Development of Divergence and Interdependence-based Hybrid Weighting Scheme (DIHWS) for accurate assessment of regional drought

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dc.authoridKartal, Veysi/0000-0003-4671-1281
dc.authoridKARAKOYUN, ERKAN/0000-0003-2821-9103
dc.contributor.authorMukhtar, Alina
dc.contributor.authorAli, Zulfiqar
dc.contributor.authorKartal, Veysi
dc.contributor.authorKarakoyun, Erkan
dc.contributor.authorYousaf, Mahrukh
dc.contributor.authorSammen, Saad Sh.
dc.date.accessioned2024-12-24T19:24:27Z
dc.date.available2024-12-24T19:24:27Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractAccurate ensembles of precipitation data play an important role in precise and efficient drought monitoring systems at the regional level. This article proposes a weighted aggregation scheme - the Divergence and Interdependence-based Hybrid Weighting Scheme (DIHWS) - to ensemble precipitation data for accurate regional drought analysis. The derivation of weights is based on the interdependence among meteorological observatories and the divergence from the mean characteristics of regional data. Here, the interdependence among meteorological observatories is assessed using the Bayesian Network theory. At the same time, the divergence from the mean characteristics of regional data is based on the set of equations used for regional aggregation in (Ali et al., Water Resour Manage 36:4099-4114, 2022). Consequently, the paper introduces a new regional drought index - the Bayesian Network-based Adaptive Regional Drought Index (BNARDI). BNARDI is a standardized regional index and used estimated at multiple time scales. The application of DIHWS and BNARDI is based on five regions of varying observatories. We observed smaller MAE values associated with DIHWS than its Simple Model Average (SMA) and one other of its relevant compitator in all the regions. Therefore, we conclude that the proposed weighting scheme and drought index are more reliable for regional drought monitoring and forecasting. Additionally, the research includes various forecasting models to assess their appropriateness for forecasting the new regional index. The results of this research demonstrate that no single method is suitable for forecasting complex drought data, as generated by BNARDI. Therefore, we suggest using varying methods or a hybrid of various candidate forecasting models for forecasting BNARDI.
dc.description.sponsorshipUniversity of the Punjab Lahore, Pakistan
dc.description.sponsorshipThe current research is a part of a funded research project awarded by the University of the Punjab Lahore, Pakistan (2023). Therefore, the authors are thankful to the project awarding institution.
dc.identifier.doi10.1007/s00704-024-05018-1
dc.identifier.endpage6490
dc.identifier.issn0177-798X
dc.identifier.issn1434-4483
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85193829749
dc.identifier.scopusqualityQ2
dc.identifier.startpage6473
dc.identifier.urihttps://doi.org/10.1007/s00704-024-05018-1
dc.identifier.urihttps://hdl.handle.net/20.500.12604/5998
dc.identifier.volume155
dc.identifier.wosWOS:001229216900001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Wien
dc.relation.ispartofTheoretical and Applied Climatology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.titleDevelopment of Divergence and Interdependence-based Hybrid Weighting Scheme (DIHWS) for accurate assessment of regional drought
dc.typeArticle

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