Geology topics

Laura Hayes

Publications and source records attributed to Laura Hayes.

18 recordsLinked to original sources

USGS2025

User’s guide for the National Hydrography Dataset Plus High Resolution (NHDPlus HR)

The National Hydrography Dataset Plus High Resolution (NHDPlus HR) is a scalable hydrologic geospatial fabric or framework, built from (1) the High Resolution (1:24,000-scale or better) National Hydrography Dataset (NHD), (2) nationally complete Watershed Boundary Dataset (WBD), and (3) 1/3-arc-second 3D Elevation Program (3DEP) digital elevation model (DEM) data (at a 10-meter ground spacing; or 5-meter 3DEP DEM in Alaska only). The NHDPlus HR provides a modeling and assessment framework at a local 1:24,000 scale, while nesting seamlessly into the national context. NHDPlus HR is modeled after the highly successful NHDPlus version 2 (NHDPlusV2). Like NHDPlusV2, the NHDPlus HR includes data for a nationally seamless network of stream reaches, elevation-based catchment areas, flow surfaces, and value-added attributes that enhance stream-network navigation, analysis, and data display. However, NHDPlus HR provides much greater spatial detail than NHDPlusV2, while NHDPlusV2 is, at present, more complete in its attribution of additions, removals, and diversions, as well as stream connectivity. This user’s guide is intended to provide necessary information and guidance in the use of NHDPlus HR data.

Scientific Investigations Report↗

USGS2022

Predicted uranium and radon concentrations in New Hampshire (USA) groundwater—Using Multi Order Hydrologic Position as predictors

Two radioactive elements, uranium (U) and radon (Rn), which are of potential concern in New Hampshire (NH) groundwater, are investigated. Exceedance probability maps are tools to highlight locations where the concentrations of undesirable substances in the groundwater may be elevated. Two forms of statistical analysis are used to create exceedance probability maps for U and Rn in NH groundwater. The first, Boosted Regression Tree (BRT), was selected for estimating U exceedance values. It computes exceedance values directly using the Bernoulli distribution function. The second method of statistical analysis used for Rn to determine exceedance probabilities is ordinary least squares (OLS) regression. In the process of determining exceedance probabilities for U and Rn, the utility of a new dataset is investigated. That new predictor dataset is the Multi-Order Hydrologic Position (MOHP) dataset. MOHP raster datasets have been produced nationally for the conterminous United States at a 30-m resolution. The concept behind MOHP is that, for any given point on the earth's surface, there is the potential for a longer groundwater flow path as one goes deeper beneath the land surface. MOHP predictors were tested in both models. Three MOHP predictors were found useful in the BRT model and two in the OLS model. MOHP data were found useful as predictors along with other site characteristics in predicting U and Rn exceedance probabilities in New Hampshire groundwater.

