Statistical downscaling of climate change scenarios of rainfall and temperature over Indira Sagar Canal Command area in Madhya Pradesh, India

Shukla, Rituraj and Deo, Ravinesh and Khare, Deepak (2015) Statistical downscaling of climate change scenarios of rainfall and temperature over Indira Sagar Canal Command area in Madhya Pradesh, India. In: 2015 IEEE 14th International Conference on Machine Learning and Applications, 9-11 Dec 2015, Miami, Florida, USA.


General circulation models (GCMs) have been employed by climate agencies to predict future climate change. A challenging issue with GCM output for local relevance is their coarse spatial resolution of the projected variables. Statistical Downscaling Model (SDSM) identifies relationships between large-scale predictors (i.e., GCM-based) and local-scale predictands using multiple linear regression models. In this study (SDSM) was applied to downscale rainfall and temperature from GCMs. The data from single station located in the Indira Sagar canal command area at Madhya Pradesh, India were used as input of the SDSM. The study included calibration and validation with large-scale atmospheric variables encompassing the NCEP reanalysis data, the future estimation due to a climate scenario, which is HadCM3 A2. Results of the downscaling experiment demonstrate that during the calibration and validation stages, the SDSM model can be well acceptable regard its performance in the downscaling of daily rainfall and temperature. For a future period (2010-2099), the SDSM model estimated an increase in total average annual rainfall and annual average temperature for station. This indicates that the area of station considered will be wet and humid in the future. Also, the mean temperature is projected to rise to 1.50 C to 2.50 C for present study area. However, the model projections show a rise in mean daily precipitation with varying percentage in the months of July (0.59% to 2.09%) and August (0.79% to 1.19) under A2 of HadCM3 model for future periods.

Statistics for USQ ePrint 28975
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to Published version in accordance with the copyright policy of the publisher. Dr R C Deo acknowledges support from University of Southern Queensland Academic Division 'Research Activation Incentive Scheme (RAIS)' grant 2015.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 15 Mar 2016 05:02
Last Modified: 27 Jun 2016 01:52
Uncontrolled Keywords: statistical downscaling, GCM data, scenario generation, canal command, SDSM
Fields of Research : 04 Earth Sciences > 0401 Atmospheric Sciences > 040104 Climate Change Processes
04 Earth Sciences > 0401 Atmospheric Sciences > 040102 Atmospheric Dynamics
04 Earth Sciences > 0401 Atmospheric Sciences > 040105 Climatology (excl.Climate Change Processes)
05 Environmental Sciences > 0502 Environmental Science and Management > 050206 Environmental Monitoring
Socio-Economic Objective: D Environment > 96 Environment > 9603 Climate and Climate Change > 960303 Climate Change Models
E Expanding Knowledge > 97 Expanding Knowledge > 970104 Expanding Knowledge in the Earth Sciences
Identification Number or DOI: 10.1109/ICMLA.2015.75

Actions (login required)

View Item Archive Repository Staff Only