Earth and Environmental Sciences

Riparian ecosystems provide numerous services that are critical to integrated, sustainable water management. Their ecological functions face various threats, however, including the construction of impervious surfaces that alter watershed hydrology. The understanding of risks and the design of adequate solutions to the threats posed by impervious cover requires assessment throughout entire watersheds. Yet few assessments have considered parcel-scale changes over larger extents, particularly using readily available public data. Seeking to better characterize recent patterns and to understand how characterizations differ with alternative spatial resolutions and assumptions, we assessed statewide change in impervious land cover within riparian areas in Washington State, USA. Leveraging open data from a public decision-support application, we generated estimates based on high-resolution (1 m) change detections for 2011 to 2017, intersected with riparian areas defined from the current management guidance. As an illustrative contrast, we constructed estimates based on the 2011 to 2016 change in a national dataset of 30 m resolution land cover within a fixed buffer on a coarser stream network. Complementing these depictions of change, we also estimated the 2021 standing impervious area using an independent 1 m land cover layer within the management-based riparian extent for the western portion of the state. The “best available” high-resolution estimate of change indicated that riparian and floodplain impervious cover increased by hundreds of hectares a year statewide during the early and middle 2010s. New impervious cover was more prevalent within reaches associated with urban growth areas (UGAs) and in portions of the assessed extent used by highly valued Pacific salmon. The coarser contrasting approach yielded a similar overall magnitude of change, but this served to clarify methodological sources of uncertainty rather than to confirm accuracy. Notably, in addition to capturing larger blocks of impervious increase, high-resolution data revealed many individual changes that were smaller than a single 30 m × 30 m pixel. In 2021, standing impervious cover was also concentrated in UGA-associated reaches, which contained 43.5% of the impervious area despite being 5.2% of the assessed extent. Much of the observed change within the assessed extent was likely outside of the local riparian regulatory jurisdiction at the time, but the patterns revealed by high-resolution monitoring data underscore the importance of continuing to strengthen riparian protections to maintain ecosystem function.

To quantify the carbon footprint of cross-border bridges built by Chinese companies in Africa, based on the Janwani Bridge in Tanzania and the life cycle theory, it is divided into five stages: production, transportation, on-site construction, operational maintenance, and demolition and disposal. Using the emission factor method to construct carbon emission models for each stage, while considering cross-border supply chains and the addition of vegetation carbon sinks, we quantify the emissions for each stage. The research is based on the project design stage bill of quantities and construction organization data for prediction and estimation. The energy consumption parameters of construction machinery refer to the Chinese quota standards, and the energy consumption of lighting during the operation period is estimated according to the design parameters. The results show that the total carbon emissions of the life cycle of the bridge is about 41,668,548.20 kgCO2e, with the production stage being the dominant position (87.48%), and cement and reinforcing steel contributing more than 95% of the emissions during this stage. The operational maintenance stage comes second (7.28%), mainly driven by lighting electricity (accounting for 73.65% of the total emissions in this stage), attributed to the local power grid dominated by fossil fuels. Sensitivity analysis shows that the key factors are ranked as cement > reinforcing steel > electricity > diesel. Considering the reality of insufficient supply of low-carbon materials and weak infrastructure in Africa, emission reduction measures are proposed from three aspects: optimizing concrete mix proportion, controlling construction machinery, and implementing intelligent lighting. The research contribution lies in incorporating the entire cross-border transportation chain and newly added vegetation carbon sinks into the LCA boundary of bridges, while considering the dual attributes of “technology output + localized operation”, and constructing a carbon emission accounting model adapted to the built-up areas of African cities. On this basis, the carbon emission characteristics of the life cycle were quantitatively analyzed, feasible emission reduction measures in the region were proposed, and the carbon reduction potential was calculated, providing scientific basis for low-carbon control of Chinese enterprises’ overseas bridges.

