Predicting baseflow dynamics, protecting aquatic habitat, and managing legacy contaminants requires explicit characterization and prediction of groundwater discharge patterns throughout river networks. Using handheld thermal infrared (TIR) cameras, we surveyed 47 km of stream length across the Farmington River watershed (1,570 km2; CT and MA, USA), mapping locations of bank and waterline groundwater discharges based on their thermal signature. Using the observed groundwater discharge locations and predicted groundwater discharge rates from 6 variations of a numerical groundwater-flow model (MODFLOW-NWT), we compared 1) predicted groundwater-discharge rates in areas with and without observed groundwater discharge, 2) spatial patterns of observed and predicted groundwater discharge locations, and 3) density of observed groundwater discharge locations with predicted discharge rates. Five of six models reasonably predicted the spatial patterns of discharge locations along the 5th order mainstem, but fewer models predicted groundwater discharge patterns in smaller streams. Our results highlight 1) the feasibility of using TIR observations to evaluate groundwater models, 2) model parameters that influence discharge prediction accuracy (riverbed sediment and bedrock hydraulic conductivity and river-aquifer connections), and 3) current strengths and future opportunities for improved modeling of groundwater-discharge patterns.
The Des Moines River alluvial aquifer is an important source of water for Des Moines Water Works, the municipal water utility that provides residential and commercial water resources to the residents of Des Moines, Iowa, and surrounding municipalities. As an initial step in developing a better understanding of the groundwater resources of the Des Moines River alluvial aquifer, the U.S. Geological Survey constructed a steady-state numerical groundwater flow model in cooperation with Des Moines Water Works to simulate water-table elevations in the Des Moines River alluvial aquifer near Prospect Park in Des Moines under winter low-flow conditions.
A simple conceptual model consisting of a hydrogeologic framework, water budget, and inferred water-table elevation map was developed for the model area. The inferred water-table elevation map was constructed based on general knowledge of hydrogeology within the model area and was used to set calibration targets for numerical model calibration. A steady-state numerical model was constructed based on the conceptual model using MODFLOW-NWT to simulate an area of about 15 square kilometers near Prospect Park in Des Moines. Parameter ESTimation software was used for model calibration to assess and optimize performance of the horizontal hydraulic conductivity and recharge parameters. The numerical groundwater flow model and supporting data are available in the USGS data release associated with this report, which contains the model archive.
Performance of the calibrated steady-state model was assessed by comparing observed and simulated water-table elevations, as well as estimated and simulated contributions to streamflow within the model area. The difference between observed water-table elevations and simulated water-table elevations was −0.1 meter at the majority of calibration targets, with the negative value indicating an overestimation of the simulated water-table elevation value compared to the observed water-table elevation value, and the root mean square error was 0.13 meter, which represents about 20 percent of the difference in observed water-table elevations. The simulated value of contributions to streamflow within the model area was considered similar to the estimated value, increasing confidence in the ability of the model to accurately represent the groundwater flow system in the Des Moines River alluvial aquifer in the model area during winter low-flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211110","collaboration":"Prepared in cooperation with Des Moines Water Works","usgsCitation":"FitzGerald, K.M., Ha, W.S., Haj, A.E., Gruhn, L.R., Bristow, E.L., and Weber, J.R., 2022, A steady-state groundwater flow model for the Des Moines River alluvial aquifer near Prospect Park, Des Moines, Iowa: U.S. Geological Survey Open-File Report 2021–1110, 20 p., https://doi.org/10.3133/ofr20211110.","productDescription":"Report: vii, 20 p.; Data Release; Dataset","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130288","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501532,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112070.htm","linkFileType":{"id":5,"text":"html"}},{"id":393916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1110/ofr20211110.pdf","text":"Report","size":"2.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1110"},{"id":393915,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1110/coverthb.jpg"},{"id":393917,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F3CKLC","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate groundwater levels in the Des Moines River alluvial aquifer near Des Moines, Iowa"},{"id":393918,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"}],"country":"United States","state":"Iowa","city":"Des Moines","otherGeospatial":"Prospect Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.65175247192383,\n 41.611463744813506\n ],\n [\n -93.61836433410645,\n 41.611463744813506\n ],\n [\n -93.61836433410645,\n 41.63019942878951\n ],\n [\n -93.65175247192383,\n 41.63019942878951\n ],\n [\n -93.65175247192383,\n 41.611463744813506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 South Clinton Street, Suite 269
Iowa City, IA 52240
Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.
Groundwater-flow models depend on hydraulic head and flux observations for evaluation and calibration. A different type of observation—change in storage measured using repeat microgravity—can also be used for parameter estimation by simulating the expected change in gravity from a groundwater model and including the observation misfit in the objective function. The method is demonstrated using new software linked to MODFLOW input and output files and field data from the vicinity of the All American Canal in southeast California, USA. Over a 10-year period following lining of the previously highly permeable canal with concrete, gravity decreased by over 100 μGal (equivalent to about 2.5 m of free-standing water) at some locations as seepage decreased and the remnant groundwater mound dissipated into the aquifer or was removed by groundwater pumping. Simulated gravity from a MODFLOW model closely matched observations, and repeat microgravity data proved useful for constraining both hydraulic conductivity and specific yield estimates. Specific yield estimated using the infinite-horizontal slab approximation agreed well with model-derived values, and the departure from the linear, flat-water-table approximation was small, less than 2%, despite relatively large and dynamic water-table slope. First-order second-moment parameter uncertainty analysis shows reduction in uncertainty for all hydraulic conductivity and specific yield parameter estimates with the addition of repeat microgravity data, as compared to drawdown data alone.
The MODFLOW API allows other programs to control MODFLOW and interactively change variables without having to modify the source code. The MODFLOW API is based on the Basic Model Interface (BMI), which is a set of conventions that define how to initialize a simulation, update the model state by advancing in time, and finalize the run. For many existing MODFLOW coupling applications, the information provided to MODFLOW must be updated multiple times in a time step. As this capability to modify variables within a time step is not defined by the BMI, an extension to BMI was developed. This eXtended Model Interface is part of the MODFLOW API and allows such a tight coupling to other models. Examples are included for a variety of use cases, including new flexibility for users to develop custom packages without modifying the MODFLOW source code and coupling MODFLOW with other models and optimization libraries.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105257","usgsCitation":"Hughes, J.D., Russcher, M.J., Langevin, C.D., Morway, E.D., and McDonald, R.R., 2022, The MODFLOW Application Programming Interface for simulationcontrol and software interoperability: Environmental Modelling & Software, v. 148, 105257, 14 p., https://doi.org/10.1016/j.envsoft.2021.105257.","productDescription":"105257, 14 p.","ipdsId":"IP-130102","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":449429,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105257","text":"Publisher Index Page"},{"id":395044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"148","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hughes, Joseph D. 0000-0003-1311-2354 jdhughes@usgs.gov","orcid":"https://orcid.org/0000-0003-1311-2354","contributorId":2492,"corporation":false,"usgs":true,"family":"Hughes","given":"Joseph","email":"jdhughes@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":832038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russcher, Martijn J. 0000-0001-8799-6514","orcid":"https://orcid.org/0000-0001-8799-6514","contributorId":272524,"corporation":false,"usgs":false,"family":"Russcher","given":"Martijn","email":"","middleInitial":"J.","affiliations":[{"id":36257,"text":"Deltares","active":true,"usgs":false}],"preferred":false,"id":832039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langevin, Christian D. 0000-0001-5610-9759 langevin@usgs.gov","orcid":"https://orcid.org/0000-0001-5610-9759","contributorId":1030,"corporation":false,"usgs":true,"family":"Langevin","given":"Christian","email":"langevin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":832040,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morway, Eric D. 0000-0002-8553-6140 emorway@usgs.gov","orcid":"https://orcid.org/0000-0002-8553-6140","contributorId":4320,"corporation":false,"usgs":true,"family":"Morway","given":"Eric","email":"emorway@usgs.gov","middleInitial":"D.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832041,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McDonald, Richard R. 0000-0002-0703-0638 rmcd@usgs.gov","orcid":"https://orcid.org/0000-0002-0703-0638","contributorId":2428,"corporation":false,"usgs":true,"family":"McDonald","given":"Richard","email":"rmcd@usgs.gov","middleInitial":"R.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":832042,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226709,"text":"sir20215124 - 2021 - Groundwater chemistry, hydrogeologic properties, bioremediation potential, and three-dimensional numerical simulation of the sand and gravel aquifer at Naval Air Station Whiting Field, near Milton, Florida, 2015–20","interactions":[],"lastModifiedDate":"2022-04-14T16:00:18.279252","indexId":"sir20215124","displayToPublicDate":"2021-12-16T14:25:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5124","displayTitle":"Groundwater Chemistry, Hydrogeologic Properties, Bioremediation Potential, and Three-Dimensional Numerical Simulation of the Sand and Gravel Aquifer at Naval Air Station Whiting Field, near Milton, Florida, 2015–20","title":"Groundwater chemistry, hydrogeologic properties, bioremediation potential, and three-dimensional numerical simulation of the sand and gravel aquifer at Naval Air Station Whiting Field, near Milton, Florida, 2015–20","docAbstract":"The U.S. Geological Survey completed a study between 2015 and 2020 of groundwater contamination in the sand and gravel aquifer at a Superfund site in northwestern Florida. Groundwater-quality samples were collected from representative monitoring wells located along a groundwater-flow pathway and analyzed in the field and laboratory. In general, ambient groundwater in the sand and gravel aquifer is acidic, dilute, and oxic. Groundwater age-dating results indicate recharge to the contaminated parts of the aquifer occurred between the 1970s and 1980s. Natural gamma, electromagnetic induction, and borehole nuclear magnetic resonance logs indicated that aquifer hydraulic conductivities generally increased with depth as the aquifer formation material became coarser, characteristic of a prograding marginal-marine delta depositional environment. Aquifer formation material incubated with radiocarbon (carbon-14) cis-1,2-Dichloroethylene demonstrated biodegradation directly to carbon dioxide in contaminated and uncontaminated parts of the aquifer. A three-dimensional, numerical groundwater-flow MODFLOW model of the sand and gravel aquifer in the study area was constructed. The calibrated model reasonably reproduced measured groundwater heads and streamflows. Moreover, the model can be used to run simulations of outcomes of potential remedial strategies, such as monitored natural attenuation, as part of future feasibility studies in the area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215124","collaboration":"Prepared in cooperation with the U.S. Navy Naval Facilities Engineering Systems Command Southeast","usgsCitation":"Landmeyer, J.E., Swain, E.D., Johnson, C.D., Lisle, J.T., McBride, W.S., Chung, D.H., and Singletary, M.A., 2021, Groundwater chemistry, hydrogeologic properties, bioremediation potential, and three-dimensional numerical simulation of the sand and gravel aquifer at Naval Air Station Whiting Field, near Milton, Florida, 2015–20: U.S. Geological Survey Scientific Investigations Report 2021–5124, 52 p., https://doi.org/10.3133/sir20215124.","productDescription":"Report: xi, 52 p.; Data Release: Dataset","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119956","costCenters":[{"id":156,"text":"Caribbean Water Science Center","active":true,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":393011,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215124/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":393010,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M0OD8F","text":"USGS data release","linkHelpText":"MODFLOW simulator used to assess groundwater flow for the Whiting Field Naval Air Station, Milton, FL"},{"id":392549,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":392548,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5124/images/"},{"id":392547,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5124/sir20215124.XML"},{"id":392546,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5124/sir20215124.pdf","text":"Report","size":"4.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5124"},{"id":392545,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5124/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Naval Air Station Whiting Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.10304260253906,\n 30.621368403494955\n ],\n [\n -86.89773559570312,\n 30.621368403494955\n ],\n [\n -86.89773559570312,\n 30.784317689718897\n ],\n [\n -87.10304260253906,\n 30.784317689718897\n ],\n [\n -87.10304260253906,\n 30.621368403494955\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive
Suite 500
Norcross, GA 30093
Aquatic systems in and around the Long Island Sound (LIS) provide a variety of ecological and economic benefits, but in some areas of the LIS, aquatic ecosystems have become degraded by excess nitrogen. A substantial fraction of the nitrogen inputs to the LIS are transported through the groundwater-flow system. Because groundwater travel times in surficial aquifers can exceed 100 years, multiyear lags are introduced between inputs at the water table in recharge areas and discharge to inland or coastal receiving waters. The U.S. Geological Survey, in cooperation with the Connecticut Department of Energy and Environmental Protection and the U.S. Environmental Protection Agency’s Long Island Sound Study, developed a steady-state groundwater model of the watersheds draining from the northern shore of the LIS for the purpose of calculating groundwater budgets and travel times to coastal waters.
The model was developed by using the MODFLOW–NWT software and existing spatial data on aquifers, river networks, land-surface altitudes, land cover, groundwater recharge, and water use. Coastal waters were delineated on the basis of the National Wetland Inventory; all non-coastal waters were collectively termed “inland waters.” A coarse-resolution model was calibrated by using the PEST++ software, long-term records of water levels in 65 wells, stream altitudes from 477 streams, base-flow records for 14 streamgages that are relatively unaffected by withdrawals, and error metrics based on incorrectly simulated flooding and incorrectly simulated dry streams. The calibrated values were used in a fine-resolution model in which the mean absolute residuals were 4.5 meters for groundwater levels, 1.3 meters for stream altitudes, and 7,200 cubic meters per day (2.9 cubic feet per second) for base flow. About 89 percent of the terrestrial cells were correctly simulated with the water table below land surface, and nearly 90 percent of the cells representing streams were correctly simulated as having the water table above the stream bottom. Together, these metrics suggest that this model is robust for simulating regional-scale groundwater patterns.
