Atmospheric and Oceanic Sciences

Study region Gilgel Abay Watershed, Upper Blue Nile Basin, Ethiopia (2882 km²) - a volcanic highland catchment where groundwater is an increasingly stressed resource due to population growth and expanding irrigated agriculture under limited monitoring infrastructure. Study focus Five machine learning models - Random Forest (RF), Gradient Boosting (GB), Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Regression (SVR) - were evaluated for spatially explicit groundwater level (GWL) depth prediction using ten predictors from 122 observation wells (377 observations, 2016–2022), tuned via systematic grid search under 5-fold, 10-fold cross-validation and three train–test split ratios. New hydrological insights for the region RF achieved the best generalization (test R² = 0.82, RMSE = 1.55 m, train–test gap = 0.13) and GB the lowest prediction error (RMSE = 1.30 m, R² = 0.80); both ensemble methods substantially outperformed ANN (R² = 0.75), SVR (R² = 0.64) and, DT (R² = 0.62), confirming ensemble ML as the superior approach for GWL prediction in data-scarce highland watersheds. Permutation-based variable importance revealed lineament density as the dominant predictor - surpassing slope, rainfall, and elevation - establishing tectonic fracture networks, rather than climatic or topographic factors alone, as the primary control on groundwater storage in this volcanic terrain. RF and GB are recommended as operational GWL prediction tools; future work should expand borehole networks and adopt spatially blocked cross-validation for more reliable generalization estimates.

Abstract We represent the active deformation of the Aegean region with an elastic‐kinematic block model composed of a finite number of rotating crustal blocks whose boundaries coincide with major active structures. A total of 832 GPS velocities and 146 earthquake slip vectors were inverted to provide estimates of block rotations and interseismic locking on block‐bounding faults so as to best reproduce the observed deformation field. Our regional model allowed us to derive a self‐consistent set of geodetic slip rates for upper plate faults and the Hellenic subduction system, the results of which are presented in a companion paper, and to determine their corresponding slip rate deficits (geodetic moment accumulation rates). Most of the faults show either full or high levels of locking, with some exhibiting significant fault slip rate deficits, and only a few displaying very low or no coupling. Model results indicate that crustal blocks in the SE Aegean‐SW Anatolia rotate counterclockwise, whereas those in central‐NW Greece rotate clockwise. Between these domains, blocks move rapidly southwestward with trenchward‐increasing velocities. The observed block motions in the Aegean reflect the combined influence of several large‐scale tectonic processes that shape the present‐day geodynamics, including the westward tectonic escape of Anatolia, rapid slab rollback and trench retreat along the Hellenic Arc, and along‐strike variations in the Nubia‐Eurasia plate boundary.

Abstract The Hellenic subduction system is the primary locus of Nubia‐Eurasia convergence, yet the distribution of strain accumulation along it is still debated. Here, we invert 832 geodetic velocities and 146 earthquake slip vectors to assess the spatial variability of interseismic locking and provide new constraints on the rate of slip deficit accumulation. We find that the subduction interface beneath southwestern Peloponnese and across the area from offshore south Peloponnese to offshore southwest of western Crete hosts three patches of low to moderate locking (0%–40%) at 10–50 km depth whose slip rate deficits account for ∼15–30% of the total plate convergence. The offshore weakly locked patches show a good spatial correlation with large interplate earthquakes, while the patch beneath southwestern Peloponnese overlaps with a past transient slow‐slip event, suggesting that similarly locked areas along the interface can exhibit different slip behavior. Our analysis further reveals that the Hellenic Trough fault displays a low‐coupled area that spatially correlates with the coseismic slip of the 365AD Crete earthquake and also identifies a confined region offshore of Gavdos island exhibiting higher degrees of locking. Finally, our modeling indicates that the eastern Hellenic subduction system is completely uncoupled, highlighting a fundamental difference with the heterogeneous locking of the western part. This work demonstrates that although most of the Nubia/Eurasia convergence is accommodated aseismically, localized patches prone to accumulating elastic strain do exist and it provides, for the first time, evidence for a connection between interseismic locking and large subduction‐related earthquakes along the Hellenic subduction system.

