Optimizing Reference Evapotranspiration Estimation in Data-Scarce Regions Using ERA5 Reanalysis and Machine Learning

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dc.contributor.authorTunca, Emre
dc.contributor.authorNovak, Vaclav
dc.contributor.authorSarec, Petr
dc.contributor.authorKoksal, Eyup Selim
dc.date.accessioned2026-07-01T11:39:26Z
dc.date.available2026-07-01T11:39:26Z
dc.date.issued2026
dc.departmentDüzce Üniversitesi
dc.description.abstractThis study aims to optimize the estimation of reference evapotranspiration (ETo) in data-scarce regions by integrating ERA5-Land reanalysis data with machine learning (ML) models. Daily meteorological data from 33 stations across Turkey's diverse climate zones (1981-2010) were utilized to train and validate three ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM). The methodology involved rigorous quality control of ground-based observations, spatial correlation of ERA5-Land grids to station locations, and performance evaluation under various data-limited scenarios. Results indicate that while ERA5-Land provides highly accurate solar radiation (Rs) and temperature (T) data, variables like wind speed (U2) and relative humidity (RH) exhibit systematic biases. Among the used models, XGBoost demonstrated superior performance (R2 = 0.95, RMSE = 0.43 mm day-1, and MAE = 0.30 mm day-1) and computational efficiency. This study provides a robust, regionally calibrated framework that corrects reanalysis biases using ML, offering a reliable alternative for ETo estimation in areas where local measurements are insufficient for sustainable water management.
dc.identifier.doi10.3390/agronomy16020253
dc.identifier.issn2073-4395
dc.identifier.issue2
dc.identifier.scopus2-s2.0-105028784625
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/agronomy16020253
dc.identifier.urihttps://hdl.handle.net/20.500.12684/23271
dc.identifier.volume16
dc.identifier.wosWOS:001669978400001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofAgronomy-Basel
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260623
dc.subject[Keyword Not Available]
dc.titleOptimizing Reference Evapotranspiration Estimation in Data-Scarce Regions Using ERA5 Reanalysis and Machine Learning
dc.typeArticle

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