Earth and Environmental Sciences

Marine ecosystem degradation may reduce state revenues, increase recovery spending, and weaken fiscal sustainability, yet Indonesia does not yet have a routine governance mechanism that links marine natural capital valuation to fiscal-risk assessment in the State Budget Financial Note. This article develops a governance design, Blue Natural Capital Value at Risk (BNC-VaR), to translate changes in marine ecosystem conditions into fiscal-exposure signals for Indonesian public finance. Ecological condition indicators, such as fish-stock status, coral-reef condition, and mangrove extent, are converted into traceable valuation parameters and then into structured outputs, including fiscal-exposure scenarios, budget-relevance notes, and medium-term fiscal-sustainability readings across revenue, expenditure, deficit, and financing channels. The design treats ecological change as affecting the fiscal position through mediated and disclosable pathways rather than automatic causal effects. It adapts Value at Risk as a risk logic for public fiscal governance rather than as a conventional market-based probabilistic measure. Using theory synthesis and a model-paper approach across six analytical stages, the study produces five design principles, four formal propositions, and a five-component institutional architecture, with the Directorate General of State Assets Management positioned as a valuation custodian. As a conceptual contribution, BNC-VaR offers an operational architecture and implementation roadmap for future empirical testing in Indonesia and other archipelagic or marine-resource-dependent fiscal systems.

Governance–Sustainability has increasingly emerged as an important framework for understanding how institutional capacity influences long-term development outcomes. This study empirically examines the relationships among Institutional Quality, Energy Transition, and Population Health and Development Outcomes within the ASEAN-10 states. Drawing upon the theoretical perspectives of New Institutionalism and Ecological Modernization Theory, the study employs Structural Equation Modeling (SEM) to analyze balanced longitudinal panel dataset (2000–2020) (N = 210 observations). The empirical results indicate that Institutional Quality is significantly associated with Energy Transition outcomes (β = −0.981, p < 0.001), while Energy Transition is significantly associated with Population Health and Development Outcomes (β = −0.982, p < 0.001). Furthermore, the findings provide evidence of a significant mediation effect, indicating that the relationship between Institutional Quality and Population Health and Development Outcomes operates indirectly through Energy Transition processes. The study contributes to the governance and sustainability literature by integrating institutional quality, sustainability transitions, and development outcomes within a unified Governance–Sustainability framework and supporting a Multi-Level Governance perspective. From a policy standpoint, the findings highlight the importance of strengthening institutional capacity and sustainability governance to support long-term development objectives. Overall, the results demonstrate that Institutional Quality functions as a foundational condition for sustainability transitions, while Energy Transition serves as a key mechanism linking governance capacity with Population Health and Development Outcomes in the ASEAN context.

Landslides are a significant natural hazard in regions with complex topographic, geological, and climatic conditions, where they can constrain sustainable territorial development and threaten infrastructure, land use, and environmental safety. This study aims to assess and map landslide susceptibility in Southern Primorye in order to support hazard-informed territorial planning and risk reduction. The analysis integrates vegetation, precipitation, geological, and topographic predictors with documented landslide occurrence data. A presence-only landslide susceptibility modeling approach was applied using the OneClassSVM algorithm with a radial basis function kernel. The results show that the highest susceptibility is associated with lower slope segments and coastal landforms composed of loose unconsolidated deposits and partly covered by sparse woodland. Surface runoff, subsurface flow, lithological conditions, and precipitation patterns were identified as the principal factors contributing to slope instability, while field observations confirmed that anthropogenic slope cutting related to road infrastructure may act as an additional local trigger. The model demonstrated moderate but acceptable predictive performance and allowed the delineation of areas with elevated landslide susceptibility. The resulting susceptibility map provides a regional-scale basis for more sustainable land-use planning, infrastructure placement, and landslide risk mitigation in Southern Primorye and in other regions with comparable environmental conditions.

