地球科学最新文献

{"title":"Spatiotemporal Patterns of Intermittent Snow Cover From PlanetScope Imagery Using Deep Learning","authors":"Zhaocheng Wang,&nbsp;Jaya Venkatesh Jaya Baskar,&nbsp;Maneesh Sarma Sistla Naga Sai,&nbsp;Bohumil Svoma,&nbsp;Enrique R. Vivoni","doi":"10.1029/2025GL116582","DOIUrl":"https://doi.org/10.1029/2025GL116582","url":null,"abstract":"<p>Monitoring snow cover in regions with intermittent dynamics is a significant challenge due to the rapid changes occurring in snow accumulation and ablation over complex terrain. We trained a deep learning model with lidar-derived labels and PlanetScope CubeSat imagery to map near-daily snow cover dynamics at 3-m resolution. The model demonstrated a high accuracy in the Salt and Verde River basins of Arizona and strong transferability to other sites in the western United States. Temporal analysis of snow line from 2021 to 2023 revealed distinct patterns of snowpack dynamics driven by seasonal and interannual climatic variability. The high-resolution snow persistence maps also unveiled significant subgrid variability in snow cover at point and watershed scales, influenced by elevation, aspect, and vegetation cover. These findings illustrate the potential of integrating high-resolution CubeSat imagery with deep learning models to enhance our understanding of intermittent snowpack spatiotemporal variability in complex terrain.</p>","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"52 13","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025GL116582","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cold Plasma Heating by Lower Hybrid Waves Excited by Warm Proton Ring Distribution in the Inner Magnetosphere","authors":"Bopu Feng,&nbsp;Zhihai Ouyang,&nbsp;Xiongdong Yu,&nbsp;Yangyang Wang,&nbsp;Haimeng Li,&nbsp;Zhou Chen,&nbsp;Ying Xiong,&nbsp;Kemin Tengchen,&nbsp;Meng Zhou,&nbsp;Rongxin Tang,&nbsp;Zhigang Yuan,&nbsp;Xiaohua Deng","doi":"10.1029/2025GL116142","DOIUrl":"https://doi.org/10.1029/2025GL116142","url":null,"abstract":"<p>In the Earth's magnetosphere, lower hybrid (LH) waves are frequently observed due to their low excitation threshold. These waves exhibit efficient coupling with electrons and ions, leading to electron heating parallel to the background magnetic field and ion heating in the perpendicular direction. Simulation results suggest that LH waves can be locally excited by a proton ring distribution. However, it is important to note that, as of now, direct satellite observations confirming this excitation mechanism have not been reported in the inner magnetosphere. In this study, we investigate a representative case of LH wave generation triggered by a warm proton ring distribution. During this event, observations combined with simulations also reveal the significant enhancements in perpendicular cold proton fluxes.</p>","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"52 13","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025GL116142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mercury Deposition in South China Across the Ordovician-Silurian Transition: Implications for Climate Change","authors":"Weiliang Kong,&nbsp;Zhen Qiu,&nbsp;Jiaqiang Zhang,&nbsp;Wen Liu,&nbsp;Qin Zhang","doi":"10.1029/2024GC012122","DOIUrl":"https://doi.org/10.1029/2024GC012122","url":null,"abstract":"<p>Most of the global climate changes are closely associated with volcanic activity. However, the link between global cooling during the Ordovician-Silurian (O-S) transition and volcanism remains unclear due to limited constraints on large volcanic events before, during, and after the peak of the Hirnantian glaciation (PHG). Here, we present high-resolution mercury (Hg) concentrations and isotopes from South China across the O-S transition to assess volcanic activity and its contributions to climate changes. Anomalous Hg enrichments and volcanic-range Hg isotopes in samples above volcanic ash layers confirm the effectiveness of Hg as a tracer of volcanic events. Variations in Hg isotopes across the O-S transition reveal multiple Hg sources related to volcanic activity, that is, dominant volcanic Hg input during pre-PHG and post-PHG-2, deposition of volcanic-sourced atmospheric Hg(II) to seawater during PHG, and increased terrestrial Hg input from enhanced weathering during post-PHG-1. We propose that prolonged global cooling was driven by an albedo catastrophe caused by volcanic aerosols and sustained by reduced atmospheric CO₂ levels due to enhanced organic carbon burial and weathering. This study highlights Hg as a tracer of volcanic activity and provides new evidence on the role of volcanism in driving climate changes across the O-S transition.