HSPF-based watershed-scale water quality modeling and uncertainty analysis.

Affiliation

Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA. [Email]

Abstract

This paper presents findings on uncertainties, introduced through digital elevation model (DEM) resolution and DEM resampling, in watershed-scale flow and water quality (NO3, P, and total suspended sediment) simulations. The simulations were performed using the Better Assessment Science Integrating Point and Nonpoint Sources/Hydrological Simulation Program Fortran watershed modeling system for two representative study watersheds delineated with both the original DEMs of four different resolutions (including 3.5, 10, 30, and 100 m) and the resampled DEMs of three different resolutions (including 10, 30, and 100 m), creating 14 simulation scenarios. Parameter uncertainties were quantified by means of the GLUE approach and compared to input data uncertainties. Results from the 14 simulation scenarios showed that there was a common increasing trend in errors of simulated flow and water quality parameters when the DEM resolution became coarser. The errors involved in the watershed with a mild slope were found to be substantially (up to 10 times) greater than those of the other watershed with a relatively steep slope. It was also found that sediment was the most sensitive and NO3 was the least sensitive parameters to the variation in DEM resolution, as evidenced by the maximum normalized root mean square error (NRMSE) of 250% in the simulated sediment concentration and 11% in the simulated NO3 concentration, respectively. Moreover, results achieved from the resampled (particularly coarser) DEMs were significantly different from corresponding ones from original DEMs. By comparing uncertainties from different sources, it was found that the parameter-induced uncertainties were higher than the resolution-induced uncertainties particularly in simulated NO3 and P concentrations for studied watersheds. The findings provide new insights into the sensitivity and uncertainty of water quality parameters and their simulation results, serving as the guidelines for developing and implementing water quality management and watershed restoration plans.

Keywords

BASINS,DEM resampling,DEM resolution,GLUE,Sensitivity analysis,Uncertainty analysis,

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