Accurate trajectory prediction of drifting objects is critical for improving survival rates and optimizing search efficiency in maritime search and rescue (SAR) operations. This study presents a comprehensive, end-to-end evaluation of drift prediction performance in the South Sea of Korea. To achieve this, object-specific leeway coefficients were derived from field experiments using three types of manikin drifters (with lifejacket, without lifejacket, and with wetsuit), and their trajectories were simulated using both a probabilistic Monte Carlo framework (OpenDrift) and a deterministic empirical approach based on the IAMSAR manual. The simulations were driven by high-resolution hydrodynamic (SCHISM) and atmospheric (ECMWF) forcing fields, and prediction performance was evaluated using multiple complementary metrics, including Normalized Cumulative Lagrangian Separation (NCLS), Root Mean Square Error (RMSE), Location Prediction Conformance (LPC), and a newly introduced metric, the Conformance-Effort Ratio (CER), which explicitly quantifies the trade-off between search coverage and required search effort. The results show that NCLS can yield systematically conservative evaluations under short travel-distance conditions, primarily due to its normalization structure, where the denominator (i.e., cumulative trajectory-length sum) remains small. This highlights a structural limitation of single-metric evaluation and underscores the necessity of a multi-metric assessment framework. When evaluated using CER, the IAMSAR approach achieves near-complete containment (LPC > 99%) by conservatively expanding the search area, but at the cost of substantially increased search effort. In contrast, the OpenDrift approach maintains a reasonable containment level within a significantly smaller search area, demonstrating intensive spatial distribution characteristics. These findings demonstrate that probabilistic drift modeling, supported by auxiliary indicators like CER, can provide a robust decision support framework for prioritizing high-probability search zones and optimizing resource allocation in SAR operations. Rather than serving as a direct replacement for conventional methods, such approaches offer strong potential as a complementary tool for improving operational efficiency under resource-constrained conditions.

Introduction Monitoring of estuarine fish biodiversity is often constrained by the inherent limitations of traditional survey methods and the complex, dynamic environmental conditions of estuarine habitats. Environmental DNA (eDNA) metabarcoding has emerged as a robust molecular tool for aquatic biodiversity assessment. Nevertheless, its complementary potential to conventional bottom trawling remains understudied in estuarine ecosystems. In this study, we integrated eDNA metabarcoding and bottom trawling to investigate the spatiotemporal dynamics of fish diversity in the Oujiang River Estuary (ORE). Methods Fish assemblage data were collected seasonally across four sampling periods at five fixed sites within the ORE. MiFish-U primers targeting the 12S rRNA gene were utilized for eDNA amplification. And twelve aquatic environmental variables were quantified to disentangle correlations between fish community structure and ambient environmental conditions. Multiple statistical approaches, including alpha diversity analysis, Principal Coordinate Analysis (PCoA) and PERMANOVA, were applied to quantify spatiotemporal shifts in fish assemblages, identify fish-environment correlations, and compare community discrepancies between the two survey methods. Results and discussion The combined approach detected a total of 100 fish species across 84 genera and 45 families. Specifically, eDNA metabarcoding identified 72 fish species, while bottom trawling captured 48 species, with only 20 species shared between the two methods. Fish assemblages exhibited distinct seasonal variations, with both survey methods revealing higher species richness during wet seasons. Temporal fluctuations in water temperature, dissolved oxygen, salinity and nutrient concentrations constituted the primary environmental drivers structuring estuarine fish assemblages, whereas spatial heterogeneity across sampling sites exerted no statistically significant influence on community composition. eDNA metabarcoding showed unique advantages in detecting pelagic, migratory, cryptic and endangered fish species, supporting effective biodiversity monitoring in topographically intricate estuarine waters. In contrast, bottom trawling provided reliable morphological identification and quantitative abundance data for demersal fish taxa, which helped resolve ambiguous species annotations derived from eDNA sequencing. In addition, seven IUCN-listed threatened fish species were documented during the field investigation. Collectively, our findings demonstrate that eDNA metabarcoding and bottom trawling serve as highly complementary, rather than mutually exclusive, tools for fish biodiversity assessment. The integration of the two methods enables a more comprehensive and accurate evaluation of estuarine fish diversity. This study validates the feasibility and efficacy of the combined monitoring framework for macrotidal estuaries and provides valuable scientific references for the ecological conservation and management of the Oujiang River Estuary as well as other similar coastal ecosystems.

