This study aims to develop a stochastic method (SM_GSTR) for generating short-time (i.e., hourly) rainstorm events at all grids (named gridded rainstorm events) in a region. The proposed SM_GSTR model is developed by the non-normal correlated multivariate Monte Carlo simulation (MMCS) method (Wu et al. 2006) with the statistical properties and spatiotemporal correlation structures of the four event-based gridded rainfall characteristics. The radar-based rainfall data on 20 typhoon events at 336 grids in a basin located in north Taiwan, Nankan River watershed, are used in the model development and demonstration. The results from the model demonstration indicate that the proposed SM_GSTR model can reproduce a great number of gridded rainfall characteristics, of which, the statistical properties in time and space have a good fit to those from the observations in association with the acceptable deviation; thus, it can reasonably emulate the behavior of the rain field in both time and space. It is expected that the resulting massive rainfall-induced disasters (e.g., inundation and landslide) from the physical-based numerical model with the simulated gridded rainstorms by the proposed SM_GSTR model can be applied to establish an alternative artificial intelligence (AI) model for effectively forecasting the hydrologic variables (e.g., runoff and water-level).


  • A stochastic model for generating a rain field is develop under consideration of correlation of rainfalls in time and space.

  • The rainstorm event consists of five gridded rainfall characteristics.

  • The resulting rain field is composed of simulated rainstorm at all grids.

  • A correlated multivariate Monte Carlo method is applied.

  • The statistical properties of simulated rainstorms at all grids can be persisted.

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