Sustainable intensification of agriculture (SIA) aims to increase agricultural output while reducing or not increasing negative environmental impacts, yet evidence on the production effects of reduced chemical inputs remains limited. To narrow this gap, this study examines how SIA impacts grain production by treating China’s “Chemical Fertilizer Use Zero-Growth Policy” (Zero-Growth Policy) as a natural experiment, by using provincial-level data from 2008 to 2022. The study indicates the following: Firstly, the policy exerted no negative impact on grain production and even boosted grain production. Secondly, this production grain operated mainly through an increased grain sown-area share and higher cropping intensity, while yield per unit area remained statistically unchanged. Thirdly, heterogeneity analysis suggests that the positive association between the policy and grain production is relatively stronger in non-main grain production areas than in main grain production areas. These findings provide preliminary evidence that policy-oriented sustainable intensification may be compatible with grain output growth in the sample period.

Quality of life is a multidimensional concept that includes characteristics of the residence and the wider living environment. The aim of the paper is to examine whether age groups differ in satisfaction with selected spatial characteristics of the residence and the immediate living environment, and whether comparable differences occur in the perceived healthy living environment. The research considers age and perceived quality of life on three distinct levels: specific spatial characteristics of the residence (for example, the presence of a balcony, terrace, daylighting and window view) with selected environmental characteristics of the immediate surroundings (open view, view of green areas, accessibility of green areas) and a broader perception of a healthy living environment (general perception of health and well-being). The research is based on questionnaire results that included 473 participants. The data were analyzed using analysis of variance, Tukey–Kramer comparisons and correlation analysis. The results show statistically significant differences between age groups, especially in regard to satisfaction with the presence of a balcony, terrace or atrium, to daylighting and the quality of the window view in the residences, with the satisfaction being lowest in the younger and highest in the older age group. Interestingly, there were no statistically significant differences in the general perception of a healthy living environment. The correlation analysis further showed that satisfaction and age was associated with home ownership, dwelling type, residential location and housing-cost burden, whereas proximity to green areas was not linearly associated with age. The findings showed that specific age groups perceived the quality of the living environment more pronouncedly when linked with specific spatial characteristics of the dwelling rather than the broader perception of the living environment.

Population ageing is reshaping the use and evaluation of everyday urban green spaces, especially in old urban districts where nearby public spaces support walking, resting, exercise, and social contact. Conventional age-friendly assessments often emphasise whether formal infrastructure is provided, but facility provision alone does not ensure experiential fit with older adults’ functional capacities, daily routines, and social practices. This exploratory multiple-case study examines user-initiated informal adaptations in three neighbourhood-scale green spaces in Gulou District, Nanjing, China. Facility audits, approximately 30 h of non-participant observation, semi-structured interviews with 36 older users, and 220 valid questionnaires were combined through cross-case coding and qualitative triangulation. Three adaptation types were identified: supplementary, modifying, and appropriative adaptations. These practices were interpreted as context-dependent behavioural signals potentially associated with safety and convenience, ergonomic support and material-thermal comfort, social accessibility and spatial accommodation, and social support. Adaptation patterns varied descriptively across sites and age groups in relation to facility conditions, spatial organisation, activity intensity, and user characteristics. The findings suggest that, when interpreted alongside facility audits, interviews, and site context, older adults’ everyday adaptations may help identify possible mismatches between formal provision and actual use, offering a low-cost interpretative perspective for inclusive, incremental, and socially sustainable green-space renewal.