Under the dual pressures of global climate change and rapid urbanization, the spatial contradiction between urban expansion and flash flood disasters in mountainous dam areas is increasingly evident. However, the mechanisms by which the multi-dimensional characteristics of urban expansion affect regional flash flood susceptibility (FFS) remain unclear, limiting scientific guidance for source-level disaster prevention. This study uses Zhaotong City, a flash flood-prone area in the lower Jinsha River basin of southwestern China, as a case study. Using land use and multi-source remote sensing data from 2000 and 2025, we identify urban expansion patterns and morphological characteristics, apply the XGBoost-SHAP model to evaluate flash flood susceptibility and determine dominant factors, and employ the generalized additive model (GAM) to quantify the nonlinear responses of expansion dimensions to FFS. Results show the following: (1) Urban expansion in Zhaotong City is primarily edge (51%) and leapfrog (46%), clustering along river valleys, dam areas, and transportation corridors. (2) The XGBoost model performs well (AUC = 0.877). Elevation, slope, normalized difference vegetation index (NDVI), and precipitation are the primary natural factors influencing FFS. About 15.66% of the city falls within the high/very high FFS zones, mainly in the Zhaolu Dam area, riverbanks of main and tributary streams, and the urban built-up area. (3) Urban expansion-related indicators explain 28.6% of the spatial variation in FFS, with leapfrog expansion as the primary driver (contribution rate 32.75%). Disorderly urban growth and morphological imbalance significantly increase flash flood susceptibility. This study provides a scientific basis for spatial planning, flash flood prevention and control, and climate-adaptive urban development in similar mountainous dam areas in Southwest China and Asia, supporting regional sustainable development goals.

Port decarbonization strategies often prioritize emissions under direct port authority control while overlooking dominant indirect sources. This study proposes an approach that combines Life-Cycle Assessment (LCA) and expert elicitation. While existing studies often rely on descriptive emission inventories, this paper demonstrates the value of combining quantitative life-cycle data with expert judgment. The methodology is applied to the Port of Sines, Portugal’s largest port by cargo volume and handling capacity. The LCA revealed that Scope 3 emissions account for over 99% of total greenhouse gas emissions, with ocean-going vessels as the main contributors. The expert elicitation process prioritized energy-related measures such as renewable energy, alternative fuels, electrification, and energy efficiency, while onshore power supply and ship–port interface measures received lower priority. By comparing the results, the study reveals a misalignment between the most significant emission sources (Scope 3 emissions, particularly ocean-going vessels) and commonly prioritized decarbonization measures (measures addressing Scopes 1 and 2). The main contribution lies in combining LCA findings and expert inputs to actively inform strategic decision making, helping ports realign decarbonization strategies toward high-impact measures and providing transferable insights for other ports pursuing net-zero objectives.

This study provides the first comprehensive assessment of microplastic (MP) contamination within oyster bed ecosystems of the Kingdom of Bahrain. Sediment, water, and oyster samples were collected from six sites representing diverse environmental conditions. Raman spectroscopy identified the presence of 12 distinct polymer types, with polypropylene (PP), polyurethane (PU), poly(ethylene terephthalate)/diamine/multi-walled carbon nanotube (PET/diamine/MWCNT), and fluorinated ethylene propylene (FEP) being the most prevalent. MPs occurred predominantly as fragments, films, and pellets, with black being the most common color across all matrices. MP abundances ranged from 750 to 1850 MPs/kg dry weight in sediments, 2100–9600 MPs/L in water, and 1.78–5.25 MPs/individual in oysters, with particles (<50 µm) most frequent in oyster tissues. Although spatial variation was evident across regions, detected polymers included types associated with known ecotoxicological risks. No significant correlation was observed between sediment grain size and MP abundance, suggesting that additional hydrodynamic or anthropogenic factors may influence MP distribution. Overall, this study provides critical baseline data on MP contamination in Bahrain’s marine environments and highlights the need for continued monitoring to assess potential risks to marine ecosystems and seafood safety. It also contributes to the limited understanding of MPs in the Arabian Gulf, informing future monitoring, conservation and policy initiatives that support long-term environmental sustainability.

