Literature DB >> 20570317

Estimation of local extreme suspended sediment concentrations in California Rivers.

Yves Tramblay1, André Saint-Hilaire, Taha B M J Ouarda, Florentina Moatar, Barry Hecht.   

Abstract

The total amount of suspended sediment load carried by a stream during a year is usually transported during one or several extreme events related to high river flow and intense rainfall, leading to very high suspended sediment concentrations (SSCs). In this study quantiles of SSC derived from annual maximums and the 99th percentile of SSC series are considered to be estimated locally in a site-specific approach using regional information. Analyses of relationships between physiographic characteristics and the selected indicators were undertaken using the localities of 5-km radius draining of each sampling site. Multiple regression models were built to test the regional estimation for these indicators of suspended sediment transport. To assess the accuracy of the estimates, a Jack-Knife re-sampling procedure was used to compute the relative bias and root mean square error of the models. Results show that for the 19 stations considered in California, the extreme SSCs can be estimated with 40-60% uncertainty, depending on the presence of flow regulation in the basin. This modelling approach is likely to prove functional in other Mediterranean climate watersheds since they appear useful in California, where geologic, climatic, physiographic, and land-use conditions are highly variable. Copyright 2010 Elsevier B.V. All rights reserved.

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Year:  2010        PMID: 20570317     DOI: 10.1016/j.scitotenv.2010.05.001

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  1 in total

1.  Estimating and Predicting Metal Concentration Using Online Turbidity Values and Water Quality Models in Two Rivers of the Taihu Basin, Eastern China.

Authors:  Hong Yao; Wei Zhuang; Yu Qian; Bisheng Xia; Yang Yang; Xin Qian
Journal:  PLoS One       Date:  2016-03-30       Impact factor: 3.240

  1 in total

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