A spatial time series framework for simulating daily precipitation at regional scales

A spatiotemporal stochastic simulation approach for constructing maps of daily precipitation at regional scales in a hindcast mode is proposed in this paper. Parametric temporal trend models of precipitation are first established at the available rain gauges. Temporal trend model parameters are then regionalized in space accounting for their spatial autoand cross-correlation, as well as for their relationships with auxiliary spatial information such as terrain elevation. The resulting residual values at the rain gauges are modeled as a realization of a stationary spatiotemporal process. Sequential simulation is then used to generate alternative synthetic realizations of daily precipitation fields, which reproduce: (i) the rain gauge measurements, and (ii) their histogram and a model for their spatiotemporal correlation over the entire study region and time period of interest. In addition, a post-processing transformation allows reproduction of the rainfall histogram at particular dates, while preserving the observed