Abstract. Wetland and upland ecosystems play significant but opposing roles in the global methane (CH4) budget, acting as natural sources and sinks, respectively. Two of the most common approaches for measuring CH4 fluxes (FCH4) are chambers, which measure fluxes at fine spatial scales (ca. 1 m2), and eddy covariance (EC) towers, which integrate fluxes across larger footprints (ca. 100–10 000 m2). Although chamber and EC observations have been combined in various syntheses and databases to estimate CH4 budgets, a unified cross-site evaluation of FCH4 estimates at plot and ecosystem scales is lacking. As a first step toward a systematic spatiotemporal scaling of EC tower and chamber footprints, we quantified differences in site-level aggregate FCH4 between EC and chamber measurements (ΔFCH4) across ten wetland and upland sites at half-hourly, hourly, daily, weekly, monthly, and annual timescales. We found that ecosystem-scale median FCH4 was consistently higher than plot-scale FCH4 at all temporal scales, with the smallest difference at the daily timescale (multi-site median ΔFCH4: 1.36 nmol m−2 s−1; median ecosystem-scale FCH4 = 1.56 nmol m−2 s−1, median plot-scale FCH4 = 0.06 nmol m−2 s−1) and the largest at annual scales (2.58 nmol m−2 s−1; median ecosystem-scale FCH4 = 25.91 nmol m−2 s−1, median plot-scale FCH4 = 6.55 nmol m−2 s−1). In general, the agreement between ecosystem- and plot-scale FCH4 decreased with finer temporal resolution (from Spearman ρ = 0.95 at the annual scale to ρ = 0.65 at the half-hourly scale), while ΔFCH4 variation was greatest at daily-to-annual scales. Key environmental predictors of ΔFCH4 across the ten sites included plot-scale spatial heterogeneity, dominant vegetation type, vapor pressure deficit, atmospheric pressure, and friction velocity at the daily and monthly scales. Wind direction was a significant predictor only at the monthly scale, suggesting EC footprint effects at these sites. These findings suggest that accounting for variability in EC footprint extent, chamber measurement placement, and measurement artifacts is key to reconciling multi-scale FCH4 observations across diverse ecosystems and refining CH4 budgets.

Abstract. The rapid warming of the Arctic is accelerating permafrost thaw and mobilising large, previously frozen organic-carbon reservoirs. Retrogressive thaw slumps (RTS) are dynamic hotspots of abrupt permafrost disturbance that expose deep, millennial-aged material to erosion and transport. To assess the fate of slump-derived organic matter (OM), we analysed samples from (i) the seasonally thawed active layer, (ii) Holocene and Pleistocene permafrost, (iii) freshly thawed debris, and (iv) runoff across four RTS of contrasting sizes and ecological settings on the Peel Plateau, north-western Canada. We specifically quantified OM abundance, thermal stability, and radiocarbon content, complemented by thermally-sliced pyrolysis–gas chromatography–mass spectrometry (Ts-Py-GCMS) for molecular fingerprints. Our results show that OM age and stability primarily reflect geomorphic feature type. Permafrost, debris, and runoff contain radiocarbon-depleted, thermally stable carbon, whereas active-layer OM is younger and more labile, with minor contributions of stabilised, higher-energy fractions. Ts-Py-GCMS shows that low-temperature fractions are dominated by carbohydrate- and cellulose-derived pyrolysates, while higher-temperature fractions contain aromatic and long-chain aliphatic compounds consistent with more processed or mineral-associated OM. The close similarity between permafrost, debris, and runoff indicates that RTS predominantly export ancient, thermally stable OM with limited early-stage alteration. These findings highlight that a substantial portion of thaw-mobilised particulate carbon likely remains stable during initial transport, rather than being rapidly mineralised at the point of thaw. This protected carbon may instead get redistributed through runoff and river networks and stored in downstream sediments. Its contribution to greenhouse-gas release and Arctic carbon-climate feedbacks therefore depends on its downstream fate.

ISO 14046:2014 is nowadays the international standard for measuring the Water Footprint (WF) of products, processes, and organizations, following a life cycle approach. The Available Water Remaining (AWARE) is an ISO 14046-compliant characterization model to measure the WF characterization factors (CFs) at national and sub-national levels considering only the quantity aspect of the impact, named Water Scarcity Footprint (WSF). Despite being established on international consensus, AWARE is calculated with global water balance models and does not entail data of hydrological dynamics at high spatial resolution. Thus, the resulting WSF values may be inaccurate for local studies. We calculate sub-regional CFs at hyper-resolution (sub-basin scale) to highlight water shortages that are not visible using current methods and improve the reliability of WSF of products and processes. Hydrological information (i.e., water availability) was retrieved from the Italian national water balance model with 1 km spatial resolution (BIGBANG 7.0), and water consumption time series (2012−2021) were obtained at the municipality level. Results show that the recalculated CF for Tuscany (= 41.8 m 3 world equiv. m −3 ) is ∼26% higher than the original AWARE's CF, suggesting that the method is not able to capture the variability of water dynamics at the sub-basin level. WSF results for three representative crops (tomato, apple and corn) are influenced by the new CFs, increasing between 50% and 100%. The proposed approach can improve the reliability of WSF assessments and can be extended to the entire Italian territory and other territories in the world.

