Decadal Oscillation in the Predictability of Palmer Drought Severity Index in California

Severity of drought in California (U.S.) varies from year-to-year and is highly influencedby precipitation in winter months, causing billion-dollar events in single drought years. Improvedunderstanding of the variability of drought on decadal and longer timescales is essential to supportregional water resources planning and management. This paper presents a soft-computing approachto forecast the Palmer Drought Severity Index (PDSI) in California. A time-series of yearly datacovering more than two centuries (1801–2014) was used for the design of ensemble projections tounderstand and quantify the uncertainty associated with interannual-to-interdecadal predictability.With a predictable structure elaborated by exponential smoothing, the projections indicate for thehorizon 2015–2054 a weak increase of drought, followed by almost the same pace as in previousdecades, presenting remarkable wavelike variations with durations of more than one year. Resultswere compared with a linear transfer function model approach where Pacific Decadal Oscillation andEl Niño Southern Oscillation indices were both used as input time series. The forecasted pattern showsthat variations attributed to such internal climate modes may not provide more reliable predictionsthan the one provided by purely internal variability of drought persistence cycles, as present in thePDSI time series