Table of Contents

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Notes on books, journal articles, and other texts that I read.


Journal articles

Cochard et al. (2021)

        file = {articles/cochard_2021.pdf},
        doi = {10.1007/s13595-021-01067-y},
        keywords = {droughts, physiology, plant-hydraulics},
        pages = {55},
        volume = {78},
        year = {2021},
        journal = {Annals of Forest Science},
        title = {{SurEau}: a mechanistic model of plant water
                  relations under extreme drought},
        author = {H. Cochard and F. Pimont and J. Ruffault and
                  N. Martin-StPaul}


The authors discuss the SurEau model implementation, a mechanistic plant hydraulics model that is the basis of the model we use---SurEau-Ecos.

The model considers water flow to follow Fickian law along water potential gradients in relation to an interface conductance. This allows to calculate water fluxes across plant compartments. The water content of a compartment is calculated through a mass balance. The model distinguishes between apoplastic and symplastic flow that is driven by water potential in the apoplasm and the symplasm, respectively. The water potential in the apoplasm is calculated in relation to the water content and capacitance. Water potential in the symplasm is derived from pressure–volume curves. The water flow through the plant accumulates to give transpiration that is calculated from the vapour pressure deficit and the gas-phase conductance, that is to say the cuticle and the stomatal conductance.

From a technical point of view, the model has two time loops. A forward simulation with a time step about 0.01 s, where water is moved through different plant compartments according to Fickian law. Accumulating fluxes across compartment boundaries gives differences in water mass. The plant compartments are (1) leaf, (2) branch, (3) trunk, and (4) roots. Then, the water mass difference is accumulated to compute the fluxes through the plant. With these, water fluxes, water potential, and hydraulic conductance are calculated.

In the second loop, the large time step with about 1 s updates boundary conditions and parameter values of the SPAC. This time step considers transpiration fluxes, the energy balance, cavitation, and the temperature dependence of physical properties of water. Photosynthesis and plant growth can also be computed in this time step.


Ruffault et al. (2022)° present a further development of SurEau, called SurEau-Ecos.


Thu Mar 28 17:17:52 2024: Should re-read to fully understand the methodology.

Schweiger et al. (2023)

        file = {articles/schweiger_2023.pdf},
        keywords = {trees, droughts, physiology},
        doi = {10.1111/oik.10136},
        volume = {2023},
        pages = {e10136},
        year = {2023},
        journal = {Oikos},
        author = {A.~H. Schweiger and T. Zimmermann and C. Poll and
                  S. Marhan and V. Leyrer and B.~J. Berauer },
        title = {The need to decipher plant drought stress along the
                  soil--plant--atmosphere continuum}


Drought stress can be induced by edaphic and atmospheric droughts.

Edaphic parameters (soil moisture, soil water potential) and atmospheric parameters (vapour pressure deficit) constitute the boundary conditions of the soil–plant–atmosphere continuum (SPAC). The authors argue that these two parameter sets can be used in combination with plant water status parameters (plant water content, leaf water potential) to quantitatively diagnose drought stress in plants.

A possible research opportunity is the underexplored effect of vapour pressure deficit on plant drought stress. The authors survey the literature and conduct a rainfall manipulation experiment with Triticum aestivum (common wheat)° that shows that vapour pressure deficit is a major control on plant functioning. Despite its significance, it is usually not taken into consideration in hydrology, ecology, and climate science.


Carminati and Javaux (2020)° present a mechanistic model description for soil–plant water relations. The authors note that

Such models can be used to generate hypotheses for scenario experiments (below) and/or can be used for experimental planning to predict strength of treatments that would have to be applied to obtain a certain plant stress response (comparable to statistical power tests for experimental planning).

On the value of coupling SPAC models with other mechanistic models, the authors note that it might allow addressing scaling relations from plant to ecosystem level—see, for example, Higgins et al. (2012)°.


Thu Mar 28 16:46:53 2024: This might be interesting to motivate Gregor's forthcoming paper.

Author: ilhan özgen xian

Created: 2024-03-28 Thu 17:19