Lag Time and Cumulative Effects of Climate Factors on Drought in North China Plain
The growing concern surrounding climate change has gradually drawn attention to the influence of climate factors on drought occurrence. In order to effectively prevent the occurrence of drought and reasonably utilize water resources, the vegetation health index (VHI) was used to characterize drought...
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Veröffentlicht in: | Water (Basel) 2023-10, Vol.15 (19), p.3428 |
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description | The growing concern surrounding climate change has gradually drawn attention to the influence of climate factors on drought occurrence. In order to effectively prevent the occurrence of drought and reasonably utilize water resources, the vegetation health index (VHI) was used to characterize drought in North China Plain (NCP) in this study. Furthermore, six climate factors: air temperature (AT), precipitation (P), evapotranspiration (ET), specific humidity (SH), soil moisture (SM), and soil temperature (ST) were selected. The pole symmetric mode decomposition (PSMD) and improved gridded trend test (IGT) were used to analyze the spatial–temporal characteristics of drought and climate factors in NCP from 1982 to 2020. By calculating the cumulative climatic factors of 0 months, 1 month, 2 months, and 3 months, the correlation between drought and the climatic factors with different cumulative scales was analyzed. The results showed that: (1) from 1982 to 2020, the drought in NCP showed a downward trend and the climate factors showed an upward trend; (2) with the increase in AT, P, ET, SH, SM, and ST, VHI showed an upward trend, and SM showed the strongest correlation with VHI; (3) the optimal cumulative lag time (CLT) for AT, P, ET, SH, SM, and ST were 1.67 months, 1.48 months, 1.95 months, 1.69 months, 0.89 months, and 1.81 months, respectively; and (4) AT was the main driving factor of drought in NCP. This study contributes to the early warning and prediction of drought events, providing a scientific basis for water management authorities in drought management and decision making, and mitigating the negative impacts of drought on socio-economic aspects. |
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In order to effectively prevent the occurrence of drought and reasonably utilize water resources, the vegetation health index (VHI) was used to characterize drought in North China Plain (NCP) in this study. Furthermore, six climate factors: air temperature (AT), precipitation (P), evapotranspiration (ET), specific humidity (SH), soil moisture (SM), and soil temperature (ST) were selected. The pole symmetric mode decomposition (PSMD) and improved gridded trend test (IGT) were used to analyze the spatial–temporal characteristics of drought and climate factors in NCP from 1982 to 2020. By calculating the cumulative climatic factors of 0 months, 1 month, 2 months, and 3 months, the correlation between drought and the climatic factors with different cumulative scales was analyzed. The results showed that: (1) from 1982 to 2020, the drought in NCP showed a downward trend and the climate factors showed an upward trend; (2) with the increase in AT, P, ET, SH, SM, and ST, VHI showed an upward trend, and SM showed the strongest correlation with VHI; (3) the optimal cumulative lag time (CLT) for AT, P, ET, SH, SM, and ST were 1.67 months, 1.48 months, 1.95 months, 1.69 months, 0.89 months, and 1.81 months, respectively; and (4) AT was the main driving factor of drought in NCP. This study contributes to the early warning and prediction of drought events, providing a scientific basis for water management authorities in drought management and decision making, and mitigating the negative impacts of drought on socio-economic aspects.