Articles | Volume 13, issue 5
Geosci. Model Dev., 13, 2433–2450, 2020
Geosci. Model Dev., 13, 2433–2450, 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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Cited articles

Allen, S. T., Kirchner, J. W., and Goldsmith, G. R.: Predicting spatial patterns in precipitation isotope (δ2H and δ18O) seasonality using sinusoidal isoscapes, Geophys. Res. Lett., 45, 4859–4868,, 2018. 
Barbeta, A. and Peñuelas, J.: Relative contribution of groundwater to plant transpiration estimated with stable isotopes, Sci. Rep., 7, 10580,, 2017. 
Benettin, P., Bailey, S. W., Rinaldo, A., Likens, G. E., McGuire, K. J., and Botter, G.: Young runoff fractions control streamwater age and solute concentration dynamics, Hydrol. Process., 31, 2982–2986,, 2017. 
Beria, H., Larsen, J. R., Ceperley, N. C., Michelon, A., Vennemann, T., and Schaefli, B.: Understanding snow hydrological processes through the lens of stable water isotopes, Wiley Interdiscip. Rev. Water, 5, e1311,, 2018. 
Beria, H., Larsen, J. R., Michelon, A., Ceperley, N. C., and Schaefli, B.: Data for the manuscript “HydroMix v1.0: a new Bayesian mixing framework for attributing uncertain hydrological sources” (Version 1.0), Zenodo,, 2019. 
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.