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USGS2010

Vegetation classification and distribution mapping report: Canyon de Chelly National Monument

Executive Summary: The classification and distribution mapping of the vegetation of Canyon de Chelly National Monument (CACH) and surrounding environment was accomplished through a multi-agency effort between 2003 and 2007. The National Park Service’s Southern Colorado Plateau Network facilitated the team that conducted the work, which comprised the U.S. Geological Survey’s Southwest Biological Science Center and Fort Collins Science Center, Navajo Natural Heritage Program, Northern Arizona University, and NatureServe. The project team described 48 plant communities for CACH—35 of which were described from quantitative classification based on field-relevé data collected in 2004. Five additional plant communities were based on field relevés collected in a previous study. The team derived four additional plant communities from field observations during the photointerpretation phase of the project, and field documented them during accuracy assessment. The National Vegetation Classification Standard served as a conceptual framework for assigning these plant communities to the alliance and association level. Ten of the 48 plant communities were designated “park specials”, that is, plant communities with insufficient data to describe them as new alliances or associations. The project team also developed a spatial vegetation map database representing CACH, with three different map-class schemas: base, group, and management map classes. The base map classes represented the finest level of spatial detail. Photointerpreters delineated initial polygons through manual interpretation of 2003/2004 1:12,000-scale true color aerial photography supplemented by occasional computer screen digitizing on a mosaic of digitized aerial photos. These polygons were labeled with base map classes during photointerpretation. Field visits verified interpretation concepts. The vegetation map database includes • ? 53 base map classes, which consist of associations and park specials classified with the quantitative analysis • ? additional associations noted during photointerpretation • ? non-vegetated land cover, such as infrastructure, land use, and geological land cover. The base map classes consist of 4,718 polygons in the project area. A field-based accuracy assessment of the base map classes showed the overall accuracy to be 50.8% The group map classes represent aggregations of the base map classes, approximating the group level of the National Vegetation Classification Standard, Version 2 (Federal Geographic Data Committee 2008). Terrestrial ecological systems, as described by NatureServe (Comer et al. 2003), were used as a first approximation of the group level. The project team identified 16 group map classes in this project. The overall accuracy of the group map classes was determined using the same accuracy assessment data as for the base map classes. The overall accuracy of the group representation of vegetation was 79.9%. In consultation with park staff, the team developed management map classes that consisted of park-defined groupings of base map classes and were intended to represent a balance between maintaining required accuracy and providing a focus on vegetation of particular interest or import to park managers. The 28 management map classes have an overall accuracy of 77.1%. While the main products of this project are the vegetation classification and the vegetation map database, a number of ancillary geographic information system and digital database products were also produced that can be used independently, or to augment the main products. These products include shapefiles of the location of field-collected data and relational databases of field-collected data.

Report↗

USGS1994

Ecological perspective: Linking ecology, GIS, and remote sensing to ecosystem management

Awareness of significant human impacts on the ecology of Earth's landscapes is not new (Thomas 1956). Over the past decade (Forman and Godron 1986, Urban et a1. 1987) applications of geographic information systems (GIS) and remote sensing technologies have supported a rapid rise in landscape.stale research. The heightened recognition within the research community of the ecological linkages between local sites and larger spatial scales has spawned increasing calls for more holistic management of landscapes (Noss 1983, Harris 1984, Risser 1985, Norse et al. 1986, Agee and Johnson 1988, Franklin 1989, Brooks and Grant 1992, Endangered Species Update-Special Issue 1993, Crow 1994, Grumbine 1994). As a result agencies such as the U.S. Forest Service, U.S. Fish and Wildlife Service, and National Park Service are now converging on "ecosystem management" as a new paradigm to sustainably manage wildlands and maintain biodiversity. However, as this transition occurs, several impediments to implementation of this new paradigm persist, including (1) significant uncenainty among many land managers about the definition and goals of ecosystem management, (2) inadequate ecological information on the past and present processes and structural conditions of target ecosystems, (3) insufficient experience on the part of land managers with the rapidly diversifying array of GIS and remote sensing tools to effectively use them to support ecology-based land management, and (4) a paucity of intimate, long-term relationships between people (including land managers) and the particular landscape communities to which they belong. This chapter provides an ecological perspective on these issues as applied to ecosystem management in a southwestern U.S. landscape.

Book chapter↗

USGS2006

Hydrogeologic framework refinement, ground-water flow and storage, water-chemistry analyses, and water-budget components of the Yuma area, southwestern Arizona and southeastern California

