Spatio-temporal regression kriging model of mean daily temperature for Croatia

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. Ahmed S, de Marsily G (1987) Comparison of geostatistical methods for estimating transmissivity using data on transmissivity and specific capacity. Water Resour Res 23(9):1717–1737

    Article  Google Scholar 

  2. Antonić O, Križan J, Marki A, Bukovec D (2001) Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks. Ecol Model 138(1):255–263. https://doi.org/10.1016/S0304-3800(00)00406-3

    Article  Google Scholar 

  3. Bajić A (1989) Severe bora on the northern Adriatic part I: statistical analysis. Hrvatski Meteorološki Časopis 24(24), 1–9 Retrieved from https://www.bib.irb.hr/524287

  4. Belušić D, Bencetić Klaić Z (2004) Estimation of bora wind gusts using a limited area model. Tellus Ser A Dyn Meteorol Oceanogr 56(4):296–307. https://doi.org/10.1111/j.1600-0870.2004.00068.x

    Article  Google Scholar 

  5. Benali A, Carvalho AC, Nunes JP, Carvalhais N, Santos A (2012) Estimating air surface temperature in Portugal using MODIS LST data. Remote Sens Environ 124:108–121. https://doi.org/10.1016/j.rse.2012.04.024

    Article  Google Scholar 

  6. Berezowski T, Szczeniak M, Kardel I, Michalowski R, Okruszko T, Mezghani A, Piniewski M (2016) CPLFD-GDPT5: high-resolution gridded daily precipitation and temperature dataset for two largest Polish river basins. Earth System Science Data 8(1):127–139. https://doi.org/10.5194/essd-8-127-2016

    Article  Google Scholar 

  7. Brinckmann S, Krähenmann S, Bissolli P (2016) High-resolution daily gridded datasets of air temperature and wind speed for Europe. Earth System Science Data 8:491–516. https://doi.org/10.5194/essd-8-491-2016

    Article  Google Scholar 

  8. Carrera-Hernández JJ, Gaskin SJ (2007) Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico. J Hydrol 336(3–4):231–249. https://doi.org/10.1016/j.jhydrol.2006.12.021

    Article  Google Scholar 

  9. Cindrić K, Pasarić Z, Gajić-Čapka M (2010) Spatial and temporal analysis of dry spells in Croatia. Theor Appl Climatol 102:171–184. https://doi.org/10.1007/s00704-010-0250-6

    Article  Google Scholar 

  10. Courault D, Monestiez P (1999) Spatial interpolation of air temperature according to atmospheric circulation patterns in southeast France. Int J Climatol 378:365–378. https://doi.org/10.1002/(SICI)1097-0088(19990330)19:4<365::AID-JOC369>3.0.CO;2-E

    Article  Google Scholar 

  11. Dodson R, Marks D (1997) Daily air temperature interpolated at high spatial resolution over a large mountainous region. Clim Res 8(1):1–20. https://doi.org/10.3354/cr008001

    Article  Google Scholar 

  12. Frei C (2014) Interpolation of temperature in a mountainous region using nonlinear profiles and non-Euclidean distances. Int J Climatol 34(5):1585–1605. https://doi.org/10.1002/joc.3786

    Article  Google Scholar 

  13. Frick C, Steiner H, Mazurkiewicz A, Riediger U, Rauthe M, Reich T, Gratzki A (2014) Central European high-resolution gridded daily datasets (HYRAS): mean temperature and relative humidity. Meteorol Z 23(1):15–32. https://doi.org/10.1127/0941-2948/2014/0560

    Article  Google Scholar 

  14. Gasch CK, Hengl T, Gräler B, Meyer H, Magney TS, Brown DJ (2015) Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: the cook agronomy farm dataset. Spat Stat 14:70–90. https://doi.org/10.1016/j.spasta.2015.04.001

    Article  Google Scholar 

  15. Gräler B, Pebesma E, Heuvelink G (2016) Spatio-temporal interpolation using gstat. R J 8(1):204–218. https://doi.org/10.1007/978-3-319-17885-1

    Article  Google Scholar 

  16. Haylock MR, Hofstra N, Klein Tank AMG, Klok EJ, Jones PD, New M (2008) A European daily high-resolution gridded dataset of surface temperature and precipitation for 1950-2006. J Geophys Res-Atmos 113(20):D20119. https://doi.org/10.1029/2008JD010201