Groundwater recharge in irrigated agricultural landscapes and surrounding watersheds is critical for sustainable water management and environmental flows. In irrigated Mediterranean regions, quantifying this process is complicated by substantial interannual and spatial variability in precipitation, irrigation practices, and evapotranspiration (ET), which introduces significant uncertainty. Here, we assess field-scale spatiotemporal variability in potential and actual contributions to aquifer replenishment across Mediterranean intermontane irrigated basins. Potential estimates were derived from a remote sensing ET water-balance residual (RSET-WB) and soil water balance modeling (SWBM), whereas the actual component was inferred from groundwater-level fluctuations using the water-table fluctuation method (WTFM). Results reveal strong spatial and crop-specific contrasts among basins and fields. In SWBM, irrigation-season variability was primarily associated with soil available water storage (AWS) and crop type, whereas non-irrigation season patterns were explained largely by interbasin differences in wet-season precipitation. Crop-specific patterns differed between methods, with alfalfa dominating RSET-WB residual estimates and grain and pasture lands showing greater SWBM-derived dry-season deep percolation below the root zone. Within SWBM, low-AWS fields also showed enhanced growing-season drainage. WTFM estimates indicated relatively balanced water table recharge between wet and dry seasons across most basins, contrasting with the wet-season dominance shown by RSET-WB and SWBM. Long-term averages (2008-2023) from RSET-WB and SWBM suggest that the dry season accounted for about 29-34% of annual potential recharge, while the wet-season fraction ranged from 54% to 78% of precipitation. Collectively, these findings underscore that irrigation return flow and late-season precipitation are critical to sustaining groundwater potential recharge in Mediterranean agricultural lands, supporting managed aquifer recharge strategies such as early- or off-season irrigation in low-AWS pasture grasslands.

Abstract Ocean tides are a major energy reservoir in Earth's climate system, yet global tide models systematically fail to reproduce observed tidal energetics. This limitation arises because existing models do not adequately represent how tide‐generated internal waves in the deep ocean regulate ocean tide energy, accounting only for their initial generation and neglecting the wave stresses associated with their subsequent propagation. These wave stresses can be out of phase with the tidal flow and substantially modify tidal amplitudes. Here we explicitly represent these wave stresses in a global tide model and assess their impact on the energetics of global tides. We show that including these wave stresses is essential for reproducing observed tidal energetics and enables energetically consistent simulations even at coarse spatial resolution. These results highlight a previously missing physical process in tide models and facilitate more accurate and efficient modeling of global ocean tides.

Abstract. Hydroxymethanesulfonate (HMS) is a critical source of particulate sulfur, formed by formaldehyde (HCHO) and sulfur dioxide (SO2) in droplets. Current models relying on bulk aqueous-phase HMS formation only explain ∼ one-third of unexplained sulfate concentrations, leaving gaps in atmospheric sulfur budget, especially in polluted and cold environments. Using Born–Oppenheimer molecular dynamics simulations, we explored HMS and its isomer hydroxymethyl sulfite (HMSi) formation mechanisms across aqueous phase and air–water/ice interfaces. Air–water interfaces enable nearly barrierless HMS formation (0.6 kcal mol−1) via unique stepwise water-mediated proton transfer, preferring HMS over HMSi (0.6 vs. 6.1 kcal mol−1), which contrasts sharply with the competitive pathways observed in the bulk aqueous phase (7.7 vs. 7.6 kcal mol−1). In contrast, protonation of formaldehyde under strongly acidic conditions reverses reaction selectivity, favoring HMSi formation over HMS. Importantly, these reaction mechanisms remain viable at air–ice interfaces in cold environments including polar areas and the upper troposphere, revealing ice surfaces as previously overlooked yet significant sites for atmospheric organosulfate formation. Our findings suggest that interfacial mechanisms may provide efficient pathways for HMS and HMSi formation in both polluted and cold environments, helping to reconcile model-observation discrepancies in the atmospheric sulfur budget.

