The Water Quality Analysis Simulation Program (WASP) helps users interpret and predict water quality responses to natural phenomena and manmade pollution for various pollution management decisions. WASP is a dynamic compartment-modeling program for aquatic systems, including both the water column and the underlying benthos. WASP allows the user to investigate 1, 2 and 3 dimensional systems and a variety of pollutant types—including both conventional pollutants (e.g., dissolved oxygen, nutrients, phytoplankton, etc.) and toxic materials. WASP has capabilities of linking with hydrodynamic and watershed models which allows for multi-year analyses under varying meteorological and environmental conditions. WASP was originally developed by HydroScience, Inc. in 1970 and was later adapted by the US Environmental Protection Agency’s Large Lakes Research Station (LLRS) for applications to the Great Lakes. The LLRS first publicly released the model in 1981. WASP has undergone continuous development since that time and this year will mark its 50th anniversary. This paper follows the development of WASP from its origin to the latest release of the model in 2020, documenting its evolution and present structure and capabilities.

The transport timescales residence time, exposure time, and age of water are evaluated for the St. Louis Bay estuary, Mississippi, to investigate the impacts of freshwater inflows and tidal dynamics from Mississippi Sound on the estuary’s transport characteristics. The timescales are explicitly defined and computed using a hydrodynamic model and tracer experiments for a set of 11 hydrologic scenarios designed to represent permanent low, average, and high flow conditions. Results indicate that (1) the estuary’s residence time can vary between 2.0 and 134.5 days during high and low flow conditions, respectively; (2) under low flow conditions dispersive processes caused by the tidal dynamics at the estuary’s open boundary may be dominant, and the returning flows can increase the exposure times up to 30% in relation to the residence times; (3) during high flow conditions advective transport caused by the freshwater flows may be dominant resulting in exposure times similar to the residence times; and, ...

Camacho, R.A. and Martin, J.L., 2013. Bayesian Monte Carlo for evaluation of uncertainty in hydrodynamic models of coastal systems. Uncertainty analysis constitutes the set of procedures and strategies conducted to identify, quantify, and report the impacts of different sources of errors on the predictions of a numerical model. Although during the last couple of decades several investigations in different areas of water resources have explicitly discussed the importance of a new modeling paradigm where model predictions are reported along with uncertainty estimates, uncertainty analysis remains an emerging topic in the field of hydrodynamic modeling. Presently, several unresolved issues remain with regard to the applicability, benefits, and limitations of existing strategies for quantification of uncertainty, and the identification of the principal sources of uncertainty in practical applications. In this document we apply the Bayesian Monte Carlo (BMC) method as a strategy to perform uncertainty analysis in coastal modeling studies. BMC is based on robust principles of Bayesian inference and Monte Carlo simulations, and has been successfully applied for studies of groundwater modeling, hydrologic modeling, and water quality modeling in the past. The procedural aspects of the method are discussed in detail, and the implementation of the method is demonstrated for the hydrodynamic model of the St. Louis Bay estuary, Mississippi (USA) for the evaluation of the impacts of input data errors. Results indicate that BMC is an effective strategy for uncertainty analyses, although in professional practice its use may be limited by computational requirements. We also point out that the effectiveness of the method may be affected by the correct selection of the likelihood function and the model error variance.

Water resources modelers face the challenge of dealing with numerous uncertainties due to the lack of knowledge of the natural systems, numerical approaches used in modeling (equations, parameters, structures, solutions), and field data collected to set up and evaluate models. Propagation of parameter uncertainty into model results is a relevant topic in environmental hydrology. Uncertainty analyses improve assessment of hydrological modeling. There is a need in modern hydrology of developing and testing uncertainty analysis methods that support hydrological model evaluation. In this research the propagation of model parameter uncertainty into streamflow model results is evaluated. The Hydrological Simulation Program – FORTRAN (HSPF) supported by the US Environmental Protection Agency was evaluated using hydroenvironmental data from the Luxapallila Creek watershed located in Mississippi and Alabama, USA. The uncertainty bounds of model outputs were computed using the Monte Carlo simulation and Harr’s point estimation methods. Analysis of parameter uncertainty propagation on streamflow simulations from 12 HSPF parameters was accomplished using 5,000 Monte Carlo random samples and 24 Harr selected points for each selected parameter. The comparison showed that Harr’s method could be an appropriate initial indicator of parameter uncertainty propagation on streamflow simulations, particularly for hydrology models with several parameters.

The lumped watershed model, Hydrologic Simulation Program FORTRAN — HSPF, was used to simulate hydrologic processes in the Luxapallila Creek watershed in Mississippi and Alabama. Analysis of parameter uncertainty propagation on streamflow simulations from 12 HSPF parameters was accomplished using 5,000 Monte Carlo random samples. Parameter sensitivity was performed individually using Monte Carlo simulations for each parameter. Triangular distributions were developed for each parameter and the most probable value for each parameter extracted from previous model calibration efforts. Daily simulated certainty bounds were evaluated using two criteria: the percentage of observed flows within the HSPF 90% certainty bounds (Reliability) and the width of the HSPF 90% certainty bounds (Sharpness). Three years of daily observed streamflow data were clustered into three groups to assess the model Reliability and Sharpness by each class: below normal, normal, and above normal flows. In general, the model Reliability was better for below normal flows than normal and above normal flows. Four out of 12 parameters were found to account the majority of parameter uncertainty propagation. Median model Sharpness was reduced by 8% using four parameters instead of 12 parameters while model Reliability decreased just 0.5%.