The rapid urbanization of coastal zones has brought to light ecological and environmental issues at the junction between land and sea. Accurately identifying key areas for ecological restoration in coastal zones, as well as implementing projects for such protection and restoration, are effective strategies for addressing these challenges and ensuring the ecological security and stability of coastal zones. This study integrated terrestrial and marine spaces, employing the research logic of “patch (ecological sources)–network (ecological networks)–region (ecological restoration areas)” to establish a research framework for identifying key areas for ecological restoration of coastal zones. The findings presented in this paper demonstrate the following: (1) The ecological sources and ecological corridors in coastal ecological networks are primarily distributed across woodland, grassland, waters, and marine protected areas. This includes 19,233.48 km2 of land ecological sources and 6099.52 km2 of sea ecological sources, with the overall length of ecological corridors reaching 3154.59 km. (2) The ecological pinch points of the key areas are primarily situated in Jinzhou, Panjin, the southern part of Yingkou, and the Lushunkou district of Dalian. It is imperative to enhance the ecological functions within these regions. (3) The ecological barriers in the key areas are mainly concentrated in the central and western regions of Dalian. These areas should be rehabilitated based on land type and marine functional area classification in future endeavors. This study provides a scientific reference for the formulation and implementation of related coastal zone national ecological restoration plans.

Reservoir drawdown zones are repeatedly affected by water-level fluctuations and anthropogenic regulation, making vegetation recovery an important issue for ecological restoration and sustainable reservoir management. This study focused on Cynodon dactylon, a dominant herbaceous species in the drawdown zones of five reservoirs in the Jinsha River Basin, southwestern China. Drawing on the existing concept of stress memory, which emphasizes the retained effects of previous environmental stress exposure on subsequent plant responses, we established an integrated assessment framework based on species dominance, functional traits, landscape pattern indices, and the soil seed bank. This framework was used to evaluate variation in the stress memory of C. dactylon across different successional stages and inundation gradients. The results showed that the overall stress memory of C. dactylon increased with successional progression in both the upper and lower zones, indicating continuous adaptive accumulation under long-term hydrological disturbance. The memory reflected by individual component indicators also generally increased, although their accumulation patterns varied among indicators. These findings suggest that dominance, functional traits, landscape pattern, and the soil seed bank can jointly characterize the adaptive responses of C. dactylon during vegetation recovery. Overall, the stress memory framework provides a systematic approach for identifying stage-specific vegetation changes, evaluating restoration potential, and informing ecological restoration and sustainable management in reservoir drawdown zones.

[Objective] This study aims to reveal the spatiotemporal evolution and transition patterns of green utilization efficiency of cultivated land (GUECL) across Chinese provinces and to identify multidimensional configurational pathways for improving efficiency. [Method] Carbon emissions and total carbon sinks were incorporated into the evaluation index system of GUECL. The super-efficiency SBM model was used to measure GUECL. A three-dimensional analytical framework of “driving forces–external foundations–internal conditions” was then constructed. Exploratory Spatio-Temporal Data Analysis and the fsQCA method were combined to examine the spatiotemporal evolution characteristics and multiple configurational pathways. [Results] (1) From 2013 to 2023, GUECL showed a fluctuating upward trend, with the mean value increasing from 0.550 to 0.835. Spatially, it presented a pattern of high efficiency in Northeast China and low efficiency in Southwest China. (2) The local spatial structure of GUECL was generally stable, although its spatiotemporal transition paths fluctuated to some extent. The cooperative effects in northeastern and western provinces were stronger than the competitive effects. The spatiotemporal evolution showed strong path dependence and lock-in effects, and the spatial association pattern was mainly positive, indicating a high degree of spatial integration. (3) Efficiency improvement was driven by the coupling of multiple factors. Four specific configurations were identified and further summarized into three typical pathways: a socially driven and economic-foundation-led pathway assisted by resource conditions; an economic- and technological-foundation-led pathway dominated by resource conditions and assisted by policy support; and a multi-factor synergistic pathway. [Conclusion] GUECL is driven by the combined and synergistic effects of driving forces, external foundations, and internal conditions. Therefore, differentiated regional strategies should be adopted to promote the precise matching and coordinated governance of multiple factors, thereby supporting the green and high-quality development of agriculture.

Abstract This study evaluates a co-production of knowledge (CPK) process for developing climate change adaptation (CCA) indicators for measuring and assessing adaptation progress tailored to diverse municipal contexts. The CPK process involved 10 small to medium-sized Norwegian municipalities and consisted of iterative cycles of municipal homework, joint workshops, and researcher-led revisions of an initial indicator set. Using a mixed-method evaluation framework, we assessed both the CPK process and four expected outcomes: usable and relevant indicators, cross-departmental collaboration, anchoring of CCA in municipal practice, and intermunicipal knowledge exchange. The results show improvements in indicator usability and municipal CCA knowledge, and modest gains in interdisciplinary collaboration. However, limited municipal resources, discontinuous participation, and weak political and institutional anchoring constrained longer-term outcomes. The study highlights key design considerations for CPK with resource-constrained local authorities, including a strong focus on engagement and familiarity early on, fostering cross-participant mentorship, and supporting long-term network building.