Abstract Understanding the composition of carbonaceous aerosols, black carbon (BC) and organic aerosols (OA), remains a major challenge in atmospheric science. Using data from two aircraft campaigns with identical instrumentation over Europe and East Asia, we analyze statistical relationships between concentrations of five trace gases (CO, NO 2 , HCHO, O 3 , and SO 2 ) with BC and OA in order to estimate carbonaceous aerosol in urban pollution plumes. We show that across both campaigns, CO is the best proxy for BC ( R 2 ≈ 0.6). In plumes, OA shows statistical links with NO 2 , O 3 , and CO, reflecting the combined influence of emissions, and secondary organic aerosol formation. Linear regressions based on trace gases remain limited, especially for OA, whereas the use of nonlinear machine-learning regression improves the quantification of BC and OA ( R 2 ≈ 0.9 for BC, R 2 ≈ 0.7 for OA). However, the number of flights is limited, the results should not be interpreted as applicable to flights in other regions and seasons. Our findings indicate that co-emitted and co-produced trace gases contain information for quantifying carbonaceous aerosol in urban pollution plumes. This potential is more robust for BC, whereas OA remains more complex to estimate because it depends on multiple predictors.

Under the background of global climate warming, glaciers in the Three-River Headwaters Region, as a crucial component of the “Asian Water Tower,” exert profound influences on regional water resource security and ecological stability through their mass balance variations. Due to the scarcity of in situ observations caused by the harsh high-altitude environment, long-term monitoring based on remote sensing techniques is urgently required. In this study, the geodetic method was employed, using the SRTM-C DEM acquired in 2000 as the reference, and recent glacier surface DEMs were generated from high-resolution ZiYuan-3 tri-stereo imagery obtained during 2024–2025. Through refined DEM co-registration, differencing, and systematic error corrections, the glacier mass balance in the Three-River Headwaters Region from 2000 to 2025 was systematically estimated. The results indicate that the glaciers in the study area exhibited an overall negative mass balance during the study period, with significant spatial heterogeneity. Among the sub-regions, the Lancang River source region experienced the most pronounced mass loss (−0.70 ± 0.07 m w.e. yr−1), whereas the Yellow River source region showed the lowest mass loss (−0.37 ± 0.09 m w.e. yr−1). Compared with earlier studies, glacier mass loss has accelerated in recent years and is closely associated with regional climatic characteristics. This study provides a scientific basis for understanding glacier changes and their hydrological and ecological impacts in the Three-River Headwaters Region.

Multi-source remote sensing is transforming landslide susceptibility assessment from static terrain-based zonation toward observation-driven spatiotemporal inference and dynamic map updating. Satellite precipitation products, interferometric synthetic aperture radar (InSAR) deformation time series, optical image sequences, land-cover products, and multi-temporal terrain observations provide complementary evidence of hydrometeorological forcing, slope kinematics, land-system regulation, and geomorphic reorganization. However, these observation streams differ substantially in spatial support, temporal resolution, physical meaning, and uncertainty structure and therefore cannot be reliably integrated as generic predictors without process-aware interpretation. This review synthesizes recent progress in remote sensing-enabled dynamic landslide susceptibility assessment by linking four key components: dynamic factor construction from Earth observation data, spatiotemporal representation and learning, susceptibility map updating, and validation under temporal and spatial independence. The reviewed literature is organized around four process roles: rainfall- and soil moisture-related forcing, kinematic state and response captured by InSAR, land-system and ecological regulation derived from optical time series, and geomorphic memory represented by multi-temporal digital elevation models (DEMs). We further examine how these signals are encoded and integrated through temporal models, graph-based representations, attention mechanisms, and hybrid frameworks, with particular emphasis on consistency among process role, data structure, mapping unit, inference target, and validation design. Current progress remains constrained by temporally coarse landslide inventories, cross-scale incompatibility among remote sensing products, uneven and insufficiently process-aware multimodal fusion, and limited physical interpretability. Future advances require event-resolved inventories, uncertainty-aware multimodal fusion, process-consistent spatiotemporal learning, and validation designs that explicitly test whether susceptibility maps can be updated in a scientifically defensible manner as new Earth observation data become available.