Dissolved oxygen (DO) dynamics in estuarine ecosystems are shaped by both anthropogenic activities and climate variability, which jointly influence oxygen concentrations and overall ecosystem health. This study examined temporal and spatial variations in DO across three coastal sectors in Eastern South America. DO levels were classified as hypoxia (<2.0mgL-¹),intermediate(2.0–4.0mgL¹), and optimum (>4.0mgL-¹).Hypoxia was associated with elevated BOD, ammonia, and phosphorus, particularly in the Metropolitan sector, where low DO persisted year-round. In this sector, hypoxia rates exceeded 40% during multiple years (2005–2008 and 2010–2013). The North (2005) and South sectors (2005 and 2007) also experienced hypoxia, mainly during dry periods, with DO levels below 2.0 mg L⁻¹ in specific years. Climate variability, especially El Niño–Southern Oscillation (ENSO) events, intensified hypoxia during droughts. In the Metropolitan sector, consecutive El Niño and La Niña years (2006 and 2008) resulted in a 40% hypoxia rate. The Northern sector exhibited 38% hypoxia during the 2005 El Niño event. Increased water movement under favourable oxygen conditions enhanced oxygenation. Salinity, temperature,pH, and spatial heterogeneity were also significant determinants. These findings indicate that oxygen dynamics are regulated by both persistent pollution and interannual climate variability. The results highlight the need for integrated management strategies that address anthropogenic impacts and the rising frequency of climate extremes.

Abstract. Observation-based characteristics of the dayside ionosphere are important for the knowledge of the coupling between the solar wind, magnetosphere and ionosphere. Therefore, this paper presents descriptions and quantitative analyses of characteristics of the polar dayside ionosphere during the winter. We use EISCAT Svalbard radar (ESR) fast elevation scans to obtain both altitudinal and latitudinal information of the ionospheric parameters electron density Ne, electron temperature Te, and ion temperature Ti. We determine the location of the open-closed field line boundary (OCB) and divide the ionosphere into three regions based on their position relative to the OCB: on closed field lines, along the OCB, and in the polar cap. We first show two case examples, illustrative of the method and the dynamic response of the ionosphere to variable solar wind. We then statistically investigate how the parameters vary from closed to open field lines across the OCB and with altitude in the three regions. Finally, we compare the obtained OCB latitudes with the ones obtained in previous studies. Overall, significant differences in the ionospheric parameters can be seen between the three latitude regions. In general, observed enhancements in Te peak in the F-region on open field lines just poleward of the OCB, reaching up to 4° poleward. In particular, Te is highest between 11:00–13:00 MLT where the ESR is most likely below the cusp. During this interval, the gradient in Te from closed to open field lines peaks. Additionally, Ne appears to be slightly enhanced poleward of the OCB at most altitudes and maximizes just below 300 km on open field lines, increasing with a factor 1.2 from closed field lines. In the E-region, Ne decreases with increasing latitude into the polar cap, especially pre-noon. Further, we observe that the ratio between Ne in the E and F regions is larger on closed than on open field lines. In addition, the variability in the ion temperature Ti appears to be larger on open field lines. Together, these result contribute to a quantification of characteristics of the dayside auroral ionosphere with respect to both altitude and latitude.

Abstract The electron diffusion region (EDR), which can be divided into inner and outer EDR, is the crucial region where the magnetic field topology changes and strong energy conversion between fields and plasmas occurs during magnetic reconnection. The outer EDR is characterized by energy conversion from the particles to the fields ( J · E ′ < 0) and can extend tens of ion inertial lengths as a single layer in the outflow direction. Using high‐resolution data from the Magnetospheric Multiscale (MMS) mission, we identified an outer EDR under a weak guide field in the Earth's magnetotail. However, J · E ′ within the outer EDR exhibits significant electron‐scale fluctuations, which may be caused by tearing mode instability. These observations provide new insights into the microphysics of magnetic reconnection in tearing unstable and weak guide field regime, manifesting the role of electron‐scale dynamics in mediating energy dissipation.

Abstract The 2025 Mw7.1 Dingri earthquake is the largest normal‐faulting event in southern Tibetan plateau recorded with near‐field observations. By integrating back‐projection imaging, multi‐point‐source inversion, and finite‐fault modeling, we reveal that the rupture propagated at variable speeds in a cascading manner across a complex conjugate fault network, generating significant high‐frequency radiation at the fault junction. Near‐field waveforms directly document the slip along the western boundary of the Dengmecuo graben as coseismic. The spatiotemporal evolution of simultaneous rupture along both boundaries of the graben suggests a possible structural connectivity at depth between two conjugate faults. Mainshock nucleation was likely promoted by sustained stress loading following the 2015 Gorkha earthquake, together with local stress perturbations from recent regional earthquakes and the foreshock sequence. These processes bridge long‐term interseismic deformation and the dramatic seismic rupture of the Dingri earthquake, illustrating a typical slow‐to‐fast failure process.