Abstract. Peatlands play a crucial role in the global carbon cycle, acting as long-term carbon sinks. However, their stability is increasingly threatened by climate change, particularly through rising temperatures and the intensification of droughts. This study focuses on the Bernadouze peatland in the Pyrenees Mountains and aims to validate a newly implemented Sphagnum Plant Functional Type (PFT) in the ISBA land surface model, assess the temporal evolution of carbon fluxes over the past 64 years, and investigate the factors influencing carbon accumulation, with a particular emphasis on drought events. The model was validated using in situ data, demonstrating reasonable carbon flux estimations. Using this validated model, we reconstructed the net ecosystem exchange (NEE) dynamics of the Bernadouze peatland from 1959 to 2022. The results reveal significant interannual variability in NEE, largely driven by air temperature and water table depth. While the peatland has remained a carbon sink, extreme droughts such as those in 1989, 1994, 2003, and most recently 2022 have led to substantial CO2 emissions. Our findings suggest that although increasing temperatures have extended the growing season and enhanced gross primary productivity (GPP), the rising frequency and intensity of droughts pose a long-term risk to peatland carbon storage. The dryness index developed in this study appears to be a strong predictor of summer and annual NEE, offering a potential tool for estimating carbon fluxes in peatlands lacking direct measurements.

Abstract. Mercury (Hg) poses a global threat due to its long-range transport and transformation into methylmercury (MeHg), a potent neurotoxin that bioaccumulates in aquatic food webs. While global and regional efforts to reduce anthropogenic Hg emissions are ongoing, the implications of these policies for future Hg deposition and consequent MeHg levels in fish remain uncertain. This study synthesizes published modeling studies to examine projected relationships among Hg emissions, atmospheric deposition, and lake fish MeHg concentrations by 2050 under various policy scenarios. While models reveal a strong linear relationship between emissions and deposition (R2 = 0.79), and a moderate correlation between Hg deposition and fish MeHg (R2 = 0.63), these trends contrast with observational data, which often show nonlinear or more complex responses. Our analysis suggests that these relationships are partly shaped by shared structural assumptions within current modeling frameworks, particularly the treatment of atmospheric deposition as the dominant driver of future fish MeHg change and the representation of relatively short response times between changing Hg inputs and fish exposure, with limited representation of the time-lagged buffering provided by legacy Hg stores. Within these constraints, atmospheric deposition and lake area emerged as key predictors, with higher deposition and smaller lakes associated with higher modeled fish MeHg levels. Notably, despite wide variation in model structures, including differences in atmospheric chemistry, emission inventories, and food web dynamics, these linear trends persisted. By identifying these consistent patterns and the assumptions that support them, this study provides a benchmark for integrating currently underrepresented processes such as ecological complexity and climate-driven ecosystem feedbacks, into future assessments supporting the Minamata Convention.

Abstract. Andean tropical montane forests are highly biodiverse ecosystems with a carbon storage capacity comparable to lowland forests. However, their response to climate change remains uncertain, as species photosynthesis depends on their thermal acclimation capacity. This study evaluates the variability of photosynthetic traits across montane and lowland tree species using a leaf level photosynthesis model and data from a transplant experiment across three elevations varying in their mean annual temperatures (14, 22, and 26 °C) in the tropical Andes. Six montane species and two lowland species were analyzed to assess photosynthetic responses to environmental conditions. We find that intraspecific variability in photosynthetic parameters, such as the apparent maximum carboxylation capacity and the apparent maximum electron transport rate, is key to accurately model photosynthesis in these ecosystems. Apparent maximum carboxylation capacity was identified as the primary determinant of diurnal variations in photosynthesis, especially under varying thermal environments. Additionally, stomatal conductance was highly variable and responded to vapor pressure deficit, suggesting that stomatal regulation is crucial for adaptation to environmental changes. Sensitivity analysis revealed that at higher altitudes (14 °C), photosynthetically active radiation and temperature were the main limiting factors for photosynthesis, while at lower altitudes (22 °C), vapor pressure deficit was the dominant factor. Finally, the study demonstrates that the common use, within global vegetation models, of average parameters from lowland species to simulate montane forest is inadequate as such parameterizations tend to underestimate montane forest photosynthesis by up to 65 %. It is also recommended that vegetation models incorporate both intra- and interspecific variability to improve predictions of the carbon cycle in tropical Andean forests and their response to climate change.

