地球科学最新文献

{"title":"Erosion control performance of geomats on silty soil slopes under simulated rainfall","authors":"Rui Zhang ,&nbsp;Xiwei Zhang ,&nbsp;Kang Chen ,&nbsp;Yipeng Guo ,&nbsp;Yanhua Yu ,&nbsp;Huanhua Cai","doi":"10.1016/j.geotexmem.2026.01.007","DOIUrl":"10.1016/j.geotexmem.2026.01.007","url":null,"abstract":"<div><div>Geomats are widely used for erosion control on slopes, yet quantitative links between their structural characteristics and erosion resistance remain insufficiently understood. This study investigates the influence of geomat geometry on erosion mitigation of silty slopes reinforced with five geomats. Erosion tests under simulated rainfall were conducted. An integrated structural–hydraulic–erosion framework was applied to relate geomat structural parameters, including porosity, pore-structure characteristics, compressed thickness, and mass per unit area, to hydraulic indicators (surface flow velocity and kinetic energy of surface runoff) and erosion indicators, including collected runoff mass, soil loss, sediment concentration, eroded area fraction, and maximum connected erosion area fraction. The results show that geomats substantially reduced erosion relative to the bare slope, decreasing cumulative soil loss by up to 89.1 %, sediment concentration by up to 84.3 %, and kinetic energy of surface runoff by 87.7–95.2 %. Geomats reduced erosion-domain connectivity and inhibited the development of continuous scouring channels, indicating effective attenuation of near-surface hydraulic forcing. Porosity and compressed thickness emerged as the dominant structural controls on erosion resistance. Geomats with porosity ≤26 % and compressed thickness ≥16 mm exhibited the best performance under the tested conditions. These findings provide mechanism-informed, preliminary guidance for erosion control on geomat-covered silty slopes.</div></div>","PeriodicalId":55096,"journal":{"name":"Geotextiles and Geomembranes","volume":"54 3","pages":"Pages 443-459"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study on the performance and mechanism of ammonia nitrogen and phosphorus removal in bioretention facilities enhanced by aluminum-based P-inactivation agent","authors":"Jing Li ,&nbsp;Wenhua Chen ,&nbsp;Shuai Yuan ,&nbsp;Weihang Cai ,&nbsp;Hua Yang ,&nbsp;Fei Li ,&nbsp;Wei Cao ,&nbsp;Shupo Liu ,&nbsp;Zhenming Zhou","doi":"10.1016/j.pce.2026.104311","DOIUrl":"10.1016/j.pce.2026.104311","url":null,"abstract":"<div><div>Bioretention facilities are widely utilized in sponge city infrastructure; however, conventional substrate fillers exhibit limited efficiency in removing nitrogen (N) and phosphorus (P). This limitation necessitates the selection of high-performance active fillers to enhance the N and P removal capabilities of bioretention facilities. This study compared the ammonia nitrogen (NH₄⁺-N) and P removal performance of four substrate fillers—bio-ceramsite, volcanic rock, quartz sand, and aluminum-based P-inactivation agent (Al-PIA)—to identify the optimal substrate filler. Under high pollutant loading conditions, the optimal thickness of the selected filler for NH₄⁺-N and P removal was determined. The NH₄⁺-N and P removal performance of bioretention facilities utilizing Al-PIA was then evaluated under low and high pollutant load concentrations, and the effects of the drying period on NH₄⁺-N and P removal were assessed. Additionally, the P removal mechanisms of Al-PIA, as well as the N and P removal pathways in the bioretention facility, were elucidated. Results indicated no significant difference in NH₄⁺-N removal among the four fillers (P &gt; 0.05). However, the Al-PIA exhibited the highest total phosphorus (TP) removal, with a mean removal efficiency of 72.46 %, establishing it as the optimal filler. The most effective Al-PIA layer thickness was 12 cm, achieving mean removal efficiencies of 84.67 % for NH₄⁺-N and 95.35 % for TP. Under various pollution load concentrations, the effluent NH₄⁺-N and TP concentrations from bioretention facilities utilizing Al-PIA complied with China's Class IV surface water standards, and demonstrate excellent NH₄⁺-N and P removal stability and interference resistance under varying drying periods. P removal by Al-PIA was primarily governed by physical adsorption, electrostatic attraction, surface precipitation, and ligand exchange. In the bioretention facility, N removal was facilitated by physical adsorption in the planting soil, plant uptake, adsorption by Al-PIA, and subsequent microbial nitrification. The removal of P was mainly attributed to adsorption by Al-PIA (87.40 %) and plant uptake and assimilation (10.40 %).