The ground-water and surface-water system in the Yuma area in southwestern Arizona and southeastern California is managed intensely to meet water-delivery requirements of customers in the United States, to manage high ground-water levels in the valleys, and to maintain treaty-mandated water-quality and quantity requirements of Mexico. The following components in this report, which were identified to be useful in the development of a ground-water management model, are: (1) refinement of the hydrogeologic framework; (2) updated water-level maps, general ground-water flow patterns, and an estimate of the amount of ground water stored in the mound under Yuma Mesa; (3) review and documentation of the ground-water budget calculated by the Bureau of Reclamation, U.S. Department of the Interior (Reclamation); and (4) water-chemistry characterization to identify the spatial distribution of water quality, information on sources and ages of ground water, and information about the productive-interval depths of the aquifer. A refined three-dimensional digital hydrogeologic framework model includes the following hydrogeologic units from bottom to top: (1) the effective hydrologic basement of the basin aquifer, which includes the Pliocene Bouse Formation, Tertiary volcanic and sedimentary rocks, and pre-Tertiary metamorphic and plutonic rocks; (2) undifferentiated lower units to represent the Pliocene transition zone and wedge zone; (3) coarse-gravel unit; (4) lower, middle, and upper basin fill to represent the upper, fine-grained zone between the top of the coarse-gravel unit and the land surface; and (5) clay A and clay B. Data for the refined model includes digital elevation models, borehole lithology data, geophysical data, and structural data to represent the geometry of the hydrogeologic units. The top surface of the coarse-gravel unit, defined by using borehole and geophysical data, varies similarly to terraces resulting from the down cutting of the Colorado River. Clay A is nearly the same as the previous conceptual hydrogeologic model definition (Olmsted and others, 1973), except for a minor westward extension from the city of Yuma. Clay B is extended to the southerly international boundary and increased in areal extent by about two-thirds of the original extent (Olmsted and others, 1973). The other hydrogeologic units generally are the same as in the previous conceptual hydrogeologic model. Before development, the Colorado and Gila Rivers were the sources of nearly all the ground water in the Yuma area through direct infiltration of water from river channels and annual overbank flooding. After construction of upstream reservoirs and clearing and irrigation of the floodplains, the rivers now act as drains for the ground water. Ground-water levels in most of the Yuma area are higher now than they were in predevelopment time. A general gradient of ground-water flow toward the natural discharge area south of the Yuma area still exists, but many other changes in flow are evident. Ground water in Yuma Valley once flowed away from the Colorado River, but now has a component of flow towards the river and Mexicali Valley. A ground-water mound has formed under Yuma Mesa from long-term surface-water irrigation; about 600,000 to 800,000 acre-ft of water are stored in the mound. Ground-water withdrawals adjacent to the southerly international boundary have resulted in water-level declines in that area. The reviewed and documented water budget includes the following components: (1) recharge in irrigated areas, (2) evapotranspiration by irrigated crops and phreatophytes, (3) ground-water return flow to the Colorado River, and (4) ground-water withdrawals (including those in Mexicali Valley). Recharge components were calculated by subtracting the amount of water used by crops from the amount of water delivered. Evapotranspiration rates were calculated on the basis of established methods, thus were appropriate for input to the ground-water flow model developed by the Bureau of Reclamation (William Greer, hydrologist, Bureau of Reclamation, written commun., 2005). Evapotranspiration by crops and phreatophytes were calculated by using crop coefficient methods and meteorological data. Other methods of calculating evapotranspiration rates by using combinations of satellite imagery and ground-based data could be used for higher spatial and temporal resolution. Ground-water return flow during years of low flow on the Colorado River (1972–82, 1987–92, and 1994–96) averaged 79,000 acre-ft per year. Ground-water withdrawal data for 1970–99 were similar to other estimates made by the U.S. Geological Survey for the Yuma area. New water-chemistry data were collected in 12 wells and 8 canals/drains to characterize spatial patterns in chemical constituents, determine isotopic ages of water, infer possible sources of ground water, and locate the vertical intervals of the aquifer that contribute most water to wells. Depth-dependent samples were collected at one of the wells (YM-10). A large quantity of water-quality data were compiled from Bureau of Reclamation and U.S. Geological Survey records and merged into the U.S. Geological Survey National Water Information System database. New samples were analyzed for major ions, nutrients, stable isotopes of oxygen and hydrogen, tritium ( 3 H), and carbon-14 ( 14 C) (along with C 13 /C 12 ratios). Light values of oxygen-18 ( 18 O) and deuterium ( 2 H, D) in well 242-2 indicate recharge from the Colorado River. Heavy water samples from wells 242-22, CADC, and Mesa del Sol indicate local recharge sources. Tritium data indicate there is young water in wells in the valleys and near the edge of Yuma Mesa, while older water is found far from the Colorado River. 14 C data indicate that water from wells near the southerly international boundary is at least several thousand years old.

Arizona, California↗

USGS2014

Metadata Wizard: an easy-to-use tool for creating FGDC-CSDGM metadata for geospatial datasets in ESRI ArcGIS Desktop

Creating compliant metadata for scientific data products is mandated for all federal Geographic Information Systems professionals and is a best practice for members of the geospatial data community. However, the complexity of the The Federal Geographic Data Committee’s Content Standards for Digital Geospatial Metadata, the limited availability of easy-to-use tools, and recent changes in the ESRI software environment continue to make metadata creation a challenge. Staff at the U.S. Geological Survey Fort Collins Science Center have developed a Python toolbox for ESRI ArcDesktop to facilitate a semi-automated workflow to create and update metadata records in ESRI’s 10.x software. The U.S. Geological Survey Metadata Wizard tool automatically populates several metadata elements: the spatial reference, spatial extent, geospatial presentation format, vector feature count or raster column/row count, native system/processing environment, and the metadata creation date. Once the software auto-populates these elements, users can easily add attribute definitions and other relevant information in a simple Graphical User Interface. The tool, which offers a simple design free of esoteric metadata language, has the potential to save many government and non-government organizations a significant amount of time and costs by facilitating the development of The Federal Geographic Data Committee’s Content Standards for Digital Geospatial Metadata compliant metadata for ESRI software users. A working version of the tool is now available for ESRI ArcDesktop, version 10.0, 10.1, and 10.2 (downloadable at http:/www.sciencebase.gov/metadatawizard ).

