Earth and Environmental Sciences

High Resolution Image Download MS PowerPoint Slide The burden of wildland fires to human health is well established, but less is known about how these impacts are distributed among vulnerable population groups and whether and to what extent wildland fire smoke affects certain parts of the country repeatedly. Moreover, much of the literature has focused on impacts from fine particles, but very few researchers have investigated deaths and illnesses attributable to ozone associated with wildland fires. This paper uses a 12-year time series of CMAQ model-predicted PM 2.5 and ozone concentrations between 2007 and 2018 and the BenMAP-CE tool to quantify the number, distribution, and economic value of these impacts. We calculate measures of inequality, including the Gini coefficient, to determine how these impacts are distributed among populations and locations. We estimate cumulatively tens of thousands of wildland fire PM 2.5 - and ozone-attributable deaths, valued at $130B. Impacts occur disproportionately among those most vulnerable, as characterized by socioeconomic status in the Social Vulnerability Index, who experience a greater air pollution mortality burden as compared to those less vulnerable. A relatively small number of counties in northern California and Idaho experience a disproportionately high number of days of wildland fire PM 2.5 exposure. The Gini coefficient and Atkinson index indicate that counties with vulnerable populations are inequitably impacted, suggesting an opportunity for policies that could promote a more equitable burden of smoke to human health.

Abstract In snow‐fed, mountainous karst regions, aquifers sustain and are connected to streamflow via springs and distributed groundwater exchanges (DGE) including inflows (DGI) and losses (DGL). Despite their significance, there is limited understanding of the complex flow paths linking aquifers and streams, particularly regarding flow path source and residence time, and thus system resilience to future shifts in recharge. This study advances a combined approach using discharge, solute, and solute isotope ( 87 Sr/ 86 Sr, 234 U/ 238 U) mass balances alongside spring environmental tracer data to quantify DGE and characterize DGI in a snow‐fed karst watershed. The integration of DGI solute isotope composition and mineral saturation indices, along with a comparison to spring 3 H‐informed apparent ages, allowed for a novel assessment of flow path dynamics. Results reveal the dominance of karst‐conduit‐influenced contributions to DGI during high and low flow. Karst‐conduit‐influenced springs and DGI showed large increases in residence times from high to low flow periods. This is explained by a shift in the presence from flow in quick response conduit and shallow soil flow paths during spring runoff to carbonate matrix flow paths during baseflow. Because of the significant spring and DGI contribution to river baseflow, insight into these groundwater sources has important implications for system resilience and the potential storage volume available that can buffer impacts of climate variability.

Abstract. Deep Learning (DL) has recently emerged as a promising approach for statistical climate downscaling. In this study, we investigate the use of pre-training in this context, building on the DeepESD model developed for the Spanish National Adaptation Plan (PNACC), which uses ERA5 predictors and the 5 km ROCIO-IBEB national gridded predictand dataset. We evaluate the effectiveness of different fine-tuning strategies to adapt this pre-trained model to alternative national and regional station (point-based) datasets. The objective is to develop downstream downscaling methods that maintain consistency with the original national-scale model while capturing the specific characteristics of regional and local datasets. We analyze the benefits of fine-tuning, focusing on the improved consistency and robustness of the resulting models. Using eXplainable Artificial Intelligence (XAI) techniques, we examine the relationships learned by the models and compare the resulting climate change signals. Our results demonstrate that pre-training provides a robust foundation for statistical downscaling, particularly in cases with limited spatial and/or temporal data availability (e.g. local high-resolution datasets available only for short periods), thereby reducing epistemic uncertainty and improving the reliability of future climate projections. Overall, this approach represents a step toward standardizing DL-based downscaling models to ensure more coherent and consistent climate projections across national and regional scales.

