Performance Comparison of Geostatistical Algorithms for Incorporating Elevation into the Mapping of Precipitation
This paper presents three 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 at 36 climatic stations in a 5,000 km2
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: 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 elevation and rainfall records at surrounding stations. The three multivariate geostatistical algorithms outperform other interpolators, in particular 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).
IV International Conference on GeoComputation
, Mary Washington College, Fredericksburg, VA, USA, 25-28 July 1999.http://www.geovista.psu.edu/sites/geocomp99/Gc99/023/gc_023.htm