New Hampshire↗

USGS2019

Flood-inundation maps for Lake Champlain in Vermont and New York

In 2016, digital flood-inundation maps along the shoreline of Lake Champlain in Addison, Chittenden, Franklin, and Grand Isle Counties in Vermont and northern Clinton County in New York were created by the U.S. Geological Survey (USGS) in cooperation with the International Joint Commission (IJC). This report discusses the creation of updated static digital flood-inundation mapping, in 2018, to include the entire shoreline of Lake Champlain in the United States. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at http://water.usgs.gov/osw/flood_inundation/ , depict estimates of the areal extent of flooding corresponding to selected water-surface elevations (stages) at the USGS lake gages on Lake Champlain. As a result of the record setting floods of May 2011 in Lake Champlain and the Richelieu River, the U.S. and Canadian governments requested that the IJC issue a reference for a study to identify how flood forecasting, preparedness, and mitigation could be improved in the Lake Champlain–Richelieu River Basin. The IJC submitted the Lake Champlain–Richelieu River Plan of Study to the governments of Canada and the United States in 2013. The flood-inundation maps in this study are one aspect of the task work outlined in the IJC 2013 Plan of Study. Wind and seiche effects (standing oscillating wave with a long wavelength) that can influence flooding along the Lake Champlain shoreline were not represented. The flood-inundation maps reflect 11 stages for Lake Champlain that are static for the entire area of the lake. Near-real-time stages at the USGS gages on Lake Champlain may be obtained from the USGS National Water Information System website at http://waterdata.usgs.gov/ ( https://doi.org/10.5066/F7P55KJN ) or from the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/ . Updated static flood-inundation boundary extents were created for Lake Champlain in Franklin, Chittenden, Addison, Rutland, and Grand Isle Counties in Vermont and Clinton, Essex, and Washington Counties in New York by using recently acquired (2009, 2012, 2014, and 2015) light detection and ranging (lidar) data. The corresponding flood-inundation maps may be referenced to any of the four active USGS lake gages on Lake Champlain. Of these four active lake gages, USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y.; USGS lake gage 04294500, Lake Champlain at Burlington, Vt.; USGS lake gage 04279085 Lake Champlain north of Whitehall, N.Y.; and USGS lake gage 04294413, Lake Champlain at Port Henry, N.Y., only the Richelieu River (Lake Champlain) at Rouses Point, N.Y., gage also serves as a National Weather Service prediction location. Lake Champlain static flood-inundation map boundary extents corresponding to the May 2011 peak flood stage (103.20 feet [ft], National Geodetic Vertical Datum of 1929 [NGVD 29], as recorded at the USGS Rouses Point lake gage, were compared to the flood-inundation area extents determined from satellite imagery for the May 2011 flood (which incorporated documented high-water marks from the flood of May 2011) and were found to be in good agreement. The May 2011 flood is the highest recorded lake water level (stage) at the Rouses Point, N.Y., and Burlington, Vt., lake gages. Flood stages greater than 101.5 ft (NGVD 29) exceed the “major flood stage” as defined by the National Weather Service for USGS lake gage 04295000. Updated digital elevation models (DEMs) were created from the recent lidar data for Lake Champlain in Vermont and New York. These DEMs were used in determining the flood-inundation boundary and associated depth grids for 11 flood stages at 0.5-ft or 1-ft intervals from 100.0 to 106.0 ft (NGVD 29) as referenced to the USGS lake gages. In addition, the May 2011 flood-inundation area for elevation 103.20 ft (NGVD 29) (102.77 ft, North American Vertical Datum of 1988) was determined from these updated DEMs. The availability of these maps, along with online information regarding current stages at the USGS lake gages and forecasted high-flow stages from the National Weather Service at USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y., will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.

New York, Vermont↗

USGS2017

Assessing models of arsenic occurrence in drinking water from bedrock aquifers in New Hampshire

Three existing multivariate logistic regression models were assessed using new data to evaluate the capacity of the models to correctly predict the probability of groundwater arsenic concentrations exceeding the threshold values of 1, 5, and 10 micrograms per liter (µg/L) in New Hampshire, USA. A recently released testing dataset includes arsenic concentrations from groundwater samples collected in 2004–2005 from a mix of 367 public-supply and private domestic wells. The use of this dataset to test three existing logistic regression models demonstrated enhanced overall predictive accuracy for the 5 and 10 μg/L models. Overall accuracies of 54.8, 76.3, and 86.4 percent were reported for the 1, 5, and 10 μg/L models, respectively. The state was divided by counties into northwest and southeast regions. Regional differences in accuracy were identified; models had an average accuracy of 83.1 percent for the counties in the northwest and 63.7 percent in the southeast. This is most likely due to high model specificity in the northwest and regional differences in arsenic occurrence. Though these models have limitations, they allow for arsenic hazard assessment across the region. The introduction of well-type (public or private), well depth, and casing length as explanatory variables may be appropriate measures to improve model performance. Our findings indicate that the original models generalize to the testing dataset, and should continue to serve as an important vehicle of preventative public health that may be applied to other groundwater contaminants in New Hampshire.

