Rainfall estimation using multiple linear regression based statistical downscaling for Piperiya watershed in Chhattisgarh

Authors

  • S. K. CHANDNIHA Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee, 247667,
  • M. L. KANSAL Department of Water Resources Development and Management, Indian Institute of Technology, Roorkee, 247667,

DOI:

https://doi.org/10.54386/jam.v18i1.911

Keywords:

Climate change, HadCM3,, SDSM,, statistical downscaling, Piperiya watershed

Abstract

Climatic variability and its behavior is a complex phenomenon that is directly associated with uncertainties. In the climate change study, particularly in hydrological aspects, it is necessary to identify the parameters (predictors) that are directly or indirectly associated with predictands. The forecasted results are directly associated with the selection of predictors. In the present study, the statistical downscaling model (SDSM) has been advocated to downscale the daily rainfall in Piperiya watershed of Chhattisgarh state. SDSM is based on multiple linear regression (MLR) technique. The daily rainfall data (1961-2001) of the Piperiya watershed in Chhattisgarh is considered as input (predictand) to the model. The model has been calibrated and validated on the basis of rainfall period of 1961-1990 and 1991-2001 respectively with large scale predictors of National Centre for Environmental Prediction (NCEP) reanalysis data. Finally, monthly rainfall is predicted on the basis of forecasted future daily rainfall for the periods of 2020s, 2050s and 2080s under the consideration of HadCM3 A2 and B2 emission scenarios. 

Downloads

Published

01-06-2016

How to Cite

S. K. CHANDNIHA, & M. L. KANSAL. (2016). Rainfall estimation using multiple linear regression based statistical downscaling for Piperiya watershed in Chhattisgarh. Journal of Agrometeorology, 18(1), 106–112. https://doi.org/10.54386/jam.v18i1.911

Issue

Vol. 18 No. 1 (2016): June

Section

Research Paper

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

This is a human-readable summary of (and not a substitute for) the license. Disclaimer.

You are free to:

Share — copy and redistribute the material in any medium or format

Adapt — remix, transform, and build upon the material

The licensor cannot revoke these freedoms as long as you follow the license terms.

    Under the following terms:

    Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

    NonCommercial — You may not use the material for commercial purposes.

    ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

    No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

    Notices:

    You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.

    No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.