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Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 613 records · Page 34Linked to original sources
USGS1987

An interactive program to display user-generated or file-based maps on a personal computer monitor

PC MAP-MAKER is an ADVANCED BASIC program written to provide users of IBM XT, IBM AT, and compatible computers with a straight-forward, flexible method to display geographical data on a color or monochrome PC (personal computer) monitor. Data can be political boundaries such as State and county boundaries; natural curvilinear features such as rivers, drainage areas, and geological contacts; and points such as well locations and mineral localities. Essentially any point defined by a latitude and longitude and any line defined by a series of latitude and longitude values can be displayed using the program. PC MAP MAKER allows users to view tabular data from U.S. Geological Survey files such as WATSTORE (National Water Data Storage and Retrieval System) in a map format in a time much shorter than required by sending the data to a line plotter. The screen image can be saved to disk for recall at a later date, and hard copies can be printed with a dot matrix printer. The program is user-friendly, using menus or prompts to guide user input. It is fully documented and structured to allow the user to tailor the program to the user 's specific needs. The documentation includes a tutorial designed to introduce users to the capabilities of the program using the State of Colorado as a demonstration map area. (Author 's abstract)

Open-File Report↗
USGS1972

A summary of peak stages and discharges in New York for the flood of June 1972

Intense rainfall associated with Tropical Storm Agnes in late June 1972 resulted in the greatest flood disaster in the history of New York State. Flooding began in Westchester County on June 19 and was followed by widespread flooding in the Southern Tier and the Fingers Lakes region on June 23. The purpose of this report is to make available preliminary peak stage and discharge data for New York streams. Detailed analyses of precipitation, stage and discharge hydrographs, flood frequency, and flood profiles will be presented in subsequent reports. Inundation maps for 39 quadrangles were prepared.

New York↗
USGS2007

Using a remote sensing/GIS model to predict southwestern Willow Flycatcher breeding habitat along the Rio Grande, New Mexico

Introduction The Southwestern Willow Flycatcher (Empidonax traillii extimus; hereafter SWFL) is a federally endangered bird (USFWS 1995) that breeds in riparian areas in portions of New Mexico, Arizona, southwestern Colorado, extreme southern Utah and Nevada, and southern California (USFWS 2002). Across this range, it uses a variety of plant species as nesting/breeding habitat, but in all cases prefers sites with dense vegetation, high canopy, and proximity to surface water or saturated soils (Sogge and Marshall 2000). As of 2005, the known rangewide breeding population of SWFLs was roughly 1,214 territories, with approximately 393 territories distributed among 36 sites in New Mexico (Durst et al. 2006), primarily along the Rio Grande. One of the key challenges facing the management and conservation of the Southwestern Willow Flycatcher is that riparian areas are dynamic, with individual habitat patches subject to cycles of creation, growth, and loss due to drought, flooding, fire, and other disturbances. Former breeding patches can lose suitability, and new habitat can develop within a matter of only a few years, especially in reservoir drawdown zones. Therefore, measuring and predicting flycatcher habitat - either to discover areas that might support SWFLs, or to identify areas that may develop into appropriate habitat - requires knowledge of recent/current habitat conditions and an understanding of the factors that determine flycatcher use of riparian breeding sites. In the past, much of the determination of whether a riparian site is likely to support breeding flycatchers has been based on qualitative criteria (for example, 'dense vegetation' or 'large patches'). These determinations often require on-the-ground field evaluations by local or regional SWFL experts. While this has proven valuable in locating many of the currently known breeding sites, it is difficult or impossible to apply this approach effectively over large geographic areas (for example, the middle Rio Grande). The SWFL Recovery Plan (USFWS 2002) recognizes the importance of developing new approaches to habitat identification, and recommends the development of drainage-scale, quantitative habitat models. In particular, the plan suggests using models based on remote sensing and Geographic Information System (GIS) technology that can capture the relatively dynamic habitat changes that occur in southwestern riparian systems. In 1999, Arizona Game and Fish Department (AGFD) developed a GIS-based model (Hatten and Paradzick 2003) to identify SWFL breeding habitat from Landsat Thematic Mapper imagery and 30-m resolution digital elevation models (DEMs). The model was developed with presence/absence survey data acquired along the San Pedro and Gila rivers, and from the Salt River and Tonto Creek inlets to Roosevelt Lake in southern Arizona (collectively called the project area). The GIS-based model used a logistic regression equation to divide riparian vegetation into 5 probability classes based upon characteristics of riparian vegetation and floodplain size. This model was tested by predicting SWFL breeding habitat at Alamo Lake, Arizona, located 200 km from the project area (Hatten and Paradzick 2003). The GIS-based model performed as expected by identifying riparian areas with the highest SWFL nest densities, located in the higher probability classes. In 2002, AGFD applied the GIS-based model throughout Arizona, for riparian areas below 1,524 m (5,000 ft) elevation and within 1.6 km of perennial or intermittent waters (Dockens et al. 2004). Overall model accuracy (using probability classes 1-5, with class 5 having the greatest probability of nesting activity) for predicting the location of 2001 nest sites was 96.5 percent; accuracy decreased when fewer probability classes were defined as suitable. Map accuracy, determined from errors of commission, increased in higher probability classes in a fashion similar to errors of omission. Map accuracy, li

Colorado, New Mexico↗