Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran
Drought is one of the most significant environmental disasters, especially in arid and semi-arid regions. Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world. One of these indicators is the Palmer drought severity index (PDS...
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| Veröffentlicht in: | Journal of arid land 2020-03, Vol.12 (2), p.318-330 |
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| description | Drought is one of the most significant environmental disasters, especially in arid and semi-arid regions. Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world. One of these indicators is the Palmer drought severity index (PDSI), which is used in many parts of the world to assess the drought situation and continuation. In this study, the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995–2014 according to meteorological data from six weather stations in the province. A statistical downscaling model (SDSM) was used to apply the output results of the general circulation model in Fars Province. To implement data processing and prediction of climate data, a statistical period 1995–2014 was considered as the monitoring period, and a statistical period 2019–2048 was for the prediction period. The results revealed that there is a good agreement between the simulated precipitation (
R
2
>0.63;
R
2
, determination coefficient; MAE |
| doi_str_mv | 10.1007/s40333-020-0095-5 |
| format | Article |
| fullrecord | <record><control><sourceid>wanfang_jour_proqu</sourceid><recordid>TN_cdi_wanfang_journals_ghqkx202002011</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><wanfj_id>ghqkx202002011</wanfj_id><sourcerecordid>ghqkx202002011</sourcerecordid><originalsourceid>FETCH-LOGICAL-c347t-b091efc16c20c32152467fc9ead87bedfe3753d4fd34f1c97353fda4c0a758f3</originalsourceid><addsrcrecordid>eNp1kUtLAzEYRYMoWGp_gLuA4MpoMslMOu5ErRYqFtp9iHmM0TZpkxkf-OdNHcGV4YPwwTk3kAvAMcHnBGN-kRimlCJcYIRxXaJyDwwKUjPE-ZjugwGpOEMVr_ghGKX0gvOpxqxmZAC-5tFop1oXPAwWrk1rQgyr0DglV1DH0DXPLXQeyug0lF7DZNYO_WzRNFlLsEvON3B-s5j-AIubxcMllFDJZGBqO_258ycyJjiP4c15Zc7gNEp_BA6sXCUz-r2HYDm5XV7fo9nj3fT6aoYUZbxFT7gmxipSqQIrWpCyYBW3qjZSj_mT0dZQXlLNrKbMElVzWlKrJVNY8nJs6RCc9rHv0lvpG_ESuujzg6J53r5-FPnb8hCSwZMe3MSw7Uxq_8iCYVzyqqpxpkhPqRhSisaKTXRrGT8FwWJXh-jrEDlU7OoQZXaK3kmZ9Y2Jf8n_S99-RYwh</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2400576690</pqid></control><display><type>article</type><title>Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran</title><source>SpringerNature Journals</source><source>Alma/SFX Local Collection</source><creator>Dehghan, Sheida ; Salehnia, Nasrin ; Sayari, Nasrin ; Bakhtiari, Bahram</creator><creatorcontrib>Dehghan, Sheida ; Salehnia, Nasrin ; Sayari, Nasrin ; Bakhtiari, Bahram</creatorcontrib><description>Drought is one of the most significant environmental disasters, especially in arid and semi-arid regions. Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world. One of these indicators is the Palmer drought severity index (PDSI), which is used in many parts of the world to assess the drought situation and continuation. In this study, the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995–2014 according to meteorological data from six weather stations in the province. A statistical downscaling model (SDSM) was used to apply the output results of the general circulation model in Fars Province. To implement data processing and prediction of climate data, a statistical period 1995–2014 was considered as the monitoring period, and a statistical period 2019–2048 was for the prediction period. The results revealed that there is a good agreement between the simulated precipitation (
R
2
>0.63;
R
2
, determination coefficient; MAE<0.52; MAE, mean absolute error; RMSE<0.56; RMSE, Root Mean Squared Error) and temperature (
R
2
>0.95, MAE<1.74, and RMSE<1.78) with the observed data from the stations. The results of the drought monitoring model presented that dry periods would increase over the next three decades as compared to the historical data. The studies showed the highest drought in the meteorological stations Abadeh and Lar during the prediction period under two future scenarios representative concentration pathways (RCP4.5 and RCP8.5). According to the results of the validation periods and efficiency criteria, we suggest that the SDSM is a proper tool for predicting drought in arid and semi-arid regions.