    Article  Google Scholar 

  17. Hengl T, Heuvelink GBM, Rossiter DG (2007) About regression-kriging: from equations to case studies. Comput Geosci 33(10):1301–1315. https://doi.org/10.1016/j.cageo.2007.05.001

    Article  Google Scholar 

  18. Hengl T, Heuvelink GBM, Tadić MP, Pebesma EJ (2012) Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Theor Appl Climatol 107(1–2):265–277. https://doi.org/10.1007/s00704-011-0464-2

    Article  Google Scholar 

  19. Hengl T, Kilibarda M, Carvalho-Ribeiro E D, Reuter H I (2015) Worldgrids—a public repository and a WPS for global environmental layers. WorldGrids at http://worldgrids.org/doku.php

    Google Scholar 

  20. Hengl T, Nussbaum M, Wright MN, Heuvelink GBM, Gräler B (2018) Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 6:e5518. https://doi.org/10.7717/peerj.5518

    Article  Google Scholar 

  21. Heuvelink GBM, Griffith DA (2010) Space-time geostatistics for geography: a case study of radiation monitoring across parts of Germany. Geogr Anal 42(2):161–179. https://doi.org/10.1111/j.1538-4632.2010.00788.x

    Article  Google Scholar 

  22. Hiebl J, Frei C (2016) Daily temperature grids for Austria since 1961---concept, creation and applicability. Theor Appl Climatol 124(1–2):161–178. https://doi.org/10.1007/s00704-015-1411-4

    Article  Google Scholar 

  23. Hiebl J, Auer I, Böhm R, Schöner W, Maugeri M, Lentini G, Spinoni J, Brunetti M, Nanni T, Perčec Tadić M, Bihari Z, Dolinar M, Müller-Westermeier G (2009) A high-resolution 19611990 monthly temperature climatology for the greater Alpine region. Meteorol Z 18(5):507–530. https://doi.org/10.1127/0941-2948/2009/0403

    Article  Google Scholar 

  24. Hofstra N, Haylock M, New M, Jones P, Frei C (2008) Comparison of six methods for the interpolation of daily, European climate data. J Geophys Res 113(D21):D21110. https://doi.org/10.1029/2008JD010100

    Article  Google Scholar 

  25. Holden ZA, Swanson A, Klene AE, Abatzoglou JT, Dobrowski SZ, Cushman SA, Squires J, Moisen GG, Oyler JW (2016) Development of high-resolution (250 m) historical daily gridded air temperature data using reanalysis and distributed sensor networks for the US Northern Rocky Mountains. Int J Climatol 36(10):3620–3632. https://doi.org/10.1002/joc.4580

    Article  Google Scholar 

  26. Horvath K, Ivatek-Šahdan S, Ivančan-Picek B, Grubišić V (2009) Evolution and structure of two severe cyclonic bora events: contrast between the northern and southern Adriatic. Weather Forecast 24(4):946–964. https://doi.org/10.1175/2009WAF2222174.1

    Article  Google Scholar 

  27. Huang R, Zhang C, Huang J, Zhu D, Wang L, Liu J (2015) Mapping of daily mean air temperature in agricultural regions using daytime and nighttime land surface temperatures derived from TERRA and AQUA MODIS data. Remote Sens 7(7):8728–8756. https://doi.org/10.3390/rs70708728

    Article  Google Scholar 

  28. Hunter RD, Meentemeyer RK (2005) Climatologically aided mapping of daily precipitation and temperature. J Appl Meteorol 44(10):1501–1510. https://doi.org/10.1175/JAM2295.1

    Article  Google Scholar 

  29. Hutchinson MF, McKenney DW, Lawrence K, Pedlar JH, Hopkinson RF, Milewska E, Papadopol P (2009) Development and testing of Canada-wide interpolated spatial models of daily minimum-maximum temperature and precipitation for 1961-2003. J Appl Meteorol Climatol 48(4):725–741. https://doi.org/10.1175/2008JAMC1979.1

    Article  Google Scholar 

  30. Ivatek-Sahdan S, Ivancan-Picek B (2006) Effects of different initial and boundary conditions in ALADIN/HR simulations during MAP IOPs. Meteorol Z 15(2):187–197. https://doi.org/10.1127/0941-2948/2006/0117

    Article  Google Scholar 

  31. Janatian N, Sadeghi M, Sanaeinejad SH, Bakhshian E, Farid A, Hasheminia SM, Ghazanfari S (2017) A statistical framework for estimating air temperature using MODIS land surface temperature data. Int J Climatol 37(3):1181–1194. https://doi.org/10.1002/joc.4766