Glacier boundary extraction on the Tibetan Plateau (TP) faces persistent challenges due to rugged terrain, seasonal snow, extensive debris cover, and topographic shadows. Traditional methods utilizing single-source or single-temporal data often yield limited accuracy. Thus, we propose an automated Double Random Forest (Double-RF) framework integrating single- and multi-temporal features from Sentinel-1 (SAR) and Sentinel-2 (Optical) data within the Google Earth Engine. We established a multidimensional feature space comprising spectral, textural, polarimetric, and topographic attributes. Feature optimization was performed using importance metrics and out-of-bag (OOB) error. A hierarchical classification strategy was employed: the first RF identifies clean glaciers and glaciers in shadow, while the second RF executes refined boundary extraction of debris-covered glaciers to mitigate spectral confusion. The results indicate that the Double-RF method significantly achieves an overall accuracy exceeding 0.84 across all sub-basins and reaching above 0.95 at best. The derived glacier inventory reveals a distinct spatial pattern: higher concentrations in the western and peripheral regions compared to the eastern and interior TP. Glaciers are predominantly distributed on shaded aspects with gentle-to-moderate slopes, highlighting the combined influence of climatic gradients and topographic controls. This multi-source, multi-temporal fusion strategy provides a robust methodological foundation for long-term glacier monitoring over the TP.

Geospatial foundation models are a new frontier in artificial intelligence, designed to understand and analyze spatial data at scale. Trained on huge sets of EO data, these models can support a wide range of applications—from monitoring natural disasters to guiding urban development and tracking climate change. To this aim, researchers and practitioners need to fine-tune the foundation models for specific tasks, utilizing a relatively small amount of additional data. As a result, geospatial foundation models are reshaping how we observe, manage, and protect our planet. Fine-tuning a geospatial foundation model requires carefully curated training datasets that reflect specific regions, time periods, or tasks—such as detecting deforestation or mapping urban growth. Yet preparing these datasets is often labor-intensive, involving steps like selecting relevant imagery, aligning spatial formats, and generating accurate labels. In practice, this means that the effectiveness of GFMs hinges on the availability of AI-ready data. This bottleneck limits the accessibility and scalability of GFMs for scientific and operational applications. In this work, we introduce a software library designed to automate these preparatory steps, streamlining the transformation of geospatial datasets into consistent, high-quality inputs for GFM fine-tuning. By reducing technical overhead and ensuring data readiness, the library enables faster, more reliable, and more inclusive adaptation of foundation models to local environmental challenges and specialized domain needs.

