Atmospheric and Oceanic Sciences

Abstract Techniques for identifying stable, high-quality in-stream habitat are increasingly needed to support fish conservation and river restoration under changing flow and sediment regimes. This study presents a multi-criteria mapping framework that integrates hydraulic modelling, geomorphic form and unit classification, and riparian canopy cover assessment to identify suitable redd conditions for egg incubation planning. The framework was tested in the Folly River, Nova Scotia, using an inner Bay of Fundy Atlantic salmon egg incubation project as a case study. High flow and low flow hydraulic conditions were simulated in HEC-RAS to derive flow velocity and water depth layers, while the Geomorphic Form Variation approach and Geomorphic Unit Tool were used to map geomorphic complexity and unit assemblages. Riparian canopy cover derived from LiDAR-based canopy height models provided an additional habitat condition layer. These outputs were spatially overlaid using criteria associated with a stable incubation site to map alternative locations with suitable incubation conditions and wetted extents under low flow were then used to assess longitudinal connectivity. Extreme fluctuations in weather patterns pose additional challenges for selecting suitable egg incubation sites. Therefore, we stress-tested our suitable sites in BASEMENT software to evaluate morphodynamic stability under high flow. The framework identified targeted redd sites and provides a transferable approach for habitat assessment, restoration planning, and climate-resilient river management.

Abstract Near‐seafloor marine sediments on active margins have been shown to be stronger than those on passive margins. However, the reasons for this are not fully understood. To investigate the role of intrinsic properties, we performed resedimentation and oedometer tests and measured undrained shear strength, grain size, mineralogy, and plasticity on sediments from three active margins (Nankai, Cascadia, Surveyor Fan) and three passive margins (Amazon Fan, Carolina, and New Jersey) and compared them to their in situ field measurements. We find that all shear strengths on resedimented samples fall within the expected range for normal consolidation. However, Nankai and Cascadia exhibit anomalously high undrained shear strength and low porosity in situ, which cannot be reproduced with 1‐D consolidation experiments. These results suggest that other factors occurring on active margins contribute to strengthening near‐seafloor sediments such as the repeated exposure to earthquakes and lateral shear strain induced by non‐uniaxial stress paths.

Ecosystem services in the middle reaches of the Yellow River Basin have undergone substantial changes under the combined influence of climate variability and intensive human activities. However, the long-term evolution of habitat-related landscape conditions and soil conservation capacity, as well as the major factors associated with their spatial heterogeneity, remain insufficiently understood at the basin scale. In this study, multi-source remote sensing and statistical datasets from 1985 to 2023 were integrated with the InVEST model, GeoDetector analysis, and the LCB-PLUS land-use simulation framework to examine changes in land use, habitat-quality proxies, and soil conservation patterns. The habitat quality index was interpreted as a land-use-based proxy of potential habitat condition rather than a direct measurement of biodiversity status or ecological functionality. The results showed that land-use change during 1985–2023 was mainly characterized by cropland reduction, construction land expansion, and bidirectional conversion between forestland and grassland. Approximately 1.2 × 10 6 hm² of cropland was converted to construction land, while ecological restoration was associated with the conversion of more than 8 × 10 4 hm² of unused land into forestland and grassland. The modeled habitat-quality index exhibited moderate temporal fluctuations, with relatively high values concentrated in mountainous ecological zones such as the Lüliang Mountains and the southern Taihang Mountains. Soil conservation (SC), quantified using the sediment retention estimation framework of the InVEST model based on the USLE approach, showed clear spatial heterogeneity, with stronger conservation capacity mainly distributed in southwestern mountainous areas with steep terrain and dense vegetation cover. GeoDetector analysis indicated that annual precipitation, NDVI, slope, and population density had relatively strong statistical associations with the spatial patterns of the habitat-quality proxy and SC. Interactions among natural and socioeconomic variables enhanced explanatory power compared with single factors, suggesting that the spatial heterogeneity of ecosystem service proxies was associated with combined influences rather than isolated drivers. Scenario simulations for 2035 showed that different land-use pathways may result in modest but detectable differences in ecosystem service indicators. Based on the joint performance of habitat quality and SC, the ecological-protection-oriented pathway was identified as the most favorable scenario among the simulated alternatives, while cropland protection served as a supplementary stabilizing pathway and rapid development represented a high-risk pathway requiring careful constraint. Overall, this study provides a basin-scale assessment of long-term landscape dynamics and their implications for ecosystem service patterns in the middle reaches of the Yellow River. The findings provide a spatially explicit reference for ecological management and scenario-based planning in ecologically fragile regions. Nevertheless, the results should be interpreted as exploratory because they are subject to uncertainties related to model parameterization, data resolution, and scenario assumptions.

