Article(id=1311351161681183578, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2026.20250363, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740672000000, receivedDateStr=2025-02-28, revisedDate=1743091200000, revisedDateStr=2025-03-28, acceptedDate=1744041600000, acceptedDateStr=2025-04-08, onlineDate=1790580080671, onlineDateStr=2026-09-28, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790580080671, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790580080671, creator=13701087609, updateTime=1790580080671, updator=13701087609, issue=Issue{id=1311351152067830064, tenantId=1146029695717560320, journalId=1205116964453384197, year='2026', volume='44', issue='1', pageStart='1', pageEnd='246', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790580078378, creator='13701087609', updateTime=1790588459421, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311386304772927717, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311386304772927718, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=24, endPage=28, ext={EN=ArticleExt(id=1311351161869927259, articleId=1311351161681183578, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Risk Assessment of Drought Disaster in China Based on Text Information Mining, columnId=null, journalTitle=Water Resources and Power, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Drought disaster is the combined effects of disaster-causing, socioeconomic and environmental factors. The textual information of historical drought disasters in China was used to classify drought disaster. The method of hotspot analysis was employed to identify prone areas of drought disaster. Based on three elements of hazard, exposure, and vulnerability, the risk assessment of drought disaster was investigated. The results show that three types of key information including water and rainfall conditions, drought severity and social impact extracted from drought disaster texts were well applied to their classification. During the period of 1949-2020 in China, it experienced an average of about 170 drought disasters per year, with mild, moderate, and severe droughts accounting for 52.4%, 27.6%, and 20.0%, respectively. The prone areas of drought disaster mainly occurred in Henan, Shanxi, Shaanxi and Inner Mongolia etc. The levels of the hazard, exposure and vulnerability in Henan, Hubei and Shandong accounted for the higher or above. The levels of drought disaster risk in Henan, Hubei, Sichuan, and Shandong, as the major grain producing areas, were the higher or above. The results of the study can provide a reference for drought disaster mitigation.

, authors=Jing-lei ZHU1, 2, 3, Xin-jun TU1, 2, 3, Xin-bei WANG1, 2, 3, Kai-rong LIN1, 2, 3, Mei-xian LIU1, 2, 3, Zhi-yong LIU1, 2, 3, authorsList=Jing-lei ZHU, Xin-jun TU, Xin-bei WANG, Kai-rong LIN, Mei-xian LIU, Zhi-yong LIU, authorCompany=null, correspAuthors=Xin-jun TU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311351162297746268, articleId=1311351161681183578, tenantId=1146029695717560320, journalId=1205116964453384197, language=CN, title=基于文本信息挖掘的中国旱灾风险评估, columnId=1207627659488632924, journalTitle=水电能源科学, columnName=水文水资源与环境, runingTitle=null, highlight=null, articleAbstract=

旱灾是致灾因子与社会经济及环境共同作用的结果。采用中国历史旱灾文本信息进行旱灾分级,通过热点分析识别旱灾频发区,基于危险性、暴露性和脆弱性三要素开展旱灾风险评估。结果表明,从水雨情、旱情和社会影响三个方面提取的旱灾关键信息,能够较好地应用于旱灾分级。1949~2020年,中国年均旱灾约170次,其中轻度、中度和重度旱灾占比分别为52.4%、27.6%、20.0%,旱灾频发区主要出现在河南、山西、陕西和内蒙古等地。危险性、暴露性和脆弱性均为较高及以上的有河南、湖北和山东。作为中国粮食主产区的河南、湖北、四川、山东等地为较高及以上旱灾风险区。研究结果可为抗旱减灾工作提供参考。

, authors=朱静蕾1, 2, 3, 涂新军1, 2, 3, 王欣贝1, 2, 3, 林凯荣1, 2, 3, 刘梅先1, 2, 3, 刘智勇1, 2, 3, authorsList=朱静蕾, 涂新军, 王欣贝, 林凯荣, 刘梅先, 刘智勇, authorCompany=null, correspAuthors=涂新军, authorNote=

朱静蕾(2000-),女,硕士研究生,研究方向为水文水资源,E-mail:

, correspAuthorsNote=
涂新军(1972-),男,教授、博导,研究方向为水文水资源,E-mail:
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朱静蕾(2000-),女,硕士研究生,研究方向为水文水资源,E-mail:

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基于文本信息挖掘的中国旱灾风险评估
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朱静蕾 1, 2, 3 , 涂新军 1, 2, 3 , 王欣贝 1, 2, 3 , 林凯荣 1, 2, 3 , 刘梅先 1, 2, 3 , 刘智勇 1, 2, 3
水电能源科学 | 水文水资源与环境 2026,44(1): 24-28
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水电能源科学 |水文水资源与环境 2026 , 44 (1) : 24 -28
基于文本信息挖掘的中国旱灾风险评估
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朱静蕾1, 2, 3 , 涂新军1, 2, 3 , 王欣贝1, 2, 3, 林凯荣1, 2, 3, 刘梅先1, 2, 3, 刘智勇1, 2, 3
作者信息
  • 1.中山大学水资源与环境研究中心,广东 广州 510275
  • 2.广东省华南地区水安全调控工程技术研究中心,广东 广州 510275
  • 3.南方海洋科学与工程广东省实验室,广东 珠海 519080
通讯作者:
涂新军(1972-),男,教授、博导,研究方向为水文水资源,E-mail:
作者简介:

