A Bayesian approach to infer nitrogen loading  rates from crop and land-use types surrounding  private wells in the Central Valley, California

Ransom, Katherine M.; Bell, Andrew M.; Barber, Quinn E.; Kourakos, George; Harter, Thomas

doi:https://doi.org/10.5194/hess-22-2739-2018

Articles | Volume 22, issue 5

https://doi.org/10.5194/hess-22-2739-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-22-2739-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 5

Research article

|

07 May 2018

Research article |

| 07 May 2018

A Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley, California

Katherine M. Ransom, Andrew M. Bell, Quinn E. Barber, George Kourakos, and Thomas Harter

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Final revised paper (published on 07 May 2018)
Preprint (discussion started on 17 Jan 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'review', Anonymous Referee #1, 22 Jan 2018
- AC1: 'Reply to Referee # 1 Comments', Katherine Ransom, 24 Jan 2018
RC2: 'Review of hess-2017-733', Anonymous Referee #2, 03 Feb 2018
- AC2: 'Reply to Referee # 2 Comments', Katherine Ransom, 20 Feb 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Publish subject to minor revisions (review by editor) (15 Mar 2018) by Philippe Ackerer

AR by Katherine Ransom on behalf of the Authors (07 Apr 2018) Author's response Manuscript

ED: Publish as is (12 Apr 2018) by Philippe Ackerer

AR by Katherine Ransom on behalf of the Authors (18 Apr 2018)

Short summary

We estimated a probability distribution of nitrogen loading rates for crop and land-use groups from regional groundwater data. Water & natural land use had the lowest estimated rates, while dairy land use had the highest. Most results compare favorably to previous estimates, though mass balance estimates for several crop groups were higher than our model estimates. The information can provide a better assessment of land-use impacts to water quality absent information on farm nutrient management.