Open-File Report↗

USGS1994

Permeability of soils in Mississippi

The permeability of soils in Mississippi was determined and mapped using a geographic information system (GIS). Soil permeabilities in Mississippi were determined to range in value from nearly 0.0 to values exceeding 5.0 inches per hour. The U.S. Soil Conservation Service's State Soil Geographic Data Base (STATSGO) was used as the primary source of data for the determination of area-weighted soil permeability. STATSGO provides soil layer properties that are spatially referenced to mapped areas. These mapped areas are referred to as polygons in the GIS. The polygons arc boundaries of soils mapped as a group and are given unique Map Unit Identifiers (MUIDs). The data describing the physical characteristics of the soils within each polygon are stored in a tabular data base format and are referred to as attributes. The U.S. Soil Conservation Service developed STATSGO to be primarily used as a guide for regional resource planning, management, and monitoring. STATSGO was designed so that soil information could be extracted from properties tables at the layer level, combined by component, and statistically expanded to cover the entire map unit. The results of this study provide a mapped value for permeability which is representative of the vertical permeability of soils in that area. The resultant permeability map provides a representative vertical soil permeability for a given area sufficient for county, multi- county, and area planning, and will be used as the soil permeability data component in the evaluation of the susceptibility of major aquifers to contami- nation in Mississippi.

Water-Resources Investigations Report↗

USGS2009

Geologic map of the St. Joe quadrangle, Searcy and Marion Counties, Arkansas

This map summarizes the geology of the St. Joe 7.5-minute quadrangle in the Ozark Plateaus region of northern Arkansas. Geologically, the area lies on the southern flank of the Ozark dome, an uplift that exposes oldest rocks at its center in Missouri. Physiographically, the St. Joe quadrangle lies within the Springfield Plateau, a topographic surface generally held up by Mississippian cherty limestone. The quadrangle also contains isolated mountains (for example, Pilot Mountain) capped by Pennsylvanian rocks that are erosional outliers of the higher Boston Mountains plateau to the south. Tomahawk Creek, a tributary of the Buffalo River, flows through the eastern part of the map area, enhancing bedrock erosion. Exposed bedrock of this region comprises an approximately 1,300-ft-thick sequence of Ordovician, Mississippian, and Pennsylvanian carbonate and clastic sedimentary rocks that have been mildly deformed by a series of faults and folds. The geology of the St. Joe quadrangle was mapped by McKnight (1935) as part of a larger area at 1:125,000 scale. The current map confirms many features of this previous study, but it also identifies new structures and uses a revised stratigraphy. Mapping for this study was conducted by field inspection of numerous sites and was compiled as a 1:24,000-scale geographic information system (GIS) database. Locations and elevations of sites were determined with the aid of a global positioning satellite receiver and a hand-held barometric altimeter that was frequently recalibrated at points of known elevation. Hill-shade-relief and slope maps derived from a U.S. Geological Survey 10-m digital elevation model as well as U.S. Geological Survey orthophotographs from 2000 were used to help trace ledge-forming units between field traverses within the Upper Mississippian and Pennsylvanian part of the stratigraphic sequence. Strikes and dips of beds were typically measured along stream drainages or at well-exposed ledges. Beds dipping less than 2 degrees are shown as horizontal. Structure contours constructed on the base of the Boone Formation were hand drawn based on elevations of control points on both lower and upper contacts of the Boone Formation as well as other limiting information on their maximum or minimum elevations.

