Atmospheric and Oceanic Sciences

In arid regions, mudflows are triggered by extreme precipitation in hydrologically sensitive basins. In the district of Coronel Gregorio Albarracín Lanchipa (Tacna, Peru), these processes mainly affect areas adjacent to ravines and steep slopes. This study delineates zones susceptible to flooding and mudflows using sequential hydrological–hydraulic modelling. Hydrometeorological records, high-resolution topography (0.1 m), and soil and land-cover data were integrated. Simulations were performed using HEC-HMS and HEC-RAS, and peak discharges were estimated for return periods (RP) of 25, 50, 100, and 200 years. Hydrographs were analyzed to assess basin response under extreme rainfall conditions. Model performance was evaluated through event-based validation using the March 2020 mudflow event. Estimated peak discharges were 11.9, 17.3, 25.3, and 122.4 m³/s for RP25, RP50, RP100, and RP200, respectively. The RP200 value is consistent with a historical event (~200 m³/s) reported in the 1920s. Results indicate a marked increase between RP100 and RP200, suggesting a nonlinear basin response under extreme conditions. Validation yielded an F1-score of 0.92, indicating strong agreement between observed and simulated extents. Simulations identified impacts on two population centers, one educational facility, 1.5 km of roads, and 44.6 ha of agricultural land, highlighting the importance of incorporating extreme scenarios in risk management and land-use planning.

Synthetic aperture radar (SAR) and light detection and ranging (LiDAR) are two fundamental active remote sensing technologies. Their synergistic use can significantly enhance the effectiveness of remote sensing applications. However, existing studies primarily focus on product-level integration, and direct mapping between SAR images and LiDAR remains a challenge due to cross-dimensional modality differences. In this paper, a deep learning-based SAR-to-LiDAR (SAR2LiDAR) matching framework is proposed, which enables direct matching between SAR images and LiDAR point clouds. The framework features a dataset generation method that creates accurate 2D-3D correspondences between SAR images and LiDAR point clouds. It explicitly models the geometric and scattering visibility relationships of SAR imaging, and employs an autoencoder-based deep learning model with specific global feature representation and cross-modal alignment capabilities. The proposed model is trained and evaluated using airborne LiDAR datasets from five urban regions: Barcelona, Hong Kong, Los Angeles, Tarragona, and Girona. Extensive qualitative and quantitative experiments demonstrate that the proposed SAR2LiDAR framework achieves accurate and robust matching performance across diverse urban scenarios, attaining an average recall@1 of 95.64%, which significantly outperforms the best competing methods by 13%. Moreover, an average recall@1 of 91.68% on out-of-distribution (OOD) test regions demonstrates the strong generalization capability of the proposed framework and its potential for large-scale remote sensing applications.

Elkhorn coral (Acropora palmata) is a threatened reef-building species that plays a critical role in Caribbean coastal ecosystems. Efficient, large-scale monitoring of A. palmata is essential for evaluating restoration success, yet traditional in situ surveys remain costly and spatially constrained. In this study, we acquired high-resolution (1.8 cm) uncrewed aerial vehicle (UAV) imagery of a coral reef within the United States Virgin Islands’ (USVI) St. Croix East End Marine Park (STXEEMP) and applied deep learning object detection to identify individual A. palmata colonies. We utilized two convolutional neural network architectures, FasterRCNN and MaskRCNN. FasterRCNN was used as an initial screening tool to identify the optimal imagery dataset from several candidates. After identifying the dataset, we used MaskRCNN with an iterative annotation refinement procedure in which initial model predictions were used to augment the training data and achieved an F1 score of 0.78. Detection accuracy was strongly influenced by colony size and apparent water depth, with markedly high accuracy for corals wider than 0.3 m (F1 = 0.87) and located in shallower waters (F1 = 0.81). Beyond detection, MaskRCNN’s polygon outputs enabled the measurement of the individual colony area and the generation of high-resolution coral density maps. These products complement broader-scale prediction and mapping approaches and provide fine-scale, management-relevant information. Although this study was conducted at a single reef site during one acquisition period, the results suggest that UAV-based deep learning workflows offer a promising approach for coral reef monitoring that could support restoration assessments and conservation decision-making, pending validation across additional sites, seasons, and environmental conditions.

