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Development and evaluation of a principal component-based composite drought index considering temporal lag dependencies among indices

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Resumo:This study introduces a composite drought index (CDI) that integrates multiple drought indices, including the Simplified Standardized Precipitation Index (SSPI), Simplified Standardized Precipitation-Evapotranspiration Index (SSPEI), soil moisture measured at depths of 0–10 cm (SM1) and 10–40 cm (SM2), Normalized Difference Vegetation Index (NDVI), and Vegetation Health Index (VHI), using principal component analysis (PCA). Data for Precipitation, temperature, SM1, SM2, NDVI, and VHI were re-gridded to a spatial resolution of 0.25° × 0.25° and used to compute SSPI and SSPEI over 3-, 6-, 9-, and 12-month timescales for grid points across Iran. SM1, SM2, NDVI, and VHI were similarly aggregated at these timescales and standardized using Box-Cox transformation. To facilitate PCA, the temporal lag dependency was adjusted to align all indices with SSPI as the primary reference, eliminating lag correlations. The analysis revealed a strong correlation between SSPI and SSPEI (r > 0.8) across most grids and timescales, alongside significant but weaker correlations with SM1 and SM2 (r > 0.5), VHI (r > 0.6), and NDVI (r > 0.4). The first principal component (PC1), representing the CDI, captured the majority of variance in the data matrix. Additional PCs explaining over 10% of the variance were combined to form a weighted version of the index (CDIw). While CDI showed the strongest correlation with SSPI and SSPEI, CDIw exhibited greater correlations with SM1, SM2, VHI, and NDVI, though with a slight reduction in its relationship with SSPI and SSPEI. Both CDI and CDIw demonstrated strong correlations with the Palmer Drought Severity Index, confirming their effectiveness in monitoring drought conditions in the study area.
Autores principais:Raziei, Tayeb
Outros Autores:Miri, Morteza; Santos, João; Zand, Mehran; Pereira, Luis S.
Assunto:Composite drought index Lag correlation CDI and CDIw drought indices Vegetation and soil moisture indices Palmer drought severity index
Ano:2025
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso embargado
Instituição associada:Instituto Politécnico de Beja
Idioma:inglês
Origem:Repositório Institucional do IPBeja
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author Raziei, Tayeb
author2 Miri, Morteza
Santos, João
Zand, Mehran
Pereira, Luis S.
author2_role author
author
author
author
author_facet Raziei, Tayeb
Miri, Morteza
Santos, João
Zand, Mehran
Pereira, Luis S.
author_role author
country_str PT
creators_json_txt [{\"Person.name\":\"Raziei, Tayeb\"},{\"Person.name\":\"Miri, Morteza\"},{\"Person.name\":\"Santos, João\"},{\"Person.name\":\"Zand, Mehran\"},{\"Person.name\":\"Pereira, Luis S.\"}]
datacite.creators.creator.creatorName.fl_str_mv Raziei, Tayeb
Miri, Morteza
Santos, João
Zand, Mehran
Pereira, Luis S.
datacite.date.Accepted.fl_str_mv 2025-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2025-12-22T12:02:47Z
datacite.date.embargoed.fl_str_mv 2025-12-22T12:02:47Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_f1cf
datacite.subjects.subject.fl_str_mv Composite drought index
Lag correlation
CDI and CDIw drought indices
Vegetation and soil moisture indices
Palmer drought severity index
datacite.titles.title.fl_str_mv Development and evaluation of a principal component-based composite drought index considering temporal lag dependencies among indices
dc.creator.none.fl_str_mv Raziei, Tayeb
Miri, Morteza
Santos, João
Zand, Mehran
Pereira, Luis S.
dc.date.Accepted.fl_str_mv 2025-01-01T00:00:00Z
dc.date.available.fl_str_mv 2025-12-22T12:02:47Z
dc.date.embargoed.fl_str_mv 2025-12-22T12:02:47Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv https://repositorio.ipbeja.pt/handle/20.500.12207/6994
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv Springer Nature
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_f1cf
dc.subject.none.fl_str_mv Composite drought index
Lag correlation
CDI and CDIw drought indices
Vegetation and soil moisture indices
Palmer drought severity index
dc.title.fl_str_mv Development and evaluation of a principal component-based composite drought index considering temporal lag dependencies among indices
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_6501
description This study introduces a composite drought index (CDI) that integrates multiple drought indices, including the Simplified Standardized Precipitation Index (SSPI), Simplified Standardized Precipitation-Evapotranspiration Index (SSPEI), soil moisture measured at depths of 0–10 cm (SM1) and 10–40 cm (SM2), Normalized Difference Vegetation Index (NDVI), and Vegetation Health Index (VHI), using principal component analysis (PCA). Data for Precipitation, temperature, SM1, SM2, NDVI, and VHI were re-gridded to a spatial resolution of 0.25° × 0.25° and used to compute SSPI and SSPEI over 3-, 6-, 9-, and 12-month timescales for grid points across Iran. SM1, SM2, NDVI, and VHI were similarly aggregated at these timescales and standardized using Box-Cox transformation. To facilitate PCA, the temporal lag dependency was adjusted to align all indices with SSPI as the primary reference, eliminating lag correlations. The analysis revealed a strong correlation between SSPI and SSPEI (r > 0.8) across most grids and timescales, alongside significant but weaker correlations with SM1 and SM2 (r > 0.5), VHI (r > 0.6), and NDVI (r > 0.4). The first principal component (PC1), representing the CDI, captured the majority of variance in the data matrix. Additional PCs explaining over 10% of the variance were combined to form a weighted version of the index (CDIw). While CDI showed the strongest correlation with SSPI and SSPEI, CDIw exhibited greater correlations with SM1, SM2, VHI, and NDVI, though with a slight reduction in its relationship with SSPI and SSPEI. Both CDI and CDIw demonstrated strong correlations with the Palmer Drought Severity Index, confirming their effectiveness in monitoring drought conditions in the study area.