Scientific Investigations Map↗

USGS2023

User’s Guide to planetary image analysis and geologic mapping in ArcGIS Pro

Geologic maps are valuable tools in planetary science. Though planetary geologic maps are similar to terrestrial (Earthbased) geologic maps, the nature of planetary exploration introduces unique challenges for geologic mappers. Terrestrial geologic mappers prepare products from field-based observation, often comparing or refining those with aerial and (or) orbital images. Planetary geologic mapping relies almost exclusively on remote observations, which are made by orbiting spacecraft. Therefore, with a few exceptions for locations with rovers, landers, or crewed surface missions, planetary geologic mappers are not able to observe their map area in detail or at smaller scales. As a result, they must interpret and describe their map features differently than those used in terrestrial geologic maps. For example, terrestrial geologic mappers commonly divide units by lithology (rock type) or grain size. However, planetary geologic mappers often do not have detailed enough information to know what type of rock or grain size is present, and instead must divide the planet’s surface into geologic units using differences in tone, color, and surface texture (at multiple scales). Cross-cutting relationships, where apparent, can provide excellent—and often crucial—observations for identifying and subdividing geologic units using orbital datasets. The process of creating planetary maps has evolved over time, from original hand-drawn maps created during the early 1800s through the late 1900s, to the fully digital products created today. Modern-day planetary geologic mapping uses Geographic Information Systems (GIS) software and tools to visualize data, delineate units and landforms, and accurately convey spatial relationships to a map user using cartographic features represented by points, lines, and polygons. This tutorial was written to familiarize both new and experienced planetary geologic mappers with ArcGIS Pro, a commonly used GIS software package developed by Esri. This tutorial introduces new planetary geologic mappers to fundamental concepts and best practices in planetary geologic mapping. For mappers with experience using ArcMap (a previous version of ArcGIS), this tutorial will help to familiarize users with new changes in layout and functionality, so current projects may be migrated into the ArcGIS Pro environment. No prior knowledge is required, although a general familiarity with geology, GIS, and planetary science is recommended. This tutorial includes links to helpful glossaries of common GIS terms and other GIS and planetary science resources in appendix 1. For additional information on planetary geologic mapping, see the U.S Geological Survey (USGS) Astrogeology NASA Planetary Geologic Mapping Program website and the Planetary Mapping Guidelines. Numerous ArcGIS tutorials are also available from Esri’s Tutorials page, through the Esri Academy catalog, and Esri’s ArcGIS Pro Resources page.

Techniques and Methods↗

USGS2009

Selected ground-water-quality data in Pennsylvania - 1979-2006

This study, by the U.S. Geological Survey (USGS) in cooperation with the Pennsylvania Department of Environmental Protection (PADEP), provides a compilation of ground-water-quality data for a 28-year period (January 1, 1979, through December 31, 2006) based on water samples from wells and springs. The data are from 14 source agencies or programs—Borough of Carroll Valley, Chester County Health Department, Montgomery County Health Department, Pennsylvania Department of Agriculture, Pennsylvania Department of Environmental Protection 2002 Pennsylvania Water-Quality Assessment, Pennsylvania Department of Environmental Protection Agency Act 537 Sewage Facilities Program, Pennsylvania Department of Environmental Protection-Ambient and Fixed Station Network, Pennsylvania Department of Environmental Protection–North-Central Region, Pennsylvania Department of Environmental Protection–South-Central Region, Pennsylvania Drinking Water Information System, Pennsylvania Topographic and Geologic Survey, Susquehanna River Basin Commission, U.S. Environmental Protection Agency, and the U.S. Geological Survey. The ground-water-quality data from the different source agencies or programs varied in type and number of analyses; however, the analyses are represented by 11 major analyte groups: antibiotics, major ions, microorganisms (bacteria, viruses, and other microorganisms), minor ions (including trace elements), nutrients (predominantly nitrate and nitrite as nitrogen), pesticides, pharmaceuticals, radiochemicals (predominantly radon or radium), volatiles (volatile organic compounds), wastewater compounds, and water characteristics (field measurements, predominantly field pH, field specific conductance, and hardness). For the USGS and the PADEP–North-Central Region, the pesticide analyte group was broken down into fungicides, herbicides, and insecticides. Summary maps show the areal distribution of wells and springs with ground-water-quality data statewide by source agency or program. Summary data tables by source agency or program provide information on the number of wells and springs and samples collected for each of the 35 watersheds and analyte groups. The number of wells and springs sampled for ground-water-quality data varies considerably across Pennsylvania. Of the 24,772 wells and springs sampled, the greatest concentration of wells and springs is in the southeast (Berks, Bucks, Chester, Delaware, Lancaster, Montgomery, and Philadelphia Counties) and in the northwest (Erie County). The number of wells and springs sampled is relatively sparse in north-central (Cameron, Elk, Forest, McKean, Potter, and Warren Counties) Pennsylvania. Little to no data are available for approximately one-fourth of the state. Nutrients and water characteristics were the most frequently sampled major analyte groups—43,025 and 30,583 samples, respectively. Minor ions and major ions were the next most frequently sampled major analyte groups–26,972 and 13,115 samples, respectively. For the remaining 10 major analyte groups, the number of samples collected ranged from a low of 24 samples (antibiotic compounds) to a high of approximately 4,674 samples (microorganisms). The number of samples that exceeded a maximum contaminant level (MCL) or secondary maximum contaminant level (SMCL) by major analyte group also varied. Of the 4,674 samples in the microorganism analyte group, 50.2 percent had water that exceeded an MCL. Of the 4,528 samples collected and analyzed for volatile organic compounds, 23.5 percent exceeded an MCL. Other major analyte groups that frequently exceeded MCLs or SMCLs included major ions (18,343 samples and a 27.7 percent exceedence), minor ions (26,972 samples, 44.7 percent exceedence), pesticides (4,868 samples, 0.7 percent exceedence), water characteristics (30,583 samples, 19.3 percent exceedence), and radiochemicals (1,866 samples, 9.6 percent exceedence). Samples collected and analyzed for antibiotics (24 samples), fungicides (1,273 samples), herbicides (1,470 samples), insecticides (1,424 samples), nutrients (43,025 samples), pharmaceuticals (28 samples), and wastewater compounds (328 samples) had the lowest exceedences of 0.0, 2.4, 1.2, <1.0, 8.3, 0.0, and <1.0 percent, respectively.