New Hampshire↗

USGS2016

Flood-inundation maps for Lake Champlain in Vermont and in northern Clinton County, New York

Digital flood-inundation maps for an approximately100-mile length of Lake Champlain in Addison, Chittenden, Franklin, and Grand Isle Counties in Vermont and northern Clinton County in New York were created by the U.S. Geological Survey (USGS) in cooperation with the International Joint Commission (IJC). The flood-inundationmaps, which can be accessed through the International Joint Commission (IJC) Web site at http://www.ijc.org/en_/ , depict estimates of the areal extent flooding correspondingto selected water levels (stages) at the USGS lake gage on the Richelieu River (Lake Champlain) at Rouses Point, N.Y. (station number 04295000). In this study, wind and seiche effects (standing oscillating wave with a long wavelength) were not taken into account and the flood-inundation mapsreflect 11 stages (elevations) for Lake Champlain that are static for the study length of the lake. Near-real-time stages at this lake gage, and others on Lake Champlain, may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/ , which also forecasts flood hydrographs at the Richelieu River (Lake Champlain) at Rouses Point. Static flood boundary extents were determined for LakeChamplain in Addison, Chittenden, Franklin, and Grand Isle Counties in Vermont and northern Clinton County in New York using recently acquired (2013–2014) lidar (light detection and ranging) and may be referenced to any of the five USGS lake gages on Lake Champlain. Of these five lakgages, USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y., is the only USGS lake gage that is also a National Weather Service prediction location. Flood boundary extents for the Lake Champlain static flood-inundation map corresponding to the May 201 flood(103.2 feet [ft], National Geodetic Vertical Datum [NGVD] 29) were evaluated by comparing these boundary extents against the inundation area extents determined for the May 2011 flood (which incorporated documented high-water marksfrom the flood of May 201) (Bjerklie and others, 2014). A digital elevation model (DEM) was created by USGS, within a geographic information system (GIS), from the recently flown and processed light detection and ranging(lidar) data (2013–2014) in Vermont and the lake shore area of northern Clinton County in New York. The lidar data have a vertical accuracy of 0.3 to 0.6-ft (9.6 to 18.0-centimeters [cm]) and a horizontal resolution of 2.3 to 4.6 ft (0.7 to 1.4 meters). This DEM was used in determining the floodboundary for 11 flood stages at 0.5-ft intervals from 100.0 to104.0 ft (NGVD 29) and 1-ft intervals from 104.0 to 106.0 ft (NGVD 29) as referenced to the USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y. In addition, the May 2011 flood-inundation area for elevation103.20 ft (NGVD 29) (102.77 ft, North American Vertical Datum [NAVD] 88) was determined from this DEM. The May 2011 flood is the highest recorded lake water level (stage)at the Rouses Point, N.Y., lake gage. Flood stages greater than 101.5 ft (NGVD 29) exceed the “major flood stage”as defined by the NationalWeather Service for USGS lake gage 04295000. The availability of these maps, along with Internet information regarding current stage from the USGS lake gage and forecasted high-flow stages from the NationalWeather Service, will provide emergency management personnel and residents with information that is critical for flood responseactivities such as evacuations and road closures, as well as for post-flood recovery eforts.

New York, Vermont↗

USGS2015

Water data to answer urgent water policy questions: Monitoring design, available data and filling data gaps for determining the effectiveness of agricultural management practices for reducing tributary nutrient loads to Lake Erie -- Addendum describing new, expanded, and planned monitoring sites

This Addendum describes new, expanded, and planned water monitoring sites in the Lake Erie drainage basin that were initiated subsequent to the preparation of Betanzo et al. (2015), the primary report with which this Addendum is associated. In addition to the new water monitoring sites, new programs have been initiated that focus on expansion of agricultural management practices to reduce nutrient transport to Lake Erie and facilitate data sharing between researchers involved with agricultural management and water-quality monitoring. This Addendum evaluates the applicability of these new monitoring sites and programs for answering the case-study policy question explored in Betanzo et al. (2015), “How effective are agricultural management practices at reducing nutrients from nonpoint sources at the watershed scale in the Lake Erie drainage basin?” The water monitoring data needs for answering the case-study policy question identified in Table 18 of Betanzo et al. (2015) were used to identify relevant monitoring sites in this Addendum, specifically focusing on total phosphorus (TP), dissolved reactive phosphorus (DRP)1 , and streamflow data (the complete list of parameters needed appears in Table 3 of Betanzo et al. (2015)). The new information summarized in this Addendum consists of water monitoring and agricultural management activities conducted by agencies and organizations whose data were compiled in the nutrient data set described in Betanzo et al. (2015), and programs identified in public news releases. Although this information is considered to be comprehensive and complete as of February 2015, there may be other new or planned water monitoring programs, of which we are not aware, that are not included here.