Study region Upper Great Ruaha River Basin, Tanzania. Study focus Climate change impacts on water availability were assessed using the Soil and Water Assessment Tool Plus (SWAT+) model forced by climate projections from ten bias-adjusted and downscaled global climate models under SSP1–2.6, SSP3–7.0, and SSP5–8.5 scenarios. Simulations were conducted for a historical period (1961–1990) and two future periods (2036–2065 and 2070–2099). Climate impacts were quantified by comparing future and historical simulations, while hydrological extremes were evaluated using flow duration curves. New hydrological insights (1) Annual temperature was projected to increase under all scenarios by 1.8–1.9°C (SSP1–2.6), 2.3–4.0°C (SSP3–7.0), and 2.6–4.9°C (SSP5–8.5). (2) Wet-season rainfall was projected to increase by up to 24% under SSP5–8.5 in the far-future, while reductions of up to 12.5% are projected under SSP1–2.6 in the mid-future during the onset of the rainy season. (3) These changes increased evapotranspiration, surface runoff, and water-yield, while reducing percolation, baseflow, and river discharge by up to 7.8% under SSP1–2.6 in the mid-future. (4) High-flows were projected to increase by more than 75% in the mid-future across all scenarios, while low-flows declined by more than 10% both future periods. The changes pose substantial risks to agriculture, water supply, energy generation, and local livelihoods. The findings provide local evidence to support climate-resilient water resources management and adaptation planning in the basin.

Geospatial monitoring is crucial for landslide research and hazard mitigation. This paper provides a comprehensive overview of contemporary landslide monitoring methods and lays the groundwork for a unified monitoring framework. An in-depth bibliometric analysis and critical review of state-of-the-art approaches developed over the past decade are presented. The study proposes a new classification and systematization of geospatial monitoring methods based on dimensionality (1D, 2D, and 3D) and referencing approach (absolute or relative). The reviewed methods include geodetic techniques, photogrammetry, laser scanning, global satellite navigation systems, UAVs, radar interferometry, and various sensors. The operational characteristics, advantages, and limitations of the existing methods are analyzed with respect to monitoring accuracy, spatial coverage, temporal resolution, and applicability to different deformation conditions. A comparative analysis and systematization of monitoring methods according to landslide velocity classes are presented. This framework links achievable observation accuracy and monitoring frequency to landslide dynamics. Based on the analysis, a refined workflow for geospatial landslide monitoring is proposed. The workflow integrates monitoring design, observation network configuration, data integration, statistical analysis, and forecasting stages. The analysis indicates that effective landslide monitoring requires integrated multi-sensor systems. Future developments are expected to focus on geospatial and non-geospatial data integration, monitoring automation, and next-generation monitoring system design.

Vegetation Optical Depth (VOD) from L-band microwave radiometry is widely used to monitor vegetation water status and ecosystem dynamics, but existing SMAP and SMOS products at 25–40 km resolution cannot resolve fine-scale canopy heterogeneity. We develop and evaluate a framework for estimating forest Global Navigation Satellite System Transmissometry (GNSS-T)-calibrated VOD at 30 m resolution by fusing Sentinel-1A Synthetic Aperture Radar (SAR), Harmonized Landsat–Sentinel-2 optical reflectance, GEDI lidar canopy structure, and climate variables (precipitation and air temperature aggregated over same-day, 7-, 30-, and 60-day windows), all calibrated using ground-based (GNSS-T). A self-supervised learning (SSL) framework based on Vision Transformers, pretrained on unlabeled satellite imagery and fine-tuned with GNSS-derived VOD from five forest sites in the United States and Switzerland, generally outperforms Random Forest and XGBoost across SAR-only, optical-only, and fusion configurations. Feature importance analysis reveals that medium-term climate drivers, particularly 60-day precipitation and 7-day temperature, consistently outrank SAR backscatter, while SAR captures rain-driven VOD peaks that optical sensors miss due to cloud screening. The framework demonstrates robust temporal generalization across deciduous and evergreen forest types, though spatial generalization remains the critical next step as the GNSS-T network expands. These results establish GNSS-T-calibrated multi-sensor fusion with domain-specific SSL pretraining as a viable pathway toward operational 30 m L-band VOD estimation.

Abstract Mineral dust drives Arctic climate variability, but accurate source attribution remains difficult due to plume mixing from the different regions. Using observations and HYSPLIT modeling, we characterize a record‐breaking trans‐continental dust intrusion (March 13–19, 2013) traversing from North Africa to the Arctic. Distinct from single‐source transport, a novel “cumulative‐snowballing” phenomenon‐Saharan dust uplifted into the free troposphere mixed with Central and East Asian emissions‐was identified. This blended plume was dominated by East (45.22%) and Central (23.62%) Asian sources, while retaining a non‐negligible Saharan contributions (17.34%). Driven by extreme multi‐regional emissions, the mid‐latitude westerly jet exported this dust across the North Pacific in the middle to lower troposphere, further a blocking‐like anticyclone opened a meridional pathway for continuous poleward intrusion. These findings demonstrate that Arctic aerosol loading can result from consecutive multi‐continental injections, highlighting the importance of considering complex mixing states in climate assessments.

