Articles | Volume 14, issue 8
https://doi.org/10.5194/bg-14-2199-2017
https://doi.org/10.5194/bg-14-2199-2017
Reviews and syntheses
 | 
02 May 2017
Reviews and syntheses |  | 02 May 2017

Reviews and syntheses: Isotopic approaches to quantify root water uptake: a review and comparison of methods

Youri Rothfuss and Mathieu Javaux

Abstract. Plant root water uptake (RWU) has been documented for the past five decades from water stable isotopic analysis. By comparing the (hydrogen or oxygen) stable isotopic compositions of plant xylem water to those of potential contributive water sources (e.g., water from different soil layers, groundwater, water from recent precipitation or from a nearby stream), studies were able to determine the relative contributions of these water sources to RWU.

In this paper, the different methods used for locating/quantifying relative contributions of water sources to RWU (i.e., graphical inference, statistical (e.g., Bayesian) multi-source linear mixing models) are reviewed with emphasis on their respective advantages and drawbacks. The graphical and statistical methods are tested against a physically based analytical RWU model during a series of virtual experiments differing in the depth of the groundwater table, the soil surface water status, and the plant transpiration rate value. The benchmarking of these methods illustrates the limitations of the graphical and statistical methods while it underlines the performance of one Bayesian mixing model. The simplest two-end-member mixing model is also successfully tested when all possible sources in the soil can be identified to define the two end-members and compute their isotopic compositions. Finally, the authors call for a development of approaches coupling physically based RWU models with controlled condition experimental setups.

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Plant root water uptake (RWU) has been documented for the past 5 decades from water stable isotopic analysis. In this paper, we review the different methods for reconstructing RWU profiles on the basis of isotopic information and confront them with each other during a series of virtual experiments. Finally, we call for a development of approaches coupling physically based RWU models with controlled condition experimental setups.
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