Observed daily rainfall data during baseline period i.e. 1961-1990 of four raingauge stations namely Akluj, Baramati, Bhor and Malsiras located in the Nira River basin in Central India were analyzed to study the impact of climate change on rainfall. LARS-WG incorporating 15 GCM’s from the CMIP3 predictions for A1B, A2 and B1 emission scenarios was used to statistically downscale the daily rainfall data during three time spans centred at 2020’s, 2055’s and 2090’s. Uncertainty in GCMs rainfall predictions was analyzed on monthly, seasonal and annual scales. Kolmogorov-Smirnov test, Student’s t-test, and Fisher test have shown average to good performance during synthetic rainfall data generation for all the stations. The analysis of the data shows that the uncertainty in the prediction increases with the timescale. Also, the variability in the predictions is smaller in annual values followed by seasonal and monthly values. Maximum uncertainty is observed in A2 scenario followed by A1B and B1 Scenarios. Monsoon months show minimum uncertainty in all the scenarios. The rainfall of Dec, Mar, Apr and May months are expected to increase in first two spans while expected to decrease in the last time span 2080 -2099 under all the scenarios. The monsoon month’s rainfall is expected to increase slightly in future for all the scenarios. Baramati shows maximum increase in annual rainfall for all scenarios while rainfall at Malsiras is expected to decrease only during third time span for all three scenarios.
Murumkar, Asmita Ramkrishna and Arya, Dhyan Singh, "Rainfall Variability Analysis In The Nira River Basin Using Multi-Model GCM Ensemble" (2014). CUNY Academic Works.