中国水土流失研究热点区的空间分布制图
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摘要
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。
China is one of the countries facing the most serious water erosion in the world. One key issue is to accurately map the distribution of existing researches about water erosion. The paper used the China Academic Journal Network Publishing Database as the data source and applied the natural language processing method to carry out place-name information extraction and research hotness modeling for mapping Chinese soil erosion and water conservation research hot regions during 1980—2017. The RUSLE(Revised Universal Soil Loss Equation) model was then applied for mapping Chinese water erosion intensity. Finally, the spatial relationship between the research hot regions and the distribution of water erosion intensity is compared and discussed. The results show that since 1980, the hot regions of water erosion research in China have mainly been distributed in the Loess Plateau and Guizhou Plateau, involving Shaanxi, Ningxia, Inner Mongolia, Gansu, Guizhou, and Heilongjiang provinces. There are 171 counties(districts or cities), accounting for 5.33% of the total land area, where the level of research hotness was judged to be moderate, or above moderate. The RUSLE model simulation indicated that severe water erosion is mainly distributed in the Loess Plateau and Yunnan-Guizhou Plateau, involving Shaanxi, Ningxia, Gansu, Shanxi, Guizhou, Yunnan, and Sichuan provinces. There are 251 counties(districts or cities), accounting for 7.04% of the total land area, with erosion rates greater than 20 t hm~(-2) a~(-1). There existing abvious spatial differences between the map of the research hot regions and the model simulation map of water erosion intensity. Analysis of the above spatial coupling model is helpful to assess the rationality of the allocation of scientific research resources.
China is one of the countries facing the most serious water erosion in the world. One key issue is to accurately map the distribution of existing researches about water erosion. The paper used the China Academic Journal Network Publishing Database as the data source and applied the natural language processing method to carry out place-name information extraction and research hotness modeling for mapping Chinese soil erosion and water conservation research hot regions during 1980—2017. The RUSLE(Revised Universal Soil Loss Equation) model was then applied for mapping Chinese water erosion intensity. Finally, the spatial relationship between the research hot regions and the distribution of water erosion intensity is compared and discussed. The results show that since 1980, the hot regions of water erosion research in China have mainly been distributed in the Loess Plateau and Guizhou Plateau, involving Shaanxi, Ningxia, Inner Mongolia, Gansu, Guizhou, and Heilongjiang provinces. There are 171 counties(districts or cities), accounting for 5.33% of the total land area, where the level of research hotness was judged to be moderate, or above moderate. The RUSLE model simulation indicated that severe water erosion is mainly distributed in the Loess Plateau and Yunnan-Guizhou Plateau, involving Shaanxi, Ningxia, Gansu, Shanxi, Guizhou, Yunnan, and Sichuan provinces. There are 251 counties(districts or cities), accounting for 7.04% of the total land area, with erosion rates greater than 20 t hm~(-2) a~(-1). There existing abvious spatial differences between the map of the research hot regions and the model simulation map of water erosion intensity. Analysis of the above spatial coupling model is helpful to assess the rationality of the allocation of scientific research resources.
引文
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[10] 于亢亢,赵华,钱程,高健.环境态度及其与环境行为关系的文献评述与元分析.环境科学研究,2018,31(6):1000-1009.
[11] 周景博,刘亮.未来气候变化对中国小麦产量影响的差异性研究——基于Meta回归分析的定量综述.中国农业气象,2018,39(3):141-151.
[12] Hu Y,Han Y,Zhang Y.Information extraction and spatial distribution of research hot regions on rocky desertification in China.Applied Sciences,2018,8(11):2075.
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[15] Hong Y,Nix H A,Hutchinson M F,Booth T H.Spatial interpolation of monthly mean climate data for China.International Journal of Climatology,2005,25(10):1369-1379.
[16] Wischmeier W H,Johnson C B,Cross B V.Soil erodibility nomograph for farmland and construction sites.Journal of Soil and Water Conservation,1971,26(5):189-193.
[17] 马娜,胡云锋,庄大方,张学利.基于遥感和像元二分模型的内蒙古正蓝旗植被覆盖度格局和动态变化.地理科学,2012,32(2):251-256.
[18] 蔡崇法,丁树文,史志华,黄丽,张光远.应用USLE模型与地理信息系统IDRISI预测小流域土壤侵蚀量的研究.水土保持学报,2000,14(2):19-24.
