An evaluation of methods for the estimation of tributary mass loads
- 1 June 1989
- journal article
- Published by American Geophysical Union (AGU) in Water Resources Research
- Vol. 25 (6) , 1379-1389
- https://doi.org/10.1029/wr025i006p01379
Abstract
Tributary loading estimation methods were evaluated by conducting retrospective studies with comprehensive sets of field data for flow rates, nutrients, heavy metals, and polychlorinated biphenyls. Three broad classes of loading estimation methods were investigated: simple averaging methods, ratio estimation methods, and regression methods. Estimators were evaluated using Monte Carlo sampling studies in which random subsamples of complete loading records were used to estimate annual loadings. These estimates were then compared to “true” loadings determined by calculations using the entire record. No group of estimators were found to be superior for all test cases considered. However, individual estimation approaches within each group often provided low error estimates. Results were inconsistent among test cases and these inconsistencies appeared to be related to specific test case characteristics such as the strength and form of the flow‐concentration relationship and the nature of the annual hydrograph. Ratio estimators appeared to be more robust to sources of bias than other estimation approaches.Keywords
This publication has 12 references indexed in Scilit:
- Estimating constituent loadsWater Resources Research, 1989
- Monte Carlo studies of sampling strategies for estimating tributary loadsWater Resources Research, 1987
- Accuracy and precision of methods for estimating river loadsEarth Surface Processes and Landforms, 1987
- River Loads Underestimated by Rating CurvesWater Resources Research, 1986
- Evaluation of River Load Estimation Methods for Total PhosphorusJournal of Great Lakes Research, 1981
- Estimating solute transport in streams from grab samplesWater Resources Research, 1979
- Comparison of Some Ratio EstimatorsJournal of the American Statistical Association, 1965
- Some Finite Population Unbiased Ratio and Regression EstimatorsJournal of the American Statistical Association, 1959
- NOTES ON BIAS IN ESTIMATIONBiometrika, 1956
- Unbiased Ratio EstimatorsNature, 1954