Can spatio-temporal geostatistical methods improve high resolution regionalisation of meteorological variables?

Ecological and hydrological models applied over regional domains generally require the input of spatial meteorological time series. We investigate the potential improvements to space–time regionalisations of sparse meteorological data sets when including information on temporal correlations between successive measurements of minimum temperature (Tmin), maximum temperature (Tmax) and precipitation (P) from 112 stations across Central Oregon. We compared a number of increasingly complex geostatistical models based on Kriging with a baseline inverse distance weighting algorithm. We varied the number of interpolation data used in both space and time and assessed the impact on interpolation skill. Furthermore, we assessed the error and bias reduction resulting from aggregating estimates over increasingly large temporal supports. We hypothesised that incorporating temporal information would decrease errors, and that error and bias would be reduced when considering estimates aggregated over longer time periods. We found that, contrary to our expectations, incorporation of information on temporal autocorrelation decreased interpolation skill by ∼5% for Tmin and Tmax. However, inclusion of temporal autocorrelation improved results for P by ∼10%. Increasing the temporal aggregation of estimates was shown to decrease error by up to 50% and bias by up to 30% (daily vs. annual support). These results indicate that instantaneous error may be diluted for phase lagged or integrating elements of the state vector, such as soil moisture, when implementing such surfaces in modelling applications. Results were more successful for temperature than precipitation (daily % error for jack-knife estimates of Tmin = 52, Tmax = 13, P = 97), reflecting the stochastic nature of precipitation, and problems with non-linearity for the Kriging algorithm.