Nearly half of the world's population relies on nonsewered sanitation, and in urban areas, intermittent onsite storage of wastewater prior to road-based transport to treatment is common. Anerobic biological activity during storage generates methane, contributing to greenhouse gas emissions and posing challenges for climate mitigation. Current estimates of methane emissions rely on methane correction factors (MCFs) in the Intergovernmental Panel on Climate Change (IPCC) guidelines that are based on theoretical assumptions and increasingly viewed as not valid. In this Perspective, we use 1349 data points from onsite storage of wastewater in 11 cities to show that obtaining accurate city-wide estimates is complicated by highly variable properties of in situ wastewater and storage conditions and hence biological production of methane. When it comes to greenhouse gas emissions from nonsewered sanitation, the sector has so far focused on storage, but as evidence suggests these estimates are too high, we propose that emphasis should in fact be placed on the entire service chain.

Abstract In many peri-urban areas of the Global South, formal water supply systems are insufficient. This gap is largely filled by a diverse informal water market and widespread informal water use, both of which depend heavily on groundwater. Peri-urban low-income communities are particularly affected. Research in such contexts requires participatory and interdisciplinary approaches. This paper aims to equip hydrogeologists with the knowledge needed to implement participatory management, thereby fostering deeper engagement with participatory methodologies and strengthening interdisciplinary collaboration. The findings are also intended to support NGOs in planning more comprehensive water projects by integrating hydrogeological considerations. Two low-income communities in peri-urban Jaipur, India, representing different hydrogeological, infrastructural, and socio-cultural conditions, were selected as study areas in cooperation with a local NGO. The study comprised four components: initial socio-hydrogeological assessments to gain baseline information; water workshops to raise awareness and exchange knowledge on groundwater; the recruitment and training of women assistants to teach a small group more advanced groundwater-related skills; and finally, the production of films to disseminate the results. The research demonstrated that the applied methods generated relevant insights and enabled the implementation of a participatory approach. However, future studies should engage more thoroughly with the literature on participatory methodologies, ideally in collaboration with social scientists experienced in such approaches. Despite these limitations, the study contributes to the growing field of participatory and socio-hydrogeological research and shows that these approaches can enhance groundwater awareness among local communities and NGOs while sensitizing hydrogeologists to the importance of inter- and transdisciplinary work.

The prediction horizon length is a crucial parameter in model predictive control (MPC) that determines how far in advance the future wave exciting force must be predicted for real-time control. This paper presents an optimal solution for the prediction horizon length in MPC for a point absorber wave energy converter (PA-WEC) operating under irregular wave conditions. To maximize absorption power and energy absorption efficiency, the dimensions of the floating body of PA-WEC are tuned according to the sea state. The linear superposition principle is investigated within the MPC formulation to assess its applicability and to determine whether the absorbed power under irregular waves can be represented as a superposition of the corresponding regular wave components. The resulting absorbed power is compared with that obtained using a constant damping control method as a reference. Finally, an empirical solution for identifying the optimal prediction horizon length in MPC for irregular waves is proposed based on the wave spectrum.