Indiana, Michigan, New York, Ohio, Pennsylvania↗

USGS2015

Water data to answer urgent water policy questions: Monitoring design, available data and filling data gaps for determining the effectiveness of agricultural management practices for reducing tributary nutrient loads to Lake Erie

Throughout its history, the United States has made major investments in assessing natural resources, such as soils, timber, oil and gas, and water. These investments allow policy makers, the private sector and the American public to make informed decisions about cultivating, harvesting or conserving these resources to maximize their value for public welfare, environmental conservation and the economy. As policy issues evolve, new priorities and challenges arise for natural resource assessment, and new approaches to monitoring are needed. For example, informed conservation and use of the nation’s finite fresh water resources in the context of increasingly intensive land development is a priority for today’s policy decisionmakers. There is a need to evaluate whether today’s water monitoring programs are generating the information needed to answer questions surrounding these new policy priorities. The Northeast-Midwest Institute (NEMWI), in cooperation with the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Program, initiated this project to explore the types and amounts of water data needed to address water-quality related policy questions of critical concern to today’s policy makers. The collaborating entities identified two urgent water policy questions and conducted case studies in the Northeast-Midwest region to determine the water data needed, water data available, and the best ways to fill the data gaps relative to those questions. This report details the output from one case study and focuses on the Lake Erie drainage basin, a data-rich area expected to be a best-case scenario in terms of water data availability.

Indiana, Michigan, New York, Ohio, Pennsylvania↗

USGS2012

Estimated probability of arsenic in groundwater from bedrock aquifers in New Hampshire, 2011

Probabilities of arsenic occurrence in groundwater from bedrock aquifers at concentrations of 1, 5, and 10 micrograms per liter (µg/L) were estimated during 2011 using multivariate logistic regression. These estimates were developed for use by the New Hampshire Environmental Public Health Tracking Program. About 39 percent of New Hampshire bedrock groundwater was identified as having at least a 50 percent chance of containing an arsenic concentration greater than or equal to 1 µg/L. This compares to about 7 percent of New Hampshire bedrock groundwater having at least a 50 percent chance of containing an arsenic concentration equaling or exceeding 5 µg/L and about 5 percent of the State having at least a 50 percent chance for its bedrock groundwater to contain concentrations at or above 10 µg/L. The southeastern counties of Merrimack, Strafford, Hillsborough, and Rockingham have the greatest potential for having arsenic concentrations above 5 and 10 µg/L in bedrock groundwater. Significant predictors of arsenic in groundwater from bedrock aquifers for all three thresholds analyzed included geologic, geochemical, land use, hydrologic, topographic, and demographic factors. Among the three thresholds evaluated, there were some differences in explanatory variables, but many variables were the same. More than 250 individual predictor variables were assembled for this study and tested as potential predictor variables for the models. More than 1,700 individual measurements of arsenic concentration from a combination of public and private water-supply wells served as the dependent (or predicted) variable in the models. The statewide maps generated by the probability models are not designed to predict arsenic concentration in any single well, but they are expected to provide useful information in areas of the State that currently contain little to no data on arsenic concentration. They also may aid in resource decision making, in determining potential risk for private wells, and in ecological-level analysis of disease outcomes. The approach for modeling arsenic in groundwater could also be applied to other environmental contaminants that have potential implications for human health, such as uranium, radon, fluoride, manganese, volatile organic compounds, nitrate, and bacteria.

New Hampshire↗

USGS2009

Methods for Estimating Withdrawal and Return Flow by Census Block for 2005 and 2020 for New Hampshire

