Supporting Datasets for "Extreme Winter Precipitation Drives Recharge of Mountain Groundwater in the Sierra Nevada and Cascades of the Western United States"

Results and relevant information need to estimate changes in TWS, groundwater storage, and associated uncertainties associated with the article "Extreme Winter Precipitation Drives Recharge of Deep Mountain Groundwater" by Swarr et al.Dataset S1 – Lists of GNSS stations from each processing center (NGL, CWU, and MEaSUREs) used to invert vertical displacement of the Earth’s surface for changes in TWS. A list of stations that were excluded from each dataset is also included to display the stations that were identified during our postprocessing steps to exhibit some influence from non-elastic hydrologic loading, volcanic deformation, and/or high noise levels and chosen to be excluded from our analysis.Dataset S2 – Lists of lakes, reservoirs, and stream gaging stations from the California Data Exchange Center (CDEC) and the United States Geological Survey (USGS) used to estimate changes in surface water storage as well as stream discharge within the Sierra Nevada and Cascades, respectively. For estimating stream discharge from the Cascades region, we identify USGS gaging stations within 0.1 degrees from the edge of the Cascades polygon. We then identify gaging stations that have an 80% or greater overlap of the stream name they are installed on, assuming several stations may exist on the same stream, retaining the gaging station that has the minimum distance from the Cascades polygon boundary.Dataset S3 – To account for interseismic strain accumulation along the Cascadia Subduction Zone, we remove a variant of the Li et al. (2018) model that represents a 4/9 elastic + 5/9 viscous superposition of the models presented by Li et al. During our postprocessing we remove the reported east, north, and vertical velocities (columns 4,5, and 6; units mm/yr) from each station contained within the dataset. The four digit ID code, latitude, and longitude of each GNSS station contained within this dataset is listed as columns 1, 2, and 3.Dataset S4 – Gridded 0.25-degree resolution dataset (NetCDF) of estimated TWS throughout the western US and associated uncertainty (95% conf. limit) between January 2006 and June 28, 2024. As we use vertical displacement time series from NGL, CWU, and MEaSUREs, the compiled dataset contains the three GNSS-inferred estimates of TWS and associated uncertainties.Dataset S5 – Gridded 0.25-degree resolution dataset (NetCDF) of estimated groundwater storage throughout the western US and associated uncertainty (95% conf. limit) between January 2006 and June 28, 2024. The TWS estimate used in estimating changes in groundwater storage reflects an error weighted mean of the three GNSS-inferred estimates of TWS using vertical displacement time series from the three different processing centers considered here. Uncertainties in groundwater storage are computed by taking the individual uncertainties of our error weighted GNSS-inferred TWS, SNODAS SWE, and NLDAS soil moisture in quadrature.Dataset S6 – Monthly averaged estimates of change in TWS relative to the long-term mean storage (mm equivalent water thickness) and associated uncertainty (95% conf. limit) within the Sierra Nevada and Cascades between January 2006 and June 2024. Here, the estimates of TWS and uncertainty reflect the error weighted mean of the three GNSS-inferred estimates of TWS derived from NGL, CWU, and MEaSUREs vertical displacement time series. The uncertainty in the weighted mean is represented by the square root of the inverse of the sum of variances of the three TWS estimates. As noted previously, we consider the uncertainties of the three GNSS-inferred estimates of TWS to be uncorrelated (see Methods section “GNSS-Inferred Estimates of TWS”).Dataset S7 – Monthly averaged estimates of change in groundwater storage relative to the long-term mean storage (mm equivalent water thickness) and associated uncertainty (95% conf. limit) within the Sierra Nevada and Cascades between January 2006 and June 2024. Groundwater storage was computed by removing estimates of water stored within snow, soil, and surface reservoir/lakes from our error-weighted GNSS-inferred estimate of TWS. Uncertainties in groundwater storage are computed by taking the individual uncertainties of our error weighted GNSS-inferred TWS, SNODAS SWE, and NLDAS soil moisture in quadrature.