SPATIO-TEMPORAL INTERPOLATION OF PRECIPITATION INCLUDING COVARIATES: DURING MONSOON PERIODS IN PAKISTAN

The space-time interpolation of precipitation has significant contribution to river control, reservoir operations, forestry interest and flash flood watches etc. The changes in environmental covariates and spatial covariates make space-time estimation of precipitation a challenging task. In the present paper, we use a generalized additive model with Gaussian link function to account for the effect of covariates; the resulting output is partitioned into two parts; trend component and residual component. The trend component is modeled on the basis of spatial artificial neural network (SANN) architecture. The residual component is assumed to be a spatio-temporal random field and is modeled using Le and Zidek (2006) hierarchical Bayesian interpolation (HBI) method. The separable stationary space-time nested covariance model and purely spatial non-stationary non-parametric covariance model for interpolation of the residual component are used. For the interpolation of the amount of precipitation at ungauged locations the interpolated residual components for ungauged locations are added to the respective interpolated trend components. The results of two covariance functions are compared by means of cross-validations and suggest that HBI including covariates provides minimum mean square prediction error if the nested spatio-temporal stationary covariance model is used.