Advances in Climate Change Research最新文献

{"title":"The interaction between thermokarst lake drainage and ground subsidence accelerates permafrost degradation","authors":"Yi-Ning Yu ,&nbsp;Feng-Ming Hui ,&nbsp;Yu Zhou ,&nbsp;Xiao Cheng ,&nbsp;Ming-Hu Ding","doi":"10.1016/j.accre.2025.01.003","DOIUrl":"10.1016/j.accre.2025.01.003","url":null,"abstract":"<div><div>Since it is not viable to directly evaluate permafrost change based on remote sensing, thermokarst lake drainage (TLD) and ground subsidence serve as indicators for depicting permafrost degradation. Unfortunately, the interaction between these two land surface processes as well as their joint effect remain unclear. In this study, based on a homogenized Landsat-Sentinel archive, TLD was detected in the Lena Basin during 2000–2022 thawing seasons using the modified LandTrendr algorithm. Only 9.7% of thermokarst lakes (TLs) experienced remarkable drainage, TLs larger than 30 hm² were more prone to undergone drainage processes. The drainage proportion among TLs with different extents all exceeded 10% during 2013–2015, suggesting the gradual drainage which lasted for three years or longer was likely to be the dominating type. The subsidence rates (−1.64 ± 0.89 to −1.94 ± 1.41 mm per year) surrounding drained TLs were higher than regional average (−1.40 ± 1.19 to −1.60 ± 1.26 mm per year). As the distance to drained TLs decreased, the proportion of subsidence measurements, rates, and seasonal subsidence magnitude exhibited consistent increasing trends. The subsidence rate was higher in the direction of more intense drainage than that in other directions. The ground subsidence trigger TLD by providing meltwater and reducing structural support, while TLD in turn contributes to ground subsidence by forming drainage channels. More importantly, our findings proved that their interaction further accelerates permafrost degradation, which is critical for more accurately modeling the complex permafrost degradation processes under the warmer and wetter Arctic climate.</div></div>","PeriodicalId":48628,"journal":{"name":"Advances in Climate Change Research","volume":"16 1","pages":"Pages 109-124"},"PeriodicalIF":6.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679547","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":"Wind power correction model designed by the quantitative assessment for the impacts of forecasted wind speed error","authors":"Zhi-Qi Xu ,&nbsp;Tong Xue ,&nbsp;Xin-Yu Chen ,&nbsp;Jin Feng ,&nbsp;Gu-Wei Zhang ,&nbsp;Cheng Wang ,&nbsp;Chun-Hui Lu ,&nbsp;Hai-Shan Chen ,&nbsp;Yi-Hui Ding","doi":"10.1016/j.accre.2024.12.006","DOIUrl":"10.1016/j.accre.2024.12.006","url":null,"abstract":"<div><div>The errors in wind power forecast will incur additional cost. It is critical to quantify the relationship between forecasting error in wind speed and power output. Unlike previous works that have rarely considered the speed error, we propose a comprehensive and repeatable wind power forecast correction model that quantitatively assess the impacts of speed error on power error, based on the power curves, speed predictions and distribution of speed forecast error. In this correction model, the power forecast error is obtained by calculating the mathematical expectation. The mathematical expectation of the wind power error is equal to the integral of the wind power error multiplied by its associated probability. Additionally, power forecast error and its probability are constructed as a function of speed forecast error and speed forecast error probability, respectively. To evaluate the model performance, numerical simulations are carried out in Guilin, Xiangyang and Xihai. The results suggest that the model can reduce the biases between observed and forecasted power, with the correlation coefficients increasing by over 15% in Guilin and Xihai. Furthermore, the root mean square error exhibits notable decline, with a reduction of over 35%, from 0.34 to 0.21 MW, from 0.42 to 0.27 MW and from 0.39 to 0.24 MW in the three aforementioned locations, respectively. This study contributes to enhancing the efficiency of wind power generation.</div></div>","PeriodicalId":48628,"journal":{"name":"Advances in Climate Change Research","volume":"16 1","pages":"Pages 73-81"},"PeriodicalIF":6.