Abstract There have been significant wet biases in the simulation of precipitation over the Tibetan Plateau (TP) for a long time. One of the important reasons is that current numerical models cannot accurately describe the influence of small‐scale orography. In the orographic gravity wave drag (OGWD) parameterization used to describe small‐scale orographic effects, this study develops a high‐precision calculation method of sub‐grid mountain sharpness that varies with geographical areas to revise and optimize the current constant mountain sharpness parameter (results in a constant drag coefficient at the surface). Batch numerical experiments on precipitation over the TP are carried out using the Yin‐He Global Spectral Model. The results show that the newly calculated drag coefficient of revised OGWD scheme improves the simulation of precipitation and atmospheric circulation over the TP. The new mountain sharpness overall increases the OGWD over the TP on the westerly circulation. According to the vertical vorticity equation, changes in OGWD enhance the positive vorticity over the southern TP. Compared to the original scheme, the revised OGWD experiment exhibits a cyclonic circulation difference over the southern TP, which weakens the flow of water vapor into the TP. The water vapor budget of the main precipitation area over the western TP decreases by about 12.66%, thus reducing precipitation and wet biases over the western TP.

What controls the occurrence of giant earthquakes remains a central question in seismology. We reveal a notably simple-but previously overlooked-relationship between earthquake size and fault dip in subduction zones. Global earthquake statistics show a robust dependence of earthquake size on fault dip, with the probability of extreme events peaking on ultralow-angle faults. Through three case studies, we demonstrate that earthquakes tend to grow larger when the fault geometry is aligned with the regional stress field. We further discuss how such favorable stress configurations are generated along subduction interfaces and emphasize the critical importance of monitoring the temporal evolution of stress in subduction zones to better forecast future megaearthquakes.

Abstract Changes in cloud fraction associated with Central Pacific and Eastern Pacific El Niños are analyzed using monthly cloud data from the International Satellite Cloud Climatology Project, meteorological variables from the European Center for Medium Range Weather Forecasting reanalysis version 5, and radiation data from the Clouds and the Earth's Radiant Energy System. We observe robust increases in low cloud fraction in the tropical Southeast Pacific (SEP, 120°–80°W 0°–20°S) during El Niños. The increases in SEP low‐cloudiness are associated with CP‐El Niño variability. Increases in SEP Sea Surface Temperature during CP‐El Niños are accompanied by a warmer tropical free troposphere, increased cold advection, and reduced overlying cirrus, facilitating the growth of low clouds. Northeast Pacific cloud decks decrease during El Niños, consistent with weaker free‐tropospheric heating outside the tropics. Coupled CMIP6 model experiments are unable to replicate increased SEP low cloud due to a misrepresentation of El Niño Southern Oscillation diversity.

Study region Jiziwan region of the Yellow River basin, China. Study focus In the Yellow River basin, ecological protection and the development of high-quality infrastructure are of great strategic significance for China. To date, the ecological protection of the Yellow River basin has faced severe challenges, among which frequent droughts have become key limiting factors. However, the spatiotemporal structure and occurrence pattern of drought in the Jiziwan region of the Yellow River basin are difficult to accurately represent, and the mechanisms by which the driving factors affect drought are unknown. Therefore, in this study, the temperature–vegetation drought index (TVDI), 3D drought identification algorithm, structural equation modelling (SEM) system and multivariate cross-wavelet transform were used to reveal the spatiotemporal characteristics of drought events and their response mechanisms to driving factors New hydrological insights for the region From 2001–2020, the annual TVDI increased significantly (0.011/year), with a spatial distribution showing a pattern of higher values in the west and lower values in the east. Seasonal differences were evident, with spring being the most severe drought season and winter showing the fastest increase in TVDI (0.012/year). A total of 15 drought events were identified, with the 2015–2016 event being the most severe (24-month duration, 52.58% of the area affected, and an intensity of 4.635 ×10 5 km²·month). Soil moisture mitigates drought through direct and indirect pathways, while surface temperature and solar radiation exacerbate drought via energy balance processes. Precipitation is the key negative moderator of drought, with the largest total impact (-0.69), highlighting its critical role in alleviating drought. Atmospheric circulation factors (AO, DMI, PNA) interact with drought (TVDI) through multiple timescale resonance cycles (4–56 months), collectively influencing drought development by altering regional water-heat balance.

