Article(id=1311998559034114515, tenantId=1146029695717560320, journalId=1311385791232049272, issueId=1311998371833934610, articleNumber=null, orderNo=null, doi=10.3878/j.issn.1006-9895.2508.25090, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747843200000, receivedDateStr=2025-05-22, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790734432228, onlineDateStr=2026-09-30, pubDate=1784044800000, pubDateStr=2026-07-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790734432228, onlineIssueDateStr=2026-09-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790734432228, creator=13701087609, updateTime=1790734432228, updator=13701087609, issue=Issue{id=1311998371833934610, tenantId=1146029695717560320, journalId=1311385791232049272, year='2026', volume='50', issue='4', pageStart='1017', pageEnd='1326', issueExtLink='null', onlineDate='null', pubDate='1784044800000', pubDateStr='2026-07-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790734387596, creator='13701087609', updateTime=1790739443587, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1312019578272702984, tenantId=1146029695717560320, journalId=1311385791232049272, issueId=1311998371833934610, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1312019578272702985, tenantId=1146029695717560320, journalId=1311385791232049272, issueId=1311998371833934610, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1265, endPage=1277, ext={EN=ArticleExt(id=1311998559336104404, articleId=1311998559034114515, tenantId=1146029695717560320, journalId=1311385791232049272, language=EN, title=Changes and Influencing Factors of Compound Hot–Dry Events in the Mongolian Plateau over the Past 40 Years, columnId=1311998374337934099, journalTitle=Chinese Journal of Atmospheric Sciences, columnName=Articles, runingTitle=null, highlight=null, articleAbstract=

In the context of global warming, the spatiotemporal correlation between extreme drought and heatwave events has intensified over time. Their compounding effects not only amplified their individual impacts but also exacerbated the pressure on water resources, agriculture, and ecological environments. Based on the fifth-generation reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5), this study calculated the standardized precipitation evapotranspiration index (SPEI) and the standardized temperature index (STI) and employed the Frank Copula function to construct the standardized compound dry and hot index (SCDHI). This approach systematically revealed the spatiotemporal evolution characteristics of compound hot–dry events in the Mongolian Plateau between 1982 and 2023. Additionally, the study integrated a random forest regression model to identify and quantify the key climatic factors that influenced compound hot–dry events in the region. The results indicated that over the past four decades, the intensity, the time proportion, and the affected area proportion of compound hot–dry events in the Mongolian Plateau exhibited a significant decreasing trend. Among the climatic variables, STI was the most influential factor affecting the compound events, with a feature importance value of approximately 0.5. This was followed by SPEI, temperature, and potential evapotranspiration (PET), with feature importance values of 0.4, 0.1, and 0.1, respectively, while precipitation had a relatively weak impact. This study underscored the need to incorporate the long-term impacts of compound hot–dry events into the protection and management of the grassland ecosystem of the Mongolian Plateau. It emphasized the importance of developing adaptive management strategies based on scientific predictions and risk assessments to enhance the resilience and sustainability of the ecosystem.

, authors=Xiaorui ZHU1, Zhiwei YU1, Lei JU1, Pengjia LI1, Hao LIU1, Zhanli SUN2, Enliang GUO3, Guojie WANG4, Qiang LIU4, 5, Lijuan MIAO1, authorsList=Xiaorui ZHU, Zhiwei YU, Lei JU, Pengjia LI, Hao LIU, Zhanli SUN, Enliang GUO, Guojie WANG, Qiang LIU, Lijuan MIAO, authorCompany=null, correspAuthors=Lijuan MIAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright © 2026 Chinese Journal of Atmospheric Sciences. All rights reserved., 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=1311998564281188852, articleId=1311998559034114515, tenantId=1146029695717560320, journalId=1311385791232049272, language=CN, title=近40年蒙古高原复合干热事件变化及影响要素研究, columnId=1311998374501511957, journalTitle=大气科学, columnName=论文, runingTitle=null, highlight=null, articleAbstract=

在全球变暖的背景下,极端干旱与热浪事件的时空关联性日益增强,其叠加效应不仅加剧了各自的影响,还进一步加重了对水资源、农业和生态环境的冲击。本研究基于欧洲中期天气预报中心第五代再分析资料,计算了标准化降水蒸散发指数和标准化温度指数,并采用Frank Copula函数构建了标准化复合干热指数,揭示了1982~2023年蒙古高原复合干热事件的演变特征。同时,结合随机森林回归模型,识别影响蒙古高原复合干热事件的关键气候要素。研究结果表明,过去四十多年,蒙古高原复合干热事件强度、发生时间占比和影响面积占比整体呈下降趋势,中等强度事件渐为频繁,事件结构由两极分布向中等强度集中。STI是影响研究区复合干热事件最重要的气候变量,特征重要性值约为0.5,其次是SPEI,特征重要性值约为0.4,而温度、潜在蒸散发和降水的影响相对较弱,特征重要性值均低于0.1。因此,本研究强调在气候变化背景下,蒙古高原草地生态系统的保护与管理需充分考虑复合干热事件的长期影响,并基于科学预测与风险评估制定适应性管理策略,以提升生态系统的韧性与可持续性。

, authors=朱晓蕊1, 余志巍1, 鞠蕾1, 李鹏嘉1, 刘昊1, 孙战利2, 郭恩亮3, 王国杰4, 刘强4, 5, 缪丽娟1, authorsList=朱晓蕊, 余志巍, 鞠蕾, 李鹏嘉, 刘昊, 孙战利, 郭恩亮, 王国杰, 刘强, 缪丽娟, authorCompany=null, correspAuthors=缪丽娟, authorNote=

朱晓蕊,女,硕士生,主要从事气候变化与土地利用研究。E-mail:

