Regional flow duration curves: Geostatistical techniques versus multivariate regression

Advances in Water Resources
By: , and 

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Abstract

A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs is of great importance for locations characterized by sparse or missing streamflow observations. We present a detailed comparison of two methods which are capable of predicting an FDC at ungauged basins: (1) an adaptation of the geostatistical method, Top-kriging, employing a linear weighted average of dimensionless empirical FDCs, standardised with a reference streamflow value; and (2) regional multiple linear regression of streamflow quantiles, perhaps the most common method for the prediction of FDCs at ungauged sites. In particular, Top-kriging relies on a metric for expressing the similarity between catchments computed as the negative deviation of the FDC from a reference streamflow value, which we termed total negative deviation (TND). Comparisons of these two methods are made in 182 largely unregulated river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform similarly throughout flow-regimes, with average Nash-Sutcliffe Efficiencies 0.566 and 0.662, (0.883 and 0.829 on log-transformed quantiles) for the geostatistical and the linear regression models, respectively. The differences between the reproduction of FDC's occurred mostly for low flows with exceedance probability (i.e. duration) above 0.98.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Regional flow duration curves: Geostatistical techniques versus multivariate regression
Series title Advances in Water Resources
DOI 10.1016/j.advwatres.2016.06.008
Volume 96
Year Published 2016
Language English
Publisher Elsevier
Contributing office(s) National Research Program - Central Branch
Description 12 p.
First page 11
Last page 22
Online Only (Y/N) N
Additional Online Files (Y/N) N