The data within this data release presents information used to characterize the groundwater-flow system and the development of a groundwater-flow model in the active model area. Conceptual and numerical models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center, in close cooperation with 18 water-resource agencies and stakeholders, to assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources.

The data within this data release presents information used to characterize the groundwater-flow system and the development of a groundwater-flow model in the active model area. Conceptual and numerical models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center, in close cooperation with 18 water-resource agencies and stakeholders, to assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources.

The files and folders in this data release contain the input and output files and MATLAB algorithms used for simulations described in the associated journal article (https://doi.org/10.1007/s10040-024-02868-x). The algorithms implement a data-driven, mechanistic model of vertical infiltration through the unsaturated zone and recharge to the water table that is developed from water-balance concepts. The model of infiltration and recharge is defined in terms of observed states (such as, the water-table altitude) and unobserved states (such as, fluxes through the unsaturated zone and recharge to the water table) and includes both diffuse and preferential flow through the unsaturated zone to the water table. Estimates of the daily contributions to recharge at the water table from diffuse and preferential flow are performed by interpreting daily time-series records of observations of water-table altitude and meteorological inputs (such as, the liquid precipitation rate, snowmelt rate, and the Potential Evapotranspiration (PET) rate). The modeling approach used here is an extension of concepts of modeling infiltration and rapid recharge originally presented in Shapiro and Day-Lewis (2021) https://doi.org/10.1029/2020WR029110 and Shapiro and others (2022) (https://doi.org/10.1111/gwat.13206). The model of infiltration and recharge to the water table is applied to daily records available at 32 U.S. Geological Survey (USGS) Climate Response Network (CRN) wells located in the Delaware River Basin (DRB) in the eastern United States from January 1, 2005, through December 31, 2021. The daily water-table altitude and the meteorological records described in the associated journal article (https://doi.org/10.1007/s10040-024-02868-x) are included as input files to the MATLAB algorithms described in this data release.

The files and folders in this data release contain the input and output files and MATLAB algorithms used for simulations described in the associated journal article (https://doi.org/10.1007/s10040-024-02868-x). The algorithms implement a data-driven, mechanistic model of vertical infiltration through the unsaturated zone and recharge to the water table that is developed from water-balance concepts. The model of infiltration and recharge is defined in terms of observed states (such as, the water-table altitude) and unobserved states (such as, fluxes through the unsaturated zone and recharge to the water table) and includes both diffuse and preferential flow through the unsaturated zone to the water table. Estimates of the daily contributions to recharge at the water table from diffuse and preferential flow are performed by interpreting daily time-series records of observations of water-table altitude and meteorological inputs (such as, the liquid precipitation rate, snowmelt rate, and the Potential Evapotranspiration (PET) rate). The modeling approach used here is an extension of concepts of modeling infiltration and rapid recharge originally presented in Shapiro and Day-Lewis (2021) https://doi.org/10.1029/2020WR029110 and Shapiro and others (2022) (https://doi.org/10.1111/gwat.13206). The model of infiltration and recharge to the water table is applied to daily records available at 32 U.S. Geological Survey (USGS) Climate Response Network (CRN) wells located in the Delaware River Basin (DRB) in the eastern United States from January 1, 2005, through December 31, 2021. The daily water-table altitude and the meteorological records described in the associated journal article (https://doi.org/10.1007/s10040-024-02868-x) are included as input files to the MATLAB algorithms described in this data release.

Comma-separated value (.csv) flat files of wells (point data) with attributes, including calculated alkalinity and assigned hydrogeochemical facies, for the continental United States (CONUS). The files are generated by the salinity calculation workflow in the './output' directory.

The data within this data release presents information used to characterize the groundwater-flow system and the development of a groundwater-flow model in the active model area. Conceptual and numerical models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center, in close cooperation with 18 water-resource agencies and stakeholders, to assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources.

The data within this data release presents information used to characterize the groundwater-flow system and the development of a groundwater-flow model in the active model area. Conceptual and numerical models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center, in close cooperation with 18 water-resource agencies and stakeholders, to assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources.