Abstract. The terrestrial biosphere plays a critical role in regulating carbon and water fluxes. Rising global temperatures increase atmospheric dryness, which in turn raises atmospheric water demand on vegetation and places. Some plants regulate transpiration losses by closing stomata, at the cost of reduced carbon uptake. Quantifying stomatal regulation and detecting early onset of vegetation stress at large scales remains a challenge. Sap flow in stems responds to water potential gradients between the roots and the atmosphere, and therefore provides a window into transpiration and stomatal regulation. Based on SAPFLUXNET measurements of sap flow across tropical, temperate and boreal biomes, we demonstrate how variations in the diurnal cycle of sap flow as a function of vapor pressure deficit (VPD) measurements can elucidate the different levels of plant hydraulic stress. We derive two metrics based on sub-daily responses of sap flow to VPD: the morning sensitivity, given by the slope of the bi-variate relationship, and the area of the diurnal sap flow – VPD curve. We find that seasonal variations in the morning slope are positively associated with top soil moisture (0–30 cm). The area of the diurnal cycle, characterizing the degree of daily hysteresis between sap flow and VPD, increases with sap flow downregulation before peak VPD and is sensitive to temperature and soil moisture variability at seasonal time scales. While in situ sensors can provide continuous sap flow data, we aim to evaluate the potential to estimate descriptors of the diurnal cycle using temporally sparse data. In particular, as sap flow is connected to changes in water storage, which can be estimated using microwave remote sensing, we examine the degree to which the slope and area can be estimated for several acquisition strategies that vary in terms of the numbers of observations and acquisition times. We propose that sub-daily microwave observations, with at least three sub-daily overpasses could be used to characterize the sub-daily hysteresis and enable improved monitoring of tree hydraulic stress and, consequently, biosphere dynamics.

Future methane (CH4) emissions from natural wetlands are predicted to increase due to global warming, leading to positive feedback on climate change. However, the magnitude of this increase remains highly uncertain. Here we present novel ensemble simulations of seven state-of-the-art terrestrial biosphere models to estimate wetland CH4 emissions (eCH4) during the twenty-first century. Our estimates suggest that for every 1 °C increase in global land surface temperature, there is a 24 ± 10 Tg CH4 yr−1 increase in eCH4. We also identify an emergent relationship between contemporary temperature dependence and projected eCH4. When constrained by 163 site-year eddy-covariance measurements of eCH4, we show that wetland emissions can increase by 50–60% by the 2090s relative to the 2010s under a high-warming scenario. The projected decadal increase in eCH4 from the 2010–2019 baseline to the 2030s would very likely (90% probability) offset an amount equivalent in scale to 8–10% of anthropogenic eCH4 at the 2020 level, comparable to the reductions committed under the Global Methane Pledge. However, the constraint is dominated by mid- and high-latitude observations, with limited tropical coverage, and uncertainties in projected wetland inundation contribute substantially to uncertainty in eCH4. Our findings reduce the uncertainty in projected wetland methane–climate feedback and highlight its potential impacts on methane mitigation efforts to slow global warming. Enhanced future methane emissions from global wetlands under warming could substantially offset the emissions reduction goals of the Global Methane Pledge, according to ensemble simulations from terrestrial biosphere models.