With the wide application of generative models in the field of SAR image inpainting, inadequate reconstruction quality of scattering characteristics and insufficient global coherence of semantic logic remain the core challenges of such tasks. To address these issues, this paper proposes a Feature-Encoding Diffusion Model (FEDM). Guided by local valid regions, the proposed model accurately learns the microwave backscattering distribution law of ground features through a SAR-specific Variational Auto-Encoder (SAR-VAE), thus improving the reconstruction accuracy of backscattering statistics. Meanwhile, it integrates semantic embedding and cross-attention mechanism to strengthen the semantic constraints of SAR scenes, ensuring the logical rationality of the ground feature layout. With progressive diffusion generation and sliding window strategy, the model achieves high-quality reconstruction with coherent semantics and consistent global spatial structure for large-scale missing regions. Experiments on public datasets including OSdataset, SEN1-2, SRSDD-v1.0 and MRSSC show that the proposed method achieves excellent performance in terms of scattering characteristic reconstruction quality and globally coherent generation of semantic logic, and realizes high-quality SAR image inpainting.

Study region The study region is the catchments that overlie, and are adjacent to, the Cambrian Limestone Aquifer in northern Australia. Study focus The Cambrian Limestone Aquifer is the largest water resource in the Northern Territory, and the only reliable water resource in the region. Water resources development has historically only occurred in the Daly and Roper catchments, but there are now increasing development pressures on the water resources in other catchments. The groundwater discharge through springs is culturally, ecologically and economically important and protected through water allocation plans. The springs in the Daly and Roper have been well characterised but the other sparsely populated, undeveloped catchments, have not had the same level of investigation. This study aims to use remotely sensed evapotranspiration to identify groundwater discharge areas across ∼750,000 km 2 of northern Australia. New hydrological insights for the region The current conceptualisation of the CLA has discharge occurring in the Roper, Daly and Nicholson catchments. This study has identified small groundwater discharge locations at the basin margins in the Victoria, Limmen Bight, McArthur, Robinson and Calvert catchments. These additional groundwater discharge locations need to be assessed for their source aquifer before development occurs in these regions to ensure the protection of their cultural, ecological and economic values.

Abstract F 10.7 , Mg II, and F 30 are widely used proxies for variability in solar Extreme UltraViolet (EUV, 5–120 nm) irradiance. An alternative proxy for solar EUV variability is Q EUV , an indirect measure of total solar EUV irradiance, retrieved from observations of Earth's Far UltraViolet (FUV, 120–300 nm) airglow and from empirically modeled solar EUV spectra. Daily Q EUV derived from FUV airglow is available from the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics (TIMED) Global Ultraviolet Imager (GUVI) and Global‐scale Observations of the Limb and Disk (GOLD) missions, as is Q EUV from empirical solar EUV spectrum. Spectral measurements from GOLD are also used to retrieve lower‐middle thermospheric temperature, termed as T disk . The availability of GOLD T disk and Q EUV provides a new opportunity to reassess the effectiveness of various solar EUV proxies for thermospheric use. This investigation showed that Q EUV has the best correlation with GOLD T disk compared to all the other EUV proxies. Therefore, it can be used as an alternate proxy for total solar EUV variability, on day‐to‐day to solar cycle time scales.

Soil microbial communities play a pivotal ecological role in contaminated environments. However, conventional metagenomic approaches struggle to distinguish between “potential function holders” and “in situ metabolically active executors”. Here, we employed a method combining fluorescent d -amino acid labeling, fluorescence-activated cell sorting, and metagenomics (FDAA-FACS-Metagenomics) to capture and profile active microbes in complex soils. The secondary addition of As(V) and Sb(V) enhanced the community’s reductive activity toward these metalloids, reshaping the active assemblages. Clostridium was markedly enriched, and several low-abundance members were activated as true executors of the reduction process. MAGs recovered via FDAA-FACS revealed an active core community with functional partitioning: some taxa participated directly in As(V)/Sb(V) reduction, while others contributed to community stability through tolerance and metabolic support. Notably, a Desulfitobacteriaceae genome (MAG29) harbored both arrAB and anrAB gene clusters, a complete Wood-Ljungdahl carbon fixation pathway, and nitrogen fixation genes. These genomic features suggest the potential for a multifunctional metabolic lifestyle involving metalloid reduction, carbon fixation, and nitrogen transformation. Such metabolic versatility may enable MAG29 to contribute to coupled carbon–nitrogen cycling and metalloid transformation under contaminated environmental conditions. These findings emphasize the important ecological roles of rare, metabolically active microbes in metalloid transformation and soil ecosystem functioning.

