The in-stream water quality model, QUAL2Kw, can provide guidance in watershed management decisions by linking changes in nutrient loads to responses in water quality. This model is particularly useful for determining wasteload allocations, aiding in total maximum daily load analyses, and developing numeric nutrient criteria. Unfortunately, states struggle to balance the data collection and modeling requirements to accomplish many of these water quality management tasks due to limited resources. This commonly results in routine data collection and monitoring efforts that do not satisfy the data requirements for modeling. To address this disconnect, this study presents a data collection and parameter calibration methodology suited to meet general QUAL2Kw modeling requirements. Then, with the goal of identifying a range of numeric nitrogen and phosphorus criteria, this general data collection and modeling strategy was applied to sites throughout Utah. To help automate and test scenarios targeted at tracking effects of loading and response combinations, a nutrient criteria tool was also developed to interface with these QUAL2Kw models. By implementing the tool on these models, input concentrations of ammonium (NH4+) ranging from 10 to 101 µg/L and inorganic phosphorus (PO4-) ranging from 1 to 14 µg/L were found to exceed thresholds of bottom algae, gross primary productivity, and ecosystem respiration. Conversely, NH4+ concentrations above 3,500 µg/L and PO4- above 490 µg/L exceeded dissolved oxygen thresholds of 5-6 mg/L in some applications. Some limitations of using mechanistic models in this manner were identified, including model capabilities (e.g., steady-state versus dynamic), inclusion of appropriate processes, uncertainty in calibrated parameters, and site-specific conditions. Although many problems will require more complex modeling efforts with significantly larger data collection efforts, this approach provides an initial framework that aids in the judicial use of resources to aid in watershed management decisions.

The in-stream water quality model, QUAL2Kw, can provide guidance in watershed management decisions by linking changes in nutrient loads to responses in water quality. This model is particularly useful for determining wasteload allocations, aiding in total maximum daily load analyses, and developing numeric nutrient criteria. Unfortunately, states struggle to balance the data collection and modeling requirements to accomplish many of these water quality management tasks due to limited resources. This commonly results in routine data collection and monitoring efforts that do not satisfy the data requirements for modeling. To address this disconnect, this study presents a data collection and parameter calibration methodology suited to meet general QUAL2Kw modeling requirements. Then, with the goal of identifying a range of numeric nitrogen and phosphorus criteria, this general data collection and modeling strategy was applied to sites throughout Utah. To help automate and test scenarios targeted at tracking effects of loading and response combinations, a nutrient criteria tool was also developed to interface with these QUAL2Kw models. By implementing the tool on these models, input concentrations of ammonium (NH4+) ranging from 10 to 101 µg/L and inorganic phosphorus (PO4-) ranging from 1 to 14 µg/L were found to exceed thresholds of bottom algae, gross primary productivity, and ecosystem respiration. Conversely, NH4+ concentrations above 3,500 µg/L and PO4- above 490 µg/L exceeded dissolved oxygen thresholds of 5-6 mg/L in some applications. Some limitations of using mechanistic models in this manner were identified, including model capabilities (e.g., steady-state versus dynamic), inclusion of appropriate processes, uncertainty in calibrated parameters, and site-specific conditions. Although many problems will require more complex modeling efforts with significantly larger data collection efforts, this approach provides an initial framework that aids in the judicial use of resources to aid in watershed management decisions.