High-frequency sensing of nitrate to improve numerical model performance: insights from an extensively modeled spring

Abstract:

Understanding the physics of widespread nitrate contamination in surface and subsurface water is vital for mitigating downstream eutrophication and water quality impairment. Though high frequency sensor data has become readily available and computational models more accessible, the integration of these two methods for improved prediction is underdeveloped. The objective of this study was to utilize high-frequency data to advance our understanding and model representation of nitrate transport in an agricultural karst watershed in Kentucky, USA. We collected two-years of in situ 15-minute nitrate data and analyzed source-timing dynamics by comparing discharge-concentration behavior across dozens of events. Thereafter, we integrated high-frequency sensing data into a watershed numerical model to better represent modeled physics and reduce overall process uncertainty. High frequency sensing results suggest a rapid response of the spring nitrate to precipitation inputs with an initial dilution during the rising limb, followed by a maxima shortly into the recession, and a return to baseflow conditions later in the recession. Hysteresis behavior at the spring is complex and includes linear (n = 1), clockwise (n = 11), counterclockwise (n = 11), and figure-eight (n = 11) shapes. After integrating the high frequency data, the best performing model simulation correctly characterized both HI and FI for 22 of 34 events. When hysteresis indices are integrated into the numerical model evaluation scheme, modeled prediction bounds were reduced by 32±13%. Further, the uncertainty regarding the modeled fluxes of nitrate via the quick, intermediate, and slow flow pathways was reduced by 40%. Taken together, this study suggests that discharge-concentration indices derived from high-frequency sensor data can be successfully be integrated into numerical models to improve process representation and reduce uncertainty.