Analysis of drought severity-area-frequency curves using a general circulation model and scenario uncertainty

With increasing water scarcity around the world, exacerbated by spatial and temporal variability of drought incidences along with the uncertainties associated with climate change, droughts are receiving much attention these days. This paper investigates the impact of climate change on severity-area-frequency (SAF) curves for annual droughts in the Kansabati River basin, India. Historical droughts are compared with historical SAF curves and with SAF curves developed on the basis of projected rainfall using a general circulation model and scenario uncertainty. A downscaling method, based on Bayesian Neural Network (BNN), is applied to project precipitation from six GCM models using two scenarios. Standardized precipitation indices (SPI 3 and SPI 12) are used as drought indices for construction of SAF curves for two periods (2001-2050 and 2051-2100). The results show that there are likely to be more severe droughts in 2001-2050 with more spatial extent than those that have occurred historically.