Pennsylvania↗

USGS2015

California State Waters Map Series — Offshore of Point Reyes, California

This publication about the Offshore of Point Reyes map area includes ten map sheets that contain explanatory text, in addition to this descriptive pamphlet and a data catalog of geographic information system (GIS) files. Sheets 1, 2, and 3 combine data from four different sonar surveys to generate comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic features (highlighted in the perspective views on sheet 4) such as the flat, sediment-covered seafloor in Drakes Bay, as well as abundant “scour depressions” on the Bodega Head–Tomales Point shelf (see sheet 9) and local, tectonically controlled bedrock uplifts. To validate geological and biological interpretations of the sonar data shown in sheets 1, 2, and 3, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are summarized on sheet 6. Sheet 5 is a “seafloor character” map, which classifies the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. Sheet 7 is a map of “potential habitats,” which are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Sheet 8 compiles representative seismic-reflection profiles from the map area, providing information on the subsurface stratigraphy and structure of the map area. Sheet 9 shows the distribution and thickness of young sediment (deposited over the last about 21,000 years, during the most recent sea-level rise) in both the map area and the larger Salt Point to Drakes Bay region, interpreted on the basis of the seismic-reflection data, and it identifies the Offshore of Point Reyes map area as lying within the Bodega Head–Tomales Point shelf, Point Reyes bar, and Bolinas shelf domains. Sheet 10 is a geologic map that merges onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery (sheets 1, 2, 3), seafloor-sediment and rock samples (Reid and others, 2006), digital camera and video imagery (sheet 6), and high-resolution seismic-reflection profiles (sheet 8), as well as aerial-photographic interpretation of nearshore areas. The information provided by the map sheets, pamphlet, and data catalog have a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues.

California↗

USGS2014

Assessing potential effects of highway runoff on receiving-water quality at selected sites in Oregon with the Stochastic Empirical Loading and Dilution Model (SELDM)

In 2012, the U.S. Geological Survey and the Oregon Department of Transportation began a cooperative study to demonstrate use of the Stochastic Empirical Loading and Dilution Model (SELDM) for runoff-quality analyses in Oregon. SELDM can be used to estimate stormflows, constituent concentrations, and loads from the area upstream of a stormflow discharge site, from the site of interest and in the receiving waters downstream of the discharge. SELDM also can be used to assess the potential effectiveness of best management practices (BMP) for mitigating potential effects of runoff in receiving waters. Nominally, SELDM is a highway-runoff model, but it is well suited for analysis of runoff from other land uses as well. This report provides case studies and examples to demonstrate stochastic-runoff modeling concepts and to demonstrate application of the model. Basin characteristics from six Oregon highway study sites were used to demonstrate various applications of the model. The highway catchment and upstream basin drainage areas of these study sites ranged from 3.85 to 11.83 acres and from 0.16 to 6.56 square miles, respectively. The upstream basins of two sites are urbanized, and the remaining four sites are less than 5 percent impervious. SELDM facilitates analysis by providing precipitation, pre-storm streamflow, and other variables by region or from hydrologically similar sites. In Oregon, there can be large variations in precipitation and streamflow among nearby sites. Therefore, spatially interpolated geographic information system data layers containing storm-event precipitation and pre-storm streamflow statistics specific to Oregon were created for the study using Kriging techniques. Concentrations and loads of cadmium, chloride, chromium, copper, iron, lead, nickel, phosphorus, and zinc were simulated at the six Oregon highway study sites by using statistics from sites in other areas of the country. Water‑quality datasets measured at hydrologically similar basins in the vicinity of the study sites in Oregon were selected and compiled to estimate stormflow-quality statistics for the upstream basins. The quality of highway runoff and some upstream stormflow constituents were simulated by using statistical moments (average, standard deviation, and skew) of the logarithms of data. Some upstream stormflow constituents were simulated by using transport curves, which are relations between stormflow and constituent concentrations. Stochastic analyses were done by using SELDM to demonstrate use of the model and to illustrate the types of information that stochastic analyses may provide: 1. An analysis was done to demonstrate use of dilution factors as an initial reconnaissance tool for comparing relative risk among sites. 2. An analysis of hardness-dependent, water-quality criteria was done to illustrate the effects of variations in hardness and flow on the application and interpretation of such criteria. This analysis shows that hardness-dependent criteria can vary by an order of magnitude among storm events because hardness is diluted by stormflows. 3. An analysis of uncertainties in input and output values was done to demonstrate that properly selected robust datasets are needed to represent conditions at a site of interest. This analysis shows that the rate of water-quality exceedances that are measured or simulated may depend on sample size and the luck of the draw. 4. An analysis was done to demonstrate that SELDM and other Monte Carlo models may generate extreme values from input statistics, which may or may not be feasible based on physicochemical or hydrological limits. 5. An analysis of BMP modeling methods was done to demonstrate use of the model for estimating treatment requirements for meeting water-quality objectives. 6. An analysis of the use of grab sampling and nonstochastic upstream modeling methods was done to evaluate the potential effects on modeling outcomes. Additional analyses using surrogate water-quality datasets for the upstream basin and highway catchment were provided for six Oregon study sites to illustrate the risk-based information that SELDM will produce. These analyses show that the potential effects of highway runoff on receiving-water quality downstream of the outfall depends on the ratio of drainage areas (dilution), the quality of the receiving water upstream of the highway, and the concentration of the criteria of the constituent of interest. These analyses also show that the probability of exceeding a water-quality criterion may depend on the input statistics used, thus careful selection of representative values is important.

