In the past several decades, field studies have shown that woody plants can access substantial volumes of water from the pores and fractures of bedrock1,2,3. 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 processes4,5,6. Here we present a lower-bound estimate of the contribution of bedrock water storage to transpiration across the continental United States using distributed, publicly available datasets. Temporal and spatial patterns of bedrock water use across the continental United States 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. Our findings indicate that 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.
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All of the datasets generated in this study are available in the Hydroshare repository at https://doi.org/10.4211/hs.a2f0d5fd10f14cd189a3465f72cba6f351. The precipitation data are available from the PRISM Climate Group56 at https://prism.oregonstate.edu/. The evapotranspiration data are available from Penman–Monteith–Leuning Evapotranspiration V2 (PML_V2)58 at https://github.com/gee-hydro/gee_PML. The snow cover data are available from NASA’s MODIS/Terra Snow Cover Daily64 at https://nsidc.org/data/MOD10A1/versions/6. The soil data are available from the USDA’s gNATSGO41 database at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625 and in the Hydroshare repository. The landcover data are available from the USGS’s National Land Cover Database40 at https://www.usgs.gov/centers/eros/science/national-land-cover-database?qt-science_center_objects=0#qt-science_center_objects. The biome data are available from NASA’s MODIS/Terra+Aqua Land Cover Type Yearly65 at https://lpdaac.usgs.gov/products/mcd12q1v006/. The Köppen66 climate data are available at https://people.eng.unimelb.edu.au/mpeel/koppen.html. The above ground biomass19 data are available at https://daac.ornl.gov/VEGETATION/guides/Global_Maps_C_Density_2010.html. With the exception of the gNATSGO and aboveground biomass data, all of the raster datasets are accessible via Google Earth Engine62. Google Earth Engine access URLs can be found in the code accompanying this study (see Code Part 2, Section 1). Source data are provided with this paper.
Codes are available from https://github.com/erica-mccormick/widespread-bedrock-water-use or https://doi.org/10.5281/zenodo.4904036.
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We thank R. Breunig. We acknowledge funding support from the USDA Forest Service Pacific Southwest Research Station, the National Science Foundation Graduate Research Fellowship Program and the US Department of Energy, Office of Science, Office of Biological Environmental Research under award number DESC0018039.
The authors declare no competing interests.
Peer review information Nature thanks Ying Fan, Huade Guan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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Extended data figures and tables
Extended Data Fig. 1 Flow chart of the methodology for bedrock storage deficit and capacity calculations.
Workflow for the calculation of total and annual bedrock water storage deficits (Sbedrock and Dbedrock,Y, respectively). Data products (solid thick border) are reported with their spatial resolution. Calculations and thresholds are reported in white boxes (Methods). Masking procedures exclude areas where output fluxes significantly exceed input fluxes (top right) and include areas where woody vegetation is established on shallow soils (middle right). These masks are applied to the water budget calculation (left and bottom) to arrive at conservative estimates of Sbedrock and Dbedrock,Y at the CONUS scale.
a–d, Annual bedrock water storage deficit, Dbedrock,Y, for 2011 (a), 2014 (b), 2015 (c) and 2017 (d).
Extended Data Fig. 4 Median annual bedrock water storage deficit constitutes more than a quarter of mean annual precipitation in some places.
The magnitude of median Dbedrock divided by mean annual precipitation shown as a percent for California (left) and Texas (right). Mean annual precipitation was calculated in Google Earth Engine62 in the Google Colaboratory environment using the PRISM Daily Spatial Climate Data set AN81d data product56,57.
The distribution of bedrock water storage capacity, Sbedrock, for locations meeting masking and calculation criteria. Where Sbedrock is greater than zero, bedrock water storage is needed to explain observed ET (Methods).
Extended Data Fig. 6 Distribution of bedrock water storage capacity varies by Köppen climate type and biome.
a, Boxplots show median, interquartile range and 1.5 times the interquartile range of Sbedrock across Köppen climate type66 (left) and biome (MODIS landcover classifications65) (right) for locations which meet analysis criteria (Methods). The number of pixels in each category is given above each box. The 25th percentile is non-zero for many biomes and climates. b, Maps indicating the locations associated with each climate (left) and biome (right). Biome and climate subgroups with less than 2,000 km2 are excluded. Summary statistics of groupings are presented in Extended Data Table 1. Post hoc tests (Kruskal–Wallis and Dunn’s tests) reveal statistically significant differences (P = <0.001) of median Sbedrock between all climate group pairings and between all biome group pairings. Boxplots and statistical analyses were processed using the Google Earth Engine62 Python API.
Extended Data Fig. 7 Soil and bedrock water storage capacity at locations where rock moisture use by plants has been documented.
Soil water storage capacity Ssoil (brown) and median Dbedrock,2004–2017 (blue) for locations with documented plant use of rock moisture, that is, bedrock water storage from the unsaturated zone. Superscripts denote locations that are masked, for not being classified as woody vegetation (‡), having soil depth greater than 1.5 m (*) or because the cumulative 2003–2017 evapotranspiration exceeds precipitation (†) (Methods, Extended Data Fig. 1). Data were sourced from the literature review (Methods). References for field studies: refs. 20,69,70,71,72,73,74,75,76,77,78,79,80
Extended Data Fig. 8 Comparison of Sbedrock and median Dbedrock to calculations using double the published soil water storage capacity values.
a, Bedrock water storage capacity (Sbedrock) assuming soil water storage capacity (Ssoil) is double that reported by gNATSGO41 to account for the possibility of soils providing water to ET at saturation, which is commonly estimated as double field capacity. b, Sbedrock without doubling of Ssoil. c, d, Median annual bedrock water storage deficit, Dbedrock,2003–2017, with doubled (c) and original (d) Ssoil.
Extended Data Fig. 9 Bedrock water storage capacity calculated with published values of root-zone storage capacity.
a, b, Two versions of bedrock water storage capacity (Sbedrock) are calculated using root-zone storage capacity (Sr) published by Wang-Erlandsson et al. 61 at a 0.5° (roughly 50 km) resolution with input and output fluxes from Climatic Research Unit Time Series version 3.22 (CRU TS3.22)67 (a) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)68 (b). To arrive at Sbedrock, Ssoil is subtracted from the maximum Sr reported in Wang-Erlandsson et al. 61.
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McCormick, E.L., Dralle, D.N., Hahm, W.J. et al. Widespread woody plant use of water stored in bedrock. Nature 597, 225–229 (2021). https://doi.org/10.1038/s41586-021-03761-3
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