Performance-weighted methods for estimating monthly streamflow at ungauged sites

SummaryRecords of natural streamflow are essential to nearly all water resources projects. In spite of the wide range of available techniques for estimating monthly streamflow records at ungauged sites, few studies have sought to compare their merits. Precedent has dictated the nearly ubiquitous use of the drainage-area ratio (DAR) technique. We evaluate several monthly streamflow information-transfer methods across nearly 1300 minimally-impacted streamflow gages in the continental US from the Hydroclimatic Data Network (HCDN). The DAR method is shown to be quite robust; however, other methods of standardizing streamflow, such as standardizing flows by means (SM) or normalizing flows with means and standard deviations (SMS), are shown to be superior for a subset (∼42%) of sites. The performance of SM and SMS, relative to DAR is shown to be generally correlated with the at-site aridity ratio. We introduce a performance-weighted transfer method that weights each estimator in proportion to its performance, as predicted by basin descriptors including at-site precipitation, aridity ratio, drainage area and ratios of those descriptors between the ungauged and index site. Our blended estimators showed promising results, exhibiting greater Nash–Sutcliffe efficiencies than DAR at 60% of sites considered. Our national comparative assessment demonstrates the merits and disadvantages of several techniques, including the concept of performance weighting. We also introduce regional hydroclimatic regression equations for estimating the mean and variance of monthly streamflows at ungauged sites in the conterminous US.

► We provide a systematic approach to evaluating streamflow record estimators. ► We evaluate three techniques for estimating streamflow records at ungauged sites. ► More advanced methods are shown to be hampered by parameter uncertainty. ► We present a blended technique for more-accurately estimating streamflow records. ► These results highlight the potential value of blended estimators.