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.
Statistical downscaling of general circulation model outputs to precipitation, evaporation and temperature using a key station approach
D. A. Sachindra, F. Huang, A. Barton, B. J. C. Perera; Statistical downscaling of general circulation model outputs to precipitation, evaporation and temperature using a key station approach. Journal of Water and Climate Change 1 December 2016; 7 (4): 683–707. doi: https://doi.org/10.2166/wcc.2016.021
Download citation file: