Subimal, Ghosh and Pradeep, Mujumdar (2008) Correction for Bias in Downscaling GCM Simulations for Hydrologic Impact Assessment. In: Water Down Under 2008, Adelaide, Australia, Adelai.Full text not available from this repository.
In a statistical downscaling model, it is important to remove the bias of General Circulations Model (GCM) outputs resulting from various assumptions about the geophysical processes. One conventional method for correcting such bias is standardisation, which is used prior to statistical downscaling to reduce systematic bias in the mean and variances of GCM predictors relative to the observations or National Centre for Environmental Prediction/ National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data. A major drawback of standardisation is that it may reduce the bias in the mean and variance of the predictor variable but it is much harder to accommodate the bias in large-scale patterns of atmospheric circulation in GCMs (e.g. shifts in the dominant storm track relative to observed data) or unrealistic inter-variable relationships. While predicting hydrologic scenarios, such uncorrected bias should be taken care of; otherwise it will propagate in the computations for subsequent years. A statistical method based on equi-probability transformation is applied in this study after downscaling, to remove the bias from the predicted hydrologic variable relative to the observed hydrologic variable for a baseline period. The model is applied in prediction of monsoon stream flow of Mahanadi River in India, from GCM generated large scale climatological data.
|Item Type:||Conference Paper|
|Department/Centre:||Division of Mechanical Sciences > Civil Engineering|
|Date Deposited:||13 Oct 2011 10:49|
|Last Modified:||13 Oct 2011 10:49|
Actions (login required)