Urban forests are not merely green amenities; they support critical ecosystem functioning and services vital for healthy, resilient cities. Recognising urban forests as core infrastructure is essential to reversing the loss of mature trees, preserving biodiversity, and maintaining liveability amid increasing climate and environmental pressures. Although the benefits of urban forests for climate resilience, biodiversity, and public health are broadly acknowledged, policies to protect and enhance these vital ecosystems are often limited, underfunded, and inadequately enforced. As mature canopy loss today takes decades to be replaced (if ever), immediate and sustained investment is crucial to safeguard urban forests. This urgency reveals four interconnected gaps in current urban forest management and stewardship. First, urban forests require recognition, investment, and maintenance as essential infrastructure contributing to urban resilience, including biodiversity support, and to maximise the delivery of key ecosystem services such as cooling and carbon sequestration. Second, equitable access to greenspaces across all communities must be ensured to redress long-standing social and environmental injustices. Third, integrating urban forests into broader climate and biodiversity governance frameworks is critical to mainstreaming their management and protection. Lastly, resilience must be strengthened through evidence-based management practices responsive to evolving environmental changes and social contexts. These priorities must be complemented with strong legal protections, rigorous enforcement of legislation against illegal tree removal, and robust community engagement supported by integrated urban planning and improved monitoring. Without these, the ecological, social, and economic benefits provided by urban forests will remain threatened. By reframing urban forests as essential living infrastructure embedded in legal, financial, and planning frameworks, cities can become cooler, healthier, more biodiverse, and socially just. This framework offers timely guidance for policymakers to prioritise urban forests within climate resilience and sustainability strategies, securing benefits for current and future generations.

Abstract Coal-fired power generation dominates China’s electricity mix and accounts for over one third of fossil CO2 emissions, with substantial air pollutants. We estimate nitrogen oxides (NOx) and co-emitted CO2 emissions from more than 1,000 coal-fired power plants in China during 2021–2024 using a lightweight top-down framework. The method combines TROPOMI NO2 observations, ERA5 wind fields, and variable NOx/NO2 ratios from GEOS-CF within an improved directional derivative approach to derive gridded anthropogenic NOx flux. Plant-level NOx emissions are obtained by integrating weighted contributions from multiple plants within a 15 km radius at monthly temporal resolution, facility-specific CO2/NOx emission ratios are applied to infer CO2. Estimated annual NOx emissions decrease from 5.49 to 4.78 Mt during 2021–2024, while CO2 emissions decrease from 4.61 to 4.10 Gt. Corresponding uncertainties are 30.9%–31.5% for NOx and 31.3%–32.0% for CO2. A confidence assessment based on internal statistical consistency and external physical plausibility constraints classifies 85.1% of plant-level estimates as high and medium confidence, showing good agreement between observational indicators and derived emissions and supporting the robustness of the framework. The approach performs less reliably for low-capacity facilities and for regions with sparse satellite coverage or relatively high wind speeds.

Abstract In recent years, several reservoirs in the western U.S. have approached or exceeded critically low storage during drought conditions; such events could be particularly impactful to water and energy availability if they are widespread across the region or exacerbated by climate change. However, projected changes in low storage and near-minimum-power-pool (MPP) risk remain poorly quantified across large spatial domains. Here, we assess simulated low storage frequency (LSF) across 94 western U.S. reservoirs using a calibrated offline hedging release model driven by projected reservoir inflows under future climate scenarios. We evaluate near-MPP risk for 30 reservoirs for which reservoir-specific thresholds are known. Projected low storage responses are spatially and seasonally heterogeneous. Across the ensemble, annual LSF declines in many future simulations, and near-MPP exposure remains limited for most reservoirs. Thus, within the climate-driven inflow projections and hedging framework, future projections do not produce a widespread increase in critically LSF across western U.S. hydropower reservoirs. This broad pattern masks important regional and seasonal differences, with elevated late-season vulnerability emerging in California and parts of the Southwest. LSF is more strongly associated with precipitation than with temperature, with drier years producing elevated risk across nearly every reservoir and warming amplifying risk in California and the Southwest. These results indicate that estimating operational threshold risks from climate-driven inflow projections remain challenging, particularly for low storage conditions that matter most for hydropower vulnerability.

