Predictive association between meteorological drought and climate indices in the state of Sinaloa, northwestern Mexico
The goal is to calculate predictive models capable of making reliable associations between meteorological drought indices (MDr) (standardized precipitation index (SPI), agricultural standardized precipitation index (aSPI), reconnaissance drought index (RDI) and effective reconnaissance drought index...
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description | The goal is to calculate predictive models capable of making reliable associations between meteorological drought indices (MDr) (standardized precipitation index (SPI), agricultural standardized precipitation index (aSPI), reconnaissance drought index (RDI) and effective reconnaissance drought index (eRDI)), and climate indices (CI) (Atlantic multidecadal oscillation (AMO), North Atlantic oscillation (NAO), oceanic El Niño index (ONI), and Pacific decadal oscillation (PDO)) from 7 weather stations in Sinaloa for the period 1969–2018. From the National Water Commission (CONAGUA) and the National Meteorological Service (SMN), free online data on precipitation and temperature (maximum and minimum) were obtained. For the calculation of MDr, Drought Indices Calculator (DrinC) software was used. CI were obtained from the National Oceanic and Atmospheric Administration (NOAA
2022
) online database. To evaluate association, Pearson and Spearman correlations (initial correlations) were applied. For the models, linear and nonlinear regressions were used. To establish whether the correlations (initial and model correlations) were significantly different from 0, a hypothesis test was applied (between the correlation coefficients and the critical correlation coefficients). The CI with the greatest association with MDr are ONI and PDO. Only two stations (La Concha and Rosario) registered significant predictive capacity, expressed in 12 models. At La Concha and Rosario stations, the best indices, scales, and time steps to predict MDr are RDI–3 (Jul–Sept) and aSPI–3 (Jul–Sept), respectively. Although the models had R
2
values of 0.231 ≤ R
2
≤ 0.384, all the correlations (0.481 ≤ correlations ≤ 0.620) are significantly different from 0. This study provides, for the first time for Sinaloa, models that accurately predict MDr through four CI. Application of these models can prevent overexploitation and contamination of water resources in this purely agricultural state, considered the breadbasket of Mexico. |
doi_str_mv | 10.1007/s12517-022-11146-7 |
format | Article |
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2022
) online database. To evaluate association, Pearson and Spearman correlations (initial correlations) were applied. For the models, linear and nonlinear regressions were used. To establish whether the correlations (initial and model correlations) were significantly different from 0, a hypothesis test was applied (between the correlation coefficients and the critical correlation coefficients). The CI with the greatest association with MDr are ONI and PDO. Only two stations (La Concha and Rosario) registered significant predictive capacity, expressed in 12 models. At La Concha and Rosario stations, the best indices, scales, and time steps to predict MDr are RDI–3 (Jul–Sept) and aSPI–3 (Jul–Sept), respectively. Although the models had R
2
values of 0.231 ≤ R
2
≤ 0.384, all the correlations (0.481 ≤ correlations ≤ 0.620) are significantly different from 0. This study provides, for the first time for Sinaloa, models that accurately predict MDr through four CI. Application of these models can prevent overexploitation and contamination of water resources in this purely agricultural state, considered the breadbasket of Mexico.</description><identifier>ISSN: 1866-7511</identifier><identifier>EISSN: 1866-7538</identifier><identifier>DOI: 10.1007/s12517-022-11146-7</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Atmospheric forcing ; Atmospheric models ; Calculators ; Climate ; Coefficients ; Contamination ; Correlation coefficient ; Correlation coefficients ; Drought ; Drought index ; Earth and Environmental Science ; Earth science ; Earth Sciences ; El Nino ; El Nino phenomena ; Hydrologic data ; Mathematical models ; Meteorological services ; North Atlantic Oscillation ; Ocean-atmosphere system ; Original Paper ; Overexploitation ; Precipitation ; Prediction models ; Reconnaissance ; Standardized precipitation index ; Water pollution ; Water resources ; Weather stations</subject><ispartof>Arabian journal of geosciences, 2023, Vol.16 (1), Article 79</ispartof><rights>Saudi Society for Geosciences and Springer Nature Switzerland AG 2023. 