Earth and Environmental Sciences

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

Hyperspectral infrared observations are an important data source for numerical weather prediction (NWP) because they provide rich information on the vertical structure of atmospheric temperature and humidity. However, most existing deep learning methods mainly focus on one-way retrieval from radiances to atmospheric profiles, while the reverse radiance simulation process and the consistency between atmospheric state space and radiance observation space are insufficiently considered. In this study, we propose SIMBA, a unified bidirectional retrieval–forward simulation framework for FY-4A/GIIRS hyperspectral infrared radiance modeling toward NWP applications. The framework jointly performs atmospheric profile retrieval and radiance reconstruction, introduces a cycle-consistency constraint to strengthen the coupling between the two processes, and employs a bidirectional Mamba state-space module to capture long-range dependencies along pressure levels. Using collocated FY-4A/GIIRS observations and ERA5 reanalysis data, the proposed method is evaluated for temperature retrieval, specific humidity retrieval, long-wave radiance reconstruction, and medium wave radiance reconstruction. Experimental results show that SIMBA outperforms several representative deep learning baselines across both retrieval and reconstruction tasks, while ablation experiments confirm the contribution of the bidirectional design and cycle-consistency mechanism. These results demonstrate that the proposed framework is effective for joint atmospheric profile retrieval and hyperspectral infrared radiance modeling, and suggests potential for future Jacobian-related analysis and NWP-oriented extensions.

Forest-steppe ecotones exhibit pronounced spatiotemporal heterogeneity and complex climate–vegetation interactions, posing significant challenges for vegetation dynamics prediction. Existing models often struggle to capture long-range temporal dependencies, preserve spatial continuity across heterogeneous transition zones, and provide ecologically interpretable insights. To address these limitations, we developed a bidirectional Geo-Spatial Mamba (Geo-S-Mamba) architecture with a multi-objective loss function incorporating spatial continuity constraints based on the first law of geography. The model was trained using multi-source geospatial datasets and independently validated during 2019–2023. The results show that Geo-S-Mamba achieved an R2 of 0.93. Moreover, both the bidirectional mechanism and the spatial-continuity loss improved the PSDI by approximately 0.08. The model effectively captured annual variations in NDVI and covariation among vegetation groups. Post hoc symmetric causal learning based on Pearl’s structural causal theory indicated that precipitation was the primary driver of grassland vegetation dynamics. Temperature and radiation influenced NDVI mainly through boundary-dependent effects. Overall, this framework can estimate changes in the spatial distribution of plant communities across heterogeneous environments and provides a scientific basis for further research on forest–steppe ecotones.

ABSTRACT This study investigates the meteorological characteristics and associated human thermal discomfort of downslope windstorms known as ‘Vento Norte’ (VNOR; ‘North Wind’ in Portuguese) occurring in the city of Santa Maria, southern Brazil, over 20 years (2004–2023). VNOR events are characterized by unseasonable strong northerly winds, abrupt temperature rises and significant drops in relative humidity. Utilising hourly meteorological data, we identified 189 VNOR episodes, predominantly occurring during austral winter months (June–August), reflecting a seasonal pattern. VNOR events significantly altered local thermal comfort conditions, as demonstrated through a comparative analysis of five biometeorological indices: Effective Temperature with Wind (TEFW), Thermal Discomfort Index (TDI), Temperature–Humidity Index (THI), Human Discomfort Index (HDI) and Humidex (HU). Statistical analyses revealed significant differences between VNOR and non‐VNOR periods, with VNOR episodes consistently associated with elevated human thermal discomfort. A detailed examination of representative events further illustrated abrupt changes in meteorological parameters, highlighting the potential for rapid thermal stress in the local population. The present analysis demonstrates the importance of monitoring VNOR phenomena and their thermal impacts and suggests how these downslope windstorms alter local atmospheric conditions, providing an environmental baseline that could support future risk‐awareness frameworks and biometeorological monitoring protocols in southern Brazil.

