Statistical downscaling of multi-site daily rainfall in a South Australian catchment using a Generalized Linear Model

The intention of this study was to identify a suitable Generalized Linear Model (GLM) for modelling multi‐site daily rainfall in the Onkaparinga catchment in South Australia and to examine the suitability of the model for downscaling of General Circulation Model (GCM) rainfall projections. A GLM was applied and multi‐site daily rainfall was downscaled using National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis datasets. Nineteen large‐scale atmospheric and circulation variables were selected at first and these were eventually reduced, based on correlation with daily rainfall, to 10 final variables to be used in the model. First, logistic regression was used to identify the wet and dry days, then wet day rainfall was modelled using a gamma distribution. The model was fitted for a calibration period (1991–2010) and it was then validated over the period 1981–1990. Several summary statistics including mean, standard deviation, number of wet days, maximum rainfall amount and lag 1 and lag 2 autocorrelations were used to check the model performance. The 2.5th and 97.5th percentiles of the simulated rainfall statistics were plotted against the observed rainfall statistics and it was shown that most of the observed statistics were within these bounds. Area averaged and station wise monthly, seasonal and annual totals for observed and simulated rainfall were estimated and compared. The overall performance of the GLM to downscale rainfall was considered satisfactory. However, a few discrepancies were observed in different performance statistics. Parameterization of the model to capture the local convective variability of rainfall would increase the model performance. It was found overall that the GLM can be applied for downscaling of GCM rainfall projections for this catchment