Articles | Volume 13, issue 5
https://doi.org/10.5194/gmd-13-2433-2020
https://doi.org/10.5194/gmd-13-2433-2020
Model description paper
 | 
27 May 2020
Model description paper |  | 27 May 2020

HydroMix v1.0: a new Bayesian mixing framework for attributing uncertain hydrological sources

Harsh Beria, Joshua R. Larsen, Anthony Michelon, Natalie C. Ceperley, and Bettina Schaefli

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Harsh Beria on behalf of the Authors (07 Oct 2019)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (25 Oct 2019) by Bethanna Jackson
RR by Anonymous Referee #1 (06 Nov 2019)
RR by Anonymous Referee #4 (27 Nov 2019)
ED: Reconsider after major revisions (12 Dec 2019) by Bethanna Jackson
AR by Harsh Beria on behalf of the Authors (17 Mar 2020)  Author's response   Manuscript 
ED: Publish as is (27 Apr 2020) by Bethanna Jackson
AR by Harsh Beria on behalf of the Authors (29 Apr 2020)
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Short summary
We develop a Bayesian mixing model to address the issue of small sample sizes to describe different sources in hydrological mixing applications. Using composite likelihood functions, the model accounts for an often overlooked bias arising due to unweighted mixing. We test the model efficacy using a series of statistical benchmarking tests and demonstrate its real-life applicability by applying it to a Swiss Alpine catchment to obtain the proportion of groundwater recharged from rain vs. snow.