Evapotranspiration strongly couples land and atmosphere to regulate water, carbon and energy fluxes across tropical South America. Ongoing deforestation and fires reduce the capacity of deep-rooted trees to recycle moisture, while intensifying droughts further alter the timing and magnitude of evapotranspiration. Here we present a high-resolution, data-constrained hydrological modelling analysis to isolate the effects of anthropogenic disturbances and droughts on evapotranspiration and vegetation function across the Amazon and adjacent biomes from 2003 to 2020. We find that evapotranspiration declines from deforestation persist 21–22% longer than those caused by fire or drought alone. When these stresses co-occur, evapotranspiration losses intensify by 36% and persist 66% longer than the average impact of individual stressors. Across neighbouring biomes, we find that grasslands and savannas in the Cerrado are most vulnerable to droughts, with evapotranspiration recovery often exceeding seven years, while Pantanal wetlands recover rapidly due to sustained moisture availability. Furthermore, vegetation productivity declines under compounding stresses despite concurrent greening trends. Our findings reveal that recurrent human disturbances erode ecosystem resilience, threatening long-term ecological stability. Isolating the human footprint on evapotranspiration is pivotal to guide sustainable land-use transitions that preserve land–atmosphere coupling in South America’s tropical ecosystems. Human-driven disturbances intensify and prolong evapotranspiration declines across South American ecosystems, disrupting the water balance and threatening ecological resilience, according to hydrological modelling and remote sensing data.

Abstract. This study explores the use of relaxation experiments in 2 machine learning-based weather prediction (MLWP) models to identify sources of subseasonal predictability in comparison to a traditional numerical weather prediction (NWP) system. Tropical relaxation involves nudging specific tropical regions of a model toward reanalysis data to isolate their influence on forecast skill. We apply this technique to Pangu-Weather (fully data-driven) and NeuralGCM (hybrid) and compare the experiments to the Unified Forecast System (UFS). The focus is on the week 3–4 forecast of 2 major precipitation events in western North America in winter 2022/2023, both linked to Madden–Julian Oscillation (MJO) activity. For the 2 cases, MLWP models exhibit higher forecast skill than the UFS at subseasonal lead times. Though tropical relaxation improves the skill in all forecast systems, gains are greater for UFS, reflecting the MLWP models' stronger baseline performance. A Rossby wave source (RWS) analysis shows that tropical relaxation consistently improves the large-scale dynamic processes associated with the tropical–extratropical teleconnections leading to both events. These results highlight the potential of relaxation experiments as an effective diagnostic for understanding and improving subseasonal forecasts, especially in emerging MLWP systems.

Abstract The current generation of Earth System Models (ESMs) exhibit substantial biases and inter-model spread in representing ocean deoxygenation and the expansion of tropical Oxygen Minimum Zones (OMZs). Improving dissolved oxygen simulation is challenging as it depends on uncertain physical and biogeochemical processes and their interactions. A machine learning–based emulator, O2EMU, aims to reduce model biases and inter-model spread by replacing ESMs’ biogeochemical parameterizations with the learned relationships between dissolved oxygen and physical variables. The emulator first learns from the historical shipboard and autonomous observations of dissolved oxygen and temperature and salinity from ocean reanalyses. The learned relationships are then applied to the temperature and salinity outputs from an ensemble of ESM historical simulations. The results show significantly improved climatological OMZ structure and long-term oxygen trends including the shoaling and expansion of the tropical Atlantic OMZs. Application of O2EMU to ESM simulations can reduce the inter-model spread by 70–75% and the root mean square error by 50–60%, offering a computationally efficient and scalable alternative to standard biogeochemical models, and providing a stepping stone towards hybrid biogeochemical projections that blend mechanistic ESM dynamics with observationally constrained tracer distributions.

Since 2013, China has implemented strict air cleaning policies, yet atmospheric nitrate (NO3−) pollution remains inadequately mitigated in many regions. It is widely recognized that enhanced atmospheric oxidation promotes NO3− formation, undercutting the effectiveness of coal combustion source (CCS) controls. However, the impact of contributions from multiple non-coal combustion sources (NCCS) has consistently been overlooked. Here, we quantify the formation pathways and sources of NO3− in China over the past twelve years through long-term measurements of the dual-isotope composition (δ15N and δ18O) of NO3− combined with machine learning. Our results reveal that, despite substantial shifts in NO3− formation pathways, its formation efficiency remains largely invariant. Moreover, while CCS has reduced its contribution to NO3− by 12.3% as of 2025 in China, four NCCSs, enhanced by the energy transition and climate warming, are emitting more NOx precursors in many regions. Individually, each of these NCCSs now contributes at a level comparable to that of CCS. We estimate a nationally averaged decrease in NO3− concentration of only 7.3% in China in the future, even if CCS’s contribution is further halved relative to 2025 haze day levels. Our study underscores the urgency of implementing coordinated multi-source NOx control strategies to achieve sustained improvements in air quality.