Phytoplankton functional groups distribution and dynamics builds nutrients and carbon energy pathways in the ocean. They respond fast to changes in their environment, and the understanding of their dynamics relies on regular sampling to resolve daily to weekly scales. The Bonifacio Cyclonic Gyre (BCG) is an area prone to intermittent phytoplankton blooms triggered by westerly winds, and close to coastal areas capable of fuelling open waters with nutrients from flooding. To study phytoplankton evolution in this dynamical area, the distribution of phytoplankton functional groups in the surface waters of the western Mediterranean Sea was investigated semi-continuously (30 min) using an automated CytoSense flow cytometer coupled to a FerryBox, onboard the ferry Le Carthage, between October 20, 2016 and January 12, 2017 and along the route from Tunis (Tunisia) to Genova (Italy). The BCG signature was identified by its surface temperature anomaly and evidenced significant higher abundances of the RedPicoProk, the RedPico, the HsNano and the OraNano over the full sampling period, except for the RedNano1 and RedNano2 and the OraPicoProk. Their estimated chlorophyll a concentration per group were higher within the gyre than outside, but did not affect the carbon/chlorophyll a ratio. A high carbon/chlorophyll a ratio can indicate recently uplifted cells that are not yet adapted to high light conditions. In this case, the observed ratios suggest that the cells were not recently upwelled, but instead had time to adjust their photo physiology to surface conditions. During 19–20 December, extreme overflow occurred over Corsica and Sardinia, considered as one of the highest rainfall events of the past 20 years. The runoff was evidenced by the low salinity intrusion that lasted several days after the storm. After the runoff and within the Bonifacio gyre region, biomasses of all groups increased and remained high until the end of the sampling, except for the RedPico. The runoff triggered an early bloom end of December that persisted for several weeks, being trapped and repleted by the uplift of the isopycnals from the BCG. Regular observation of the sea surface temperature and salinity together with phytoplankton community structure provides essential insights into the fast response of phytoplankton communities to an extreme event at unprecedented resolution.

Introduction The Forest-Steppe Ecotone (FSE) is a highly heterogeneous and climate-sensitive transition zone facing intensified degradation due to increasing disturbances. Conventional single-index remote sensing methods often fail to adequately capture the complex, multi-dimensional ecological dynamics of these sensitive ecotones. This study investigated the Baiyin Aobao Forest Farm, an FSE in Northeastern China, to develop a more robust monitoring method. Methods We utilized long-term Landsat archives (1990–2022) to track long-term transition patterns and cyclical changes of land cover types. Based on stability observed during 2017–2022, we leveraged Sentinel-2 imagery to calculate five complementary spectral indices (NDVI, SAVI, NDMI, CI, and BI2). Principal Component Analysis (PCA) was applied to determine objective weights for constructing the Herbaceous Composite Evaluation Index (HCEI). UAV-derived Fractional Vegetation Cover (FVC) was used to quantitatively validate the HCEI (R 2 = 0.87, RMSE = 0.082, MAE = 0.065) and establish a five-level classification system (Grade I, Excellent, to Grade V, Very Poor). Results Results indicate that 84% of grassland maintained stability, with dynamic exchanges mainly occurring between sparse vegetation and grassland. Vegetation indices peaked around 2019 (optimal ecological status), declining after 2020 with increased surface exposure and accelerated degradation. Spatially, Grade I–II grasslands were concentrated in the northeast, while Grade IV–V were distributed in the southwest. Grade I declined by approximately 8.2% (2019–2022), whereas Grade V increased. Grade III was identified as a key transitional state and early warning indicator. Markov prediction was confined to short-term analysis due to limited time series. Discussion This integrated HCEI framework effectively combines multi-source remote sensing data to support FSE monitoring and restoration. The complementary dynamics between grassland and sparse vegetation, and the spatial gradient of degradation, provide critical insights for zonal management and adaptive restoration strategies in vulnerable forest-steppe ecotones.

This study examines the effect of data center construction on urban energy-environmental performance (EEP) in China using a quasi-experimental empirical design. We construct a city-level panel by geocoding 97 data centers from OpenStreetMap and matching them to 282 prefecture-level cities during 2008–2023. These facilities correspond to 46 treated cities across three deployment waves in 2013, 2017, and 2020. Using a multi-period difference-in-differences framework with two-way fixed effects, we find that data center construction is associated with a 0.040-unit increase in urban EEP, equivalent to approximately 29% of one standard deviation. The result remains positive across alternative fixed-effect structures, city-specific trends, province-by-year fixed effects, a leave-one-out sensitivity test excluding major digital hubs, and controls for overlapping policies including Low-Carbon City pilots, Carbon Trading pilots, and Smart City initiatives. Event-study estimates show no evidence of differential pre-treatment trends and suggest that the effect emerges gradually after data center construction. The finding is further supported by placebo tests, Goodman-Bacon decomposition, and Callaway–Sant’Anna group-time average treatment effect estimation. Mechanism analysis indicates that the improvement in EEP is consistent with three complementary channels: green technological innovation, economic complexity upgrading, and digital-real economy integration. Heterogeneity analysis suggests that the estimated effect is larger in western cities, which may reflect both greater marginal returns to digital infrastructure and more favorable renewable-energy endowments, although this interpretation remains suggestive because facility-level electricity-source data are unavailable. Overall, the findings suggest that data centers should not be viewed only as electricity-intensive facilities. When embedded in local innovation systems, industrial upgrading processes, and real-economy digital applications, they may also function as enabling infrastructure for urban green transformation.

