Wet and dry spell analysis of Global Climate Model-generated precipitation using power laws and wavelet transforms |
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| Authors: | Ashok K Mishra Mehmet ?zger Vijay P Singh |
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| Institution: | (1) Department of Biological and Agricultural Engineering and Civil Engineering, Texas A&M University, College Station, TX 77843, USA;(2) Hydraulics Division, Civil Engineering Department, Civil Engineering Faculty, Istanbul Technical University, 34469 Istanbul, Turkey |
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| Abstract: | 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. |
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