</p>","PeriodicalId":50422,"journal":{"name":"Geochemistry Geophysics Geosystems","volume":"26 7","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024GC012122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating net rainfall calculation in deep learning-based surrogate modeling frameworks for 2D flood prediction","authors":"Juan F. Farfán-Durán ,&nbsp;Carlos Montalvo ,&nbsp;Luis Cea ,&nbsp;João P. Leitão","doi":"10.1016/j.jhydrol.2025.133632","DOIUrl":"10.1016/j.jhydrol.2025.133632","url":null,"abstract":"<div><div>This study proposes a novel deep learning (DL)-based surrogate model that incorporates the calculation of net rainfall using the SCS-CN method, providing a flexible framework for evaluating the influence of rainfall events under different antecedent moisture conditions (AMC). The proposed framework involves establishing a ground truth model (Iber-SWMM) and defining the necessary terrain features and rainfall patterns for training the surrogate. A benchmark surrogate model using only gross rainfall, replicating methodologies from previous studies, is also developed for comparison. The trained models are then applied to predict water depth maps using test rainfall patterns under different scenarios, both with and without net rainfall. The results demonstrate that the proposed surrogate model reduces the computational times of Iber-SWMM by 2 to 4 orders of magnitude while outperforming the benchmark surrogate in all the measures. It presents satisfactory accuracy in water depth prediction, with 80% to 95% of predictions within a -0.2 to 0.2 m error range and hit ratios between 0.87 to 0.91 in terms of flooded pixels in the more extreme events. These outcomes are comparable to those achieved by a physics-based model on one of the test events. The study also suggests future lines for refinement.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"661 ","pages":"Article 133632"},"PeriodicalIF":5.9,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536090","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":"Quantifying intra-urban socio-economic and environmental vulnerability to extreme heat events in Johannesburg, South Africa.","authors":"Craig Parker, Craig Mahlasi, Tamara Govindasamy, Lebohang Radebe, Nicholas Brian Brink, Christopher Jack, Madina Doumbia, Etienne Kouakou, Matthew Chersich, Gueladio Cisse, Sibusisiwe Makhanya","doi":"10.1007/s00484-025-02971-y","DOIUrl":"https://doi.org/10.1007/s00484-025-02971-y","url":null,"abstract":"<p><p>Urban populations face increasing vulnerability to extreme heat events, particularly in rapidly urbanising Global South cities where environmental exposure intersects with socioeconomic inequality and limited healthcare access. This study quantifies heat vulnerability across Johannesburg, South Africa, by integrating high-resolution environmental data with socio-economic and health metrics across 135 urban wards. We examine how historical urban development patterns influence contemporary vulnerability distributions using principal component analysis and spatial statistics. Environmental indicators (Land Surface Temperature (LST), vegetation indices, and thermal field variance) were combined with socioeconomic and health variables (including indicators on crowded dwellings and healthcare access, self-reporting of chronic diseases) in a comprehensive vulnerability assessment. Principal Component Analysis revealed three primary dimensions explaining 56.6% (95% CI: 52.4-60.8%) of the total variance: urban heat exposure (31.5%), health status (12.8%), and socio-economic conditions (12.3%). Built-up areas showed weak but significant correlations with heat indices (ρ = 0.28, p < 0.01), while higher poverty levels demonstrated moderate positive correlations with LST (ρ = 0.41, p < 0.001). The spatial analysis identified significant clustering of vulnerability (Global Moran's I = 0.42, p < 0.001), with distinct high-vulnerability clusters in historically disadvantaged areas. Alexandra Township showed the highest vulnerability(HVI score: 0.87, LST: 29.8 °C ± 0.4 °C, NDVI: 0.08 ± 0.02), with factors characterising the high vulnerability in that area including limited healthcare access and extreme heat exposure. Northern suburbs formed a significant low-vulnerability cluster (Mean HVI = 0.23 ± 0.07, p < 0.001), benefiting from greater vegetation coverage and better healthcare access. These findings demonstrate how historical planning decisions continue to shape contemporary environmental health risks, with vulnerability concentrated in areas of limited healthcare access and high extreme heat exposure. Results suggest the need for targeted interventions that address both environmental and social dimensions of heat vulnerability, particularly focusing on expanding healthcare access in identified hotspots and implementing community-scale green infrastructure in high-risk areas. This study provides an evidence-based framework for prioritising heat-resilience initiatives in rapidly urbanising Global South cities while highlighting the importance of addressing historical inequities in urban adaptation planning.</p>","PeriodicalId":588,"journal":{"name":"International Journal of Biometeorology","volume":" ","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144537674","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":"Exploring the impact of streetscapes on waterlogging risk from a 15-minute walkability perspective","authors":"Bin Li ,&nbsp;Changxiu Cheng ,&nbsp;Kaixuan Dai","doi":"10.1016/j.apgeog.2025.103699","DOIUrl":"10.1016/j.apgeog.2025.103699","url":null,"abstract":"<div><div>Understanding the impact of streetscapes on waterlogging risk is critical for enhancing urban resilience. This study uses street view images and navigation data to investigate the spatial variability of waterlogging probability and dominant factors within a 15-min walkable radius of residential communities, applying interpretable machine learning. Focusing on Beijing's main urban area, the results reveal that within 150 m of waterlogging points, buildings have the greatest impact, followed by walls and roads, while sidewalks and plants play a minimal