Physics and Chemistry of the Earth最新文献

{"title":"Agricultural drought assessment using deep learning and multi-sensor remote sensing data integration","authors":"Prashant Kumar,&nbsp;Sonvane Chetan Chandrakant,&nbsp;Sudhanshu Ranjan,&nbsp;Akshar Tripathi","doi":"10.1016/j.pce.2025.104006","DOIUrl":"10.1016/j.pce.2025.104006","url":null,"abstract":"<div><div>This study proposes a Deep Learning Multi-Layer Perceptron Neural Network (DLMLPNN) model-based Drought Index (DI), capable of handling large amounts of remotely sensed data from different sensors, for the Gaya district of South Bihar. Apart from Synthetic Aperture RADAR (SAR) data from Sentinel-1, the multispectral data from Sentinel-2 was to generate vegetation (NDVI) and moisture indices (NDMI) for the Gaya district in South Bihar. Further, rainfall data from the Tropical Rainfall Measuring Mission (TRMM) along with surface soil moisture data from the Soil Moisture Active Passive (SMAP) satellite, thermal data from Landsat-8 Operational Land Imager (OLI) and CH₄ and O₃ concentration data from Sentinel-5P are used. These remote sensing datasets were used as input for training the DLMLPNN to predict the Normalized Differential Moisture Index (NDMI) as an indicator of soil moisture. It was observed that the model estimated the NDMI with R² statistics of 0.87 and 0.852 in the training and testing phases respectively. The NDMI gave a high correlation of more than 60 % with the ground collected Volumetric Soil Moisture (VSM). Feature Importance (FI) score was also computed to find out the contribution of each parameter used in the estimation of soil moisture. Based upon the weightage of each parameter used in the estimation of NDMI, a novel DI of the Gaya region was prepared for 2023. This index is the first of its kind for multi-sensor and multi-parameter drought analysis for the region and can be used to indicate drought conditions in other drought-prone areas.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104006"},"PeriodicalIF":3.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470262","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":"Investigation on plastic-aggregates in coastal and marine pollution: Distribution, possible formation process, and disintegration prospects","authors":"Sudeshna Chell ,&nbsp;Mijanur Mondal ,&nbsp;Uday Kumar Ghorui ,&nbsp;Uttiya Dey ,&nbsp;Sabyasachi Chakrabortty ,&nbsp;Kousik Das ,&nbsp;Harish Puppala","doi":"10.1016/j.pce.2025.104000","DOIUrl":"10.1016/j.pce.2025.104000","url":null,"abstract":"<div><div>Plastic-aggregates are made up from unused or waste plastic and natural aggregates which have recently been emerged as a significant addition to the existing emerging contaminants list mainly in the coastal environment. The transformation from plastics/microplastics to Plastic-aggregates signifies a crucial shift in our understanding and use of plastics and prompting us to reconsider their fundamental characteristics along with possible environmental threats. When plastic waste is incinerated for the purpose of disposal, it combines with organic and inorganic substances present in the surrounding environment, leading to a new type of material. Besides, some natural factors (physical, chemical, biological or in combination) also act upon discarded plastics to combine with rocks and other earthen materials to form plastic-aggregates. Our research aims to build fundamental knowledge and critically review the possible formation process, classification, and possible degradation of all such polymer-rock compounds along with their impact on the ecosystem. The knowledge gap related to the degradation and release of secondary pollutants from these agglomerates is to be addressed urgently in future research. Development and standardization of proper sampling and reporting procedures for plastic-aggregates can enhance our understanding related to their impacts on human health as well as to the entire environment as these aggregates contain different toxic chemicals.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104000"},"PeriodicalIF":3.