Nitrate Hysteresis as a Tool for Revealing Storm‐Event Dynamics and Improving Water Quality Model Performance. Issue 1 (17th January 2023)

• Record Type: Journal Article
• Authors: Husic, Admin; Fox, James F.; Clare, Evan; Mahoney, Tyler; Zarnaghsh, Amirreza
• Is Part Of: Water resources research. Volume 59:Issue 1(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-01-17
• Subjects: hysteresis -- sensors -- uncertainty -- modeling -- karst -- nitrate; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2022WR033180
• Languages: English
• ISSNs: 0043-1397

Abstract

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. Plain Language Summary: Understanding how contamination arrives at streams and springs is important for managing our water resources. Recent advancements in sensor technology allow us to get large amounts of nutrient data without the need to perform laboratory analysis for each data point. However, these data have not been integrated into numerical models that can be used to predict the pathways of contamination. In this study, we blend these two methods (sensing and modeling) and find that the large data sets generated by sensors can be used to improve numerical model predictions and reduce uncertainty associated with the model outputs. Future studies should consider how to best utilize large amounts of existing sensing data to mitigate contamination and improve water resources. Key Points: High variability in spring nitrate hysteresis‐shape contrasts single‐shape dominance in surface streams Rapid versus slow connection of nitrate to the karst spring is primarily a function of antecedent aquifer conditions Integrating high‐frequency sensing into numerical model evaluation reduces prediction uncertainty