role. Streetscapes such as roads, walls, buildings, and sidewalks exhibit an inverted U-shaped trend in their impact on waterlogging risk, whereas sky and vegetation, particularly grass, significantly reduce the risk. Dominant factors vary spatially: in areas near airports and railways, dense buildings increase waterlogging risk, while walls and open sky mitigate it; in the eastern business district, walls and sky are the main contributors to waterlogging, but grass alleviates it; in decentralized areas, open skies reduce risk, while buildings exacerbate it; in the northern periphery, dispersed buildings and walls help reduce waterlogging; and along the Fifth Ring Road, walls, sky, and trees play a stronger role. Spatially, high-risk zones are concentrated along major roads, particularly between the 3rd and 4th Ring Roads in the south, forming a U-shaped pattern, while areas near the 2nd Ring Road show lower risk. These findings provide actionable insights for urban planning, streetscape design, and Waterlogging prevention strategies.</div></div>","PeriodicalId":48396,"journal":{"name":"Applied Geography","volume":"182 ","pages":"Article 103699"},"PeriodicalIF":4.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524007","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":"Assessing Regional Climate Trends in West Africa Under Geoengineering: A Multimodel Comparison of UKESM1 and CESM2","authors":"Francis Nkrumah,&nbsp;Kwesi A. Quagraine,&nbsp;Gandome Mayeul Leger Davy Quenum,&nbsp;Daniele Visioni,&nbsp;Hubert A. Koffi,&nbsp;Nana Ama Browne Klutse","doi":"10.1029/2024JD043117","DOIUrl":"https://doi.org/10.1029/2024JD043117","url":null,"abstract":"<p>This study investigates West Africa's climate vulnerability under stratospheric aerosol injection (SAI), using UKESM1 and CESM2 models. We analyzed temperature and precipitation responses for 2050–2069 relative to 2015–2034 under SSP2-4.5 and ARISE-SAI-1.5 scenarios. Our approach involved evaluating temperature and precipitation anomalies, applying signal-to-noise ratio (SNR) analysis—defined as the ratio of the forced climate response to internal variability—to assess signal robustness, and using cumulative distribution (CDF) and probability density (PDF) functions to explore shifts in precipitation extremes. Results indicate that under SSP2-4.5, both models project significant warming. UKESM1 simulates increases near 1.8°C, while CESM2 projects between 1.0°C and 1.2°C. Under ARISE-SAI-1.5, UKESM1 shows pronounced cooling, with temperatures dropping up to 0.3°C below the reference period at some latitudes. CESM2 shows a more uniform cooling, with temperatures between 0°C and 0.3°C above the reference. SNR analysis reveals robust, statistically significant temperature changes across the region, clearly emerging above natural variability by midcentury. Precipitation changes, however, show lower SNR values and greater spatial uncertainty, suggesting weaker and less predictable hydrological responses. CDF and PDF analyses highlight complex shifts in precipitation extremes, suggesting that while SAI could counteract warming trends, it may introduce additional variability and uncertainty in rainfall projections. These results emphasize the importance of multimodel comparisons in assessing geoengineering impacts on regional climates, as differing sensitivities to radiative forcing and feedback can produce divergent outcomes.</p>","PeriodicalId":15986,"journal":{"name":"Journal of Geophysical Research: Atmospheres","volume":"130 13","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024JD043117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Seismic Fragility and Vulnerability Assessment for Buildings (ASFRAVA-B): Integrating probabilistic seismic design into performance-based engineering practices","authors":"Adrian Ulza ,&nbsp;Yunita Idris ,&nbsp;Muhammad Aulia Ramadhan ,&nbsp;Syamsidik ,&nbsp;Zahra Amalia","doi":"10.1016/j.ijdrr.2025.105679","DOIUrl":"10.1016/j.ijdrr.2025.105679","url":null,"abstract":"<div><div>Practical assessment of seismic fragility and vulnerability is critical for effective earthquake engineering and risk mitigation. This study introduces ASFRAVA-B, an open-source, automated framework designed to derive seismic fragility and vulnerability functions for building assessments. ASFRAVA-B employs the capacity spectrum method and built-in statistical procedures to efficiently generate seismic fragility and vulnerability curves. The framework supports various statistical fitting methods, including multiple stripes analysis with maximum likelihood estimation, generalized linear models with logit and probit links, and machine-learning logistic regression, with three regularization options. It offers flexibility for users to choose the fragility function. The applicability of ASFRAVA-B is demonstrated through two case studies: (1) an evaluation of how masonry infill affects the seismic fragility and vulnerability of a two-story school building compared to an open frame; and (2) a comparative exercise against Nonlinear Time History Analysis (NLTHA) of a four-story, three-dimensional multiple-degree-of-freedom building model using incremental dynamic analysis. Results show that the infilled frame generally exhibits a higher probability of exceedance than the open frame, despite demonstrating greater stiffness and strength in the pushover curve. This result highlights the necessity of considering masonry infill walls in structural models, particularly for low-rise structures where such infill significantly contributes to overall stiffness. In the comparative analysis, ASFRAVA-B tends to slightly overestimate damage probabilities at lower intensity measures, yet provides substantial computational efficiency compared to the detailed NLTHA model. Sensitivity analyses demonstrate that the configuration of ground motion records exerts a moderate influence on fragility curves, emphasizing the importance of careful record selection for reliable outcomes.