Model Library

Capabilities

• WASP supports 1D, 2D, and 3D modeling;
• It can be linked to hydrodynamic models and watershed models to simulate water flow and transport processes;
• User-friendly interface;
• It has a modular design;
• It has a eutrophication module;
• WASP uses a flexible compartmental approach to model advective and dispersive transport, with hydrodynamics driven by the kinematic wave equation.

Limitations

• It requires an extensive amount of data and a significant amount of time (Kayode and Muthukrishna, 2018; Ejigu, 2021);
• Low accuracy in simulating sinkable/floatable materials, near-field effects or mixing zones (Costa et al., 2021), microalgae and periphyton dynamics (Kayode and Muthukrishna, 2018), variation of suspended solids loading in the river (Ejigu, 2021), and inflows of fine silt and clay sediments from the tributaries (Di Vittorio et al., 2023);
• It does not incorporate some variable processes (i.e. non-aqueous phase liquids, metals speciation, segment drying, and mixing zone processes) (Ejigu, 2021);
• Linking multi-dimensional hydrodynamic models requires substantial site-specific linking efforts (Kannel et al., 2011);
• Does not consider different groups of CBOD, environmental parameters and benthic algae (Srinivas and Singh, 2018);
• WASP does not deal with non-point source pollution and there is no provision for developing TMDLs and to perform uncertainty analysis (Rai et al., 2024);
• It cannot replicate particle settling and resuspension processes within the lake (Di Vittorio et al., 2023).

Inputs

Hydrodynamic data, Pollutant loadings, Boundary conditions, Kinetic parameters, Meteorological data

Outputs

• Simulation results of time-series hydrological, conventional pollutants (N, P, DO, BOD, sediment oxygen demand, algae, periphyton), organic chemicals, metals, mercury, pathogens, temperature) in waterbodies.
• Predictions for algal growth, eutrophication, and nutrient cycling.

References

Muhammetoglu, A., Kocer, M. A. T., & Durmaz, S. (2022). Evaluation of different management scenarios for trout farm effluents using dynamic water quality modeling. Environmental Monitoring and Assessment, 194(312). https://doi.org/10.1007/s10661-022-09978-7

Spitz, F. J., & DePaul, V. T. (2023). Simulation of flow and eutrophication in the central Salem River, New Jersey. U.S. Geological Survey, 2022-5047.

Objectives

The objective of the study was to determine the impacts of effluents from flow-through rainbow trout farms on the water quality of a receiving stream and to simulate the effects of best management scenarios on water quality using a dynamic water quality model.

The objective of the study were to: (1) assess available data collected as part of an associated monitoring study of the central Salem River, (2) supplement those data through additional monitoring, (3) develop a surface-water-quality model of the central Salem River to serve as a tool for evaluating nutrient-loading processes and identifying nutrient sources, (4) use the model to test alternative management scenarios for establishing a nutrient TMDL for the river.