Functional zoning of national parks is critical for balancing ecological conservation and resource utilization, yet traditional methods suffer from strong subjectivity and high spatial fragmentation. Using the proposed Nanling National Park as a case study, we developed a coupling framework integrating the InVEST model with an improved ant colony optimization (ACO) algorithm. The InVEST model assessed three ecosystem services: habitat quality, carbon storage, and water yield. Model outputs were independently cross-validated against external data, yielding a Pearson correlation of r = 0.74 ( p &amp;lt; 0.001) between habitat quality and camera-derived species richness, R 2 = 0.81 between modeled and observed forest carbon densities, and a mean relative error of 7.3% between modeled and gaged runoff. The improved ACO introduced three landscape-oriented strategies: non-uniform pheromone initialization, spatial neighborhood-constrained heuristic function, and adaptive pheromone evaporation. Integration was achieved through a four-channel coupling mechanism that embedded ecosystem service outputs into the ACO search process. Results showed a mean habitat quality index of 0.72, total carbon storage of 47.82 million tons, and annual water yield of 2.172 billion m 3 . The improved ACO increased convergence speed by 32.6% and achieved a spatial continuity index of 0.912, outperforming NSGA-II, PSO, and GA across six indicators. The optimized zoning comprised core protection (53.95%), general control (32.04%), science education and recreation (8.01%), and traditional utilization (6.00%). The core zone contained 72.1% of high-quality habitats with an aggregation index of 91.6%. A spatial Kappa coefficient of 0.82 with existing reserve boundaries and a 78.6% capture rate of Class-I protected wildlife habitats validated the scheme. Monte Carlo uncertainty analysis gave a 95% confidence interval of 52.3–55.6% for the core zone proportion, and a Sobol’ global sensitivity analysis based on a Saltelli quasi-Monte Carlo sequence of 18,432 model evaluations with 30 independent stochastic replicates per sample point and 1,000 bootstrap resamples yields 95% BCa confidence intervals with absolute half-widths below 0.022 for all eight algorithmic parameters and relative half-widths below 10% for the three dominant parameters ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"> <mml:mi>γ</mml:mi> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"> <mml:mi>β</mml:mi> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"> <mml:mi>α</mml:mi> </mml:math> ), statistically distinguishing them and isolating ACO stochasticity (4.7%) from parameter-induced variance (95.3%). This coupling framework provides scientific and quantitative support for the functional zoning of subtropical montane national parks comparable to Nanling.

Due to the rapid acceleration of urbanisation and the increasing occurrence of extreme rainfall events, underpasses have become critical hotspots of urban flooding vulnerability. In this study, we investigated 36 underpasses in Hangzhou using the Urban Flood Inundation Model (UFIM) to systematically evaluate their drainage performance. A high-resolution hydraulic simulation framework was developed by integrating terrain data, drainage pipe networks, pumping stations, and land-use information. Based on the maximum tolerable hourly rainfall derived from multi-scenario simulations, the facilities were divided into high-, medium-, and low-vulnerability groups. Our quantitative and spatial analyses reveal a pronounced core–periphery disparity: 41.7% of the underpasses were highly vulnerable (drainage threshold ≈ 61.3 mm/h), exhibiting significant spatial agglomeration in the older urban core. In contrast, facilities in newly developed peripheral areas demonstrated better drainage performance (threshold up to 75.6 mm/h). Furthermore, the backwater effect from downstream rivers at flood stages significantly constrains pump efficiency by increasing the static head requirement. Based on these spatial vulnerabilities and thresholds, targeted infrastructure optimisation and spatial planning strategies are proposed, shifting the focus from uniform engineering upgrades to vulnerability-based drainage capacity enhancements.

Abstract Slack pressure gradients in summer may be associated with sunny warm weather, promoting temperatures above the normal – especially in areas away from the sea. However, in warm‐sector weather, the air is moist and condensation may occur over comparatively cool seas, fog perhaps drifting on shore, or the chance of inland fog by night. This case illustrates the many factors that must be taken into consideration when trying to forecast the formation and advection of sea fog, as well as the effect of cooling inland. Forecasters can add value to numerical output, using experience and observed data.

Soil pH is a fundamental geochemical parameter with direct implications for environmental quality, but its spatial drivers in geologically complex mountain regions remain poorly understood. This study investigated surface soil pH across 452 sites in Pingquan City, a semi-arid, lithologically heterogeneous mountainous area of Hebei Province, China. The results show that the soil in Pingquan City is predominantly alkaline, with higher pH in southwestern and northeastern areas and lower pH in the northwest. Soil pH ranged from 4.62 to 9.98, with strong positive spatial autocorrelation. Comprehensive quality assessment indicated that the overall soil quality is moderately low. GeoDetector analysis identified average annual temperature, soil texture, elevation, and bedrock lithology as dominant structural drivers, with bi-factor enhancement interactions. GeoSHAP further uncovered two local effects: precipitation exerts a positive influence on pH in carbonate-rock-dominated areas, reversing the leaching–acidification pattern; and temperature functions as a proxy variable integrating co-varying topography, parent material, and texture rather than a direct thermal driver. The combined application of spatial autocorrelation, GeoDetector, and GeoSHAP provides an effective framework for identifying spatial phenomena, discriminating dominant drivers, and explaining local variations. These findings support regional soil quality assessment and land management, and provide a geochemical baseline for safeguarding groundwater resources in mountainous regions.