[19] 胡云锋,王倩倩,刘越,李军,任旺兵.国家尺度社会经济数据格网化原理和方法.地球信息科学学报,2011,13(5):573-578.
[20] Liu J Y,Zhang Q,Hu Y F.Regional differences of China′s urban expansion from late 20th to early 21st century based on remote sensing information.Chinese Geographical Science,2012,22(1):1-14.
[21] Ji G X,Zhao J C,Yang X,Yue Y L,Wang Z.Exploring China′s 21-year PM10 emissions spatiotemporal variations by DMSP-OLS nighttime stable light data.Atmospheric Environment,2018,191:132-141.
[22] Renard K G,Foster G R,Weesies G A,McCool D K,Yoder D C.Predicting Soil Erosion by Water:A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE).Washington,DC:U.S.Department of Agriculture,Agricultural Handbook,1997:27-28.
[23] 杨冉冉,徐涵秋,林娜,何慧,曾宏达.基于RUSLE的福建省长汀县河田盆地区土壤侵蚀定量研究.生态学报,2013,33(10):2974-2982.
[24] 冯强,赵文武.USLE/RUSLE中植被覆盖与管理因子研究进展.生态学报,2014,34(16):4461-4472.
[25] Fu B J,Zhao W W,Chen L D,Zhang Q J,Lü Y H,Gulinck H,Poesen J.Assessment of soil erosion at large watershed scale using RUSLE and GIS:a case study in the Loess Plateau of China.Land Degradation & Development,2005,16(1):73-85.
[26] Fu B J,Yu L,Lü Y H,He C S,Zeng Y,Wu B F.Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China.Ecological Complexity,2011,8(4):284-293.
[27] 刘国彬,王兵,卫伟,蔡进军,陈云明,毕华兴,刘广全,魏安智.黄土高原水土流失综合治理技术及示范.生态学报,2016,36(22):7074-7077.
[28] 宋同清,彭晚霞,杜虎,王克林,曾馥平.中国西南喀斯特石漠化时空演变特征、发生机制与调控对策.生态学报,2014,34(18):5328-5341.
[29] 刘发勇,熊康宁,兰安军,詹奉丽,犹珀玉,艾玉.贵州省喀斯特石漠化与水土流失空间相关分析.水土保持研究,2015,22(6):60-64,71-71.
[30] 熊康宁,李晋,龙明忠.典型喀斯特石漠化治理区水土流失特征与关键问题.地理学报,2012,67(7):878-888.
[31] Wei T,Li M H,Wu C S,Yan X Y,Fan Y,Di Z R,Wu J S.Do scientists trace hot topics?Scientific Reports,2013,3:2207.
[2] 中华人民共和国水利部.第一次全国水利普查水土保持情况公报.中国水土保持,2013,(10):2-3,11-11.
[3] Galaz V,Crona B,Daw T,Bodin ?,Nystr?m M,Olsson P.Can web crawlers revolutionize ecological monitoring?Frontiers in Ecology and the Environment,2010,8(2):99-104.
[4] Sadilek A,Kautz H,Silenzio V.Predicting disease transmission from geo-tagged micro-blog data//Proceedings of the 26th AAAI Conference on Artificial Intelligence.Toronto,Ontario,Canada:AAAI Press,2012.
[5] Dredze M,Paul M J,Bergsma S,Tran H.Carmen:a twitter geolocation system with applications to public health//Proceedings of the AAAI Publications,Workshops at the 27th AAAI Conference on Artificial Intelligence.Bellevue,Washington USA:AAAI Press,2013.
[6] Cameron M A,Power R,Robinson B,Yin J.Emergency situation awareness from twitter for crisis management//Proceedings of the 21st International Conference on World Wide Web.Lyon,France:ACM,2012.
[7] 王曙,吉雷静,张雪英,赵仁亮,陈晓丹,余浩.面向网页文本的地理要素变化检测.地球信息科学学报,2013,15(5):625-634.
[8] 仇培元,陆锋,张恒才,余丽.蕴含地理事件微博客消息的自动识别方法.地球信息科学学报,2016,18(7):886-893.
[9] 裴韬,郭思慧,袁烨城,张雪英,袁文,高昂,赵志远,薛存金.面向公共安全事件的网络文本大数据结构化研究.地球信息科学学报,2019,21(1):2-13.