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144587873","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":"Spatiotemporal variability and source attribution of PM2.5/PM10 ratios: Aerosol type classification and AQI evaluation across seventy monitoring stations in Delhi and Haryana, India","authors":"Ram Pravesh Kumar ,&nbsp;Ravent Rana ,&nbsp;Arti Choudhary ,&nbsp;Ranjit Singh","doi":"10.1016/j.pce.2025.104005","DOIUrl":"10.1016/j.pce.2025.104005","url":null,"abstract":"<div><div>The present study examines PM2.5 and PM10 levels at 70 CAAQMS stations across Delhi and Haryana from 2020 to 2023. The aerosol-type classifications were employed based on PM2.5/PM10 ratios, revealing a significant source of particulate matter. PM2.5 levels increased from 84.31 μg/m³ in 2020 to 154.64 μg/m³ in 2023, while PM10 rose from 171.12 μg/m³ to 268.46 μg/m³, both exceeding NAAQS limits and posing risks to human health and the environment. The lower concentration in 2020 was linked to reduced activities during the COVID-19 lockdowns. Chandni Chowk had the highest PM2.5 levels in 2020, but by 2023, New Moti Bagh had become the most polluted. For PM10, Chandni Chowk recorded the highest concentration in 2020 and 2021, while Anand Vihar surpassed it in 2022 and 2023. Seasonal variation showed higher PM2.5/PM10 ratios in winter and post-monsoon, and lower ratios in the monsoon and pre-monsoon seasons due to climatic factors. Delhi has higher PM2.5/PM10 ratios than Haryana, likely due to greater vehicular density and industrial activity. PM2.5/PM10 ratio analysis reveals that mixed-anthropogenic type (IIb1) and mixed dust type (IIb2) dominate the aerosols in Delhi and Haryana. The AQI results reveal that PM2.5 is the dominant pollutant in Delhi and Haryana across all seasons (2020–2023), with the highest values during winter, 220 and 179 days and in post-monsoon, 280 and 302 days, respectively. The study emphasises the need for stricter emission controls, particularly in Delhi, and aligns with SDG 11, supporting urgent policies to combat air pollution and protect community health and the environment.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104005"},"PeriodicalIF":3.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312800","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":"Managing climate change risks in urban areas: determinants of climate change responses by informal settlement dwellers","authors":"Sani Abubakar Mashi ,&nbsp;Amina Ibrahim Inkani ,&nbsp;Elizabeth Dorsuu Jenkwe ,&nbsp;Martins Momoh","doi":"10.1016/j.pce.2025.104004","DOIUrl":"10.1016/j.pce.2025.104004","url":null,"abstract":"<div><div>Urban areas are central to climate change (CC) risk management but are among the most affected by its impacts, particularly informal settlements, where vulnerability is highest. However, little information exists on how informal sector dwellers respond to CC effects. This study examines the determinants of CC response strategies among informal sector households in Abuja Federal Capital City, Nigeria. A structured questionnaire survey was conducted with 388 purposefully selected households across five informal settlements, and logistic regression analysis was used to identify key factors influencing their CC responses. Findings show that while 55 %–83 % of respondents across the study locations were aware of CC response strategies, actual adoption was low. Only 27 % used fans or air conditioners for cooling, and 10 % adjusted seasonal activities in response to changing climate conditions. The remaining 16 response strategies, including solar energy use, flood prevention, tree planting, migration, and infrastructural adjustments, were each used by less than 7 % of households. Overall, CC responses in these informal settlements were uncoordinated, reactive, and lacked institutional support. Barriers to CC response adoption included lack of knowledge (24 %), inadequate infrastructure (23 %), financial constraints (21 %), and time limitations (19 %). Logistic regression analysis identified education, income, and occupation as significant positive determinants of CC response adoption, while marital status had a negative influence. These factors explained 80 % of the variation in CC responses, with a model accuracy of 94.6 %. To enhance CC resilience among informal sector households, effective strategies are needed, including intensive disaster risk education, economic support through grants and microfinance, and infrastructural upgrades such as drainage systems and green spaces. Institutional interventions are essential to support coordinated and sustainable climate adaptation in urban informal settlements. Upgrading programs are needed to provide some physical infrastructure to make the area more CC resilient.