Data for machine learning predictions of pH in the glacial aquifer system, northern USA

Dates

Publication Date: 2020-12-16
Start Date: 1988-01-15
End Date: 2018-09-04

Citation

Brown, C.J., Belitz, K., Erickson, M.L., Elliott, S.M., Kauffman, L.J., Ransom, K.M., Reddy, J.E., and Stackelberg, P.E., 2020, Data for machine learning predictions of pH in the glacial aquifer system, northern USA: U.S. Geological Survey data release, https://doi.org/10.5066/P9RF0R6E.

Summary

A boosted regression tree (BRT) model was developed to predict pH conditions in three-dimensions throughout the glacial aquifer system (GLAC) of the contiguous United States using pH measurements in samples from 18,258 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and when coupled with long flow paths, results in the most alkaline conditions. Conversely, in areas where glacial sediments are thin and carbonate-poor, pH conditions remain acidic. At depths typical of drinking-water supplies, predicted pH > 7.5 – which is associated with arsenic mobilization – occurs more frequently than predicted pH [...]

Summary

A boosted regression tree (BRT) model was developed to predict pH conditions in three-dimensions throughout the glacial aquifer system (GLAC) of the contiguous United States using pH measurements in samples from 18,258 wells and predictor variables that represent aspects of the hydrogeologic setting. Model results indicate that the carbonate content of soils and aquifer materials strongly controls pH and when coupled with long flow paths, results in the most alkaline conditions. Conversely, in areas where glacial sediments are thin and carbonate-poor, pH conditions remain acidic. At depths typical of drinking-water supplies, predicted pH > 7.5 – which is associated with arsenic mobilization – occurs more frequently than predicted pH < 6 – which is associated with water corrosivity and the mobilization of other trace elements. A novel aspect of this model was the inclusion of numerically based estimates of groundwater flow characteristics (age and flow path length) as predictor variables. The sensitivity of pH predictions to these variables was consistent with hydrologic understanding of groundwater flow systems and the geochemical evolution of groundwater quality. The model was not developed to provide precise estimates of pH at any given location. Rather, it can be used to more generally identify areas where contaminants may be mobilized into groundwater and where corrosivity issues may be of concern to set priorities among areas for future groundwater monitoring.

Data are provided in 2 tables and 3 compressed files that contain various files associated with the BRT model.

The 2 tables include:
1) pH_Predictions_GLAC_GeochMod_Dataset.csv: This table is generally a subset of the pH dataset (the measured pH data for well sites that were separated into the training and testing dataset files “trnData.txt” and “testData.txt” included in model_archive.7z) that was used to model pH conditions but includes some additional wells from Wilson and others (2019). The table includes pH, general chemical characteristics, and concentrations of major and trace elements, calculated parameters, and mineral saturation indices (SI) computed with PHREEQC (Parkhurst and Appelo, 2013) for 9,655 groundwater samples from wells in the GLAC.
2) pH_Predictions_GLAC_Variable_Descriptions.txt: A table listing all variables (short abbreviation and long description) used in the model, including the importance rank of the variable, units, and reference.

The 3 compressed files include:
1) model_archive.7z: contains 15 files associated with the BRT model
2) rstack_dom.7z: rstack_dom.txt
3) rstack_pub.7z : rstack_pub.txt
Refer to the README.txt file in model_archive.7z for information about the files in the archive and how to use them to run the BRT model. "The "rstack" files represent raster stacks which are a collection of raster layer objects with the same spatial extent and resolution and which are vertically aligned. Rstack.dom consists of raster layer objects at the depth typically used for domestic supplies and rstack.pub, those at the depth typically used for public supplies.

Contacts

Point of Contact :: Craig Brown
Originator :: Craig Brown, Kenneth Belitz, Melinda L Erickson, Sarah M Elliott, Leon J Kauffman, Katherine M Ransom, James E Reddy, Paul E Stackelberg
Metadata Contact :: Craig Brown, U.S. Geological Survey
Publisher :: U.S. Geological Survey
Distributor :: U.S. Geological Survey - ScienceBase
USGS Mission Area :: Water Resources
SDC Data Owner :: Earth System Processes Division

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pH_Predictions_GLAC_Metadata.xml “Metadata” Original FGDC Metadata	View	90.92 KB	application/fgdc+xml
rstack_dom.7z “Domestic supply explanatory model variables”		586.53 MB	application/x-7z-compressed
rstack_pub.7z “Public supply explanatory model variables”		586.23 MB	application/x-7z-compressed
pH_Predictions_GLAC_GeochMod_Dataset.csv “pH and geochemical data”		1.34 MB	text/csv
pH_Predictions_GLAC_Variable_Descriptions.txt “BRT model variable descriptions”		13.94 KB	text/plain
model_archive.7z “BRT model archive”		59.22 MB	application/x-7z-compressed

Related External Resources

Type: Related Primary Publication

Stackelberg, P.E., Belitz, K., Brown, C.J., Erickson, M.L., Elliott, S.M., Kauffman, L.J., Ransom, K.M. and Reddy, J.E. (2020), Machine learning predictions of pH in the glacial aquifer system, northern USA. Groundwater. Accepted Author Manuscript. https://doi.org/10.1111/gwat.13063	https://doi.org/10.1111/gwat.13063

Purpose

Data on pH measurements were compiled for wells withdrawing water from the glacial aquifer system. These pH data were combined with measured and calculated data from 97 predictor variables that integrate information on geology, hydrology, climate and other factors that might affect pH conditions. A machine learning model (boosted regression trees) was developed to predict pH conditions throughout the glacial aquifer system.

Data for machine learning predictions of pH in the glacial aquifer system, northern USA

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