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679544","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":"Climate warming and wetting poses a severe threat to permafrost engineering stability on the Qinghai‒Xizang Plateau","authors":"Xiao-Ming Xu ,&nbsp;Zhong-Qiong Zhang ,&nbsp;Bo-Wen Tai ,&nbsp;Si-Ru Gao ,&nbsp;Yu-Zhong Yang ,&nbsp;Qing-Bai Wu","doi":"10.1016/j.accre.2025.02.001","DOIUrl":"10.1016/j.accre.2025.02.001","url":null,"abstract":"<div><div>Permafrost underpins engineering in cold regions but is highly sensitive to climate change. The mechanisms linking climate warming, precipitation changes, and permafrost degradation to infrastructure stability remain poorly understood on the Qinghai‒Xizang Plateau (QXP). Here, we present a multi-factor framework to quantify climate impacts on permafrost engineering stability. Our findings reveal a 26.7% decline in permafrost engineering stability from 2015 to 2100, with areas of extremely poor stability expanding by 0.3 × 10⁴ km² per decade (SSP2-4.5) and 0.6 × 10⁴ km² per decade (SSP5-8.5). Meanwhile, regions with relatively better stability shrink by 2.0 × 10⁴ km² and 2.9 × 10⁴ km² per decade, respectively. These changes driven primarily by a warming and wetting climate pattern. Moreover, engineering stability is maintained in northwestern and interior regions, whereas warmer, ice-saturated areas in the central plateau and southern Qilian Mountains degrade rapidly. Notably, cold permafrost is warming faster than warm permafrost, increasing its vulnerability. These insights provide a critical basis for guiding the future design, construction, and maintenance of permafrost infrastructure, enabling the development of adaptive engineering strategies that account for projected climate change impacts.</div></div>","PeriodicalId":48628,"journal":{"name":"Advances in Climate Change Research","volume":"16 1","pages":"Pages 93-108"},"PeriodicalIF":6.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679546","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":"Assessing the added value of convection-permitting modeling for urban climate research: A case study in eastern China","authors":"Lin Pei ,&nbsp;Shi-Guang Miao ,&nbsp;Xiang-Yu Huang ,&nbsp;Zhong-Wei Yan ,&nbsp;Deliang Chen","doi":"10.1016/j.accre.2025.01.004","DOIUrl":"10.1016/j.accre.2025.01.004","url":null,"abstract":"<div><div>Accurate urban-resolving climate data are essential for urban climate research and applications. However, General Circulation Models (GCMs) often lack the resolution and urban representation needed to provide reliable fine-scale climate information over urban areas. Convection-permitting modeling (CPM) has emerged as a promising solution to this challenge, despite its computational demands. Evaluating the added value of CPM for specific regions is crucial. In this study, we utilized the Weather Research and Forecasting (WRF) model coupled with a single-layer urban canopy model, as a regional climate model, to assess the performance and added value of CPM at both regional (urban clusters) and local (megacity) scales. With an optimized dynamic downscaling scheme, we conducted 3-km-resolution CPM and 9-km-resolution dynamic downscaling modeling (DDM) during the summer of 2020 in eastern China, where most cities and urban clusters are located. At the local scale, CPM well reproduced observed precipitation rates at daily and sub-daily time scales, greatly improved the overestimation of drizzle-to-light rainfall events and underestimation of heavy-to-torrential rain events in ERA5 reanalysis data. Additionally, CPM effectively captured diurnal variations in precipitation across six sub-regions of eastern China, a capability lacking in DDM and ERA5. Moreover, CPM successfully reproduced the observed urban heat island intensity in Beijing by capturing the heterogeneous air temperature distribution, outperforming ERA5 and DDM. Our findings highlight the considerable added value of CPM in simulating sub-daily precipitation variations and urban heat island intensity over urban areas of China. These insights will greatly enhance future high-resolution regional climate simulations and climate change projections over urban areas in China.</div></div>","PeriodicalId":48628,"journal":{"name":"Advances in Climate Change Research","volume":"16 1","pages":"Pages 1-11"},"PeriodicalIF":6.