Simulated groundwater budgets were compiled for the entire study area, for each HUC12 (Hydrologic Unit Code no. 12) watershed and its adjacent coastal waters, if applicable, within the study area, and for 14 coastal-embayment watersheds. Most groundwater (90.6 percent of inflows) discharged to inland waters, with smaller fractions to coastal waters (7.0 percent) and well withdrawals (2.4 percent). When computed for HUC12 watersheds with coastal discharge, the portions of groundwater discharging to coastal waters ranged from 0.02 to 66 percent of groundwater outflows, with a median of 13 percent. Within priority-embayment watersheds, the portions of groundwater discharging to coastal waters ranged from 2 to 56 percent, with a median of 15 percent.
Groundwater travel times also were simulated for the entire study area, for each HUC12 watershed and its adjacent coastal waters, if applicable, within the study area and for 14 priority coastal embayments. Within the entire study area, the median groundwater travel time was 1.9 years, with an interquartile range of 0.1 to 5.9 years. Sensitivity analysis of groundwater travel times within a subbasin in the study area indicates that the travel times are a function of the grid resolution, with coarser grids resulting in shorter median travel times. Travel times for groundwater discharging to coastal waters were similar to travel times for groundwater discharging to inland waters, with a median of 1.9 years. Median travel times for the HUC12 watersheds ranged from 0.9 to 53.5 years, with a median of 1.8 years. Among HUC12 watersheds that include coastal areas, travel times for groundwater discharging to coastal waters ranged from less than 1 to 61.6 years, with a median of 2.8 years. The HUC12 watersheds with the longest simulated travel times were in the urban area near New York City where the model performance is less accurate. Median travel times for groundwater discharging to coastal waters within the priority-embayment watersheds ranged from less than 1 to 18.6 years, with a median of 2.3 years.
A more focused analysis was conducted for the Niantic River watershed to demonstrate the applicability of the regional model to local-scale nitrogen-transport analyses by using nitrogen-input and -attenuation rates from literature sources. Nitrogen inputs were estimated by using land-cover-based loading factors, and attenuation was estimated by using attenuation factors based on geologic zones and soil properties. Based on this analysis, groundwater transports an estimated 22,000 kilograms of nitrogen per year (2.9 kilograms of nitrogen per hectare per year) to streams, rivers, and coastal waters within the Niantic River watershed. Approximately 36 percent of discharging nitrogen is from atmospheric-deposition sources, 38 percent is from fertilizers, and 26 percent is from septic systems. Most of the groundwater-transported nitrogen (88 percent) discharges first to streams and rivers, with only 12 percent discharging directly to coastal waters. Travel times for groundwater-transported nitrogen ranged from less than 1 day to more than 100 years, with a median of 1.6 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215116","collaboration":"Prepared in cooperation with the United States Environmental Protection Agency’s Long Island Sound Study and the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Barclay, J.R., and Mullaney, J.R., 2021, Simulation of groundwater budgets and travel times for watersheds on the north shore of Long Island Sound, with implications for nitrogen-transport studies: U.S. Geological Survey Scientific Investigations Report 2021–5116, 84 p., https://doi.org/10.3133/sir20215116.","productDescription":"Report: x, 84 p.; 2 Data Releases","numberOfPages":"84","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-117840","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":391933,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91TQ895","text":"USGS data release","linkHelpText":"Summary data on groundwater budgets and travel times for watersheds on the north shore of Long Island Sound"},{"id":391932,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BLHPIT","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH groundwater flow models of steady-state conditions in coastal Connecticut and adjacent areas of New York and Rhode Island, as well as a nitrogen transport model of the Niantic River watershed"},{"id":391931,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5116/sir20215116.pdf","text":"Report","size":"30.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5116"},{"id":391930,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5116/coverthb.jpg"}],"country":"United States","state":"Connecticut, New York, Rhode Island","otherGeospatial":"Long island Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.9324951171875,\n 40.826280356677124\n ],\n [\n -71.45782470703125,\n 40.826280356677124\n ],\n [\n -71.45782470703125,\n 41.50857729743935\n ],\n [\n -73.9324951171875,\n 41.50857729743935\n ],\n [\n -73.9324951171875,\n 40.826280356677124\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
A previously constructed numerical model simulating the regional groundwater flow system in the vicinity of the Wright-Patterson Air Force Base near Dayton, Ohio, was updated to incorporate current hydrologic stresses and conditions and improve the usefulness of the model for water-supply planning and protection. The original model, which simulated conditions from 1997 to 2001, was reconstructed with the most recently available U.S. Geological Survey groundwater modeling software and recalibrated to represent average groundwater flow conditions for the period of October 2018.
The steady-state, three-dimensional, three-layer MODFLOW model of the aquifer encompasses about 241 square miles in Montgomery, Greene, and Clark Counties. The Great Miami buried-valley aquifer consists of glacial sands and gravels in a buried bedrock valley. The shale bedrock in the area is poorly permeable, but the glacial deposits can yield as much as 2,000 gallons per minute to wells. As groundwater is the primary source of drinking water in the heavily populated study area, groundwater pumping from the buried-valley aquifer represents the largest time-varying stress in the groundwater flow model. The model simulated 228 pumped wells. Hydraulic conductivities in the model ranged from less than 1 foot per day to 450 feet per day. Simulated recharge rates ranged from 6 inches per year to 12.2 inches per year. Boundary conditions and aquifer properties were unchanged from the previous model. Model grid spacing and orientation also were not modified from the previous model.
Parameter estimation software was used to optimize model input parameters by matching simulated values to observed (estimated or measured) values. Calibrated parameters included horizontal hydraulic conductivity, vertical hydraulic conductivity, riverbed conductance, and recharge. Model calibration used measured water levels (hydraulic heads) from 124 observation wells, and streamflow gain/loss measurements from select reaches of the Mad River and its tributaries were compared with simulated streamflow gain/loss. Performance of the updated model is similar to previous studies. Eighty-one percent of simulated hydraulic heads were within 10 feet of the measured hydraulic heads, but comparison of the simulated streamflow gain/loss with the measured gain/loss indicates that streamflow gain/loss is not well represented by the updated model.
The particle tracking program MODPATH was used to calculate groundwater flow paths from recharge areas to selected existing and proposed groundwater withdrawal sites that service Wright-Patterson Air Force Base. Areas contributing groundwater to withdrawal sites were delineated based on 1-, 5-, and 10-year groundwater travel times. In addition, groundwater flow paths were calculated to simulate a groundwater release at eight sites near Wright-Patterson Air Force Base.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215115","collaboration":"Prepared in cooperation with the U.S. Air Force Civil Engineering Center, Wright-Patterson Air Force Base","usgsCitation":"Riddle, A.D., 2021, Update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio: U.S. Geological Survey Scientific Investigations Report 2021–5115, 36 p., https://doi.org/ 10.3133/ sir20215115.","onlineOnly":"Y","ipdsId":"IP-119316","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":391514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5115/sir20215115.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5115"},{"id":391515,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FN1JK4","text":"USGS data release","linkHelpText":"MODFLOW 6 and MODPATH 7 model data sets used for the update of the groundwater flow model for the Great Miami buried-valley aquifer in the vicinity of Wright-Patterson Air Force Base near Dayton, Ohio"},{"id":391513,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5115/coverthb.jpg"}],"contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
The Partridge River Basin (PRB) covers 156 square miles in northeastern Minnesota with headwaters in the Mesabi Iron Range. The basin is characterized by extensive wetlands, lakes, and streams in poorly drained and often thin glacial material overlying Proterozoic bedrock. To better understand the interaction between these extensive surface water features and the groundwater system, a three-dimensional, steady-state, groundwater-flow model of the PRB was developed by the U.S. Geological Survey in cooperation with the Great Lakes Indian Fish & Wildlife Commission using the finite-difference computer code MODFLOW-NWT. The model simulates steady-state base flow in streams and groundwater interactions using the streamflow routing (SFR2) package. Existing mining features including tailings basins, stockpiles, pumped mine pits, and flooded mine pits were simulated using either high hydraulic conductivity zones or the drain (DRN) package. The unsaturated zone flow (UZF) package was used to better represent the groundwater system in areas with a high water table and for wetlands often associated with such areas. UZF typically is used to represent unsaturated zone processes but also can simulate the rejection of recharge and groundwater discharge to the land surface when the water table is near land surface. The steady-state model used data from the 2011 to 2013 period when 2011 high-resolution land surface (light detecting and ranging [lidar]) data were available that reflected land-surface and water elevations from mining activity in the basin. The parameter-estimation software suite PEST_HP was used to obtain a best fit of the modeled to measured groundwater levels, streamflow, pit inflow rates, and mapped peat deposits. The PEST calibration used the target residuals from two models with the same model parameters and targets from two separate periods: (1) a 1995–2015 calibration model, which provided a larger number of calibration targets, and (2) a 2011–2013 mining conditions model, which included calibration targets that reflected conditions consistent with the modeled mine-workings topography.
Calibration of the PRB model resulted in ranges of glacial horizontal hydraulic conductivity parameters that generally agreed with literature values and other models of the region. Horizontal hydraulic conductivity of the bedrock was higher in the upper bedrock layers where numerous and continuous fractures have been observed and lower in the deeper bedrock layers. Average basin-wide calibrated infiltration was 5.3 inches per year. An average of 4.6 inches per year of infiltration crosses the water table and becomes recharge and 0.7 inch per year is rejected by UZF due to saturated conditions at the land surface. Simulated groundwater runoff (the sum of rejected recharge and groundwater seepage to the land surface) can either be routed to streams or removed from the model as evapotranspiration. The calibrated model indicates relatively shallow groundwater-flow paths dominating and approximately 50 percent of the stream base flow coming from groundwater runoff.
The 2011–2013 mining conditions model was then used to develop five model scenarios simulating the response of the groundwater and surface-water system to potential hydrologic stress. The purpose of these mine pit scenarios is to present a possible workflow to quantify a model’s uncertainty for a given model forecast and serve as a possible guide for initial data collection that may improve a future model’s ability to make such a forecast. The scenarios included one scenario with the currently existing Peter Mitchell pit at final buildout and flooded to an elevation of 1,500 feet, and four scenarios with a hypothetical, new mine pit plus the flooded Peter Mitchell at final buildout. The five model scenarios were used to forecast streamflow at six locations in the PRB, pit inflow rates for the new mine pits and the flooded Peter Mitchell pit, and the average depth to water in 12 wetlands. A linear uncertainty analysis was performed using information from the PEST calibration and tools in the PyEMU python package to assess model uncertainty propagation to the model forecasts. Streamflows generally were reduced with future mining and the greatest streamflow reductions occurred from the flooded Peter Mitchell Pit, probably due to its large size. Average depth to groundwater in wetlands was most affected the closer the wetland was to a new mine pit.
Linear uncertainty methods were also used to evaluate data worth, which is the ability for potential new groundwater elevation observations to reduce the uncertainty in scenario forecasts. Data worth was performed for a grid of new hydraulic head observations. Overall, areas with nonnegligible data worth generally corresponded to wetland areas with no groundwater seepage to land surface from UZF. These model behaviors indicated that the land-surface boundary condition simulated by the UZF package was pinning the groundwater elevations to the land surface in areas with groundwater seepage (33 percent of the 2011–2013 base conditions model) such that the sensitivity to new observations in these areas was minimal. Therefore, representing wetlands as boundary conditions minimized the usefulness of data worth calculations because wetland areas were present over a large part of the model domain.
