中国是世界上水土流失最为严重的国家之一。准确掌握既有水土流失研究的空间分布格局是一项基础性工作。以中国知网学术期刊数据库作为数据源,应用自然语言处理方法,对1980-2017年中国水土流失研究地区进行了地名信息提取及研究热点建模...中国是世界上水土流失最为严重的国家之一。准确掌握既有水土流失研究的空间分布格局是一项基础性工作。以中国知网学术期刊数据库作为数据源,应用自然语言处理方法,对1980-2017年中国水土流失研究地区进行了地名信息提取及研究热点建模;继而应用RUSLE模型模拟,得到全国土壤侵蚀强度的空间分布;在上述研究基础上,对研究热点地区与侵蚀强度之间的空间耦合关系进行了对比分析。结果表明:(1)1980年以来,中国水土流失研究热点区主要分布在黄土高原及贵州高原,涉及陕西、宁夏、内蒙古、甘肃、贵州以及黑龙江等省区;中等及以上热度的县(区、市)共171个,占全国国土总面积的5.33%。(2)RUSLE模型模拟表明,严重的土壤侵蚀主要分布在黄土高原及云贵高原,涉及陕西、宁夏、甘肃、山西、贵州、云南、四川等省区;侵蚀模数大于20 t hm-2 a-1的县(区、市)共251个,占全国国土总面积的7.04%。(3)研究热点地图与水土流失强度模型模拟地图之间存在空间差异。对特定空间耦合模式的分析有助于判断科研资源配置的合理性。展开更多
Desertification research plays a key role in the survival and development of all mankind.The Normalized Comprehensive Hotspots Index(NCH)is a comprehensive index that reveals the spatial distribution of research hotsp...Desertification research plays a key role in the survival and development of all mankind.The Normalized Comprehensive Hotspots Index(NCH)is a comprehensive index that reveals the spatial distribution of research hotspots in a given research field based on the number of relevant scientific papers.This study uses Web Crawler technology to retrieve the full text of all Chinese journal articles spanning the 1980s-2018 in the Chinese Academic Journal full-text database(CAJ)from CNKI.Based on the 253,055 articles on desertification that were retrieved,we have constructed a research hotspot extraction model for desertification in China by means of the NCH Index.This model can reveal the spatial distribution and dynamic changes of research hotspots for desertification in China.This analysis shows the following:1)The spatial distribution of research hotspots on desertification in China can be effectively described by the NCH Index,although its application in other fields still needs to be verified and optimized.2)According to the NCH Index,the research hotspots for desertification are mainly distributed in the Agro-Pastoral Ecotone and grassland in Inner Mongolia,the desertification areas of Qaidam Basin in the Western Alpine Zone and the Oasis-Desert Ecotone in Xinjiang(including the extension of the central Tarim Basin to the foothills of the Kunlun Mountains,the sporadic areas around the Tianshan Mountains and the former hilly belt of the southern foothills of the Altai Mountains).Among these three,the Agro-Pastoral Ecotone in the middle and eastern part of Inner Mongolia includes the most prominent hotspots in the study of desertification.3)Since the 1980s,the research hotspots for desertification in China have shown a general downward trend,with a significant decline in 219 counties(10.37%of the study area).This trend is dominated by the projects carried out since 2002.The governance of desertification in the eastern part of the Inner Mongolia-Greater Khingan Range still needs to be strengthened.The distribution of desertification climate types reflects the distribution of desertification in a given region to some extent.The Normalized Comprehensive Hotspots Index provides a new approach for researchers in different fields to analyze research progress.展开更多
A new sequential data assimilation method named "Monte Carlo H ∞ filter" is introduced based on H ∞ filter technique and Monte Carlo method in this paper. This method applies to nonlinear systems in condit...A new sequential data assimilation method named "Monte Carlo H ∞ filter" is introduced based on H ∞ filter technique and Monte Carlo method in this paper. This method applies to nonlinear systems in condition of lacking the statistical properties of observational errors. In order to compare the as- similation capability of Monte Carlo H ∞ filter with that of the ensemble Kalman filter (EnKF) in solving practical problems caused by temporal correlation or spatial correlation of observational errors, two numerical experiments are performed by using Lorenz (1963) system and shallow-water equations re- spectively. The result is that the assimilation capability of the new method is better than that of EnKF method. It is also shown that Monte Carlo H ∞ filter assimilation method is effective and suitable to nonlinear systems in that it does not depend on the statistical properties of observational errors and has better robustness than EnKF method when the statistical properties of observational errors are varying. In addition, for the new method, the smallest level factor founded by search method is flow-dependent.展开更多