</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"143 ","pages":"Article 104311"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microplastics in the environment: Sources, impacts, degradation strategies and energy recovery options-A rigorous review","authors":"J. Aravind Kumar ,&nbsp;A. Annam Renita ,&nbsp;S. Sathish ,&nbsp;D. Prabu ,&nbsp;Ashwin Jacob ,&nbsp;Ahmad Hosseini-Bandegharaei ,&nbsp;M. Kavisri ,&nbsp;Meivelu Moovendhan","doi":"10.1016/j.pce.2026.104323","DOIUrl":"10.1016/j.pce.2026.104323","url":null,"abstract":"<div><div>Microplastics, pervasive in the environment, have emanated as a pressing environmental implication due to their widespread dispersal and potential adverse effects on ecosystems and human health. This comprehensive review succumbs an in-depth scrutiny of microplastics, encompassing their sources, distributions, and environmental impacts. Sources of microplastics encompass a wide array of industrial and household activities, ranging from daily care products to the food industry and common household items. In addition, algae play a key part in the degrading processes that microplastics undergo, with macro- and microalgae being major players in remediation initiatives. To understand the flowing ecological effects, the complex relationships that microplastics have with marine organisms, especially those that are part of the marine food web are examined. Furthermore, cutting-edge process technologies like anaerobic digestion, hydrothermal liquefaction (HTL), and thermal hydrolysis process (THP) present viable paths for managing microplastics, with a focus on energy recovery via co-digestion procedures. The review additionally presents potential directives for forthcoming research, highlighting the necessity of continuing efforts to maximize cleanup tactics, lessen environmental effects, and protect ecosystems around the world from the increasingly dangerous threat of microplastic pollution. Biodegradation strategies for disintegrating such microplastic were also highlighted and explored at the outset.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"143 ","pages":"Article 104323"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seasonal variation and distribution of microplastics in surface water and sediments of Coimbatore Lakes, India","authors":"Davis Kaimalayil Ephsy,&nbsp;Selvaraju Raja","doi":"10.1016/j.gr.2025.12.013","DOIUrl":"10.1016/j.gr.2025.12.013","url":null,"abstract":"<div><div>This study quantified the abundance, distribution, and characteristics of microplastics in surface water and sediments from five lakes in the Coimbatore District: Kumaraswamy Lake, Ukkadam Lake, Kuruchi Lake, Singanallur Lake, and<!--> <!-->Sulur Lake. The highest microplastic abundance was found in the<!--> <!-->surface water of Kuruchi Lake (14.08 ± 0.63 particles/L) at site S5 during the monsoon, and in the surface sediments of Kumaraswamy Lake (13.33 ± 0.33 particles/g) at site S6 during summer. Spatial distribution patterns indicated that lakes receiving urban runoff, domestic wastewater inflow, and inputs from fishing and recreational activities exhibited higher microplastic concentrations. Seasonal variations showed elevated microplastic abundance in summer sediments and monsoon surface water samples. Microplastics were identified using Attenuated total reflectance- Fourier Transform Infrared Spectroscopy (ATR-FTIR) and Differential Scanning Calorimetry (DSC)), revealing Linear low-density polyethylene (LLDPE), High-density polyethylene (HDPE), Polyethylene terephthalate (PET), and Polypropylene (PP). These microplastic occurred in white, transparent, black, blue, yellow, and pink colors and appeared as films, fragments, thin pieces, and fibres. Characteristic DSC melting peaks were observed 200 °C for PET, 167.98 °C for PP, 126.70 °C for LLDPE, and 130.02 °C for HDPE. The lake’s pollution load index is categorized as risk level 1, indicating a low level of microplastic pollution. The presence and distribution of these microplastics suggest potential ecological risks to freshwater organisms and possible implications for human health.</div></div>","PeriodicalId":12761,"journal":{"name":"Gondwana Research","volume":"154 ","pages":"Pages 310-322"},"PeriodicalIF":7.