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USGS2002

Preliminary geophysical characterization of two oil production sites, Osage County, Oklahoma - Osage Skiatook Petroleum Environmental Research Project

Ground electromagnetic and dc resistivity geophysical surveys were used to interpret the subsurface distribution of salinized soil, water, and bedrock at two sites (A and B) and to characterize the larger scale hydrologic setting. Measurements were made on grids of about 1000 square meters using a very shallow penetrating (less than 10 m) electromagnetic (EM) geophysical system (EM31). At site A, high subsurface conductivities (more than 100 millisiemens per meter) found below disposal ponds extended down the local hydrologic gradient to below the normal level of near by Lake Skiatook. At site B, areas of highest subsurface electrical conductivity were offset about 10 m from the center of salt scars. The area of high subsurface electrical conductivity extends in the subsurface below the normal level of Skiatook Lake. DC resistivity soundings were made in and around the two sites in order to characterize deeper (30-60 m) electrical properties of the subsurface lithology and ground water. These soundings indicate that the tight shale that dominates the local lithology is moderately electrically conductive (5 milliseimems per meter). DC soundings done in several areas at the Skiatook Lake shoreline indicate an electrically conductive (less than 10 millisiemens per meter) zone exists below the shore even away from the oil production sites. This conductive zone may indicate a mixing between fresh lake water and local ground water that has high dissolved solids. Borehole geophysical logs at site B and laboratory rock property measurements are currently being used to refine interpretation of ground geophysical measurements.

Oklahoma↗

USGS2013

2010 Joint United States-Canadian Program to explore the limits of the Extended Continental Shelf aboard U.S. Coast Guard Cutter Healy--Cruise HLY1002

In August and September 2010, the U.S. Geological Survey, in cooperation with Natural Resources Canada, Geological Survey of Canada, conducted bathymetric and geophysical surveys in the Beaufort Sea and eastern Arctic Ocean aboard the U.S. Coast Guard Cutter Healy. The principal objective of this mission to the high Arctic was to acquire data in support of a delineation of the outer limits of the U.S. and Canadian Extended Continental Shelf in the Arctic Ocean, in accordance with the provisions of Article 76 of the United Nations Convention on the Law of the Sea. The Healy was accompanied by the Canadian Coast Guard icebreaker Louis S. St-Laurent. The scientific parties on board the two vessels consisted principally of staff from the U.S. Geological Survey (Healy), and the Geological Survey of Canada and the Canadian Hydrographic Service (Louis). The crew also included marine-mammal observers, Native-community observers, ice observers, and biologists conducting research of opportunity in the Arctic Ocean. Despite interruptions necessitated by three medical emergencies, the joint survey proved largely successful. The Healy collected 7,201 trackline-kilometers of swath (multibeam) bathymetry (47,663 square kilometers) and CHIRP subbottom data, with accompanying marine gravity measurements, and expendable bathythermograph data. The Louis acquired 3,673 trackline-kilometers of multichannel seismic (airgun) deep-penetration reflection data along 25 continuous profiles, as well as 34 sonobuoy refraction stations and 9,500 trackline-kilometers of single-beam bathymetry. The coordinated efforts of the two vessels resulted in seismic-reflection-profile data that were of much higher quality and continuity than if the data had been acquired with a single vessel alone. The equipment-failure rate of the seismic equipment aboard the Louis was greatly reduced when the Healy led as the ice breaker. When ice conditions proved too severe to deploy the seismic system, the Louis led the Healy, resulting in much improved quality of the swath bathymetric and CHIRP subbottom data in comparison with data collected either by the Healy in the lead or the Healy working alone. During periods when the Healy was operating alone (principally when the Louis was diverted for emergency medical evacuations or ship repairs), the Healy was able to deploy a piston-core-sampler (10 meters maximum potential recovery depending on configuration). The coring operations resulted in recovery of cores at five locations ranging from 2.4 to 5.7 meters in length from water depths ranging from 1,157 to 3,700 meters. One of these cores sited on the Alaskan margin recovered the first reported occurrence of methane hydrate from the Arctic Ocean. Ancillary science objectives, including ice observations and deployment of ice-monitoring buoys and water-column sampling to measure acidification of Arctic waters were successfully conducted. The water-column sampling included using 10 full-ocean-depth, water-sampling casts with accompanying conductivity-temperature-depth measurements. Except for the data deemed proprietary, data from the cruise have been archived and are available for download at the National Geophysical Data Center and at cooperating organizations. Outreach staff and guest teachers aboard the two vessels provided near-real-time connection between the research activities and the public through online blogs, web pages, and other media.

