Quantifying Spatial Changes in the Structure of Water Quality Constituents in a Large Prairie River within Two Frameworks of a Water Quality Model

Abstract

Abstract:A global sensitivity analysis was carried out on a water quality model to quantify the spatial changes in parameter sensitivity of a model of a large prairie river, the South Saskatchewan River (SSR). The method is used to assess the relative impacts of major nutrient loading sources and a reservoir on the river's water quality. The river completely freezes over during winter; hence, the sensitivity analysis was carried out seasonally, for winter and summer, to account for the influence of ice-covered conditions on nutrient transformations. Furthermore, the integrity of the river's aquatic ecosystem was examined through the inter-relationship between variables and comparing hierarchy index values and water quality indices at four locations along the river. Sensitivities of model parameters varied slightly at different locations along the river, with the phytoplankton growth rate being the most influential parameter. Nitrogen and phosphorus transformation processes were more sensitive in winter, while chlorophyll-a and dissolved oxygen parameters showed higher sensitivity in summer. A more complicated correlation between variables was observed downstream of the junction of the Red Deer River. Our results reveal that the lower correlation between variables may suggest a more balanced and healthier system, although further analysis is needed to support this statement.

Modelling the possible impacts of climate change on the thermal regime and macroinvertebrate species of a regulated prairie river

“…WASP is intended to assist in the interpretation and prediction of water quality responses to both natural phenomena and man-made pollution and water management impacts and, in this way, to support management decision making. The WASP model incorporates several different kinetic modules and has been applied to model various water quality processes, including eutrophication (Ernst & Owens, 2009), phytoplankton, nutrient dynamics (Akomeah, Chun, & Lindenschmidt, 2015;Hosseini, Chun, & Lindenschmidt, 2016;Hosseini, Chun, Wheater, & Lindenschmidt, 2017), metal transport and transformation (Carroll et al, 2000;Caruso & Bishop, 2009;Lin, Larssen, Vogt, Feng, & Zhang, 2011), and the transport and fate of toxicants (Franceschini & Tsai, 2010;Meric et al, 2013). The WASP model incorporates several different kinetic modules and has been applied to model various water quality processes, including eutrophication (Ernst & Owens, 2009), phytoplankton, nutrient dynamics (Akomeah, Chun, & Lindenschmidt, 2015;Hosseini, Chun, & Lindenschmidt, 2016;Hosseini, Chun, Wheater, & Lindenschmidt, 2017), metal transport and transformation (Carroll et al, 2000;Caruso & Bishop, 2009;Lin, Larssen, Vogt, Feng, & Zhang, 2011), and the transport and fate of toxicants (Franceschini & Tsai, 2010;Meric et al, 2013).…”

“…The lower SSR, from Gardiner Dam to the confluence with the North Saskatchewan River, was discretized into 673 longitudinal segments, each 500 m in length (Hosseini et al, 2016). Hydrodynamic data were estimated using 187 surveyed cross-sectional profiles extracted from a hydrologic Engineering Center River Analysis System model based on annual mean flow provided by the Water Security Agency (Hosseini, Chun, et al, 2017).…”

Geospatial Modeling of River Systems

“…Hosseini et al [75] and Meissner et al [76,77] used different modelling approaches to determine the effects of spatial variability and indicators on aquatic ecology. Hosseini et al [75] applied a deterministic water quality model to determine the impact of location along a river on the sensitivity of ecological parameters (e.g., oxygen demand and growth rate) to water-quality variables (e.g., dissolved oxygen and chlorophyll-a concentrations).…”

“…Hosseini et al [75] applied a deterministic water quality model to determine the impact of location along a river on the sensitivity of ecological parameters (e.g., oxygen demand and growth rate) to water-quality variables (e.g., dissolved oxygen and chlorophyll-a concentrations). The difference in their spatial extents consisted of reaches of the same river, upstream and downstream of a large Prairie reservoir.…”

Toward a Comprehensive Water-Quality Modeling of Barnegat Bay: Development of ROMS to WASP Coupler

“…With the addition of the wetting and drying capability , ROMS is suitable for modeling tidal dynamics and water circulation, temperature, salinity, and sediment transport in a shallow, back-barrier estuary. The Water Quality Analysis Simulation Program (WASP; Ambrose et al, 1988;Di Toro, Fitzpatrick, and Thomann, 1983;Wool, Davie, and Rodriguez, 2003), developed by the U.S. Environmental Protection Agency (EPA), incorporates watershed nutrient loading and internal nutrient cycling to calculate dissolved oxygen (DO), oxygen demand, nutrient concentrations, sediment, and phytoplankton dynamics and has been widely used for water-quality and TMDL assessment in rivers, reservoirs, lakes, and estuaries (Abdelrhman, 2015;Camacho et al, 2014;Franceschini and Tsai, 2010;Hosseini, Chun, and Lindenschmidt, 2016;Kaufman, 2011;Lindenschmidt, 2006 and references therein;Tetra Tech, 2012, 2015. WASP can receive hydrodynamic information from other models (EFDC, DYNHYD, RIVMOD, CE-QUAL-RIV1, SWMM) through a binary hydrodynamic linkage file; however, a coupling between ROMS and WASP was not available prior to this study.…”