Oregon↗

USGS2014

Influence of item distribution pattern and abundance on efficiency of benthic core sampling

ore sampling is a commonly used method to estimate benthic item density, but little information exists about factors influencing the accuracy and time-efficiency of this method. We simulated core sampling in a Geographic Information System framework by generating points (benthic items) and polygons (core samplers) to assess how sample size (number of core samples), core sampler size (cm 2 ), distribution of benthic items, and item density affected the bias and precision of estimates of density, the detection probability of items, and the time-costs. When items were distributed randomly versus clumped, bias decreased and precision increased with increasing sample size and increased slightly with increasing core sampler size. Bias and precision were only affected by benthic item density at very low values (500–1,000 items/m 2 ). Detection probability (the probability of capturing ≥ 1 item in a core sample if it is available for sampling) was substantially greater when items were distributed randomly as opposed to clumped. Taking more small diameter core samples was always more time-efficient than taking fewer large diameter samples. We are unable to present a single, optimal sample size, but provide information for researchers and managers to derive optimal sample sizes dependent on their research goals and environmental conditions.

Wetlands↗

USGS2014

Decision support system development at the Upper Midwest Environmental Sciences Center

A Decision Support System (DSS) can be defined in many ways. The working definition used by the U.S. Geological Survey Upper Midwest Environmental Sciences Center (UMESC) is, “A spatially based computer application or data that assists a researcher or manager in making decisions.” This is quite a broad definition—and it needs to be, because the possibilities for types of DSSs are limited only by the user group and the developer’s imagination. There is no one DSS; the types of DSSs are as diverse as the problems they help solve. This diversity requires that DSSs be built in a variety of ways, using the most appropriate methods and tools for the individual application. The skills of potential DSS users vary widely as well, further necessitating multiple approaches to DSS development. Some small, highly trained user groups may want a powerful modeling tool with extensive functionality at the expense of ease of use. Other user groups less familiar with geographic information system (GIS) and spatial data may want an easy-to-use application for a nontechnical audience. UMESC has been developing DSSs for almost 20 years. Our DSS developers offer our partners a wide variety of technical skills and development options, ranging from the most simple Web page or small application to complex modeling application development.

Upper Midwest↗

USGS2015

Land-cover types, shoreline positions, and sand extents derived From Landsat satellite imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1984 to 2014

The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. These studies, however, have traditionally focused on sandy shorelines and sandy barrier-island systems, without consideration of impacts to coastal wetlands. The goal of the Barrier Island and Estuarine Wetland Physical Change Assessment project is to integrate a wetland-change assessment with existing coastal-change assessments for the adjacent sandy dunes and beaches, initially focusing on Assateague Island along the Maryland and Virginia coastline. Assateague Island was impacted by waves and storm surge associated with the passage of Hurricane Sandy in October 2012, including erosion and overwash along the ocean-facing sandy shoreline as well as erosion and overwash deposition in the back-barrier and estuarine bay environments. This report serves as an archive of data that were derived from Landsat 5 and Landsat 8 imagery from 1984 to 2014, including wetland and terrestrial habitat extents; open-ocean, back-barrier, and estuarine mainland shoreline positions; and sand-line positions along the estuarine mainland and barrier shorelines from Assateague Island, Maryland to Metompkin Island, Virginia. The geographic information system data files with accompanying formal Federal Geographic Data Committee metadata can be downloaded from the Data Downloads page.

Maryland, Virginia↗

USGS2010

Field note--Successful establishment of a phytoremediation system at a petroleum-hydrocarbon contaminated shallow aquifer--Trends, trials, and tribulations

We report the establishment of a mixed hybrid poplar ( Populus spp .) and willow ( Salix spp .) phytoremediation system at a fuel-contaminated site. Several approaches were used to balance competing goals of cost-effectiveness yet successful tree establishment without artificial irrigation or trenching. Bare root and unrooted cuttings were installed using either: (1) 1.2 m deep holes excavated with an 8 cm diameter auger using a direct-push rig and backfilled with the excavated, in situ soil; (2) 1.2 m deep holes created with a 23 cm diameter auger attached to a Bobcat rig and backfilled with clean topsoil from offsite; and (3) shallow holes between 15–30 cm deep that were created with a 1.3 cm diameter rod and no backfill. Tree mortality from initial plantings indicated contaminated zones not quantified in prior site investigations and remedial actions. Aquifer heterogeneity, underground utilities, and prior remediation infrastructure hampered the ability of the site to support a traditional experimental design. Total stem length and mortality were measured for all planted trees and were incorporated into a geographic information system. Planting early in the growing season, augering a larger diameter hole, and backfilling with clean, uncontaminated topsoil was cost effective and allowed for greater tree cutting growth and survival.

