Geology topics

Douglas A. Freehafer

Publications and source records attributed to Douglas A. Freehafer.

6 recordsLinked to original sources

USGS2020

Mercury in fish from streams and rivers in New York State: Spatial patterns, temporal changes, and environmental drivers

Mercury (Hg) concentrations in freshwater fish across the state of New York frequently exceed guidelines considered harmful to humans and wildlife, but statewide distribution and temporal changes are not well known for the state’s streams and rivers. We analyzed existing data to describe recent spatial patterns, identify key environmental drivers, and assess temporal changes. Size classes within sportfishes and prey fishes formed ‘functional taxa’ (FT), and standardized scores were generated from 2007-2016 data for 218 sites. Muscle Hg in >1 sportfish FT exceeded human-health guidelines of 50 ng/g (sensitive populations) and 300 ng/g (general population, GP) at 93% and 56% of sites, respectively, but exceeded 1000 ng/g (a state threshold) at only 10% of sites. Whole-body Hg in >1 prey fish FT exceeded wildlife thresholds of 40 ng/g and 100 ng/g at 91% and 51% of sites, respectively. Environmental drivers of recent spatial patterns include extent of forest cover and storage, the latter an indicator of wetlands. Standardized Hg scores increased with increasing atmospheric Hg deposition and storage across rural ‘upland’ regions of New York. However, scores were not related to atmospheric deposition in more-developed ‘lowland’ regions due to the limited methylation potential of urban landscapes. Comparisons of 2010-2015 sportfish Hg concentrations with those of 1998 and 2000-2005 showed inconsistent temporal changes both among and within eight sites examined. Some recent stream and river fish Hg spatial patterns differed from those of lake-based studies, highlighting the importance of New York’s flowing waters to future Hg monitoring and risk assessment.

New York↗

USGS2017

Estimation of low-flow statistics at ungaged sites on streams in the Lower Hudson River Basin, New York, from data in geographic information systems

A variety of watershed properties available in 2015 from geographic information systems were tested in regression equations to estimate two commonly used statistical indices of the low flow of streams, namely the lowest flows averaged over 7 consecutive days that have a 1 in 10 and a 1 in 2 chance of not being exceeded in any given year (7-day, 10-year and 7-day, 2-year low flows). The equations were based on streamflow measurements in 51 watersheds in the Lower Hudson River Basin of New York during the years 1958–1978, when the number of streamflow measurement sites on unregulated streams was substantially greater than in subsequent years. These low-flow indices are chiefly a function of the area of surficial sand and gravel in the watershed; more precisely, 7-day, 10-year and 7-day, 2-year low flows both increase in proportion to the area of sand and gravel deposited by glacial meltwater, whereas 7-day, 2-year low flows also increase in proportion to the area of postglacial alluvium. Both low-flow statistics are also functions of mean annual runoff (a measure of net water input to the watershed from precipitation) and area of swamps and poorly drained soils in or adjacent to surficial sand and gravel (where groundwater recharge is unlikely and riparian water loss to evapotranspiration is substantial). Small but significant refinements in estimation accuracy resulted from the inclusion of two indices of stream geometry, channel slope and length, in the regression equations. Most of the regression analysis was undertaken with the ordinary least squares method, but four equations were replicated by using weighted least squares to provide a more realistic appraisal of the precision of low-flow estimates. The most accurate estimation equations tested in this study explain nearly 84 and 87 percent of the variation in 7-day, 10-year and 7-day, 2-year low flows, respectively, with standard errors of 0.032 and 0.050 cubic feet per second per square mile. The equations use natural values of streamflow and watershed properties; logarithmic transformations yielded less accurate equations inconsistent with some conceptualized relationships.

New York↗

USGS2015

Development of flood regressions and climate change scenarios to explore estimates of future peak flows

A new Web-based application, titled “Application of Flood Regressions and Climate Change Scenarios To Explore Estimates of Future Peak Flows”, has been developed by the U.S. Geological Survey, in cooperation with the New York State Department of Transportation, that allows a user to apply a set of regression equations to estimate the magnitude of future floods for any stream or river in New York State (exclusive of Long Island) and the Lake Champlain Basin in Vermont. The regression equations that are the basis of the current application were developed in previous investigations by the U.S. Geological Survey (USGS) and are described at the USGS StreamStats Web sites for New York ( http://water.usgs.gov/osw/streamstats/new_york.html ) and Vermont ( http://water.usgs.gov/osw/streamstats/Vermont.html ). These regression equations include several fixed landscape metrics that quantify aspects of watershed geomorphology, basin size, and land cover as well as a climate variable—either annual precipitation or annual runoff. The application uses predictions of future annual precipitation from five climate models and two future greenhouse gas emissions scenarios and provides results that are averaged over three future periods—2025 to 2049, 2050 to 2074, and 2075 to 2099. Results are presented in ensemble form as the mean, median, maximum, and minimum values among the five climate models for each greenhouse gas emissions scenario and period. These predictions of future annual precipitation are substituted into either the precipitation variable or a water balance equation for runoff to calculate potential future peak flows. This application is intended to be used only as an exploratory tool because (1) the regression equations on which the application is based have not been adequately tested outside the range of the current climate and (2) forecasting future precipitation with climate models and downscaling these results to a fine spatial resolution have a high degree of uncertainty. This report includes a discussion of the assumptions, uncertainties, and appropriate use of this exploratory application.

