Using multiple linear regression and Geographic Information System techniques, we modelled the spatial distribution of mean monthly precipitation for the seasonal and annual periods in a mountainous region of 10,590 km2, located in the central area of the Cantabrian Coast, Spain. We used precipitation data measured at 117 stations for the period 1966–1990, using 84 stations for function development and reserving 33 for validation tests. The best model developed used five topographic descriptors as independent variables: elevation, distance from the coastline, distance from the west, and a measurement of elevation and slope means into homogeneous areas. These topographic variables were calculated as raster models with 200 m resolution.
The model accounted for most of the spatial variability in mean precipitation, with an adjusted R² between 0.58 and 0.67. The standard error was approximately 10% and the mean absolute error ranged from 8.1 to 26.1 mm, which represented 13– 19% of observed precipitation. Regression enabled us to estimate precipitation in areas where there are no nearby stations and where topography has a major influence on the precipitation.
Spatial modelling of a climate variable is of interest because many other environmental variables depend on climate. Accurate climate data only exist for point locations, the meteorological stations, as a result of which values at any other point in the terrain must be inferred from neighbouring stations or from relationships with other variables.
Many studies (Kurtzman and Kadmon, 1999; Oliver and Webster, 1990; Philip and Watson, 1982; Mitas and Mitasova, 1988) model the spatial distri- bution of a climate variable using interpolation methods. These techniques can obtain satisfactory results from limited data, based mainly on the geographic situation of the sampling points, on the topological relationships between these points, and on the value of the variable to be measured. However, interpolation methods only consider spatial relation- ships among sampling points, and do not take into account other properties of the landscape.