, correspAuthorsNote=
缪丽娟,女,博士,副教授,主要从事气候变化与土地利用、土地系统变化研究。E-mail:
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Here, x represents year, y represents the SCDHI value, R2 denotes the coefficient of determination, and p indicates the significance level., figureFileSmall=NiCGin/q3AYG3uPeZW1VNw==, figureFileBig=ZIFPm/VOYgTxmvBW65Sczg==, tableContent=null), ArticleFig(id=1311998572082594373, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=CN, label=图1, caption=1982~2023年蒙古高原标准化复合干热指数(SCDHI)的年际变化趋势(单位:a−1)。其中,x表示年份,y表示SCDHI值,R2为决定系数,p为显著性水平。, figureFileSmall=NiCGin/q3AYG3uPeZW1VNw==, figureFileBig=ZIFPm/VOYgTxmvBW65Sczg==, tableContent=null), ArticleFig(id=1311998572279726662, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=EN, label=Fig.2, caption=Spatial distribution of the variation trend of SCDHI in the Mongolian Plateau from 1982 to 2023 (units: a−1). Here, p indicates the significance level., figureFileSmall=pEntghlm1U4l7GRWJVm+tw==, figureFileBig=kSOZ++XD/QbQfW6agQZ1VQ==, tableContent=null), ArticleFig(id=1311998572342641223, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=CN, label=图2, caption=1982~2023年蒙古高原SCDHI变化趋势的空间分布(单位:a−1)。其中,p为显著性水平。, figureFileSmall=pEntghlm1U4l7GRWJVm+tw==, figureFileBig=kSOZ++XD/QbQfW6agQZ1VQ==, tableContent=null), ArticleFig(id=1311998572405555784, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=EN, label=Fig.3, caption=Temporal variations in the time proportion of compound hot-dry events in the Mongolian Plateau from 1982 to 2023: (a) all grades and (b–f) Grade 0 to Grade 4 events. Grade 0: abnormal events; Grade 1: mild events; Grade 2: moderate events; Grade 3: severe events; Grade 4: extreme events. Here, x represents year, y represents the time proportion, R2 denotes the coefficient of determination, and p indicates the significance level., figureFileSmall=5R+acPA3TdJPXmOquyyWoQ==, figureFileBig=0P/V0HlPNGL1h5yUlZH3Ow==, tableContent=null), ArticleFig(id=1311998572468470345, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=CN, label=图3, caption=1982~2023年蒙古高原(a)全部以及(b–f)Grade 0至Grade 4等级复合干热事件发生时间占比随时间的变化。Grade 0:反常事件;Grade 1:轻度事件;Grade 2:中等事件;Grade 3:严重事件;Grade 4极端事件。其中,x表示年份,y表示发生时间占比,R2为决定系数,p为显著性水平。, figureFileSmall=5R+acPA3TdJPXmOquyyWoQ==, figureFileBig=0P/V0HlPNGL1h5yUlZH3Ow==, tableContent=null), ArticleFig(id=1311998572535579210, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=EN, label=Fig.4, caption=Spatial distributions of the time proportion of compound hot-dry events in the Mongolian Plateau from 1982 to 2023: (a) all grades and (b–f) Grade 0 to Grade 4 events. Grade 0: abnormal events; Grade 1: mild events; Grade 2: moderate events; Grade 3: severe events; Grade 4: extreme events., figureFileSmall=grVLvcAzZNTyRH/1xxo8/w==, figureFileBig=1dhe4SsUSi4fo7DDITXU4w==, tableContent=null), ArticleFig(id=1311998572632048203, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=CN, label=图4, caption=1982~2023年蒙古高原(a)全部以及(b–f)Grade 0至Grade 4等级复合干热事件发生时间占比的空间分布。Grade 0:反常事件;Grade 1:轻度事件;Grade 2:中等事件;Grade 3:严重事件;Grade 4极端事件。, figureFileSmall=grVLvcAzZNTyRH/1xxo8/w==, figureFileBig=1dhe4SsUSi4fo7DDITXU4w==, tableContent=null), ArticleFig(id=1311998572724322892, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=EN, label=Fig.5, caption=Temporal variations in the affected area proportion of compound hot-dry events in the Mongolian Plateau from 1982 to 2023: (a) all grades and (b–f) Grade 0 to Grade 4 events. Grade 0: abnormal events; Grade 1: mild events; Grade 2: moderate events; Grade 3: severe events; Grade 4: extreme events. Here, x represents year, y represents the affected area proportion, R2 denotes the coefficient of determination, and p indicates the significance level., figureFileSmall=8Lt8O/wC2batdNE5Iq0gHw==, figureFileBig=uQKlFbghRjS/Hn+x4SpIpg==, tableContent=null), ArticleFig(id=1311998572799820365, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=CN, label=图5, caption=1982~2023年蒙古高原(a)全部以及(b–f)Grade 0至Grade 4等级复合干热事件影响面积占比随时间的变化。Grade 0:反常事件;Grade 1:轻度事件;Grade 2:中等事件;Grade 3:严重事件;Grade 4极端事件。其中,x表示年份,y表示影响面积占比,R2为决定系数,p为显著性水平。, figureFileSmall=8Lt8O/wC2batdNE5Iq0gHw==, figureFileBig=uQKlFbghRjS/Hn+x4SpIpg==, tableContent=null), ArticleFig(id=1311998572866929230, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=EN, label=Fig.6, caption=Feature importance values (FIV) of various factors influencing SCDHI. STI: Standardized Temperature Index; SPEI: Standardized Precipitation Evapotranspiration Index; PET: Potential Evapotranspiration., figureFileSmall=uethi0OXGWoGSmL5AnNyFg==, figureFileBig=bvgc4hv8i4n1hCHfxnuCKg==, tableContent=null), ArticleFig(id=1311998572942426703, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=CN, label=图6, caption=影响SCDHI各特征变量的特征重要性值(FIV)。STI、SPEI和PET分别为标准化温度指数、标准化降水蒸散发指数和潜在蒸散发。, figureFileSmall=uethi0OXGWoGSmL5AnNyFg==, figureFileBig=bvgc4hv8i4n1hCHfxnuCKg==, tableContent=null), ArticleFig(id=1311998573009535568, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=EN, label=Fig.7, caption=Temporal variations in feature importance values (FIV) of different variables influencing SCDHI from 1982 to 2023. STI, SPEI, and PET represent the Standardized Temperature Index, Standardized Precipitation Evapotranspiration Index, and Potential Evapotranspiration, respectively. Here, x represents year, y represents the feature importance value of each variable, and p indicates the significance level., figureFileSmall=BJJRSwrrw7kNAZP1PjIRQA==, figureFileBig=mHVQKVLcqAXpIymbCIfWwQ==, tableContent=null), ArticleFig(id=1311998573080838737, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=CN, label=图7, caption=1982~2023年SCDHI 各特征变量特征重要性值(FIV)随时间的变化。STI、SPEI 和 PET 分别表示标准化温度指数、标准化降水蒸散发指数和潜在蒸散发。其中,x表示年份,y表示各变量的特征重要性值,p为显著性水平。, figureFileSmall=BJJRSwrrw7kNAZP1PjIRQA==, figureFileBig=mHVQKVLcqAXpIymbCIfWwQ==, tableContent=null), ArticleFig(id=1311998573135364690, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=EN, label=Tab.1, caption=