Abstract A novel classification of three‐dimensional heat wave types is applied to Middle Europe over 1979–2022. Heat waves are classified according to their vertical structure of temperature anomalies in the ERA5 reanalysis into near‐surface (HWG), lower‐tropospheric (HWL), higher‐tropospheric (HWH), and omnipresent (HWO). Jenkinson–Collison classification is used to link circulation types (CTs) to individual heat wave types, and their climatological characteristics are studied in station‐based E‐OBS data. We show that the surface temperature anomalies are largest during HWG, which also are characterized by the driest conditions before onset. In all heat wave types, CTs with southerly flow are more common compared to the June–September climatology, but differences among the heat wave types are found for other CTs. In HWG, the CT occurring most frequently is indeterminate flow, corresponding to a little pronounced pressure field with no clear role of anticyclonic vorticity or flow direction. The expected pattern of increased anticyclonic and decreased cyclonic flow is clearly manifest only for HWH, while it is reversed during HWG. An important role of anticyclonic circulation supporting gradual warming is found before the onset of most heat wave types, except for HWH. The differences in circulation characteristics among the heat wave types are smaller after their termination, which is often associated with relatively cold westerly flow. The analysis contributes to better understanding the interrelationships among different heat wave types, atmospheric circulation, and other driving mechanisms.

Excessive infiltration and inflow (I&I) of environmental water into sewer networks renders conventional wastewater treatment ineffective and costly. Physical inspection techniques (e.g., Closed Circuit Television and Sonar) are the go-to methods for diagnosis but are expensive ($5–15 USD/m). Chemical fingerprinting is over an order of magnitude cheaper ($0.5–1.5 USD/m) but the diagnosis is delayed due to reliance on laboratory analysis and is error-prone using deterministic algorithms. To overcome such limitations, sewer samples ( n = 21) were collected from a small (0.5 km 2 ) coastal district of Shenzhen and analyzed initially for 9 tracers including pharmaceutical compounds unique to sewage, but later focusing on 3 tracers (electrical conductivity (EC), radon-222 ( 222 Rn), and ammonia nitrogen (NH 3 −N)). New data from 31 environmental water samples were critical for constraining endmember compositions. A Bayesian Chemical Mass Balance (CMB) model distinguished and quantified seawater, groundwater, and rainwater intrusion probabilistically. The model using 3 tracers under dry-weather conditions (or 4 when δ 18 O was included for rainwater inflow during wet-weather events) yielded sewage proportion estimates comparable to that of 8 or 9 tracers, differing only by 3.0% ± 2.5%, although the mean relative error was twice as high. At four sewage wells, raw sewage accounted for 32.3 ± 13.3%, 12.8 ± 5.3%, 41.9 ± 15.1% and 33.6 ± 13.4% along the flow paths in August 2021. Significant temporal variability was evident at one well, with raw sewage contributions ranging from 21.2 ± 6.7% in August 2021 to 58.8 ± 8.0% in November 2022 and 50.8 ± 37.5% in January 2025.

Study region The Zambezi River Basin (ZRB) is a large transboundary basin in south-central Africa shared by eight countries. It provides vital water resources for hydropower, agriculture, ecosystems, and human use across the region. Study focus The remote sensing-based Water Accounting Plus (WA+) framework was applied to the ZRB from 2003 to 2023 to quantify its water balance. The WA + methodology was used to overcome sparse ground data by leveraging satellite-derived datasets such as precipitation ( P ), actual evapotranspiration ( ET a ) and land cover and use (LULC). New hydrological insights for the region The WA + water balance for the ZRB reveals that P (1280.5 km³/year) is the dominant inflow, while ET a (1095.6 km³/year) constitutes 85.5% of the gross inflow. This leaves a long-term mean surface water yield ( P–ET a ) of 184.9 km³ /year. A substantial portion of the inflow (115.9 km³/year) is lost to storage, suggesting significant basin-scale water-storage potential. The exploitable water averages 106.6 km³ /year, of which 19.9 km³ /year is reserved outflow and 0.2 km³ /year is non-utilisable outflow; thus, about 86.4 km³ /year remains available for use. Currently, 44.5 km³ /year is utilised flow, leaving 42.0 km³ /year that is still undeveloped. Overall, the basin sustains a net-positive water balance, though water availability declined over the study period, coinciding with a general downward tendency in P and elevated ET a in recent years; however, the basin-scale trends in P and ET a were not statistically significant.