International Journal of Phytoremediation↗

USGS2003

Simulation of net infiltration and potential recharge using a distributed-parameter watershed model of the Death Valley region, Nevada and California

This report presents the development and application of the distributed-parameter watershed model, INFILv3, for estimating the temporal and spatial distribution of net infiltration and potential recharge in the Death Valley region, Nevada and California. The estimates of net infiltration quantify the downward drainage of water across the lower boundary of the root zone and are used to indicate potential recharge under variable climate conditions and drainage basin characteristics. Spatial variability in recharge in the Death Valley region likely is high owing to large differences in precipitation, potential evapotranspiration, bedrock permeability, soil thickness, vegetation characteristics, and contributions to recharge along active stream channels. The quantity and spatial distribution of recharge representing the effects of variable climatic conditions and drainage basin characteristics on recharge are needed to reduce uncertainty in modeling ground-water flow. The U.S. Geological Survey, in cooperation with the Department of Energy, developed a regional saturated-zone ground-water flow model of the Death Valley regional ground-water flow system to help evaluate the current hydrogeologic system and the potential effects of natural or human-induced changes. Although previous estimates of recharge have been made for most areas of the Death Valley region, including the area defined by the boundary of the Death Valley regional ground-water flow system, the uncertainty of these estimates is high, and the spatial and temporal variability of the recharge in these basins has not been quantified. To estimate the magnitude and distribution of potential recharge in response to variable climate and spatially varying drainage basin characteristics, the INFILv3 model uses a daily water-balance model of the root zone with a primarily deterministic representation of the processes controlling net infiltration and potential recharge. The daily water balance includes precipitation (as either rain or snow), snow accumulation, sublimation, snowmelt, infiltration into the root zone, evapotranspiration, drainage, water content change throughout the root-zone profile (represented as a 6-layered system), runoff (defined as excess rainfall and snowmelt) and surface water run-on (defined as runoff that is routed downstream), and net infiltration (simulated as drainage from the bottom root-zone layer). Potential evapotranspiration is simulated using an hourly solar radiation model to simulate daily net radiation, and daily evapotranspiration is simulated as an empirical function of root zone water content and potential evapotranspiration. The model uses daily climate records of precipitation and air temperature from a regionally distributed network of 132 climate stations and a spatially distributed representation of drainage basin characteristics defined by topography, geology, soils, and vegetation to simulate daily net infiltration at all locations, including stream channels with intermittent streamflow in response to runoff from rain and snowmelt. The temporal distribution of daily, monthly, and annual net infiltration can be used to evaluate the potential effect of future climatic conditions on potential recharge. The INFILv3 model inputs representing drainage basin characteristics were developed using a geographic information system (GIS) to define a set of spatially distributed input parameters uniquely assigned to each grid cell of the INFILv3 model grid. The model grid, which was defined by a digital elevation model (DEM) of the Death Valley region, consists of 1,252,418 model grid cells with a uniform grid cell dimension of 278.5 meters in the north-south and east-west directions. The elevation values from the DEM were used with monthly regression models developed from the daily climate data to estimate the spatial distribution of daily precipitation and air temperature. The elevation values were also used to simulate atmosp

Water-Resources Investigations Report↗

USGS2013

Tools for beach health data management, data processing, and predictive model implementation

This fact sheet describes utilities created for management of recreational waters to provide efficient data management, data aggregation, and predictive modeling as well as a prototype geographic information system (GIS)-based tool for data visualization and summary. All of these utilities were developed to assist beach managers in making decisions to protect public health. The Environmental Data Discovery and Transformation (EnDDaT) Web service identifies, compiles, and sorts environmental data from a variety of sources that help to define climatic, hydrologic, and hydrodynamic characteristics including multiple data sources within the U.S. Geological Survey and the National Oceanic and Atmospheric Administration. The Great Lakes Beach Health Database (GLBH-DB) and Web application was designed to provide a flexible input, export, and storage platform for beach water quality and sanitary survey monitoring data to compliment beach monitoring programs within the Great Lakes. A real-time predictive modeling strategy was implemented by combining the capabilities of EnDDaT and the GLBH-DB for timely, automated prediction of beach water quality. The GIS-based tool was developed to map beaches based on their physical and biological characteristics, which was shared with multiple partners to provide concepts and information for future Web-accessible beach data outlets.

Fact Sheet↗

USGS2016

Estimated historical distribution of grassland communities of the Southern Great Plains

