A hydrological record extension model for reconstructing streamflows from tree‐ring chronologies

Characterizing drought events is an important factor in designing and operating water resource projects, but instrumental hydrologic records are generally short (<100 years). Because estimates of drought statistics can improve when longer records are available, we developed a stochastic model to extend instrumental streamflows based on tree‐ring chronologies. This Record Extension plus Noise (or REXTN) model consists of an autoregressive term to account for the temporal persistence of streamflows, predictor variables with longer records, and a noise term. The noise term was included to avoid underestimating the variability of the flows and to generate multiple extensions, which offer the possibility of quantifying the uncertainty of drought statistics such as the critical drought. For cases where having multiple extensions is not desirable, a statistically based algorithm was developed to select a single extended record. Using a simulation experiment, model REXTN was found to perform better than other existing reconstruction methods. The model was then applied to extend streamflows of the Poudre River, CO, USA, based on tree ring‐chronologies back to the year 1600, and the reconstructions were used to determine drought statistics such as duration and magnitude. When results based on the classical linear regression model were compared with those calculated by model REXTN, the latter was found to better match flow and drought statistics from the instrumental records, as well as to give a broader range of drought duration and magnitude. The REXTN model provides a useful addition to the range of tools available to hydrologists for coping with the uncertainty associated with water resource management under future climate scenarios. Copyright © 2014 John Wiley & Sons, Ltd.