In the past several decades, field studies have shown that woody plants can access substantial volumes of water from the pores and fractures of bedrock. If, like soil moisture, bedrock water storage serves as an important source of plant-available water, then conceptual paradigms regarding water and carbon cycling may need to be revised to incorporate bedrock properties and processes. Here we present a lower-bound estimate of the contribution of bedrock water storage to transpiration across the continental United States (CONUS) using distributed, publicly available datasets. We find:

  • Temporal and spatial patterns of bedrock water use across the CONUS indicate that woody plants extensively access bedrock water for transpiration.
  • Plants across diverse climates and biomes access bedrock water routinely and not just during extreme drought conditions.
  • On an annual basis in California, the volumes of bedrock water transpiration exceed the volumes of water stored in human-made reservoirs, and woody vegetation that accesses bedrock water accounts for over 50% of the aboveground carbon stocks in the state.
  • Plants commonly access rock moisture, as opposed to groundwater, from bedrock and that like soil moisture, rock moisture is a critical component of terrestrial water and carbon cycling.
Schematic of an idealized rooting profile, where the root-zone is broken up into soil and bedrock components

Available Resources

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

Data Products

Data Products

All of the resulting data products from this project are available on Hydroshare. Relevant map data are also accessible via Google Earth Engine.


Google Earth Engine

We used Google Earth Engine in the Google Colab Python API environment for the bulk of the analyses associated with this paper. This is a free service, however it requires users to sign-up prior to use.



Check out the paper (DOI 10.1038/s41586-021-03761-3) and press, including press releases from UT Austin and Simon Fraser University.


Publicly Available Data Used in Analyses

See above for more information on getting started with Google Earth Engine (GEE).

Thumbnail of South America with generic PML data shown as color


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'.

Thumbnail of CONUS with generic PRISM data shown as color


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

Thumbnail schematic of data sources for gNATSGO and generic map of CONUS

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.

Thumbnail of Western US with generic NLCD data shown as color

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'.

Thumbnail of North America with MODIS snow data shown as color

Snow Cover

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

Random thumbnail showing MODIS data across a river drainage

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) ; MODIS Landcover (GEE), and above-ground Carbon (Spawn et al., 2020) (data) (personal GEE asset).

Personal Assets Hosted on GEE

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.

Thumbnail showing Dbedrock data across CONUS

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).

Thumbnail showing outline of masked areas in generic river valley

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.

Thumbnail showing color and topography across a mountain in CA

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!

Thumbnail of western US with generic colors

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.