Classification criteria for intensity of compound hot-dry events

, figureFileSmall=null, figureFileBig=null, tableContent=
等级类型SCDHI
Grade 0反常事件(−0.8, −0.5]
Grade 1轻度事件(−1.3, −0.8]
Grade 2中等事件(−1.6, −1.3]
Grade 3严重事件(−2.0, −1.6]
Grade 4极端事件≤−2.0
), ArticleFig(id=1311998573231833683, tenantId=1146029695717560320, journalId=1311385791232049272, articleId=1311998559034114515, language=CN, label=表1, caption=

复合干热事件强度等级划分标准

, figureFileSmall=null, figureFileBig=null, tableContent=
等级类型SCDHI
Grade 0反常事件(−0.8, −0.5]
Grade 1轻度事件(−1.3, −0.8]
Grade 2中等事件(−1.6, −1.3]
Grade 3严重事件(−2.0, −1.6]
Grade 4极端事件≤−2.0
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近40年蒙古高原复合干热事件变化及影响要素研究
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朱晓蕊 1 , 余志巍 1 , 鞠蕾 1 , 李鹏嘉 1 , 刘昊 1 , 孙战利 2 , 郭恩亮 3 , 王国杰 4 , 刘强 4, 5 , 缪丽娟 1
大气科学 | 论文 2026,50(4): 1265-1277
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大气科学 |论文 2026 , 50 (4) : 1265 -1277
近40年蒙古高原复合干热事件变化及影响要素研究
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朱晓蕊,女,硕士生,主要从事气候变化与土地利用研究。E-mail:

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朱晓蕊,女,硕士生,主要从事气候变化与土地利用研究。E-mail:

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朱晓蕊1 , 余志巍1, 鞠蕾1, 李鹏嘉1, 刘昊1, 孙战利2, 郭恩亮3, 王国杰4, 刘强4, 5, 缪丽娟1
作者信息
  • 1南京信息工程大学地理科学学院, 南京210044
  • 2德国莱布尼茨经济转型体农业发展政策研究所, 德国哈勒06120
  • 3内蒙古师范大学地理科学学院, 呼和浩特010022
  • 4南京信息工程大学遥感与测绘工程学院, 南京210044
  • 5南京信息工程大学气候系统预测与变化应对全国重点实验室, 南京210044
通讯作者:
缪丽娟,女,博士,副教授,主要从事气候变化与土地利用、土地系统变化研究。E-mail:
作者简介:

朱晓蕊,女,硕士生,主要从事气候变化与土地利用研究。E-mail:

Changes and Influencing Factors of Compound Hot–Dry Events in the Mongolian Plateau over the Past 40 Years
Xiaorui ZHU1 , Zhiwei YU1, Lei JU1, Pengjia LI1, Hao LIU1, Zhanli SUN2, Enliang GUO3, Guojie WANG4, Qiang LIU4, 5, Lijuan MIAO1
Affiliations
  • 1School of Geographical Sciences, Nanjing University of Information Science & Technology, Nanjing 210044
  • 2Leibniz Institute of Agricultural Development in Transition Economies, Halle, Germany 06120,
  • 3College of Geographical Science, Inner Mongolia Normal University, Hohhot 010022
  • 4School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, Nanjing 210044
  • 5State Key Laboratory of Climate System Prediction and Risk Management, Nanjing University of Information Science & Technology, Nanjing 210044
出版时间: 2026-07-15 doi: 10.3878/j.issn.1006-9895.2508.25090
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在全球变暖的背景下,极端干旱与热浪事件的时空关联性日益增强,其叠加效应不仅加剧了各自的影响,还进一步加重了对水资源、农业和生态环境的冲击。本研究基于欧洲中期天气预报中心第五代再分析资料,计算了标准化降水蒸散发指数和标准化温度指数,并采用Frank Copula函数构建了标准化复合干热指数,揭示了1982~2023年蒙古高原复合干热事件的演变特征。同时,结合随机森林回归模型,识别影响蒙古高原复合干热事件的关键气候要素。研究结果表明,过去四十多年,蒙古高原复合干热事件强度、发生时间占比和影响面积占比整体呈下降趋势,中等强度事件渐为频繁,事件结构由两极分布向中等强度集中。STI是影响研究区复合干热事件最重要的气候变量,特征重要性值约为0.5,其次是SPEI,特征重要性值约为0.4,而温度、潜在蒸散发和降水的影响相对较弱,特征重要性值均低于0.1。因此,本研究强调在气候变化背景下,蒙古高原草地生态系统的保护与管理需充分考虑复合干热事件的长期影响,并基于科学预测与风险评估制定适应性管理策略,以提升生态系统的韧性与可持续性。

