The Use of Bayesian Mixing Models for Root Water Uptake: A Critical Review

Abstract

Isotope-based Bayesian mixing models (BMM) are widely used in ecohydrology to infer where plants acquire water from the soil, yet clear guidance on their application to root water uptake (RWU) remains limited. This review synthesizes existing BMM applications for RWU estimation and critically examines three fundamental challenges that constrain their robustness and interpretability. First, RWU inference is often severely underdetermined because the number of isotopic tracers is far smaller than the number of potential soil water sources or depth layers, placing fundamental limits on parameter identifiability. Second, RWU estimates are sensitive to model configuration choices, particularly source grouping and prior specification. A key conceptual insight emerging from this review is that so-called “non-informative” Dirichlet priors can become strongly informative as the number of sources increases, leading to divergent uptake patterns inferred from the same dataset. Third, inappropriate specification of error structures can misrepresent how isotopic variability is propagated into the likelihood function, inflating posterior uncertainty or biasing inferred RWU proportions. Looking forward, we argue that further progress in BMM-based RWU inference requires moving beyond discrete, depth-resolved formulations toward physically grounded and vertically continuous inference frameworks with well-justified error structures. Such developments, together with explicit consideration of identifiability and model dimensionality, are essential for the robust use of hydrogen and oxygen stable isotopes in quantifying root water uptake patterns.