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><citedby>FETCH-LOGICAL-c1647-5c3b8d07f9795a85962fa054f5faf09cb1c12ab6208c2a3865e5a9f3b345d7ac3</citedby><cites>FETCH-LOGICAL-c1647-5c3b8d07f9795a85962fa054f5faf09cb1c12ab6208c2a3865e5a9f3b345d7ac3</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/s12517-022-11146-7$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12517-022-11146-7$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Cárdenas, Omar Llanes</creatorcontrib><title>Predictive association between meteorological drought and climate indices in the state of Sinaloa, northwestern Mexico</title><title>Arabian journal of geosciences</title><addtitle>Arab J Geosci</addtitle><description>The goal is to calculate predictive models capable of making reliable associations between meteorological drought indices (MDr) (standardized precipitation index (SPI), agricultural standardized precipitation index (aSPI), reconnaissance drought index (RDI) and effective reconnaissance drought index (eRDI)), and climate indices (CI) (Atlantic multidecadal oscillation (AMO), North Atlantic oscillation (NAO), oceanic El Niño index (ONI), and Pacific decadal oscillation (PDO)) from 7 weather stations in Sinaloa for the period 1969–2018. From the National Water Commission (CONAGUA) and the National Meteorological Service (SMN), free online data on precipitation and temperature (maximum and minimum) were obtained. For the calculation of MDr, Drought Indices Calculator (DrinC) software was used. CI were obtained from the National Oceanic and Atmospheric Administration (NOAA
2022
) online database. To evaluate association, Pearson and Spearman correlations (initial correlations) were applied. For the models, linear and nonlinear regressions were used. To establish whether the correlations (initial and model correlations) were significantly different from 0, a hypothesis test was applied (between the correlation coefficients and the critical correlation coefficients). The CI with the greatest association with MDr are ONI and PDO. Only two stations (La Concha and Rosario) registered significant predictive capacity, expressed in 12 models. At La Concha and Rosario stations, the best indices, scales, and time steps to predict MDr are RDI–3 (Jul–Sept) and aSPI–3 (Jul–Sept), respectively. Although the models had R
2
values of 0.231 ≤ R
2
≤ 0.384, all the correlations (0.481 ≤ correlations ≤ 0.620) are significantly different from 0. This study provides, for the first time for Sinaloa, models that accurately predict MDr through four CI. Application of these models can prevent overexploitation and contamination of water resources in this purely agricultural state, considered the breadbasket of Mexico.</description><subject>Atmospheric forcing</subject><subject>Atmospheric models</subject><subject>Calculators</subject><subject>Climate</subject><subject>Coefficients</subject><subject>Contamination</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>Drought</subject><subject>Drought index</subject><subject>Earth and Environmental Science</subject><subject>Earth science</subject><subject>Earth Sciences</subject><subject>El Nino</subject><subject>El Nino phenomena</subject><subject>Hydrologic data</subject><subject>Mathematical models</subject><subject>Meteorological services</subject><subject>North Atlantic Oscillation</subject><subject>Ocean-atmosphere system</subject><subject>Original Paper</subject><subject>Overexploitation</subject><subject>Precipitation</subject><subject>Prediction models</subject><subject>Reconnaissance</subject><subject>Standardized precipitation index</subject><subject>Water pollution</subject><subject>Water resources</subject><subject>Weather stations</subject><issn>1866-7511</issn><issn>1866-7538</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9UE1LAzEUDKJgrf4BTwGvruZjs9k9SvELKgrqOWSzL23KdlOT1Oq_N7WiN08zPGaGeYPQKSUXlBB5GSkTVBaEsYJSWlaF3EMjWleZCF7v_3JKD9FRjAtCqprIeoTenwJ0ziT3DljH6I3TyfkBt5A2AANeQgIffO9nzuged8GvZ_OE9dBh07ulToDdkAMgZsRpDjim7dFb_OwG3Xt9jgcf0nwDMUEY8AN8OOOP0YHVfYSTHxyj15vrl8ldMX28vZ9cTQtDq1IWwvC27oi0jWyErkVTMauJKK2w2pLGtNRQptuKkdowzetKgNCN5S0vRSe14WN0tstdBf-2zhXUwq9D7hUVkxVreMNKklVspzLBxxjAqlXIv4VPRYna7qt2-6q8r_reV8ls4jtTzOJhBuEv-h_XFzJJf8o</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Cárdenas, Omar Llanes</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope></search><sort><creationdate>2023</creationdate><title>Predictive association between meteorological drought and climate indices in the state of Sinaloa, northwestern