Item talk:Q330276
From geokb
{
"DOI": {
"doi": "10.5066/p9tu0r2t",
"prefix": "10.5066",
"suffix": "p9tu0r2t",
"identifiers": [],
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"creators": [
{
"name": "Lombard, Melissa A",
"nameType": "Personal",
"givenName": "Melissa A",
"familyName": "Lombard",
"affiliation": [],
"nameIdentifiers": [
{
"schemeUri": "https://orcid.org",
"nameIdentifier": "https://orcid.org/0000-0001-5924-6556",
"nameIdentifierScheme": "ORCID"
}
]
}
],
"titles": [
{
"title": "Datasets for assessing the impact of drought on arsenic exposure from private domestic wells in the conterminous United States"
}
],
"publisher": "U.S. Geological Survey",
"container": {},
"publicationYear": 2021,
"subjects": [
{
"subject": "Environmental Health, Geochemistry, Hydrology, Water Quality"
}
],
"contributors": [],
"dates": [
{
"date": "2021",
"dateType": "Issued"
}
],
"language": null,
"types": {
"ris": "DATA",
"bibtex": "misc",
"citeproc": "dataset",
"schemaOrg": "Dataset",
"resourceType": "Dataset",
"resourceTypeGeneral": "Dataset"
},
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"relationType": "IsCitedBy",
"relatedIdentifier": "10.1021/acs.est.9b05835",
"relatedIdentifierType": "DOI"
}
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"descriptions": [
{
"description": "Documented in this data release are data used to model and map the probability of arsenic being greater than 10 micrograms per liter in private domestic wells throughout the conterminous United States during drought conditions (Lombard and others, 2020). The model used to predict the probability of arsenic exceeding 10 micrograms per liter in private domestic wells was previously developed and documented by Ayotte and others (2017). Independent variables in the model include groundwater recharge and annual precipitation. In order to assess the impact of drought these variables were altered to simulate drought by reducing the 30-year average annual values by 25 and 50 percent. The impact of drought was also assessed by using groundwater recharge and precipitation values from the year 2012 when approximately 66 percent of the contiguous United States experienced drought. Data sources for groundwater recharge and precipitation for the year 2012 differ from those used in the original model and the drought simulations, therefore a 30-year average climate model was also produced using these new data sources (Thornton and others, 2018; Hay, 2019). Data are documented from the original model, the drought simulations with reduced values of groundwater recharge and precipitation, the year 2012 and the average annual precipitation and groundwater recharge from 1981 - 2010 from the new data sources. The model input data that were used to make the prediction maps are within a zipped folder (Prediction_Input_Data.zip) that contains 50 files, one for each model predictor variable. These include the predictor variables from the original model as well as the updated precipitation and groundwater recharge variables for the year 2012 and the average annual values based on the years1981 - 2010, and groundwater recharge and precipitation variables that were systematically decreased for drought simulations. The model prediction outputs are within a zipped folder (Prediction_Output_Data.zip) that contains 10 tif-format raster files, one for each of the eight drought simulations, one for the year 2012, and one for the updated average annual precipitation and groundwater recharge variables for 1981 - 2010. A third zipped folder (Change_Prob_Maps.zip) contains 10 tif-raster files that show the change in probability of arsenic exceeding 10 micrograms per liter in private domestic wells based on the drought simulations and the data used for the year 2012. References: Lombard, M.A., Daniel, J., Jeddy, Z., Hay, L.E., Ayotte, J.D., 2020, Assessing the impact of drought on arsenic exposure from private domestic wells in the conterminous United States, Environmental Science and Technology, http://dx.doi.org/10.1021/acs.est.9b05835. Ayotte, J.D., Medalie, L., Qi, S.L., Backer, L.C., Nolan, B.T., 2017, Estimating the High Arsenic Domestic Well Population in the Conterminous United States, Environmental Science and Technology, 51, (21), 12443 - 12545. Thornton, P. E.; Thornton, M. M.; Mayer, B. W.; Wei, Y.; Devarakonda, R.; Vose, R. S.; Cook, R. B.,2018, Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3. In ORNL Distributed Active Archive Center. Hay, L. E., 2019, Application of the National Hydrologic Model Infrastructure with the Precipitation-Runoff Modeling System (NHM-PRMS), by HRU Calibrated Version: U.S. Geological Survey data release.",
"descriptionType": "Abstract"
}
],
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