Study Region: The Nile River Basin (NRB), spanning 11 countries in East Africa and supporting over 300 million people. Study Focus: This study synthesizes three decades of research using an AI-enhanced PRISMA review to assess impacts of climate change, land-use change, and infrastructure on water availability and risks across the basin. New Hydrological Insights for the Region: The review reveals pronounced spatial heterogeneity in hydrological responses. In the Blue Nile, discharge trends are mixed and often statistically insignificant, with recent changes increasingly linked to land-use dynamics rather than climate alone. In the White Nile, hydrology shows multi-decadal variability rather than consistent long-term trends. A major regime shift in 1961, associated with a positive Indian Ocean Dipole (IOD), raised Lake Victoria by about 2 m and increased baseline outflows. Flows declined through the 1990s, dropped sharply in the early 2000s due to dam operations, and reached record highs in 2020 following IOD-driven extreme rainfall. Attribution studies indicate climate change has likely contributed to increased White Nile flows in recent decades, although uncertainties remain regarding the roles of climate, land use, and human interventions. Future rainfall and river flow projections show wide divergence, ranging from − 10 % to + 25 % for rainfall and − 20 % to + 40 % for discharge by late century, highlighting uncertainty in future water availability and increasing pressure on transboundary water management.

Abstract This study investigates the role of moisture in double rainbands over South China through combining ensemble sensitivity analysis and Lagrangian backward tracing. During 10–11 May 2022, a high‐impact double‐rainband event hit South China with coexisting inland and coastal heavy rainfall. Ensemble sensitivity analysis reveals that the coastal rainfall was most sensitive to upstream moisture conditions over the South China Sea (SCS). By comparison, the inland rainfall was more related to nearby moisture. Using the Hybrid Single‐Particle Lagrangian Integrated Trajectory model, the respective moisture pathways and sources for the two rainfall areas were further quantified and linked to the sensitivity results. Dominant moisture pathways (∼80%) for coastal rainfall originated from the western Pacific and the SCS. Most moisture was absorbed from the SCS along these pathways, and was finally transported by low‐level southerly/southwesterly jets toward the target area, making the SCS the primary moisture source (>30%) and sensitive region for coastal rainfall. For inland rainfall, southerly and northerly trajectories related to the frontal system accounted for a comparable proportion. Northern sources, including local moisture recycling, contributed more for inland rainfall than for coastal rainfall. Analysis of two additional double‐rainband events confirmed moisture paths were more diverse for inland rainband with increased moisture contribution from northern sources. The frontal system and monsoon activity significantly affected the relative importance of northern sources, the Bay of Bengal and the Arabian Sea, respectively. The findings underscore the necessity of considering area‐specific moisture features to improve forecasts of double rainbands.

Gross primary productivity (GPP) is central to terrestrial carbon cycling and forest carbon sink assessment. Using Google Earth Engine, MODIS GPP, ERA5-Land meteorological data, and forest extent masks, this study examined GPP dynamics and climatic controls in China’s northeast, southern, and southwest forest regions from 2005 to 2025. GPP increased overall in all three regions, with higher values in the south and lower values in the north. Climatic drivers differed regionally: in the northeast, GPP responded positively to temperature, while VPD slightly exceeded temperature in the dominant-control area; in the southern region, temperature was the main driver but VPD remained important; in the southwest, temperature dominated larger areas, whereas moisture-related controls showed stronger spatial heterogeneity. Piecewise analysis identified temperature–VPD turning points of 11.74 °C, 10.43 °C, and 25.64 °C for the northeast, southwest, and southern regions, respectively. Two-dimensional temperature–VPD binning further revealed nonlinear GPP distributions and distinct optimal hydrothermal combinations across regions. These results show that warming effects on forest productivity are region-specific and constrained by atmospheric dryness, providing evidence for assessing China’s forest carbon sink responses to climate change.

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.

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 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.

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.