The influence of contrasting suspended particulate matter transport regimes on the bias and precision of flux estimates.
A large database (507 station-years) of daily suspended particulate matter (SPM) concentration and discharge data from 36 stations on river basins ranging from 600 km(2) to 600,000 km(2) in size (USA and Europe) was collected to assess the effects of SPM transport regime on bias and imprecision of flux estimates when using infrequent surveys and the discharge-weighted mean concentration method. By extracting individual SPM concentrations and corresponding discharge values from the database, sampling frequencies from 12 to 200 per year were simulated using Monte Carlo techniques. The resulting estimates of yearly SPM fluxes were compared to reference fluxes derived from the complete database. For each station and given frequency, bias was measured by the median of relative errors between estimated and reference fluxes, and imprecision by the difference between the upper and lower deciles of relative errors. Results show that the SPM transport regime of rivers affects the bias and imprecision of fluxes estimated by the discharge-weighted mean concentration method for given sampling frequencies (e.g. weekly, bimonthly, monthly). The percentage of annual SPM flux discharged in 2% of time (Ms(2)) is a robust indicator of SPM transport regime directly related to bias and imprecision. These errors are linked to the Ms(2) indicator for various sampling frequencies within a specific nomograph. For instance, based on a deviation of simulated flux estimates from reference fluxes lower than +/-20% and a bias lower than 1% or 2%, the required sampling intervals are less than 3 days for rivers with Ms(2) greater than 40% (basin size<10,000 km(2)), between 3 and 5 days for rivers with Ms(2) between 30 and 40% (basin size between 10,000 and 50,000 km(2)), between 5 and 12 days for Ms(2) from 20% to 30% (basin size between 50,000 and 200,000 km(2)), 12-20 days for Ms(2) in the 15-20% range (basin size between 200,000 and 500,000 km(2)).
Moatar
Florentina
F
GEEAC, UPRES-EA 2100, Université de Tours, Parc de Grandmont, 37200 Tours, France. florentina.moatar@univ-tours.fr
Person
Gwenaelle
G
Meybeck
Michel
M
Coynel
Alexandra
A
Etcheber
Henri
H
Crouzet
Philippe
P
D005602:France; D019015:Geologic Sediments / Q000032:analysis*; D005858:Germany; D009010:Monte Carlo Method; D045483:Rivers; D014481:United States; D014872:Water Movements; D014873:Water Pollutants
D014873:Water Pollutants