Improving the Areal Estimation of Rainfall in Galicia (NW Spain) Using Digital Elevation Information

Abstract

Rainfall is an intermittent phenomenon in both space and time and it displays large spatio-temporal variability. The most commonly used interpolation methods provide good estimates of the total amount of rainfall but they do not model accurately its complex spatio-temporal structure. Better descriptions of rainfall spatial variability should be obtained from a digital elevation model. The application of a geostatistical technique that should improve rainfall estimation by integrating elevation data in Galicia (NW Spain) is discussed. The algorithm used is kriging with an external drift. The results are compared with methods that do not account for the elevation information data, such as ordinary kriging, conditional simulation and the inverse squared-distance weighting. The data set used in this exercise consists of monthly rainfall data from a maximum of 121 pluviographs corresponding to a period of 48 months (from January 1998 to December 2001) and a digital elevation model with cells of 500 m by 500 m size covering an area of 29750 km². For 15 out of 48 monthly semivariograms a pure nugget effect was observed and during 33 months spatial dependence was modelled by a nugget effect component plus a spherical, exponential or Gaussian component. Gaussian conditional simulation gave higher rainfall mean values than ordinary kriging and kriging with external drift. However, kriging with external drift accounting for elevation gave results that were thought to be the best descriptor of the effect of topography on the rainfall. The results, while in the line of similar applications in other fields, favor the geostatistical methods including the secondary information; however, the scores of the different methods are very similar, making it difficult to justify complex geostatistical analysis in this specific case study. Reasons for this performance should be found in the weak spatial correlation of the rainfall and between the rainfall and elevation.