Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 5
Articles | Volume 13, issue 5
https://doi.org/10.5194/gmd-13-2433-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-2433-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 5
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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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.
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