AN INNOVATIVE WATER QUALITY MODELING PROJECT FOR THE BEAUFORT RIVER
Abstract:Bordered on the west by the Broad River and on the east by the Beaufort River, the Beaufort-Jasper Water & Sewer Authority provides wastewater service to all of the Beaufort and Port Royal area with the exception of the U.S. Marine Corps facilities. Permitted discharges of reclaimed water to the Beaufort River total about 9 million gallons per day (mgd) with actual discharges averaging about 3.5 mgd and evenly split between municipal and military facilities. The discharged flows are anticipated to increase to 12 mgd over the next 20 years due to growth in the municipal population.
The Beaufort River is a complex estuarine river system that is home to shellfish grounds and fisheries nursery habitats. The River also serves as receiving water for treated wastewater from four significant water reclamation facilities. While not uncommon for coastal areas, the river is on the South Carolina 303(d) list of impaired waters for low dissolved oxygen. The Clean Water Act stipulates that Total Maximum Daily Loads (TMDLs) must be determined for all waters on the 303(d) list. Critical to the development of defensible TMDLs is the linkage between the impairment and the source of the impairment. For the Beaufort River, a regional approach was necessary to address its complex river system issues and protect the highly sensitive eco-systems.
The BJWSA wastewater facilities are operating above 70% of permitted capacity and are scheduled to be replaced by a regional Water Reclamation Facility in June 2006. In order to support the expedited NPDES permitting efforts needed to support the schedule for BJWSA's planned water reclamation facility, a new approach to develop a predictive dissolved oxygen model was required. The regional wastewater management approach, planned for the Beaufort River, was developed and spearheaded by Dean Moss, General Manager of the BJW&SA. His vision called for consolidating the military and civilian wastewater discharges into a single, higher quality discharge. However, this plan required extensive data collection and modeling to prove its validity. Toward that end, the coordinated efforts of the BJWSA, JJG, USGS, and ADM team led to the establishment of an extensive network of eight continuous monitoring stations in the Beaufort and Broad River Basins. From this monitoring information, an empirical model of the system was developed that made accurate predictions of the response of in stream dissolved-oxygen concentrations due to changing hydrologic, meteorological, and point-source loading conditions. Sensitivities were performed for each treatment plant in the system and the dissolved oxygen concentration impact at each monitoring station location. Potential wastewater management scenarios were evaluated for developing the optimal loading from the various dischargers, while simultaneously protecting the integrity of the Beaufort River. This modeling effort confirmed the regional approach proposed by the BJW&SA was the best solution to protect the Beaufort River and its resources.
For many years, data mining and artificial neural networks (ANN) have provided valuable forecasting information to marketing and financial institutions. ANN differs from traditional water quality modeling methodologies because it does not use equations based on physics and biology when forecasting. This eliminates the inherent biases in the weight placed on physical and biological factors (and their interpretation) in traditional modeling. Instead, ANN relies on a statistical model that is created from the detailed analysis of environmental information. For watershed management, the information is elicited from the extensive database of meteorological and water quality data.
During the course of the model development, the BJWSA spearheaded a partnership effort with the federal government (USGS and EPA) and state regulatory officials (SC DHEC), as well as its consulting firms. SC DHEC technical staff, as well as EPA Region IV, provided technical review of the project. Regulators were included in the project process as participants to insure their full understanding of this innovative modeling approach and to confirm the technical validity of the model.
The ANN technology applications for water quality modeling beyond forecasting the sensitivity of point-source loads on instream dissolved-oxygen concentrations are extensive. The ANN approach can easily be applied to complex and dynamic water systems where traditional approaches have previously been employed, whether successfully or unsuccessfully. Now, the BJWSA is utilizing a cutting edge application of that existing technology. The result – for the first time, ANN was being used as a water quality model that is more accurate, cost-efficient, and time-efficient than physics based models.
Because the model is faster and less expensive to run, watershed managers will have a cost-effective decision-making tool at their disposal that can produce more information in less time, and with greater accuracy than physics-based modeling. The speed and accuracy of the model using the ANN technology is impressive: Results that take hours to produce with physics-based modeling only takes minutes with the ANN model. The accuracy of the results is significantly higher than typically seen with traditional modeling approaches. By using this modeling approach, a TMDL was established and BJWSA received a NPDES Permit for its new Water Reclamation Facility.
Document Type: Research Article
Publication date: 2005-01-01
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