ever-increasing human development on societal and economic level. The lax registration policy in Vienna requires action and the lack of research in the outdoor heating area remains a blind spot in the context of urban sustainability: in times of global warming using fossil fuels to heat outdoor spaces seems questionable in itself.

Abstract Per- and polyfluoroalkyl substances (PFASs) are globally detected in humans, yet regional variations in PFAS exposure remain insufficiently characterized. Herein, by utilizing target and nontarget analysis with machine learning, differences in PFAS exposure were assessed based on 1088 serum samples of the population from SX, GD, HeB, and HuB regions in China. Nontarget analysis identified 49 PFASs, among which bis(trifluoromethane)sulfonimide (Ntf2), C8 per- and polyfluoroalkyl ether carboxylic acid, C12 hydrogen-substituted polyfluoroalkyl (linear) carboxylic acid, 8:2 fluorotelomer unsaturated carboxylic acid, 1-hydroxy-6:2 fluorotelomer sulfonic acid, haloxyfop, and leflunomide were reported in human serum for the first time. Target analysis indicated total PFAS concentrations of 9.4, 17, 25, and 29 ng/mL in the SX, GD, HeB, and HuB regions, respectively, with compositional profiles differing in the proportions of specific PFAS classes. Machine learning classifiers achieved an accuracy of 0.89 in distinguishing samples from the four regions, revealing significant interregional disparity. Furthermore, PFNA, Ntf2, PFOA, and 6:2 Cl-PFESA were identified as distinctive exposure biomarkers for SX, GD, HeB, and HuB, respectively, each being closely associated with local industrial activities. Overall, this study elucidates the region-specific characteristics of human PFAS exposure in China, providing a scientific basis for regionally tailored health risk assessment and pollution control strategies.

Localized sediment siltation poses a major engineering challenge that restricts the operational safety of water-related infrastructure, such as coastal ports and pile-supported wharfs. Pneumatic jet desilting technology provides a new solution for this problem. By combining stratified suspended sediment sampling with high fidelity particle image velocimetry (PIV) measurements, four transient dynamic stages of bed sediment suspension are identified. Furthermore, the vertical distribution characteristics of non-uniform sediment are quantified. The results show an energy saturation effect during the transfer of gas kinetic energy to the water body. The vertical flow velocity in the core region follows a power-law relationship with the incident gas flow rate. In the multiphase flow field, the distribution of non-uniform particles is governed by the mechanical competition between turbulent entrainment in the bubble wake and gravity settling. This competition leads to vertical spatial sorting. Based on the multiphase kinetic energy dissipation mechanism, a dimensionless generalized suspension index is constructed. Consequently, a predictive equation for the vertical concentration distribution of suspended sediment under pneumatic jets is derived. This equation theoretically reveals the spatial distribution laws of sediment under submerged gas jets. The equation provides an analytical basis for parameter optimization and energy control in pneumatic desilting engineering.

The “Kashmir Seismic Gap” is located between the epicentral regions of the 1905 Kangra and the 2005 Kashmir earthquakes. The area corresponds to the Jhelum basin and encompasses the Kashmir Valley. The geodetic measurements indicate that the Kashmir Himalaya accommodates ∼12 mm/yr of India-Asia convergence and ∼5 mm/yr of dextral shear. However, except for the known Karakoram Fault, which is further north of the area under investigation, there are no convincing reports of any prominent dextral faulting from the region. In this article, we have carried out remote sensing and GIS based geomorphic investigation including topographic derivatives such as drainage geometry, basin divide offsets, and elevation-χ analysis that consistently indicate an active accommodation of the missing dextral shear. The dextral slip is manifested in the deformed (offset) lateral divides of the Kashmir Valley (KV) and preferential deflection of the drainage along the Central Kashmir Fault (CKF). The χ-metric, which is used to examine the divide stability, also shows a change in its nature corresponding to the Central Kashmir Fault. The drainage on the SE lateral divide displays the most conspicuous evidence of the dextral shearing, which has resulted in the capture of through-going drainage within Kashmir Valley, and the beheaded stream continues to flow southwards. This event of drainage reorganisation has left a ‘wind-gap’ in the divide near Banihal. Furthermore, the hypothesis of dextral shearing is tested in the larger tectonic context together with the ∼ N-S oriented Jhelum Fault (JF) and Kishtwar Fault (KF). Both the Jhelum Fault and Kishtwar Fault are active sinistral faults and can impart anti-clockwise rotation to the region in between. The rotation between blocks of the Kashmir Himalaya can induce the observed dextral shear, which is consistent across the entire Kashmir Himalaya and the Kashmir Valley. Both of which, when calculated with the ∼ 4–6 Ma age of the Kashmir Valley, imply ∼ 5–7 mm/yr of dextral shear, similar to the estimates from geodesy.