Annals of Forest Science

, Volume 67, Issue 8, pp 804–804 | Cite as

Homogenisation of climate time series from ICP Forests Level II monitoring sites in Germany based on interpolated climate data

  • Daniel ZicheEmail author
  • Walter Seidling
Original Article


  • • The aim of our work was to homogenise the meteorological dataset of German ICP Forests Level II sites (n = 73) by the aid of interpolations based on climate data from the German meteorological network (DWD).

  • • For each site daily values of climate variables (temperature, precipitation, solar radiation, relative humidity and wind speed) were interpolated by ordinary kriging after the removal of global trends for each day over a period of 11 y. The quality of the method was estimated by cross validation. The standard normal homogeneity test for single shifts was applied repetitively to detect inhomogenities in all time series (n = 594) using the interpolated dataset as reference.

  • • Our results indicate that: the accuracy of the interpolation method was highest for maximum air temperature and lowest for wind speed; homogenisation improved the quality of the climate time series and had the largest impact on solar radiation and wind speed; the correlation of interpolated and measured climate was stronger within the DWD network than within the ICP Forests Level II network, due to a generally higher variance (precipitation) or a systematic deviation (wind speed, minimum air temperature).

  • • We suggest the use of external climate data for homogenisation procedures within the quality assurance/quality control of the ICP Forests Level II programme. The high prediction errors of precipitation and wind speed demonstrate the need for the on — site survey within the monitoring programme.


