Contraste de los patrones de concentración de NO3-N en dos manantiales cársticos de Iowa (EEUU): conocimientos acerca del almacenamiento y suministro de nitrógeno en el acuífero

Schilling, Keith E.; Jones, Christopher S.; Clark, Ryan; Libra, Robert D.; Liang, Xiuyu; Zhang, You‐Kuan

doi:10.1007/s10040-019-01935-y

Cited by 6 publications

(5 citation statements)

References 51 publications

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“…The recent growing body of nitrate sensing literature in karst settings reinforces the concept of karst springs as dynamic integrators of storm‐event dynamics. Complex contributions of quick and slow flow can cause nitrate dilution and mobilization within the same event (Huebsch et al., 2014), which are tied to the underlying maturity of the karst structure (Schilling et al., 2019). Schilling et al.…”

Section: Resultsmentioning

confidence: 99%

“…Complex contributions of quick and slow flow can cause nitrate dilution and mobilization within the same event (Huebsch et al, 2014), which are tied to the underlying maturity of the karst structure (Schilling et al, 2019). Schilling et al (2019) showed, using sensing data, that in an immature karst system in Iowa, USA, nitrate stored in overburden material can provide a constant source of high concentration material whereas in a neighboring mature system event recharge via sinkholes significantly diluted spring concentrations. The nitrate response in Royal Spring both resembles the mature system, where dilution is common, but also the immature systems where events are characterized by mobilization of nitrate during storm recession.…”

Section: Hysteresis Evaluation and Uncertainty Reductionmentioning

confidence: 99%

“…The recent growing body of nitrate sensing literature in karst settings reinforces the concept of karst springs as dynamic integrators of storm-event dynamics. Complex contributions of quick and slow flow can cause nitrate dilution and mobilization within the same event (Huebsch et al, 2014), which are tied to the underlying maturity of the karst structure (Schilling et al, 2019). Schilling et al (2019) showed, using sensing data, that in an immature karst system in Iowa, USA, nitrate stored in overburden material can provide a constant source of high concentration material whereas in a neighboring mature system event recharge via sinkholes significantly diluted spring concentrations.…”

Section: Hysteresis Evaluation and Uncertainty Reductionmentioning

confidence: 99%

“…High‐frequency sensors for the continuous in situ monitoring of nitrate have improved our understanding of nutrient export from karst environments (Opsahl et al., 2017; Schilling et al., 2019; Wang, Li, et al., 2020; Yue et al., 2019). Particularly, sensors allow us to collect data at time scales (<15 min) that are providing novel insight into the drivers of hydrology and biogeochemistry (Rode et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

“…These tools include hydrograph separation (Ford et al., 2019), numerical modeling (Husic, Fox, Adams, Ford, et al., 2019), and high‐frequency sensing data (Mellander et al., 2012). However, few studies to our knowledge have tested and extended the above concepts for nitrate transport in karst using nitrate sensor data (Schilling et al., 2019; Zhang et al., 2020) and none have utilized nitrate sensing together with numerical modeling to help constrain uncertainty regarding nitrate contamination. The increasing availability of high‐frequency sensing data (Fenton et al., 2017; Schilling et al., 2019) and the improved computational capabilities of modeling packages (Husic et al., 2020) allows these knowledge gaps to be advanced at this time.…”

Section: Introductionmentioning

confidence: 99%

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

Husic

Fox

Clare

et al. 2023

Water Resources Research

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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.

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Section: Resultsmentioning

confidence: 99%