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146033602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DOC-governed metal solubility and mobility in river sediments: Integrating machine learning, causal pathways, and geochemical simulations","authors":"Fahmida Sultana ,&nbsp;Zia Ahmed ,&nbsp;Fei Zhang ,&nbsp;Tasrina R. Choudhury ,&nbsp;M. Safiur Rahman","doi":"10.1016/j.pce.2026.104322","DOIUrl":"10.1016/j.pce.2026.104322","url":null,"abstract":"<div><div>This study explores the complex interactions between sediment texture, dissolved organic carbon (DOC) levels, and water chemistry in influencing the solubility and mobility of toxic metals (Cd, Ni, Zn, Cu, Cr, Pb) in river sediments. A multi-tiered approach integrating machine learning, Structural Equation Modeling (SEM), and geochemical simulations was employed to understand metal behavior in the Meghna River, Bangladesh. Redundancy Analysis (RDA) revealed that sediment texture and DOC fractions are the primary drivers of metal mobility, with clay content contributing the most to variation in metal concentrations (Variance Inflation Factor (VIF) values for clay = 3.50). The study employed Random Forest (RF) and XGBoost models to predict metal concentrations, achieving exceptional predictive accuracy with Area Under the Curve (AUC) values of 1.000 for Ni, Zn, Cr, and Pb, and 0.964 for Cd. Regression models demonstrated strong performance with R² values of 0.963 for Pb, 0.938 for Ni, and 0.928 for Zn, highlighting the robustness of DOC and sediment texture in explaining metal variability. SEM analysis indicated that pH mediates the DOC–metal relationship, with standardized path coefficients for DOC retention and metal mobility being −0.475 and 0.96 for Zn, respectively. The GIS-based Metal Mobility Index (MMI) and Soil Mobility Index (SMI) predicted high-risk zones for metal mobility, with an AUC of 0.91, effectively distinguishing between high and low mobility regions. These findings provide critical insights into metal transport dynamics and offer valuable tools for river sediment management and metal contamination risk assessment.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"143 ","pages":"Article 104322"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geotechnical and microstructural assessment of gas condensate–contaminated clayey gravel","authors":"Amin Falamaki ,&nbsp;Abdosalam Dashti ,&nbsp;Aghileh Khajeh ,&nbsp;Amir Hossein Vakili ,&nbsp;Amir Hossein Karimi","doi":"10.1016/j.pce.2026.104340","DOIUrl":"10.1016/j.pce.2026.104340","url":null,"abstract":"<div><div>This study investigates the detrimental effects of gas condensate contamination on the geotechnical properties of clayey gravel (GC) soil, addressing a pressing environmental and geotechnical engineering challenge. Soil samples were systematically contaminated with gas condensate at concentrations of 0, 2, 4, 6, and 8% by dry weight and subjected to comprehensive geotechnical testing, including Atterberg limits, modified Proctor compaction, direct shear, unconfined compressive strength (<em>UCS</em>), and California bearing ratio (<em>CBR</em>). Testing of Atterberg limits revealed progressive reductions in soil consistency, with the liquid limit decreasing by 15.2% (from 30.9% to 26.2%) and the plastic limit by 13.5% (from 21.0% to 18.16%) at maximum contamination. Modified Proctor compaction tests identified a critical threshold at 4% contamination, where maximum dry density initially increased by 1.2% before declining by 4.5% at higher concentrations, while optimum moisture content decreased by 28.6%. Strength characterization showed severe degradation, with <em>UCS</em> experiencing a 68.8% reduction (from 938.49 to 293.07 kPa) and <em>CBR</em> values decreasing by 52.3% at 100% relative density. Direct shear tests demonstrated substantial weakening of shear strength parameters, with cohesion declining by 53% and friction angle by 25%. These findings underscore the severe implications of gas condensate contamination for soil stability and highlight the urgency of implementing mitigation measures to safeguard infrastructure and environmental integrity at gas condensate storage sites.