4.6 Article

Causes of systematic over- or underestimation of low streamflows by use of index-streamgage approaches in the United States

Journal

HYDROLOGICAL PROCESSES
Volume 25, Issue 14, Pages 2211-2220

Publisher

WILEY-BLACKWELL
DOI: 10.1002/hyp.7976

Keywords

bias; low streamflow; short-term streamgages; ungaged basins; intermittent streams; hydrologic similarity

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Low-flow characteristics can be estimated by multiple linear regressions or the index-streamgage approach. The latter transfers streamflow information from a hydrologically similar, continuously gaged basin ('index streamgage') to one with a very limited streamflow record, but often results in biased estimates. The application of the index-streamgage approach can be generalized into three steps: (1) selection of streamflow information of interest, (2) definition of hydrologic similarity and selection of index streamgage, and (3) application of an information-transfer approach. Here, we explore the effects of (1) the range of streamflow values, (2) the areal density of streamgages, and (3) index-streamgage selection criteria on the bias of three information-transfer approaches on estimates of the 7-day, 10-year minimum streamflow (Q(7,10)). The three information-transfer approaches considered are maintenance of variance extension, base-flow correlation, and ratio of measured to concurrent gaged streamflow (Q-ratio invariance). Our results for 1120 streamgages throughout the United States suggest that only a small portion of the total bias in estimated streamflow values is explained by the areal density of the streamgages and the hydrologic similarity between the two basins. However, restricting the range of streamflow values used in the index-streamgage approach reduces the bias of estimated Q(7,10) values substantially. Importantly, estimated Q(7,10) values are heavily biased when the observed Q(7,10) values are near zero. Results of the analysis also showed that Q(7,10) estimates from two of the three index-streamgage approaches have lower root-mean-square error values than estimates derived from multiple regressions for the large regions considered in this study. Published in (C) 2011 by John Wiley & Sons, Ltd.

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