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104004"},"PeriodicalIF":3.0,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307103","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":"Spatial assessment of snow cover patterns in the Sutlej River Basin using machine learning approaches and remote sensing data","authors":"Abhilash Gogineni ,&nbsp;Ravindra Vitthal Kale ,&nbsp;Srija Roy ,&nbsp;Prakhar Modi ,&nbsp;Pramod Kumar","doi":"10.1016/j.pce.2025.103996","DOIUrl":"10.1016/j.pce.2025.103996","url":null,"abstract":"<div><div>Snow cover information plays a significant role in the hydrology and climate of Himalayan river basins, making it an essential parameter for understanding seasonal flow variations in these regions. This study investigates the spatial variation of snow cover concerning elevation, slope, and aspect ratio across the Sutlej River Basin (SRB) over three seasons, monsoon, winter, and summer, from 2013 to 2021. The study was conducted on the Google Earth Engine (GEE) platform, using two machine–learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), to classify the Landsat satellite data. The study results reveal that the Random Forest classification consistently demonstrated better performance across all three seasons, showing higher overall accuracy and Kappa coefficient values. A decadal increasing trend in Snow Cover Area (SCA) was observed throughout the Sutlej River Basin (SRB). Furthermore, topographic parameters such as elevation, slope, and aspect significantly influenced the spatial distribution of snow cover, showing patterns that contrast with broader climate trends. Specifically, higher elevations particularly those above 4500 m consistently retained substantial snow cover across all seasons. Slopes between 30° and 45°, classified as intermediate gradients, provided an optimal balance between steepness and flatness, promoting maximum snow retention. Regarding aspect, northern and northeastern-facing slopes showed the highest snow accumulation due to reduced solar radiation, which aids in preserving snow during warmer periods. Further, the results highlight the influence of climate variability, with a declining trend in summer snow cover and an increasing trend in monsoon snow cover observed over the past three years (2019–2021).</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103996"},"PeriodicalIF":3.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297249","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":"The search for small intrusions of the Stepnyak gold-bearing type within the Akmola region of Kazakhstan: A multi-geophysical technique and unmanned aerial vehicle approach","authors":"Aliya Maussymbayeva ,&nbsp;Gulzada Umirova ,&nbsp;Strukova Polina ,&nbsp;Aigerim Abdullina ,&nbsp;Nurbol Tileuberdi ,&nbsp;Samuel Nunoo","doi":"10.1016/j.pce.2025.103988","DOIUrl":"10.1016/j.pce.2025.103988","url":null,"abstract":"<div><div>Current data reveals about a hundred gold-bearing geological bodies have been identified in the Akmola region of Kazakhstan. Such ore-bodies are characterized by various quartz veins that occur often as stockworks, and new genetic types. The region looks promising in terms of the formation of gold-bearing rocks in deep horizons, since favorable factors of ore-forming geological structures have been identified. This assertion is supported by the known gold occurrences, and historic data from detailed exploration work, which prove the prospects for identifying economically viable gold deposits for development. Irrespective of the promising gold-rich terrain, the existence of complex geological structures, presence of extensive colluvium or loose sediments cover, coupled with poor geological to geophysical information, impedes exploration successes. This paper shows that considering the aforementioned challenges, a composite geophysical methods of aeromagnetic, and electrical techniques through unmanned aerial vehicle (UAV), marked by induced polarization in the modification of the median gradient can be effective. The results made it possible to solve the problems of detailing the geological and tectonic structure of the area, the spatial positions, and identification of deeply buried ore bodies. The adopted combined-approach projects subtle geological structure of the site, identifies promising geological bodies of interest for steppe-type gold mineralization, and demonstrated the applicability of geophysical methods in the search for gold-bearing zones.