Application of L‐moments and Bayesian inference for low‐flow regionalization in Sefidroud basin,Iran |
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| Authors: | Esmaeel Dodangeh Saeed Soltani Ali Sarhadi Jenq‐Tzong Shiau |
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| Affiliation: | 1. Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran;2. Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Ontario, Canada;3. Department of Hydraulic and Ocean Engineering, National Cheng Kung University, Tainan, Taiwan, Republic of China |
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| Abstract: | Reliable estimation of low flows at ungauged catchments is one of the major challenges in water‐resources planning and management. This study aims at providing at‐site and ungauged sites low‐flow frequency analysis using regionalization approach. A two‐stage delineating homogeneous region is proposed in this study. Clustering sites with similar low‐flow L‐moment ratios is initially conducted, and L‐moment‐based discordancy and heterogeneity measures are then used to detect unusual sites. Based on the goodness‐of‐fit test statistic, the best‐fit regional model is identified in each hydrologically homogeneous region. The relationship between mean annual 7‐day minimum flow and hydro‐geomorphic characteristics is also constructed in each homogeneous region associated with the derived regional model for estimating various low‐flow quantiles at ungauged sites. Uncertainty analysis of model parameters and low‐flow estimations is carried out using the Bayesian inference. Applied in Sefidroud basin located in northwestern Iran, two hydrologically homogeneous regions are identified, i.e. the east and west regions. The best‐fit regional model for the east and west regions are generalized logistic and Pearson type III distributions, respectively. The results show that the proposed approach provides reasonably good accuracy for at‐site as well as ungauged‐site frequency analysis. Besides, interval estimations for model parameters and low flows provide uncertainty information, and the results indicate that Bayesian confidence intervals are significantly reduced when comparing with the outcomes of conventional t‐distribution method. Copyright © 2013 John Wiley & Sons, Ltd. |
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| Keywords: | low flow regionalization L‐moments Bayesian inference multivariate regression uncertainty analysis cluster analysis |
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