Groundwater is an essential freshwater resource in the Western Himalayas, where increasing anthropogenic pressure and environmental variability are raising concerns regarding groundwater quality and water security. However, regionally integrated assessments of groundwater-quality variability across the Western Himalayan states remain limited. This study evaluates groundwater quality across Jammu and Kashmir, Himachal Pradesh, and Uttarakhand using groundwater-monitoring data obtained from the Central Ground Water Board (CGWB). A total of 338 observation wells monitored during 2019–2022 were analyzed using the weighted arithmetic Water Quality Index (WQI) based on Bureau of Indian Standards (BIS) and World Health Organization (WHO) drinking-water guidelines. Spatial and temporal variability were examined through hydrochemical, correlation, and geospatial analyses. The results reveal substantial regional and district-level variability in groundwater quality across the Western Himalayas. Groundwater in Himachal Pradesh and Uttarakhand is predominantly classified as excellent to good, whereas Jammu and Kashmir exhibit greater hydrochemical heterogeneity and localized groundwater deterioration. Elevated WQI values are concentrated within foothill and valley-transition districts, while high-altitude recharge zones generally maintain lower WQI values. Hydrochemical analyses indicate that groundwater-quality variability is primarily associated with mineralization processes, lithological controls, and localized anthropogenic influences. Temporal analysis further indicates moderate groundwater-quality improvement between 2019 and 2022, particularly in parts of Jammu and Kashmir. Overall, the findings demonstrate that groundwater systems across the Western Himalayas remain largely controlled by hydrogeological conditions but are increasingly modified by localized anthropogenic pressures. Strengthened groundwater monitoring, protection of recharge zones, and targeted management of vulnerable foothill and valley-transition environments will be essential for sustaining long-term water security in this climate-sensitive mountain region.

Flooding poses a serious challenge in rapidly growing mountain cities, where steep relief, wadi networks, and expanding urban surfaces concentrate runoff along narrow drainage pathways. This study applies a terrain-based Height Above Nearest Drainage (HAND) workflow within a GIS environment to map flood susceptibility and infrastructure exposure across the Abha, Khamis Mushait, and Ahad Rafidah catchment in the Aseer Region of Saudi Arabia. A 30 m digital elevation model was processed in PCRaster to derive flow direction, flow accumulation, stream networks, subcatchments, and HAND surfaces under four contributing-area thresholds of 1, 5, 10, and 20 km2. The scenario design evaluates how drainage-representation uncertainty affects susceptibility and exposure estimates. Susceptibility was summarized for cumulative HAND classes of ≤5, ≤10, ≤20, and ≤30 m, then intersected with filtered building footprints and the road network to estimate infrastructure exposure. The analysis shows that mapped susceptibility varies with drainage representation, but the most critical building and road exposure remains concentrated within the same low-lying urban–wadi zone across all scenarios. The mapped extent of the HAND ≤ 5 m class declined from 367 km2 under the 1 km2 scenario to 99 km2 under the 20 km2 scenario. Buildings within HAND ≤ 5 m decreased from 26,449 to 5633, while road segments within the same class declined from 8758 to 1393. Even under more conservative stream thresholds, exposure remains focused within this same urbanized drainage belt, indicating persistent localized susceptibility. The findings show that HAND can be used as a practical first-pass screening tool for identifying flood-susceptible terrain and prioritizing exposed infrastructure in data-scarce environments, while the scenario-based threshold testing improves confidence in identifying robust hotspots for follow-up hydraulic modeling and urban risk management.

