Abstract:
The climate impact studies, particularly in hydrology, often require
climate information at fine scale for present as well as future scenario. Global
Climate Model (GCM) estimates climate change scenarios on coarse spatial resolution.
Therefore, different techniques have been evolved to downscale the coarsegrid
scale GCM data to finer scale surface variables of interest. In the present study,
the Statistical Downscaling Model (SDSM) has been applied to downscale daily
precipitation from simulated GCM data. SDSM utilizes Multiple Linear Regression
(MLR) technique. The daily precipitation data (1961–2001) representing Tawa
region has been considered as input (predictand) to the model. The model has
been calibrated (1961–1991) and validated (1992–2001) with screened large-scale
predictors of (National Centre for Environmental Prediction (NCEP) reanalysis
data. The prediction of future daily rainfall for the study area has been carried out
for the period 2020s, 2050s and 2080s corresponding to HadCM3 A2 variables. The
calibration and validation results confirm the SDSM model acceptability slightly at
a lower degree. The results of the downscaled daily precipitation for the future
period indicate an increasing trend in the mean daily precipitation.