蒙古高原  /  复合干热事件  /  标准化复合干热指数  /  时空演变  /  影响要素

In the context of global warming, the spatiotemporal correlation between extreme drought and heatwave events has intensified over time. Their compounding effects not only amplified their individual impacts but also exacerbated the pressure on water resources, agriculture, and ecological environments. Based on the fifth-generation reanalysis data from the European Centre for Medium-Range Weather Forecasts (ERA5), this study calculated the standardized precipitation evapotranspiration index (SPEI) and the standardized temperature index (STI) and employed the Frank Copula function to construct the standardized compound dry and hot index (SCDHI). This approach systematically revealed the spatiotemporal evolution characteristics of compound hot–dry events in the Mongolian Plateau between 1982 and 2023. Additionally, the study integrated a random forest regression model to identify and quantify the key climatic factors that influenced compound hot–dry events in the region. The results indicated that over the past four decades, the intensity, the time proportion, and the affected area proportion of compound hot–dry events in the Mongolian Plateau exhibited a significant decreasing trend. Among the climatic variables, STI was the most influential factor affecting the compound events, with a feature importance value of approximately 0.5. This was followed by SPEI, temperature, and potential evapotranspiration (PET), with feature importance values of 0.4, 0.1, and 0.1, respectively, while precipitation had a relatively weak impact. This study underscored the need to incorporate the long-term impacts of compound hot–dry events into the protection and management of the grassland ecosystem of the Mongolian Plateau. It emphasized the importance of developing adaptive management strategies based on scientific predictions and risk assessments to enhance the resilience and sustainability of the ecosystem.

