Burton, A., Fowler, H.J., Kilsby, C.G. and O’Connell, P.E. 2010. A stochastic model for the spatial-temporal simulation of non-homogeneous rainfall occurrence and amounts. Water Resources Research, 46, W11501, doi:10.1029/2009WR008884.

Abstract

The Non-homogeneous Spatial Activation of Raincells (NSAR) model is presented which provides a continuous spatial-temporal stochastic simulation of rainfall exhibiting spatial non-stationarity in both amounts and occurrence. Spatial non-stationarity of simulated rainfall is important for hydrological modeling of mountainous catchments where orographic effects on precipitation are strong. Such simulated rainfall fields support the current trend towards distributed hydrological modeling.

The NSAR model extends the Spatial Temporal Neyman-Scott Rectangular Pulses (STNSRP) model, which has a homogeneous occurrence process, by generating raincells with a spatially non-homogeneous Poisson process. An algorithm to simulate non-homogeneous raincell occurrence is devised. This utilizes a new efficient and accurate algorithm to simulate raincells from an infinite 2D Poisson process, in which only raincells relevant to the application are simulated.

A 4009km2 Pyrenean catchment exhibiting extreme orographic effects provides a suitable case-study comprising seven daily raingauge records with hourly properties estimated using regional downscaling relationships. Both the NSAR and the STNSRP models are fitted to five calibration raingauges. Simulated hourly fields are validated using the remaining two raingauges providing the first validation of time series sampled from STNSRP or NSAR processes at locations not used in model fitting. The NSAR model exhibits considerable improvement over the STNSRP model particularly with respect to non-homogeneous rainfall occurrence at both daily and hourly resolutions. Further, the NSAR simulation provides an excellent match to the spatially non-homogeneous observed daily mean, proportion dry, variance, coefficient of variation, auto-correlation, skewness coefficient, cross-correlation and extremes, and to the hourly proportion dry and variance properties.