The purpose of this project was to map the estimated distribution of grassland communities of the Southern Great Plains prior to Euro-American settlement. The Southern Great Plains Rapid Ecoregional Assessment (REA), under the direction of the Bureau of Land Management and the Great Plains Landscape Conservation Cooperative, includes four ecoregions: the High Plains, Central Great Plains, Southwestern Tablelands, and the Nebraska Sand Hills. The REA advisors and stakeholders determined that the mapping accuracy of available national land-cover maps was insufficient in many areas to adequately address management questions for the REA. Based on the recommendation of the REA stakeholders, we estimated the potential historical distribution of 10 grassland communities within the Southern Great Plains project area using data on soils, climate, and vegetation from the Natural Resources Conservation Service (NRCS) including the Soil Survey Geographic Database (SSURGO) and Ecological Site Information System (ESIS). The dominant grassland communities of the Southern Great Plains addressed as conservation elements for the REA area are shortgrass, mixed-grass, and sand prairies. We also mapped tall-grass, mid-grass, northwest mixed-grass, and cool season bunchgrass prairies, saline and foothill grasslands, and semi-desert grassland and steppe. Grassland communities were primarily defined using the annual productivity of dominant species in the ESIS data. The historical grassland community classification was linked to the SSURGO data using vegetation types associated with the predominant component of mapped soil units as defined in the ESIS data. We augmented NRCS data with Landscape Fire and Resource Management Planning Tools (LANDFIRE) Biophysical Settings classifications 1) where NRCS data were unavailable and 2) where fifth-level watersheds intersected the boundary of the High Plains ecoregion in Wyoming. Spatial data representing the estimated historical distribution of grassland communities of the Southern Great Plains are provided as a 30 x 30-meter gridded surface (raster dataset). This information will help to address the priority management questions for grassland communities for the Southern Great Plains REA and can be used to inform other regional-level land management decisions.

Southern Great Plains↗

USGS2015

Potential groundwater recharge for the State of Minnesota using the Soil-Water-Balance model, 1996-2010

Groundwater recharge is one of the most difficult components of a water budget to ascertain, yet is an important boundary condition necessary for the quantification of water resources. In Minnesota, improved estimates of recharge are necessary because approximately 75 percent of drinking water and 90 percent of agricultural irrigation water in Minnesota are supplied from groundwater. The water that is withdrawn must be supplied by some combination of (1) increased recharge, (2) decreased discharge to streams, lakes, and other surface-water bodies, and (3) removal of water that was stored in the system. Recent pressure on groundwater resources has highlighted the need to provide more accurate recharge estimates for various tools that can assess the sustainability of long-term water use. As part of this effort, the U.S. Geological Survey, in cooperation with the Minnesota Pollution Control Agency, used the Soil-Water-Balance model to calculate gridded estimates of potential groundwater recharge across Minnesota for 1996‒2010 at a 1-kilometer (0.621-mile) resolution. The potential groundwater recharge estimates calculated for Minnesota from the Soil-Water Balance model included gridded values (1-kilometer resolution) of annual mean estimates (that is, the means for individual years from 1996 through 2010) and mean annual estimates (that is, the mean for the 15-year period 1996−2010). The Soil-Water-Balance model uses a modified Thornthwaite-Mather soil-water-balance approach, with components of the soil-water balance calculated on a daily basis. A key advantage of this approach includes the use of commonly available geographic information system data layers that incorporate land cover, soil properties, and daily meteorological data to produce temporally and spatially variable gridded estimates of potential recharge. The Soil-Water-Balance model was calibrated by using a combination of parameter estimation techniques, making manual adjustments of model parameters, and using parameter values from previously published Soil-Water-Balance models. Each calibration simulation compared the potential recharge estimate from the model against base-flow estimates derived from three separate hydrograph separation techniques. A total of 35 Minnesota watersheds were selected for the model calibration. Meteorological data necessary for the model included daily precipitation, minimum daily temperature, and maximum daily temperature. All of the meteorological data were provided by the Daymet dataset, which included daily continuous surfaces of key climatological data. Land-cover data were provided by the 2001 and 2006 National Land Cover Database: the 2001 classification was used from 1994 through 2003, and the 2006 classification was used from 2004 through 2010. Soil data used in the model included hydrologic soils group and the available soil-water capacity. These soil data were obtained from the Natural Resources Conservation Service Soil Survey Geographic (SSURGO) database and the State Soil Geographic (STATSGO) database. The statewide mean annual potential recharge rate from 1996–2010 was 4.9 inches per year. Potential recharge estimates increased from west to east across Minnesota. The mean annual potential recharge estimates across Minnesota at a 1-km resolution for the overall simulation period (1996–2010) ranged from less than 0.1 to 17.8 inches per year. Some of the lowest potential recharge rates for the simulation period were in the Red River of the North Basin of northwestern Minnesota, and generally were between 1.0 and 1.5 inches per year. The highest potential recharge rates were in northeastern Minnesota and the Anoka Sand Plain in central Minnesota. Eighty-eight percent of the potential recharge rates (by grid cell) were between 2 and 8 inches per year from 1996–2010. Only about 3 percent of all the potential recharge estimates (by grid cell) were less than 2 inches per year, and 9 percent of estimates were greater than 8 inches per year. On an annual basis, however, potential recharge rates were as high as 27.2 inches per year. The highest annual mean recharge estimate across the State was for 2010, and the lowest mean recharge estimate was for 2003. Although precipitation variability partially explained the annual differences in potential recharge estimates, precipitation alone did not account for these differences, and other factors such as antecedent moisture conditions likely were important. Also, because precipitation gradients across the State can vary from year to year, the dominant land-cover class and hydrologic soil group combinations for a particular region had a large effect on the resulting potential recharge value. During 1996–2010, April had the greatest monthly mean potential recharge compared to all other months, accounting for a mean of 30 percent of annual potential recharge in this single month.

Minnesota↗