文摘中国是世界上水土流失最为严重的国家之一。准确掌握既有水土流失研究的空间分布格局是一项基础性工作。以中国知网学术期刊数据库作为数据源,应用自然语言处理方法,对1980-2017年中国水土流失研究地区进行了地名信息提取及研究热点建模;继而应用RUSLE模型模拟,得到全国土壤侵蚀强度的空间分布;在上述研究基础上,对研究热点地区与侵蚀强度之间的空间耦合关系进行了对比分析。结果表明:(1)1980年以来,中国水土流失研究热点区主要分布在黄土高原及贵州高原,涉及陕西、宁夏、内蒙古、甘肃、贵州以及黑龙江等省区;中等及以上热度的县(区、市)共171个,占全国国土总面积的5.33%。(2)RUSLE模型模拟表明,严重的土壤侵蚀主要分布在黄土高原及云贵高原,涉及陕西、宁夏、甘肃、山西、贵州、云南、四川等省区;侵蚀模数大于20 t hm-2 a-1的县(区、市)共251个,占全国国土总面积的7.04%。(3)研究热点地图与水土流失强度模型模拟地图之间存在空间差异。对特定空间耦合模式的分析有助于判断科研资源配置的合理性。
基金The National Key Research and Development Program of China(2016YFC0503701,2016YFB0501502)The Strategic Priority Research Program of Chinese Academy of Sciences(XDA19040301,XDA20010202,XDA23100201)The Key Project of the High Resolution Earth Observation System in China(00-Y30B14-9001-14/16)
文摘Desertification research plays a key role in the survival and development of all mankind.The Normalized Comprehensive Hotspots Index(NCH)is a comprehensive index that reveals the spatial distribution of research hotspots in a given research field based on the number of relevant scientific papers.This study uses Web Crawler technology to retrieve the full text of all Chinese journal articles spanning the 1980s-2018 in the Chinese Academic Journal full-text database(CAJ)from CNKI.Based on the 253,055 articles on desertification that were retrieved,we have constructed a research hotspot extraction model for desertification in China by means of the NCH Index.This model can reveal the spatial distribution and dynamic changes of research hotspots for desertification in China.This analysis shows the following:1)The spatial distribution of research hotspots on desertification in China can be effectively described by the NCH Index,although its application in other fields still needs to be verified and optimized.2)According to the NCH Index,the research hotspots for desertification are mainly distributed in the Agro-Pastoral Ecotone and grassland in Inner Mongolia,the desertification areas of Qaidam Basin in the Western Alpine Zone and the Oasis-Desert Ecotone in Xinjiang(including the extension of the central Tarim Basin to the foothills of the Kunlun Mountains,the sporadic areas around the Tianshan Mountains and the former hilly belt of the southern foothills of the Altai Mountains).Among these three,the Agro-Pastoral Ecotone in the middle and eastern part of Inner Mongolia includes the most prominent hotspots in the study of desertification.3)Since the 1980s,the research hotspots for desertification in China have shown a general downward trend,with a significant decline in 219 counties(10.37%of the study area).This trend is dominated by the projects carried out since 2002.The governance of desertification in the eastern part of the Inner Mongolia-Greater Khingan Range still needs to be strengthened.The distribution of desertification climate types reflects the distribution of desertification in a given region to some extent.The Normalized Comprehensive Hotspots Index provides a new approach for researchers in different fields to analyze research progress.
基金Supported by the National Natural Science Foundation of China (Grant Nos. 40275032, 40505005 and 40405019) Opening Foundation of Institute of Heavy Rain, CMA (Grant No. IHR2006G13)
文摘A new sequential data assimilation method named "Monte Carlo H ∞ filter" is introduced based on H ∞ filter technique and Monte Carlo method in this paper. This method applies to nonlinear systems in condition of lacking the statistical properties of observational errors. In order to compare the as- similation capability of Monte Carlo H ∞ filter with that of the ensemble Kalman filter (EnKF) in solving practical problems caused by temporal correlation or spatial correlation of observational errors, two numerical experiments are performed by using Lorenz (1963) system and shallow-water equations re- spectively. The result is that the assimilation capability of the new method is better than that of EnKF method. It is also shown that Monte Carlo H ∞ filter assimilation method is effective and suitable to nonlinear systems in that it does not depend on the statistical properties of observational errors and has better robustness than EnKF method when the statistical properties of observational errors are varying. In addition, for the new method, the smallest level factor founded by search method is flow-dependent.