Seagrass meadows rank among the most productive coastal ecosystems, yet our understanding of their nutrient-acquisition strategies in oligotrophic environments remain fragmented. Current research tends to focus on isolated processes, such as leaf or root uptake, without adequately integrating morpho-physiological traits, sediment heterogeneity, or microbial interactions. This fragmentation constrains our ability to predict seagrass responses to global environmental change. This review synthesizes seagrass nutritional ecology and shows that species in oligotrophic systems employ complementary strategies, including high-affinity foliar absorption, extensive root proliferation, and internal resorption. Crucially, these traits exhibit high plasticity and are modulated by intricate plant-microbe mutualisms, such as nitrogen fixation and phosphorus solubilization. We further demonstrate how sediment properties and hydrodynamic create spatial heterogeneity that dictates site-specific nutrient bioavailability. We propose three priority research directions: (1) deciphering the genetic and epigenetic drivers of phenotypic plasticity adaptation to oligotrophy; (2) applying stable isotope tracers and comparative genomics to quantify the net functional benefits of the seagrass microbiome; and (3) assessing long-term impacts of synergistic stressors (e.g., warming and eutrophication) on meadow structure and biogeochemical functions. By treating oligotrophic environments as natural laboratories, this review informs restoration efforts and the need to safeguard ecosystem services globally, particularly blue carbon sequestration and biodiversity conservation.

The accurate classification of seabed sediment and benthic covers in shallow-water environments remains a key challenge for marine activities and oceanographic research. However, coastal areas of shallow waters are influenced by complex dynamic environments, making it difficult to obtain authentic sediment and benthic-cover samples. Therefore, to address the problem of few-shot classification of seabed sediment and benthic covers, a few-shot classification algorithm of seabed sediment and benthic covers based on the fusion model of airborne LiDAR bathymetry (ALB) and multispectral images is proposed in this article. Based on the extracted features, a scale-invariant feature transform-progressive sample consensus (SIFT-PROSAC) algorithm and perspective transform model were constructed to achieve feature fusion. Then, multi-modal feature selection is realized using a formal concept analysis-Relief-F (FCA-Relief-F) algorithm. Finally, a graph attention network-prototype network (GAT-PN) model was established to classify five types of sediment and benthic cover (coral reef, stone, sand, vegetation, and coastal zone). To validate the effectiveness of the proposed method, experimental data from actual measurements at Ganquan Island in the Xisha Islands of China were used. Compared to other classical classifiers, the GAT-PN algorithm achieves a higher classification accuracy, with an overall accuracy (OA) and Kappa coefficient of 97.50% and 0.97, respectively. The findings of this study provide effective technical support for marine engineering and related fields.

Identifying wild bird species associated with highly pathogenic avian influenza (HPAI) is essential for optimizing surveillance and mitigating spillover risks. This study analyzes Brazil's nationwide HPAI surveillance data (up to July 2025), comprising 1,153 records across 127 bird and mammal species from 525 municipalities. Using a multi-model framework-including chi-square association tests, binary logistic regression, and spatial Generalized Additive Models (GAMs). Species-outbreak associations were significative and positive for Thalasseus maximus (χ² = 237.34, p < 0.0001), Thalasseus acuflavidus (χ² = 216.12, p < 0.0001), Sterna hirundo (χ² = 83.88, p < 0.0001), and Sterna hirundinacea (χ² = 77.56, p < 0.0001). An optimized logistic regression model (model 3) highlighted T. acuflavidus as the strongest predictor of HPAI (OR = 80.74; 95% CI: 21.85-298.39; p < 0.001), achieving good predictive performance (AUC = 0.85; Pseudo R² = 0.48). To account for spatial dependence, we fit a binomial GAM incorporating a bivariate longitude-latitude spatial smoother, significantly improving model fit (AUC = 0.96; Pseudo R² = 0.58) and effectively accounting for residual spatial autocorrelation (Moran's I = 0.0399, z = 2.10, p = 0.044). Outbreaks were concentrated along Brazil's southeastern coast, overlapping with high-density poultry zones, while inland spread remained sporadic, suggesting migratory routes as key transmission pathways. These results underscore the critical role of seabirds-particularly T. acuflavidus-in HPAI H5N1 dynamics in Brazil. The enhanced predictive power of the spatial GAM supports its utility in risk mapping. We recommend integrating biodiversity data with spatial modeling to guide targeted surveillance in high-risk coastal areas, reducing spillover threats to poultry and wild populations.