Mongolian Plateau  /  Compound Hot–Dry Events  /  Standardized Compound Dry and Hot Index  /  Spatiotemporal Evolution  /  Influencing Factors
朱晓蕊, 余志巍, 鞠蕾, 李鹏嘉, 刘昊, 孙战利, 郭恩亮, 王国杰, 刘强, 缪丽娟. 近40年蒙古高原复合干热事件变化及影响要素研究. 大气科学, 2026 , 50 (4) : 1265 -1277 . DOI: 10.3878/j.issn.1006-9895.2508.25090
Xiaorui ZHU, Zhiwei YU, Lei JU, Pengjia LI, Hao LIU, Zhanli SUN, Enliang GUO, Guojie WANG, Qiang LIU, Lijuan MIAO. Changes and Influencing Factors of Compound Hot–Dry Events in the Mongolian Plateau over the Past 40 Years[J]. Chinese Journal of Atmospheric Sciences, 2026 , 50 (4) : 1265 -1277 . DOI: 10.3878/j.issn.1006-9895.2508.25090
干旱和高温是影响全球陆地生态系统稳定性的最主要的极端天气事件,在许多过渡性气候区域,这两种事件往往不是独立发生的,而是常常叠加形成复合干热事件(王熙乔等, 2023; 郭祎等, 2025)。复合干热事件是指降水不足与温度异常升高在同一时间段内发生并相互作用的极端事件(Wu and Liu, 2024)。近年来,复合干热事件引发了学者们的广泛关注。1994年夏季,东亚地区经历了一次极其严重的复合干热事件(吕俊梅等, 2006);2022年夏季,中国长江中下游地区再次遭遇历史性的复合干热事件,其持续时间和强度均突破历史记录(郭祎等, 2025)。研究表明,2000年后相较于20世纪末期,全球复合干热事件的发生天数显著增加,每年累计增加了2~10 d(Mukherjee and Mishra, 2021)。这类事件的危害程度往往超过单一的干旱或极端高温事件的影响,造成了植被退化、粮食减产、水资源短缺等负面影响(尹德震等, 2022)。特别是在干旱区和半干旱区,复合干热事件显著加剧了植被脆弱性和影响了植被生态系统的稳定性(Zscheischler et al., 2018; 赵冬苑等, 2025)。在复合干热事件影响下,全球植被退化概率分别比单一干旱或极端高温条件下增加了7%和28%(Hao et al., 2021)。此外,复合干热事件还会延长植被生态系统的恢复时间。相比单一干旱,复合干热事件使植被生态系统的恢复时间平均延长超过1个月(Yao et al., 2024)。联合国政府间气候变化专门委员会第六次评估报告(IPCC AR6)指出,在气候变暖背景下,未来复合干热事件的发生频率和强度将继续增加(Intergovernmental Panel on Climate Change, 2023)。因此,深入理解复合干热事件的演变特征,对于干旱半干旱地区制定有效的灾害风险管理策略具有重要意义。
蒙古高原作为全球重要的草原生态系统分布区,不仅为农牧民提供了丰富的自然资源,而且是我国北方重要的生态屏障(邵亚婷等, 2021)。长期以来,草地和畜牧业是蒙古高原许多农牧民家庭生计的基础,是当地社会经济的重要组成部分(Miao et al., 2021)。然而,蒙古高原是典型的干旱和半干旱区,生态系统脆弱,极易受到气候变化的影响(吕振涛等, 2021; 马丹萌等, 2022)。近年来,蒙古高原正经历气温升高和干旱加剧的双重压力,这对草原生态系统和畜牧业生产力造成了不可逆转的损失。研究表明,自20世纪60年代以来,蒙古高原年降水量显著下降,减少趋势为−2.30 mm·(10 a)−1,温度上升速度是北半球陆地平均上升速度的3倍,这进一步加重了当地水资源短缺和草地退化问题(那音太等, 2019; Cai et al., 2024)。在此背景下,对极端天气的时空演变特征进行深入的定量分析尤为重要,不仅能够为蒙古高原草地生长提供重要参考,还可以推动当地畜牧业经济的可持续发展。
由于复合干热事件涉及高温、干旱等多个变量,因此表征时需综合考虑这些变量的共同作用。目前,常用的方法主要包括事件重叠法和构建复合干热事件指数。事件重叠法主要通过时间序列分析,确定不同变量极端事件在时间上的重叠或同时发生情况,但是无法对于复合事件的强度进行再分析(Zhang et al., 2022)。而构建综合型指数,能够很好地弥补事件重叠分析带来的局限性。通过使用联合概率分布能够有效拟合干旱和热浪同时发生的可能性,并通过游程理论的大小对事件的强度进行量化(Guo et al., 2023)。近年来,复合事件指数的构建被广泛应用于极端天气事件的研究。以复合干热指数为例,它不仅可以监测长期的复合干热事件,还为量化次月尺度事件的时空变化提供了科学依据(Li et al., 2021)。
当前对于蒙古高原气候变化的研究主要关注单一的极端事件(张煦庭等, 2017; 秦福莹等, 2018),如极端干旱、极端高温等,但对于复合干热事件的衡量以及驱动要素方面的研究还相对较少(刘诗梦和吴和俐, 2021)。因此,本研究旨在解决以下两个科学问题:(1)探究1982~2023年蒙古高原复合干热事件时空演变规律。(2)解析驱动蒙古高原复合干热事件变化的关键气候变量。研究结果能够为政府制定应对草地生态系统气候变化的策略提供重要参考,并为气候韧性规划和应急响应机制的建设提供有力支持。
蒙古高原位于亚洲东北部(37°39'N~53°32'N,87°43'E~126°06'E),包括中国内蒙古自治区和蒙古国全境,其地势西高东低,海拔介于88~4323 m。蒙古高原位于干旱、半干旱区,是典型的温带大陆性气候,四季分明,气温差异较大,冬季寒冷漫长,夏季炎热短暂,年平均气温在1.5°C~16°C,年均降水量通常在200~400 mm,降水主要集中在5~10月(秦福莹等, 2018; Luo et al., 2021; 超宝等, 2024)。由于独特的气候特征,蒙古高原常年干旱,气温波动剧烈,形成典型的干热环境,干热事件频发,对植被的健康生长造成显著危害(王磊斌等, 2022; Li et al., 2024)。