forest monitoring climate kriging homogeneity QA/QC 


  1. Alexandersson H., 1986. A homogeneity test applied to precipitation data. J. Climatol. 6: 661–675.CrossRefGoogle Scholar
  2. Bala G., Caldeira K., Wickett M., Phillips T.J., Lobell D.B., Delire C., and Mirin A., 2007. Combined climate and carbon-cycle effects of largescale deforestation. Proc. Natl. Acad. Sci. USA 104: 6550–6555.PubMedCrossRefGoogle Scholar
  3. Daly C., Halbleib M., Smith J.I., Gibson W.P., Doggett M.K., Taylor G.H., Curtis J., and Pasteris P.P., 2008. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28: 2031–2064.CrossRefGoogle Scholar
  4. De Vries W., Vel E., Reinds G.J., Deelstra H., Klap J.M., Leeters E.E.J.M., Hendriks C.M.A., Kerkvoorden M., Landmann G., Herkendell J., Haussmann T., and Erisman J.W., 2003. Intensive monitoring of forest ecosystems in Europe — 1. Objectives, set-up and evaluation strategy. For. Ecol. Manage. 174: 77–95.CrossRefGoogle Scholar
  5. Diodato N. and Ceccarelli M., 2005. Interpolation processes using multivariate geostatistics for mapping of climatological precipitation mean in the Sannio Mountains (southern Italy). Earth. Surf. Proc. Land. 30: 259–268.CrossRefGoogle Scholar
  6. Dorninger M., Schneider S., and Steinacker R., 2008. On the interpolation of precipitation data over complex terrain. Meteorol. Atmos. Phys. 101: 175–189.CrossRefGoogle Scholar
  7. Ferretti M., Bussotti F., Cenni E., and Cozzi A., 1999. Implementation of quality assurance procedures in the Italian programs of forest condition monitoring. Water Air Soil Pollut. 116: 371–376.CrossRefGoogle Scholar
  8. Gallo K.P., Easterling D.R., and Peterson T.C., 1996. The influence of land use/land cover on climatological values of the diurnal temperature range. J. Climate 9: 2941–2944.CrossRefGoogle Scholar
  9. Houston T.D. and Hiederer R., 2009. Applying quality assurance procedures to environmental monitoring data: a case study. J. Environ. Monitor. 11: 774–781.CrossRefGoogle Scholar
  10. Hutchinson M.F., Mckenney D.W., Lawrence K., Pedlar J.H., Hopkinson R.F., Milewska E., and 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. Clim. 48: 725–741.CrossRefGoogle Scholar
  11. Innes J.L., 1994. Climatic sensitivity of temperate forests. Environ. Pollut. 83: 237–243.PubMedCrossRefGoogle Scholar
  12. Kang S.Y., Kim S., and Lee D., 2002. Spatial and temporal patterns of solar radiation based on topography and air temperature. Can. J. For. Res. 32: 487–497.CrossRefGoogle Scholar
  13. Klingaman N.P., Butke J., Leathers D.J., Brinson K.R., and Nickl E., 2008. Mesoscale simulations of the land surface effects of historical logging in a moist continental climate regime. J. Appl. Meteorol. Clim. 47: 2166–2182.CrossRefGoogle Scholar
  14. Luo W., Taylor M.C., and Parker S.R., 2008. A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. Int. J. Climatol. 28: 947–959.CrossRefGoogle Scholar
  15. Miller D.G., Rivington M., Matthews K.B., Buchan K., and Bellocchi G., 2008. Testing the spatial applicability of the Johnson-Woodward method for estimating solar radiation from sunshine duration data. Agric. For. Meteorol. 148: 466–480.CrossRefGoogle Scholar
  16. O’Neal M.A., Hanson B., Leathers D.J., and Goldstein A., 2009. Estimating land cover — induced increases in daytime summer temperatures near Mt. Adans, Washington. Phys. Geogr. 30: 130–143.Google Scholar
  17. Österle H., Werner P.C., and Gerstengarbe F.W., 2006. Qualitätsprüfung, Ergänzung und Homogenisierung der täglichen Datenreihen in Deutschland, 1951–2003: ein neuer Datensatz. 7. Deutsche Klimatagung. Klimatrends: Vergangenheit und Zukunft. http://www. Scholar
  18. Peterson T.C., 2003. Assessment of urban versus rural in situ surface temperatures in the contiguous United States: No difference found. J. Climate 16: 2941–2959.CrossRefGoogle Scholar
  19. Peterson T.C., Easterling D.R., Karl T.R., Groisman P., Nicholls N., Plummer N., Torok S., Auer I., Boehm R., Gullett D., Vincent L., Heino R., Tuomenvirta H., Mestre O., Szentimrey T., Salinger J., Forland E.J., Hanssen-Bauer I., Alexandersson H., Jones P., and Parker D., 1998. Homogeneity adjustments of in situ atmospheric climate data: A review. Int. J. Climatol. 18: 1493–1517.CrossRefGoogle Scholar
  20. Solberg S., Dobbertin M., Reinds G.J., Lange H., Andreassen K., Garcia Fernandez P., Hildingsson A., and de Vries W., 2009. Analyses of the impact of changes in atmospheric deposition and climate on forest growth in European monitoring plots: A stand growth approach. For. Ecol. Manage. 258: 1735–1750.CrossRefGoogle Scholar
  21. Spadavecchia L. and Wiliams M., 2009. Can spatio-temporal geostatistical methods improve high resolution regionalisation of meteorological variables? Agric. For. Meteorol. 149: 1105–1117.CrossRefGoogle Scholar
  22. Stahl K., Moore R.D., Floyer J.A., Asplin M.G., and McKendry I.G., 2006. Comparison of approaches in a large region with complex topography and highly variable station density. Agric. For. Meteorol. 139: 224–236.CrossRefGoogle Scholar
  23. Strack J.E., Pielke R.A., Steyaert L.T., and Knox R.G., 2008. Sensitivity of June near-surface temperatures and precipitation in the eastern United States to historical land cover changes since European settlement. Water Resour. Res. 44: W11401.CrossRefGoogle Scholar
  24. Thornton F.C., Running S.W., and White M.A., 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol. 190: 214–251.CrossRefGoogle Scholar
  25. Vicente-Serrano S.M., Saz-Sanchez M.A., and Cuadrat J.M., 2003. Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature. Clim. Res. 24: 161–180.CrossRefGoogle Scholar

Copyright information

© Springer S+B Media B.V. 2010

Authors and Affiliations

  1. 1.Institute of Forest Ecology and Forest Inventory at the Johann Heinrich von Thünen InstituteEberswaldeGermany

Personalised recommendations