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Load Estimation Method Using Distributions with Covariates: A Comparison with Commonly Used Estimation Methods
Author(s) -
Raymond Sébastien,
Mailhot Alain,
Talbot Guillaume,
Gag Patrick,
Rousseau Alain N.,
Moatar Florentina
Publication year - 2014
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/jawr.12147
Subject(s) - covariate , hydrograph , statistics , environmental science , sampling (signal processing) , regression , streamflow , range (aeronautics) , series (stratigraphy) , regression analysis , mean squared error , linear regression , estimation , hydrology (agriculture) , mathematics , drainage basin , computer science , geography , paleontology , materials science , cartography , filter (signal processing) , geotechnical engineering , engineering , composite material , computer vision , biology , management , economics
Load estimates obtained using an approach based on statistical distributions with parameters expressed as a function of covariates (e.g., streamflow) (distribution with covariates hereafter called DC method) were compared to four load estimation methods: (1) flow‐weighted mean concentration; (2) integral regression; (3) segmented regression (the last two with Ferguson's correction factor); and (4) hydrograph separation methods. A total of 25 datasets (from 19 stations) of daily concentrations of total dissolved solids, nutrients, or suspended particulate matter were used. The selected stations represented a wide range of hydrological conditions. Annual flux errors were determined by randomly generating 50 monthly sample series from daily series. Annual and interannual biases and dispersions were evaluated and compared. The impact of sampling frequency was investigated through the generation of bimonthly and weekly surveys. Interannual uncertainty analysis showed that the performance of the DC method was comparable with those of the other methods, except for stations showing high hydrological variability. In this case, the DC method performed better, with annual biases lower than those characterizing the other methods. Results show that the DC method generated the smallest pollutant load errors when considering a monthly sampling frequency for rivers showing high variability in hydrological conditions and contaminant concentrations.