The U.S. Geological Survey, in cooperation with the New Hampshire Department of Environmental Services, estimated the amount of water demand, consumptive use, withdrawal, and return flow for each U.S. Census block in New Hampshire for the years 2005 (current) and 2020. Estimates of domestic, commercial, industrial, irrigation, and other nondomestic water use were derived through the use and innovative integration of several State and Federal databases, and by use of previously developed techniques. The New Hampshire Water Demand database was created as part of this study to store and integrate State of New Hampshire data central to the project. Within the New Hampshire Water Demand database, a lookup table was created to link the State databases and identify water users common to more than one database. The lookup table also allowed identification of withdrawal and return-flow locations of registered and unregistered commercial, industrial, agricultural, and other nondomestic users. Geographic information system data from the State were used in combination with U.S. Census Bureau spatial data to locate and quantify withdrawals and return flow for domestic users in each census block. Analyzing and processing the most recently available data resulted in census-block estimations of 2005 water use. Applying population projections developed by the State to the data sets enabled projection of water use for the year 2020. The results for each census block are stored in the New Hampshire Water Demand database and may be aggregated to larger political areas or watersheds to assess relative hydrologic stress on the basis of current and potential water availability.

Open-File Report↗

USGS2008

Methods for and estimates of 2003 and projected water use in the Seacoast Region, Southeastern New Hampshire

New methods were developed to estimate water use in 2003 and future water demand in 2017 and 2025 in the Seacoast region in southeastern New Hampshire, which has experienced a 37-percent population increase during 1980 to 2000. Water-use activities for which estimates were developed include water withdrawal, delivery, demand, consumptive use, release, return flow, and transfer by registered and aggregated unregistered (less than 20,000 gallons per day (gal/d)) users at the census-block and town scales. Estimates of water use rely on understanding what influences water demand and its associated consumptive use, because changes in demand and consumptive use affect withdrawal and return flow. Domestic water demand was estimated using a per capita water demand model that related metered deliveries to domestic users with census block and block-group data. The model was used to predict annual, summer, and winter per capita water-demand coefficients for each census block. Significant predictors of domestic water demand include population per housing unit, median value of owner-occupied single family homes, median year of housing construction (with 1900 as the base value), population density, housing unit density, and proportion of housing units that are in urban areas. Mean annual domestic per capita water-demand coefficient in the Seacoast region was 75 gal/d; the coefficient increased to 91 gal/d during the summer and decreased to 65 gal/d during the winter. Domestic consumptive use was estimated as the difference between annual and winter domestic water demand. Estimates of commercial and industrial water demand were based on coefficients derived from reported use and metered deliveries. Projections of water demand in 2017 and 2025 were determined by using the housing and employee projections for those years developed through a Transportation Demand Model and applying current domestic and non-domestic coefficients. Water demand in 2003 was estimated as 25.8 million gallons per day (Mgal/d), 35 percent of which was during the summer months of June, July, and August. Domestic water demand was 18.6 Mgal/d (72 percent), commercial water demand was 3.7 Mgal/d (14 percent), industrial water demand was 2.9 Mgal/d (11 percent), irrigation water demand was 0.3 Mgal/d (1 percent), and mining and aquaculture water demand was 0.2 Mgal/d (1 percent). Domestic consumptive use for the Seacoast region was 16 percent of domestic water demand, which translates to a loss of 3 Mgal/d over the entire Seacoast region. In 2003, water withdrawal was 771.2 Mgal/d, of which 742.2 Mgal/d was instream use for hydroelectric power generation and thermoelectric power cooling. The remaining 29.0 Mgal/d was withdrawn by community water systems (22.6 Mgal/d; 72 percent), domestic users (6.4 Mgal/d; 21 percent), commercial users (1.0 Mgal/d; 3 percent), industrial users (1.0 Mgal/d; 3 percent), irrigation (0.2 Mgal/d; 1 percent) and other users (less than 0.1 Mgal/d). Return flow for 2003 was 772.2 Mgal/d, of which 742.0 Mgal/d was returned following use for hydroelectric power generation and thermoelectric plant cooling. The remaining 30.2 Mgal/d was returned by community wastewater systems (20.2 Mgal/d; 68 percent), domestic users (7.8 Mgal/d; 26 percent), commercial users (1.2 Mgal/d; 3 percent), industrial users (0.8 Mgal/d; 3 percent), and other users (0.1 Mgal/d). Domestic water demand is projected to increase by 54 percent to 28.7 Mgal/d from 2003 to 2025 based on projection of future population growth. Non-domestic (commercial, industrial, irrigation, and mining) water demand is projected to increase by 66 percent to 11.8 Mgal/d from 2003 to 2025.

Scientific Investigations Report↗

USGS2004