Assessing interannual variability in nitrogen sourcing and  retention through hybrid Bayesian watershed modeling

Miller, Jonathan W.; Karimi, Kimia; Sankarasubramanian, Arumugam; Obenour, Daniel R.

doi:https://doi.org/10.5194/hess-25-2789-2021

Articles | Volume 25, issue 5

https://doi.org/10.5194/hess-25-2789-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Frontiers in the application of Bayesian approaches in water...

https://doi.org/10.5194/hess-25-2789-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 5

Research article

|

26 May 2021

Research article |

| 26 May 2021

Assessing interannual variability in nitrogen sourcing and retention through hybrid Bayesian watershed modeling

Jonathan W. Miller, Kimia Karimi, Arumugam Sankarasubramanian, and Daniel R. Obenour

Supplement

https://doi.org/10.5194/hess-25-2789-2021-supplement

Data sets

obelab/FallsJordanWshHESS: JordanFallsWshHESS Dataset J. W. Miller, K. Karimi, A. Sankarasubramanian, and D. R. Obenour https://doi.org/10.5281/zenodo.4776993

Short summary

Within a watershed, nutrient export can vary greatly over time and space. In this study, we develop a model to leverage over 30 years of streamflow, precipitation, and nutrient sampling data to characterize nitrogen export from various livestock and land use types across a range of precipitation conditions. Modeling results reveal that urban lands developed before 1980 have remarkably high levels of nitrogen export, while agricultural export is most responsive to precipitation.