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103988"},"PeriodicalIF":3.0,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280339","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":"Assessing meteorological droughts in the Çoruh River basin, Turkey: A probabilistic approach using SPI, SPEI, and copulas","authors":"Tolga Barış Terzi,&nbsp;Osman Üçüncü","doi":"10.1016/j.pce.2025.104002","DOIUrl":"10.1016/j.pce.2025.104002","url":null,"abstract":"<div><div>This study presents a comprehensive analysis of meteorological drought characteristics in the Çoruh River Basin (ÇRB), utilizing both the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). While previous research has examined droughts in the region, this study marks the first application of SPEI, offering a more holistic evaluation by integrating temperature-driven evapotranspiration effects. Drought events were identified using run theory, and their characteristics were systematically analyzed through univariate and bivariate frequency analyses. The Lognormal distribution was identified as the most suitable marginal distribution for both drought duration and severity, based on goodness-of-fit tests. Dependence structures between drought characteristics were modeled using copula functions, with the Gaussian copula providing the best fit for SPI and the Frank copula for SPEI. Return period analysis revealed a strong interdependence between drought duration and severity, with SPEI demonstrating superior capability in capturing prolonged drought events compared to SPI, particularly under rising temperature conditions. The findings underscore the limitations of precipitation-only indices in drought assessment and highlight the critical importance of incorporating temperature-sensitive indices like SPEI for accurate drought characterization. By employing advanced statistical methods, including copula-based modeling and joint return period analysis, this study establishes a robust framework for evaluating meteorological droughts and their probabilistic characteristics. The results emphasize the necessity of using temperature-inclusive drought indices for reliable risk assessments and climate adaptation planning. This research provides valuable insights for policymakers, hydrologists, and water resource managers, offering a foundation for enhanced drought monitoring, early warning systems, and mitigation strategies in the ÇRB and other drought-prone regions.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104002"},"PeriodicalIF":3.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271246","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":"Coordination or imbalance? Understanding the relationship between economic resilience and ecological resilience in Hunan Province","authors":"Han Hu ,&nbsp;Kegao Yan ,&nbsp;Houbao Fan ,&nbsp;Tiangui Lv ,&nbsp;Yuan Huang ,&nbsp;Xinmin Zhang ,&nbsp;Yan Feng","doi":"10.1016/j.pce.2025.103997","DOIUrl":"10.1016/j.pce.2025.103997","url":null,"abstract":"<div><div>The transition from conflict to harmonious coexistence between economy and environment has consistently garnered significant attention. Particularly in the current era where uncertainty and risk have become prevalent themes, the coordination of economic resilience (EnR) and ecological resilience (ElR) remains a critically important and unresolved topic of discussion. We innovatively measure EnR and ElR from the dimensions of “resistance-absorption-renewal ability” based on evolutionary resilience theory and further investigate these concepts through the application of coupling coordination degree (CCD), relative development degree, and optimal parameters-based geographical detector methods. The result shows: The spatiotemporal distribution of EnR and ElR in Hunan Province exhibits regional specificity, with weak resilience and strong resilience areas presenting clustered distributions, showing clear east-west hierarchical patterns. The CCD falls between 0.27 and 0.66, predominantly in an antagonistic state, with a noticeable upward trend. Changsha and Huaihua are the cities with the highest and lowest CCD, respectively. Hunan Province has the most cities with ecological resilience lag types, followed by the systematic balanced type. Public environmental attention (PEA), technological innovation (TI), infrastructure (IC), and social welfare (SW) are the dominant factors influencing the CCD of EnR and ElR. The explanatory power of most interaction terms exceeds that of single factors, with the majority being in the bivariate enhancement type, accounting for 86.7 %. This study contributes by expanding traditional economic-ecological research into a resilience perspective, offering insights for achieving coupling coordination amidst uncertainty.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 103997"},"PeriodicalIF":3.