Articles | Volume 13, issue 5
Geosci. Model Dev., 13, 2433–2450, 2020
https://doi.org/10.5194/gmd-13-2433-2020
Geosci. Model Dev., 13, 2433–2450, 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 et al.

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Interactive discussion

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
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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.