Estimating uncertainty associated with the standardized precipitation index

ABSTRACT We investigate methodological uncertainties associated with the standardized precipitation index (SPI) that result from limited record length, trends, and outliers. We use long, homogenous records from 14 Italian stations to investigate how specific features in the precipitation record affect construction of an underlying gamma probability function. We apply a resampling scheme to the long records in order to estimate confidence intervals associated with a range of precipitation characteristics. Stability in parameter estimation increases nonlinearly as record length increases. The resulting SPI estimates for 30‐year reference periods have considerably more uncertainty than those made from 60‐year records. In general, increasing record length beyond 60‐years has limited benefits and, in the presence of a trend, may increase uncertainty. Extreme events also have significant influence on SPI estimates, even for records exceeding 60 years. Despite using stations from different geographic regions, each with unique precipitation characteristics, we find consistent confidence interval estimates across stations. These confidence intervals can be applied to specific time series to identify how trends, changes in variability, and outliers during a particular reference period influence SPI values. Estimates of the standardized precipitation index (SPI) have inherent uncertainties associated with limited record length, trends, and outliers. Despite the uniqueness of individual precipitation records, there is consistency in SPI confidence interval estimates across stations and distinct patterns based on record length. Such estimates can be applied to specific time series to identify how trends, changes in variability, and outliers during a particular reference period influence SPI values.