In 2004, the U.S. Geological Survey initiated a large-scale regional study of the Denver Basin aquifer system to evaluate the hydrologic effects of continued pumping and document an updated groundwater-flow model useful for appraisal of hydrologic conditions (Paschke, 2011). This data release includes spatial datasets used as input for a three-dimensional groundwater-flow model of the Denver Basin aquifer system. Spatial datasets were developed for six Denver Basin bedrock aquifers and five intervening confining units including, from oldest to youngest, the Laramie-Fox Hills aquifer (KLF), Laramie confining unit (KLC), lower Arapahoe aquifer (LKA), Arapahoe confining unit (KAC), upper Arapahoe aquifer (UKA), Denver lower confining unit (TKDLC), Denver aquifer (TKD), Denver upper confining unit (TKDUC), lower Dawson aquifer (LTDW), Dawson confining unit (TDWC), and upper Dawson aquifer (UTDW). Maps of the base altitude and lateral extent of each aquifer were developed for the 11 aquifer and confining units to define the hydrogeologic framework for the model. The BasePoints.zip folder contains 11 point shapefiles of the data points for altitude of the base of each bedrock aquifer and confining unit and one shapefile with locations of wells in the Denver Basin having geophysical logs ("PP1770_seo_geologs_points"). The "BaseContours.zip" folder contains 11 polyline shapefiles of generalized lines of equal base altitude for each bedrock aquifer and confining unit derived from the base-altitude points, and the "Extents.zip" folder contains 11 polygon shapefiles representing the extent of each aquifer and confining unit. Maps of silt-plus-sand thickness were developed for the six bedrock aquifers and were used to estimate hydraulic conductivity and specific yield in the groundwater-flow model. The "SandPoints.zip" folder contains six point shapefiles of data points for silt-plus-sand thickness of each bedrock aquifer, and the "SandContours.zip" folder contains six polyline shapefiles of generalized lines of equal silt-plus-sand thickness derived from the silt-plus-sand thickness points. Shapefiles in the zipped folders are named using the abbreviation or name of the aquifer or confining unit as shown on Table A2 of Paschke (2011).

Geothermal well data from Southern Methodist University (SMU, 2021) and the U.S. Geological Survey (Sass et al., 2005) were used to create maps of estimated background conductive heat flow across the greater Great Basin region of the western US. The heat flow maps in this data release were created using a process that sought to remove hydrothermal convective influence from predictions of background conductive heat flow. Heat flow maps were constructed using a custom-developed iterative process using weighted regression, where convectively influenced outliers were de-emphasized by assigning lower weights to measurements that are very different from the estimated local trend (e.g., local convective influence). The weighted regression algorithm is 2D LOESS (locally estimated scatterplot smoothing; Cleveland et al., 1992), which was used for local linear regression, and smoothness was controlled by varying the number of nearby points used for each local interpolation. Three maps are included in this data release, allowing comparison of the influence of measurement confidence: all wells are equal-weight, and two different published categorizations of measurement quality were used to de-emphasize low-quality measurements. Each map is an estimate of background conductive heat flow as a function of assumed data quality, and a point coverage is also provided for all wells in the compiled dataset. The point coverage includes an important new attribute for geothermal wells: the residual, which can be interpreted as the well’s departure from estimated background heat flow conditions, and the value of residual may be useful in identifying hydrothermal or groundwater influence on conductive heat flow. References Cleveland, W. S., Grosse, E., Shyu, W. M, 1992, Local regression models. Chapter 8 of Statistical Models in S eds J.M. Chambers and T.J. Hastie, Wadsworth & Brooks/Cole. Sass, J. H., S.S. Priest, A.H. Lachenbruch, S.P. Galanis, Jr., T.H. Moses, Jr., J.P. Kennelly, Jr., R.J. Munroe, E.P. Smith, F.V. Grubb, R.H. Husk, Jr., and C.W. Mase, 2005, Summary of supporting data for USGS regional heat flow studies of the Great Basin, 1970-1990, USGS Open file Report, 2005-1207. SMU Regional Heat Flow Database, retrieved from http://geothermal.smu.edu on March 29, 2021.

Geothermal well data from Southern Methodist University (SMU, 2021) and the U.S. Geological Survey (Sass et al., 2005) were used to create maps of estimated background conductive heat flow across the greater Great Basin region of the western US. The heat flow maps in this data release were created using a process that sought to remove hydrothermal convective influence from predictions of background conductive heat flow. Heat flow maps were constructed using a custom-developed iterative process using weighted regression, where convectively influenced outliers were de-emphasized by assigning lower weights to measurements that are very different from the estimated local trend (e.g., local convective influence). The weighted regression algorithm is 2D LOESS (locally estimated scatterplot smoothing; Cleveland et al., 1992), which was used for local linear regression, and smoothness was controlled by varying the number of nearby points used for each local interpolation. Three maps are included in this data release, allowing comparison of the influence of measurement confidence: all wells are equal-weight, and two different published categorizations of measurement quality were used to de-emphasize low-quality measurements. Each map is an estimate of background conductive heat flow as a function of assumed data quality, and a point coverage is also provided for all wells in the compiled dataset. The point coverage includes an important new attribute for geothermal wells: the residual, which can be interpreted as the well’s departure from estimated background heat flow conditions, and the value of residual may be useful in identifying hydrothermal or groundwater influence on conductive heat flow. References Cleveland, W. S., Grosse, E., Shyu, W. M, 1992, Local regression models. Chapter 8 of Statistical Models in S eds J.M. Chambers and T.J. Hastie, Wadsworth & Brooks/Cole. Sass, J. H., S.S. Priest, A.H. Lachenbruch, S.P. Galanis, Jr., T.H. Moses, Jr., J.P. Kennelly, Jr., R.J. Munroe, E.P. Smith, F.V. Grubb, R.H. Husk, Jr., and C.W. Mase, 2005, Summary of supporting data for USGS regional heat flow studies of the Great Basin, 1970-1990, USGS Open file Report, 2005-1207. SMU Regional Heat Flow Database, retrieved from http://geothermal.smu.edu on March 29, 2021.