INTRODUCTION: Dengue is a growing health threat in Pakistan due to climate, urbanization, and inadequate sanitation. Despite recurrent seasonal outbreaks, comprehensive evidence on the dengue burden in Pakistan remains limited due to due to underreporting, incomplete data capture, and inconsistencies in diagnostic practices. METHODS: We analyzed laboratory-confirmed dengue cases from January 2012 to December 2022 to estimate incidence rates per 1,000 individuals across Pakistan at the subnational level including all 4 provinces, 86 districts, and a federal territory, stratified by 5 distinct age groups (below 5 years, 5-14 years, 15-49 years, 50-69 years, and 70 + years) and sex. The population estimates were adjusted to account for variations in treatment-seeking behavior and the utilization of partnering laboratories to ensure that the estimated incidence rates accurately represent the broader population. RESULTS: The incidence rate of dengue in Pakistan exhibited an increasing trend. Across all age groups, the 15-49 years age group had the highest incidence rates, with 56 cases per 1,000 individuals in 2022 (estimated 1.6 million cases). Sex analysis showed a higher proportion of dengue cases in males compared to females, consistent across all age groups. Spatial analysis revealed regional disparities, with higher incidence rates reported from Sindh compared to other provinces. In 2022, Sindh had the highest incidence rate of 45.63 (95% UI: 34.13-60.68) per 1000 individuals (2.63 million estimated cases), while Baluchistan had the lowest incidence rates of 0.44 (95% UI: 0.08-4.48) per 1,000 individuals (6,785 estimated cases). All age groups experienced increasing incidence, with the 15-49 age group showing the most pronounced rise. CONCLUSION: The findings underscore the growing burden of dengue in Pakistan, highlighting the urgent need for effective public health interventions and enhanced data collection mechanisms to better understand and address this rising public health concern.

Introduction Hull coatings protect marine vessels against corrosion and degradation. Reliable assessment of coating condition during dry-docks and dockside inspections is essential for maintenance planning. Colour imaging enables automated above-surface visual inspection; however, image segmentation remains challenging due to highly overlapping colour distributions. Circular hue histogram thresholding is commonly used for this purpose, but it may blend distinct coating states or amplify artefacts, thereby reducing segmentation reliability. Methods This study introduces a representational reformulation of circular histogram smoothing for coating-state segmentation. Instead of treating smoothing as a non-negative convolution operation, the proposed reformulation interprets it as a local correlation-based similarity measure between hue histogram and a smoothing kernel. This maps the response ranges from [0,+∞) to a signed and bounded interval [−1,1], explicitly distinguishing structural agreement from disagreement between histogram patterns and the kernel. A mode extraction method is developed based on this reformulation and integrated into circular thresholding procedures. Results Experimental evaluation was conducted using 50 images of actual ship hull coating surfaces acquired during dry-dock inspection, with three cleanliness conditions: clean, rusted, and stained. Compared with conventional convolution-based smoothing, the proposed similarity-based formulation improved discrimination of valid histogram modes and reduced false peak detection. Segmentation results demonstrated more stable and consistent separation of coating states under overlapping colour conditions. Discussion By reformulating histogram smoothing as a signed similarity representation, the proposed framework enhances segmentation robustness without increasing computational complexity. The approach provides improved interpretability of histogram structure and supports reliable real-time automated visual inspection, contributing to more consistent and responsive maintenance decision-making in marine and offshore operations.

