Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall

Abstract

This paper presents three multivariate geostatistical algorithms for incorporating a digital elevation model into the spatial prediction of rainfall: simple kriging with varying local means; kriging with an external drift; and colocated cokriging. The techniques are illustrated using annual and monthly rainfall observations measured at 36 climatic stations in a 5000 km 2 region of Portugal. Cross validation is used to compare the prediction performances of the three geostatistical interpolation algorithms with the straightforward linear regression of rainfall against elevation and three univariate techniques: the Thiessen polygon; inverse square distance; and ordinary kriging. Larger prediction errors are obtained for the two algorithms (inverse square distance, Thiessen polygon) that ignore both the elevation and rainfall records at surrounding stations. The three multivariate geostatistical algorithms outperform other interpolators, in particular the linear regression, which stresses the importance of accounting for spatially dependent rainfall observations in addition to the colocated elevation. Last, ordinary kriging yields more accurate predictions than linear regression when the correlation between rainfall and elevation is moderate (less than 0.75 in the case study).