Long-Time Memory in Drought via Detrended Fluctuation Analysis

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dc.authoridTatlı, Hasan / 0000-0002-1960-0618
dc.contributor.authorTatlı, Hasan
dc.contributor.authorDalfes, H. Nüzhet
dc.date.accessioned2025-01-27T20:38:53Z
dc.date.available2025-01-27T20:38:53Z
dc.date.issued2020
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractThe persistence of drought events largely determines the severity of socioeconomic and ecological impacts, unfortunately the performance of current weather forecasting models (WFM) to simulate such events is subject to great uncertainties. This study is investigating time-domain characteristics of drought persistence over Turkey by applying the detrended fluctuation analysis (DFA) method to the Palmer drought severity index (PDSI). The existence of long-range power-law correlation in PDSI fluctuations is demonstrated for time scales ranging from monthly to decadal. Understanding of such statistical patterns in PDSI values can definitely be a step forward in drought predictability. From a climatological point of view, it is found that the areas with high level DFA scaling exponent (generalized Hurst) indicate the areas of higher sensitivity to droughts and associated risks. Furthermore, the characteristics of the persistence of the PDSI in climate zones have also been examined by applying the Holdridge Life Zones (HLZ) classification. HLZ classification over Turkey leads to two climate-zones: cool-temperate and warm-temperate. In addition, when topography is taken in account, montane (cool-temperate) and lower-montane (warm-temperate) climate zones can be treated as two different zones. It has been observed that the predictable index (PI) of the PDSI derived from the DFA Hurst exponent is relatively high in the cool-temperate and montane climate zones compared to others. In fact, very different PI values were also obtained in a few HLZ climate classes within the same climate zone and with same vegetation index (i.e. steppe, dry-forest, warm-forest etc.).
dc.identifier.doi10.1007/s11269-020-02493-9
dc.identifier.endpage1212
dc.identifier.issn0920-4741
dc.identifier.issn1573-1650
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85078636931
dc.identifier.scopusqualityQ1
dc.identifier.startpage1199
dc.identifier.urihttps://doi.org/10.1007/s11269-020-02493-9
dc.identifier.urihttps://hdl.handle.net/20.500.12428/23788
dc.identifier.volume34
dc.identifier.wosWOS:000507698300001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofWater Resources Management
dc.relation.publicationcategoryinfo:eu-repo/semantics/openAccess
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20250125
dc.subjectDFA
dc.subjectDrought
dc.subjectPDSI
dc.subjectHLZ
dc.subjectLong-memory
dc.subjectTurkey
dc.titleLong-Time Memory in Drought via Detrended Fluctuation Analysis
dc.typeArticle

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