    Article  Google Scholar 

  32. Jarvis CH, Stuart N (2001) A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part II: the interaction between number of guiding variables and the type of interpolation method. J Appl Meteorol 40(6):1075–1084. https://doi.org/10.1175/1520-0450(2001)040<1075:ACASFI>2.0.CO;2

    Article  Google Scholar 

  33. Kilibarda M, Bajat B (2012) PlotGoogleMaps: the R-based web-mapping tool for thematic spatial data. GEOMATICA 66(1):37–49. https://doi.org/10.5623/cig2012-007

    Article  Google Scholar 

  34. Kilibarda M, Hengl T, Heuvelink GBM, Gräler B, Pebesma E, Perčec Tadic M, Bajat B (2014) Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. J Geophys Res-Atmos 119(5):2294–2313. https://doi.org/10.1002/2013JD020803

    Article  Google Scholar 

  35. Kilibarda M, Tadić MP, Hengl T, Luković J, Bajat B (2015) Global geographic and feature space coverage of temperature data in the context of spatio-temporal interpolation. Spat Stat 14:22–38. https://doi.org/10.1016/j.spasta.2015.04.005

    Article  Google Scholar 

  36. Klein Tank AMG et al (2002) Daily dataset of 20th-century surface air temperature and precipitation series for the European climate assessment. Int. J. of Climatol. 22:1441–1453 Data and metadata available at http://www.ecad.eu

    Article  Google Scholar 

  37. Kloog I, Nordio F, Coull BA, Schwartz J (2014) Predicting spatiotemporal mean air temperature using MODIS satellite surface temperature measurements across the Northeastern USA. Remote Sens Environ 150:132–139. https://doi.org/10.1016/J.RSE.2014.04.024

    Article  Google Scholar 

  38. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2 (IJCAI'95), vol 3. Morgan Kaufmann Publishers Inc, San Francisco, pp 1137–1143

    Google Scholar 

  39. Krähenmann S, Ahrens B (2013) Spatial gridding of daily maximum and minimum 2 m temperatures supported by satellite observations. Meteorog Atmos Phys 120(1–2):87–105. https://doi.org/10.1007/s00703-013-0237-9

    Article  Google Scholar 

  40. Kurtzman D, Kadmon R (1999) Mapping of temperature variables in Israel: a comparison of different interpolation methods. Clim Res 13(1):33–43 Retrieved from http://www.jstor.org/stable/24866021

    Article  Google Scholar 

  41. Li X, Zhou Y, Asrar GR, Zhu Z (2018) Creating a seamless 1 km resolution daily land surface temperature dataset for urban and surrounding areas in the conterminous United States. Remote Sens Environ 206(January):84–97. https://doi.org/10.1016/j.rse.2017.12.010

    Article  Google Scholar 

  42. Menne MJ, Durre I, Vose RS, Gleason BE, Houston TG (2012) An overview of the global historical climatology network-daily database. J Atmos Ocean Technol 29(7):897–910. https://doi.org/10.1175/JTECH-D-11-00103.1

    Article  Google Scholar 

  43. Odeh I, McBratney A, Chittleborough D (1995) Further results on prediction of soil properties from terrain attributes: heterotopic cokriging and regression-kriging. Geoderma 67(3–4):215–226. https://doi.org/10.1016/0016-7061(95)00007-B

    Article  Google Scholar 

  44. Osborn TJ, Jones PD (2014) The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth. Earth Syst Sci Data 6(1):61–68. https://doi.org/10.5194/essd-6-61-2014

    Article  Google Scholar 

  45. Oyler JW, Ballantyne A, Jencso K, Sweet M, Running SW (2015) Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature. Int J Climatol 35(9):2258–2279. https://doi.org/10.1002/joc.4127

    Article  Google Scholar 

  46. Oyler JW, Dobrowski SZ, Holden ZA, Running SW (2016) Remotely sensed land skin temperature as a spatial predictor of air temperature across the conterminous United States. J Appl Meteorol Climatol 55(7):1441–1457. https://doi.org/10.1175/JAMC-D-15-0276.1

    Article  Google Scholar 

  47. Parmentier B, McGill B, Wilson AM, Regetz J, Jetz W, Guralnick RP, Schildhauer M (2014) An assessment of methods and remote-sensing derived covariates for regional predictions of 1 km daily maximum air temperature. Remote Sens 6(9):8639–8670. https://doi.org/10.3390/rs6098639