Cova Eirós (Triacastela, Lugo, NW Iberia) holds the most comprehensive palaeolithic archaeological sequence in the northwestern Iberian Peninsula, with stratigraphic levels containing rich and well-preserved fauna and lithic industries spanning roughly from -at least- 41 ka calBP to 14 ka calBP. Within this chronological timespan, several significant climatic events took place, most notably the transition between Marine Isotopic Stages (MIS) 3 and 2, a period during which Neanderthal populations went extinct and were subsequentially replaced by Anatomically Modern Humans (AMH). In this context, the zooarchaeological record from levels 3 (~42–39 ka calBP), 2 (~36–35 ka calBP) and 1 (>17 ka BP) provides a key dataset for reconstructing the climatic and environmental conditions under which these hominin groups lived, as well as establishing a palaeoclimatic sequence encompassing MIS 3, the immediate transition to MIS 2, and a fully developed MIS 2. To achieve this, a multi-proxy approach based on the faunal record has been employed, integrating methods for palaeoclimatic reconstruction (Bioclimatic Model), palaeoenvironmental reconstruction (Habitat Weighting) and a traditional zooarchaeological analysis. This comprehensive methodology aims to infer the ecological conditions in which both Neanderthals and AMH populations thrived and elucidating their interactions with the surrounding animal communities across different time periods. Results indicate that throughout the sequence, climatic conditions were consistently colder and slightly drier than today, yet the region maintained a relatively humid and wooded environment, which may have functioned as a refuge area. Moreover, the zooarchaeological evidence reveals shifts in territorial exploitation and subsistence strategies among hunter-gatherer groups, with a progressive decrease in occupation intensity over time.

Abstract Soft proton contamination presents a persistent threat to the performance of space‐based X‐ray observatories such as ESA's upcoming Solar‐wind Magnetosphere Ionosphere Link Explorer (SMILE). This study develops a machine‐learning framework for modeling soft proton fluxes in the 92.2–159.7 keV range, tailored to SMILE's elliptical, highly inclined orbit. Using past data from ESA's Cluster mission and a solar‐cycle‐based extrapolation scheme derived from Solar Cycle 23, we simulate the average space weather conditions SMILE is likely to encounter during its first 3 years of operation. The proposed model leverages XGBoost, a nonlinear ensemble learning algorithm, and introduces a rotating quarterly train–test split strategy to overcome distributional mismatches present in earlier models. This new approach improves generalization and substantially reduces underprediction bias during periods of elevated proton flux, achieving a test set of 0.45 and reducing bias from −0.30 to −0.10 log(1/c/s/sr/keV). Analysis of the spatial distribution of the models' predicted proton flux for the SMILE mission shows good agreement with known magnetospheric structures, including lobe/cusp regions and solar‐cycle‐dependent enhancements. The results of this study provide a data‐driven estimate of the ambient soft proton environment along the SMILE orbit.

Atmospheric oxidation of nitrogen-containing volatile chemical products (N-VCPs) significantly affects air quality, with the formation of carcinogenic nitrosamines being particularly concerning. Benzimidazoles (BZIs) are high-production chemicals and a type of N-VCP. However, their atmospheric oxidation mechanisms and subsequent impacts remain unclear. Here, using the simplest benzimidazole (BZI) as a representative compound, we employed theoretical calculations and toxicological modeling to investigate the • OH-initiated reaction mechanism of BZI and assess the toxicity of their oxidation products (OPs). The calculated reaction rate constant for the BZI + • OH reaction is 1 × 10 –11 cm 3 molecule –1 s –1 at 298 K and 1 atm, which is comparable to those of other studied N-VCPs. The • OH-addition intermediate is dominantly formed in the BZI + • OH reaction, which further leads to the formation of organonitrates, alkoxy radicals, hydroperoxides, and carcinogenic nitrosamines under conditions where NO, HO 2 ·, and O 2 coexist. Toxicity predictions indicate that almost all OPs are more toxic than BZI. This study clarifies a previously unrecognized pathway to the formation of nitrosamines via the • OH-addition intermediate, beyond the conventional N–H H-abstraction intermediate, thereby refining the reaction diversity (two distinct pathways) and benefiting the assessment of the atmospheric impact caused by the oxidation of N-VCPs.