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Modeled Watershed Runoff Associated with Variations in Precipitation Data with Implications for Contaminant Fluxes
Citation:
GOLDEN, H. E., C. D. KNIGHTES, E. COOTER, AND R. L. DENNIS. Modeled Watershed Runoff Associated with Variations in Precipitation Data with Implications for Contaminant Fluxes. Presented at Third Interagency Conference on Research in the Watersheds, Estes Park, CO, September 08 - 11, 2008.
Impact/Purpose:
Conduct hydrology and water quality process studies to enhance process representations in watershed models.
Description:
Watershed-scale fate and transport models are important tools for estimating the sources, transformation, and transport of contaminants to surface water systems. Precipitation is one of the primary inputs to watershed biogeochemical models, influencing changes in the water budget of the surface, shallow subsurface, and deep groundwater zones, and as a result the transport of contaminants to surface water systems. Knowledge of precipitation across watersheds is notably imperfect, and most watershed fate and transport studies focus on observed data at a few sites within or near the watersheds. However, as numerous new generations of simulated and radar detected datasets of precipitation become increasingly refined, available, and applied to watersheds for a more spatially resolved estimate of precipitation, questions remain as to how these will affect modeled runoff generation in watersheds and thus contaminant fluxes. We utilize observed and simulated precipitation data across representative wet, dry, and normal years (2001-2003) in the southeastern USA to assess how variations in precipitation estimates affect daily and monthly modeled runoff. We apply daily observed and simulated precipitation as input parameters to a grid-based watershed mercury (Hg) model (GBMM v2.0, Tetra Tech, 2006) that computes daily mass balances for hydrology, sediment, and mercury within each GIS raster grid cell and produces flux estimates of each to a tributary network. Preliminary results suggest that although modeled precipitation resolves spatial issues associated with sparsely located precipitation gage data, observed precipitation data generates daily and monthly variations in runoff closer to observed runoff than modeled precipitation data. Results also illustrate that simulated precipitation data do not reflect rapid changes in daily rainfall and subsequent runoff responses as well as observed precipitation data. The study highlights the importance of calculating uncertainties associated with precipitation and how these uncertainties are reflected in modeled runoff generation, particularly as it is linked to watershed contaminant fluxes.