Particle deposition models were originally developed for pulmonary drug delivery and radiation dosimetry, yet their accuracy is also critical for assessing inhaled pollutants. Despite their widespread use, these models have been validated mainly against total deposition fractions in idealized geometries, leaving their performance against spatially resolved data largely untested. We implemented a probabilistic Markov chain deposition model that represents the airway tree as a graph of cylindrical segments. Per-segment capture probabilities combine impaction, sedimentation, and diffusion, with a piecewise airflow-split rule (local cross-sectional area proximally, distal-subtree volume distally). Three impaction kernels (Chan-Lippmann, Yeh-Schum, Zhang) were evaluated against the Lung Anatomy + Particle Deposition Mouse Archive (LAPDMouse), which reports spatially resolved per-airway deposition counts and per-mouse breathing parameters for 34 mice exposed to 0.5, 1, and 2 µm aerosols. Since LAPDMouse reports only spatial distributions of captured particles, agreement was evaluated as distributions over segmented airways. The piecewise model reproduces the experimental depth-wise distribution across all three particle sizes, placing 41%–55% of predicted captured particles in generation 12 and beyond, spanning the 41%–49% observed experimentally; among the three kernels, Chan-Lippmann tracks the experimental distal tail most closely. Lobe-wise residuals are moderate but structurally similar across all three impaction kernels, pointing to the 1-D reduction of proximal airflow allocation rather than a kernel-specific failure, consistent with CFD studies on realistic airway geometries. These findings are relevant for microplastic exposure assessment: analytical 1-D models can reproduce the captured-particle distribution, but regional predictions remain sensitive to how realistic anatomy is reduced to a branching graph, cylindrical airway segments, and simplified flow partitioning.

High-fidelity land use/land cover (LULC) maps are fundamental for monitoring ecosystem dynamics and supporting sustainable environmental planning. While Landsat imagery supports long-term ecological analysis, its medium resolution presents significant informatics challenges, such as mixed pixels and severe class imbalances among natural habitats. This study proposes a systematic informatics framework for generating large-scale training datasets by integrating public land-cover products with Landsat 8 multispectral imagery. We evaluated 252 experimental combinations of cloud-cover thresholds, spectral bands, and loss functions across U-Net, DeepLabv3+, and Swin-UPerNet architectures. U-Net achieved the highest overall performance, particularly excelling in dominant classes like forest and farmland. Conversely, Swin-UPerNet and Focal Tversky Loss (FTL) provided a critical relative advantage in identifying “hard-to-classify” natural classes—grassland and wetland—which are inherently difficult to distinguish from dominant classes due to spectral overlap and visual ambiguity. The FTL further enhanced minority class F1-scores by up to 10%. While the RGB + NIR + SWIR1/2 combination was optimal, configurations utilizing cloud thresholds ≥20% proved that increased data volume (> 10,000 samples) effectively offsets moderate noise. Ultimately, these findings suggest that a hybrid architecture—integrating Swin-Transformer's global context modeling with U-Net's progressive upsampling—represents the most promising informatics pathway for next-generation, medium-resolution ecological monitoring.

Abstract The Best-Fit Assessment for Numerical Models (BAM) is a Python-based, open-source, modular, and versatile statistical tool designed to primarily evaluate the performance of numerical models in volcanology. BAM was developed to assess models that simulate the transport and deposition of volcanic mass flows, namely pyroclastic density currents, lava flows, lahars, and debris avalanches. BAM makes use of matrix arrays in the form of raster pairs, chiefly meant to compare the footprint of flow model outputs against user-provided observed geological evidence, such as mapped deposits. This comparison is achieved via a best-fit assessment, which, firstly, includes the computation of length metrics (e.g., percent-length ratio), a confusion matrix (i.e., statistical contingency table), and traditional similarity metrics (e.g., the Jaccard similarity coefficient, Dice-Sørensen coefficient, precision, and sensitivity). Secondly, BAM introduces an approach to incorporate any branching of the footprint geometry, termed the skeleton-aggregated percent-length ratio, and a method to more strictly evaluate areas of overestimation or underestimation, the function-transformed false positives and false negatives, respectively. These transformed results are reincorporated into the traditional similarity metrics to yield innovative and insightful function-transformed similarity metrics, completing the best-fit assessment procedure. This collection of measures establishes BAM as a robust framework to validate, calibrate, and benchmark numerical models focused on inundation areas for volcanic mass flows.

Urban wetlands are increasingly promoted as nature-based solutions for climate mitigation, yet their greenhouse gas outcomes remain highly variable and, in some cases, counterproductive. While wetlands can sequester substantial amounts of carbon, methane and nitrous oxide emissions often offset these gains, particularly in urban environments where hydrology, nutrient loading, and disturbance regimes are tightly managed. This Perspective argues that carbon outcomes in urban wetlands are not determined by ecological potential alone but by governance decisions that shape design, infrastructure operation, monitoring, and institutional coordination. Rather than presenting new empirical data, we reframe existing biogeochemical knowledge through a policy science lens to identify controllable decision points that determine whether urban wetlands function as net climate assets or as liabilities. We outline four interlinked strategies: governance-informed riparian and wetland design, low-carbon operation of urban water infrastructure, adaptive monitoring and measurement-reporting and verification systems, and institutional alignment through water rights and regulatory frameworks. Together, these strategies reposition urban wetlands from passive ecosystem services to actively managed climate-friendly infrastructure. Recognizing wetlands as governable systems provides a pragmatic pathway for integrating urban wetlands into decarbonization strategies under real-world institutional and resource constraint scenarios.