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271215","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":"Coupled effect of orography and rainfall on canopy heights in the Western Ghats and Meghalaya Plateau of India","authors":"Sandipan Paul ,&nbsp;Priyank Pravin Patel ,&nbsp;Bodo Bookhagen","doi":"10.1016/j.pce.2025.104003","DOIUrl":"10.1016/j.pce.2025.104003","url":null,"abstract":"<div><div>The Western Ghats and Meghalaya Plateau in India act as orographic barriers to incoming moisture from the Arabian Sea and the Bay of Bengal during the Indian Summer Monsoon (ISM), respectively, causing some of the highest global rainfall rates on their windward sides. Here, we investigate links between topographic parameters (elevation, hillslope, and relief) and rainfall distribution and the impact of the coupled topography-rainfall system on the vegetation height along these two barriers, which has not been studied previously. We use satellite-retrieved (TRMM) and gauge-interpolated (IMD) rainfall datasets along with spaceborne-LiDAR derived canopy height measurements. In both regions, the occurrence of orography-induced peak rainfall does not co-locate with the highest elevation; rather, rainfall peaks are usually at ∼750 m (±250 m) altitude for both regions, being situated about 25–30 km windward of their elevation peaks. The forced orographic lifting at these elevations is sufficient to create rainfall maxima. In the Western Ghats, rainfall peaks correlate with high topographic relief, but this relation varies in magnitude along the strike from south to north. Local mountain passes (Thalghat and Palghat Gaps) in the Western Ghats allow moisture movement further inland. The elevated central Meghalaya Plateau shows a stronger orographic control that induces higher rainfall. Rainfall distribution is primarily determined by these orographic barriers and their orientation to the prevailing wind direction. Topography-induced rainfall gradients significantly influence the canopy height and cause higher canopy clusters around rainfall peaks. We also delineate distinct homogenous zones to indicate the different topoclimatic-vegetation responses within these two regions.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104003"},"PeriodicalIF":3.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329865","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":"Research on regional short-term precipitation quantitative forecast revision technology integrating weather feature information","authors":"Feisheng Chen ,&nbsp;Weiping Lu ,&nbsp;Huasheng Zhao ,&nbsp;Zhixiang Xiao ,&nbsp;Jingwen Sun ,&nbsp;Yu Jiang","doi":"10.1016/j.pce.2025.104001","DOIUrl":"10.1016/j.pce.2025.104001","url":null,"abstract":"<div><div>This study proposes a method to improve the accuracy of heavy precipitation forecasts by incorporating weather system information into a random forest (RF) regression model. Based on meteorological elements from the European Centre for Medium Range Weather Forecasts (ECMWF) model, this method utilizes dimensionality reduction techniques to transform the four-dimensional training dataset into a three-dimensional one. Key weather systems, such as the 500 hPa trough line and the 850 hPa-925hPa shear line, are introduced as predictive factors into the RF model. Finally, the method corrects heavy precipitation forecasts using the China Meteorological Administration's Shanghai 3-km regional model and forecaster experience. The RF model demonstrated superior performance over the ECMWF model in predicting precipitation events, particularly excelling in forecasting heavy rain and above. From April to September 2022, the RF model achieved an 18 % increase in correct forecasts for heavy rain and more intense precipitation events, along with notably better TS scores across various intensities of rainstorms. Additionally, the RF model provided a more accurate representation of precipitation fall areas and intensities, closely aligning with actual rainfall patterns. This enhanced accuracy was particularly evident when the predicted rain band shapes and trends in the model corresponded with real-world observations, often resulting in a slightly stronger intensity of the forecasted rain. These findings demonstrate that the integration of weather system features into the RF model significantly improves the accuracy in forecasting heavy rain and more severe precipitation events. This advancement represents a more effective and reliable tool for meteorological applications, especially in predicting intense rainfall scenarios.</div></div>","PeriodicalId":54616,"journal":{"name":"Physics and Chemistry of the Earth","volume":"140 ","pages":"Article 104001"},"PeriodicalIF":3.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338421","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}