The uniformity performance characteristic in remote sensing data products is a metric that helps evaluate how an image accurately represents radiometric responses to geophysical variability at the Earth’s surface or within the atmosphere. These uniformity attributes are used in Earth remote sensing science applications such as investigating water quality, land-cover/land-use classifications, and crop health assessments. To ensure these applications perform effectively, it is crucial to maintain and regularly assess the uniformity of calibrated remote sensing data products. In the Landsat missions, data from the onboard radiometric reference Solar diffuser device are used as the standard approach to derive the calibration parameters, ensuring the maintenance of both absolute radiance and radiometric uniformity through quarterly updates. This publication presents an alternative approach that leverages Earth scene data to assess the on-orbit uniformity performance of the Landsat 9 (L9) mission. The analysis characterizes uniformity at a specific set of spatial positions (the focal-plane boundary zones) that are intrinsic to the Operational Land Imager (OLI) focal plane design. By employing these Earth data statistics, the article further explores the temporal trends in the radiometric uniformity results. The results shown in this article demonstrate that for pushbroom imaging design systems with staggered sensor chip focal plane architectures, the co-registered pixels, i.e., overlapping measurements of the same ground target viewed by different detectors, offer a self-contained mechanism for assessing product uniformity. Remarkably, this alternative approach achieves a level of radiometric quality comparable to that delivered by a stable, well-characterized onboard Solar diffuser calibration device. The findings demonstrate the potential and capability of such Earth scene statistics in maintaining radiometric uniformity in calibrated data products to better than 1% (1-sigma) throughout the mission’s operational lifetime.

Rainwater harvesting systems (RWHS) are increasingly being adopted globally as part of green infrastructure to complement climate change adaptation and sustainable stormwater management. This study reviews the adoption of RWHS in non-domestic sectors, including commercial, industrial, and institutional settings, situating the topic within the broader challenges of water security in urban areas. This review aims to synthesise existing evidence on the key drivers, barriers, trends, and research gaps shaping RWHS adoption beyond the conventional domestic household scale. Following the PRISMA 2020 guidelines, peer-reviewed manuscripts published since 2000 were systematically identified and screened from the Scopus and Web of Science databases, focusing explicitly on adoption-related outcomes in non-domestic contexts. The findings indicate that RWHS adoption is primarily driven by anticipated water bill savings, sustainability, corporate environmental commitments, and stormwater management benefits, while major barriers include high initial capital costs, regulatory uncertainty, technical and maintenance complexity, and concerns regarding water quality and system reliability. This review also reveals sector-specific trends, such as the integration of RWHS with green building certification schemes and smart water technologies, and the prioritisation of process and utility water uses in non-domestic sectors. Overall, the findings indicate that non-domestic RWHS have substantial potential to contribute to sustainable stormwater management, but wider uptake depends on targeted policy support, economic incentives, clearer regulatory frameworks, and improved technical guidance, alongside further empirical research addressing the identified knowledge gaps.

Superpoint Transformers use superpoints as the basic processing units, thereby significantly reducing the number of tokens processed by Transformers. However, they have been seldom employed in point cloud roof plane instance segmentation, and existing superpoint Transformers suffer from limited performance due to the use of low-quality superpoints. To address this challenge, we establish a set of criteria that high-quality superpoints for Transformers should satisfy and introduce a corresponding two-stage superpoint generation process. The superpoints generated by our method not only have accurate boundaries, but also exhibit consistent geometric sizes and shapes, which greatly benefit the feature learning of superpoint Transformers. To compensate for the limitations of deep learning features when the training set size is limited, we incorporate multidimensional handcrafted features into the model. Additionally, we design a decoder that combines a Kolmogorov–Arnold Network with a Transformer module to improve instance prediction and mask extraction. Finally, our network’s predictions are refined using traditional algorithm-based post-processing. For evaluation, we annotated a real-world dataset and corrected annotation errors in the existing RoofN3D dataset. Experimental results show that our method achieves state-of-the-art performance on our dataset, as well as both the original and corrected RoofN3D datasets. Our model also shows significant advantages over existing methods when handling data with low point density, large density variations, or low 3D point precision. Moreover, it is not sensitive to plane boundary annotations during training, significantly reducing the annotation burden. We will release our code, trained models, and datasets.

A rift event set off a domino effect of geologic processes that created conditions ripe for Antarctica’s glaciation, a new study suggests.