Abstract Urban areas are widely viewed as central to the global carbon challenge, yet estimates of the urban contribution to global CO2 emissions vary substantially because studies define “urban” and allocate emissions using different boundaries and accounting perspectives. We address this challenge by introducing a globally consistent, administrative-boundary framework that aligns emissions with governance units and distinguishes urban centres, peri-urban areas, and rural areas across all subnational administrative divisions worldwide. This governance-aligned classification provides a consistent basis for urban emissions typologies, enabling comparable urban emission quantification across regions and accounting perspectives. Combining high-resolution territorial emissions inventories (EDGAR, ODIAC, CEDS) with global consumption-based footprints (GGMCF), we estimate that in 2022 urban areas (urban centres + peri-urban areas) accounted for 72-76% of global territorial CO2 emissions, with urban centres contributing 30-37% and 39-42% from peri-urban areas. Using consumption-based data, we estimate that urban areas accounted for ~82% of CO2 emissions in 2015. We further show that the sectoral composition of urban territorial emissions has shifted from 1970 to 2022, with the energy sector increasing in prominence and relative contributions from buildings and industry declining. Finally, comparing territorial and consumption-based estimates across subnational units, we map consumption–production imbalances and find that 63% of regions are net embodied-emissions importers. These results provide a critical update to global assessments of urban emissions and demonstrate the value of pairing territorial and consumption-based accounting on governance-relevant boundaries for interpreting responsibility and identifying mitigation leverage points.

Flood-season lake spatiotemporal dynamics are vital for ecological security and socioeconomic development, requiring consistent high-resolution monitoring. However, precipitation fluctuations and sediment turbidity significantly alter water quality, while blurred boundaries between water and floodplain wetlands challenge precise monitoring. To address these issues, this study proposes a water body extraction method leveraging polarimetric Synthetic Aperture Radar data. utilizes the maximum between-class variance algorithm for initial segmentation, optimizes the threshold via a genetic algorithm, and employs dynamic morphological operations to refine boundary details. The method was validated using 2015–2025 Sentinel-1 flood-season time series of Dongting Lake on Google Earth Engine. The results demonstrate that the proposed method achieves stable and accurate water extraction across various years and seasons, with an overall accuracy surpassing 0.93, confirming its robustness and broad applicability. Furthermore, the spatiotemporal hydrodynamics and driving mechanisms of Dongting Lake were analyzed by integrating the extracted water areas with multi-source data, including water level, precipitation, discharge, temperature, and sunshine duration. Findings indicate that the flood-season water area exhibited a fluctuating trend, initially increasing and subsequently decreasing, peaking at 2202.26 km2 in 2020 and dropping to 614.04 km2 in 2025, a pattern primarily driven by extreme meteorological events such as heavy rainfall and prolonged droughts. Spatially, inundation patterns were characterized by deeper water in the north and shallower depths in the south, separated by a topographically higher central region. Regression analysis revealed a robust correlation between water area and water level with an R2 of 0.931, providing a quantitative reference for water level estimation in ungauged regions. Additionally, discharge and precipitation were positively correlated with water area, whereas temperature and sunshine duration exerted a negligible influence. This study supports flood regulation in the Dongting Lake basin and provides a robust framework for analyzing lake dynamics using long-term SAR data.

High Resolution Image Download MS PowerPoint Slide Exposure to ambient air pollution, including ozone and fine particulate matter (PM 2.5 ), is the world’s leading environmental health risk factor. Estimating how this burden may change in the future depends on projecting population growth and age structure as well as understanding how future meteorological changes may impact the production and removal of pollutants from the atmosphere. The net impact of these factors on a global scale has not been well-characterized. Here, we leverage recent meteorology, exposure, and mortality output from general circulation, atmospheric chemistry, and health impact models to isolate how changes in meteorology and populations will impact future global air-pollution-related mortality and the associated monetized impacts by the degree of global temperature change. In contrast to previous studies, we estimate that changes in meteorologically driven air pollution, in the absence of pollutant precursor emission changes, will result in 180 000 fewer deaths annually by 2100 relative to current levels, an annual monetized benefit of $7.3 trillion. Reductions are driven by decreases in PM 2.5 -attributable mortality in populated regions but are substantially offset by global increases in ozone-related mortality. We also highlight striking regional differences in the sign of net pollutant impacts by 2100, with net pollution decreases in the Northern Hemisphere driven by reductions in nitrate aerosol, while increases in both ozone and organic aerosol at higher temperatures lead to net increases in pollutant impacts in the Southern Hemisphere. Lastly, we assess sensitivities of these results to meteorological projections, health impact functions, and 10 000 future warming scenarios.

