Application of Statistical Downscaling Model for Prediction of Future Rainfall in Bhudhabalanga River Basin, Odisha (India)
Satyapriya Behera1, Deepak Khare2, Prabhash Kumar Mishra3, Sangitarani Sahoo4

1Satyapriya Behera, M.Tech Scholar, Indian Institute of Technology Roorkee, Roorkee (Uttarakhand), India.
2Deepak Khare, Prof. WRD&M, Indian Institute of Technology Roorkee, Roorkee (Uttarakhand), India.
3Prabhash Kumar Mishra, Scientest, National Institute of Hydrology, Roorkee (Uttarakhand), India.
4Sangitarani Sahoo, M.Tech Scholar, Indian Institute of Technology Roorkee, Roorkee (Uttarakhand), India.

Manuscript received on 15 April 2016 | Revised Manuscript received on 25 April 2016 | Manuscript Published on 30 April 2016 | PP: 24-31 | Volume-5 Issue-4, April 2016 | Retrieval Number: D4473045416/16©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: The impact of climate change in the hydrology sector, often require fine scale spatial resolution climate information for studying present as well as future scenario. Global climate Models (GCMs) assess climate change scenarios on coarse partial resolution. There are different techniques to downscale to downscale coarser grid scale data to finer scale as coarse resolution of GCMs data cannot be used directly to asses climate impact for a particular area. Therefore downscaling of Global climate Models (GCMs) output is important to estimate regional climate change impacts. Precipitation is one of the important climate variables that is used as inputs in hydrologic models in many water resources studies. In this present study, Statistical Downscaling Model (SDSM) has been adopted to downscale daily precipitation to generate future climate outputs for Budhabalanga river basin in Odisha. Multiple linear regression (MLR) technique is used in SDSM. The daily precipitation data (1961-2001) representing Budhabalanga river catchment area has been used as input of the SDSM Model. The model has been calibrated and validated with large-scale National Central for Environmental Prediction (NCEP) reanalysis data for the period 1961-1990 and 1991-2001 respectively. The prediction of future daily precipitation for the period 2025s, 2050s and 2080s for the study area has been carried out corresponding to Hadley Centre Coupled Model version 3 (HadCM3 A2 and B2). The study results show that during the calibration and validation, confirm the SDSM model acceptability in regards to its downscaling performance for daily and annual rainfall. The results of the downscaled daily precipitation for future period indicates an increasing trend for the period 2025s and 2050s where as decrease in trend for the period 2080s for mean daily precipitation.
Keywords: Climate Change, Global Climate Model, Scenario Generation, Statistical Downscaling, Precipitation, Budhabalanga Basin

Scope of the Article: Application Specific ICs (ASICs)