Statistical modeling for forecasting land surface temperature increase in Taiwan from 2000 to 2023 using three knots cubic spline
Taiwan is highly mountainous, making it the world's fourth-highest island. The main island is distinguished by the contrast between its eastern two-thirds, which consist primarily of rough forest-covered mountains. Taiwan's climate is influenced by the east Asian monsoon, whereas regions o...
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| Veröffentlicht in: | Modeling earth systems and environment 2024-04, Vol.10 (2), p.2793-2801 |
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| creator | Abdulmana, Sahidan Prasetya, Tofan Agung Eka Garcia-Constantino, Matias Lim, Apiradee |
| description | Taiwan is highly mountainous, making it the world's fourth-highest island. The main island is distinguished by the contrast between its eastern two-thirds, which consist primarily of rough forest-covered mountains. Taiwan's climate is influenced by the east Asian monsoon, whereas regions of central and southern Taiwan have a tropical monsoon climate. Climate change is causing the monsoon to become increasingly irregular, unreliable, and even deadly, with more severe rainfall and worsening dry spells. Land surface temperature (LST) is an essential parameter because the warmth rising off Earth’s landscapes influences our world’s weather and climate patterns. Therefore, the objectives of this study are: (i) to investigate the daytime LST annual seasonal patterns and trends, and (ii) to forecast LST increase in sub-regions and regions in Taiwan. The data used in this study was time series data of daytime LST from 2000 to 2023 from the moderate resolution imaging spectroradiometer (MODIS) website. The natural cubic spline method with eight knots was used to investigate the annual seasonal patterns of daytime LST. The linear regression model was applied to model the LST trends, and a cubic spline with 2, 3, and 4 knots was then applied to forecast LST trends over 23 years. Moreover, the multivariate regression model was used to adjust the spatial correlation and to evaluate the increase in daytime LST. The results demonstrate that daytime LST in Taiwan has increased on average by 0.151 °C per decade. Most of the daytime LST by sub-regions had a consistent increase. Furthermore, daytime LST in all regions had increasing trends. Using three knots of cubic spline to forecast daytime LST trends illustrates the significant increase trends compared with other spline knots. In conclusion, daytime LST in Taiwan is gradually increasing and the reasons for these trends toward daytime LST need to be explored in future studies. |
| doi_str_mv | 10.1007/s40808-023-01926-9 |
| format | Article |
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The main island is distinguished by the contrast between its eastern two-thirds, which consist primarily of rough forest-covered mountains. Taiwan's climate is influenced by the east Asian monsoon, whereas regions of central and southern Taiwan have a tropical monsoon climate. Climate change is causing the monsoon to become increasingly irregular, unreliable, and even deadly, with more severe rainfall and worsening dry spells. Land surface temperature (LST) is an essential parameter because the warmth rising off Earth’s landscapes influences our world’s weather and climate patterns. Therefore, the objectives of this study are: (i) to investigate the daytime LST annual seasonal patterns and trends, and (ii) to forecast LST increase in sub-regions and regions in Taiwan. The data used in this study was time series data of daytime LST from 2000 to 2023 from the moderate resolution imaging spectroradiometer (MODIS) website. The natural cubic spline method with eight knots was used to investigate the annual seasonal patterns of daytime LST. The linear regression model was applied to model the LST trends, and a cubic spline with 2, 3, and 4 knots was then applied to forecast LST trends over 23 years. Moreover, the multivariate regression model was used to adjust the spatial correlation and to evaluate the increase in daytime LST. The results demonstrate that daytime LST in Taiwan has increased on average by 0.151 °C per decade. Most of the daytime LST by sub-regions had a consistent increase. Furthermore, daytime LST in all regions had increasing trends. Using three knots of cubic spline to forecast daytime LST trends illustrates the significant increase trends compared with other spline knots. In conclusion, daytime LST in Taiwan is gradually increasing and the reasons for these trends toward daytime LST need to be explored in future studies.