Concerns about mitigation deterrence have prompted calls for pathways that avoid multigigatonne reliance on carbon dioxide removal (CDR), yet such pathways can also discourage near-term investment in CDR, leaving the technologies technically and economically underprepared if large-scale deployment becomes necessary due to unfulfilled emission reduction pledges. Here we model a new pathway in which CDR and decarbonization “Co-Scale” aggressively in parallel without one undermining the other, with the intention that we can readily course-correct if effort in one domain does not materialize. We treat this scenario as a stylized upper bound of what is climatically achievable under ideal enabling conditions, rather than what is immediately feasible under current technical, economic, and environmental constraints. We compare this pathway with two conventional designs, i.e., CDR-Led and Decarb-Led. In CDR-Led, large-scale CDR can substitute for deep decarbonization, while in Decarb-Led, rapid emissions reductions are prioritized and CDR plays a limited complementary role. We find that compared to these two conventional scenarios, Co-Scale reaches net zero CO 2 seven years earlier, accumulates 4 times more net negative CO 2 by 2100, cuts the 1.5 °C overshoot duration by roughly half, and limits end-of-century warming to 1 °C rather than 1.37–1.39 °C. Relative to the recent focus on Decarb-Led pathways, Co-Scale’s main constraints is the feasibility of geological carbon storage, while food, water, and cost pressures are comparatively less restrictive.

Abstract. Marine stratocumulus clouds play a central role in Earth's climate system by reflecting incoming solar radiation and exerting a strong cooling effect. Their organization into open and closed mesoscale cellular morphologies can be thought of as an example of bistable dynamics driven by aerosol–cloud interactions and mesoscale processes. From the perspective of non-equilibrium thermodynamics, these structures are an example of a far-from-equilibrium open system that continuously produces and exports entropy. While entropy production has been studied in idealized deep convective systems, it has not yet been quantified for shallow clouds. Here, we compute and decompose the internal entropy production of open- and closed-cell stratocumulus using an ensemble of large-eddy simulations. We show that the overall entropy production of stratocumulus is low, reflecting the limited vertical extent and corresponding reduced ability to utilize the energy fluxes at the system's boundaries. Moist processes dominate the overall irreversibility, which, combined with their low entropy production, leads to a mechanical efficiency about an order of magnitude smaller than in deep convective systems. Although the dominant irreversible processes differ between open- and closed-cell regimes, the distributions of total entropy production largely overlap across the ensemble, limiting the ability to distinguish the dynamics of individual cases based solely on total entropy production.

Study region. The Cagayan Valley Region (Region II) in northern Luzon, Philippines, includes the Cagayan River Basin, the country’s longest river system and largest catchment. The basin is a major rice- and corn-producing area, but recurrent tropical-cyclone and monsoon floods cause damage almost every year. Study focus. This study assesses future flood hazard, exposure, and private-sector damage by integrating climate, land-use, and population scenarios within a common time frame. Climate forcing came from three CMIP6 GCMs – ACCESS-CM2, CanESM5, and EC-Earth3-Veg-LR – under four SSPs, and bias-corrected rainfall drove the RRI model. Future land use was projected by combining Land Change Modeler susceptibility scores with population- and crop-area demand constraints, while population distribution was estimated from government projections and spatial covariates. By overlaying projected land use, population, and inundation, the study quantifies crop-specific exposure, separates climate- and land-use-driven contributions, and evaluates land-use regulation. New hydrological insights. This integrated assessment shows that simulated peak discharge increases across all future SSP scenarios because rainfall events exhibit higher short-duration intensity and sharper temporal peaks. The 100-year inundated area increases by more than 31% under higher-forcing scenarios. Under the medium population scenario, exposed population increases by about 100%, from approximately 1 million at present to 2 million by 2070. Climate change mainly increases frequent-flood exposure, whereas land-use expansion amplifies rare-flood exposure and damage. Risk-informed land-use regulation reduces future housing losses by up to 154 million PHP yr −1 .