Probabilistic capture zones were estimated for each of the mines in the model scenarios. A capture zone represents the area contributing recharge to a model feature, like a well or a mine pit, and can be calculated by forward tracking particles from the water table. By using Monte Carlo techniques, it is possible to generate estimated capture zones that include the probability of recharge capture given the uncertainty present in the model. Monte Carlo techniques use randomly generated model parameter sets sampled from a plausible parameter range to create many possible realizations. The resulting capture zone arrays were calculated by tallying the total number of realizations in which a particle from a model cell was captured by the feature. Probabilities from the Monte Carlo runs ranged from 1 (captured in 100 percent of the runs) near the pits to 0 (captured in 0 percent of the runs) at the edges of the capture zone. Capture zones were not always spatially continuous; for example, the capture zone for the proposed mine pits south of the flooded Peter Mitchell pit was discontinuous with capture surrounding the proposed mine pit and north of the flooded Peter Mitchell pit. This northern section represents deeper groundwater flow paths that originate in the topographic high, move under the flooded pit, and discharge into the proposed pit. This pattern of capture indicates the possibility of some deeper flow through the upper fractured bedrock when the shallow groundwater flow system is modified. These results underscore that future site-specific applications of the base condition model require the input of site-specific data and recalibration to focus on the site of interest.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215038","collaboration":"Prepared in cooperation with the Great Lakes Indian Fish & Wildlife Commission","usgsCitation":"Haserodt, M.J., Hunt, R.J., Fienen, M.N., and Feinstein, D.T., 2021, Groundwater/surface-water interactions in the Partridge River Basin and evaluation of hypothetical future mine pits, Minnesota: U.S. Geological Survey Scientific Investigations Report 2021–5038, 94 p., https://doi.org/10.3133/sir20215038.","productDescription":"Report: ix, 87 p.; Data Release; Dataset","numberOfPages":"102","onlineOnly":"Y","ipdsId":"IP-123210","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391131,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5038/sir20215038.xml","text":"Report xml","size":"277 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5038 xml"},{"id":391130,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":391132,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5038/images"},{"id":391129,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VODOU8","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT and MODPATH models, capture zones and uncertainty data analysis for the Partridge River Basin, Minnesota"},{"id":391127,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5038/coverthb.jpg"},{"id":391128,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5038/sir20215038.pdf","text":"Report","size":"69.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5038"}],"country":"United States","state":"Minnesota","otherGeospatial":"Partridge River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.25,\n 47.4\n ],\n [\n -91.75,\n 47.4\n ],\n [\n -91.75,\n 47.8\n ],\n [\n -92.25,\n 47.8\n ],\n [\n -92.25,\n 47.4\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive,
Madison, WI 53726
The fresh groundwater system at Fire Island National Seashore in New York is one of the natural resources that is most vulnerable to climate change; the various federally listed threatened or endangered species that live on Fire Island, including the piping plover, roseate tern shorebird, and seabeach amaranth may be affected by changes in the groundwater system. The U.S. Geological Survey, in cooperation with the National Park Service, developed a three-dimensional groundwater-flow model to simulate climate-change-related changes in depth to the water table and depth to freshwater/saltwater interfaces on Fire Island. An existing SEAWAT three-dimensional variable-density groundwater flow and transport model was converted to a MODFLOW–NWT three-dimensional finite-difference groundwater model with the Seawater Intrusion (SWI2) package and recalibrated using the UCODE_2005 automatic calibration software. The simulated groundwater divide was found to be skewed strongly toward the ocean shore in response to the modeled wave setup and tidal pumping overheight.
Effects of climate change include sea-level rise and changes in groundwater recharge rates. Sea-level rise scenarios included specified uniform steady states at 0.2-, 0.4-, and 0.6-meter increases above the 2015 level, applied to the existing topography. A high-recharge scenario was created by increasing 2015 recharge rates by 10 percent. Under all scenarios except the low-recharge scenario, the depth to the water table and the thickness of the unsaturated zone decreased. The thickness of the freshwater lens decreased under every scenario. Resulting maps were generated on a 25-meter grid and indicate changes in areas where natural resources may be vulnerable because of projected climate changes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205117","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Misut, P.E., and Dressler, S., 2021, Simulation of water-table and freshwater/saltwater interface response to climate-change-driven sea-level rise and changes in recharge at Fire Island National Seashore, New York: U.S. Geological Survey Scientific Investigations Report 2020–5117, 47 p., https://doi.org/10.3133/sir20205117.","productDescription":"Report: vii, 47 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-082635","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":387031,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95TBIMW","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used to simulate water-table and freshwater/saltwater interface response to climate-change-driven sea-level rise and changes in recharge at the Fire Island National Seashore, New York"},{"id":387039,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205104","text":"Scientific Investigations Report 2020–5104","linkHelpText":"- Simulated Effects of Sea-Level Rise on the Shallow, Fresh Groundwater System of Assateague Island, Maryland and Virginia"},{"id":387038,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205080","text":"Scientific Investigations Report 2020–5080","linkHelpText":"- Simulation of Water-Table Response to Sea-Level Rise and Change in Recharge, Sandy Hook Unit, Gateway National Recreation Area, New Jersey"},{"id":387030,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5117/sir20205117.pdf","text":"Report","size":"21.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5117"},{"id":387029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5117/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island National Seashore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.31314086914062,\n 40.614994915836924\n ],\n [\n -73.2513427734375,\n 40.612909950230936\n ],\n [\n -73.06869506835938,\n 40.65563874006118\n ],\n [\n -72.84072875976562,\n 40.730608477796636\n ],\n [\n -72.75146484374999,\n 40.763901280945866\n ],\n [\n -72.76931762695312,\n 40.77534183237267\n ],\n [\n -72.83798217773438,\n 40.74725696280421\n ],\n [\n -72.96157836914061,\n 40.72228267283148\n ],\n [\n -73.08792114257812,\n 40.66918118282895\n ],\n [\n -73.2403564453125,\n 40.637925243274374\n ],\n [\n -73.30215454101562,\n 40.63375667842965\n ],\n [\n -73.32687377929688,\n 40.62020704520565\n ],\n [\n -73.31314086914062,\n 40.614994915836924\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The latest release of MODFLOW 6, the current core version of the MODFLOW groundwater modeling software, debuted a new package dubbed the “mover” (MVR). Using a generalized approach, MVR facilitates the transfer of water among any arbitrary combination of simulated features (i.e., pumping wells, stream, drains, lakes, etc.) within a MODFLOW 6 simulation. Four “rules” controlling the amount of water transferred from a providing feature to a receiving feature are currently available. In this way, MVR can represent natural connections between features, for example streams entering or exiting lakes, and perhaps more interestingly, it also can transfer water among simulated features to more accurately simulate water management. An example model representative of an agricultural setting demonstrates some of the available MVR connections. For example, an irrigation event that transfers surface water from an irrigation delivery ditch to multiple cropped areas demonstrates a “one-to-many” connection that is possible within MVR. Conversely, irrigation or precipitation runoff from multiple fields may be routed to a particular stream segment using “many-to-one” MVR connections. MVR supports many additional connection types, several of which are demonstrated by the included example problem.
A three-dimensional hydrogeologic framework of the Hot Springs anticlinorium beneath Hot Springs National Park, Arkansas, was constructed to represent the complex hydrogeology of the park and surrounding areas to depths exceeding 9,000 feet below ground surface. The framework, composed of 6 rock formations and 1 vertical fault emplaced beneath the thermal springs, was discretized into 19 layers, 429 rows, and 576 columns and incorporated into a 3-dimensional steady-state groundwater-flow model constructed in MODFLOW-2005. Historical daily mean thermal spring flows were simulated for one stress period of approximately 34 years (1980–2014), chosen to represent the period of record for historical climate data used in the quantification of the boundary conditions. The groundwater-flow model was manually calibrated to historical daily mean thermal spring flows of 88,000 cubic feet per day observed over a 12-year period of record (1990–1995 and 1998–2005) at the thermal springs collection system. Calibration was achieved by calculating starting heads and general head boundary conditions from the Bernoulli equation and then adjusting the horizontal and vertical hydraulic conductivities of the rock formations and vertical fault and the hydraulic conductance of head-dependent flux boundaries. The groundwater-flow model was coupled to a surface-water model developed in the Precipitation-Runoff Modeling System (PRMS) by using PRMS-simulated gravity drainage as a specified flux recharge boundary condition in the groundwater-flow model. Together, the coupled models were used to (1) locate the areas of groundwater recharge to the thermal springs in the discretized hydrogeologic framework by using forward and reverse particle-tracking capabilities of MODPATH, (2) simulate the effects of variable recharge rates on the spring flows at the thermal springs, and (3) assess possible effects of climate and land-use change on the long-term variability of spring flows at the thermal springs.
Forward and backward particle-tracking maps indicated that the most prevalent areas of recharge in the discretized hydrogeologic framework used in this study were within about 0.6–0.9 mile of the thermal springs. Forward particle tracking indicated a recharge area southwest of the thermal springs that corresponded to a location where the predominant lithologies are the Arkansas Novaculite, Hot Springs Sandstone, and Bigfork Chert. Backward particle tracking indicated a second localized area of recharge to the northeast of the thermal springs that corresponded to a location where the dominant lithology is the Bigfork Chert. The groundwater-flow model indicated that the most probable recharge formations are the Arkansas Novaculite, Bigfork Chert, and Hot Springs Sandstone.
The simulated effects of climate and land-use changes on the variability of the spring-flow rates at the thermal springs generally resulted in reductions of thermal spring flow attributed to urban development and more extreme climates characterized by elevated mean surface air temperatures. The groundwater-flow model predicted a linear relation between the thermal spring discharge and the cumulative recharge volume applied to the hydrogeologic framework, and the positive slope of the predicted relation between recharge and simulated thermal spring flow indicates that more extreme precipitation events that supply more recharge may in fact increase the thermal spring-flow rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215045","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Hart, R.M., Ikard, S.J., Hays, P.D., and Clark, B.R., 2021, Effects of climate and land-use change on thermal springs recharge—A system-based coupled surface-water and groundwater-flow model for Hot Springs National Park, Arkansas: U.S. Geological Survey Scientific Investigations Report 2021–5045, 38 p., https://doi.org/10.3133/sir20215045.","productDescription":"Report: viii, 38 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-091576","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"links":[{"id":386401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5045/coverthb.jpg"},{"id":386402,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5045/sir20215045.pdf","text":"Report","size":"43.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5045"},{"id":386403,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SBJVVL","text":"USGS data release","linkHelpText":"Model inputs and outputs for simulating and predicting the effects of climate and land-use changes on thermal springs recharge—A system-based coupled surface-water and groundwater-flow model for Hot Springs National Park, Arkansas"},{"id":386404,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5045/images"}],"country":"United States","state":"Arkansas","otherGeospatial":"Hot Springs National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.1475830078125,\n 34.487881874939866\n ],\n [\n -92.96012878417969,\n 34.487881874939866\n ],\n [\n -92.96012878417969,\n 34.57273337081573\n ],\n [\n -93.1475830078125,\n 34.57273337081573\n ],\n [\n -93.1475830078125,\n 34.487881874939866\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Groundwater models have evolved to encompass more aspects of the water cycle, but the incorporation of realistic boundary conditions representing surface water remains time-consuming and error-prone. We present two Python packages that robustly automate this process using readily available hydrography data as the primary input. SFRmaker creates input for the MODFLOW SFR package, while Linesink-maker creates linesink string input for the GFLOW analytic element program. These programs can reduce weeks or even months of manual effort to a few minutes of execution time, and carry the added advantages of reduced potential for error, improved reproducibility and facilitation of step-wise modeling through reduced dependency on a particular conceptual model or discretization. Two real-world examples at the county to multi-state scales are presented.
The Osage Nation of northeastern Oklahoma, conterminous with Osage County, covers about 2,900 square miles. The area is primarily rural with 62 percent of the land being native prairie grass, and much of the area is used for cattle ranching and extraction of petroleum and natural gas. Protection of water rights are important to the Osage Nation because of its reliance on cattle ranching and the potential for impairment of water quality by petroleum extraction. Additionally, the potential for future population increases, demands for water from neighboring areas such as the Tulsa metropolitan area, and expansion of petroleum and natural-gas extraction on water resources of this area further the need for the Osage Nation to better understand its water availability. Therefore, the U.S. Geological Survey, in cooperation with the Osage Nation, completed a hydrologic investigation to assess the status and availability of surface-water and groundwater resources in the Osage Nation.
A transient integrated hydrologic-flow model was constructed using the U.S. Geological Survey fully integrated hydrologic-flow model called the MODFLOW One-Water Hydrologic Model. The integrated hydrologic-flow model, called the Osage Nation Integrated Hydrologic Model (ONIHM), was constructed and uses an orthogonal grid of 276 rows and 289 columns, and each grid cell measures 1,312.34 feet (ft; 400 meters) per side, with eight variably thick vertical layers that represented the alluvial and bedrock aquifers within the study area, including the alluvial aquifer, the Vamoosa-Ada aquifer, and the minor Pennsylvanian bedrock aquifers, and the confining units. Landscape and groundwater-flow processes were simulated for two periods: (1) the 1950–2014 period from January 1950 through September 2014 and (2) the forecast period from October 2014 through December 2099. The 1950–2014 period ONIHM simulated past conditions using measured or estimated inputs, and the forecast-period ONIHM simulated three separate potential forecast conditions under constant dry, average, or wet climate conditions using calibrated input values from the 1950–2014 period ONIHM.
The 1950–2014 period ONIHM was calibrated by linking the Parameter Estimation software (PEST) with the MODFLOW One-Water Hydrologic Model. PEST uses statistical parameter estimation techniques to identify the best set of parameter values to minimize the difference between measured or estimated calibration targets and their simulated equivalent values (residuals). Tikhonov regularization and singular-value decomposition-assist features of PEST were used during the calibration process. The 1950–2014 period ONIHM was calibrated to 713 measured groundwater levels at 195 wells; 95,636 estimated monthly mean groundwater levels at 124 wells; 5,307 measured streamflows at 13 streamgages; and 8,679 simulated mean monthly streamflows at 10 streamgages extracted from a surface-water model by adjusting 231 parameters. The estimated groundwater-level observations and streamflows were included as observations to improve the spatial and temporal density of observation targets during calibration. The best set of parameter values obtained during the calibration process of the 1950–2014 model was then used as the input parameter values for the forecast model simulations. A comparison of the calibration targets to their corresponding simulated values indicated that the model adequately reproduced streamflows and groundwater levels for some streamgages and wells and underestimated streamflows and groundwater levels at other locations. Measured and simulated streamflows correlated adequately with a coefficient of determination of 0.938, as did water levels with a coefficient of determination of 0.795. The 1950–2014 period ONIHM underestimated certain groundwater levels and streamflows, but generally measured or estimated calibration targets correlated well with simulated equivalents, which indicated that the model can adequately simulate the response of the hydrologic system to stresses in the 1950–2014 and forecast periods.