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w15193428</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>air temperature ; Analysis ; Aquatic resources ; China ; climate ; Climate change ; Data assimilation ; Datasets ; Drought ; Droughts ; Ecosystems ; evapotranspiration ; Global temperature changes ; Precipitation ; Precipitation (Meteorology) ; prediction ; Regression analysis ; Remote sensing ; socioeconomics ; Soil moisture ; soil temperature ; soil water ; specific humidity ; Temperature ; Trends ; Vegetation ; water management ; Water shortages</subject><ispartof>Water (Basel), 2023-10, Vol.15 (19), p.3428</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c364t-862f8602396d0a78cc6a212ddd5a0042a71e3f19ca1c1294e6e027f0321d7ae03</citedby><cites>FETCH-LOGICAL-c364t-862f8602396d0a78cc6a212ddd5a0042a71e3f19ca1c1294e6e027f0321d7ae03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Zhang, Zezhong</creatorcontrib><creatorcontrib>Wang, Zipeng</creatorcontrib><creatorcontrib>Lai, Hexin</creatorcontrib><creatorcontrib>Wang, Fei</creatorcontrib><creatorcontrib>Li, Yanbin</creatorcontrib><creatorcontrib>Feng, Kai</creatorcontrib><creatorcontrib>Qi, Qingqing</creatorcontrib><creatorcontrib>Di, Danyang</creatorcontrib><title>Lag Time and Cumulative Effects of Climate Factors on Drought in North China Plain</title><title>Water (Basel)</title><description>The growing concern surrounding climate change has gradually drawn attention to the influence of climate factors on drought occurrence. In order to effectively prevent the occurrence of drought and reasonably utilize water resources, the vegetation health index (VHI) was used to characterize drought in North China Plain (NCP) in this study. Furthermore, six climate factors: air temperature (AT), precipitation (P), evapotranspiration (ET), specific humidity (SH), soil moisture (SM), and soil temperature (ST) were selected. The pole symmetric mode decomposition (PSMD) and improved gridded trend test (IGT) were used to analyze the spatial–temporal characteristics of drought and climate factors in NCP from 1982 to 2020. By calculating the cumulative climatic factors of 0 months, 1 month, 2 months, and 3 months, the correlation between drought and the climatic factors with different cumulative scales was analyzed. The results showed that: (1) from 1982 to 2020, the drought in NCP showed a downward trend and the climate factors showed an upward trend; (2) with the increase in AT, P, ET, SH, SM, and ST, VHI showed an upward trend, and SM showed the strongest correlation with VHI; (3) the optimal cumulative lag time (CLT) for AT, P, ET, SH, SM, and ST were 1.67 months, 1.48 months, 1.95 months, 1.69 months, 0.89 months, and 1.81 months, respectively; and (4) AT was the main driving factor of drought in NCP. This study contributes to the early warning and prediction of drought events, providing a scientific basis for water management authorities in drought management and decision making, and mitigating the negative impacts of drought on socio-economic aspects.</description><subject>air temperature</subject><subject>Analysis</subject><subject>Aquatic resources</subject><subject>China</subject><subject>climate</subject><subject>Climate change</subject><subject>Data assimilation</subject><subject>Datasets</subject><subject>Drought</subject><subject>Droughts</subject><subject>Ecosystems</subject><subject>evapotranspiration</subject><subject>Global temperature changes</subject><subject>Precipitation</subject><subject>Precipitation (Meteorology)</subject><subject>prediction</subject><subject>Regression analysis</subject><subject>Remote sensing</subject><subject>socioeconomics</subject><subject>Soil moisture</subject><subject>soil temperature</subject><subject>soil water</subject><subject>specific humidity</subject><subject>Temperature</subject><subject>Trends</subject><subject>Vegetation</subject><subject>water management</subject><subject>Water shortages</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdUU1LAzEQXURBqT34DwJe9NCar81mj2W1KhQVqedlyCZtym6iSVbx3xupiDhzmOHx3vCYVxRnBM8Zq_HVBylJzTiVB8UJxRWbcc7J4Z_9uJjGuMO5eC1liU-K5xVs0NoOGoHrUDMOYw_Jvmt0Y4xWKSJvUNPbAZJGS1DJhww5dB38uNkmZB168CFtUbO1DtBTD9adFkcG-qinP3NSvCxv1s3dbPV4e98sVjPFBE8zKaiRAlNWiw5DJZUSQAntuq6E7I9CRTQzpFZAFKE110JjWhnMKOkq0JhNiov93dfg30YdUzvYqHTfg9N-jC0jJSOirBnJ1PN_1J0fg8vuWiorIWQtucis-Z61gV631hmfAqjcnR6s8k4bm_FFVdGScUZ4FlzuBSr4GIM27WvIrwqfLcHtdyLtbyLsCzPKefU</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Zhang, Zezhong</creator><creator>Wang, Zipeng</creator><creator>Lai, Hexin</creator><creator>Wang, Fei</creator><creator>Li, Yanbin</creator><creator>Feng, Kai</creator><creator>Qi, Qingqing</creator><creator>Di, Danyang</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>20231001</creationdate><title>Lag Time and Cumulative Effects of Climate Factors on Drought in North China