Change in Rainfall Patterns in the Hilly Region of Uttarakhand due to the Impact of Climate Change

Abstract

Uttarakhand, a Himalayan state of India, may experience an increase in temperature of 1.4°C to 5.8°C by 2100 due to global warming. The rise in temperature may melt the glaciers of the state and may have some significant impact on the rainfall. In this study, we have quantified the changes in the rainfall of the state. Also, an attempt has been made to evaluate the impact of climate change on rainfall. The future rainfall can be estimated by using a global circulation model (GCM). However, due to the very coarse spatial resolution of the different GCM, we cannot use them directly. For matching this spatial inequality between the GCM output and historical precipitation data, we used the statistical downscaling technique. In the present study, we have examined the suitability of the artificial neural network with principal component analysis for downscaling the rainfall for different hilly districts of the state. We used the GCM model developed by Canadian Earth System Model, and the Indian metrological department gridded rainfall data. We performed the analysis for the different scenarios to visualize the impact of climate change on rainfall trends for all nine hilly districts of Uttarakhand. Results show that there was a clear indication of climate change in upper Himalayan Districts like Pithoragarh, Rudraprayag, and Chamoli, which was observed from the peak of monthly rainfall. The percentage change of monsoon rainfall in the future may go up to 200 % in the case of RCP8.5, and the change maybe around 180% for RCP4. Also, the volume of rainfall may increase in the case of RCP8.5 from July to September as compared to the historical data, i.e., there may be a shifting of monsoon rainfall in the future.

Long-term hydrological simulation for the estimation of snowmelt contribution of Alaknanda River Basin, Uttarakhand using SWAT

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HEC-HMS based rainfall-runoff model for Punpun river basin

“…The data for rainfall-runoff (R-R) modeling ranges from a digital elevation model (DEM) to derive physical basin characteristics, soil characteristics (for parameters about infiltration loss and soil moisture holding capability) and land use classes derived from satellite images etc. (Banerjee et al 2020;Kumar & Bhattacharjya 2021). With the advent of GIS, these data can be easily processed and analyzed to estimate hydrological modeling parameters (Prasad & Narayanan 2016;Seekao & Pharino 2016;Kumar & Himanshu 2017).…”