Abstract
High resolution gridded mean daily temperature datasets are valuable for research and applications in agronomy, meteorology, hydrology, ecology, and many other disciplines depending on weather or climate. The gridded datasets and the models used for their estimation are being constantly improved as there is always a need for more accurate datasets as well as for datasets with a higher spatial and temporal resolution. We developed a spatio-temporal regression kriging model for Croatia at 1 km spatial resolution by adapting the spatio-temporal regression kriging model developed for global land areas. A geometrical temperature trend, digital elevation model, and topographic wetness index were used as covariates together with measurements from the Croatian national meteorological network for the year 2008. This model performed better than the global model and previously developed models for Croatia, based on MODIS land surface temperature images. The R2 was 97.8% and RMSE was 1.2 °C for leave-one-out and 5-fold cross-validation. The proposed national model still has a high level of uncertainty at higher altitudes leaving it suitable for agricultural areas that are dominant in lower and medium altitudes.
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Acknowledgments
The authors would like to thank to the National Oceanic and Atmospheric Administration (NOAA) for providing GSOD data and Croatian Meteorological and Hydrological Service (http://meteo.hr) for CMDT dataset. We would also like to thank Hengl et al. (2012) for reproducible research paper published in Theoretical and Applied Climatology Journal and the R-sig-geo community for developing free and open tools for space-time modeling.
Funding
This study was funded by Serbian Ministry of Education, Science and Technological Development with Grant No. III 47014 and TR 36035 and by Horizon 2020 Research and Innovation programme under Grant agreement No. 821964.
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Sekulić, A., Kilibarda, M., Protić, D. et al. Spatio-temporal regression kriging model of mean daily temperature for Croatia. Theor Appl Climatol 140, 101–114 (2020). https://doi.org/10.1007/s00704-019-03077-3
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DOI: https://doi.org/10.1007/s00704-019-03077-3