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679653","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":"Evaluation of the performance of WRF9km in simulating climate over the upper Yellow River Basin","authors":"Yi-Jia Li ,&nbsp;Xue-Jia Wang ,&nbsp;Xiao-Hua Gou ,&nbsp;Qi Wang ,&nbsp;Tinghai Ou ,&nbsp;Guo-Jin Pang ,&nbsp;Mei-Xue Yang ,&nbsp;Lan-Ya Liu ,&nbsp;Li-Ya Qie ,&nbsp;Tao Wang ,&nbsp;Jia-Yu Wang ,&nbsp;Si-Hao Wei ,&nbsp;Xiao-Lai Cheng","doi":"10.1016/j.accre.2024.12.003","DOIUrl":"10.1016/j.accre.2024.12.003","url":null,"abstract":"<div><div>Understanding the current climate in the Yellow River Basin is essential for accurately predicting future climate change and assessing its impacts on water resources and ecosystems; however, existing models exhibit notable biases in this region, primarily due to low resolution and errors in driving data and model domains. Using <em>in-situ</em> station observation data, CN05.1 gridded meteorological observation dataset, along with the ERA5 and MERRA2 reanalysis datasets, the performance of the WRF9km in simulating temperature and precipitation from 1980 to 2016 was comprehensively evaluated. Results indicate that the WRF9km model effectively captures the spatial pattern of air temperature, with a spatial correlation exceeding 0.86 (at the 95% confidence level) and a cold bias of −2.8 °C compared to CN05.1. This bias is primarily due to the underestimation of downward radiation and the overestimation of surface albedo. However, the WRF9km model fails to reproduce the observed warming trend across the entire region, especially during the summer. For precipitation, the WRF9km model generally reproduces the observed spatial pattern, with spatial correlation coefficients above 0.80 for all seasons except winter (at the 95% confidence level). However, the model overestimates precipitation relative to CN05.1 and underestimates it when compared to MERRA2. The precipitation bias is mainly attributed to the misrepresentation of wind fields and moisture by the WRF9km model. Regarding precipitation trends, different datasets yield divergent results, indicating substantial inter-annual variability that is difficult for the WRF9km to capture. Compared to the driving ERA5 data, the WRF9km model reduces cold biases between November and December, as well as wet biases across all seasons. The model also better simulates the winter warming trend in the western part of the UYRB and the summer wetting trend in the northern part. The evaluation of the WRF9km model provides valuable insights for the development of dynamical downscaling in terrain complex regions, especially for improving the surface albedo scheme and input driving data.</div></div>","PeriodicalId":48628,"journal":{"name":"Advances in Climate Change Research","volume":"16 1","pages":"Pages 58-72"},"PeriodicalIF":6.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679543","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":"Future climatic risks faced by the Beautiful China Initiative: A perspective for 2035 and 2050","authors":"Zhong-Xue Ma ,&nbsp;Hui-Juan Cui ,&nbsp;Quan-Sheng Ge","doi":"10.1016/j.accre.2025.01.002","DOIUrl":"10.1016/j.accre.2025.01.002","url":null,"abstract":"<div><div>Identifying high-risk areas for climatic disasters and their overlaps during the implementation of the Beautiful China Initiative fills a critical gap in disaster risk research, which often lacks quantitative analyses of the combined risks from multiple disasters. This study evaluates key climatic risks and their overlaps, including heavy storms, heatwaves, and droughts, that may affect the Beautiful China Initiative objectives in 2035 and 2050. The analysis is based on three shared socioeconomic pathways (SSP126, SSP370, and SSP585), incorporating vulnerability, disaster risks, and exposure levels. The findings indicate that the severity of climatic risks in China will intensify over time and with climate warming. The western regions will face more severe single-climate risks, while the eastern regions will encounter increasingly severe comprehensive climatic risks. In the western regions, by 2035, the Tarim Basin in Xinjiang is projected to experience heatwave risks exceeding Level 5, while Tibet and Qinghai will face drought risks above Level 6. By 2050, more areas will escalate to Level 6 and 7 risks. In the eastern regions, by 2035, Shandong, Henan, the Pearl River Delta, and the Beijing, Tianjin, Hebei region are expected to face comprehensive risks from heavy storms, heatwaves, and droughts. By 2050, the overlapping high-risk areas will expand, covering the eastern parts of the Yangtze River Economic Belt. Furthermore, higher radiative forcing scenarios are associated with increased risks. This study provides critical insights for developing targeted disaster prevention and management systems across different regions of China, offering guidance for the effective implementation of the Beautiful China Initiative.