Using a key-station approach, statistical downscaling of monthly general circulation model outputs to monthly precipitation, evaporation, minimum temperature and maximum temperature at 17 observation stations located in Victoria, Australia was performed. Using the observations of each predictand, over the period 1950–2010, correlations among all stations were computed. For each predictand, the station which showed the highest number of correlations above 0.80 with other stations was selected as the first key-station. The stations that were highly correlated with that key-station were considered as the member stations of the first cluster. By employing this same procedure on the remaining stations, the next key-station was found. This procedure was performed until all stations were segregated into clusters. Thereafter, using the observations of each predictand, regression equations (inter-station regression relationships) were developed between the key-stations and the member stations for each calendar month. The downscaling models at the key-stations were developed using reanalysis data as inputs to them. The outputs of HadCM3 pertaining to A2 emission scenario were introduced to these downscaling models to produce projections of the predictands over the period 2000–2099. Then the outputs of these downscaling models were introduced to the inter-station regression relationships to produce projections of predictands at all member stations.
- general circulation model
- key station
- multi-linear regression
- statistical downscaling
- First received 25 November 2014.
- Accepted in revised form 18 February 2016.
- © IWA Publishing 2016