</description><identifier>ISSN: 2363-6203</identifier><identifier>EISSN: 2363-6211</identifier><identifier>DOI: 10.1007/s40808-023-01926-9</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Chemistry and Earth Sciences ; Climate change ; Computer Science ; Daytime ; Earth and Environmental Science ; Earth Sciences ; Earth System Sciences ; Ecosystems ; Environment ; Knots ; Land surface temperature ; Math. Appl. in Environmental Science ; Mathematical Applications in the Physical Sciences ; Mathematical models ; Monsoons ; Mountains ; Original Article ; Physics ; Rainfall ; Regions ; Regression models ; Seasonal variations ; Spectroradiometers ; Statistical analysis ; Statistical models ; Statistics for Engineering ; Surface temperature ; Trends ; Wind</subject><ispartof>Modeling earth systems and environment, 2024-04, Vol.10 (2), p.2793-2801</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c270t-3a5d5fdfb940566a4a8ac5e5fc5883d59a05747d816bab96c3347106f077e02f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s40808-023-01926-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s40808-023-01926-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Abdulmana, Sahidan</creatorcontrib><creatorcontrib>Prasetya, Tofan Agung Eka</creatorcontrib><creatorcontrib>Garcia-Constantino, Matias</creatorcontrib><creatorcontrib>Lim, Apiradee</creatorcontrib><title>Statistical modeling for forecasting land surface temperature increase in Taiwan from 2000 to 2023 using three knots cubic spline</title><title>Modeling earth systems and environment</title><addtitle>Model. Earth Syst. Environ</addtitle><description>Taiwan is highly mountainous, making it the world's fourth-highest island. The main island is distinguished by the contrast between its eastern two-thirds, which consist primarily of rough forest-covered mountains. Taiwan's climate is influenced by the east Asian monsoon, whereas regions of central and southern Taiwan have a tropical monsoon climate. Climate change is causing the monsoon to become increasingly irregular, unreliable, and even deadly, with more severe rainfall and worsening dry spells. Land surface temperature (LST) is an essential parameter because the warmth rising off Earth’s landscapes influences our world’s weather and climate patterns. Therefore, the objectives of this study are: (i) to investigate the daytime LST annual seasonal patterns and trends, and (ii) to forecast LST increase in sub-regions and regions in Taiwan. The data used in this study was time series data of daytime LST from 2000 to 2023 from the moderate resolution imaging spectroradiometer (MODIS) website. The natural cubic spline method with eight knots was used to investigate the annual seasonal patterns of daytime LST. The linear regression model was applied to model the LST trends, and a cubic spline with 2, 3, and 4 knots was then applied to forecast LST trends over 23 years. Moreover, the multivariate regression model was used to adjust the spatial correlation and to evaluate the increase in daytime LST. The results demonstrate that daytime LST in Taiwan has increased on average by 0.151 °C per decade. Most of the daytime LST by sub-regions had a consistent increase. Furthermore, daytime LST in all regions had increasing trends. Using three knots of cubic spline to forecast daytime LST trends illustrates the significant increase trends compared with other spline knots. In conclusion, daytime LST in Taiwan is gradually increasing and the reasons for these trends toward daytime LST need to be explored in future studies.</description><subject>Chemistry and Earth Sciences</subject><subject>Climate change</subject><subject>Computer Science</subject><subject>Daytime</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Earth System Sciences</subject><subject>Ecosystems</subject><subject>Environment</subject><subject>Knots</subject><subject>Land surface temperature</subject><subject>Math. Appl. in Environmental Science</subject><subject>Mathematical Applications in the Physical Sciences</subject><subject>Mathematical models</subject><subject>Monsoons</subject><subject>Mountains</subject><subject>Original Article</subject><subject>Physics</subject><subject>Rainfall</subject><subject>Regions</subject><subject>Regression models</subject><subject>Seasonal variations</subject><subject>Spectroradiometers</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Statistics for Engineering</subject><subject>Surface temperature</subject><subject>Trends</subject><subject>Wind</subject><issn>2363-6203</issn><issn>2363-6211</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9UEtLAzEQXkTBUvsHPAU8r06STbI5SvEFBQ_Wc5hmk7q13V2TLOLRf25qRW8ehplhvgfzFcU5hUsKoK5iBTXUJTBeAtVMlvqomDAueSkZpce_M_DTYhbjBgCoZFJqPSk-nxKmNqbW4pbs-sZt225NfB_25SzmS9632DUkjsGjdSS53eACpjE40nY2OIz7gSyxfceO-NDvCMseJPW5M07GuNdIL8E58tr1KRI7rlpL4pDN3Flx4nEb3eynT4vn25vl_L5cPN49zK8XpWUKUslRNMI3fqUrEFJihTVa4YS3oq55IzSCUJVqaipXuNLScl4pCtKDUg6Y59Pi4qA7hP5tdDGZTT-GLlsaphUXigomM4odUDb0MQbnzRDaHYYPQ8Hs0zaHtE1-zHynbXQm8QMpZnC3duFP-h_WF4N6gnQ</recordid><startdate>20240401</startdate><enddate>20240401</enddate><creator>Abdulmana, Sahidan</creator><creator>Prasetya, Tofan Agung Eka</creator><creator>Garcia-Constantino, Matias</creator><creator>Lim, Apiradee</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TN</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope></search><sort><creationdate>20240401</creationdate><title>Statistical modeling for forecasting land surface temperature