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A comparison of river load estimation techniques: application to dissolved organic carbon
Author(s) -
Cooper D. M.,
Watts C. D.
Publication year - 2002
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.525
Subject(s) - extrapolation , environmental science , variance (accounting) , statistics , sampling (signal processing) , soil science , dissolved organic carbon , estimation , logarithm , rating curve , carbon fibers , mathematics , hydrology (agriculture) , econometrics , computer science , chemistry , algorithm , geology , sediment , environmental chemistry , geotechnical engineering , economics , filter (signal processing) , business , mathematical analysis , composite number , paleontology , accounting , management , computer vision
The transport of dissolved organic carbon (DOC) from land to ocean is a significant component of the global carbon cycle, and good estimates of the load transported in rivers are needed. Three load estimation techniques are investigated using field data from an upland catchment in Wales. The methods use approximately weekly concentration measurements with corresponding discharge measurement, supplemented by 15 min measurements of discharge. Annual load estimates are obtained for the years 1985–2000 with the following treatment of supplementary information: (i) exclusion using simple extrapolation; (ii) inclusion using the ratio method; (iii) inclusion using the rating curve method with a straight line fitted to the logarithms of load and discharge data. A variance estimate is given for the three load estimation methods. The influence of additional opportunistic DOC concentration measurements on load estimation is discussed and investigated. The importance of an unbiased sampling procedure for simple extrapolation is demonstrated, but the inclusion of opportunistic data from high discharge samples is shown to improve rating curve estimates of load, reducing their variance. The strong influence of parameter uncertainty in the load variance for the rating curve method is demonstrated, as well as the sensitivity of the method to model assumptions. Copyright © 2002 John Wiley & Sons, Ltd.