Influence of non-stationarity and auto-correlation of climatic records on spatio-temporal trend and seasonality analysis in a region with prevailing arid and semi-arid climate, Iran
Trend and stationarity analysis of climatic variables are essential for understanding climate variability and provide useful information about the vulnerability and future changes, especially in arid and semi-arid regions. In this study, various climatic zones of Iran were investigated to assess the...
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Veröffentlicht in: | Journal of arid land 2020-11, Vol.12 (6), p.964-983 |
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description | Trend and stationarity analysis of climatic variables are essential for understanding climate variability and provide useful information about the vulnerability and future changes, especially in arid and semi-arid regions. In this study, various climatic zones of Iran were investigated to assess the relationship between the trend and the stationarity of the climatic variables. The Mann-Kendall test was considered to identify the trend, while the trend free pre-whitening approach was applied for eliminating serial correlation from the time-series. Meanwhile, time series stationarity was tested by Dickey-Fuller and Kwiatkowski-Phillips-Schmidt-Shin tests. The results indicated an increasing trend for mean air temperature series at most of the stations over various climatic zones, however, after eliminating the serial correlation factor, this increasing trend changes to an insignificant decreasing trend at a 95% confidence level. The seasonal mean air temperature trend suggested a significant increase in the majority of the stations. The mean air temperature increased more in northwest towards central parts of Iran that mostly located in arid and semiarid climatic zones. Precipitation trend reveals an insignificant downward trend in most of the series over various climatic zones; furthermore, most of the stations follow a decreasing trend for seasonal precipitation. Furthermore, spatial patterns of trend and seasonality of precipitation and mean air temperature showed that the northwest parts of Iran and margin areas of the Caspian Sea are more vulnerable to the changing climate with respect to the precipitation shortfalls and warming. Stationarity analysis indicated that the stationarity of climatic series influences on their trend; so that, the series which have significant trends are not static. The findings of this investigation can help planners and policy-makers in various fields related to climatic issues, implementing better management and planning strategies to adapt to climate change and variability over Iran. |
doi_str_mv | 10.1007/s40333-020-0100-z |
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In this study, various climatic zones of Iran were investigated to assess the relationship between the trend and the stationarity of the climatic variables. The Mann-Kendall test was considered to identify the trend, while the trend free pre-whitening approach was applied for eliminating serial correlation from the time-series. Meanwhile, time series stationarity was tested by Dickey-Fuller and Kwiatkowski-Phillips-Schmidt-Shin tests. The results indicated an increasing trend for mean air temperature series at most of the stations over various climatic zones, however, after eliminating the serial correlation factor, this increasing trend changes to an insignificant decreasing trend at a 95% confidence level. The seasonal mean air temperature trend suggested a significant increase in the majority of the stations. The mean air temperature increased more in northwest towards central parts of Iran that mostly located in arid and semiarid climatic zones. Precipitation trend reveals an insignificant downward trend in most of the series over various climatic zones; furthermore, most of the stations follow a decreasing trend for seasonal precipitation. Furthermore, spatial patterns of trend and seasonality of precipitation and mean air temperature showed that the northwest parts of Iran and margin areas of the Caspian Sea are more vulnerable to the changing climate with respect to the precipitation shortfalls and warming. Stationarity analysis indicated that the stationarity of climatic series influences on their trend; so that, the series which have significant trends are not static. The findings of this investigation can help planners and policy-makers in various fields related to climatic issues, implementing better management and planning strategies to adapt to climate change and variability over Iran.