Authors: Estiarte M1,2 and Vicca S3
Reviewer: Lee H4, Reinsch S5
Measurable unit: ratio (unitless); Measurement scale: site; Equipment costs: €€; Running costs: €; Installation effort: medium; Maintenance effort: medium; Knowledge need: medium to high; Measurement mode: data logger
Water stress (WS) in terrestrial ecosystems develops when soil water is depleted below a threshold. The depletion of water, or water deficit, implies decreases in the soil water potential that propagate through the soil–plant–atmosphere continuum, typically reducing activity of plants and of soil organisms. WS thus has a strong impact on ecosystem functioning and, in the longer-term, also on ecosystem structure. A few indices to quantify WS have been proposed aiming to indicate how vegetation activity is constrained by water depletion (Myers, 1988; Granier et al., 1999; Vicca et al., 2012). WS indices incorporate information on precipitation characteristics, which make them useful in analysing climate treatment effects where the timing and intensity of rain differs and for between-year comparisons in observational studies. WS indices also incorporate information on soil properties (soil water holding capacity) and rooting depth (vegetation exploration of the soil), which facilitates across site comparisons, i.e. along climatic gradients or to compare land-use change and cover.
3.8.1 What and how to measure?
Indices of plant WS can be obtained from direct measures of plant water potential (i.e. Myers, 1988; see protocol 5.9 Psychrometry for Water Potential measurements). Alternatively, whole ecosystem WS can be estimated from measurements of soil water content (SWC). Granier et al. (1999), worked with the relative extractable water (REW) defined as the ratio of the actual content of extractable water to the maximum amount that can be extracted over the entire soil profile reached by roots. They considered that WS starts when the actual water content decreases below 40% of the maximum water content, i.e. REW < 0.4. This threshold was defined empirically after the value above which the transpiration to potential evapotranspiration ratio keeps constant, and below which the ratio decreases linearly as SWC decreases, indicating stomatal closure to regulated transpiration (Granier et al., 1999). Hence, at RES < 0.4, changes in SWC affect plant functioning, while above this threshold, SWC dynamics have little influence on plant activity (Granier et al., 1999) unless the soil is water saturated.
Long-term monitoring of SWC across the soil profile using TDR sensors (see section 3.1 above) may be limited by economic constraints for the instrumentation in several plots, or by the need to preserve the integrity of the experimental plots. A less invasive alternative is a plant–soil water budget model. The modelling of the SWC requires daily meteorological data and the description of the soil and vegetation properties at least at site level (Granier et al., 2007; Longepierre et al., 2014). Modelling allows the estimation of water balance both in reference plots under the natural water regime and in plots with modified precipitation. Modelling the water balance in climate-change experiments, especially under precipitation-manipulation treatments, is highly advised because it completes the description of the variability in ecosystem water relationships. Modelling results help with the interpretation of treatment effects, between-year variability, and facilitate the integration of the experiments into wider across-sites syntheses.
A mid-term option is recording SWC to root depth at site level outside the manipulation plots. This reduces instrumentation costs and perturbation within experimental plots, provides field data for across-site comparisons and, at the same time, can improve the estimation of the SWC within the treated plots through fine-tuning of the parameters during modelling. If experimental manipulations or equipment change the SWC over time inside the experimental plots differently than outside the plots, this method cannot be used. This is the case in, for example, long-term experiments altering rain.
Gold standard
Measuring SWC. Requires the measurement of SWC at all soil layers reached by the roots and the characterisation of the size of the soil water reservoir at each layer down to maximum root depth (we consider a layer the whole horizon if it is fully reached by roots or only the fraction of the horizon down to maximum root depth (see Vicca et al., 2012). The amount of maximum extractable water for plants for a soil layer (EWmax) is defined as:
EWmax = (SWCfc – SWCwp) * thickness of soil layer
where SWCfc is the soil water content at field capacity for the layer and SWCwp is the soil water content at wilting point. The water that can be extracted from a soil layer at a specific soil water content (SWC) is
EW = (SWC – SWCwp) * thickness of soil layer
The maximum total amount of extractable water (TEWmax) and the actual total amount of extractable water (TEW) integrate the entire rooting zone and are thus calculated as the sum of all the n soil layers.
TEWmax =EW1max + … EWnmax
TEW = EW1 + EW2 + …+ EWn
The relative extractable water (REW) is defined as:
REW = TEW / TEWmax
Two WS indices can be estimated: i) the duration of the WS, as the number of days when REW < 0.4, with 0.4 the threshold value for the development of stress, and ii) the intensity of the WS (IWS) calculated as
IWS = sum(maximum[0; (0.4 – REW) / 0.4])
Other methods
Modelling soil water content. Water balance models require continuous meteorological data and parameters describing the soil and the vegetation. A simple model uses the daily precipitation and the estimated potential evapotranspiration obtained from meteorological measurements at site level, or within the plots in case the precipitation or temperature are altered by the manipulation. Soil data to estimate maximum SWC and drainage is required for soil layers down to the rooting depth. Required vegetation description includes maximum leaf area index (LAI) and its phenology if LAI is not constant (e.g. in deciduous species), maximum rooting depth, root density across soil layers and canopy coverage.
