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Journals / Research in Agricultural Sciences / 2026 / Volume: 57 Issue: 3

Combining Climate Datasets for Temperature Mapping

Sıcaklık Haritalaması için İklim Veri Kümelerinin Birleştirilmesi

Gizem Mısırlıoğlu(Karadeniz Technical University)0000-0003-0894-9686
Selçuk Gümüş(KARADENIZ TECHNICAL UNIVERSITY)0000-0002-6942-160X
Stefano Grigolato(University of Padua)0000-0002-2089-3892
Pages
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Publication Date
2026-09-16
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Further validation using a denser and independent station network would strengthen the applicability of the approach at finer spatial scales.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Accurate temperature information is essential for climate assessment, environmental modelling, and hydrological analysis; however, mountainous regions with sparse meteorological networks often lack spatially continuous observations.
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Key Findings & Data:Based on statistical performance metrics, IDW provided the most reliable spatial representation and was therefore selected as a reference surface, and a hybrid machine-learning model was subsequently developed using a Random Forest algorithm by integrating the WorldClim v2.1 and CHELSA v2.1 datasets.
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Conclusion & Impact:Further validation using a denser and independent station network would strengthen the applicability of the approach at finer spatial scales.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:CHELSA · IDW · Python

Abstract

Accurate temperature information is essential for climate assessment, environmental modelling, and hydrological analysis; however, mountainous regions with sparse meteorological networks often lack spatially continuous observations. This study aims to identify a suitable interpolation method for observed temperature data and to develop a hybrid modelling approach to improve temperature estimation in data-scarce regions. Temperature records from eight stations in Türkiye’s Eastern Black Sea Region were analysed using Inverse Distance Weighting (IDW), Kriging, and Spline interpolation. Based on statistical performance metrics, IDW provided the most reliable spatial representation and was therefore selected as a reference surface, and a hybrid machine-learning model was subsequently developed using a Random Forest algorithm by integrating the WorldClim v2.1 and CHELSA v2.1 datasets. The hybrid model showed strong agreement with the IDW-derived reference surface (MAE = 0.32, RMSE = 0.48, R² = 0.94), highlighting the potential of integrating open-source climate datasets in data-scarce mountainous regions. Further validation using a denser and independent station network would strengthen the applicability of the approach at finer spatial scales.

Özet

İklim değerlendirmesi, çevre modellemesi ve hidrolojik analiz için doğru sıcaklık bilgisi şarttır; ancak, seyrek meteorolojik ağlara sahip dağlık bölgelerde genellikle sürekli gözlemler bulunmamaktadır. Bu çalışma, gözlemlenen sıcaklık verileri için uygun bir enterpolasyon yöntemi belirlemeyi ve veri kıtlığı olan bölgelerde sıcaklık tahminini iyileştirmek için hibrit bir modelleme yaklaşımı geliştirmeyi amaçlamaktadır. Türkiye'nin Doğu Karadeniz Bölgesi'ndeki sekiz istasyondan elde edilen sıcaklık kayıtları, Ters Mesafe Ağırlıklandırma (IDW), Kriging ve Spline enterpolasyon yöntemleri kullanılarak analiz edilmiştir. İstatistiksel performans ölçütlerine göre, IDW en güvenilir mekansal temsili sağlamış ve bu nedenle referans yüzey olarak seçilmiştir; daha sonra WorldClim v2.1 ve CHELSA v2.1 veri setlerini entegre ederek Rastgele Orman algoritması kullanılarak hibrit bir makine öğrenme modeli geliştirilmiştir. Hibrit model, IDW’den türetilen referans yüzeyle güçlü bir uyum göstermiştir (MAE = 0,32, RMSE = 0,48, R² = 0,94) ve bu sonuç, veri kıtlığı bulunan dağlık bölgelerde açık kaynaklı iklim veri setlerinin bütünleştirilme potansiyelini ortaya koymuştur. Daha yoğun ve bağımsız bir istasyon ağıyla gerçekleştirilecek doğrulama, yaklaşımın daha ayrıntılı mekânsal ölçeklerde uygulanabilirliğini güçlendirecektir.

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Cite This Article

Mısırlıoğlu, G., Gümüş, S., Grigolato, S. (2026). Combining Climate Datasets for Temperature Mapping. Research in Agricultural Sciences, Volume: 57 Issue: 3. https://doi.org/10.17097/agricultureatauni.1858624

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