In the 1950–2014 period ONIHM, the calibrated mean horizontal hydraulic conductivity for layer 1 alluvial aquifer was 30.7 feet per day, and the seven lower layers had a calibrated mean horizontal hydraulic conductivity of less than 3.3 feet per day. The mean calibrated groundwater-level residual was 16.6 ft, and the mean calibrated streamflow residual of the Arkansas River at Ralston, Oklahoma, streamgage (U.S. Geological Survey station 07152500) was within 6 percent (373 cubic feet per second) of mean measured streamflow for the 1950–2014 period ONIHM.
The ONIHM simulated landscape fluxes of precipitation; groundwater applied by irrigation wells; evapotranspiration from precipitation, groundwater, and irrigation; runoff from precipitation; and deep percolation from precipitation. The largest loss of water from the landscape was evapotranspiration from precipitation with a calibrated mean annual outflow of 32 inches (in.): mean annual precipitation was about 36 in. Calibrated mean annual runoff and deep percolation (recharge to the water table) rates were 4.7 inches per year (in/yr) and 0.70 in/yr, respectively, for the 1950–2014 period ONIHM.
The calibrated 1950–2014 period ONIHM groundwater fluxes included net farm net recharge (calculated as the difference between the inflow of recharge to the water table and the outflow of evapotranspiration from the water table such that negative values indicate that evapotranspiration from the water table was greater than deep percolation [recharge to the water table] and vice versa). Net farm net recharge was the largest flux from the groundwater system with a mean annual net outflow of 153.4 cubic feet per second. Stream leakage was the largest flux to the groundwater system with a mean annual net inflow of 152.5 cubic feet per second, indicating that, on average, the groundwater/surface-water interaction was a “losing” system where stream water leaked into the subsurface and recharged the water table. Simulated monthly trends demonstrated that net stream leakage was the largest inflow to the groundwater-flow system for 10 of the 12 months; for the other 2 months (January and March), farm net recharge (January) and net storage (March) were the largest inflow to the groundwater-flow system.
A saline groundwater interface map was created for the study and compared to the water levels from the final stress period of the 1950–2014 model to identify the presence of fresh/marginal groundwater throughout the study area. Fresh/marginal groundwater was characterized as groundwater with less than 1,500 milligrams per liter of total dissolved solids. Fresh/marginal groundwater thickness ranged from 0 to 438.2 ft within the study area. The thickest regions of fresh/marginal groundwater were in the eastern part of the study area near Sand Creek, Bird Creek, and Hominy Creek and in the Arkansas River alluvial aquifer in the region downstream from the Arkansas River at Ralston, Okla.
Like the 1950–2014 model, forecast model results for the landscape indicated that transpiration from precipitation was the largest flux out of the landscape for all three forecasts, constituting 77, 73, and 58 percent of precipitation for the dry, average, and wet forecasts, respectively. The dry and average forecast landscape fluxes demonstrated similar trends and magnitudes, whereas the wet forecast landscape fluxes indicated the largest changes compared to the average forecast fluxes. Most notably, runoff increased from a mean of 1.1 and 1.6 in/yr for the dry and average forecasts, respectively, to 10 in/yr for the wet forecast. Similar changes occurred for the other wet forecast landscape fluxes.
The calibrated 1950–2014 period ONIHM simulated three forecasts to assess the effects of potential climatic changes on the hydrologic system from October 2014 to December 2099. The three forecasts simulated theoretical dry, average, and wet conditions using precipitation and potential evapotranspiration datasets from selected years in the calibrated 1950–2014 period ONIHM. Annual precipitation amounts were 26.89, 35.47, and 50.73 in. for the dry, average, and wet forecasts, respectively. Groundwater-flow component forecast results indicated that stream leakage is always a net inflow to the groundwater-flow system for dry, average, and wet conditions, meaning the study area stream network is always predominantly a “losing” regime where stream water infiltrates into the underlying aquifer. Storage was only a net outflow from the groundwater-flow system and indicated a replenishment to groundwater storage that resulted in an increase in groundwater levels only during the wet forecast. Further, these gains in groundwater storage for the wet forecast occurred only during February through June.
Mean fresh/marginal groundwater saturated thicknesses were 125 and 126 ft for the dry and average forecast conditions, respectively, and wet forecast average thickness was 145 ft and ranged from 0 to 443 ft. The spatial extents of fresh/marginal groundwater at the end of the dry, average, and wet forecast model periods (December 2099) did not change substantially from the end of the 1950–2014 model period (September 2014).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205141","collaboration":"Prepared in cooperation with the Osage Nation","usgsCitation":"Traylor, J.P., Mashburn, S.L., Hanson, R.T., and Peterson, S.M., 2021, Assessment of water availability in the Osage Nation using an integrated hydrologic-flow model: U.S. Geological Survey Scientific Investigations Report 2020–5141, 96 p., https://doi.org/10.3133/sir20205141.","productDescription":"Report: xiii, 96 p.; 2 Interactive Figures; Data Release; Dataset","numberOfPages":"114","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102662","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":384320,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5141/coverthb.jpg"},{"id":384321,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141.pdf","text":"Report","size":"9.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141"},{"id":384322,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure8.pdf","text":"Figure 8 (layered)","size":"626 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 8","linkHelpText":"— Supergroups for the Osage Nation Integrated Hydrologic Model (note: some supergroups are hidden; in order to see a given supergroup, the reader may need to turn off layers for the overlying supergroups)."},{"id":384324,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91OKQ2C","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-One Water Hydrologic Model integrated hydrologic-flow model used to evaluate water availability in the Osage Nation"},{"id":384323,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure14.pdf","text":"Figure 14 (layered)","size":"711 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 14","linkHelpText":"— Simulated groundwater-level altitude contours for the final stress period of the calibrated Osage Nation Integrated Hydrologic Model (September 30, 2014), dry forecast (December 31, 2099), average forecast (December 31, 2099), and wet forecast (December 31, 2099). This figure is a layered PDF."},{"id":384325,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas, Oklahoma","otherGeospatial":"Osage Nation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.99578857421875,\n 36.13565654678543\n ],\n [\n -95.99853515625,\n 37.00035919622158\n ],\n [\n -95.97930908203125,\n 37.081475648860525\n ],\n [\n -96.29241943359375,\n 37.13623498442895\n ],\n [\n -96.48193359375,\n 36.96306042436515\n ],\n [\n -96.9873046875,\n 36.94989178681327\n ],\n [\n -97.12188720703125,\n 36.6992553955527\n ],\n [\n -97.14385986328125,\n 36.36822190085111\n ],\n [\n -96.6412353515625,\n 36.213255233061844\n ],\n [\n -96.26220703125,\n 36.11125252076156\n ],\n [\n -95.99578857421875,\n 36.13565654678543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
The 1973 Oklahoma Water Law (82 OK Stat § 82-1020.5) requires that the Oklahoma Water Resources Board (OWRB) conduct hydrologic investigations of the State’s groundwater basins to support a determination of the maximum annual yield for each groundwater basin (hereinafter referred to as an “aquifer”). The maximum annual yield allocated per acre of land is known as the equal-proportionate-share (EPS) pumping rate. At present (2021), the OWRB has not yet established a maximum annual yield and EPS pumping rate for the Salt Fork Red River aquifer. To provide updated information to the OWRB that could support evaluation and determination of an appropriate maximum annual yield, the U.S. Geological Survey (USGS), in cooperation with the OWRB, conducted a hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater availability in the Salt Fork Red River aquifer.
The Salt Fork Red River aquifer in Greer, Harmon, and Jackson Counties of southwestern Oklahoma is composed of about 274.5 square miles of alluvium and terrace deposits associated with the Salt Fork Red River. The mean annual recharge rate to the Salt Fork Red River aquifer for the period 1980–2015 was estimated to be about 2.94 inches per year, or 10.0 percent of the mean annual precipitation for the same period (29.4 inches per year). This 1980–2015 mean annual recharge rate is equivalent to a mean annual recharge rate of about 38,000 acre-feet per year (acre-ft/yr) for the Salt Fork Red River aquifer excluding about 19,764 acres comprising the Mulberry Creek and Horse Creek terraces. The mean annual recharge rates upgradient and downgradient from USGS streamgage 07300500 Salt Fork Red River at Mangum, Okla. (hereinafter referred to as the “Mangum gage”), apportioned by aquifer area (41.5 and 58.5 percent, respectively), were about 16,000 and 22,000 acre-ft/yr, respectively. Mean annual groundwater use for the study period (1980–2015) was 3,532.7 acre-ft/yr; about 77 percent of that groundwater use was for irrigation, and about 23 percent was for public supply. Most groundwater use for irrigation was associated with wells in the Martha terrace.
A hydrogeologic framework was developed for the Salt Fork Red River aquifer and included a definition of the aquifer extent and potentiometric surface, as well as a description of the textural and hydraulic properties of aquifer materials. The hydrogeologic framework was used in the construction of the numerical groundwater-flow model of the Salt Fork Red River aquifer described in this report. A conceptual model for the Salt Fork Red River aquifer that reasonably represents the groundwater-flow system was developed to constrain the construction and calibration of the numerical model. The conceptual-model water budget estimated mean annual inflows to, and outflows from, the Salt Fork Red River aquifer for the period 1980–2015 and included a subaccounting of mean annual inflows and outflows for the portions of the aquifer that were upgradient and downgradient from the Mangum gage.
The numerical groundwater-flow model of the Salt Fork Red River aquifer was constructed by using MODFLOW-2005 with the Newton formulation solver. The model of the Salt Fork Red River aquifer was spatially discretized into 1,050 rows, 1,125 columns, about 170,000 active cells measuring 200 by 200 feet (ft), and a single convertible layer. The model was temporally discretized into 432 monthly transient stress periods (each with two time steps to improve model stability). An initial steady-state stress period represented mean annual inflows to, and outflows from, the aquifer and produced a solution that was used as the initial condition for subsequent transient stress periods as well as some groundwater-availability scenarios. The model was calibrated to water-table-altitude observations at selected wells and base-flow observations at selected streamgages.
The simulated saturated thickness of the Salt Fork Red River aquifer was determined by subtracting the altitude of the aquifer base from the simulated water-table altitude at the end of the numerical-model period (2015). The simulated saturated thickness was more than 75 ft in a paleochannel in the Dodson terrace near the Texas border. The mean aquifer thickness (sum of saturated and unsaturated) was 49.62 ft, and the mean saturated thickness was 28.55 ft. A simulated mean transmissivity of 1,024 feet squared per day was computed from the calibrated hydraulic conductivity and saturated thickness of each cell. The simulated available water in storage at the end of the numerical-model period (2015) was 526,117 acre-feet (acre-ft); about 42 percent of that total was available upgradient from the Mangum gage, and about 58 percent of that total was available downgradient from the Mangum gage (including the Mangum terrace).
Three types of groundwater-availability scenarios were run using the calibrated numerical model. These scenarios were used to (1) estimate the EPS pumping rate that ensures a minimum 20-, 40-, and 50-year life of the aquifer, (2) quantify the potential effects of projected well withdrawals on groundwater storage over a 50-year period, and (3) simulate the potential effects of a hypothetical 10-year drought on base flow and groundwater storage. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were about 0.51, 0.48, and 0.48 acre-foot per acre per year, respectively. Given the 155,929-acre modeled aquifer area, these rates correspond to annual yields of about 78,800, 74,900, and 74,700 acre-ft/yr, respectively. For the 20-year EPS scenario, decreasing and increasing recharge by 10 percent resulted in a 6-percent change in the EPS pumping rate in both cases; for the 40- and 50-year EPS scenarios, decreasing and increasing recharge by 10 percent resulted in a 7-percent change in the EPS pumping rate in both cases.
Projected 50-year pumping scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage of the Salt Fork Red River aquifer and base flows in the Salt Fork Red River. The effects of well withdrawals were evaluated by quantifying differences in groundwater storage and base flow in four 50-year scenarios, which applied (1) no groundwater pumping, (2) mean pumping rates for the study period (1980–2015), (3) 2015 pumping rates, and (4) increasing demand pumping rates at simulated wells. The increasing demand pumping rates assumed a cumulative 20.4-percent increase in pumping over 50 years based on 2010–60 demand projections for southwestern Oklahoma. Groundwater storage after 50 years with no pumping was 535,000 acre-ft, or 8,900 acre-ft (1.7 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean water-table-altitude increase of 0.48 ft. Groundwater storage after 50 years of pumping at the mean rate for the study period (1980–2015) was 519,900 acre-ft, or 6,200 acre-ft (1.2 percent) less than the initial groundwater storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.34 ft. Groundwater storage at the end of the 50-year period with 2015 pumping rates was 513,100 acre-ft, or 13,000 acre-ft (2.5 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.71 ft. Groundwater storage at the end of the 50-year period with increasing demand pumping rates was 509,700 acre-ft, or 16,500 acre-ft (3.1 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean water-table-altitude decline of 0.89 ft.
