Stochastic Simulation of Rainfall Data Using a Markov Chain Model Calibrated to Dynamically Downscaled Climate Data

Abstract:

This study used high resolution spatially distributed rainfall data produced by NSW/ACT Regional Climate Modelling (NARCliM) project. NARCliM dynamically downscaled four Global Climate Models using three Regional Climate Models within the Weather Research and Forecasting model to generate gridded climate data at 10 km spatial resolution for south eastern Australia. These dataset are being used in this project to evaluate the urban water security of reservoirs on the east coast of Australia. A stochastic model to simulate rainfall series was developed for runoff generation using parameters calibrated to NARCliM. This study has developed a Markov Chain model, which simulates the occurrence of daily rainfall using the transition probability of dry and wet days, while the precipitation for the wet days are generated using a two parameter gamma distribution. We have identified significant seasonal and intra- to inter-decadal variations of the model parameters at our field site. Incorporating the temporal variability (for instance, calibrating the model parameters to each decade independently), we have found that the model satisfactorily preserves the daily, monthly and annual characteristics of the NARCliM rainfall. In addition to the temporal variability, we have observed that the model parameters vary spatially at our site with potential orographic effects that vary both seasonally and decadally. However, the parameters of the model fitted to the NARCliM rainfall are significantly different from the parameters fitted to the ground-based climate station rainfall. Suitability of the model for the generation of long time series (e.g. 1000 years) required for reservoir simulation will be discussed.