increase in Taiwan from 2000 to 2023 using three knots cubic spline</title><author>Abdulmana, Sahidan ; Prasetya, Tofan Agung Eka ; Garcia-Constantino, Matias ; Lim, Apiradee</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-3a5d5fdfb940566a4a8ac5e5fc5883d59a05747d816bab96c3347106f077e02f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Chemistry and Earth Sciences</topic><topic>Climate change</topic><topic>Computer Science</topic><topic>Daytime</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Earth System Sciences</topic><topic>Ecosystems</topic><topic>Environment</topic><topic>Knots</topic><topic>Land surface temperature</topic><topic>Math. Appl. in Environmental Science</topic><topic>Mathematical Applications in the Physical Sciences</topic><topic>Mathematical models</topic><topic>Monsoons</topic><topic>Mountains</topic><topic>Original Article</topic><topic>Physics</topic><topic>Rainfall</topic><topic>Regions</topic><topic>Regression models</topic><topic>Seasonal variations</topic><topic>Spectroradiometers</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Statistics for Engineering</topic><topic>Surface temperature</topic><topic>Trends</topic><topic>Wind</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Abdulmana, Sahidan</creatorcontrib><creatorcontrib>Prasetya, Tofan Agung Eka</creatorcontrib><creatorcontrib>Garcia-Constantino, Matias</creatorcontrib><creatorcontrib>Lim, Apiradee</creatorcontrib><collection>CrossRef</collection><collection>Oceanic Abstracts</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><jtitle>Modeling earth systems and environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abdulmana, Sahidan</au><au>Prasetya, Tofan Agung Eka</au><au>Garcia-Constantino, Matias</au><au>Lim, Apiradee</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Statistical modeling for forecasting land surface temperature increase in Taiwan from 2000 to 2023 using three knots cubic spline</atitle><jtitle>Modeling earth systems and environment</jtitle><stitle>Model. Earth Syst. Environ</stitle><date>2024-04-01</date><risdate>2024</risdate><volume>10</volume><issue>2</issue><spage>2793</spage><epage>2801</epage><pages>2793-2801</pages><issn>2363-6203</issn><eissn>2363-6211</eissn><abstract>Taiwan is highly mountainous, making it the world's fourth-highest island. The main island is distinguished by the contrast between its eastern two-thirds, which consist primarily of rough forest-covered mountains. Taiwan's climate is influenced by the east Asian monsoon, whereas regions of central and southern Taiwan have a tropical monsoon climate. Climate change is causing the monsoon to become increasingly irregular, unreliable, and even deadly, with more severe rainfall and worsening dry spells. Land surface temperature (LST) is an essential parameter because the warmth rising off Earth’s landscapes influences our world’s weather and climate patterns. Therefore, the objectives of this study are: (i) to investigate the daytime LST annual seasonal patterns and trends, and (ii) to forecast LST increase in sub-regions and regions in Taiwan. The data used in this study was time series data of daytime LST from 2000 to 2023 from the moderate resolution imaging spectroradiometer (MODIS) website. The natural cubic spline method with eight knots was used to investigate the annual seasonal patterns of daytime LST. The linear regression model was applied to model the LST trends, and a cubic spline with 2, 3, and 4 knots was then applied to forecast LST trends over 23 years. Moreover, the multivariate regression model was used to adjust the spatial correlation and to evaluate the increase in daytime LST. The results demonstrate that daytime LST in Taiwan has increased on average by 0.151 °C per decade. Most of the daytime LST by sub-regions had a consistent increase. Furthermore, daytime LST in all regions had increasing trends. Using three knots of cubic spline to forecast daytime LST trends illustrates the significant increase trends compared with other spline knots. In conclusion, daytime LST in Taiwan is gradually increasing and the reasons for these trends toward daytime LST need to be explored in future studies.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s40808-023-01926-9</doi><tpages>9</tpages></addata></record> |
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| subjects | Chemistry and Earth Sciences Climate change Computer Science Daytime Earth and Environmental Science Earth Sciences Earth System Sciences Ecosystems Environment Knots Land surface temperature Math. Appl. in Environmental Science Mathematical Applications in the Physical Sciences Mathematical models Monsoons Mountains Original Article Physics Rainfall Regions Regression models Seasonal variations Spectroradiometers Statistical analysis Statistical models Statistics for Engineering Surface temperature Trends Wind |
| title | Statistical modeling for forecasting land surface temperature increase in Taiwan from 2000 to 2023 using three knots cubic spline |
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