Publication
Modelling drought classes time series for groundwater drought assessment and prediction in Algarve region
| dc.contributor.author | Moreira, Elsa | |
| dc.contributor.author | Neves, Maria C. | |
| dc.date.accessioned | 2025-03-01T10:30:08Z | |
| dc.date.available | 2025-03-01T10:30:08Z | |
| dc.date.issued | 2024-12-17 | en_US |
| dc.date.updated | 2025-02-27T22:10:06Z | |
| dc.description.abstract | Log-linear quasi-association models have been successfully applied to analyze and predict drought class transitions derived from standardized precipitation index (SPI) time series in Portugal. This kind of model proved to be suitable for fitting the SPI drought transitions and is considered a reliable tool for capturing the dynamics of drought severity changes since it models the probabilities associated with transitions in drought severity over specific time periods. In the context of groundwater drought monitoring, the standardized groundwater index (SGI) is used and is computed from groundwater levels available from the SNIRH piezometric network. The aim is to employ similar models to model the transitions between SGI drought classes and use them to analyze and predict transitions in groundwater drought classes one or two months in advance. The purpose is also to evaluate the effectiveness of these tools in predicting short-term transitions in groundwater drought. The findings contribute to improving water management practices and enhancing early warning systems to mitigate the impacts of drought in the Algarve, with potential applications in other parts of the world. | eng |
| dc.description.version | info:eu-repo/semantics/publishedVersion | |
| dc.identifier.slug | cv-prod-4393557 | |
| dc.identifier.uri | http://hdl.handle.net/10400.1/26841 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | ECOSTA ECONOMETRICS AND STATISTICS | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Modelling drought classes time series for groundwater drought assessment and prediction in Algarve region | por |
| dc.type | conference object | en_US |
| dspace.entity.type | Publication | |
| oaire.citation.conferenceDate | 2024-12-14 | |
| oaire.citation.conferencePlace | Londres | |
| oaire.citation.title | 18th International Joint Conference on Computational and Financial Econometrics (CFE) and Computational and Methodological Statistics (CMStatistics) | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Neves | |
| person.givenName | Maria C. | |
| person.identifier.ciencia-id | F016-4214-4A45 | |
| person.identifier.orcid | 0000-0001-5726-3467 | |
| person.identifier.rid | A-7703-2013 | |
| person.identifier.scopus-author-id | 7103415176 | |
| rcaap.cv.cienciaid | F016-4214-4A45 | MARIA DA CONCEIÇÃO LOPES VIDEIRA LOURO NEVES | |
| rcaap.rights | openAccess | en_US |
| relation.isAuthorOfPublication | 5aa08a50-0e64-4d5d-acd6-d817b92e74c5 | |
| relation.isAuthorOfPublication.latestForDiscovery | 5aa08a50-0e64-4d5d-acd6-d817b92e74c5 |