Open-File Report↗

USGS2015

Identifying trout refuges in the Indian and Hudson Rivers in northern New York through airborne thermal infrared remote sensing

The locations and sizes of potential cold-water refuges for trout were examined in 2005 along a 27-kilometer segment of the Indian and Hudson Rivers in northern New York to evaluate the extent of refuges, the effects of routine flow releases from an impoundment, and how these refuges and releases might influence trout survival in reaches that otherwise would be thermally stressed. This river segment supports small populations of brook trout (Salvelinus fontinalis ), brown trout ( Salmo trutta ), and rainbow trout (Oncorhynchus mykiss ) and also receives regular releases of reservoir-surface waters to support rafting during the summer, when water temperatures in both the reservoir and the river frequently exceed thermal thresholds for trout survival. Airborne thermal infrared imaging was supplemented with continuous, in-stream temperature loggers to identify potential refuges that may be associated with tributary inflows or groundwater seeps and to define the extent to which the release flows decrease the size of existing refuges. In general, the release flows overwhelmed the refuge areas and greatly decreased the size and number of the areas. Mean water temperatures were unaffected by the releases, but small-scale heterogeneity was diminished. At a larger scale, water temperatures in the upper and lower segments of the reach were consistently warmer than in the middle segment, even during passage of release waters. The inability of remote thermal infrared images to consistently distinguish land from water (in shaded areas) and to detect groundwater seeps (away from the shallow edges of the stream) limited data analysis and the ability to identify potential thermal refuge areas.

New York↗

USGS2006

Magnitude and Frequency of Floods in New York

Techniques are presented for estimating the magnitude and frequency of flood discharges on rural, unregulated streams in New York, excluding Long Island. Peak-discharge-frequency data and basin characteristics from 388 streamflow-gaging stations in New York and adjacent states were used to develop multiple linear regression equations for flood discharges with recurrence intervals ranging from 1.25 to 500 years. A generalized least-squares (GLS) procedure was used to develop the regression equations. Separate sets of equations were developed for each of six hydrologic regions of New York; standard errors of prediction range from 14 to 43 percent. Statistically significant explanatory variables in the regression equations include drainage area, main-channel slope, percent basin storage, mean annual precipitation, percent forested area, a basin lag factor, a ratio of main-channel slope to basin slope, mean annual runoff, maximum snow depth, and percentage of basin above 1,200 feet. Drainage areas for the 388 sites used in the analyses ranged from 0.41 to 4,773 square miles. Methods of computing flood discharges from the regression equations differ, depending on whether the estimate is for a gaged or ungaged basin, and whether the basin crosses hydrologic-region or state boundaries. Examples of computations are included. Discharge-frequency estimates for an additional 122 streamflow-gaging stations with significant regulation or urbanization (including Long Island) are also included as at-site estimates. Basin characteristics, log-Pearson Type III statistics, and regression and weighted estimates of the discharge-frequency relations are tabulated for the streamflow-gaging stations used in the regression analyses. Sensitivity analyses showed that mean-annual precipitation, drainage area, mean annual runoff, and maximum snow depth are the variables to which computed discharges are most sensitive in the regression equations. Included with the report is a DVD that provides computation procedures and geographic information system spatial datasets to compute basin characteristics used in the regional regression equations and flood-frequency estimates at a specified location on a stream.

Scientific Investigations Report↗

USGS2004

Onondaga Lake Watershed – A Geographic Information System Project Phase I – Needs assessment and spatial data framework

In the fall of 2002, the Onondaga Lake Partnership (OLP) formed a Geographic Information System (GIS) Planning Committee to begin the process of developing a comprehensive watershed geographic information system for Onondaga Lake. The goal of the Onondaga Lake Partnership geographic information system is to integrate the various types of spatial data used for scientific investigations, resource management, and planning and design of improvement projects in the Onondaga Lake Watershed. A needs-assessment survey was conducted and a spatial data framework developed to support the Onondaga Lake Partnership use of geographic information system technology. The design focused on the collection, management, and distribution of spatial data, maps, and internet mapping applications. A geographic information system library of over 100 spatial datasets and metadata links was assembled on the basis of the results of the needs assessment survey. Implementation options were presented, and the Geographic Information System Planning Committee offered recommendations for the management and distribution of spatial data belonging to Onondaga Lake Partnership members. The Onondaga Lake Partnership now has a strong foundation for building a comprehensive geographic information system for the Onondaga Lake watershed. The successful implementation of a geographic information system depends on the Onondaga Lake Partnership’s determination of: (1) the design and plan for a geographic information system, including the applications and spatial data that will be provided and to whom, (2) the level of geographic information system technology to be utilized and funded, and (3) the institutional issues of operation and maintenance of the system.

Open-File Report↗