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Statistical downscaling of temperature using three techniques in the Tons River basin in Central India

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Abstract

In this study, downscaling models were developed for the projections of monthly maximum and minimum air temperature for three stations, namely, Allahabad, Satna, and Rewa in Tons River basin, which is a sub-basin of the Ganges River in Central India. The three downscaling techniques, namely, multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LS-SVM), were used for the development of models, and best identified model was used for simulations of future predictand (temperature) using third-generation Canadian Coupled Global Climate Model (CGCM3) simulation of A2 emission scenario for the period 2001–2100. The performance of the models was evaluated based on four statistical performance indicators. To reduce the bias in monthly projected temperature series, bias correction technique was employed. The results show that all the models are able to simulate temperature; however, LS-SVM models perform slightly better than ANN and MLR. The best identified LS-SVM models are then employed to project future temperature. The results of future projections show the increasing trends in maximum and minimum temperature for A2 scenario. Further, it is observed that minimum temperature will increase at greater rate than maximum temperature.

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Acknowledgments

The authors are thankful to the Department of Science and Technology (DST), New Delhi for providing financial support during the study period. We are also thankful to anonymous reviewers for their thoughtful suggestions to improve this manuscript significantly.

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Authors and Affiliations

  1. Department of Water Resources Development and Management, IIT Roorkee, Roorkee, Uttarakhand, India

    Darshana Duhan & Ashish Pandey

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  1. Darshana Duhan
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  2. Ashish Pandey
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Correspondence to Darshana Duhan.

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Duhan, D., Pandey, A. Statistical downscaling of temperature using three techniques in the Tons River basin in Central India. Theor Appl Climatol 121, 605–622 (2015). https://doi.org/10.1007/s00704-014-1253-5

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