Wet and dry spell analysis of Global Climate Model-generated precipitation using power laws and wavelet transforms

Climate model simulations for the twenty-first century point toward changing characteristics of precipitation. This paper investigates the impact of climate change on precipitation in the Kansabati River basin in India. A downscaling method, based on Bayesian Neural Network (BNN), is applied to project precipitation generated from six Global Climate Models (GCMs) using two scenarios (A2 and B2). Wet and dry spell properties of monthly precipitation series at five meteorologic stations in the Kansabati basin are examined by plotting successive wet and dry durations (in months) against their number of occurrences on a double-logarithmic paper. Straight-line relationships on such graphs show that power laws govern the pattern of successive persistent wet and dry monthly spells. Comparison of power-law behaviors provides useful interpretation about the temporal precipitation pattern. The impact of low-frequency precipitation variability on the characteristics of wet and dry spells is also evaluated using continuous wavelet transforms. It is found that inter-annual cycles play an important role in the formation of wet and dry spells.