Where to start
Granier et al. (1999) defined the method and justified the threshold for relative extractable water and Vicca et al. (2012) suggested using it to compare experiments. Good examples on its application in combination with water balance models are in Granier et al. (2007) and Longepierre et al. (2014). Myers (1988) defined an index of plant water stress from the cumulative integral obtained by frequent measurements of predawn plant water potential.
Installation, field operation, maintenance, interpretation
An initial identification and description of the soil horizons are needed to decide the number and depth of soil moisture probes. See protocols 1.2 Soil type and physical characteristics and 3.1 Soil moisture. For practical reasons of instrumentation, some authors restrict the measurements and calculations to the profile depth were 80% of fine roots occur (Gebauer et al. 2012).
Lateral water flow at deep soil layers may occur especially at the interface between soil and bedrock. Lateral flow can be unravelled by the modelling, for instance as described in Longepierre et al. (2014).
For modelling, see also protocols 4.5 Aboveground plant phenology for leaf phenology and 1.2.1 Soil types and horizons, 4.16 Functional traits and Pérez-Harguindeguy et al. (2013) for rooting depth distribution and LAI (Leaf Area Index).
3.8.2 Special cases, emerging issues, and challenges
Punctual measurements of pre-dawn plant water potential are a good complement. Additionally, provided minimum water potential for extracting water by plant species is available, plant water potentials can be modelled from the modelled soil water (e.g. Longepierre et al. 2014).
Determination of maximum root depth may be challenging in deep-rooted ecosystems and when roots enter rocks.
3.8.3 References
Theory, significance, and large datasets
Allen et al. (1998), Breda et al. (2006), Granier et al. (2000), Piedallu et al. (2011)
More on methods and existing protocols
Gebauer et al. (2012), Longepierre et al. (2014), Vicca et al. (2012)
All references
Allen, R. G., Pereira, L. S., Raes, D., & Smith, M. (1998). Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements. Food and Agricultural Organization of the United Nations.
Breda, N., Huc, R., Granier, A., & Dreyer, E. (2006). Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Annals of Forest Science, 63(6), 625-644.
Gebauer, T., Horna, V., & Leuschner, C. (2012). Canopy transpiration of pure and mixed forest stands with variable abundance of European beech. Journal of Hydrology, 442, 2-14.
Granier, A., Breda, N., Biron, P., & S. Villette, S. (1999). A lumped water balance model to evaluate duration and intensity of drought constraints in forest stands. Ecological Modelling, 116(2-3), 269-283.
Granier, A., Loustau, D., & Breda, N. (2000). A generic model of forest canopy conductance dependent on climate, soil water availability and leaf area index. Annals of Forest Science, 57(8), 755-765.
Granier, A., Reichstein, M., Breda, N., Janssens, I. A., Falge, E., Ciais, P., … Wang, Q. (2007). Evidence for soil water control on carbon and water dynamics in European forests during the extremely dry year: 2003. Agricultural and Forest Meteorology, 143(1-2), 123-145.
Longepierre, D., Mouillot, F., Ouelhazi, B., Ourcival, J. M., Rocheteau, A., Degueldre, D., & Rejeb M.N. (2014). True water constraint under a rainfall interception experiment in a Mediterranean shrubland (Northern Tunisia): confronting discrete measurements with a plant-soil water budget model. Plant Ecology, 215(7), 779-794.
Myers, B. J. (1988). Water-stress integral – a link between short-term stress and long-term growth. Tree Physiology, 4(4), 315-323.
Pérez-Harguindeguy, N., Diaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P., … Cornelissen, J. H. C. (2013). New handbook for standardised measurement of plant functional traits worldwide. Australian Journal of Botany, 61(3), 167-234.
Piedallu, C, Gégout, J. C., Bruand, A., & Seynave, I. (2011). Mapping soil water holding capacity over large areas to predict potential production of forest stands. Geoderma, 160(3), 355-366.
Vicca, S., Gilgen, A. K., Serrano, M. C., Dreesen, F. E., Dukes, J. S., Estiarte, M., … Granier, A. (2012) Urgent need for a common metric to make precipitation manipulation experiments comparable. New Phytologist, 195(3), 518-522.
Authors: Estiarte M1,2 and Vicca S3
Reviewer: Lee H4, Reinsch S5
Affiliations
1 CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
2 CREAF, Cerdanyola del Vallès, Spain
3 Centre of Excellence PLECO (Plants and Ecosystems), Department of Biology, University of Antwerp, Wilrijk, Belgium
4 NORCE Norwegian Research Centre and Bjerknes Centre for Climate Research, Bergen, Norway
5 Centre for Ecology & Hydrology, Environment Centre Wales, Bangor, UK