A hypothetical 10-year drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage. The period January 1983–December 1992 was chosen as the simulated drought period. Drought effects were quantified by comparing the results of the drought scenario to those of the calibrated numerical model (no drought) at the end of the simulated drought period (1992). To simulate the hypothetical drought, recharge in the calibrated numerical model was reduced by 50 percent during the simulated drought period (1983–92). Upstream inflows from the Salt Fork Red River, Turkey Creek, and Bitter Creek were reduced by 75 percent. Groundwater storage at the end of the drought period (1992) was 479,200 acre-ft, or 53,200 acre-ft (10.0 percent) less than the groundwater storage of the calibrated numerical model at the end of the drought period. This decrease in groundwater storage is equivalent to a mean water-table-altitude decline of 2.9 ft. At the end of the 10-year hypothetical drought period, simulated base flows at the Mangum gage and USGS streamgage 07301110 Salt Fork Red River near Elmer, Okla., had decreased by about 80 and 70 percent, respectively.
Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754-4501
Contact Us- USGS Publications Warehouse
","tableOfContents":"Groundwater is a primary source of drinking water in northern Utah County. The groundwater system is recharged mainly from precipitation in the adjacent Wasatch Mountains and infiltration of streamflow. In 2004, groundwater withdrawals were estimated to be roughly 44,500 acre-feet per year. In 2016, groundwater withdrawals were estimated to be greater than 63,400 acre-feet per year. To prepare for anticipated future increases in groundwater withdrawals, local cities identified 16 locations as feasible for managed aquifer recharge. Using an updated version of an existing U.S. Geological Survey groundwater flow model of northern Utah County, the Groundwater-Management Process for MODFLOW-2005 was used to investigate optimal managed aquifer recharge scenarios with the objective of maintaining acceptable reductions in simulated discharge at 12 groundwater discharge areas and flowing wells along Utah Lake.
The Groundwater-Management Process is applied to a 50-year (2017–66) projection of groundwater conditions using average recharge conditions and a linear increase of approximately 750 acre-feet per year of municipal groundwater withdrawals. Two sets of discharge constraints were applied. The first scenario constrains discharge to greater than or equal to 80 percent of the 2016 simulated groundwater discharge along Utah Lake. The constraint was met with a total managed aquifer recharge rate of roughly 7,300 acre-feet per year during 2042–56, and 15,600 acre-feet per year during 2057–66. A second scenario constrains discharge to greater than or equal to 90 percent of the 2016 simulated discharge. This constraint can only be met at 8 of the 12 discharge areas along Utah Lake. This required a managed aquifer recharge rate of roughly 10,000 acre-feet per year during 2042–56 and 15,400 acre-feet per year during 2057–66. For both scenarios, the Groundwater-Management Process indicated that all managed aquifer recharge sites need to be used to meet discharges constraints. The discharge constraints were informally defined on the basis of the water rights hierarchy associated with Utah Lake.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215010","collaboration":"Prepared in cooperation with the North Utah County Aquifer Council","usgsCitation":"Stolp, B.J., and Brooks, L.E., 2021, Groundwater management process simulations using an updated version of the three-dimensional numerical model of groundwater flow in northern Utah Valley, Utah County, Utah: U.S. Geological Survey Scientific Investigations Report 2021–5010, 28 p., https://doi.org/10.3133/sir20215010.","productDescription":"vi, 28 p","numberOfPages":"28","ipdsId":"IP-119330","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":383759,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5010/sir20215010.xml"},{"id":383731,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5010/images/"},{"id":383729,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5010/covrthb.jpg"},{"id":383730,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5010/sir20215010.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Utah","county":"Utah County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-111.5971,40.5784],[-111.5923,40.577],[-111.5814,40.5688],[-111.5718,40.5597],[-111.5658,40.5484],[-111.5751,40.5171],[-111.5782,40.5022],[-111.5722,40.4912],[-111.5832,40.4781],[-111.5911,40.4695],[-111.6068,40.4678],[-111.6165,40.4556],[-111.6136,40.4401],[-111.6027,40.4365],[-111.5907,40.4314],[-111.5871,40.4237],[-111.5805,40.4169],[-111.5721,40.4101],[-111.5716,40.3955],[-111.5644,40.3878],[-111.5608,40.3769],[-111.5525,40.3624],[-111.5331,40.3628],[-111.5199,40.3632],[-111.4964,40.3617],[-111.4819,40.3612],[-111.4705,40.3489],[-111.4608,40.3493],[-111.4603,40.3425],[-111.4519,40.342],[-111.4513,40.3357],[-111.4453,40.3311],[-111.4375,40.3234],[-111.4394,40.3161],[-111.4473,40.308],[-111.4528,40.3021],[-111.451,40.3008],[-111.4498,40.2949],[-111.442,40.2917],[-111.4348,40.2916],[-111.4317,40.2934],[-111.4269,40.2907],[-111.4209,40.287],[-111.4149,40.2893],[-111.4082,40.296],[-111.4004,40.2965],[-111.3956,40.2933],[-111.3883,40.2987],[-111.375,40.2977],[-111.3666,40.2918],[-111.357,40.2908],[-111.3462,40.2889],[-111.3257,40.2834],[-111.3203,40.2883],[-111.31,40.2892],[-111.3076,40.2842],[-111.3065,40.2815],[-111.3089,40.2765],[-111.2981,40.2742],[-111.2921,40.2669],[-111.2867,40.2668],[-111.279,40.2573],[-111.2778,40.2536],[-111.27,40.2513],[-111.2635,40.2413],[-111.2713,40.2377],[-111.2703,40.2205],[-111.2632,40.2127],[-111.262,40.2086],[-111.256,40.2036],[-111.2555,40.1982],[-111.2412,40.1868],[-111.2364,40.1827],[-111.2347,40.1745],[-111.2269,40.1694],[-111.2288,40.1613],[-111.2265,40.1531],[-111.2307,40.1518],[-111.2278,40.1436],[-111.2207,40.1349],[-111.2219,40.1286],[-111.225,40.1196],[-111.2288,40.1096],[-111.2342,40.1051],[-111.2355,40.0979],[-111.2422,40.0897],[-111.2344,40.0838],[-111.2387,40.0793],[-111.2375,40.0743],[-111.2406,40.0698],[-111.2479,40.0576],[-111.2373,40.0398],[-111.2301,40.0371],[-111.2157,40.0415],[-111.203,40.0505],[-111.1983,40.0414],[-111.1965,40.0337],[-111.1882,40.0268],[-111.1853,40.0218],[-111.1721,40.0199],[-111.1643,40.0144],[-111.1686,40.0077],[-111.1658,39.9949],[-111.1538,39.9917],[-111.1502,39.9889],[-111.1448,39.9894],[-111.1377,39.9857],[-111.1282,39.9761],[-111.1198,39.9733],[-111.1163,39.9678],[-111.1169,39.9629],[-111.1188,39.9597],[-111.1278,39.9539],[-111.1249,39.9493],[-111.1165,39.9461],[-111.0715,39.9458],[-111.0703,39.9022],[-111.0337,39.9015],[-111.005,39.9022],[-110.9588,39.9018],[-110.9534,39.9013],[-110.9348,39.9011],[-110.8887,39.9017],[-110.8569,39.9009],[-110.8574,39.8719],[-110.8559,39.8157],[-110.8726,39.8154],[-110.8954,39.816],[-110.9475,39.8156],[-111.0289,39.8162],[-111.0768,39.8171],[-111.0768,39.8152],[-111.1199,39.8151],[-111.1385,39.8148],[-111.1415,39.8148],[-111.19,39.8147],[-111.2086,39.8148],[-111.2469,39.8155],[-111.3086,39.8154],[-111.4146,39.8141],[-111.4176,39.8145],[-111.4918,39.8135],[-111.5277,39.8132],[-111.5343,39.8132],[-111.5834,39.813],[-111.6412,39.813],[-111.7235,39.812],[-111.7373,39.812],[-111.756,39.8117],[-111.7657,39.8106],[-111.7599,39.8214],[-111.7551,39.8275],[-111.7476,39.8333],[-111.7484,39.8379],[-111.7491,39.841],[-111.7464,39.8451],[-111.746,39.8495],[-111.7448,39.8509],[-111.7427,39.8515],[-111.7401,39.8515],[-111.7365,39.8512],[-111.7352,39.8518],[-111.7334,39.8539],[-111.7385,39.8654],[-111.7447,39.8686],[-111.7474,39.871],[-111.7493,39.8711],[-111.7563,39.8753],[-111.7606,39.8834],[-111.7559,39.8893],[-111.7613,39.8924],[-111.7639,39.8956],[-111.7689,39.8987],[-111.7762,39.8986],[-111.7829,39.9045],[-111.7826,39.9069],[-111.7868,39.9092],[-111.7882,39.9155],[-111.7854,39.921],[-111.7882,39.9267],[-111.8097,39.9399],[-111.8193,39.9387],[-111.8225,39.9477],[-111.8266,39.9482],[-111.8301,39.9395],[-111.8315,39.9365],[-111.8307,39.9344],[-111.8314,39.924],[-111.8418,39.9243],[-111.8437,39.9219],[-111.8491,39.9174],[-111.8569,39.9151],[-111.864,39.9061],[-111.8674,39.9067],[-111.8803,39.9076],[-111.8833,39.9038],[-111.8893,39.9013],[-111.8908,39.898],[-111.8998,39.8987],[-111.91,39.8953],[-111.913,39.8855],[-111.9201,39.8796],[-111.9242,39.8695],[-111.9277,39.8676],[-111.9313,39.8621],[-111.9349,39.8585],[-111.9391,39.8553],[-111.9403,39.8535],[-111.9457,39.8472],[-111.9475,39.8431],[-111.9523,39.8381],[-111.9541,39.8354],[-111.957,39.8309],[-111.957,39.8259],[-111.9583,39.8209],[-111.9607,39.8173],[-111.9642,39.8136],[-111.9726,39.8064],[-111.9762,39.8046],[-111.9846,39.8109],[-111.9888,39.8087],[-111.9906,39.8077],[-111.996,39.8077],[-111.999,39.8037],[-112.0038,39.7937],[-112.0098,39.7914],[-112.0181,39.7878],[-112.0241,39.7823],[-112.0307,39.7805],[-112.0361,39.7819],[-112.0403,39.7842],[-112.0487,39.7774],[-112.0558,39.7778],[-112.0654,39.7787],[-112.0717,39.7787],[-112.0901,39.7818],[-112.0868,39.7908],[-112.0814,39.7935],[-112.0762,39.7982],[-112.0756,39.7996],[-112.0774,39.8077],[-112.0714,39.8118],[-112.0684,39.8154],[-112.069,39.8209],[-112.0649,39.8286],[-112.0703,39.8358],[-112.0762,39.8399],[-112.0742,39.8486],[-112.0791,39.8535],[-112.0811,39.8537],[-112.0809,39.8656],[-112.07,39.8698],[-112.0642,39.8771],[-112.0583,39.8871],[-112.0523,39.8893],[-112.0577,39.8948],[-112.0643,39.9066],[-112.0697,39.9097],[-112.0751,39.9113],[-112.0794,39.9119],[-112.0848,39.9256],[-112.0932,39.9303],[-112.0968,39.9294],[-112.1004,39.9281],[-112.1035,39.9298],[-112.1061,39.9388],[-112.1039,39.946],[-112.0942,39.9541],[-112.1025,39.9654],[-112.11,39.9727],[-112.1298,39.9745],[-112.1538,39.999],[-112.1551,40.0026],[-112.1605,40.0067],[-112.1635,40.0067],[-112.1689,40.0089],[-112.1773,40.0112],[-112.1725,40.0275],[-112.1713,40.0361],[-112.172,40.0416],[-112.1738,40.047],[-112.1804,40.0561],[-112.1792,40.0674],[-112.1714,40.0692],[-112.1732,40.0747],[-112.1739,40.0792],[-112.1727,40.0851],[-112.1589,40.0883],[-112.1511,40.1082],[-112.1625,40.1168],[-112.1674,40.1255],[-112.1728,40.1295],[-112.174,40.1304],[-112.1746,40.1354],[-112.1716,40.1399],[-112.1662,40.1459],[-112.1728,40.1567],[-112.1602,40.1622],[-112.1566,40.1667],[-112.1542,40.1694],[-112.1452,40.1744],[-112.1464,40.1903],[-112.1507,40.198],[-112.1597,40.198],[-112.1513,40.2125],[-112.1549,40.2193],[-112.1652,40.2189],[-112.17,40.2238],[-112.1724,40.2279],[-112.1742,40.2306],[-112.1772,40.2338],[-112.1772,40.2388],[-112.1767,40.2424],[-112.1809,40.2497],[-112.1845,40.2542],[-112.1845,40.2574],[-112.1882,40.2619],[-112.1918,40.2664],[-112.1912,40.2687],[-112.187,40.2787],[-112.1876,40.2887],[-112.1889,40.2991],[-112.1925,40.3045],[-112.1985,40.3122],[-112.1949,40.319],[-112.1901,40.3268],[-112.1769,40.3358],[-112.1781,40.3413],[-112.183,40.3481],[-112.1896,40.3567],[-112.192,40.3621],[-112.1872,40.3739],[-112.1945,40.3784],[-112.2,40.3861],[-112.2,40.3907],[-112.1994,40.3948],[-112.197,40.4047],[-112.1946,40.4124],[-112.1958,40.4215],[-112.2043,40.4306],[-112.2073,40.4347],[-112.2085,40.4392],[-112.2122,40.4446],[-112.2128,40.4551],[-112.2116,40.4587],[-112.2002,40.4601],[-112.1869,40.4687],[-112.1814,40.4683],[-112.173,40.4674],[-112.1675,40.4642],[-112.1591,40.4624],[-112.1524,40.4669],[-112.1506,40.4687],[-112.1428,40.471],[-112.1343,40.4665],[-112.1283,40.4633],[-112.1252,40.4606],[-112.1198,40.4543],[-112.1174,40.4534],[-112.1125,40.4515],[-112.1125,40.4457],[-112.1131,40.4429],[-112.1113,40.4389],[-112.1034,40.442],[-112.0998,40.4448],[-112.092,40.447],[-112.0835,40.4466],[-112.0751,40.447],[-112.069,40.4457],[-112.0636,40.4484],[-112.0582,40.4516],[-112.0527,40.4543],[-112.0443,40.4561],[-112.0425,40.4602],[-112.0322,40.4643],[-112.0286,40.4575],[-112.0261,40.4493],[-112.0213,40.4398],[-112.0141,40.4344],[-112.0038,40.4262],[-111.9923,40.4235],[-111.9772,40.4158],[-111.9748,40.4149],[-111.97,40.4158],[-111.9663,40.4176],[-111.9627,40.4189],[-111.9561,40.4198],[-111.9531,40.4212],[-111.9513,40.4221],[-111.9434,40.4267],[-111.9307,40.433],[-111.9271,40.4348],[-111.9192,40.438],[-111.9126,40.4416],[-111.9222,40.4525],[-111.9035,40.4588],[-111.8969,40.4638],[-111.8926,40.4656],[-111.8878,40.4683],[-111.8811,40.4715],[-111.8696,40.4765],[-111.8654,40.4715],[-111.8594,40.4688],[-111.8551,40.4669],[-111.8515,40.4692],[-111.8461,40.4765],[-111.8424,40.4755],[-111.8394,40.4742],[-111.8328,40.4814],[-111.8261,40.4846],[-111.811,40.4905],[-111.8013,40.495],[-111.7928,40.4954],[-111.791,40.4959],[-111.7867,40.5072],[-111.7873,40.509],[-111.7819,40.5149],[-111.771,40.5235],[-111.7619,40.5276],[-111.7474,40.5253],[-111.7371,40.5262],[-111.7335,40.5307],[-111.7202,40.5266],[-111.7129,40.532],[-111.7038,40.5356],[-111.6935,40.5351],[-111.6869,40.5342],[-111.6796,40.5328],[-111.6669,40.541],[-111.6608,40.5432],[-111.6565,40.5532],[-111.6523,40.5554],[-111.6468,40.5568],[-111.6414,40.5608],[-111.6347,40.5699],[-111.6311,40.5672],[-111.622,40.5667],[-111.6129,40.5667],[-111.6038,40.5657],[-111.5983,40.5789],[-111.5971,40.5784]]]},\"properties\":{\"name\":\"Utah\",\"state\":\"UT\"}}]}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
