Paper No. 95126 of the Journal of the American Water Resources Association (formerly Water Resources Bulletin). Discussions are open until October 1, 1997.
SENSITWITY ANALYSIS OF SIMULATED CONTAMINATED SEDIMENT TRANSPORT1
Article first published online: 8 JUN 2007
JAWRA Journal of the American Water Resources Association
Volume 33, Issue 2, pages 313–326, April 1997
How to Cite
Fontaine, T. A. and Jacomino, V. M. F. (1997), SENSITWITY ANALYSIS OF SIMULATED CONTAMINATED SEDIMENT TRANSPORT. JAWRA Journal of the American Water Resources Association, 33: 313–326. doi: 10.1111/j.1752-1688.1997.tb03512.x
- Issue published online: 8 JUN 2007
- Article first published online: 8 JUN 2007
- sensitivity analysis;
- sediment transport;
- contaminated sediment;
ABSTRACT: A simulation analysis of contaminated sediment transport involves model selection, data collection, model calibration and verification, and evaluation of uncertainty in the results. Sensitivity analyses provide information to address these issues at several stages of the investigation. A sensitivity analysis of simulated contaminated sediment transport is used to identify the most sensitive output variables and the parameters most responsible for the output variable sensitivity. The output variables included are streamflow and the flux of sediment and Cs137. The sensitivities of these variables are measured at the field and intermediate scales, for flood and normal flow conditions, using the HSPF computer model. A sensitivity index was used to summarize and compare the results of a large number of output variables and parameters. An extensive database was developed to calibrate the model and conduct the sensitivity analysis on a 6.2 mi2 catchment in eastern Tennessee. The fluxes of sediment and Cs137 were more sensitive than streamflow to changes in parameters for both flood and normal flow conditions. The relative significance of specific parameters on output variable sensitivity varied according to the type of flow condition and the location in the catchment. An implications section illustrates how sensitivity analysis results can help with model selection, planning data collection, calibration, and uncertainty analysis.