    Article  Google Scholar 

  48. Parmentier B, McGill BJ, Wilson AM, Regetz J, Jetz W, Guralnick R, Schildhauer M (2015) Using multi-timescale methods and satellite-derived land surface temperature for the interpolation of daily maximum air temperature in Oregon. Int J Climatol 35(13):3862–3878. https://doi.org/10.1002/joc.4251

    Article  Google Scholar 

  49. Pebesma EJ (2004) Multivariable geostatistics in S: the gstat package. Comput Geosci 30(7):683–691. https://doi.org/10.1016/j.cageo.2004.03.012

    Article  Google Scholar 

  50. Pebesma EJ (2012) Spacetime: spatio-temporal data in R. J Stat Softw 51(7):1–30. https://doi.org/10.18637/jss.v051.i07

    Article  Google Scholar 

  51. Pejović M, Nikolić M, Heuvelink GBM, Hengl T, Kilibarda M, Bajat B (2018) Sparse regression interaction models for spatial prediction of soil properties in 3D. Comput Geosci 118(March):1–13. https://doi.org/10.1016/j.cageo.2018.05.008

    Article  Google Scholar 

  52. Perčec Tadić M (2010) Gridded Croatian climatology for 1961-1990. Theor Appl Climatol 102(1):87–103. https://doi.org/10.1007/s00704-009-0237-3

    Article  Google Scholar 

  53. R Development Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna ISBN 3-900051-07-0

    Google Scholar 

  54. Rosenfeld A, Dorman M, Schwartz J, Novack V, Just AC, Kloog I (2017) Estimating daily minimum, maximum, and mean near surface air temperature using hybrid satellite models across Israel. Environ Res 159(March):297–312. https://doi.org/10.1016/j.envres.2017.08.017

    Article  Google Scholar 

  55. Srivastava A, Rajeevan M, Kshirsagar S (2009) Development of a high resolution daily gridded temperature dataset ( 1969–2005 ) for the Indian region. Atmos Sci Lett 10(October):249–254. https://doi.org/10.1002/asl

    Article  Google Scholar 

  56. Stahl K, Moore RD, Floyer JA, Asplin MG, McKendry IG (2006) Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density. Agric For Meteorol 139(3–4):224–236. https://doi.org/10.1016/j.agrformet.2006.07.004

    Article  Google Scholar 

  57. Stewart SB, Nitschke CR (2017) Improving temperature interpolation using MODIS LST and local topography: a comparison of methods in south east Australia. Int J Climatol 37(7):3098–3110. https://doi.org/10.1002/joc.4902

    Article  Google Scholar 

  58. Williamson S, Hik D, Gamon J, Kavanaugh J, Flowers G (2014) Estimating temperature fields from MODIS land surface temperature and air temperature observations in a sub-arctic alpine environment. Remote Sens 6(2):946–963. https://doi.org/10.3390/rs6020946

    Article  Google Scholar 

  59. Wu T, Li Y (2013) Spatial interpolation of temperature in the United States using residual kriging. Appl Geogr 44:112–120. https://doi.org/10.1016/j.apgeog.2013.07.012

    Article  Google Scholar 

  60. Xu Y, Knudby A, Ho HC (2014) Estimating daily maximum air temperature from MODIS in British Columbia, Canada. Int J Remote Sens 35(24):8108–8121. https://doi.org/10.1080/01431161.2014.978957

    Article  Google Scholar 

  61. Yuan W, Xu B, Chen Z, Xia J, Xu W, Chen Y, Wu X, Fu Y (2014) Validation of China-wide interpolated daily climate variables from 1960 to 2011. Theor Appl Climatol 119(3–4):689–700. https://doi.org/10.1007/s00704-014-1140-0

    Article  Google Scholar 

  62. Zaninović K, Gajić-Čapka M, Perčec Tadić M, Vučetić M, Milković J, Bajić A, Cindrić K et al. (2008) Climate atlas of Croatia 1961–1990, 1971–2000. Državni hidrometeorološki zavod, Zagreb.

  63. Zhu W, Lű A, Jia S (2013) Estimation of daily maximum and minimum air temperature using MODIS land surface temperature products. Remote Sens Environ 130:62–73. https://doi.org/10.1016/j.rse.2012.10.034

    Article  Google Scholar 

Download references

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.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Branislav Bajat.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Spatio-temporal regression kriging
  • Mean daily temperature
  • R meteo package
  • Gridded data