朱静蕾(2000-),女,硕士研究生,研究方向为水文水资源,E-mail:

Risk Assessment of Drought Disaster in China Based on Text Information Mining
Jing-lei ZHU1, 2, 3 , Xin-jun TU1, 2, 3 , Xin-bei WANG1, 2, 3, Kai-rong LIN1, 2, 3, Mei-xian LIU1, 2, 3, Zhi-yong LIU1, 2, 3
Affiliations
  • 1.Center of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China
  • 2.Center of Water Security Engineering and Technology in Southern China of Guangdong, Guangzhou 510275, China
  • 3.Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519080, China
出版时间: 2026-01-25 doi: 10.20040/j.cnki.1000-7709.2026.20250363
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旱灾是致灾因子与社会经济及环境共同作用的结果。采用中国历史旱灾文本信息进行旱灾分级,通过热点分析识别旱灾频发区,基于危险性、暴露性和脆弱性三要素开展旱灾风险评估。结果表明,从水雨情、旱情和社会影响三个方面提取的旱灾关键信息,能够较好地应用于旱灾分级。1949~2020年,中国年均旱灾约170次,其中轻度、中度和重度旱灾占比分别为52.4%、27.6%、20.0%,旱灾频发区主要出现在河南、山西、陕西和内蒙古等地。危险性、暴露性和脆弱性均为较高及以上的有河南、湖北和山东。作为中国粮食主产区的河南、湖北、四川、山东等地为较高及以上旱灾风险区。研究结果可为抗旱减灾工作提供参考。

旱灾  /  风险评估  /  文本挖掘  /  热点分析  /  DPSEEA

Drought disaster is the combined effects of disaster-causing, socioeconomic and environmental factors. The textual information of historical drought disasters in China was used to classify drought disaster. The method of hotspot analysis was employed to identify prone areas of drought disaster. Based on three elements of hazard, exposure, and vulnerability, the risk assessment of drought disaster was investigated. The results show that three types of key information including water and rainfall conditions, drought severity and social impact extracted from drought disaster texts were well applied to their classification. During the period of 1949-2020 in China, it experienced an average of about 170 drought disasters per year, with mild, moderate, and severe droughts accounting for 52.4%, 27.6%, and 20.0%, respectively. The prone areas of drought disaster mainly occurred in Henan, Shanxi, Shaanxi and Inner Mongolia etc. The levels of the hazard, exposure and vulnerability in Henan, Hubei and Shandong accounted for the higher or above. The levels of drought disaster risk in Henan, Hubei, Sichuan, and Shandong, as the major grain producing areas, were the higher or above. The results of the study can provide a reference for drought disaster mitigation.

drought disaster  /  risk assessment  /  text mining  /  hotspot analysis  /  DPSEEA
朱静蕾, 涂新军, 王欣贝, 林凯荣, 刘梅先, 刘智勇. 基于文本信息挖掘的中国旱灾风险评估. 水电能源科学, 2026 , 44 (1) : 24 -28 . DOI: 10.20040/j.cnki.1000-7709.2026.20250363
Jing-lei ZHU, Xin-jun TU, Xin-bei WANG, Kai-rong LIN, Mei-xian LIU, Zhi-yong LIU. Risk Assessment of Drought Disaster in China Based on Text Information Mining[J]. Water Resources and Power, 2026 , 44 (1) : 24 -28 . DOI: 10.20040/j.cnki.1000-7709.2026.20250363
  • 国家自然科学基金面上项目(52279032)
  • 国家重点研发计划(2022YFC3002900)
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2026年第44卷第1期
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doi: 10.20040/j.cnki.1000-7709.2026.20250363
  • 接收时间:2025-02-28
  • 首发时间:2026-09-28
  • 出版时间:2026-01-25
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  • 收稿日期:2025-02-28
  • 修回日期:2025-03-28
  • 录用日期:2025-04-08
基金
国家自然科学基金面上项目(52279032)
国家重点研发计划(2022YFC3002900)
作者信息
    1.中山大学水资源与环境研究中心,广东 广州 510275
    2.广东省华南地区水安全调控工程技术研究中心,广东 广州 510275
    3.南方海洋科学与工程广东省实验室,广东 珠海 519080

通讯作者:

涂新军(1972-),男,教授、博导,研究方向为水文水资源,E-mail:
参考文献
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https://castjournals.cast.org.cn/joweb/sdnykx/CN/10.20040/j.cnki.1000-7709.2026.20250363
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Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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