Study area North-eastern Ethiopia, Lower Awash Basin Study focus The study aims to investigate the processes influencing groundwater composition and evolution using ionic ratios, chemometrics, and inverse geochemical modeling based on major ion chemistry of 164 groundwater samples that were collected from hand-dug wells, springs, and boreholes New hydrogeological insights The Results from the Gibbs plot, Ca + Mg vs SO 4 +HCO 3, Ca/Na vs HCO 3 /Na vs Ca/Na vs Mg/Na, and Chloralkaline indices showed that rock water interaction, silicate weathering, cation exchange, and evaporation are the major geochemical processes that control the groundwater chemistry. Principal component analysis identified three components: PC-I and PC-II reflect geogenic processes, whereas PC-III indicates anthropogenic influences. Hierarchical cluster analysis grouped the groundwater samples in to two main clusters: cluster I represents fresh Ca-Mg-HCO₃ type from the recharge zone alongside a mixed water type from transition zone, while cluster II includes mineral-rich samples from the discharge zone (Na-HCO 3 and Na-Cl). Inverse geochemical modeling indicated that major geochemical process that affect groundwater chemistry and evolution are dissolution of primarily silicate minerals such as plagioclase, olivine, and pyroxene, along with the precipitation of calcite and clay minerals. This study addresses the gap in conventional hydrochemical approaches in the understanding of hydrogeochemical processes by employing a quantitative approach and contributes in the formulation of policies for sustainable groundwater resource management.

Total organic carbon (TOC) is a critical parameter for evaluating hydrocarbon source rocks, but its strong spatial heterogeneity challenges accurate regional prediction using limited well sample data. Therefore, this study presents an integrated geophysical workflow for the three-dimensional (3D) quantitative prediction of TOC in the post-salt marine source rocks of the Madingo Formation, Lower Congo Basin. Calibrated with 60 measured samples, three log-based TOC models (multiple regression, improved ΔLogR, and a Back-Propagation neural network [BPNN]) were evaluated, and the optimal log-derived TOC was integrated with seismic attributes to construct a 3D TOC volume via seismic impedance inversion. Comparative results demonstrate that the BPNN outperforms traditional empirical models, yielding a correlation coefficient of R = 0.9342. The 3D prediction reveals pronounced spatiotemporal heterogeneity in TOC distribution, which is strongly controlled by sedimentary facies, reaching maximum concentrations in the deep-water slope zone and decreasing towards shallow-water zones. This integrated data-driven approach effectively addresses the limitations of discrete sample analysis, providing a quantified predictive framework that helps constrain prediction uncertainty for deep-sea hydrocarbon exploration in heterogeneous marine source rocks.

Abstract In the induced magnetotail of Mars, the neutral sheet is an important channel for atmospheric ion escape, yet the role of heavy planetary pickup ions in shaping its geometry has been unclear. Using three‐dimensional hybrid simulations, we demonstrate that these ions induce a global neutral sheet asymmetry via mass loading. Ambient plasma flow deceleration bends draped interplanetary magnetic field (IMF) lines, extending the sheet along the direction at large IMF Parker spiral angles and tilting its ‐hemisphere segment toward the plane in the Mars–Solar–Electric coordinate system at small angles. The asymmetry's magnitude is modulated by both the planetary ion production rate and solar wind dynamic pressure. These results show that Mars' magnetotail is not solely governed by IMF draping and crustal fields, but also dynamically reshaped by pickup ions.

Abstract The spatial distribution of suspended sediment load is a key indicator for watershed management by identifying primary sediment source areas and elucidating transport‐deposition patterns. However, clarifying such spatial patterns in alpine regions is challenging due to strong sediment heterogeneity and sparse hydrological monitoring data. Here, we focus on the Yarlung Tsangpo River and apply a global sediment simulator model to estimate mean annual suspended sediment loads at 17 cross‐sections along the mainstream and its tributaries. The model demonstrates good applicability in this region, with a high coefficient of determination ( R 2 = 0.97) between predicted and observed values. The sediment load exhibits significant spatial heterogeneity with a coefficient of variation (CV) of 1.31. A generally downstream increasing trend is observed, whereas the lower reach functions as a ‘sediment factory’ of the basin, contributing 92.9% of the sediment load (SL) at the basin outlet, with an average specific sediment yield (SSY) of 4495 t/km 2 and a local maximum of 18 629 t/km 2 . In contrast, the middle reach contributes only 5.9% of the sediment load and exhibits a high modern deposition rate of 81 Mt/a, far exceeding the sediment export of 14.36 Mt/a. Transport and deposition zones alternate spatially, with deposition predominantly occurring at tributary confluences and upstream of riverbed knickpoints. These insights advance the understanding of sediment production and transport mechanisms in complex alpine river systems and support informed water resource development.