Abstract Volcanic edifices show complex internal structures including discontinuities at different scales. These discontinuities can act as zones of weakness and are closely linked to volcanic activity distribution and associated hazards, making their detection and monitoring essential. In this study we investigate the presence of small‐scale faults on the flanks of Piton de la Fournaise volcano, and their implications for volcanic activity. We have carried out a careful analysis of the Interferometric Synthetic Aperture Radar (InSAR) data acquired on 30 magma intrusions occurring between October 2010 and July 2023. By filtering out long‐wavelength displacements we have revealed small‐scale differential motion of 1–3 cm indicative of faults on the northern and southern flanks of the summit. We detect 151 fault structures, 87% of which are tensile fractures located along the main NE‐SE rift zone axis and activated by intrusion‐induced dilation. Fault distribution outlines areas of deflation similar to graben‐like structures, approximately 200–300 m wide and 400–600 m long following the rift zone axes. Moreover, on the north‐western flank, nearly circular nested faults delineate areas of centripetal subsidence, and are interpreted as relict ring‐faults of past crater collapses. Electromagnetic measurements previously acquired at the volcano, indicate these faults participate in hydrothermal fluid circulation leading to rock alteration and edifice weakening along fault traces, potentially affecting the stability of the flanks. InSAR data can thus effectively reveal active structures on volcanic edifices, offering critical insights into structural weaknesses that help hazard assessment and risk mitigation.

Global warming has increased the frequency and intensity of extreme high-temperature events, with evolution patterns differing substantially under various temperature–humidity combinations. This study used observational data from 21 meteorological stations in Zhejiang Province (1980–2019) and applied a mutually exclusive classification framework based on dual thresholds of dry-bulb and wet-bulb temperatures to categorize extreme high-temperature events into dry-type (DHW), humid-type (HHW), and compound-type (CHW). The results show that DHW frequency, duration, and intensity all exhibited significant increasing trends, with frequency rising at 0.32/10a and intensity at 1.92 °C/10a. HHW occurred with low frequency and showed no significant trend across the study period. CHW intensity increased significantly at 2.85 °C/10a, while frequency and duration remained stable. Spatially, DHW concentrated in northern and central inland areas, whereas CHW dominated along the eastern coastal belt, reflecting the contrasting influences of land–sea thermal contrast and moisture availability. Urbanization showed significant positive correlations with all DHW indicators and negative correlations with HHW trends, indicating an amplifying effect on dry heat through surface warming and reduced evapotranspiration, and a suppressive effect on humid heat through reduced surface moisture availability. These findings demonstrate that the intensification of extreme heat in this region is dominated by dry-type events, and that urbanization plays a dual role in amplifying dry heat while suppressing humid heat, providing a scientific basis for differentiated heat risk management and climate-adaptive urban planning.

Abstract Stable continental regions (SCRs) are characterized by low strain rates and long earthquake recurrence intervals, but the patterns and drivers of their seismicity remain debated. This study investigates the rupture history of the Liuyuan fault in the low‐strain Beishan region of China to determine whether SCR earthquakes are regular, clustered, or random. We integrate paleoseismic trenching, IRSL and cosmogenic 10 Be dating of trench units and rockfalls, and microstructural analyses of fault‐zone materials. Our results reveal two distinct earthquake clusters at 65.5–46.2 ka and 8.8–4.6 ka, possibly separated by a long period of quiescence, which remains unconfirmed due to a sedimentary record gap. The late Pleistocene cluster is independently corroborated by cosmogenic 10 Be ages of rockfalls, which cluster at 46–48 ka and overlap within uncertainty. Microstructural evidence, including multiple generations of quartz veins and abundant phyllosilicates, points to a fluid‐driven, fault‐valve mechanism. We propose that episodic increases in deep‐sourced fluid pressure, facilitated by a steep fault geometry within a transpressional regime, trigger these rupture clusters. A comparison with other SCRs globally suggests that this clustered behavior, observed in some intraplate settings, may be controlled by key factors: a weakened lithosphere that localizes strain and transient stress perturbations from fluid migration or surface processes. These findings challenge the assumption of time‐independent seismicity in some SCRs and have significant implications for seismic hazard assessment, particularly for critical infrastructure in low‐strain environments like the Beishan region, a proposed site for high‐level radioactive waste disposal.

