Statistical Downscaling of Maximum Temperature in Hoshangabad District of India
Ankit Balvanshi1, H.L. Tiwari2, Mayank Gupta3, Akhilesh Sharma4
1Ankit Balvanshi, Assistant Professor, Department of Civil Engineering, S.I.R.T.E., Bhopal, India.
2H.L. Tiwari, Associate Professor, Department of Civil Engineering, Maulana Azad National Institute of Technology, Bhopal, India.
3Mayank Gupta, Assistant Professor, Department of Civil Engineering, S.I.R.T.E., Bhopal, India.
4Akhilesh Sharma, Assistant Professor, Department of Civil Engineering, S.I.R.T.E., Bhopal, India.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 493-496 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4484099320 | DOI: 10.35940/ijrte.C4484.099320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Global Climate ModelsCanESM2 and CGCM3 were utilised to downscale the maximum temperature for Hoshangabad district of Madhya Pradesh, India. The area of study comprises to be of 6704 km2. The predictors employed for CanESM2 were ncepmslpgl, ncepp500gl, ncepp850gl and ncepmslpas, ncepp500gl, ncepp850gl were the predictors fixed for CGCM3. The total duration of the study was from the years 1979 – 2001. The two GCMs, CGCM3 and CanESM2 were checked for their capability in downscaling the maximum temperature climatic parameter. The GCM outputs were evaluated on Nash Sutcliffe Efficiency (NSE) and coefficient of determination (r2) criterias. The period of calibration was taken to be 1979-1995 and 1996-2001 was chosen as the period of validation. GCM CanESM2 obtained NSE of 0.77, 0.75 and r2 of 0.79, 0.79 during the periods of calibration and validation respectively. It was concluded that CanESM2 model is found comparatively more suitable for downscaling of maximum temperature for Hoshangabad region. The GCM can be further employed to generate the future scenario of maximum temperature in the region.
Keywords: Global Climate Model, CGCM3, CanESM2, NSE, r2.