</description><identifier>ISSN: 1674-6767</identifier><identifier>EISSN: 2194-7783</identifier><identifier>DOI: 10.1007/s40333-020-0100-z</identifier><language>eng</language><publisher>Heidelberg: Science Press</publisher><subject>Air temperature ; Analysis ; Arid climates ; Arid regions ; Arid zones ; Climate change ; Climate variability ; Climatic zones ; Confidence intervals ; Correlation ; Correlation coefficients ; Earth and Environmental Science ; Environmental policy ; Geography ; Physical Geography ; Plant Ecology ; Precipitation ; Research Article ; Seasonal variations ; Seasonality ; Semi arid areas ; Semiarid climates ; Semiarid lands ; Stations ; Sustainable Development ; Time series ; Trends ; Variability ; Vulnerability</subject><ispartof>Journal of arid land, 2020-11, Vol.12 (6), p.964-983</ispartof><rights>Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><rights>Copyright © Wanfang Data Co. 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Arid Land</addtitle><description>Trend and stationarity analysis of climatic variables are essential for understanding climate variability and provide useful information about the vulnerability and future changes, especially in arid and semi-arid regions. In this study, various climatic zones of Iran were investigated to assess the relationship between the trend and the stationarity of the climatic variables. The Mann-Kendall test was considered to identify the trend, while the trend free pre-whitening approach was applied for eliminating serial correlation from the time-series. Meanwhile, time series stationarity was tested by Dickey-Fuller and Kwiatkowski-Phillips-Schmidt-Shin tests. The results indicated an increasing trend for mean air temperature series at most of the stations over various climatic zones, however, after eliminating the serial correlation factor, this increasing trend changes to an insignificant decreasing trend at a 95% confidence level. The seasonal mean air temperature trend suggested a significant increase in the majority of the stations. The mean air temperature increased more in northwest towards central parts of Iran that mostly located in arid and semiarid climatic zones. Precipitation trend reveals an insignificant downward trend in most of the series over various climatic zones; furthermore, most of the stations follow a decreasing trend for seasonal precipitation. Furthermore, spatial patterns of trend and seasonality of precipitation and mean air temperature showed that the northwest parts of Iran and margin areas of the Caspian Sea are more vulnerable to the changing climate with respect to the precipitation shortfalls and warming. Stationarity analysis indicated that the stationarity of climatic series influences on their trend; so that, the series which have significant trends are not static. The findings of this investigation can help planners and policy-makers in various fields related to climatic issues, implementing better management and planning strategies to adapt to climate change and variability over Iran.</description><subject>Air temperature</subject><subject>Analysis</subject><subject>Arid climates</subject><subject>Arid regions</subject><subject>Arid zones</subject><subject>Climate change</subject><subject>Climate variability</subject><subject>Climatic zones</subject><subject>Confidence intervals</subject><subject>Correlation</subject><subject>Correlation coefficients</subject><subject>Earth and Environmental Science</subject><subject>Environmental policy</subject><subject>Geography</subject><subject>Physical Geography</subject><subject>Plant Ecology</subject><subject>Precipitation</subject><subject>Research Article</subject><subject>Seasonal variations</subject><subject>Seasonality</subject><subject>Semi arid areas</subject><subject>Semiarid climates</subject><subject>Semiarid lands</subject><subject>Stations</subject><subject>Sustainable Development</subject><subject>Time series</subject><subject>Trends</subject><subject>Variability</subject><subject>Vulnerability</subject><issn>1674-6767</issn><issn>2194-7783</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1Uc1u3CAQRlUqdZXmAXpDqpRTacHYYI5RlKQrRcqlPSOMsUPqBQfYJJv36vt1HDfKKUgIzXw_o-FD6Auj3xml8keuKeec0IoSCg3y_AFtKqZqImXLj9CGCVkTIYX8hE5yvqNwRFurmm3Q320Ypr0L1uE44BADycUUH4NJvhywCT02-xKJjSm56QVZiHbyOygsTg6QPmNo53mBSXG7OSYz4ZLcooabncngOK2OZjpkn7EP2IB8XBwffbnFc3IPxk8-jBiGvyp3nrxU60T3DW-TCZ_Rx8FM2Z38f4_R78uLX-c_yfXN1fb87JpYrmghohZd2zWKU8ctM65hrKGVlIL3gvG2d6rjQydEpWzdKynapuubQVnWKdmbwfFjdLr6PpowmDDqu7hPsEDW4-39n6cKfpwKShsgfl2Jc4r3e5fLG7Oq24ayFuIAFltZNsWckxv0nGCrdNCM6iVJvSapwVcvSepn0FSrJgM3jC69Ob8v-gdvRKR0</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Mirdashtvan, Mahsa</creator><creator>Mohseni Saravi, Mohsen</creator><general>Science Press</general><general>Springer Nature B.V</general><general>Department of Range and Watershed Management,Faculty of Natural Resources,College of Agriculture and Natural Resources,University of Tehran,Karaj 3158777871,Iran</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20201101</creationdate><title>Influence of non-stationarity and auto-correlation of climatic records on