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Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates
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Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates
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Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates
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Journal Article
Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates
2020
Overview
In Mediterranean-type climates, asynchronicity between energy and water availability means that ecosystems rely heavily on the water-storing capacity of the subsurface to sustain plant water use over the summer dry season. The root-zone water storage capacity ( Smax [L]) defines the maximum volume of water that can be stored in plant accessible locations in the subsurface, but is poorly characterized and difficult to measure at large scales. Here, we develop an ecohydrological modeling framework to describe how Smax mediates root zone water storage (S [L]), and thus dry season plant water use. The model reveals that where Smax is high relative to mean annual rainfall, S is not fully replenished in all years, and root-zone water storage and therefore plant water use are sensitive to annual rainfall. Conversely, where Smax is low, S is replenished in most years but can be depleted rapidly between storm events, increasing plant sensitivity to rainfall patterns at the end of the wet season. In contrast to both the high and low Smax cases, landscapes with intermediate Smax values are predicted to minimize variability in dry season evapotranspiration. These diverse plant behaviors enable a mapping between time variations in precipitation, evapotranspiration and Smax, which makes it possible to estimate Smax using remotely sensed vegetation data − that is, using plants as sensors. We test the model using observations of Smax in soils and weathered bedrock at two sites in the Northern California Coast Ranges. Accurate model performance at these sites, which exhibit strongly contrasting weathering profiles, demonstrates the method is robust across diverse plant communities, and modes of storage and runoff generation.
