4.6 Article Proceedings Paper

The association between climate teleconnection indices and Upper Klamath seasonal streamflow: Trans-Nino Index

Journal

HYDROLOGICAL PROCESSES
Volume 23, Issue 7, Pages 973-984

Publisher

JOHN WILEY & SONS LTD
DOI: 10.1002/hyp.7200

Keywords

Trans-Nino Index; hydro-climatology; streamflow forecasting; El Nino; teleconnections; climate variability; water supply; principal components regression

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This research investigates large-scale climate features affecting inter-annual hydrologic variability of streams flowing into Upper Klamath Lake (UKL), Oregon, USA. UKL. is an arid, mountainous basin located in the rain shadow east of the crest of the Cascade Mountains in the northwestern United States. Developing accurate statistical models for predicting spring and summer seasonal streamflow volumes for UKL. is difficult because the basin has complex hydrology and a high degree of topographic and climatologic variability. In all effort to reduce streamflow forecast uncertainty, six large-scale climate indices-the Pacific North American Pattern, Southern Oscillation Index, Pacific Decadal Oscillation (PDO), Multivariate El Nino-Southern Oscillation Index, Nino 3.4, and a revised Trans-Nino Index (TNI)-were evaluated for their ability to explain inter-annual variation of the major hydrologic inputs into UKL. The TNI is the only index to show significant correlations during the current warm phase of the PDO. During the wand PDO phase (1978-present), the averaged October through December TNI is strongly correlated with the subsequent April through September streamflow (r = 0.7) and 1 April snow water equivalent (r = 0.6,). Regional analysis shows that this climate signal is not limited to UKL but is found throughout the northwestern United States. Incorporating the TNI variable into statistical streamflow prediction models results in standard errors of forecasts issued on the first of February and earlier that are 7-10% smaller than those for the models without the TNI. This, coupled with other enhancements to the statistical models, offers a significant increment of improvement in forecasts used by water managers. Copyright (C) 2009 John Wiley & Sons, Ltd.

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