本研究选取的2 m温度和降水的月尺度数据来自欧洲中期天气预报中心(ECMWF)提供的ERA5-Land再分析数据集(https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land-monthlymeans?tab=overview [2025-08-20]),时间范围为1982~2023年,空间分辨率为0.1°×0.1°。ERA5-Land再分析数据凭借高时空分辨率,不仅能精确刻画地表温度的时空变化特征,还对极端高温和强降水事件表现出优异的监测能力,是目前气候变化研究中最广泛使用的再分析数据之一(刘鸿波等, 2021; 杨洋等, 2022; 刘婷婷等, 2022)。然而,受同化系统和模型参数化方案的限制,再分析数据普遍存在系统性偏差(张蓓等, 2019; 杨劲等, 2024)。鉴于此,本研究选取美国国家海洋和大气管理局(NOAA)下属国家环境信息中心(NCEI)提供的逐日气温和降水观测数据(https://www.ncei.noaa.gov/data/global-summary-of-the-day/archive/[2025-08-20])作为验证基准,筛选研究时段内连续观测超过35年且数据完整的94个气象站点,经站点合并和时序均值处理后得到逐月气象观测数据。考虑到回归偏差订正法在中高纬度降水偏差订正中具有计算效率高、物理意义明确的优势,本研究分别对温度和降水数据进行偏差订正(杨阳等, 2022)。结果表明:经订正后,温度的均方根误差从1.2°C降至0.88°C(降低27%),降水数据的均方根误差从1.39 mm降至1.28 mm(降低8%)。此外,本研究基于Google Earth Engine平台提供的ERA5-Land 再分析数据集,获取2 m露点温度、地表向下太阳总辐射量、10 m风速以及地表气压等气象要素,以计算研究区的潜在蒸散发量(https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_LAND_MONTHLY_AGGR?hl=zh-cn [2025-08-20])。由于夏季(6~8月)是复合干热事件高频发生的季节,本研究将时间范围分别向前和向后延长一个月,以更全面地覆盖该时期的复合干热事件,最终,本研究重点关注5~9月期间发生的复合干热事件。
复合干热事件是指干旱与热浪在同一时间内发生并相互作用的极端天气事件(Wu and Liu, 2024)。为准确衡量月尺度复合干热事件时空变化特征,本研究构建了标准化复合干热指数(Standardized Compound Dry and Hot Index,简称SCDHI),该指数结合了标准化降水蒸散发指数(Standardized Precipitation Evapotranspiration Index,简称SPEI)和标准化温度指数(Standardized Temperature Index,简称STI),能够综合反映气象干旱与温度异常升高对干热事件的影响。气象干旱是指一定时间尺度内降水量持续低于该地区的长期气候平均值,导致水分供应不足的现象(Wang et al., 2024)。仅基于降水的干旱评估方法(如构建标准化降水指数)无法全面反映干旱过程(Rebetez et al., 2006)。因此,本研究采用标准化降水蒸散发指数识别干旱事件,该指数通过综合考虑降水和潜在蒸散发(Potential Evapotranspiration,简称PET)之间的水分平衡,更加能够灵敏地捕捉气温变化对干旱的影响(Vicente-Serrano et al., 2010)。本研究采用联合国粮农组织推荐的Penman-Monteith方法(FAO-PM)计算PET(Allen et al., 1998):
$ \text{PET}=\frac{0.408 \Delta\left({R}_\text{n}-G\right)+\gamma \frac{900}{{T}_\text{m}+273}{u}_{2}\left({e}_\text{s}-{e}_\text{a}\right)}{\Delta+\gamma (1+0.34{u}_{2})} \text{,} $
式中,Rn为净辐射,G为土壤热通量,γ是干湿常数,Tm为月平均温度,u2为2 m高度处风速,es为饱和水汽压,ea为实际水汽压,Δ为饱和水汽压曲线斜率。
利用第i个月的降水(Pi)与PETi的差值表示第i月的水分亏缺量,随后计算累计水分亏缺Dk:
$ {D}_{k}=\sum\limits_{i=1}^{k}\left({P}_{i}-\text{PE}{\text{T}}_{i}\right) \text{,} $
随后,利用Log-logistic概率分布函数对累计水分亏缺Dk进行拟合,根据拟合结果算累积概率,将其进行标准化处理得到SPEI。
研究进一步构建标准化温度指数,以揭示高温异常对复合干热事件的影响。标准化温度指数与标准化降水指数的计算方法相似,将月尺度温度数据时间序列拟合为正态分布,并进行归一化处理得到STI(Zscheischler et al., 2014):
$ G\left(x\right)=\frac{1}{\sigma \sqrt 2\text{π}}\int\limits_{-\infty }^{x}\exp \left(-\frac{{\left(t-\mu \right)}^{2}}{2{\sigma }^{2}}\right)\text{d}t \text{,} $
$ \text{STI}={\varphi }^{-1}(q) \text{,} $
式中,x是温度时间序列,t和σ分别是该时间段温度均值和标准差,q是累积概率。
由于干旱和高温事件通常在时间和空间上存在复杂的依赖关系,传统的单变量方法难以全面描述其相互作用和联合分布。Copula函数作为一种多变量联合分布模型,能够高效刻画多个水文气候变量(如降水和温度)之间的变异性及其依赖关系。其中,Archimedean 型以其结构简单、灵活性强而被广泛应用。在二元情形下,常见的单参数 Archimedean Copula包括Gumbel、Clayton 和 Frank Copula。其中,Gumbel 和 Clayton Copula 更适用于建模变量间的非负依赖关系,而 Frank Copula 既可以刻画正相关,也能够表达负相关(Tootoonchi et al., 2022)。因此,本研究采用Frank Copula函数构建复合干热指数SCDHI,以定量评估研究区复合干热事件的特性,从而提供对复合干热事件发生机制的更全面理解。
首先,干旱和高温分别用SPEI和STI表示,假设两个随机变量X和Y分别代表SPEI和STI,则复合干热事件的联合累积概率j可表示为
$ j=J\left(X\leqslant x,Y\geqslant y\right)=u-c\left(u,v\right) \text{,} $
式中,u和v分别是X和Y的边缘分布函数,$ c(u,v) $表示联合累积分布函数。然后,将联合累积概率j转换为标准正态分布:
$ \text{SCDHI}={\varphi }^{-1}\left(F\left(J\left(X\leqslant x,Y\leqslant y\right)\right)\right) \text{,} $
其中,$ {\varphi }^{-1} $是标准正态分布的反函数,F为拟合的分布函数。根据复合干热事件的发生概率,SCDHI可根据不同的阈值划分为不同的强度等级,具体见表1(Li et al., 2021)。
复合干热事件强度是由标准化复合干热指数计算出数值并根据不同阈值划分等级得到,SCDHI值越小,表明复合干热事件强度越高(Li et al., 2021)。复合干热事件发生时间占比是指发生复合干热事件的时间占总时间序列的比例(夏敏等, 2019)。复合干热事件影响面积占比是指复合干热事件发生区域占总研究区域的百分比(张更喜等, 2021)。通过统计研究区域内发生复合干热事件的格点数量,计算其占总格点数量的百分比,来描述特定时间段内复合干热事件的影响范围。
随机森林通过构建多棵决策树,并结合投票或平均值的方式,在处理非线性关系、特征相关性和高维数据时具有显著优势(Breiman, 2001)。由于复合干热事件强度的变化受多种气象要素的影响,这些要素之间往往存在复杂的非线性关系和相互作用,而随机森林能够有效捕捉这些复杂的关系,并准确量化各气象要素对复合干热事件强度的影响。此外,随机森林在准确性和稳定性方面表现出色,能够在处理具有高度相关和非线性的数据时提供优异的性能(陈亚松等, 2025; 邓淑丹等, 2025)。因此,本研究选取随机森林回归模型作为分析工具,基于不纯度减少的Gini重要性开展逐年建模的特征重要性分析,探讨了温度、降水、PET、SPEI和STI这五个特征变量对目标变量SCDHI的相对影响,分别从高温异常、热力需求及水分平衡等不同维度揭示复合干热事件强度变化的关键驱动过程。利用逐年建模开展特征重要性分析,并对特征变量进行重要性排序,以量化不同气象因子对复合干热事件强度的相对贡献。同时,结合时间序列分析,进一步探讨这些变量的重要性随时间的动态变化,揭示其长期演变趋势。