</div></div>","PeriodicalId":48628,"journal":{"name":"Advances in Climate Change Research","volume":"16 1","pages":"Pages 141-153"},"PeriodicalIF":6.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679489","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":"Traditional ecological knowledge in High Mountain Asia: A pathway to climate resilience in agriculture amidst changing climates","authors":"Lavanya Witharana ,&nbsp;Deliang Chen ,&nbsp;Julia Curio ,&nbsp;Anders Burman","doi":"10.1016/j.accre.2025.01.009","DOIUrl":"10.1016/j.accre.2025.01.009","url":null,"abstract":"<div><div>Traditional Ecological Knowledge (TEK) often represents centuries of empirical observation and adaptation to specific ecological conditions, which is relevant to meaningful nature-human relations. Yet, TEK is rarely taken into account. This study examines the role of TEK-based agriculture in promoting adaptation and resilience to climate change in the mountain agricultural systems of the Hindu Kush Himalaya (HKH) region. Through an extensive literature review, it identifies and synthesizes TEK-based agricultural practices, with a focus on soil and fertility management, strategies to manage agroecological disruptions and agroforestry. The findings indicate that these practices align with the climate change adaptation priorities of HKH countries, particularly in developing water- and nutrient-efficient crop cultivation systems and enhancing soil organic matter. While TEK-based agricultural methods can support regional climate change adaptation and resilience, the alteration or loss of traditional practices due to socio-economic factors may worsen the impacts of climate change. Therefore, recognizing and validating TEK within regional and local adaptation frameworks are essential for maintaining the resilience of traditional agriculture in the HKH region.</div></div>","PeriodicalId":48628,"journal":{"name":"Advances in Climate Change Research","volume":"16 1","pages":"Pages 167-182"},"PeriodicalIF":6.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679548","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":"Climatic impacts on electricity consumption of urban residential buildings in China","authors":"Yang-Yang Guo ,&nbsp;Mei-Xuan Teng ,&nbsp;Chen Zhang ,&nbsp;Sheng-Nan Wang ,&nbsp;Yi-Ming Wei","doi":"10.1016/j.accre.2024.12.004","DOIUrl":"10.1016/j.accre.2024.12.004","url":null,"abstract":"<div><div>Evaluating the effects of climate change is crucial for developing effective strategies for both mitigation and adaptation policies. However, a comprehensive quantification of the precise effects of climate change on electricity consumption in China's urban residential buildings has been hampered by the scarcity of data. Here, we employ a verified county-level unbalanced panel dataset to estimate the effect of cooling degree days (CDD) and heating degree days (HDD) on the electricity consumption of urban residential buildings in China. The results indicate that a 1% increase in CDD and HDD is linked to a corresponding rise of 0.114% and 0.457% in electricity consumption per unit of floor space in urban residential buildings, respectively. However, these effects are diminished as income increases, implying residents have more strategies to adapt to climate change as income rises. The impacts of temperature fluctuations exhibit variability across different climate zones, building heights, and construction years. Specifically, buildings in regions characterized by hot summers and cold winters, as well as those with hot summers and warm winters, exhibit greater sensitivity to temperature fluctuations compared to the buildings located in regions with severe cold and predominantly cold climates. Additionally, low-rise buildings tend to consume more electricity than multi-story and mid-to-high-rise buildings in response to temperature variation. Interestingly, new buildings are more vulnerable to temperature fluctuations than older buildings. These findings offer a comprehensive and accurate assessment of climatic impacts in different climate zones, enabling a more profound comprehension of climate change. This study provides empirical evidence that the effect of climate change on building energy use varies with building heights, addressing a critical gap in prior research.