A screening-level modeling approach to estimate nitrogen loading and standard exceedance risk, with application to the Tippecanoe River watershed, Indiana.
This paper presents a screening-level modeling approach that can be used to rapidly estimate nutrient loading, assess numerical nutrient standard exceedance risk of surface waters leading to potential classification as impaired for designated use, and explore best management practice (BMP) implementation to reduce loading with a relatively low data requirement. The modeling framework uses a hybrid statistical and process based approach to estimate source of pollutants, their transport and decay in the terrestrial and aquatic parts of watersheds. The framework is developed in the ArcGIS environment and is based on the total maximum daily load (TMDL) balance model. Nitrogen (N) is currently addressed in the framework, referred to as WQM-TMDL-N. Loading for each catchment includes non-point sources (NPS) and point sources (PS). The probability of a nutrient load to exceed a target load is evaluated using probabilistic risk assessment, by including the uncertainty associated with export coefficients of various land uses. In an application of this modeling approach to the Tippecanoe River watershed in Indiana, USA, total nitrogen (TN) loading, confidence interval and risk of standard exceedance leading to potential impairment were estimated. Model results suggest that decay coefficients decrease, and delivery fractions increase with increasing stream order. The spatial distribution pattern of delivered incremental TN yield shows a trend similar to that of the delivery fraction in this watershed. The target TN exceedance risk increases considerably when switching from Indiana draft-N benchmark to far lower EPA-proposed TN criteria, suggesting that load reduction to meet the latter criteria may benefit from BMP implementation through source control and delivery reduction.
Oak Ridge Institute for Science and Education (ORISE) Postdoctoral Research Associate, U.S. Environmental Protection Agency, National Risk Management Research Laboratory, 26 West Martin Luther King Drive, Cincinnati, OH 45268, USA. Electronic address: yang.gavin@epa.gov.
Guoxiang
G
Yang
U.S. Environmental Protection Agency, National Risk Management Research Laboratory, 26 West Martin Luther King Drive, Cincinnati, OH 45268, USA. Electronic address: best.elly@epa.gov.
Elly P H
EP
Best
The University of Texas at Austin, Center for Research in Water Resources, Austin, TX 78712, USA. Electronic address: twhit@mail.utexas.edu.
Tim
T
Whiteaker
University of Cincinnati, School of Energy, Environmental, Biological & Medical Engineering (SEEBME), Cincinnati, OH 45221, USA. Electronic address: teklitag@mail.uc.edu.
Allen
A
Teklitz
University of Cincinnati, School of Energy, Environmental, Biological & Medical Engineering (SEEBME), Cincinnati, OH 45221, USA. Electronic address: yeghialt@ucmail.uc.edu.
Lilit
L
Yeghiazarian
D004784:Environmental Monitoring / Q000379:methods*; D007196:Indiana; D008962:Models, Theoretical; D009584:Nitrogen / Q000032:analysis*; D018570:Risk Assessment; D045483:Rivers / Q000737:chemistry*; D035501:Uncertainty; D014874:Water Pollutants, Chemical / Q000032:analysis* / Q000592:standards
D014874:Water Pollutants, Chemical; D009584:Nitrogen
Loading; Nitrogen; Risk assessment; Screening-level model; Spatial scales; TMDL