spatio-temporal trend and seasonality analysis in a region with prevailing arid and semi-arid climate, Iran</title><author>Mirdashtvan, Mahsa ; Mohseni Saravi, Mohsen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c390t-646b8b5930e3c1ae5115027763d6138de9b3fb6629c4d97685bd5f9c1b97dafe3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Air temperature</topic><topic>Analysis</topic><topic>Arid climates</topic><topic>Arid regions</topic><topic>Arid zones</topic><topic>Climate change</topic><topic>Climate variability</topic><topic>Climatic zones</topic><topic>Confidence intervals</topic><topic>Correlation</topic><topic>Correlation coefficients</topic><topic>Earth and Environmental Science</topic><topic>Environmental policy</topic><topic>Geography</topic><topic>Physical Geography</topic><topic>Plant Ecology</topic><topic>Precipitation</topic><topic>Research Article</topic><topic>Seasonal variations</topic><topic>Seasonality</topic><topic>Semi arid areas</topic><topic>Semiarid climates</topic><topic>Semiarid lands</topic><topic>Stations</topic><topic>Sustainable Development</topic><topic>Time series</topic><topic>Trends</topic><topic>Variability</topic><topic>Vulnerability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mirdashtvan, Mahsa</creatorcontrib><creatorcontrib>Mohseni Saravi, Mohsen</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>Journal of arid land</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mirdashtvan, Mahsa</au><au>Mohseni Saravi, Mohsen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Influence of non-stationarity and auto-correlation of climatic records on spatio-temporal trend and seasonality analysis in a region with prevailing arid and semi-arid climate, Iran</atitle><jtitle>Journal of arid land</jtitle><stitle>J. Arid Land</stitle><date>2020-11-01</date><risdate>2020</risdate><volume>12</volume><issue>6</issue><spage>964</spage><epage>983</epage><pages>964-983</pages><issn>1674-6767</issn><eissn>2194-7783</eissn><abstract>Trend and stationarity analysis of climatic variables are essential for understanding climate variability and provide useful information about the vulnerability and future changes, especially in arid and semi-arid regions. In this study, various climatic zones of Iran were investigated to assess the relationship between the trend and the stationarity of the climatic variables. The Mann-Kendall test was considered to identify the trend, while the trend free pre-whitening approach was applied for eliminating serial correlation from the time-series. Meanwhile, time series stationarity was tested by Dickey-Fuller and Kwiatkowski-Phillips-Schmidt-Shin tests. The results indicated an increasing trend for mean air temperature series at most of the stations over various climatic zones, however, after eliminating the serial correlation factor, this increasing trend changes to an insignificant decreasing trend at a 95% confidence level. The seasonal mean air temperature trend suggested a significant increase in the majority of the stations. The mean air temperature increased more in northwest towards central parts of Iran that mostly located in arid and semiarid climatic zones. Precipitation trend reveals an insignificant downward trend in most of the series over various climatic zones; furthermore, most of the stations follow a decreasing trend for seasonal precipitation. Furthermore, spatial patterns of trend and seasonality of precipitation and mean air temperature showed that the northwest parts of Iran and margin areas of the Caspian Sea are more vulnerable to the changing climate with respect to the precipitation shortfalls and warming. Stationarity analysis indicated that the stationarity of climatic series influences on their trend; so that, the series which have significant trends are not static. The findings of this investigation can help planners and policy-makers in various fields related to climatic issues, implementing better management and planning strategies to adapt to climate change and variability over Iran.</abstract><cop>Heidelberg</cop><pub>Science Press</pub><doi>10.1007/s40333-020-0100-z</doi><tpages>20</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Air temperature Analysis Arid climates Arid regions Arid zones Climate change Climate variability Climatic zones Confidence intervals Correlation Correlation coefficients Earth and Environmental Science Environmental policy Geography Physical Geography Plant Ecology Precipitation Research Article Seasonal variations Seasonality Semi arid areas Semiarid climates Semiarid lands Stations Sustainable Development Time series Trends Variability Vulnerability |
title | Influence of non-stationarity and auto-correlation of climatic records on spatio-temporal trend and seasonality analysis in a region with prevailing arid and semi-arid climate, Iran |
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