The availability of reliable ground-truth data is one of the main bottlenecks for improving high-resolution forest attribute maps from Earth observation data. This is underpinned by the European Union (EU) Forest Strategy for 2030 that underscores the need for harmonized, cross-border forest resource assessments that integrate both remote sensing and field-based National Forest Inventory (NFI) data. However, confidentiality constraints on NFI plot coordinates present a significant barrier to aligning these datasets, thereby limiting the development of unified forest monitoring systems that can fully leverage the potential of Earth Observation data. To overcome these data-sharing limitations we explored the effectiveness of a privacy-enhancing technique, known as Federated Learning (FL), that is a form of distributed computing aimed at preserving the privacy and confidentiality of data owned by different organizations. This methodology has been tested for the collaborative modelling and mapping of forest timber volume across four European countries: Norway, Sweden, Finland, and Italy. We employed a time-series convolutional neural network (CNN) architecture tailored to integrate 40 years of Landsat or 7 years of Sentinel imagery and terrain variables with harmonized NFI data from more than 85,000 sample plots. This model architecture was used for the FL approach and compared to traditional country-specific and centralized modelling strategies. FL models achieved predictive performances comparable to the traditional models, which proofs the effectiveness of the proposed approach. Centralized or global models showed slightly reduced performance compared to the national models, highlighting the value of fine-tuning with local ground-truth data. By aligning with the EU’s forest monitoring objectives, FL facilitates the generation of harmonized models and maps of forest features, like timber volume and biomass, that are critical to support evidence-based forest policy and management. The findings underscore the potential of FL to transform collaborative environmental monitoring, particularly in domains where data confidentiality and interoperability are critical.

Urban rivers in megacities serve as major sinks for diverse emerging contaminants (ECs). However, a comprehensive understanding of their sources and dynamics remains limited. This study employs an integrated targeted and nontargeted analysis to investigate ECs in the Wenyu River basin of Beijing and its associated sewage treatment plant (STP) effluents. We identified/tentatively identified and quantified 480 ECs, with household chemicals dominating the contaminant profile. Hierarchical cluster analysis delineated five distinct anthropogenic source types: influenza prevention compounds, daily lifestyle chemicals, sunscreen/insecticide residues, rural diffuse inputs, and traffic/waterfront landscape emissions. Results indicate that STP effluents constitute the primary intermediary for the first three categories, exerting a dominant influence on riverine EC levels, demonstrated by path coefficients of 0.736 in spring and 0.585 in summer. Seasonal variations revealed clear patterns: influenza- and sunscreen-related sources showed pronounced fluctuations corresponding to flu season and sunlight exposure periods, whereas traffic-landscape sources peaked during summer rainfall events. Ecological risk assessment indicated that 91.4% of the total environmental risk originated from STP-derived ECs, with telmisartan and fipronil identified as the compounds posing the highest ecological risk. This study provides new insights into the source-specific dynamics and seasonal drivers of ECs in urban rivers, offering a scientific basis for targeted pollution control strategies.

Anaerobic digestion effluent (ADE) is an attractive feedstock for resource recovery due to its high concentrations of ammonia (NH 3 ) and phosphate (PO 4 3– ). However, the presence of elevated bicarbonate (HCO 3 – ) imparts strong buffering capacity, which inhibits PO 4 3– precipitation and increases chemical demand for NH 3 recovery. To overcome HCO 3 – interference, this study proposes an enzyme-based pretreatment that converts HCO 3 – into PO 4 3– using phosphoenolpyruvate carboxylase (PEPC). PEPC was immobilized onto carboxylated polystyrene beads via EDC/NHS coupling (PB@PEPC) to enhance stability and reusability. When applied to real ADE, 25 PB@PEPC beads achieved a 2.94-fold reduction in HCO 3 – concentration (from 6013.8 ± 339.8 mg·L –1 to 2045.5 ± 283.5 mg·L –1 ) within 12 h, while simultaneously increasing PO 4 3– concentration 10.79-fold (from 590.8 ± 148.3 mg·L –1 to 6363.3 ± 383.5 mg·L –1 ). The enriched PO 4 3– was recovered as calcium phosphate with efficiencies exceeding 95% using Ca(OH) 2 at a Ca/P molar ratio of 2.5. The elevated pH during PO 4 3– precipitation promoted NH 4 + conversion to volatile NH 3, enabling up to 82.4 ± 6.1% NH 3 recovery using a membrane contactor without additional alkaline reagents. PB@PEPC retained 86.1 ± 4.2% of its initial activity after five reuse cycles. Overall, this integrated process offers a sustainable pathway for simultaneous nutrient recovery from complex wastewater.

