Spatio-Temporal Analysis Of Climatic Data Using Additive Regression Splines

Environmental modelling often requires knowledge of the values of certain climatic variables at locations where no such information is available. When this is the case, one must rely on interpolated values derived from climatic data recorded at surrounding locations. The accuracy of the interpolated field, however, can often be critically dependent on the inclusion of additional predictor variables in the data models used to calculate the interpolated values. This is the case with precipitation, for example, as it is often influenced by the underlying topography. When interpolating precipitation data it is therefore desirable to include predictors such as elevation and topographic slope and aspect, in addition to those quantifying the data point locations, to achieve accurate precipitation surfaces. Furthermore, studies have shown that interpolation accuracy is improved by allowing for a spatially varying dependence on these topographic variables. Additional predictors will also be appropriate when analysing temporal trends in climatic data. Interpolation procedures that incorporate additional predictors in a spatially varying way can also be useful tools for analysing how the effects of certain predictors vary across the spatial extent of the region under consideration.