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"143 ","pages":"Article 104340"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of lime treatment on the microstructure and geotechnical properties of micaceous soil","authors":"Amaranatha Ginkapalli Anjaneyappa ,&nbsp;Seelam Srikanth ,&nbsp;Subhashish Dey","doi":"10.1016/j.pce.2026.104324","DOIUrl":"10.1016/j.pce.2026.104324","url":null,"abstract":"<div><div>Micaceous soils present significant challenges in geotechnical engineering due to their platy mineral structure, high compressibility and poor load-bearing capacity. The untreated soil examined in this study contained a high fraction of flaky mica and exhibited inherently weak engineering behavior, with an unconfined compressive strength (UCS) of approximately 45 kPa and low bearing resistance, rendering it unsuitable for direct use in pavement and embankment applications. Although the lime stabilization is widely adopted for improving fine-grained soils, its effectiveness and underlying mechanisms in mica-rich soils remain inadequately understood, particularly the relationship between micro-structural evolution and engineering performance. To address this gap, the present study systematically evaluates the influence of lime treatment on the mechanical and micro-structural behavior of micaceous soil. Soil samples were treated with 2%, 4%, 6% and 8% quicklime and cured for periods of up to 56 days, followed by evaluation of strength, compaction characteristics, consistency limits and micro-structural characteristics. The UCS increased substantially, reaching a peak value of 244.8 kPa at 4% lime after 56 days, representing an improvement of approximately 5.4 times compared to the untreated soil. The California Bearing Ratio (CBR) also peaked at the same lime dosage, with unsoaked CBR increasing from 3.65% to 9.34% and soaked CBR from 2.12% to 7.15%. Micro-structural analyses using SEM, EDS, XRD and FTIR revealed the formation of cementitious products, particularly calcium silicate hydrate (C–S–H) and calcium aluminates hydrate (C-A-H) phases, providing mechanistic insight into the observed strength improvements. The added value of this study lies in explicitly linking micro-structural transformations to macroscopic strength and bearing enhancement in lime stabilized micaceous soils, demonstrating that lime treatment can effectively upgrade problematic mica-rich soils to meet the engineering requirements for pavement sub-grades and embankment fills.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"143 ","pages":"Article 104324"},"PeriodicalIF":4.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How a nocturnal cold front amplified wildfire impacts on near-surface air quality downwind of the second largest US wildfire","authors":"Sandip Pal ,&nbsp;Matthew Hamel ,&nbsp;Hassanpreet Dhaliwal ,&nbsp;Diya Das ,&nbsp;Danielle Harr ,&nbsp;Tyler Danzig ,&nbsp;Temple R. Lee ,&nbsp;Kiran Menon ,&nbsp;Nicholas E. Prince ,&nbsp;Matthew Asel ,&nbsp;Wesley Burgett","doi":"10.1016/j.atmosres.2026.108841","DOIUrl":"10.1016/j.atmosres.2026.108841","url":null,"abstract":"<div><div>Ongoing global climate change has yielded a myriad of catastrophic weather hazards, including extreme heat, drought, and severe fire weather conditions across global dryland environments. A massive wildfire ignited across the Texas Panhandle between 27 and 28 Feb 2024 (i.e., Smokehouse Creek Fire, the second largest wildfire in the US history), which consumed over 1,000,000 ha of land and resulted in an overall loss of greater than &gt;$1 billion. Understanding aerosol mixing processes and the associated kinematics near the surface and within the nocturnal boundary layer (NBL) during such wildfire events is crucial for various applications, including predicting and monitoring environmental air quality (AQ), weather forecasting and transport and dispersion modeling. This study provides, for the first time, an empirical evidence of how a nocturnal cold front amplified the wildfire impact on AQ at a site located 250 km downwind of the second largest US wildfire, yielding hazardous concentrations of fine particulate matter (PM_2.5–250 μg m⁻³). Using a combination of lidar-derived aerosol backscatter, vertical velocity and horizontal wind profiles, 10 m-tower observations of meteorological parameters, radiosonde-derived thermodynamics, and near-surface PM_2.5 measurements, our analyses revealed that narrow and intense updrafts (i.e., vertical velocity of up to 5–10 m s⁻¹) along the leading edge of a nocturnal cold front triggered the entrainment of an elevated smoke plume (∼1500-2000 m above ground) down to the surface via broader and weaker downdrafts (−0.5 to −2.0 m s⁻¹). This helped explain the transport and vertical mixing pathway of the wildfire plume near ground and aloft. Results reported enhance our understanding of NBL processes and provide critical insights for improving AQ forecasting and validating aerosol transport in dispersion models.