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Climate Change Assessment of Precipitation in Tandula Reservoir System

Climate Change Assessment of Precipitation in Tandula Reservoir System The precipitation is the principle input of hydrological cycle affect availability of water in spatial and temporal scale of basin due to widely accepted climate change. The present study deals with the statistical downscaling using Statistical Down Scaling Model for rainfall of five rain gauge stations (Ambagarh, Bhanpura, Balod, Chamra and Gondli) in Tandula, Kharkhara and Gondli reservoirs of Chhattisgarh state of India to forecast future rainfall in three different periods under SRES A1B and A2 climatic forcing conditions. In the analysis, twenty-six climatic variables obtained from National Centers for Environmental Prediction were used and statistically tested for selection of best-fit predictors. The conditional process based statistical correlation was used to evolve multiple linear relations in calibration for period of 1981–1995 was tested with independent data of 1996–2003 for validation. The developed relations were further used to predict future rainfall scenarios for three different periods 2020–2035 (FP-1), 2046–2064 (FP-2) and 2081–2100 (FP-3) and compared with monthly rainfalls during base period (1981–2003) for individual station and all three reservoir catchments. From the analysis, it has been found that most of the rain gauge stations and all three reservoir catchments may receive significant less rainfall in future. The Thiessen polygon based annual and seasonal rainfall for different catchments confirmed a reduction of seasonal rainfall from 5.1 to 14.1% in Tandula reservoir, 11–19.2% in Kharkhara reservoir and 15.1–23.8% in Gondli reservoir. The Gondli reservoir may be affected the most in term of water availability in future prediction periods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of The Institution of Engineers (India): Series A Springer Journals

Climate Change Assessment of Precipitation in Tandula Reservoir System

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Publisher
Springer Journals
Copyright
Copyright © 2018 by The Institution of Engineers (India)
Subject
Engineering; Civil Engineering
ISSN
2250-2149
eISSN
2250-2157
DOI
10.1007/s40030-018-0269-8
Publisher site
See Article on Publisher Site

Abstract

The precipitation is the principle input of hydrological cycle affect availability of water in spatial and temporal scale of basin due to widely accepted climate change. The present study deals with the statistical downscaling using Statistical Down Scaling Model for rainfall of five rain gauge stations (Ambagarh, Bhanpura, Balod, Chamra and Gondli) in Tandula, Kharkhara and Gondli reservoirs of Chhattisgarh state of India to forecast future rainfall in three different periods under SRES A1B and A2 climatic forcing conditions. In the analysis, twenty-six climatic variables obtained from National Centers for Environmental Prediction were used and statistically tested for selection of best-fit predictors. The conditional process based statistical correlation was used to evolve multiple linear relations in calibration for period of 1981–1995 was tested with independent data of 1996–2003 for validation. The developed relations were further used to predict future rainfall scenarios for three different periods 2020–2035 (FP-1), 2046–2064 (FP-2) and 2081–2100 (FP-3) and compared with monthly rainfalls during base period (1981–2003) for individual station and all three reservoir catchments. From the analysis, it has been found that most of the rain gauge stations and all three reservoir catchments may receive significant less rainfall in future. The Thiessen polygon based annual and seasonal rainfall for different catchments confirmed a reduction of seasonal rainfall from 5.1 to 14.1% in Tandula reservoir, 11–19.2% in Kharkhara reservoir and 15.1–23.8% in Gondli reservoir. The Gondli reservoir may be affected the most in term of water availability in future prediction periods.

Journal

Journal of The Institution of Engineers (India): Series ASpringer Journals

Published: Feb 7, 2018

References