These data represent the network-adjusted results of relative- and absolute-gravity surveys. Relative-gravity surveys were carried out using two ZLS Corporation Burris relative-gravity meters. The effect of solid Earth tides and ocean loading were removed from the data. Instrument drift was removed by evaluating gravity change during repeated measurements at one or more base stations. Absolute-gravity surveys were carried out using a Micro-g LaCoste, Inc. A-10 absolute-gravity meter. Vertical gradients between the different measuring heights of the absolute- and relative-gravity meters were measured using a relative-gravity meter and fully-adjustable tripod, and used to correlate the measurements between the two instruments. Relative-gravity differences and absolute-gravity data were combined using a least-squares network adjustment, as implemented in the software GSadjust (https://doi.org/10.5066/P9YEIOU8). Information about the network adjustment is provided under Data Quality in the metadata.

These data represent the network-adjusted results of relative- and absolute-gravity surveys. Relative-gravity surveys were carried out using two ZLS Corporation Burris relative-gravity meters. The effect of solid Earth tides and ocean loading were removed from the data. Instrument drift was removed by evaluating gravity change during repeated measurements at one or more base stations. Absolute-gravity surveys were carried out using a Micro-g LaCoste, Inc. A-10 absolute-gravity meter. Vertical gradients between the different measuring heights of the absolute- and relative-gravity meters were measured using a relative-gravity meter and fully-adjustable tripod, and used to correlate the measurements between the two instruments. Relative-gravity differences and absolute-gravity data were combined using a least-squares network adjustment, as implemented in the software GSadjust (https://doi.org/10.5066/P9YEIOU8). Information about the network adjustment is provided under Data Quality in the metadata.

This dataset supercedes an earlier data release and includes all previous data in addition to data from 2019. These data represent the network-adjusted results of relative- and absolute-gravity surveys. Relative-gravity surveys were carried out using a Micro-g LaCoste D-series relative-gravity meter. The effect of solid Earth tides and ocean loading were removed from the data. Instrument drift was removed by evaluating gravity change during repeated measurements at one or more base stations. Absolute-gravity surveys were carried out using a Micro-g LaCoste, Inc. A-10 absolute-gravity meter. Vertical gradients between the different measuring heights of the absolute- and relative-gravity meters were measured using a relative-gravity meter and fully-adjustable tripod, and used to correlate the measurements between the two instruments. Relative-gravity differences and absolute-gravity data were combined using a least-squares network adjustment, as implemented in the software Gravnet (Hwang, C., Wang, C., Lee, L., 2002. Adjustment of relative gravity measurements using weighted and datum-free constraints. Comput. Geosci. 28, 1005–1015). Additional information about the network adjustment is provided under Data Quality. Data pre- and post-processing were carried out using GSadjust (https://github.com/usgs/sgp-GSadjust).

This dataset supercedes an earlier data release and includes all previous data in addition to data from 2019. These data represent the network-adjusted results of relative- and absolute-gravity surveys. Relative-gravity surveys were carried out using a Micro-g LaCoste D-series relative-gravity meter. The effect of solid Earth tides and ocean loading were removed from the data. Instrument drift was removed by evaluating gravity change during repeated measurements at one or more base stations. Absolute-gravity surveys were carried out using a Micro-g LaCoste, Inc. A-10 absolute-gravity meter. Vertical gradients between the different measuring heights of the absolute- and relative-gravity meters were measured using a relative-gravity meter and fully-adjustable tripod, and used to correlate the measurements between the two instruments. Relative-gravity differences and absolute-gravity data were combined using a least-squares network adjustment, as implemented in the software Gravnet (Hwang, C., Wang, C., Lee, L., 2002. Adjustment of relative gravity measurements using weighted and datum-free constraints. Comput. Geosci. 28, 1005–1015). Additional information about the network adjustment is provided under Data Quality. Data pre- and post-processing were carried out using GSadjust (https://github.com/usgs/sgp-GSadjust).

Relative-gravity surveys were carried out using a ZLS Burris relative-gravity meter. The effect of solid Earth tides and ocean loading were removed from the data. Instrument drift was removed by evaluating gravity change during repeated measurements at one or more base stations. Absolute-gravity surveys were carried out using a Micro-g LaCoste, Inc. A-10 absolute-gravity meter. Vertical gradients between the different measuring heights of the absolute- and relative-gravity meters were measured using a relative-gravity meter and fully-adjustable tripod, and used to correlate the measurements between the two instruments. Relative-gravity differences and absolute-gravity data were combined using a least-squares network adjustment, as implemented in the software GSadjust (https://doi.org/10.5066/P9YEIOU8). Information about the network adjustment is provided under Data Quality in the metadata.