基于Mann−Kendall趋势检验和Sen斜率估计法对蒙古高原复合干热事件强度(SCDHI值)变化进行分析。1982~2023年,SCDHI年均值呈现显著的波动上升趋势(p<0.05),上升速率约为0.003 a−1(图1)。这表明研究区复合干热事件在过去40多年间呈现显著减弱趋势。其中,1982~1995年,SCDHI值总体较低,多数年份的SCDHI值低于−1,表明复合干热事件强度相对较高。1995年以来,SCDHI值明显上升,2002年上升至−0.73,复合干热事件强度由Grade 1轻度事件缓和至Grade 0反常事件。2005年以后,SCDHI值波动幅度显著增大,复合干热事件强度变化更加剧烈,表现出更高的年际波动性特征,但SCDHI值始终低于轻度事件的临界值−0.8。2020年和2023年SCDHI值逐渐接近中等强度事件的临界值−1.3,复合干热事件有向着严重方向发展的趋势。
1982~2023年,蒙古高原大部分区域的SCDHI值呈现下降趋势,然而,不同区域的复合干热事件强度呈现出明显的空间分布差异(图2)。其中,中部区域SCDHI值下降趋势最为显著,速率约为−0.015 a−1,表明这些区域的复合干热事件强度显著增强(p<0.05)。相比之下,西北部地区复合干热事件强度虽略有增强,但SCDHI值变化较慢,变化速率约为−0.005 a−1。西南部和东北部区域SCDHI值表现出显著增加的趋势,表明这些区域的复合干热事件强度有所减弱。尤其在西南部区域,SCDHI值增长较快,变化速率高达0.03 a−1,对蒙古高原复合干热事件整体变化趋势的影响较为显著。
如图3,1982~2023年,蒙古高原复合干热事件发生时间占比整体呈减少趋势(p>0.05)。具体而言,除Grade 1轻度事件和Grade 2中等事件发生时间占比呈现上升趋势以外,其余各个级别干热事件的发生时间占比均呈下降趋势(图3)。从事件的不同强度等级来看,Grade 0反常事件、Grade 1轻度事件和Grade 2中等事件的发生时间占比变化较小,速率分别为−0.0025 a−1、0.0041 a−1和0.0084 a−1。而Grade 3严重事件和Grade 4极端事件的发生时间占比下降更为迅速,分别为−0.0166 a−1和−0.0444 a−1。此外,Grade 4极端事件发生时间占比下降趋势最为显著,且年际波动性较大。总体来看,研究区的高强度复合干热事件正逐渐向中低强度复合干热事件转化,这表明蒙古高原的复合干热事件正逐渐缓和。
如图4所示,1982~2023年,蒙古高原北部和东部为复合干热事件的高频发生区,而南部区域发生时间占比相对较低(图4a)。Grade 0反常事件和Grade 1轻度事件广泛分布于蒙古高原中部及部分南部地区(图4b和图4c),其中Grade 1轻度事件的发生明显频繁于其他强度等级,绝大多数区域的发生时间占比超过0.35。Grade 2中等事件和Grade 3严重事件几乎覆盖了整个蒙古高原(图4d和图4e),但发生时间占比相对较低,表明中高强度复合干热事件虽广泛分布,但其影响相对有限。随着事件强度增加,复合干热事件发生时间占比明显下降,同时高强度事件(Grade 4极端事件)的空间分布差异愈发显著(图4f)。Grade 4极端事件高频于蒙古高原西北部少量区域,部分区域的发生时间占比甚至高达0.8。
在过去四十多年中,蒙古高原超过65%的区域都经历了复合干热事件(图5)。整体来看,复合干热事件的影响面积占比虽呈现下降趋势,但并不显著(p>0.05),下降速率约为−0.098 a−1(图5a)。这一变化趋势与事件发生时间占比的演变规律相似,即高强度复合干热事件的覆盖范围逐渐缩小,而中低强度复合干热事件的影响区域则持续扩大。具体来说,Grade 0反常事件和Grade 1轻度事件的面积变化较小,未呈现明显的上升或下降趋势,变化速率分别为−0.0048 a−1和0.0079 a−1(图5b和图5c)。相比之下,Grade 2中等事件和Grade 3严重事件的面积变化更为明显,速率分别是0.016 a−1和−0.032 a−1(图5d和图5e)。Grade 4极端事件的面积年际波动性大且总体降低较为显著(p<0.05),速率约为−0.085 a−1(图5f)。这一趋势表明,蒙古高原复合干热事件的影响范围正逐渐发生变化,低强度与高强度事件影响面积有所减少,而中等强度事件的影响范围持续扩大,逐渐占据主导地位,事件结构呈现由极端化向稳定化发展的特征。
蒙古高原复合干热事件的变化受多种气候因素影响,其中,温度、降水、潜在蒸散发、标准化降水蒸散发指数和标准化温度指数对 SCDHI的贡献存在显著差异(图6)。STI具有最高的重要性值(约为0.5),高于其他特征变量,凸显了温度波动在复合干热事件中的主导作用。其次,SPEI的特征重要性值约为0.4,表明水分盈亏(干旱条件)对SCDHI的影响次之。相比之下,温度、PET和降水的贡献较小(重要性值均低于0.1),其中降水的影响几乎可忽略。这一结果证实,蒙古高原复合干热事件的强度变化主要由STI和SPEI驱动,而温度(原始变量)、PET和降水的作用有限。研究进一步强调了温度异常和水分平衡在干热事件中的协同效应,为区域气候风险评估和管理提供了更可靠的科学依据。
如图7,在大多数年份,STI的特征重要性明显高于其他特征变量,其值介于0.3~0.7,在2002年达到最大值0.64,在2023年降低到最小值0.34,其余年份未表现出明显变化趋势,通常在0.5上下小范围波动,这表明,STI是驱动SCDHI变化的主要因素。SPEI的特征重要性次于STI,其值稳定在0.3~0.5,总体呈现波动下降的趋势,表明干旱指数在复合干热事件中的影响虽相对稳定,但其主导作用正逐渐减弱。温度的特征重要性值较小,大部分年份在0.05~0.15,2023年达到0.27的峰值,其余年份均较低。PET的特征重要性值年际波动较为频繁,但通常在0.06以下,2000年后略有升高,2006年达到0.05,随后迅速回落。降水的重要性始终最低,通常低于0.04,仅在个别年份略有升高,如1984年和1989年,值分别是0.057和0.05,其余年份波动趋势并不显著。综合来看,STI和SPEI对SCDHI变化的贡献较大,而温度、PET和降水的影响相对较弱且变化幅度小,这进一步验证了温度波动(STI)和水分盈亏(SPEI)是蒙古高原复合干热事件变化的主要驱动因子。
本研究进一步采用皮尔逊相关性分析,计算了5~9月SCDHI与STI之间的相关性。结果表明,在5月至9月,SCDHI与STI之间的相关性普遍较高,相关系数介于−0.8~−0.5之间,各个月份间保持显著负相关的关系。从时间变化来看,5月和6月的相关系数分布集中,波动较小,且相关系数绝对值高于其他月份,其中5月的中位数绝对值最高,约为0.7。相比之下,7月和8月的相关性略有下降,中位数绝对值约为0.6,相关系数分布集中,波动较小,几乎没有离群值。9月的相关性波动最大,表现出较强的不稳定性,并伴随较多离群值。从空间上看,SCDHI与STI负相关区域主要位于蒙古高原西北部和东南部,中部区域呈现正负相关交错分布但相关系数绝对值较小,仅在西南部少量区域和东北角存在正相关关系。总体而言,SCDHI与STI的相关性在5月到7月较为稳定,而在9月表现出较大的波动性,这表明 STI 对蒙古高原复合干热事件强度的影响存在一定的季节性变化。尤其在夏末秋初,该影响可能受更复杂的气候因子或极端天气事件的干扰,导致相关性的不确定性增加。