Steady-state numerical groundwater-flow models were constructed for the islands of Kaua‘i, O‘ahu, and Maui to enable quantification of the hydrologic consequences of withdrawals and other stresses that can place limits on groundwater availability. The volcanic aquifers of Hawai‘i supply nearly all drinking water for the islands’ residents, freshwater for diverse industries, and natural discharge to springs, streams, and nearshore areas that support ecosystems, cultural practices, aesthetics, and recreation. Increases in groundwater withdrawal and changes in climate can cause water-table depression, saltwater rise, and reduction of natural groundwater discharge—all of which can limit fresh groundwater availability. The numerical models described in this report are designed to quantify these consequences. Separate models were created for each island using MODFLOW-2005 with the Seawater Intrusion package, which allows simulation of freshwater and saltwater in ocean-island aquifers. Calibration resulted in models that generally replicate observed water-level, stream base-flow, and spring-flow data, and simulate groundwater-flow directions and fresh groundwater thicknesses that are consistent with conceptual models. The calibrated models use hydraulic properties that are consistent with the ranges reported in previous studies. The models show that the relative distribution of fresh groundwater discharge to the ocean, streams, and springs and withdrawals for human use differ substantially among the three islands studied here. These differences indicate that consequences that limit the availability of fresh groundwater for human use are likely to differ among the three islands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205126","usgsCitation":"Izuka, S.K., Rotzoll, K., and Nishikawa, T., 2021, Volcanic Aquifers of Hawai‘i—Construction and calibration of numerical models for assessing groundwater availability on Kaua‘i, O‘ahu, and Maui: U.S. Geological Survey Scientific Investigations Report 2020-5126, 63 p., https://doi.org/10.3133/sir20205126.","productDescription":"Report: viii, 63 p.; Data Release","numberOfPages":"63","ipdsId":"IP-071367","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":383611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5126/covrthb.jpg"},{"id":383612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5126/sir20205126.pdf","text":"Report","size":"53 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383613,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K4DK2P","linkHelpText":"MODFLOW-2005 and SWI2 models for assessing groundwater availability in volcanic aquifers on Kaua‘i, O‘ahu, and Maui, Hawai‘i"},{"id":416444,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20155164","text":"Scientific Investigations Report 2015-5164","description":"Izuka, S.K., Engott, J.A., Rotzoll, Kolja, Bassiouni, Maoya, Johnson, A.G., Miller, L.D., and Mair, Alan, 2018, Volcanic aquifers of Hawai‘i—Hydrogeology, water budgets, and conceptual models (ver. 2.0, March 2018): U.S. Geological Survey Scientific Investigations Report 2015-5164, 158 p., https://doi.org/10.3133/sir20155164.","linkHelpText":"- Volcanic Aquifers of Hawai‘i—Hydrogeology, Water budgets, and Conceptual Models"},{"id":416445,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1876","text":"Professional Paper 1876","description":"Izuka, S.K., and Rotzoll, K., 2023, Volcanic aquifers of Hawaiʻi—Contributions to assessing groundwater availability on Kauaʻi, Oʻahu, and Maui: U.S. Geological Survey Professional Paper 1876, 100 p., https://doi.org/10.3133/pp1876.","linkHelpText":"- Volcanic Aquifers of Hawai‘i—Contributions to Assessing Groundwater Availability on Kaua‘i, O‘ahu, and Maui"},{"id":417944,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233010","text":"Fact Sheet 2023-3010","description":"Izuka, S.K., and Rotzoll, K., 2023, Availability of groundwater from the volcanic aquifers of the Hawaiian Islands: U.S. Geological Survey Fact Sheet 2023-3010, 4 p., https://doi.org/10.3133/fs20233010.","linkHelpText":"- Availability of Groundwater from the Volcanic Aquifers of the Hawaiian Islands"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaua'i, Maui, O'ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.73095703125,\n 20.57365332356332\n ],\n [\n -155.90423583984375,\n 20.57365332356332\n ],\n [\n -155.90423583984375,\n 21.04861794324536\n ],\n [\n -156.73095703125,\n 21.04861794324536\n ],\n [\n -156.73095703125,\n 20.57365332356332\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.33221435546875,\n 21.235622362422877\n ],\n [\n -157.62359619140625,\n 21.235622362422877\n ],\n [\n -157.62359619140625,\n 21.72505868324388\n ],\n [\n -158.33221435546875,\n 21.72505868324388\n ],\n [\n -158.33221435546875,\n 21.235622362422877\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.85931396484375,\n 21.830906665069758\n ],\n [\n -159.22622680664062,\n 21.830906665069758\n ],\n [\n -159.22622680664062,\n 22.264951388846296\n ],\n [\n -159.85931396484375,\n 22.264951388846296\n ],\n [\n -159.85931396484375,\n 21.830906665069758\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Groundwater flow model construction is often time‐consuming and costly, with development ideally focused on a specific purpose, such as quantifying well capture from water bodies or providing flow fields for simulating advective transport. As environmental challenges evolve, the incentive to re‐purpose existing groundwater flow models may increase. However, few studies have evaluated which characteristics of groundwater flow models deserve greatest consideration when re‐purposing models for groundwater age and advective transport simulations. In this paper, we compare simulated age metrics produced by three MODFLOW‐MODPATH models of the same area but with differing levels of complexity (layering and heterogeneity). Comparisons are made at three watershed scales (HUC 8 to HUC 12). Groundwater age metrics, specifically the young fraction and median age of the young and old fractions, are used for evaluation because they relate to intrinsic susceptibility of aquifers and are simpler to interpret than full age distributions used for advective transport. Results indicate that: 1. the young fraction is less sensitive to model layering than the median age of young and old fractions, suggesting that simple models may suffice for basic intrinsic susceptibility assessments; 2. water table mounding and associated discharge into partially penetrating boundaries, such as head‐water streams, is important for simulating both the young fraction and the median age of the young fraction; and 3. the influence of partially penetrating head‐water streams is maintained regardless of the porosity distribution. Results of this work should aid modelers with evaluating the appropriateness of re‐purposing existing groundwater flow models for age simulations.
Potential effects of projected climate variability on base flow and groundwater storage in the North Fork Red River aquifer, Oklahoma (USA), were estimated using downscaled climate model data coupled with a numerical groundwater-flow model. The North Fork Red River aquifer discharges groundwater to the North Fork Red River, which provides inflow to Lake Altus. To approximate future conditions, Coupled Model Intercomparison Project Phase 5 climate data were downscaled to the watershed and a time-series of scaling factors were developed and interpolated for three climate scenarios (central tendency, warmer and drier, and less warm and wetter) representing future climate conditions for the period 2045–2074. These scaling factors were then applied to a soil-water-balance model to produce groundwater recharge and evapotranspiration estimates. A MODFLOW groundwater-flow model of the North Fork Red River aquifer used the scaled recharge and evapotranspiration data to estimate changes in base flow and water-surface elevation of Lake Altus. Compared to a baseline scenario, the mean percent change in annual base flow during 2045–2074 was −10.8 and −15.9% for the central tendency and warmer/drier scenarios, respectively; the mean percent change in annual base flow for the less-warm/wetter scenario was +15.7%. The mean annual percent change in groundwater storage for the central tendency, warmer/drier, and less-warm/wetter climate scenarios and the baseline are −2.7, −3.2, and +3.0%, respectively. The range of outcomes from the climate scenarios may be influenced by variability in the downscaled climate data for precipitation more than for temperature.
","language":"English","publisher":"Springer","doi":"10.1007/s10040-020-02230-x","usgsCitation":"Labriola, L., Ellis, J., Gangopadhyay, S., Pruitt, T., Kirstetter, P., and Hong, Y., 2021, Evaluating the effects of downscaled climate projections on groundwater storage and simulated base-flow contribution to the North Fork Red River and Lake Altus, southwest Oklahoma (USA): Hydrogeology Journal, v. 28, no. 8, p. 2903-2916, https://doi.org/10.1007/s10040-020-02230-x.","productDescription":"14 p.","startPage":"2903","endPage":"2916","ipdsId":"IP-111529","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":436658,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91DWW91","text":"USGS data release","linkHelpText":"MODFLOW-NWT model used in simulations of selected climate scenarios of groundwater availability in the North Fork Red River aquifer, southwestern Oklahoma"},{"id":385978,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.00927734375,\n 33.706062655101206\n ],\n [\n -94.37255859375,\n 33.706062655101206\n ],\n [\n -94.37255859375,\n 35.47856499535729\n ],\n [\n -97.00927734375,\n 35.47856499535729\n ],\n [\n -97.00927734375,\n 33.706062655101206\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-10-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Labriola, L.G. 0000-0002-5096-2940","orcid":"https://orcid.org/0000-0002-5096-2940","contributorId":216625,"corporation":false,"usgs":true,"family":"Labriola","given":"L.G.","email":"","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellis, J.H. 0000-0001-7161-3136 jellis@usgs.gov","orcid":"https://orcid.org/0000-0001-7161-3136","contributorId":196287,"corporation":false,"usgs":true,"family":"Ellis","given":"J.H.","email":"jellis@usgs.gov","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":816475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pruitt, Tom","contributorId":257612,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":816476,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kirstetter, Pierre","contributorId":258774,"corporation":false,"usgs":false,"family":"Kirstetter","given":"Pierre","affiliations":[{"id":52282,"text":"School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73072, USA","active":true,"usgs":false}],"preferred":false,"id":816477,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hong, Yang","contributorId":258775,"corporation":false,"usgs":false,"family":"Hong","given":"Yang","affiliations":[{"id":52282,"text":"School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73072, USA","active":true,"usgs":false}],"preferred":false,"id":816478,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216163,"text":"sir20205090 - 2020 - Analysis of remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York","interactions":[],"lastModifiedDate":"2021-04-27T17:33:12.761031","indexId":"sir20205090","displayToPublicDate":"2021-04-27T13:40:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5090","displayTitle":"Analysis of Remedial Scenarios Affecting Plume Movement Through a Sole-Source Aquifer System, Southeastern Nassau County, New York","title":"Analysis of remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York","docAbstract":"A steady-state three-dimensional groundwater-flow model based on present conditions is coupled with the particle-tracking program MODPATH to assess the fate and transport of volatile organic-compound plumes within the Magothy and upper glacial aquifers in southeastern Nassau County, New York. Particles are forward tracked from locations within plumes defined by surfaces of equal concentration. Particles move toward ultimate well capture and discharge to the general head and drain boundaries representing natural receptors in the models. Because rates of advection within coarse-grained sediments typically exceed 0.1 foot per day, mechanisms of dispersion and diffusion were assumed to be negligible. Resulting particle pathlines are influenced by hydrogeologic framework features and the interplay of nearby hydrologic stresses. Simulated hydrologic effects include cones of depression near pumping wells and water-table mounding near points of treated water recharge; however, remedial pumping amounts are balanced by treated-water return, and net effects at distant regional boundaries, including freshwater/saltwater interfaces, are minor.