Relative-gravity surveys were carried out using a ZLS Burris relative-gravity meter. The effect of solid Earth tides and ocean loading were removed from the data. Instrument drift was removed by evaluating gravity change during repeated measurements at one or more base stations. Absolute-gravity surveys were carried out using a Micro-g LaCoste, Inc. A-10 absolute-gravity meter. Vertical gradients between the different measuring heights of the absolute- and relative-gravity meters were measured using a relative-gravity meter and fully-adjustable tripod, and used to correlate the measurements between the two instruments. Relative-gravity differences and absolute-gravity data were combined using a least-squares network adjustment, as implemented in the software GSadjust (https://doi.org/10.5066/P9YEIOU8). Information about the network adjustment is provided under Data Quality in the metadata.

This dataset contains the network-adjusted results of relative- and absolute-gravity surveys performed from 2019 to 2024 in and near the Santa Cruz River channel in the city of Tucson, Arizona. Relative-gravity surveys were carried out using a ZLS Burris relative-gravity meter. The effect of solid Earth tides and ocean loading were removed from the data. Instrument drift was removed by evaluating gravity change during repeated measurements at one or more base stations. Absolute-gravity surveys were carried out using a Micro-g LaCoste, Inc. A-10 absolute-gravity meter. Vertical gradients between the different measuring heights of the absolute- and relative-gravity meters were measured using a relative-gravity meter and fully-adjustable tripod, and used to correlate the measurements between the two instruments. Relative-gravity differences and absolute-gravity data were combined using a least-squares network adjustment, as implemented in the software GSadjust (https://doi.org/10.5066/P9YEIOU8). Information about the network adjustment is provided under Data Quality in the metadata. This updated version of data now includes gravity data for 2023 and 2024.

This dataset represents the network-adjusted results of relative- and absolute-gravity surveys performed from 2020 to 2023 in and near the city of Phoenix, Arizona. Relative surveys were done using a Zero Length Spring, Inc. Burris relative-gravity meter. Absolute-gravity surveys were done using a Micro-g LaCoste, Inc. A-10 absolute-gravity meter. The effect of solid Earth tides and ocean loading were removed from the data prior to network adjustment. Non-linear instrument drift was removed by evaluating gravity change during repeated measurements at one or more base stations. Vertical gradients between the relative- and absolute-gravity meters were measured at each station where both types of measurement were collected to correlate the measurements of the two instruments. Vertical gradients were measured using a relative-gravity meter and tripod set to the height of the absolute-gravity meter. Relative-gravity differences and absolute-gravity data were combined using least-squares network adjustment, as implemented in the software GSadjust (https://code.usgs.gov/sgp/gsadjust). Data are provided for 105 stations collected over 4 discrete, annual surveys.

This dataset represents the network-adjusted results of relative- and absolute-gravity surveys performed from 2020 to 2023 in and near the city of Phoenix, Arizona. Relative surveys were done using a Zero Length Spring, Inc. Burris relative-gravity meter. Absolute-gravity surveys were done using a Micro-g LaCoste, Inc. A-10 absolute-gravity meter. The effect of solid Earth tides and ocean loading were removed from the data prior to network adjustment. Non-linear instrument drift was removed by evaluating gravity change during repeated measurements at one or more base stations. Vertical gradients between the relative- and absolute-gravity meters were measured at each station where both types of measurement were collected to correlate the measurements of the two instruments. Vertical gradients were measured using a relative-gravity meter and tripod set to the height of the absolute-gravity meter. Relative-gravity differences and absolute-gravity data were combined using least-squares network adjustment, as implemented in the software GSadjust (https://code.usgs.gov/sgp/gsadjust). Data are provided for 105 stations collected over 4 discrete, annual surveys.

This dataset contains absolute-gravity measurements made using an A-10 absolute gravity meter (Micro-g Lacoste, Inc.) between 2009 and 2017 in the Big Chino Subbasin, Yavapai County, Arizona. Measurements were made about 3 times per year at a total of 33 different stations. Data are presented in tabular form, including relevant parameters used for processing. Data were output by g software (Micro-g Lacoste, Inc.) version 9.12.04.23. A correction for laser-frequency drift was applied, based on regular calibration of the HeNe laser used in the A-10 against an iodine-stabilized laser. A second soil-moisture correction was applied based on satellite soil-moisture measurements, an infiltration model, and the elevation of the terrain surrounding each station. This correction is described in detail in the accopmanying interpretive report (Kennedy and others, 2019). Kennedy, J.R., Kahler, L.M., Read, A.L., 2019, Aquifer storage change and storage properties, 2010-2017, in the Big Chino Subbasin, Yavapai County, Arizona: U.S. Geological Survey Scientific Investigations Report 2019-5060, 50 p.