</description><identifier>ISSN: 1674-6767</identifier><identifier>EISSN: 2194-7783</identifier><identifier>DOI: 10.1007/s40333-020-0095-5</identifier><language>eng</language><publisher>Heidelberg: Science Press</publisher><subject>Arid regions ; Arid zones ; Climate change and its impact on water resources in arid regions ; Climate prediction ; Climatic data ; Computer simulation ; Data analysis ; Data processing ; Disasters ; Drought ; Drought index ; Earth and Environmental Science ; Environmental monitoring ; General circulation models ; Geography ; Historical account ; History ; Mathematical models ; Meteorological data ; Physical Geography ; Plant Ecology ; Root-mean-square errors ; Semi arid areas ; Semiarid environments ; Semiarid lands ; Sustainable Development ; Weather stations</subject><ispartof>Journal of arid land, 2020-03, Vol.12 (2), p.318-330</ispartof><rights>Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020</rights><rights>Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020.</rights><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c347t-b091efc16c20c32152467fc9ead87bedfe3753d4fd34f1c97353fda4c0a758f3</citedby><cites>FETCH-LOGICAL-c347t-b091efc16c20c32152467fc9ead87bedfe3753d4fd34f1c97353fda4c0a758f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/ghqkx/ghqkx.jpg</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40333-020-0095-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40333-020-0095-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Dehghan, Sheida</creatorcontrib><creatorcontrib>Salehnia, Nasrin</creatorcontrib><creatorcontrib>Sayari, Nasrin</creatorcontrib><creatorcontrib>Bakhtiari, Bahram</creatorcontrib><title>Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran</title><title>Journal of arid land</title><addtitle>J. Arid Land</addtitle><description>Drought is one of the most significant environmental disasters, especially in arid and semi-arid regions. Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world. One of these indicators is the Palmer drought severity index (PDSI), which is used in many parts of the world to assess the drought situation and continuation. In this study, the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995–2014 according to meteorological data from six weather stations in the province. A statistical downscaling model (SDSM) was used to apply the output results of the general circulation model in Fars Province. To implement data processing and prediction of climate data, a statistical period 1995–2014 was considered as the monitoring period, and a statistical period 2019–2048 was for the prediction period. The results revealed that there is a good agreement between the simulated precipitation (
R
2
>0.63;
R
2
, determination coefficient; MAE<0.52; MAE, mean absolute error; RMSE<0.56; RMSE, Root Mean Squared Error) and temperature (
R
2
>0.95, MAE<1.74, and RMSE<1.78) with the observed data from the stations. The results of the drought monitoring model presented that dry periods would increase over the next three decades as compared to the historical data. The studies showed the highest drought in the meteorological stations Abadeh and Lar during the prediction period under two future scenarios representative concentration pathways (RCP4.5 and RCP8.5). According to the results of the validation periods and efficiency criteria, we suggest that the SDSM is a proper tool for predicting drought in arid and semi-arid regions.</description><subject>Arid regions</subject><subject>Arid zones</subject><subject>Climate change and its impact on water resources in arid regions</subject><subject>Climate prediction</subject><subject>Climatic data</subject><subject>Computer simulation</subject><subject>Data analysis</subject><subject>Data processing</subject><subject>Disasters</subject><subject>Drought</subject><subject>Drought index</subject><subject>Earth and Environmental Science</subject><subject>Environmental monitoring</subject><subject>General circulation models</subject><subject>Geography</subject><subject>Historical account</subject><subject>History</subject><subject>Mathematical models</subject><subject>Meteorological data</subject><subject>Physical Geography</subject><subject>Plant Ecology</subject><subject>Root-mean-square errors</subject><subject>Semi arid areas</subject><subject>Semiarid environments</subject><subject>Semiarid lands</subject><subject>Sustainable Development</subject><subject>Weather stations</subject><issn>1674-6767</issn><issn>2194-7783</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp1kUtLAzEYRYMoWGp_gLuA4MpoMslMOu5ErRYqFtp9iHmM0TZpkxkf-OdNHcGV4YPwwTk3kAvAMcHnBGN-kRimlCJcYIRxXaJyDwwKUjPE-ZjugwGpOEMVr_ghGKX0gvOpxqxmZAC-5tFop1oXPAwWrk1rQgyr0DglV1DH0DXPLXQeyug0lF7DZNYO_WzRNFlLsEvON3B-s5j-AIubxcMllFDJZGBqO_258ycyJjiP4c15Zc7gNEp_BA6sXCUz-r2HYDm5XV7fo9nj3fT6aoYUZbxFT7gmxipSqQIrWpCyYBW3qjZSj_mT0dZQXlLNrKbMElVzWlKrJVNY8nJs6RCc9rHv0lvpG_ESuujzg6J53r5-FPnb8hCSwZMe3MSw7Uxq_8iCYVzyqqpxpkhPqRhSisaKTXRrGT8FwWJXh-jrEDlU7OoQZXaK3kmZ9Y2Jf8n_S99-RYwh</recordid><startdate>20200301</startdate><enddate>20200301</enddate><creator>Dehghan, Sheida</creator><creator>Salehnia, Nasrin</creator><creator>Sayari, Nasrin</creator><creator>Bakhtiari, Bahram</creator><general>Science Press</general><general>Springer Nature B.V</general><general>Department of Water Engineering, Faculty of Agriculture, Shahid Bahonar University of Kerman, Kerman 