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eu_rights_str_mv embargoedAccess
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identifier.url.fl_str_mv https://repositorio.ipbeja.pt/handle/20.500.12207/6994
inst_facet_str urn:organizationAcronym:ipb{{{_:::_}}}Instituto Politécnico de Beja
instacron_str ipb
institution Instituto Politécnico de Beja
instname_str Instituto Politécnico de Beja
language eng
network_acronym_str ripb
network_name_str Repositório Institucional do IPBeja
oai_identifier_str oai:repositorio.ipbeja.pt:20.500.12207/6994
organization_str_mv urn:organizationAcronym:ipb
person_str_mv Raziei, Tayeb
Miri, Morteza
Santos, João
Zand, Mehran
Pereira, Luis S.
publishDate 2025
publisher.none.fl_str_mv Springer Nature
repo_facet_str urn:repositoryAcronym:ripb{{{_:::_}}}Repositório Institucional do IPBeja
reponame_str Repositório Institucional do IPBeja
repository_id_str urn:repositoryAcronym:ripb
service_str_mv urn:repositoryAcronym:ripb
spelling This study introduces a composite drought index (CDI) that integrates multiple drought indices, including the Simplified Standardized Precipitation Index (SSPI), Simplified Standardized Precipitation-Evapotranspiration Index (SSPEI), soil moisture measured at depths of 0–10 cm (SM1) and 10–40 cm (SM2), Normalized Difference Vegetation Index (NDVI), and Vegetation Health Index (VHI), using principal component analysis (PCA). Data for Precipitation, temperature, SM1, SM2, NDVI, and VHI were re-gridded to a spatial resolution of 0.25° × 0.25° and used to compute SSPI and SSPEI over 3-, 6-, 9-, and 12-month timescales for grid points across Iran. SM1, SM2, NDVI, and VHI were similarly aggregated at these timescales and standardized using Box-Cox transformation. To facilitate PCA, the temporal lag dependency was adjusted to align all indices with SSPI as the primary reference, eliminating lag correlations. The analysis revealed a strong correlation between SSPI and SSPEI (r > 0.8) across most grids and timescales, alongside significant but weaker correlations with SM1 and SM2 (r > 0.5), VHI (r > 0.6), and NDVI (r > 0.4). The first principal component (PC1), representing the CDI, captured the majority of variance in the data matrix. Additional PCs explaining over 10% of the variance were combined to form a weighted version of the index (CDIw). While CDI showed the strongest correlation with SSPI and SSPEI, CDIw exhibited greater correlations with SM1, SM2, VHI, and NDVI, though with a slight reduction in its relationship with SSPI and SSPEI. Both CDI and CDIw demonstrated strong correlations with the Palmer Drought Severity Index, confirming their effectiveness in monitoring drought conditions in the study area.application/pdfengSpringer NatureDevelopment and evaluation of a principal component-based composite drought index considering temporal lag dependencies among indicesRaziei, TayebMiri, MortezaSantos, JoãoZand, MehranPereira, Luis S.URLhttps://repositorio.ipbeja.pt/handle/20.500.12207/6994ISSNIsPartOf0920-4741ISSNIsPartOf1573-1650DOIIsPartOf10.1007/s11269-025-04235-12025-12-22T12:02:47Z2025-01-01T00:00:00Z2025http://purl.org/coar/access_right/c_f1cfembargoed accessComposite drought indexLag correlationCDI and CDIw drought indicesVegetation and soil moisture indicesPalmer drought severity index2480752 byteshttp://purl.org/coar/access_right/c_f1cfapplication/pdffulltexthttps://repositorio.ipbeja.pt/bitstreams/e2effb43-d31e-4739-966d-f3d25b7d11aa/downloadliteraturehttp://purl.org/coar/resource_type/c_6501journal article
spellingShingle Development and evaluation of a principal component-based composite drought index considering temporal lag dependencies among indices
Raziei, Tayeb
Composite drought index
Lag correlation
CDI and CDIw drought indices
Vegetation and soil moisture indices
Palmer drought severity index
status SINGLETON
subject.fl_str_mv Composite drought index
Lag correlation
CDI and CDIw drought indices
Vegetation and soil moisture indices
Palmer drought severity index
title Development and evaluation of a principal component-based composite drought index considering temporal lag dependencies among indices
title_full Development and evaluation of a principal component-based composite drought index considering temporal lag dependencies among indices
title_fullStr Development and evaluation of a principal component-based composite drought index considering temporal lag dependencies among indices
title_full_unstemmed Development and evaluation of a principal component-based composite drought index considering temporal lag dependencies among indices
title_short Development and evaluation of a principal component-based composite drought index considering temporal lag dependencies among indices
title_sort Development and evaluation of a principal component-based composite drought index considering temporal lag dependencies among indices
topic Composite drought index
Lag correlation
CDI and CDIw drought indices
Vegetation and soil moisture indices
Palmer drought severity index
topic_facet Composite drought index
Lag correlation
CDI and CDIw drought indices
Vegetation and soil moisture indices
Palmer drought severity index
url https://repositorio.ipbeja.pt/handle/20.500.12207/6994
visible 1