Research on the seaward shoreface boundary (SSB) over broad spatial scales is limited, restricting the understanding of its macroscale characteristics. In this study, we define a theoretical wave-base-derived seaward shoreface boundary (TWSSB) based on the intersection of wave base (half the wavelength) with seabed topography. Using high-resolution wave analysis data from the Copernicus Marine Environment Monitoring Service (CMEMS) for 2022-2023, annual and seasonal wave bases were calculated in China’s coastal waters. By combining these data with nearshore bathymetry, the annual and seasonal TWSSBs were determined. The spatial distribution of annual TWSSBs and the migration of seasonal TWSSBs along China’s coast were detailed, providing a comprehensive view of TWSSB characteristics. Results show a strong correlation between TWSSBs and local isobaths. The annual TWSSBs approximately coincide with the 10 m isobath in the Bohai Sea and Beibu Gulf, align with the 20 m isobath in the central and southern Yellow Sea, and are close to the 30 m isobath east of Hainan Island. Compared to the annual averages, seasonal TWSSBs show different horizontal shifts, typically within several kilometers (median ~3 km). The maximum seasonal range (difference between seaward and landward shifts) reaches approximately 41 km in the South China Sea. Together, these findings present a large-scale synoptic mapping of the TWSSB along the entire coast of China and demonstrate that TWSSB location is influenced by regional bathymetry and seasonal wave conditions. This theoretical, wave-based approach is expected to offer a reference for future studies on SSB and coastal morphodynamic processes.

Introduction Water scarcity remains a pressing global problem, influenced by climate variability, limited access to water storage, and considerable reliance on rain-fed agriculture, making rainwater harvesting particularly relevant in drought-prone areas, where this reliance increases vulnerability to water stress. Rainwater harvesting has become a viable adaptation strategy for reducing exposure to dry spells, strengthening resilience to climate fluctuations, and supporting sustainable agricultural practices. Methods Therefore, this study was conducted in the Beshilo sub-basin, Ethiopia, to identify suitable rainwater harvesting sites using geographic information system (GIS) and analytic hierarchy process (AHP), to enhance drought resilience, improve agricultural water availability, supporting climate adaptation planning, and promoting sustainable water resource management in water-stressed areas. Ten parameters were considered: rainfall, soil texture, hydrological soil group, topographic wetness index, land use/land cover, stream order, drainage density, slope, geology, proximity to agricultural land, and proximity to roads. Within the geographic information system environment, spatial datasets were reclassified into suitability classes, standardized to a common resolution and projection, and combined using a weighted overlay technique. Parameter weights were derived through the AHP method, yielding an acceptable consistency ratio (CR = 0.09) based on expert judgment and prior research. Results Rainfall (29%) was the most influential parameter, followed by slope (20%) and land use/land cover (15%). Drainage density, geology, and proximity-related factors exerted a relatively low influence. The sub-basin was classified into four suitability classes: low suitable, moderately suitable, highly suitable, and very highly suitable by the ensuing rainwater harvesting suitability map. Discussion Analysis revealed that very high and very low suitability classes account for 1.89 and 1.63% of the area, respectively, while 58.76% is moderately suitable and 37.72% is highly suitable. The predominance of moderately to highly suitable zones highlights significant potential for implementing rainwater harvesting structures across the basin. Generally, the findings demonstrate that geographic information systems integrated with AHP are reliable and effective tools to assist in making decisions for rainwater harvesting planning. This study aligns with similar studies conducted in semi-arid regions, including Ethiopia, offering valuable spatial insights to advance climate-resilient agriculture and sustainable water resource management in water-stressed areas.

Urban green spaces are increasingly recognised as interfaces for plant health, yet their role in shaping the diversity and spread of Phytophthora remains poorly synthesised globally. We compiled 166 records of Phytophthora –host associations from 16 peer-reviewed studies (2005–2025) across botanical gardens, parks, street trees, and managed urban landscapes. Only confirmed pathogenic interactions were included. The dataset comprised 28 Phytophthora taxa and 96 host taxa from 11 countries, yielding 133 unique associations. Diversity was highly uneven, with a few taxa dominating urban systems. Phytophthora multivora was the most frequently recorded taxon and had the broadest host range, followed by P. nicotianae , P . cinnamomi and P . ramorum . Associations were concentrated in angiosperms, especially Ericales, Myrtales, and Fagales, with Ericaceae, Myrtaceae, and Theaceae prominent. Nearly 60% of hosts were non-native, reflecting ornamental horticulture and global plant trade. Records were geographically biased, with Spain and South Africa accounting for over two-thirds of observations, while many regions remain underrepresented. A median 6-year lag between sampling and publication may delay recognition of emerging risks. Urban environments support structured Phytophthora communities shaped by host composition, environment, and human-mediated plant movement, functioning as reservoirs and sources of inoculum for surrounding ecosystems. To address management limitations, we propose a conceptual framework that integrates prevention, detection, response, and recovery into an adaptive cycle for urban green spaces, supporting pathway-based biosecurity, risk prioritisation, and coordinated surveillance. Our study highlights the need for more balanced global surveillance and coordinated management of Phytophthora in urban and peri-urban landscapes to improve urgently needed resilience and biosecurity outcomes globally.