</div></div>","PeriodicalId":48628,"journal":{"name":"Advances in Climate Change Research","volume":"16 1","pages":"Pages 25-34"},"PeriodicalIF":6.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679540","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":"Projection for the occurrence dates of heat stress in North China","authors":"Lu-Lei Bu ,&nbsp;Kai-Wen Zhang ,&nbsp;Zhi-Yan Zuo ,&nbsp;Deliang Chen","doi":"10.1016/j.accre.2025.01.011","DOIUrl":"10.1016/j.accre.2025.01.011","url":null,"abstract":"<div><div>High humidity has been causing extreme heat stress during summer in North China, presenting threats to human life. Although future changes in the intensity and frequency of heat stress have been well recognized, it remains unclear whether the alterations in humidity resulting from increasing greenhouse gases (GHG) will change the occurrence date of heat stress in North China in the future. In this study, we identify three models, including CMCC-CM2-SR5, CMCC-ESM2, and TaiESM1, as the models that most reasonably simulate the dependence of the heat stress on the near-surface specific humidity and the occurrence date of the heat stress in North China. Based on these three models, we show that the increased specific humidity during the warm season, which is caused by rising GHG emissions, will extend the occurrence date of extreme heat stress from only July and August under the SSP1-2.6 scenario to June through September under SSP5-8.5 scenario. A more prevalent occurrence of the extreme heat stress during the warm season under SSP5-8.5 scenario will cause a three-to four-fold increase in the population exposure to the Severe heat stress (wet-bulb temperature &gt; 27.5 °C) compared to the SSP1-2.6 scenario. The findings highlight that North China will suffer more prevalent and extreme heat stresses in the future due to the GHG-caused increasing humidity. The results are valuable for developing adaptation and mitigation strategies to minimize the impacts of the extreme heat stress in North China.</div></div>","PeriodicalId":48628,"journal":{"name":"Advances in Climate Change Research","volume":"16 1","pages":"Pages 82-92"},"PeriodicalIF":6.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679545","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":"Assessment of flood loss in administrative units based on improved vulnerability curves","authors":"Rui Yan ,&nbsp;Lu-Lu Liu ,&nbsp;Jie Wang ,&nbsp;Shuang Zhou ,&nbsp;Shao-Hong Wu","doi":"10.1016/j.accre.2024.12.008","DOIUrl":"10.1016/j.accre.2024.12.008","url":null,"abstract":"<div><div>The vulnerability curve is a key method for assessing flood loss risk, which contributes to the improvement of flood prevention and relief systems. However, existing vulnerability curves are typically developed on a large spatial scale, and intraregional variations in vulnerability are overlooked. This study aims to quantify the spatial heterogeneity of vulnerability within a region and elucidate the impact of vulnerability on flood losses. With Hubei and Hunan provinces in China as case study areas, vulnerability curves are constructed using a mixed-effects model. These curves are then combined with flood intensities across various return periods to estimate flood losses. In addition, the influence of vulnerability and flood intensity on flood losses is analyzed. The results indicate that the mixed-effects model is effective in constructing distinct vulnerability curves for smaller-scale administrative units (<em>e.g.</em> cities) while also assessing the overall vulnerability of the study area, and it achieves high accuracy (<em>R</em>² &gt; 0.75). The disparities in loss rates between cities increase under longer return periods. Furthermore, the variation in vulnerability between cities is the primary factor influencing flood losses for short return periods, whereas differences in hazard intensity have a greater impact on city flood losses during longer return periods. This study provides methods and recommendations for systematic flood risk reduction and proposes pathways for enhancing climate resilience.</div></div>","PeriodicalId":48628,"journal":{"name":"Advances in Climate Change Research","volume":"16 1","pages":"Pages 154-166"},"PeriodicalIF":6.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679490","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}