Once a steady-state model was developed and calibrated, eight hypothetical remedial scenarios were evaluated to hydraulically contain the volatile organic-compound plumes. Specifically, the remedial scenarios were optimized to achieve full containment by altering the pumping-well locations, adjusting the pumping rates, and adjusting the discharge locations and rates. Based on the results, total hypothetical extraction rates varied from about 5,462 gallons per minute during an anticipated near-future condition to about 13,340 gallons per minute during full hydraulic containment of all site-related compounds identified by the New York State standards, criteria, and guidance for environmental investigations and cleanup. Targeting of high-concentration zones of the plume increases the total amount of remedial pumpage necessary to capture all parts of the plume but may decrease the total amount of time necessary to operate a remedial system. Simulated time frames of advective transport ranged from about 12 years to capture zones with elevated concentrations of volatile organic compounds (mean particle travel time plus the standard deviation of travel time) to more than 100 years to capture all zones.
Groundwater-flow model analysis indicates that all the optimal plume-containment scenarios would have negligible effects on streams and the saltwater-freshwater interface along the south shore of Long Island. Massapequa, Bellmore, Seaman, and Seaford Creeks are represented by using MODFLOW drain-boundary conditions. Saltwater-freshwater interfaces are represented by using MODFLOW general head-boundary conditions where the Magothy aquifer discharges upward into saline groundwater across the Gardiners clay confining unit and the Lloyd aquifer discharges upward into saline groundwater across the Raritan confining unit.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205090","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Misut, P.E., Walter, D., Schubert, C., and Dressler, S., 2020, Analysis of remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York: U.S. Geological Survey Scientific Investigations Report 2020–5090, 83 p., https://doi.org/10.3133/sir20205090.","productDescription":"Report: vi, 83 p.; Data Release; 5 Figures","numberOfPages":"83","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105143","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":380266,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5090/sir20205090_figures.zip","text":"High-resolution figures","size":"159 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Figures 16, 18, 20, 22, and 24"},{"id":380264,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DOBQ8N","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 model use to analyze remedial scenarios affecting plume movement through a sole-source aquifer system, southeastern Nassau County, New York"},{"id":380262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5090/coverthb.jpg"},{"id":380263,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5090/sir20205090.pdf","text":"Report","size":"18.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5090"}],"country":"United States","state":"New York","county":"Nassau County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.87619018554688,\n 40.482470524589516\n ],\n [\n -73.289794921875,\n 40.482470524589516\n ],\n [\n -73.289794921875,\n 40.81796653313175\n ],\n [\n -73.87619018554688,\n 40.81796653313175\n ],\n [\n -73.87619018554688,\n 40.482470524589516\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The Washita River alluvial aquifer is a valley-fill and terrace alluvial aquifer along the valley of the Washita River in western Oklahoma that provides a productive source of groundwater for agricultural irrigation and water supply. The Oklahoma Water Resources Board (OWRB) has designated the westernmost section of the aquifer in Roger Mills and Custer Counties, Okla., as reach 1 of the Washita River alluvial aquifer; reach 1 is the focus of this report. The OWRB issued an order on November 13, 1990, that established the maximum annual yield (MAY; 120,320 acre-feet per year [acre-ft/yr]) and equal-proportionate-share (EPS) pumping rate (2.0 acre-feet per acre per year [(acre-ft/acre)/yr]) for reach 1 of the Washita River alluvial aquifer. The MAY and EPS were based on hydrologic investigations that evaluated the effects of potential groundwater withdrawals on groundwater availability in the Washita River alluvial aquifer. Every 20 years, the OWRB is statutorily required to update the hydrologic investigation on which the MAY and EPS were based. Because 30 years have elapsed since the last order was issued, the U.S. Geological Survey, in cooperation with the OWRB, conducted a new hydrologic investigation and evaluated the effects of potential groundwater withdrawals on groundwater flow and availability in the Washita River alluvial aquifer.
The Washita River is the primary source of inflow to Foss Reservoir, a Bureau of Reclamation reservoir constructed in 1961 for flood control, water supply, and recreation. Foss Reservoir provides water for Bessie, Clinton, New Cordell, and Hobart, Okla. Nearly 98 percent of the total groundwater use from the Washita River alluvial aquifer during 1967 to 2015 was for irrigation; other uses of groundwater in the study area include public supply, mining, and agriculture.
A hydrogeologic framework was developed for the Washita River alluvial aquifer and included the physical characteristics of the aquifer, the geologic setting, the hydraulic properties of hydrogeologic units, the potentiometric surface (water table), and groundwater-flow directions at a scale that captures the regional controls on groundwater flow. The Washita River alluvial aquifer consists of alluvium and terrace deposits that were transported primarily by water and range from clay to gravel in size. The terrace includes windblown deposits of silt size and, in some cases, contains gravel laid down at several levels along former courses of present-day rivers.
A conceptual flow model is a simplified description of the aquifer system that includes hydrologic boundaries, major inflow and outflow sources of the groundwater-flow system, and a conceptual water budget with the estimated mean flows between those hydrologic boundaries. During the study period 1980–2015, mean annual groundwater withdrawals, predominantly used for agricultural irrigation, totaled 5,502 acre-ft/yr, or 14 percent of aquifer outflows. When applied across the 132-square-mile aquifer area used for modeling purposes (84,366 acres), mean annual recharge of 3.15 inches per year corresponds to a mean annual recharge volume of 22,169 acre-ft/yr, or 56 percent of aquifer inflows. The annual saturated-zone evapotranspiration outflow was 11,828 acre-ft/yr for the Washita River alluvial aquifer, or about 30 percent of aquifer outflows. For the Washita River alluvial aquifer, lateral flow was 17,157 acre-ft/yr, or 44 percent of the aquifer inflows. The conceptual flow model and hydrogeologic framework were used to conceptualize, design, and build the numerical groundwater-flow model.
A numerical groundwater-flow model of the Washita River alluvial aquifer was constructed by using MODFLOW-2005. The Washita River alluvial aquifer groundwater-model grid was spatially discretized into 350-foot (ft) cells and two layers. Layer 1 represented the undifferentiated alluvium and terrace deposits of Quaternary age, and layer 2 represented the bedrock of Permian age, which was given a uniform nominal thickness of 100 ft. The groundwater-simulation period was temporally discretized into 433 monthly transient stress periods, representing January 1980 to December 2015. An initial 365-day steady-state stress period was configured to represent mean annual inflows and outflows from the Washita River alluvial aquifer for the study period. The groundwater-flow model was calibrated manually and by automated adjustment of model inputs by using PEST++. Calibration targets for the Washita River alluvial aquifer model included groundwater-level observations and reservoir-stage observations, as well as base-flow and stream-seepage estimates.
Three groundwater-availability scenarios were used in the calibrated groundwater model to (1) estimate the EPS pumping rate that retains the saturated thickness that meets the minimum 20-year life of the aquifer, (2) quantify the effects of projected pumping rates on groundwater storage over a 50-year period, and (3) evaluate how projected pumping rates extended 50 years into the future and sustained hypothetical drought conditions over a 10-year period affect base flow and groundwater in storage. The results of the groundwater-availability scenarios could be used by the OWRB to reevaluate the established MAY of groundwater from the Washita River alluvial aquifer.
EPS scenarios for the Washita River alluvial aquifer were run for periods of 20, 40, and 50 years. The 20-, 40-, and 50-year EPS pumping rates under normal recharge conditions were 1.7, 1.6, and 1.6 (acre-ft/acre)/yr, respectively. Given the aquifer area used for modeling purposes (84,366 acres), these rates correspond to annual yields of 142,579, 134,986, and 134,986 acre-ft/yr, respectively. Groundwater storage at the end of the 20-year EPS scenario was about 281,000 acre-feet (acre-ft), or about 306,000 acre-ft (52 percent) less than the starting storage. Considering the land-surface area of the Washita River alluvial aquifer and using a specific yield of 0.12, this decrease in storage was equivalent to a mean groundwater-level decline of about 30 ft. The Washita River downstream from Foss Reservoir and most of the streams in the study area were dry at the end of the 20-year EPS scenario. Foss Reservoir stage was below the dead-pool stage of 1,597 ft after about 7 years of pumping in the 20-year EPS scenario.
Four projected 50-year groundwater-use scenarios were used to simulate the effects of selected well withdrawal rates on groundwater storage in the Washita River alluvial aquifer. These four scenarios used (1) no groundwater use, (2) groundwater use at the 2015 pumping rate, (3) mean groundwater use for the simulation period, and (4) increasing groundwater use. Groundwater storage after 50 years with no groundwater use was 545,249 acre-ft, or 693 acre-ft (0.1 percent) greater than the initial groundwater storage; this groundwater storage increase is equivalent to a mean groundwater-level increase of 0.1 ft. Groundwater storage at the end of the 50-year period with 2015 pumping rates was 543,831 acre-ft, or 723 acre-ft (0.1 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.1 ft. Groundwater storage after 50 years with the mean pumping rate for the study period was 543,202 acre-ft, or 1,349 acre-ft (0.2 percent) less than the initial groundwater storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.1 ft. Groundwater storage at the end of the 50-year period with an increasing demand groundwater-pumping rate, which was 38 percent greater than the 2015 groundwater-pumping rate, was 542,584 acre-ft, or 1,967 acre-ft (0.4 percent) less than the initial storage; this groundwater storage decrease is equivalent to a mean groundwater-level decrease of 0.2 ft.
A hypothetical 10-year-drought scenario was used to simulate the effects of a prolonged period of reduced recharge on groundwater storage in the Washita River alluvial aquifer and Foss Reservoir stage and storage. To simulate the hypothetical drought, recharge in the calibrated model was reduced by 50 percent during the simulated drought period (1983–1992). Groundwater storage at the end of the drought period in December 1992 was 562,000 acre-ft, or 36,000 acre-ft (6 percent) less than the groundwater storage of the calibrated groundwater model (598,000 acre-ft). At the end of the hypothetical drought, the largest changes in saturated thickness (as great as 43.5 ft) were in the area upgradient from Foss Reservoir, particularly in the terrace at the model boundary. Substantial decreases in the Foss Reservoir stage began during the fall of 1985 in conjunction with base-flow decreases of up to 100 percent at U.S. Geological Survey streamgage 07324200 Washita River near Hammon, Okla. These lake-stage declines outpaced groundwater-level declines in the surrounding aquifer. The minimum Foss Reservoir storage simulated during the drought period was 77,954 acre-ft, which was a decrease of 46 percent from the nondrought storage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205118","collaboration":"Prepared in cooperation with the Oklahoma Water Resources Board","usgsCitation":"Ellis, J.H., Ryter, D.W., Fuhrig, L.T., Spears, K.W., Mashburn, S.L., and Rogers, I.M.J., 2020, Hydrogeology, numerical simulation of groundwater flow, and effects of future water use and drought for reach 1 of the Washita River alluvial aquifer, Roger Mills and Custer Counties, western Oklahoma, 1980–2015: U.S. Geological Survey Scientific Investigations Report 2020–5118, 81 p., https://doi.org/10.3133/sir20205118.","productDescription":"Report: xi, 81 p.; Data Release","numberOfPages":"98","onlineOnly":"Y","ipdsId":"IP-116035","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":381399,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PKMG6U","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model used in simulation of groundwater flow, and analysis of projected water use for the Washita River alluvial aquifer, western Oklahoma"},{"id":381398,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5118/sir20205118.pdf","text":"Report","size":"18.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5118"},{"id":381397,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5118/coverthb.jpg"}],"country":"United States","state":"Oklahoma","county":"Roger Mills County, Custer County","otherGeospatial":"Washita River alluvial aquifer","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-98.6305,35.812],[-98.6308,35.6387],[-98.6307,35.552],[-98.6199,35.552],[-98.6209,35.4639],[-98.8338,35.4653],[-98.9399,35.4659],[-99.0455,35.4654],[-99.1517,35.4658],[-99.3629,35.4649],[-99.3631,35.508],[-99.5755,35.5085],[-99.576,35.42],[-100.0009,35.4223],[-100.0014,35.4558],[-100.0011,35.6197],[-100.001,35.64],[-100.0015,35.8008],[-100.0015,35.8782],[-99.9742,35.8921],[-99.9566,35.8959],[-99.947,35.9009],[-99.938,35.9037],[-99.9272,35.9074],[-99.9228,35.9115],[-99.9177,35.9175],[-99.9132,35.9234],[-99.911,35.928],[-99.9082,35.9325],[-99.9049,35.9371],[-99.9038,35.9462],[-99.9045,35.9562],[-99.9051,35.9589],[-99.899,35.9698],[-99.894,35.9748],[-99.8522,36.0051],[-99.8398,36.0115],[-99.829,36.0107],[-99.8227,36.0089],[-99.8152,36.0026],[-99.8078,35.9949],[-99.8019,35.9827],[-99.8019,35.9737],[-99.8051,35.9618],[-99.809,35.9518],[-99.8111,35.9364],[-99.8099,35.9287],[-99.8087,35.9246],[-99.8007,35.9174],[-99.7938,35.9102],[-99.788,35.8962],[-99.784,35.8921],[-99.7725,35.8867],[-99.76,35.885],[-99.7521,35.8824],[-99.7372,35.8738],[-99.7258,35.8653],[-99.7189,35.8626],[-99.7149,35.854],[-99.6979,35.855],[-99.6774,35.847],[-99.6615,35.847],[-99.6558,35.8457],[-99.6416,35.8444],[-99.6291,35.84],[-99.6149,35.84],[-99.6042,35.8478],[-99.6002,35.8519],[-99.5929,35.8551],[-99.5855,35.8574],[-99.577,35.8588],[-99.5623,35.8621],[-99.5578,35.8675],[-99.5562,35.8825],[-99.5416,35.903],[-99.532,35.9076],[-99.5241,35.9185],[-99.5156,35.9281],[-99.5067,35.9481],[-99.5061,35.9535],[-99.5085,35.9608],[-99.5085,35.9649],[-99.5045,35.9703],[-99.4995,35.974],[-99.4876,35.9795],[-99.4785,35.9899],[-99.4638,35.9995],[-99.4445,36.01],[-99.4303,36.016],[-99.4144,36.0169],[-99.3928,36.017],[-99.3809,36.017],[-99.3808,35.8991],[-99.374,35.8991],[-99.3736,35.8111],[-99.0571,35.8112],[-98.7366,35.8118],[-98.6305,35.812]]]},\"properties\":{\"name\":\"Custer\",\"state\":\"OK\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Confined (or buried) aquifers of glacial origin overlain by till confining units provide drinking water to hundreds of thousands of Minnesota residents. The sustainability of these groundwater resources is not well understood because hydraulic properties of till that control vertical groundwater fluxes (leakage) to underlying aquifers are largely unknown. The U.S. Geological Survey, Iowa State University, Minnesota Geological Survey, and Minnesota Department of Health investigated hydraulic properties and groundwater flow through till confining units using field studies and heuristic MODFLOW simulations. Till confining units in the following late-Wisconsinan stratigraphic units (with locations in parentheses) were characterized: Des Moines lobe till of the New Ulm Formation (Litchfield, Minnesota), Superior lobe till of the Cromwell and Aitkin Formations (Cromwell, Minn.), and Wadena lobe till of the Hewitt Formation (hydrogeology field camp [HFC] near Akeley, Minn.). Pre-Illinoian till of the Good Thunder formation (Olivia, Minn.) was also characterized.