The seafloor is recognized as a major sink for marine debris, while deep-sea litter remains poorly investigated due to the logistical and economic constraints associated with seabed exploration. This study reports for the first time the occurrence of marine debris found on the Argentine deep seafloor using the SOI’s Remotely Operated Vehicle (ROV), providing the first documented evidence for this region. A total of 29 litter items were recorded across 55.6 km of surveyed seafloor. The highest debris abundance was recorded in submarine canyon areas (1.4 items/km), whereas the Malvinas Basin showed the lowest levels (0.1 items/km), likely associated with hydrodynamic conditions. In addition, plastic items, fishing ropes, and nets were the most frequently recorded types of debris. These findings establish a baseline of the current status of deep-sea marine debris and propose future directions for the region.

Abstract Urban weather station networks (WSNs) are widely used to monitor urban weather and climate patterns and aid urban planning. However, maintaining WSNs is expensive and labor-intensive. Here, we present a step-wise station removal procedure to thin an existing WSN in Freiburg, Germany, and analyze the ability of WSN subsets to reproduce air temperature and humidity patterns of the entire original WSN for a year following a simulated reduction of WSN density. We found that substantial reductions in station numbers after one year of full deployment are possible while retaining high predictive accuracy. A reduction from 42 to 4 stations, for instance, increased mean prediction RMSEs from 0.69 K to 0.83 K for air temperature and from 3.8% to 4.4% for relative humidity, corresponding to RMSE increases of only 20% and 16%, respectively. Predictive accuracy is worse for remote stations in forests than for stations in built-up or open settings, but better than a state-of-the-art numerical urban land-surface model (Surface Urban Energy and Water Balance Scheme). Stations located at the edges between built-up and rural areas are most valuable when reconstructing city-wide climate characteristics. Our study demonstrates the potential of thinning WSNs to maximize the efficient allocation of financial and personnel-related resources in urban climate research.

Abstract Urbanization and climate change have increased the frequency and severity of stormwater-related challenges, necessitating more effective approaches for selecting green infrastructure (GI) during the planning and design process. Although numerous studies have evaluated the environmental performance of individual GI practices, comparatively limited attention has been devoted to translating performance assessments into practical guidance for selecting and prioritizing GI practices before implementation. To address this gap, this research developed a pre-design GI prioritization framework by integrating local sensitivity analyses with three deterministic performance models. The framework quantified runoff mitigation, ecosystem-service benefits, and 20-year life-cycle costs to establish hierarchical rankings of GI practices under alternative decision-making scenarios. The framework was applied to a 266-ha redevelopment scenario in downtown Lansing, Michigan, USA, and subsequently evaluated through post-design performance prediction. Results consistently identified rain gardens, street planters, infiltration basins, wetlands, and vegetation filter strips as the highest-priority practices under the balanced environmental-economic scenario. Implementation of the resulting GI plan reduced annual runoff volume by 52%, decreased pollutant loading by 9%–15%, and generated substantial carbon sequestration benefits. The study demonstrates how sensitivity-analysis results derived from multiple performance models can be synthesized into a practical decision-support framework for pre-design GI prioritization, providing a transferable method for informing GI planning and resource-allocation decisions.

Abstract El Niño–Southern Oscillation (ENSO) precipitation diversity exhibits pronounced phase-dependent asymmetry over the tropical Pacific. Observations indicate enhanced precipitation diversity over the western North Pacific during La Niña and over the equatorial central Pacific during El Niño. This asymmetry is associated with the positively skewed distribution and intensified extremes of tropical precipitation, implying that diversity tends to increase during ENSO phases with enhanced mean precipitation. Atmosphere-only simulations reasonably reproduce the observed asymmetric features of ENSO-related precipitation diversity, whereas fully coupled simulations underestimate its amplitude and shift the center westward. Inter-model comparisons further demonstrate weak consistency between the two simulations, with atmosphere-only experiments generally reproducing stronger precipitation diversity, suggesting an important role of SST forcing in determining the asymmetric ENSO precipitation diversity across tropical Pacific regions.