A spatial time series framework for modeling daily precipitationat regional scales

In this paper, a framework for stochastic spatiotemporal modeling of daily precipitation in a hindcast mode is presented. Observed precipitation levels in space and time are modeled as a joint realization of a collection of space-indexed time series, one for each spatial location. Time series model parameters are spatially varying, thus capturing space-time interactions. Stochastic simulation, i.e., the procedure of generating alternative precipitation realizations (synthetic fields) over the space-time domain of interest (Deutsch and Journel, 1998), is employed for ensemble prediction. The simulated daily precipitation fields reproduce a data-based histogram and spatiotemporal covariance model, and identify the measured precipitation values at the rain gauges (conditional simulation). Such synthetic precipitation fields can be used in a Monte Carlo framework for risk analysis studies in hydrologic impact assessment investigations