[10] 于亢亢,赵华,钱程,高健.环境态度及其与环境行为关系的文献评述与元分析.环境科学研究,2018,31(6):1000-1009.
[11] 周景博,刘亮.未来气候变化对中国小麦产量影响的差异性研究——基于Meta回归分析的定量综述.中国农业气象,2018,39(3):141-151.
[12] Hu Y,Han Y,Zhang Y.Information extraction and spatial distribution of research hot regions on rocky desertification in China.Applied Sciences,2018,8(11):2075.
[13] Hu Y F,Han Y Q,Zhang Y Z,Zhuang Y.Extraction and dynamic spatial-temporal changes of grassland deterioration research hot regions in China.Journal of Resources and Ecology,2017,8(4):352-358.
[14] Richardson C W,Forster G R,Wright D A.Estimation of erosion index from daily rainfall amount.Transactions of the ASAE,1983,26(1):153-156.
[15] Hong Y,Nix H A,Hutchinson M F,Booth T H.Spatial interpolation of monthly mean climate data for China.International Journal of Climatology,2005,25(10):1369-1379.
[16] Wischmeier W H,Johnson C B,Cross B V.Soil erodibility nomograph for farmland and construction sites.Journal of Soil and Water Conservation,1971,26(5):189-193.
[17] 马娜,胡云锋,庄大方,张学利.基于遥感和像元二分模型的内蒙古正蓝旗植被覆盖度格局和动态变化.地理科学,2012,32(2):251-256.
[18] 蔡崇法,丁树文,史志华,黄丽,张光远.应用USLE模型与地理信息系统IDRISI预测小流域土壤侵蚀量的研究.水土保持学报,2000,14(2):19-24.
[19] 胡云锋,王倩倩,刘越,李军,任旺兵.国家尺度社会经济数据格网化原理和方法.地球信息科学学报,2011,13(5):573-578.
[20] Liu J Y,Zhang Q,Hu Y F.Regional differences of China′s urban expansion from late 20th to early 21st century based on remote sensing information.Chinese Geographical Science,2012,22(1):1-14.
[21] Ji G X,Zhao J C,Yang X,Yue Y L,Wang Z.Exploring China′s 21-year PM10 emissions spatiotemporal variations by DMSP-OLS nighttime stable light data.Atmospheric Environment,2018,191:132-141.
[22] Renard K G,Foster G R,Weesies G A,McCool D K,Yoder D C.Predicting Soil Erosion by Water:A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE).Washington,DC:U.S.Department of Agriculture,Agricultural Handbook,1997:27-28.
[23] 杨冉冉,徐涵秋,林娜,何慧,曾宏达.基于RUSLE的福建省长汀县河田盆地区土壤侵蚀定量研究.生态学报,2013,33(10):2974-2982.
[24] 冯强,赵文武.USLE/RUSLE中植被覆盖与管理因子研究进展.生态学报,2014,34(16):4461-4472.
[25] Fu B J,Zhao W W,Chen L D,Zhang Q J,Lü Y H,Gulinck H,Poesen J.Assessment of soil erosion at large watershed scale using RUSLE and GIS:a case study in the Loess Plateau of China.Land Degradation & Development,2005,16(1):73-85.
[26] Fu B J,Yu L,Lü Y H,He C S,Zeng Y,Wu B F.Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China.Ecological Complexity,2011,8(4):284-293.
[27] 刘国彬,王兵,卫伟,蔡进军,陈云明,毕华兴,刘广全,魏安智.黄土高原水土流失综合治理技术及示范.生态学报,2016,36(22):7074-7077.
[28] 宋同清,彭晚霞,杜虎,王克林,曾馥平.中国西南喀斯特石漠化时空演变特征、发生机制与调控对策.生态学报,2014,34(18):5328-5341.
[29] 刘发勇,熊康宁,兰安军,詹奉丽,犹珀玉,艾玉.贵州省喀斯特石漠化与水土流失空间相关分析.水土保持研究,2015,22(6):60-64,71-71.
[30] 熊康宁,李晋,龙明忠.典型喀斯特石漠化治理区水土流失特征与关键问题.地理学报,2012,67(7):878-888.
[31] Wei T,Li M H,Wu C S,Yan X Y,Fan Y,Di Z R,Wu J S.Do scientists trace hot topics?Scientific Reports,2013,3:2207.
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