TY  - JOUR
AU  - Sekulić, Aleksandar
AU  - Kilibarda, Milan
AU  - Protić, Dragutin
AU  - Perčec-Tadić, Melita
AU  - Bajat, Branislav
PY  - 2020
UR  - https://link.springer.com/article/10.1007/s00704-019-03077-3
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2257
AB  - 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.
PB  - Springer Nature
T2  - Theoretical and Applied Climatology
T1  - Spatio-temporal regression kriging model of mean daily temperature for Croatia
EP  - 114
SP  - 101
VL  - 140
DO  - https://doi.org/10.1007/s00704-019-03077-3
ER  - 

@article{
author = "Sekulić, Aleksandar and Kilibarda, Milan and Protić, Dragutin and Perčec-Tadić, Melita and Bajat, Branislav",
year = "2020",
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.",
publisher = "Springer Nature",
journal = "Theoretical and Applied Climatology",
title = "Spatio-temporal regression kriging model of mean daily temperature for Croatia",
pages = "114-101",
volume = "140",
doi = "https://doi.org/10.1007/s00704-019-03077-3"
}

Sekulić, A., Kilibarda, M., Protić, D., Perčec-Tadić, M.,& Bajat, B.. (2020). Spatio-temporal regression kriging model of mean daily temperature for Croatia. in Theoretical and Applied Climatology
Springer Nature., 140, 101-114.
https://doi.org/https://doi.org/10.1007/s00704-019-03077-3

Sekulić A, Kilibarda M, Protić D, Perčec-Tadić M, Bajat B. Spatio-temporal regression kriging model of mean daily temperature for Croatia. in Theoretical and Applied Climatology. 2020;140:101-114.
doi:https://doi.org/10.1007/s00704-019-03077-3 .

Sekulić, Aleksandar, Kilibarda, Milan, Protić, Dragutin, Perčec-Tadić, Melita, Bajat, Branislav, "Spatio-temporal regression kriging model of mean daily temperature for Croatia" in Theoretical and Applied Climatology, 140 (2020):101-114,
https://doi.org/https://doi.org/10.1007/s00704-019-03077-3 . .

TY  - JOUR
AU  - Sekulić, Aleksandar
AU  - Kilibarda, Milan
AU  - Protić, Dragutin
AU  - Perčec-Tadić, Melita
AU  - Bajat, Branislav
PY  - 2019
UR  - https://link.springer.com/article/10.1007/s00704-019-03077-3
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1847
AB  - 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.
PB  - Springer
T2  - Theoretical and Applied Climatology
T1  - Spatio-temporal regression kriging model of mean daily temperature for Croatia
DO  - 10.1007/s00704-019-03077-3
ER  - 

@article{
author = "Sekulić, Aleksandar and Kilibarda, Milan and Protić, Dragutin and Perčec-Tadić, Melita and Bajat, Branislav",
year = "2019",
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.",
publisher = "Springer",
journal = "Theoretical and Applied Climatology",
title = "Spatio-temporal regression kriging model of mean daily temperature for Croatia",
doi = "10.1007/s00704-019-03077-3"
}

Sekulić, A., Kilibarda, M., Protić, D., Perčec-Tadić, M.,& Bajat, B.. (2019). Spatio-temporal regression kriging model of mean daily temperature for Croatia. in Theoretical and Applied Climatology
Springer..
https://doi.org/10.1007/s00704-019-03077-3

Sekulić A, Kilibarda M, Protić D, Perčec-Tadić M, Bajat B. Spatio-temporal regression kriging model of mean daily temperature for Croatia. in Theoretical and Applied Climatology. 2019;.
doi:10.1007/s00704-019-03077-3 .

Sekulić, Aleksandar, Kilibarda, Milan, Protić, Dragutin, Perčec-Tadić, Melita, Bajat, Branislav, "Spatio-temporal regression kriging model of mean daily temperature for Croatia" in Theoretical and Applied Climatology (2019),
https://doi.org/10.1007/s00704-019-03077-3 . .