Abstract. Atmospheric rivers (ARs) have been recently identified in the Mediterranean basin, where they have been shown to play an important role in intense precipitation events over northern Italy and the Alpine chain. In fact, as demonstrated by two recent severe events (27–29 October 2018; 2–3 October 2020), the synoptic pattern conducive to heavy rainfall in this area may favour intense moisture transport from remote regions towards the Alps. In these events there was either a south-westerly moisture advection directly from the Atlantic Ocean Tropical area across the African continent, or a north-westerly transport over the Atlantic area, entering the Mediterranean in correspondence of the Gibraltar Strait. In order to identify ARs in such a complex geographical area, a well-known algorithm of objective detection has been adapted to the morphology of the Mediterranean basin and to the peculiar shape of the organized water vapour transport, which may differ from that generally observed in the ARs over the open ocean. The two above-mentioned case studies have been used for testing the procedure. The algorithm has been applied in conjunction with some additional selection criteria to identify only those AR events in the western Mediterranean that affected northern-central Italy over the last ∼ 60 years. A climatological analysis is provided and the possible correspondence between the most intense identified ARs and extreme rainfall events is investigated. Exploiting a precipitation dataset for northern-central Italy (ArCIS), some areas turned out to be particularly exposed to extreme precipitation events in the presence of ARs.

Abstract Groundwater, as the largest storage of liquid freshwater on Earth and a major component of the water cycle, is critical for supporting surface water flow, ecosystems as well as water and food security. Human dependence on groundwater has increased sharply over recent decades, contributing to wealth creation and poverty alleviation. However, anthropogenic activities, including groundwater abstraction and climate change, have modified groundwater dynamics, leading to groundwater level (GWL) changes. We analysed 20 year time series of GWLs in 47 countries distributed across a range of climatic, geographic, hydrogeological and socioeconomic contexts worldwide. Using different indicators based on three time series analysis methods, we show that almost one third of the GWLs trends are declining—thus reflecting overexploitation of groundwater—while GWLs are rising in 18% of wells—not always indicating a recovery but also the consequence of human impact on the environment. Globally, GWLs show a mix of declining and rising trends, however, at regional scale, patterns and hotspots are evident. We also explored the associated impacts of GWL change on humans and ecosystems, by reviewing 28 case study examples. We show that both rising and falling GWLs have substantial impacts on water and food security, ecosystems, infrastructure and socioeconomic wellbeing. Our findings underscore an urgent need for expanding monitoring programmes, protecting groundwater, and defining acceptable impacts to determine sustainable groundwater usage and minimise impacts of GWL change.

Abstract Recent studies have identified three distinct modes of propagation associated with the boreal summer intraseasonal oscillation (BSISO): canonical (northeastward), northward dipole, and eastward expansion. However, the impacts of these three BSISO modes on western North Pacific tropical cyclone (TC) activity remain unclear. To address this question, we identified 98 BSISO events during the boreal summer from 1979 to 2021, comprising 47 canonical, 26 northward dipole, and 25 eastward expansion events. In terms of TC genesis, the canonical and eastward expansion modes primarily influence TC genesis over the South China Sea, whereas the northward dipole mode modulates TC genesis across the western North Pacific. These differences arise from BSISO-driven shifts in the large-scale environment and associated monsoon trough displacement. In terms of TC tracks and regional impacts, TCs tied to the canonical mode disproportionately affect southern China, whereas those linked to the northward dipole mode more strongly impact eastern China. The meridional migration of synoptic-scale waves and the zonal shift of the western Pacific subtropical high underpin the observed TC impacts across China driven by various BSISO modes. These findings highlight the distinct modulation of TC activity by different BSISO modes and offer useful insights for intraseasonal TC forecasting. Significance Statement Although the boreal summer intraseasonal oscillation (BSISO) has three main propagation types: northeastward (canonical), northward dipole, and eastward expansion, how these modes affect tropical cyclone (TC) activity in the western North Pacific is unclear. Analysis of 98 BSISO events (1979–2021) reveals distinct regional impacts. Canonical and eastward expansion modes primarily modulate TC genesis in the South China Sea, while the northward dipole mode influences TCs across the western North Pacific. Consequently, canonical mode TCs primarily impact southern China, while northward dipole mode TCs more strongly affect eastern China. These differences stem from shifts in the large-scale environment, monsoon trough, synoptic-scale waves, and the western Pacific subtropical high. These findings provide valuable insights for improving intraseasonal TC forecasts.