Abstract The Paleocene–Eocene Thermal Maximum (PETM; ∼55.9 Ma) was a rapid global warming event marked by intensified hydrological cycling, enhanced continental weathering, and widespread ocean deoxygenation. Although the extent of anoxic basins during the PETM is relatively well documented, the contribution of high‐latitude continental weathering to regional ocean redox dynamics remains unclear, leaving the spatial impact of weathering on ocean biogeochemistry poorly constrained. Here, we use elemental geochemical data from sediments recovered from the Vøring Basin, North Atlantic during International Ocean Discovery Program (IODP) Expedition 396, to reconstruct variations in continental weathering intensity and redox conditions across the PETM. Our results show that high accumulation rates during the PETM coincided with low chemical weathering relative to underlying pre‐PETM strata, indicating that rapid burial limited chemical alteration, although accumulation rates in the pre‐PETM interval are not well constrained. This pattern likely reflects intensified rainfall, which primarily enhanced physical erosion and delivered large amounts of detrital material to the basin while limiting chemical weathering. The influx of fresh, reactive detrital material from the North Atlantic Igneous Province (NAIP) increased the supply of bioavailable nutrients, likely stimulating elevated primary productivity and contributing to the development of euxinic bottom waters during the onset of the PETM. On the boreal mid‐Norwegian margin, our data suggest that enhanced physical erosion, rather than chemical weathering, dominated continental responses to extreme greenhouse forcing, promoting nutrient enrichment, transient euxinia, and organic carbon burial.

Abstract Accurate prediction of Great Lakes ice cover is critical for regional weather, navigation support, and safety. Current operational models often exhibit biases, such as over‐prediction of ice concentration and delayed spring melt, potentially due to simplified parameterizations. This study addresses this issue by first characterizing ice floe size variability using 12 years (2010–2021) of satellite‐derived ice charts from the U.S. National Ice Center (NIC). We tested spatially‐ and temporally‐varying floe size parameterizations in the Finite Volume Community Ocean Model (FVCOM), comparing its performance against the standard operational configuration, which uses a constant 300 m floe size. The analysis reveals that floe sizes are highly variable and correlate with winter severity. Model simulations for a high‐ice (2019) and low‐ice (2020) winter show that variable floe size parameterizations significantly reduce model bias in ice concentration and improve categorical accuracy for ice thickness when compared to a constant floe size configuration. For example, exact categorical accuracy for Lake Superior ice thickness improved from 41% to 67% in 2019. These findings demonstrate that integrating variable ice floe size from satellite data resolves a key model deficiency and offers a practical path toward improving operational ice forecasting in the Great Lakes.

Constructing new reservoirs to ensure a reliable water supply in downstream areas and to alleviate overflow flooding along rivers during floods is an urgent task for the authorities. In this study, we developed a GIS platform that integrates a series of watershed geomorphological and hydrological models to assess the suitability of prospective dam sites. The built-in modules include watershed geomorphological analysis, rainfall analysis, flow analysis, surplus water analysis, and reservoir analysis. Using the digital elevation model, users can obtain the reservoir H-A-V curve at a prospective dam site and the upstream watershed’s geomorphological factors. A topography-based hydrological model was used to estimate available water at the dam site, and surplus water was obtained by subtracting existing water demands and/or environmental flow requirements from the available water series. Exceedance probability analysis was then conducted for the surplus water to evaluate the dam site’s feasibility for new water resources development. The system also provides a reservoir’s useful-life evaluation to determine the time required for sediment accumulation to render the reservoir unable to serve its intended purpose. Moreover, for flood control, the platform includes a built-in module for estimating design discharge for different return periods. The planned Pingxi Reservoir site in New Taipei County, Taiwan, is used as an example in this study. Detailed analytical procedures are presented to demonstrate the use of the proposed integrated GIS platform system to assess the adequacy of prospective dam sites for new water resources development.