Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates
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 ( $S_{\mathrm{max}}$ [L]) defines the ma...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2020-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/abb10b |
| _version_ | 1797747733810905088 |
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| author | David N Dralle W Jesse Hahm Daniella M Rempe Nathaniel Karst Leander D L Anderegg Sally E Thompson Todd E Dawson William E Dietrich |
| author_facet | David N Dralle W Jesse Hahm Daniella M Rempe Nathaniel Karst Leander D L Anderegg Sally E Thompson Todd E Dawson William E Dietrich |
| author_sort | David N Dralle |
| collection | DOAJ |
| description | 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 ( $S_{\mathrm{max}}$ [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 $S_{\mathrm{max}}$ mediates root zone water storage ( S [L]), and thus dry season plant water use. The model reveals that where $S_{\mathrm{max}}$ 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 $S_{\mathrm{max}}$ 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 $S_{\mathrm{max}}$ cases, landscapes with intermediate $S_{\mathrm{max}}$ values are predicted to minimize variability in dry season evapotranspiration. These diverse plant behaviors enable a mapping between time variations in precipitation, evapotranspiration and $S_{\mathrm{max}}$ , which makes it possible to estimate $S_{\mathrm{max}}$ using remotely sensed vegetation data − that is, using plants as sensors. We test the model using observations of $S_{\mathrm{max}}$ 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. |
| first_indexed | 2024-03-12T15:55:50Z |
| format | Article |
| id | doaj.art-8853220f5c854f268556baa59b6dff38 |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:55:50Z |
| publishDate | 2020-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-8853220f5c854f268556baa59b6dff382023-08-09T14:56:22ZengIOP PublishingEnvironmental Research Letters1748-93262020-01-01151010407410.1088/1748-9326/abb10bPlants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climatesDavid N Dralle0W Jesse Hahm1Daniella M Rempe2Nathaniel Karst3Leander D L Anderegg4Sally E Thompson5Todd E Dawson6William E Dietrich7Pacific Southwest Research Station, United States Forest Service , Davis, CA, United States of AmericaDepartment of Geography, Simon Fraser University , Burnaby, BC, CanadaJackson School of Geosciences, University of Texas at Austin , Austin, TX, United States of AmericaDivision of Mathematics and Science, Babson College , Wellesley, MA, United States of AmericaDepartment of Integrative Biology, University of California at Berkeley , Berkeley, CA, United States of AmericaCivil, Environmental, and Mining Engineering, The University of Western Australia , Perth, AustraliaDepartment of Integrative Biology, University of California at Berkeley , Berkeley, CA, United States of AmericaEarth and Planetary Sciences, University of California at Berkeley , Berkeley, CA, United States of AmericaIn 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 ( $S_{\mathrm{max}}$ [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 $S_{\mathrm{max}}$ mediates root zone water storage ( S [L]), and thus dry season plant water use. The model reveals that where $S_{\mathrm{max}}$ 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 $S_{\mathrm{max}}$ 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 $S_{\mathrm{max}}$ cases, landscapes with intermediate $S_{\mathrm{max}}$ values are predicted to minimize variability in dry season evapotranspiration. These diverse plant behaviors enable a mapping between time variations in precipitation, evapotranspiration and $S_{\mathrm{max}}$ , which makes it possible to estimate $S_{\mathrm{max}}$ using remotely sensed vegetation data − that is, using plants as sensors. We test the model using observations of $S_{\mathrm{max}}$ 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.https://doi.org/10.1088/1748-9326/abb10broot-zone water storage capacityseasonally dryevapotranspirationstochastic |
| spellingShingle | David N Dralle W Jesse Hahm Daniella M Rempe Nathaniel Karst Leander D L Anderegg Sally E Thompson Todd E Dawson William E Dietrich Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates Environmental Research Letters root-zone water storage capacity seasonally dry evapotranspiration stochastic |
| title | Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates |
| title_full | Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates |
| title_fullStr | Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates |
| title_full_unstemmed | Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates |
| title_short | Plants as sensors: vegetation response to rainfall predicts root-zone water storage capacity in Mediterranean-type climates |
| title_sort | plants as sensors vegetation response to rainfall predicts root zone water storage capacity in mediterranean type climates |
| topic | root-zone water storage capacity seasonally dry evapotranspiration stochastic |
| url | https://doi.org/10.1088/1748-9326/abb10b |
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