Interpolative mapping of mean precipitation in the Baltic countries by using landscape characteristics

Maps of the long-term mean precipitation involving local landscape variables were generated for the Baltic countries, and the effectiveness of seven modelling methods was compared. The precipitation data were recorded in 245 meteorological stations in 1966–2005, and 51 location-related explanatory variables were used. The similarity-based reasoning in the Constud software system outperformed other methods according to the validation fit, except for spring. The multivariate adaptive regression splines (MARS) was another effective method on average. The inclusion of landscape variables, compared to reverse distance-weighted interpolation, highlights the effect of uplands, larger water bodies and forested areas. The long-term mean amount of precipitation, calculated as the station average, probably underestimates the real value for Estonia and overestimates it for Lithuania due to the uneven distribution of observation stations.