Abstract Severe precipitation in the Yangtze River Basin (YRB) poses escalating flood risks, underscoring urgent needs for skillful subseasonal prediction. In this study, we develop an integrated dynamical‐statistical downscaling model based on overlapping circulation‐precipitation co‐evolution (OCPCE), which merges prior and concurrent circulation evolution to predict rainfall anomalies. The core innovation shifts from conventional downscaling of dynamical model‐predicted circulation to an integrated framework combining observed recent evolution with highly predictable portions of future circulation from dynamical subseasonal‐to‐seasonal (S2S) models within an optimal overlapping time window. Implemented via evolution‐based singular value decomposition, this design maximizes retention of useful initial information while suppressing lower‐skill long‐lead predictions. The OCPCE model demonstrates statistically significant deterministic skill over YRB and reliable probabilistic predictions at 10–40‐day leads, substantially outperforming direct ECMWF‐S2S predictions. This work offers a physically coherent and operationally viable framework for improving subseasonal precipitation prediction, providing critical support for early flood warning and proactive disaster prevention.

Abstract Classic Recharge Oscillator (RO) studies typically rely on spatially averaged indices, which capture the temporal characteristics of ENSO mode but provide limited information on its spatial structure. To address this limitation, a Recharge Oscillator‐based Linear Inverse Model (RO–LIM) was developed by embedding the recharge–discharge physics of ENSO into a data‐driven framework, enabling simultaneous diagnosis of its linear stability and spatial structure. Applied to reanalysis data, RO–LIM identifies a damped ENSO mode (decay rate of 0.29 years −1 ) for 1980–2023. After 2000, ENSO became more strongly damped (0.4 years −1 ) and transitioned from an eastward‐propagating eastern‐Pacific type to a westward‐propagating central‐Pacific or mixed type. This regime shift arises from weakened thermocline‐wind coupling but strengthened zonal current‐wind coupling under a La Niña‐like mean‐state change with enhanced trades and a steeper thermocline. RO–LIM provides a physically grounded and quantitative tool for diagnosing ENSO stability, spatial diversity, and their modulation by slow mean‐state change.

Abstract. Soil moisture (SM) is a critical component of the hydrological cycle, but accurately predicting it remains challenging due to the nonlinearity of soil water transport, variability in boundary conditions, and the intricate nature of soil properties. Recently, deep learning has shown promise in this domain, typically by modeling temporal dependencies for soil moisture predictions. In this study, we propose non-local neural networks (NLNNs) to convert this problem into a single-time-step, simultaneous multi-depth soil moisture forecasting. The non-local operation design includes embedded Gaussian operations and disentangled knowledge-guided operations, resulting in two variants: the self-attention non-local neural network (SA-NLNN) and the knowledge-guided non-local neural network (KG-NLNN). The knowledge-guided non-local operation is designed to capture vertical soil moisture relationships by decomposing the influences on soil moisture at a given depth into four components, each governed by distinct physical processes. The models offer visual interpretability through learned non-local weights, which reveal interactions among soil moisture across different depths, thereby enabling a qualitative representation of inter-layer connectivity. Notably, the model guided by soil moisture transport knowledge yields more stable and reasonable interpretations. With in-situ observations, we demonstrate that our proposed models perform satisfactorily. The knowledge-guided non-local operations significantly enhance accuracy and reliability. Additionally, our models adapt to diverse time-scale situations while maintaining high computational efficiency. Both models exhibit robust noise resistance, with knowledge guidance enhancing KG-NLNN's noise resistance. In summary, our work addresses the soil moisture prediction challenge in a novel way, highlighting the potential of NLNN and the importance of incorporating physic guidance in data-driven models.