Overview & Abstract

Below: A conceptual diagram of the partitioning between soil and bedrock water storage capacity, where the latter is the focus of this project.

Figure 1

Woody plant transpiration is a major control on Earth's climate system, streamflow, and human water supply. Soils are widely considered to be the primary reservoir of water for woody plants, however, plants also access water stored in the fractures and pores of bedrock, either as rock moisture (water stored in the unsaturated zone) (Schwinning, 2010) or bedrock groundwater (below the water table) (Miller, 2010). Bedrock as a water source for plants has not been evaluated over large scales, and consequently, its importance to terrestrial water and carbon cycling is poorly understood (Fan, 2019).

Here, we use remotely sensed water fluxes and soil survey data to show that woody plants routinely access significant quantities of water stored in bedrock - commonly as rock moisture -for transpiration across diverse climates and biomes. For example, in California, the volume of bedrock water transpired by woody vegetation annually exceeds that stored in man-made reservoirs, and woody vegetation that withdraws bedrock water accounts for over 50% of the above-ground carbon stocks in the state. Our findings show that bedrock water storage dynamics are a critical element of terrestrial water cycling and therefore necessary to capture the effect of shifting climate on woody ecosystems, above- and below-ground carbon storage, and water resources.

Collaborators and Affiliations

Code and Data Availability

A preprint of the original submission of the manuscript is available here

All of the code for this project is available on the GitHub repository.

All of the resulting data products from this project are available in the Hydroshare repository.

Publicly Available Data Sources

Links to original sources and Google Earth Engine dataset pages

Evapotranspiration

Penman-Monteith-Leuning Evapotranspiration V2 (PML_V2) (GitHub)
(Zhang et al., 2019). 500 m resolution. Available on GEE from 2002 to 2017. Bands used = 'Es' and 'Ec'.

Precipitation

PRISM Daily Spatial Climate Dataset
(Daly et al., 2007). 2.5 arc-minute resolution. Available on GEE from 1981 to 2021. Bands used = 'ppt.'

Soils Depth and Water Storage

Gridded National Soil Survey Geographic Database (gNATSGO)
(Soil Survey Staff, 2019). Not available on GEE, but is hosted as a personal asset for this project, with relevant inputs also available as tifs in Hydroshare respository.

Woody Vegetation

United States Geological Survey National Land Cover Database (USGS NLCD)
30 m resolution. Available on GEE from 1992 to 2017. Bands used: 'landcover'.

Snow Cover

MODIS Terra Daily Global Snow Cover
500 m resolution. Available on GEE from 2000 to 2021. Bands used: 'NDSI_Snow_Cover.''

Additional Datasets

Datasets used in the analyses of bedrock water storage but not required for making or masking project products include: Koppen Climate (Peel et al., 2007) (data) (personal GEE asset); MODIS Landcover (GEE), and above-ground Carbon (Spawn et al., 2020) (data) (personal GEE asset).

Personal Assets Hosted on Google Earth Engine

Please note: This is not a permanent repository. If these links cease to work, please find the same data, which can be added to GEE manually, stored permanently in the Hydroshare repository.

Bedrock water storage capacity

GEE collection with annual bedrock water storage capacities for water years 2003 to 2017, at 500 m resolution for CONUS and masked (see section on Masking Layers).

GEE image of total bedrock water storage capacity (Sbedrock) calculated over the water years 2003 to 2017, at 500 m resolution for CONUS and masked (see section on Masking Layers).

Masking Layers

Three masks were used to restrict analyses to places likely to use bedrock water and GEE images are given in one GEE image collection, along with the combined total of the masks. These masks include:
(1) Woody vegetation
(2) Depth to bedrock less than 1.5 m
(3) Cumulative evapotranspiration less than precipitation from 2003 to 2017. See the Methods section for more information on how these masks were created.

Root-zone storage (Sr)

GEE image of Sr (mm) taken as the maximum root-zone storage deficit from 2003 to 2017. For more information, see the Methods section, as well as Dralle et al., 2020 , which details the snow correction method utilized for this product. The original inspiration was taken from Wang-Erlandsson et al., 2017, so you may also want to check that out!

Other Products

Several other products were used in this study which, although published prior to this work, were not hosted on GEE and therefore were uploaded by the authors as personal assets for use in the code. Please see the above section in this site for links to those data and relevant personal GEE assets.