</div></div>","PeriodicalId":13915,"journal":{"name":"International journal of disaster risk reduction","volume":"127 ","pages":"Article 105679"},"PeriodicalIF":4.2,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534500","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":"Uprooting dynamics of a model tree under rockfall impact: Combined experimental and numerical insights","authors":"Chenyang Zhang,&nbsp;Chenghao Yu,&nbsp;Anthony Kwan Leung,&nbsp;Sadeghi Mohammad,&nbsp;Clarence Edward Choi","doi":"10.1002/esp.70114","DOIUrl":"https://doi.org/10.1002/esp.70114","url":null,"abstract":"<p>Climate change already increases the number of rockfalls or flow-like landslides, posing a significant threat to human lives and public infrastructure in mountainous regions. Characterizing the dynamic uprooting response during rockfall-tree interaction is essential for understanding the protective capabilities of forests in mitigating these hazards. In this study, a novel model tree is developed to evaluate the uprooting resistance against instantaneous impact. A lamina emergent torsional (LET) joint is introduced to simulate the root-soil plate rotation behaviour. Then, a large-scale pendulum experiment is used to validate the statical and dynamic uprooting responses of the model tree. A numerical model is used to back-analyse the experiments and subsequently, a parametric study is performed. Our results demonstrate that the model tree closely reproduces the static and dynamic uprooting behaviours of natural trees, providing an accessible tool for further physical model experiments on rockfall and landslide impacts. The dynamic uprooting response of a tree is governed by both impact force and contact duration. Under instantaneous impacts, three distinct response regimes are observed: quasi-static, impulse and intermediate. In most impact scenarios, trees exhibit responses in the impulse and intermediate regimes, indicating that static-based criteria are insufficient for assessing uprooting stability. Consequently, we propose a dynamic failure criterion for predicting tree uprooting during rockfall interactions based on an empirical relationship between the critical impact duration and the normalized maximum impact turning moment. This criterion enables the prediction of dynamic tree uprooting failure using rockfall velocities, rockfall masses and stem diameters.</p>","PeriodicalId":11408,"journal":{"name":"Earth Surface Processes and Landforms","volume":"50 9","pages":""},"PeriodicalIF":2.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/esp.70114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144537210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climate Change Increases Evaporative and Crop Irrigation Demand in North America","authors":"Emily L. Williams,&nbsp;John T. Abatzoglou","doi":"10.1029/2025EF005931","DOIUrl":"https://doi.org/10.1029/2025EF005931","url":null,"abstract":"<p>Across North America, warmer temperatures have increased reference evapotranspiration (ETo), taxing water resources. This problem is especially pronounced for semi-arid regions with large amounts of irrigated agriculture, such as California's Central Valley. In this region, increased ETo has increased irrigation demand, but the role of anthropogenic climate change (ACC) in driving this increase has not yet been quantified. Here, we quantified the influence of ACC on ETo and how these changes have translated into increased irrigation demand. We calculated observational ETo from ERA5-Land and counterfactual ETo that removes the forced changes simulated by 20 models from the Coupled Model Intercomparison Project Phase 6 from the observational records. At the scale of North America, we found that ACC drove a 64 mm increase in annual ETo from 1980 to 2022, compared to the observed 54 mm increase. The largest observed increases in ETo were found in the southwestern and central regions, where ACC has likely exacerbated trends linked to natural climate variability. The largest ACC contributor to increased ETo was increased vapor pressure deficit, while decreased solar radiation has tempered increased ETo. Finally, we found that ACC has increased annual crop irrigation demand in the Central Valley by 0.76 km³ during 1980–2022, with cumulative increased irrigation demand of 9.2 km³, equivalent to ∼11% of the region's groundwater loss during this time. Our findings suggest that ACC is accelerating demand for water in this already water-limited region, and this phenomenon is likely occurring in other semi-arid agricultural regions of North America.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 7","pages":""},"PeriodicalIF":7.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2025EF005931","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144524915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}