Mexico</title><author>Cárdenas, Omar Llanes</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1647-5c3b8d07f9795a85962fa054f5faf09cb1c12ab6208c2a3865e5a9f3b345d7ac3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Atmospheric forcing</topic><topic>Atmospheric models</topic><topic>Calculators</topic><topic>Climate</topic><topic>Coefficients</topic><topic>Contamination</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>Drought</topic><topic>Drought index</topic><topic>Earth and Environmental Science</topic><topic>Earth science</topic><topic>Earth Sciences</topic><topic>El Nino</topic><topic>El Nino phenomena</topic><topic>Hydrologic data</topic><topic>Mathematical models</topic><topic>Meteorological services</topic><topic>North Atlantic Oscillation</topic><topic>Ocean-atmosphere system</topic><topic>Original Paper</topic><topic>Overexploitation</topic><topic>Precipitation</topic><topic>Prediction models</topic><topic>Reconnaissance</topic><topic>Standardized precipitation index</topic><topic>Water pollution</topic><topic>Water resources</topic><topic>Weather stations</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cárdenas, Omar Llanes</creatorcontrib><collection>CrossRef</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>Arabian journal of geosciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cárdenas, Omar Llanes</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Predictive association between meteorological drought and climate indices in the state of Sinaloa, northwestern Mexico</atitle><jtitle>Arabian journal of geosciences</jtitle><stitle>Arab J Geosci</stitle><date>2023</date><risdate>2023</risdate><volume>16</volume><issue>1</issue><artnum>79</artnum><issn>1866-7511</issn><eissn>1866-7538</eissn><abstract>The goal is to calculate predictive models capable of making reliable associations between meteorological drought indices (MDr) (standardized precipitation index (SPI), agricultural standardized precipitation index (aSPI), reconnaissance drought index (RDI) and effective reconnaissance drought index (eRDI)), and climate indices (CI) (Atlantic multidecadal oscillation (AMO), North Atlantic oscillation (NAO), oceanic El Niño index (ONI), and Pacific decadal oscillation (PDO)) from 7 weather stations in Sinaloa for the period 1969–2018. From the National Water Commission (CONAGUA) and the National Meteorological Service (SMN), free online data on precipitation and temperature (maximum and minimum) were obtained. For the calculation of MDr, Drought Indices Calculator (DrinC) software was used. CI were obtained from the National Oceanic and Atmospheric Administration (NOAA
2022
) online database. To evaluate association, Pearson and Spearman correlations (initial correlations) were applied. For the models, linear and nonlinear regressions were used. To establish whether the correlations (initial and model correlations) were significantly different from 0, a hypothesis test was applied (between the correlation coefficients and the critical correlation coefficients). The CI with the greatest association with MDr are ONI and PDO. Only two stations (La Concha and Rosario) registered significant predictive capacity, expressed in 12 models. At La Concha and Rosario stations, the best indices, scales, and time steps to predict MDr are RDI–3 (Jul–Sept) and aSPI–3 (Jul–Sept), respectively. Although the models had R
2
values of 0.231 ≤ R
2
≤ 0.384, all the correlations (0.481 ≤ correlations ≤ 0.620) are significantly different from 0. This study provides, for the first time for Sinaloa, models that accurately predict MDr through four CI. Application of these models can prevent overexploitation and contamination of water resources in this purely agricultural state, considered the breadbasket of Mexico.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s12517-022-11146-7</doi></addata></record> |
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subjects | Atmospheric forcing Atmospheric models Calculators Climate Coefficients Contamination Correlation coefficient Correlation coefficients Drought Drought index Earth and Environmental Science Earth science Earth Sciences El Nino El Nino phenomena Hydrologic data Mathematical models Meteorological services North Atlantic Oscillation Ocean-atmosphere system Original Paper Overexploitation Precipitation Prediction models Reconnaissance Standardized precipitation index Water pollution Water resources Weather stations |
title | Predictive association between meteorological drought and climate indices in the state of Sinaloa, northwestern Mexico |
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