Sediment concentration prediction and statistical evaluation for annual load estimation

Wang, You-Gan and Tian, Ting (2013) Sediment concentration prediction and statistical evaluation for annual load estimation. Journal of Hydrology, 482 69-78. doi:10.1016/j.jhydrol.2012.12.043


Author Wang, You-Gan
Tian, Ting
Title Sediment concentration prediction and statistical evaluation for annual load estimation
Journal name Journal of Hydrology   Check publisher's open access policy
ISSN 0022-1694
Publication date 2013-03-04
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.jhydrol.2012.12.043
Volume 482
Start page 69
End page 78
Total pages 10
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2014
Language eng
Formatted abstract
We consider the development of statistical models for prediction of constituent concentration of riverine pollutants, which is a key step in load estimation from frequent flow rate data and less frequently collected concentration data. We consider how to capture the impacts of past flow patterns via the average discounted flow (ADF) which discounts the past flux based on the time lapsed – more recent fluxes are given more weight. However, the effectiveness of ADF depends critically on the choice of the discount factor which reflects the unknown environmental cumulating process of the concentration compounds. We propose to choose the discount factor by maximizing the adjusted R2 values or the Nash–Sutcliffe model efficiency coefficient. The R2 values are also adjusted to take account of the number of parameters in the model fit. The resulting optimal discount factor can be interpreted as a measure of constituent exhaustion rate during flood events. To evaluate the performance of the proposed regression estimators, we examine two different sampling scenarios by resampling fortnightly and opportunistically from two real daily datasets, which come from two United States Geological Survey (USGS) gaging stations located in Des Plaines River and Illinois River basin. The generalized rating-curve approach produces biased estimates of the total sediment loads by −30% to 83%, whereas the new approaches produce relatively much lower biases, ranging from −24% to 35%. This substantial improvement in the estimates of the total load is due to the fact that predictability of concentration is greatly improved by the additional predictors.
Keyword Bootstrap
Load estimation
Nash-Sutcliffe model efficiency coefficient
Rating curve
Suspended sediment
Uncertainty
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Mathematics and Physics
School of Agriculture and Food Sciences
Official 2014 Collection
 
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