目前,已有许多研究通过设定高温和干旱的阈值来定义复合干热事件,即当温度和降水同时超过或低于特定阈值时,认为发生了复合干热事件。例如,Xu et al.(2024)将日最高温度的第90百分位数和SPI<−0.8作为高温和干旱事件的阈值;Zhang et al.(2022)则将日最高温度的第95百分位数和SPEI<−1作为识别标准。这些方法简单直观,能够有效捕捉高温与干旱事件的共现特征,但阈值的选择存在一定的主观性,并且无法全面反映高温与干旱事件之间的非线性关系。为克服上述方法的局限性,Copula函数被引入复合干热事件的研究中。Copula函数能够灵活描述变量之间的非线性依赖关系,为复合指数的构建提供了新思路(Tootoonchi et al., 2022)。例如,Wu et al.(2021)利用Copula函数将SPI和STI联合,构建了混合干热事件指数(BDHI),有效揭示了不同干热条件之间的复杂关系;Chen et al.(2024)通过Copula函数计算了热浪和干旱的联合累积概率,以定量评估复合干热事件的严重程度。这些研究表明,Copula函数在刻画复合干热事件的时空特征方面具有显著优势。因此,本研究在此基础上,基于Copula函数构建了标准化复合干热指数(SCDHI),不仅可以很好地反映干旱和高温的联合特征,还可以捕捉不同时间尺度复合干热事件的演变规律。
在全球变暖背景下,复合干热事件的频率、持续时间和严重程度均呈现显著增加趋势。Zhang et al.(2022)研究发现全球变暖每升高1°C,全球陆地区域复合干热事件的持续时间将增加约10 d;Tripathy et al.(2023)指出北半球的复合干热事件更为严重;Xu et al.(2024)研究表明亚洲季风区遭受复合干热事件的频率显著高于其他区域。本研究表明,尽管近四十多年来蒙古高原复合干热事件的发生时间占比和影响面积整体呈现下降趋势,但中等强度事件的影响范围却持续扩大,逐渐成为复合干热事件的主导类型,显示出事件结构由两极分布向中等强度集中演化的趋势。这种变化趋势表明,随着时间的推移,中强度复合干热事件的空间扩展愈发显著,未来蒙古高原的生态系统和社会经济可能面临更大的挑战。
温度持续上升和干旱加剧的双重效应,是推动复合干热事件发生和强度变化的关键因素,其中温度异常常被认为是主导因素。Zhang et al.(2022)研究表明全球复合干热事件的变化主要以温度升高为主导;Min et al.(2023)研究表明全球气温上升显著增加了复合干热事件发生的可能性;Jiang et al.(2025)研究表明热浪事件的变化在导致复合干热事件频繁发生方面起主导作用。近年来,蒙古高原年平均气温显著上升,同时年降水量整体呈减少趋势,为复合干热事件的发生提供了气候背景(Luo et al., 2021)。本研究进一步发现STI是蒙古高原复合干热事件最主要的驱动因子,表明温度异常在蒙古高原复合干热事件的形成与发展中占主导地位。然而,通过随机森林回归模型分析发现,降水对蒙古高原复合干热事件强度变化的重要性最低,而Bevacqua et al.(2022)研究指出降水趋势决定了复合干热事件的发生。这一差异可能是因为SPEI本身已考虑了降水和蒸散发的平衡,使得降水的独立影响在特征重要性分析中被SPEI所覆盖。这一结果表明干热指标和区域气候特征的变化在推动复合事件发展中发挥了核心作用,为未来干旱区复合极端事件的风险评估和适应策略提供了科学依据,同时也凸显了蒙古高原作为全球气候变化研究重要区域的独特价值。
现有研究表明,在蒙古高原,极端干旱和高温事件是限制植被生长的主要原因(Li et al., 2024)。本研究聚焦于5~9月(植被生长季)的复合干热事件,为理解此类复合极端事件对植被生态系统的潜在影响提供了参考。然而,由于本研究未深入探讨植被覆盖的动态变化,因此尚未明确复合干热事件与植被覆盖之间的具体联系。例如,复合干热事件的时空分布是否直接影响了植被生产力的变化,或是否对区域生态系统的长期稳定性产生了深远影响,这些问题仍需进一步研究。未来的研究可结合高分辨率的多种植被覆盖数据,深入分析复合干热事件对植被生态系统的具体影响机制,以弥补现有研究的不足,并为区域生态管理和可持续发展提供更加全面的科学依据。
本研究基于Frank Copula函数构建了标准化复合干热指数(SCDHI),揭示了1982~2023年蒙古高原复合干热事件强度、发生时间占比、影响面积占比的时空变化特征,并结合随机森林回归模型评估了各气候变量对SCDHI变化的特征重要性,研究发现:(1)1982~2023年,蒙古高原复合干热事件强度逐渐减弱,其西南部和东北部区域强度降低最为显著,而中部和西北部的部分区域事件强度略有增强;(2)复合干热事件的整体发生时间占比逐渐下降,其中Grade 4极端事件的发生时间占比下降趋势最为显著。北部和东部区域是复合干热事件的高频发生区,而南部区域发生时间占比相对较低;(3)蒙古高原复合干热事件的影响面积占比整体上呈缩减趋势,其中仅Grade 1轻度事件和Grade 2中等事件空间影响范围呈扩展趋势,其余等级的事件均有所缩减;(4)标准化温度指数(STI)是影响蒙古高原复合干热事件最重要的气候变量,标准化降水蒸散发指数(SPEI)的重要性次之,温度、潜在蒸散发(PET)、降水的重要性值均小于0.1。研究强调,未来极端气候变化研究应进一步聚焦新型极端事件,如复合干热事件对植被生长的具体影响机理,政策制定者应采取切实可行的适应性管理策略,以降低复合极端事件对草原生态系统、农业生产和社会经济 造成的潜在风险,确保蒙古高原草地的长期稳定性和可持续发展。

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doi: 10.3878/j.issn.1006-9895.2508.25090
  • 接收时间:2025-05-22
  • 首发时间:2026-09-30
  • 出版时间:2026-07-15
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  • 收稿日期:2025-05-22
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    1南京信息工程大学地理科学学院, 南京210044
    2德国莱布尼茨经济转型体农业发展政策研究所, 德国哈勒06120
    3内蒙古师范大学地理科学学院, 呼和浩特010022
    4南京信息工程大学遥感与测绘工程学院, 南京210044
    5南京信息工程大学气候系统预测与变化应对全国重点实验室, 南京210044

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缪丽娟,女,博士,副教授,主要从事气候变化与土地利用、土地系统变化研究。E-mail:
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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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