Plain</title><author>Zhang, Zezhong ; Wang, Zipeng ; Lai, Hexin ; Wang, Fei ; Li, Yanbin ; Feng, Kai ; Qi, Qingqing ; Di, Danyang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c364t-862f8602396d0a78cc6a212ddd5a0042a71e3f19ca1c1294e6e027f0321d7ae03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>air temperature</topic><topic>Analysis</topic><topic>Aquatic resources</topic><topic>China</topic><topic>climate</topic><topic>Climate change</topic><topic>Data assimilation</topic><topic>Datasets</topic><topic>Drought</topic><topic>Droughts</topic><topic>Ecosystems</topic><topic>evapotranspiration</topic><topic>Global temperature changes</topic><topic>Precipitation</topic><topic>Precipitation (Meteorology)</topic><topic>prediction</topic><topic>Regression analysis</topic><topic>Remote sensing</topic><topic>socioeconomics</topic><topic>Soil moisture</topic><topic>soil temperature</topic><topic>soil water</topic><topic>specific humidity</topic><topic>Temperature</topic><topic>Trends</topic><topic>Vegetation</topic><topic>water management</topic><topic>Water shortages</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Zezhong</creatorcontrib><creatorcontrib>Wang, Zipeng</creatorcontrib><creatorcontrib>Lai, Hexin</creatorcontrib><creatorcontrib>Wang, Fei</creatorcontrib><creatorcontrib>Li, Yanbin</creatorcontrib><creatorcontrib>Feng, Kai</creatorcontrib><creatorcontrib>Qi, Qingqing</creatorcontrib><creatorcontrib>Di, Danyang</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Zezhong</au><au>Wang, Zipeng</au><au>Lai, Hexin</au><au>Wang, Fei</au><au>Li, Yanbin</au><au>Feng, Kai</au><au>Qi, Qingqing</au><au>Di, Danyang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Lag Time and Cumulative Effects of Climate Factors on Drought in North China Plain</atitle><jtitle>Water (Basel)</jtitle><date>2023-10-01</date><risdate>2023</risdate><volume>15</volume><issue>19</issue><spage>3428</spage><pages>3428-</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>The growing concern surrounding climate change has gradually drawn attention to the influence of climate factors on drought occurrence. In order to effectively prevent the occurrence of drought and reasonably utilize water resources, the vegetation health index (VHI) was used to characterize drought in North China Plain (NCP) in this study. Furthermore, six climate factors: air temperature (AT), precipitation (P), evapotranspiration (ET), specific humidity (SH), soil moisture (SM), and soil temperature (ST) were selected. The pole symmetric mode decomposition (PSMD) and improved gridded trend test (IGT) were used to analyze the spatial–temporal characteristics of drought and climate factors in NCP from 1982 to 2020. By calculating the cumulative climatic factors of 0 months, 1 month, 2 months, and 3 months, the correlation between drought and the climatic factors with different cumulative scales was analyzed. The results showed that: (1) from 1982 to 2020, the drought in NCP showed a downward trend and the climate factors showed an upward trend; (2) with the increase in AT, P, ET, SH, SM, and ST, VHI showed an upward trend, and SM showed the strongest correlation with VHI; (3) the optimal cumulative lag time (CLT) for AT, P, ET, SH, SM, and ST were 1.67 months, 1.48 months, 1.95 months, 1.69 months, 0.89 months, and 1.81 months, respectively; and (4) AT was the main driving factor of drought in NCP. This study contributes to the early warning and prediction of drought events, providing a scientific basis for water management authorities in drought management and decision making, and mitigating the negative impacts of drought on socio-economic aspects.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w15193428</doi><oa>free_for_read</oa></addata></record> |
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subjects | air temperature Analysis Aquatic resources China climate Climate change Data assimilation Datasets Drought Droughts Ecosystems evapotranspiration Global temperature changes Precipitation Precipitation (Meteorology) prediction Regression analysis Remote sensing socioeconomics Soil moisture soil temperature soil water specific humidity Temperature Trends Vegetation water management Water shortages |
title | Lag Time and Cumulative Effects of Climate Factors on Drought in North China Plain |
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