Technical note: An evaluation of error propagation in MixSIAR for root water uptake

Abstract. Quantifying root water uptake (RWU) using hydrogen and oxygen stable isotopes and Bayesian mixing models is widely applied in ecohydrology. But its reliability remains unclear due to multiple uncertainties and the underdetermined nature of the problem. Here we systematically evaluate error propagation in MixSIAR-based RWU estimation using synthetic datasets with known RWU profiles. Through 2916 controlled simulations, we assess the contributions of eight factors, including observed soil and plant water isotopic compositions, RWU patterns, and model configuration (source grouping, prior information, and error structure) to the variance in accuracy and uncertainty of estimated root wate uptake proportions using MixSIAR. Results show that prior information and source grouping dominate RWU inference, explaining over 40% of the total variance. By contrast, when evaluated using posterior-predicted plant water isotopic compositions, their contribution falls below 10%, while soil and plant isotopic compositions and error structure together explain over 40%. This shift results in two things: (1) it masks the influence of prior information and source grouping for inferring RWU proportions using MixSIAR, and (2) it reveals a critical limitation in the approach: accurate isotope fits of observed plant water do not guarantee correct RWU inference, since multiple RWU solutions can produce identical isotopic compositions. These findings demonstrate that prior information and source grouping should be explicitly specified and carefully justified in future applications of MixSIAR in studies of quantifying RWU using Bayesian mixing models. Also, the multi-criteria evaluation frameworks beyond posterior predicted plant water isotopic composition are needed to assess the performance of MixSIAR in isotope-based RWU estimation.

Authors

Institutions

Publication Details

Published
2026-10-27
DOI
https://doi.org/10.5194/egusphere-2026-4829
Primary Topic
Groundwater and Isotope Geochemistry
Type
preprint

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Technical note: An evaluation of error propagation in MixSIAR for root water uptake

Han Fu, Wenxiu Zou, Ruiqi Ren, Bingcheng Si et al.
2 links
Groundwater and Isotope Geochemistry
preprint

Technical note: An evaluation of error propagation in MixSIAR for root water uptake

Han Fu, Wenxiu Zou, Ruiqi Ren, Bingcheng Si, Jeffrey McDonnell, Huijie Li, Adrià Barbeta
preprint en

Abstract

Abstract. Quantifying root water uptake (RWU) using hydrogen and oxygen stable isotopes and Bayesian mixing models is widely applied in ecohydrology. But its reliability remains unclear due to multiple uncertainties and the underdetermined nature of the problem. Here we systematically evaluate error propagation in MixSIAR-based RWU estimation using synthetic datasets with known RWU profiles. Through 2916 controlled simulations, we assess the contributions of eight factors, including observed soil and plant water isotopic compositions, RWU patterns, and model configuration (source grouping, prior information, and error structure) to the variance in accuracy and uncertainty of estimated root wate uptake proportions using MixSIAR. Results show that prior information and source grouping dominate RWU inference, explaining over 40% of the total variance. By contrast, when evaluated using posterior-predicted plant water isotopic compositions, their contribution falls below 10%, while soil and plant isotopic compositions and error structure together explain over 40%. This shift results in two things: (1) it masks the influence of prior information and source grouping for inferring RWU proportions using MixSIAR, and (2) it reveals a critical limitation in the approach: accurate isotope fits of observed plant water do not guarantee correct RWU inference, since multiple RWU solutions can produce identical isotopic compositions. These findings demonstrate that prior information and source grouping should be explicitly specified and carefully justified in future applications of MixSIAR in studies of quantifying RWU using Bayesian mixing models. Also, the multi-criteria evaluation frameworks beyond posterior predicted plant water isotopic composition are needed to assess the performance of MixSIAR in isotope-based RWU estimation.

Ludong University (CN), Chinese Academy of Sciences (CN), North China University of Water Resources and Electric Power (CN), University of Saskatchewan (CA), Institute of Agrifood Research and Technology (ES), Northeast Institute of Geography and Agroecology (CN), Global Institute for Water Security (CA), University of Birmingham (GB)
National Natural Science Foundation of China, Natural Science Foundation of Heilongjiang Province, Agriculture Research System of China
Groundwater and Isotope Geochemistry
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon