Abstract
The impact of climate change often requires climatic data of spatial resolution at a precise scale to study the ongoing scenario and future scenario as well. Climate change scenarios are evaluated on coarse spatial resolution with the help of Global Climate Models (GCMs). Different methods can be used to downscale the data present at coarser scale to the finer scale. Multiple linear regressions (MLR) technique-based SDSM was employed for downscaling the obtained data from coarser scale to finer scale. Rainfall is considered as one of the principal climate factors for environmental variation studies. Statistical downscaling technique is utilized for downscaling daily precipitation data to obtain future climate data for Haridwar District in Uttarakhand. The large-scale NCEP (National Centers for Environmental Prediction) “reanalysis” data of the interval 1961–1995 is utilized for calibration, and the data of the interval 1996–2005 is utilized for the model’s validation. The estimation of future monthly based rainfall for the period 2020s (2006–2040), 2050s (2041–2070) and 2080s (2071–2099) for Haridwar District is carried out for different RCPs (2.6, 4.5, and 8.5). This examination shows a decreasing pattern of yearly rainfall for future for the period 2020s (2006–2040) by 9–18%, 2050s (2041–2070) by 12–16%, and an increase in trend in 2080s (2071–2099) compared to the present (1961–2005) scenario.
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Singh, G., Mishra, N., Thakural, L.N., Kumar, S. (2022). Statistical Downscaling of Climate Change Scenarios of Rainfall in Haridwar District of Uttarakhand, India. In: Kolhe, M.L., Jaju, S.B., Diagavane, P.M. (eds) Smart Technologies for Energy, Environment and Sustainable Development, Vol 1. Springer Proceedings in Energy. Springer, Singapore. https://doi.org/10.1007/978-981-16-6875-3_11
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