Evaluating variogram models and kriging approaches for analyzing spatial trends in precipitation simulations from global climate models

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dc.contributor.authorAamina Batool
dc.contributor.authorSufian Ahmad
dc.contributor.authorAyesha Waseem
dc.contributor.authorVeysi Kartal
dc.contributor.authorZulfiqar Ali
dc.contributor.authorMuhammad Mohsin
dc.date.accessioned2025-02-14T06:19:26Z
dc.date.available2025-02-14T06:19:26Z
dc.date.issued2025-02-07
dc.departmentFakülteler, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü
dc.description.abstractClimate change has heightened the irregularity and unpredictability of weather patterns, influencing precipitation patterns. Accurate geographical projections of precipitation and other climatic variables are critical to sustainable water resource management and disaster preparedness. Variogram models are geostatistical techniques used to examine spatial correlation. Therefore, selecting the optimum variogram model for spatial interpolation is challenging. This study used six variogram models to assess spatial trends. Leave-one-out cross-validation (LOOCV) and K-fold cross-validation approaches are used to find the best variogram model based on metrics such as mean absolute error (MAE), root mean square error (RMSE), and mean bias. In this study, correlation data of 22 GCMs within observed data are predicted over 94 locations in Pakistan from 1950 to 2014. For evaluation, ordinary kriging (OK) and universal kriging (UK) are utilized as geostatistical approaches. The study highlights the suitability of the variogram models. Pentaspherical variogram (Pen) model is suggested as suitable model due to its minimum error metrics as well as the Hol effect (Hol) model has been considered beneficial for dealing with complicated data. From the geostatistical approaches, ordinary kriging (OK) yields the best prediction. Moreover, ordinary kriging (OK) and universal kriging (UK) both yield similar outcomes across some correlation-based data of 22 GCMs within observed data. Consequently, the implication of correlation analysis, optimum variogram models, and interpolation techniques enables the precise and accurate approach in the prediction of GCM performance. The efficiency of variogram models and interpolation approaches in managing data variability helps to enhance the consistency and interpretability of climate data.
dc.identifier.citationBatool, A., Ahmad, S., Waseem, A., Kartal, V., Ali, Z., & Mohsin, M. (2025). Evaluating variogram models and kriging approaches for analyzing spatial trends in precipitation simulations from global climate models. Acta Geophysica, 1-21.
dc.identifier.doi10.1007/s11600-025-01545-1
dc.identifier.issn1895-7455
dc.identifier.urihttps://doi.org/10.1007/s11600-025-01545-1
dc.identifier.urihttps://hdl.handle.net/20.500.12604/8497
dc.identifier.wosWOS:001414823300001
dc.indekslendigikaynakWeb of Science
dc.institutionauthorKartal, Veysi
dc.institutionauthorid0000-0003-4671-1281
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofActa Geophysica
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectGCMs
dc.subjectVariogram models
dc.subjectOrdinary kriging
dc.subjectUniversal kriging
dc.subjectPrecipitation
dc.titleEvaluating variogram models and kriging approaches for analyzing spatial trends in precipitation simulations from global climate models
dc.typejournal-article

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