Nitrate Hysteresis as a Tool for Revealing Storm‐Event Dynamics and Improving Water Quality Model Performance

Abstract

Understanding the physics of nitrate contamination in surface and subsurface water is vital for mitigating downstream water quality impairment. Though high frequency sensor data have 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 for an agricultural karst spring in Kentucky, USA. We collected 2‐years of 15‐min nitrate and specific conductance data and analyzed source‐timing dynamics across dozens of events to develop a conceptual model for nitrate hysteresis in karst. Thereafter, we used the sensing data, specifically discharge‐concentration indices, to constrain modeled nitrate prediction bounds as well as the uncertainty of hydrologic and nitrogen processes, such as soil percolation and biogeochemical transformation. Observed nitrate hysteresis behavior at the spring was complex and included clockwise (n = 11), counterclockwise (n = 13), and figure‐eight (n = 10) shapes, which contrasts with surface systems that are often dominated by a single hysteresis shape. Sensing results highlight the importance of antecedent connectivity to nitrate‐rich storages in determining the timing of nitrate delivery to the spring. After integrating hysteresis analysis into our numerical model evaluation, simulated nitrate prediction bounds were reduced by 43 ± 12% and parameter uncertainty by 36 ± 20%. Taken together, this study suggests that discharge‐concentration indices derived from high‐frequency sensor data can be successfully integrated into numerical models to improve process representation and reduce modeled uncertainty.

Exceeding Turbidity versus Karst Spring Discharge during Single Rainfall Events: The Case of the Jadro Spring

“…Counterclockwise hysteresis loops have been generally related to sediment sources located distantly from the measurement location [55][56][57]. In this study, none of the four counterclockwise loops show a typical form since turbidity and discharge reach their peaks almost simultaneously.…”

Extreme Learning Machine Predicts High-Frequency Stream flow and Nitrate-N Concentrations in a Karst Agricultural Watershed

“…Previous studies suggested the importance of incorporating hysteresis metrics into numerical model evaluation (Liu et al, 2021). A recent study by Husic et al (2023) integrated nitrate hysteresis metrics into a cascading linear reservoir model simulating flow and nitrate at a daily timestep in a mature karst watershed. The authors found that including hysteresis indices as a model evaluation metric reduced uncertainty associated with both model parameterization and nitrate loading.…”

“…In karst agroecosystems, watershed nitrate concentrations during storm events initially become diluted with low nitrate concentrations from new-water on the rising limb of the hydrograph, increase following peak flows as soil and epikarst connectivity of old-water increases, and then decrease towards pre-event levels as phreatic connectivity becomes prominent during the receding limb of the hydrograph (Baran et al, 2008;Rusjan et al, 2008;Buda and DeWalle, 2009;Ford et al, 2019;Husic et al, 2019). Quantitative hysteresis index (HI) values have been used to evaluate the direction and magnitude of hysteretic behavior and therefore may be useful in numerical model evaluations to ensure models can accurately capture nitrate source and pathway variability (Liu et al, 2021;Mehdi et al, 2021;Husic et al, 2023). Given the variability in connectivity of waters throughout the soil profile during events and variable nitrate concentrations in pore waters with depth, we hypothesize that the hysteretic behavior of watershed nitrate concentrations is tightly linked with the hysteretic behavior of soil hydrology.…”

Nitrate-nitrogen dynamics in response to forestry harvesting and climate variability: Four years of UV nitrate sensor data in a shallow, gravel aquifer

“…UV nitrate sensors have been used to monitor nitrate-nitrogen concentrations in surface water, springs, wastewater and drinking water supplies (Pellerin et al, 2012;Miller et al, 2016;Burkitt et al, 2017;Miller et al, 2017;Burns et al, 2018;Hansen and Singh, 2018;Shi et al, 2022). More recently they have been applied to in-situ groundwater monitoring studies to elucidate temporal variability (Opsahl et al, 2017;Burbery et al, 85 2021;Haas et al, 2023;Husic et al, 2023). The use of UV nitrate sensors allows for reduced maintenance and calibration compared to methods that use reagents, while greatly increasing measurement frequency compared to discrete sampling programmes (Pellerin et al, 2013).…”

“…Using high-frequency UV nitrate sensors to monitor nitrate-nitrogen in groundwater can consequently provide new insights into sources, processes and pathways that are temporally variable. As the mechanisms that control 100 inorganic nitrogen movement from its source are also highly variable across catchments, high frequency monitoring can be applied to characterise a range of settings to gain new insights into inorganic nitrogen export through groundwater pathways (Liu et al, 2020;Burbery et al, 2021;Haas et al, 2023;Husic et al, 2023). Such temporally dense data provided by in situ UV nitrate sensors can then be used as the basis for time-series analysis techniques traditionally used in surface water studies that have not previously been applied in 105 groundwater quality analysis.…”