Abstrait

Bias correction of ANN based statistically ownscaled precipitation data for the Chaliyar river basin

Chithra NR, Santosh G Thampi

Any study to assess the impact of climate change on hydrology requires future climate scenarios at river basin scale. General Circulation Models (GCM) are the only reliable source for future climate scenarios, but they perform well only at coarse scale. Also, it may not be possible to straight away use the output from GCMs in hydrologic models applied at river basin scale. GCM simulations need to be downscaled to river basin scale. Uncorrected bias in the downscaled data, if any, should be corrected before the downscaled data is used in hydrologic applications. In this study, an advanced nonlinear bias correction method is applied to Artificial Neural Network (ANN) based downscaling models to obtain projections of monthly precipitation of station scale. The models were validated through application to downscale the monthly precipitation at two rain gauge stations, one in the Chaliyar river basin located in the humid tropics in Kerala, India, and other located close to it. The probable predictor variables are extracted from the National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data and simulations from the third generation Canadian Coupled Global Climate Model (CGCM3) for the twentieth century experiment, 20C3M. The potential predictors were selected based on the values of the correlation coefficient between NCEP predictors and predictand precipitation and also between NCEP predictors and GCM predictors. Separate models were developed for each station and for each of the season and separate sets of potential predictors were used in each of the models. The models were validated using the data after year 2000; the performance of the models was reasonably good except for a few extremes.