Hydraulic and geochemical field data were collected from sediment cores and a series of five piezometer nests. Each nest consisted of five to eight piezometers screened at short vertical intervals in hydrostratigraphic units including (if present) surficial aquifers, till confining units, confined/buried aquifers, and underlying bedrock. Till hydraulic conductivity was estimated from slug tests (horizontal [Kh]) and constant-rate aquifer tests in the confined aquifer (vertical [Kv]). Travel times through the till were evaluated with Darcy’s law and stable isotope concentrations. A series of heuristic MODFLOW simulations were used to evaluate groundwater fluxes through till across the range of till hydraulic properties and pumping rates observed at the field sites.
The field data demonstrated variability in hydraulic properties between and within till stratigraphic units horizontally and vertically. The variability in hydraulic properties within and between sites resulted in substantial differences in groundwater flux through till. A conceptual understanding that emerges from the vertical till profiles is that they are not homogeneous hydrostratigraphic units with uniform properties; rather, each vertical sequence is a heterogeneous mixture of glacial sediment with differing abilities to transmit water.
Till thicknesses varied from 60 to 166 feet, and till textures ranged from a sandy loam (Hewitt Formation, HFC site) to a silt loam/clay loam (Good Thunder formation, Olivia site). Till Kh varied by one to three orders of magnitude within each piezometer nest. Four piezometer nests had downward hydraulic gradients ranging from 0.04 to 0.56, and one nest had a slight upward hydraulic gradient of 0.02. The Cromwell, HFC, and Litchfield 1 sites were examples of “leaky” tills with high Kv (0.001 to 1.1 feet per day [ft/d]) and geometric mean Kh (0.03 to 0.07 ft/d) and extensive vertical hydraulic connectivity between the confined aquifer and the overlying till. Estimated groundwater travel times through these sites ranged from 1 to 81 years, and two of these sites had tritium throughout their till profiles. The tills at the other two sites, Olivia and Litchfield 2, were effective confining units that had low Kv (0.001 to 0.0005 ft/d) and geometric mean Kh (0.0002 to 0.004 ft/d). The till piezometers at these sites had no drawdown response to short-term (up to 10 hours for Olivia and up to 5 days for Litchfield) high-capacity pumping from the confined aquifer. Estimated groundwater travel times through the tills at these sites ranged from 165 to nearly 1,800 years, and tritium was only detected in the upper one-third of these till profiles. Across all sites, the till vertical anisotropy (ratio of Kh to Kv) ranged by four orders of magnitude from 0.05 at the Cromwell nest to 70 at the Litchfield 1 nest. Stable isotopes of oxygen and hydrogen indicate that groundwater throughout all five till profiles is younger than the last glacial advance into Minnesota at about 11,000 years ago.
The heuristic modeling demonstrated that, for understanding sustainability of groundwater pumping from confined aquifers, knowledge of till hydraulic properties is just as important as knowledge of aquifer hydraulic properties. Substantial differences in groundwater fluxes into and through till were observed across hydrogeologic settings representative of the field sites. Over long periods of time (hundreds of years), pumping-induced hydraulic gradients are established in confined aquifer systems and, even in low hydraulic conductivity tills, these pumping-induced hydraulic gradients increase leakage into and through till compared to ambient conditions.
In conclusion, groundwater flowing vertically downward through till confining units (leakage) replenishes water pumped from confined aquifers. Till hydraulic properties, such as those presented in this report, provide important information that can be used to quantify leakage rates through till. Till hydraulic properties are variable over short distances and profoundly affect leakage rates, demonstrating the importance of site-specific till hydraulic data for evaluating the sustainability of groundwater withdrawals from confined aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205127","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources and in collaboration with Iowa State University and the Minnesota Department of Health","usgsCitation":"Trost, J.J., Maher, A., Simpkins, W.W., Witt, A.N., Stark, J.R., Blum, J., and Berg, A.M., 2020, Hydrogeology and groundwater geochemistry of till confining units and confined aquifers in glacial deposits near Litchfield, Cromwell, Akeley, and Olivia, Minnesota, 2014–18: U.S. Geological Survey Scientific Investigations Report 2020–5127, 80 p., https://doi.org/10.3133/sir20205127.","productDescription":"Report: ix, 80 p.; 2 Data Releases; Dataset","numberOfPages":"94","onlineOnly":"Y","ipdsId":"IP-103595","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":381538,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS dataset","linkHelpText":"— USGS water data for the Nation"},{"id":381534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5127/coverthb.jpg"},{"id":381535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5127/sir20205127.pdf","text":"Report","size":"4.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5127"},{"id":381536,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IXC7D3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Geochemical data, water-level data, and slug test analysis results from till confining units and confined aquifers in glacial deposits near Akeley, Cromwell, Litchfield, and Olivia, Minnesota, 2015–2018"},{"id":381537,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KOI6T3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Heuristic MODFLOW models used to evaluate the effects of pumping groundwater from confined aquifers overlain by till confining units"}],"country":"United States","state":"Minnesota","city":"Akeley, Cromwell, Litchfield, Olivia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.5758056640625,\n 45.084672408703945\n ],\n [\n -94.48173522949219,\n 45.084672408703945\n ],\n [\n -94.48173522949219,\n 45.16364237397378\n ],\n [\n -94.5758056640625,\n 45.16364237397378\n ],\n [\n -94.5758056640625,\n 45.084672408703945\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.91549682617188,\n 46.65320687122665\n ],\n [\n -92.84614562988281,\n 46.65320687122665\n ],\n [\n -92.84614562988281,\n 46.6965511173143\n ],\n [\n -92.91549682617188,\n 46.6965511173143\n ],\n [\n -92.91549682617188,\n 46.65320687122665\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.74918365478516,\n 46.987044654111116\n ],\n [\n -94.70970153808594,\n 46.987044654111116\n ],\n [\n -94.70970153808594,\n 47.01678007415223\n ],\n [\n -94.74918365478516,\n 47.01678007415223\n ],\n [\n -94.74918365478516,\n 46.987044654111116\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.02418518066406,\n 44.74185630294231\n ],\n [\n -94.95758056640625,\n 44.74185630294231\n ],\n [\n -94.95758056640625,\n 44.79986517347138\n ],\n [\n -95.02418518066406,\n 44.79986517347138\n ],\n [\n -95.02418518066406,\n 44.74185630294231\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
A three-dimensional groundwater-flow model was developed for the aquifer system of Long Island, New York, to evaluate (1) responses of the hydrologic system to changes in natural and anthropogenic hydraulic stresses, (2) the subsurface distribution of groundwater age, and (3) the regional-scale distribution of groundwater travel times and the source of water to fresh surface waters and coastal receiving waters. The model also provides the groundwater flow components used to define model boundaries for possible inset models used for local-scale analyses.
The three-dimensional, groundwater flow model developed for this investigation uses the numerical code MODFLOW–NWT to represent steady-state conditions for average groundwater pumping and aquifer recharge for 2005–15. The particle-tracking algorithm MODPATH, which simulates advective transport in the aquifer, was used to estimate groundwater age, delineate the areas at the water table that contribute recharge to coastal and freshwater bodies, and estimate total travel times of water from the water table to discharge locations.
A three-dimensional, 1-meter (3.3-foot) topobathymetric model was used to determine land-surface altitudes for the island and seabed altitudes for the surrounding coastal waters. The mapped extents and surface altitudes of major geologic units were compiled and used to develop a three-dimensional hydrogeologic framework of the aquifer system, including aquifers and confining units. Lithologic data from deep boreholes and previous aquifer-test results were used to estimate the three-dimensional distribution of hydraulic conductivity in principal aquifers. Natural recharge from precipitation was estimated for 2005–15 using a modified Thornthwaite-Mather methodology as implemented in a soil-water balance model. Components of anthropogenic recharge—wastewater return flow, storm water inflow, and inflow from leaky infrastructure—also were estimated for 2005–15. Groundwater withdrawals for various sources, including public water supply, industrial, remediation, and agricultural, were compiled or estimated for the same period.
These data were incorporated into the model development to represent the aquifer system geometry, boundaries, and initial hydraulic properties of the regional aquifers and confining units within the Long Island aquifer system. Average hydraulic conditions—water levels and streamflows—for 2005–15 were estimated using existing data from the U.S. Geological Survey National Water Information System database. Model inputs were adjusted to best match average hydrologic conditions using inverse methods as implemented in the parameter-estimating software PEST. The calibrated model was used to simulate average hydrologic conditions in the aquifer system for 2005–15.
About 656 cubic feet per second of water was withdrawn on average annually for 2005–15 for water supply and an average of about 349 cubic feet per second of water recharged the aquifer annually from return flow and leaky infrastructure. Parts of New York City have drawdowns exceeding 25 feet, mostly because of urbanization and associated large decreases in recharge rates. Large areas in the western part of the island have drawdowns exceeding 10 feet, mostly from large groundwater withdrawals and sewering, which largely eliminates wastewater return flow. Water-table altitudes in eastern parts of the island increased by more than 2 feet in some areas as a result of wastewater return flow in unsewered areas and changes in land use. Changes in streamflows show a similar pattern as water-table altitudes. Streamflows generally decrease in western parts of the island where there are large drawdowns and increase in eastern parts of the island where water-table altitudes increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205091","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Walter, D.A., Masterson, J.P., Finkelstein, J.S., Monti, J., Jr., Misut, P.E., and Fienen, M.N., 2020, Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15: U.S. Geological Survey Scientific Investigations Report 2020–5091, 75 p., https://doi.org/10.3133/sir20205091.","productDescription":"Report: ix, 75 p.; 3 Data Releases","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112206","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":381521,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5091/images/"},{"id":381195,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5091"},{"id":381194,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5091/coverthb2.jpg"},{"id":381192,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954DLLC","text":"USGS data release","linkHelpText":"Aquifer texture data describing the Long Island aquifer system"},{"id":381191,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KWQSEJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 used to simulate groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15"},{"id":381190,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens Counties, New York, October-November 2017"},{"id":381520,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.XML"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.102783203125,\n 40.55554790286311\n ],\n [\n -73.7017822265625,\n 40.49709237269567\n ],\n [\n -72.8778076171875,\n 40.65147128144057\n ],\n [\n -72.037353515625,\n 40.91351257612758\n ],\n [\n -71.6143798828125,\n 41.16211393939692\n ],\n [\n -71.9384765625,\n 41.178653972331674\n ],\n [\n -72.191162109375,\n 41.236511201246216\n ],\n [\n -72.65808105468749,\n 41.11660732012896\n ],\n [\n -73.4490966796875,\n 40.967455873296714\n ],\n [\n -73.751220703125,\n 40.89275342420696\n ],\n [\n -74.0203857421875,\n 40.718119379753446\n ],\n [\n -74.102783203125,\n 40.55554790286311\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532