7616914111, Iran%Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad 9177949207, 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>20200301</creationdate><title>Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran</title><author>Dehghan, Sheida ; Salehnia, Nasrin ; Sayari, Nasrin ; Bakhtiari, Bahram</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c347t-b091efc16c20c32152467fc9ead87bedfe3753d4fd34f1c97353fda4c0a758f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Arid regions</topic><topic>Arid zones</topic><topic>Climate change and its impact on water resources in arid regions</topic><topic>Climate prediction</topic><topic>Climatic data</topic><topic>Computer simulation</topic><topic>Data analysis</topic><topic>Data processing</topic><topic>Disasters</topic><topic>Drought</topic><topic>Drought index</topic><topic>Earth and Environmental Science</topic><topic>Environmental monitoring</topic><topic>General circulation models</topic><topic>Geography</topic><topic>Historical account</topic><topic>History</topic><topic>Mathematical models</topic><topic>Meteorological data</topic><topic>Physical Geography</topic><topic>Plant Ecology</topic><topic>Root-mean-square errors</topic><topic>Semi arid areas</topic><topic>Semiarid environments</topic><topic>Semiarid lands</topic><topic>Sustainable Development</topic><topic>Weather stations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dehghan, Sheida</creatorcontrib><creatorcontrib>Salehnia, Nasrin</creatorcontrib><creatorcontrib>Sayari, Nasrin</creatorcontrib><creatorcontrib>Bakhtiari, Bahram</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>Dehghan, Sheida</au><au>Salehnia, Nasrin</au><au>Sayari, Nasrin</au><au>Bakhtiari, Bahram</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran</atitle><jtitle>Journal of arid land</jtitle><stitle>J. Arid Land</stitle><date>2020-03-01</date><risdate>2020</risdate><volume>12</volume><issue>2</issue><spage>318</spage><epage>330</epage><pages>318-330</pages><issn>1674-6767</issn><eissn>2194-7783</eissn><abstract>Drought is one of the most significant environmental disasters, especially in arid and semi-arid regions. Drought indices as a tool for management practices seeking to deal with the drought phenomenon are widely used around the world. One of these indicators is the Palmer drought severity index (PDSI), which is used in many parts of the world to assess the drought situation and continuation. In this study, the drought state of Fars Province in Iran was evaluated by using the PDSI over 1995–2014 according to meteorological data from six weather stations in the province. A statistical downscaling model (SDSM) was used to apply the output results of the general circulation model in Fars Province. To implement data processing and prediction of climate data, a statistical period 1995–2014 was considered as the monitoring period, and a statistical period 2019–2048 was for the prediction period. The results revealed that there is a good agreement between the simulated precipitation (
R
2
>0.63;
R
2
, determination coefficient; MAE<0.52; MAE, mean absolute error; RMSE<0.56; RMSE, Root Mean Squared Error) and temperature (
R
2
>0.95, MAE<1.74, and RMSE<1.78) with the observed data from the stations. The results of the drought monitoring model presented that dry periods would increase over the next three decades as compared to the historical data. The studies showed the highest drought in the meteorological stations Abadeh and Lar during the prediction period under two future scenarios representative concentration pathways (RCP4.5 and RCP8.5). According to the results of the validation periods and efficiency criteria, we suggest that the SDSM is a proper tool for predicting drought in arid and semi-arid regions.</abstract><cop>Heidelberg</cop><pub>Science Press</pub><doi>10.1007/s40333-020-0095-5</doi><tpages>13</tpages></addata></record> |
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| ispartof | Journal of arid land, 2020-03, Vol.12 (2), p.318-330 |
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| language | eng |
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| source | SpringerNature Journals; Alma/SFX Local Collection |
| subjects | Arid regions Arid zones Climate change and its impact on water resources in arid regions Climate prediction Climatic data Computer simulation Data analysis Data processing Disasters Drought Drought index Earth and Environmental Science Environmental monitoring General circulation models Geography Historical account History Mathematical models Meteorological data Physical Geography Plant Ecology Root-mean-square errors Semi arid areas Semiarid environments Semiarid lands Sustainable Development Weather stations |
| title | Prediction of meteorological drought in arid and semi-arid regions using PDSI and SDSM: a case study in Fars Province, Iran |
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