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108841"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable deep learning method integrating spatial self-attention for generating bias-corrected high-resolution GFS precipitation forecasts","authors":"Haiyang Wang ,&nbsp;Shufeng Lai ,&nbsp;Chongxun Mo ,&nbsp;Tao Feng ,&nbsp;Changhao Jiang ,&nbsp;Na Li","doi":"10.1016/j.atmosres.2026.108832","DOIUrl":"10.1016/j.atmosres.2026.108832","url":null,"abstract":"<div><div>Numerical weather prediction (NWP) models are subject to inherent limitations such as insufficient resolution and systematic biases, which present formidable challenges for regional precipitation forecasting. High-precision precipitation forecasting is crucial for regional flood prevention and urban flood risk reduction. This study proposes an explainable deep learning framework (DualTransBU-Net-P), integrating spatial self-attention. This framework incorporates a core downscaling-bias correction model (DualTransBU-Net), a post-processing optimization module for extreme precipitation, and SHAP (Shapley Additive Explanations) for interpretability. It performs end-to-end joint downscaling and bias correction on Global Forecast System (GFS) precipitation forecast data by integrating multi-source data. The results show that compared to existing models, the proposed architecture significantly enhances GFS precipitation forecast accuracy, improving resolution from 0.25° to 0.025°. The root mean square error (RMSE) of the test set is reduced by 4.6% to 18.7%, and the fair threat score (ETS) is improved by an average of 43.9%. Among 437 heavy-precipitation day samples, RMSE decreased for 412 samples (94.3%). The ETS under the extreme precipitation threshold (&gt;10 mm d⁻¹) increased by 50.6% to 63.1%. Furthermore, the model's performance remained high during the seasonal analysis, demonstrating strong seasonal generalization. Interpretability analysis revealed distinct decision-making mechanisms of the deep learning model during heavy precipitation under typhoon and non-typhoon conditions, with different underlying physical factors controlling these mechanisms. The combination of a self-attention mechanism and interpretable deep learning provides an effective approach for refined precipitation forecasting.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108832"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding CMCC seasonal prediction system v3.5 applications to the local scale through statistical downscaling techniques","authors":"Leonardo Aragão ,&nbsp;Andrea Borrelli ,&nbsp;Silvio Gualdi","doi":"10.1016/j.atmosres.2026.108811","DOIUrl":"10.1016/j.atmosres.2026.108811","url":null,"abstract":"<div><div>The Italian Peninsula's climate is highly influenced by its complex topography and diverse regional weather systems, making high-resolution seasonal forecasting crucial for many societal sectors. Traditional seasonal prediction models, such as the CMCC SPSv3.5 (SPS), provide valuable insights but lack the spatial resolution necessary to capture local-scale climatic details. Thus, this study aims to provide a high-resolution seasonal forecast over Italy by enhancing SPS through statistical downscaling (SD) techniques tailored to the region's demand for finer-scale climate information. The SD method involves a three-step process that utilises observational datasets (ERA5 and CHIRPS) at 1/4° horizontal resolution and two machine-learning methods based on Empirical Quantile Mapping (EQM) and <em>k</em>-Nearest Neighbours (<em>k</em>NN), translating 1° SPS forecasts into high-resolution fields by matching predicted conditions to observed patterns. Both SD methods were cross-validated over the 24-year hindcast period available (1993–2016), and the results indicate significantly enhanced seasonal predictions for the Italian Peninsula, with biases about 5–6 times smaller than those of the original SPS. The main component of this improvement is spatial accuracy, which allows the identification of domain characteristics that are unnoticed in SPS. The bias evaluated by lead time, key for seasonal forecasts, showed accuracy declining from lead month 1 onward. For instance, the 2 m temperature bias increased from −0.14/−0.31/−0.85 °C in lead month 1 to −0.68/−0.71/−1.41 °C in lead month 6 (<em>k</em>NN/EQM/SPS), highlighting the challenge of maintaining predictive skill and the need for adaptive correction strategies to enhance lead-time reliability. Combining SD techniques with SPS outputs offers a solution for high-resolution seasonal predictions, supporting climate-sensitive applications by reducing forecast bias and improving spatial accuracy.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"336 ","pages":"Article 108811"},"PeriodicalIF":4.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}