To address the challenge of vehicle-type recognition in complex traffic scenes within wide-area satellite imagery, where vehicles are easily confused with the surrounding background, this paper exploits the distinct spectral-response differences among various ground objects in multispectral data to guide the model to focus on vehicle targets. Based on satellite imagery comprising seven visible (VIS) bands and one near-infrared (NIR) band, this research is structured into a two-stage framework. In the first stage, a dedicated Preprocessing Framework for Vehicle Type Recognition (PFVTR) is developed for the satellite imagery, aiming to identify the sensitive bands within the visible-light spectrum for this specific task. Consequently, the Visible and Near-Infrared Multispectral Dataset (VNMD) is established based on the sensitive VIS bands and NIR imagery. In the second stage, to exploit the environmental robustness of the near-infrared band, the Multispectral Information Fusion-based Multimodal Vehicle Type Recognition model (MIF-MVTR) is proposed. By adaptively fusing the sensitive visible bands and the near-infrared spectrum, the model counters environmental interference to bolster recognition precision. Experimental results on the VNMD and public VEDAI datasets, as well as under complex illumination conditions, demonstrate that MIF-MVTR effectively exploits complementary spectral information to improve vehicle-type recognition performance in complex scenarios. These results further validate the robustness and generalization capability of the proposed model. Overall, spectral information plays an important role in alleviating vehicle-background confusion in wide-area traffic scenes.

Subsea blowout preventer (BOP) hydraulic control systems are safety-critical subsystems whose performance directly affects well control capability and emergency actuation reliability. Maintaining hydraulic integrity is essential because leakage-induced degradation can reduce stored actuation energy and compromise pressure delivery during critical operations. This paper presents a physics-based real-time monitoring methodology for accumulator leakage estimation in subsea BOP control systems using offshore pressure measurements. The approach estimates cycle-level leakage rates from hydraulic power unit pressure histories by analyzing pressure decay behavior during discharge cycles and applying recursive least-squares estimation (RLSE) for the adaptive tracking of leakage dynamics. To further assess whether the estimated leakage behavior reflects observable hydraulic system dynamics, a complementary signal-based consistency analysis is performed using features derived directly from the pressure measurements. The results indicate that the leakage states identified by the RLSE method correspond to statistically distinguishable and physically interpretable pressure patterns, supporting cross-method consistency. Because the methodology relies only on routinely available pressure measurements and requires no additional subsea instrumentation, the proposed framework provides a deployable approach for real-time hydraulic integrity monitoring and condition-based maintenance support.

Abstract Safeguarding groundwater, a vital resource in adaptation to global change, yet increasingly under pressure, requires robust knowledge and capacity. While strong foundations in hydrogeology remain indispensable, there is a clear need for joint, interdisciplinary and applied groundwater education at the postgraduate level to address the complex and multifaceted nature of groundwater science. Building on these arguments, the Joint Master’s Programme Groundwater and Global Change was successfully launched a decade ago. This essay presents the rationale behind this approach.

Abstract Seagrass beds are highly efficient blue carbon ecosystems. Despite covering only 0.2 % of the global ocean area, they play a critical role in carbon stocks. However, baseline data on carbon stocks in temperate seagrass beds remain scarce, constraining the assessment of regional blue carbon potential. This study, focusing on the Changshan Archipelago in the North Yellow Sea (covering 8 islands, with a total survey area of 324.06 ha), aims to quantify the carbon stock of temperate seagrass beds in this region and analyze their driving factors. Through in-situ sampling and laboratory analysis of 39 stations, this study revealed that the temperate seagrass meadows in the North Yellow Sea exhibit a distinct mono-dominant community structure, with Zostera marina being the absolutely dominant species.