Open-File Report↗

USGS2011

Infectious diseases in Yellowstone’s canid community

Each summer Yellowstone Wolf Project staff visit den sites to monitor the success of wolf reproduction and pup rearing behavior. For the purposes of wolf monitoring, Yellowstone National Park (YNP) is divided into two study areas, the northern range and the interior, each distinguished by their ecological and physiographical differences. The 1,000 square kilometer northern range, characterized by lower elevations (1,500–2,200 m), serves as prime winter habitat for ungulates and supports a higher density of wolves than the interior (20–99 wolves/1,000 km 2 versus 2–11 wolves/1,000 km 2 ). The interior of the park encompasses 7,991 square kilometers, is higher in elevation, receives higher annual snowfall, and generally supports lower densities of wolves and ungulates. During the Yellowstone Wolf Project’s 2005 observations on the northern range, researchers noticed that some wolf pups were disappearing and those that remained were unusually listless. The Slough Creek pups, at first numbering 18, dwindled to three survivors. Similar findings were mirrored at other den sites across the northern range. When annual den surveys were conducted in late July, all that remained were scattered piles of bones and fur. Coyotes suffered similar setbacks in 2005, with many of the survivors exhibiting neurological shakes and tremors. The park’s canids had been affected by something, but what? Prompted by what seemed to be a disease outbreak, the Yellowstone Wolf Project, the Yellowstone Ecological Research Center (YERC), and the University of Minnesota decided to take several collaborative approaches toward improving our understanding of the presence and role of infectious disease in Yellowstone’s canid community. Several serological studies have been conducted in the past among the park’s coyotes (Gese et al. 1997) and cougars (Biek 2006), providing a helpful foundation on which to build and compare. A serological survey was conducted, using serum samples collected during routine wolf and coyote captures over a period of 18 years (Almberg et al. 2009). Simulation models were used to explore the dynamics of canine distemper virus (Almberg et al. 2010)—one of the more prominent pathogens in terms of its effects on its hosts—and several long-term pathogen surveillance projects were initiated which are intended to someday provide a foundation for more advanced genetic-based analyses of pathogen dynamics. Since these initial efforts, the group has also expanded the research to include a study of sarcoptic mange, which began affecting wolves and coyotes in YNP in 2006 and 2007.

Yellowstone National Park↗

USGS2026

Stacked machine learning for timber identification with laser-induced breakdown spectroscopy (LIBS)

This study presents a new approach to wood species identification using laser-induced breakdown spectroscopy (LIBS) combined with stacked machine learning techniques. The research analyzed 700 samples comprising nine Dalbergia species and nine additional tropical timber species, utilizing a handheld LIBS analyzer. A stacking methodology was developed by integrating three support vector machine (SVM) models with different kernel functions (linear, polynomial, and radial) in a one-versus-all (OVA) configuration. These SVM outputs were then combined using a partial least squares discriminant analysis (PLS-DA) meta-learner. Through PCA-based variable selection, the dimensionality was reduced from 23 401 to wavelengths while maintaining classification accuracy. The stacking approach achieved a Cohen's kappa value of 0.8671 in the validation set, significantly outperforming traditional flat classifiers. Variable importance analysis revealed calcium, magnesium, and barium as crucial elements for species differentiation, with their concentrations reflecting environmental conditions and geographical origins. This research demonstrates the potential of combining LIBS spectroscopy with advanced machine learning techniques for rapid, non-invasive timber identification, which can support efforts against illegal logging and enforcement of international trade regulations.

Applied Spectroscopy↗

USGS2016

Ecosystem vulnerability to climate change in the southeastern United States

Two recent investigations of climate-change vulnerability for 19 terrestrial, aquatic, riparian, and coastal ecosystems of the southeastern United States have identified a number of important considerations, including potential for changes in hydrology, disturbance regimes, and interspecies interactions. Complementary approaches using geospatial analysis and literature synthesis integrated information on ecosystem biogeography and biodiversity, climate projections, vegetation dynamics, soil and water characteristics, anthropogenic threats, conservation status, sea-level rise, and coastal flooding impacts. Across a diverse set of ecosystems—ranging in size from dozens of square meters to thousands of square kilometers—quantitative and qualitative assessments identified types of climate-change exposure, evaluated sensitivity, and explored potential adaptive capacity. These analyses highlighted key gaps in scientific understanding and suggested priorities for future research. Together, these studies help create a foundation for ecosystem-level analysis of climate-change vulnerability to support effective biodiversity conservation in the southeastern United States.

Alabama, Arkansas, Florida, Georgia, Kentucky, Lou↗

USGS1999

Selected nutrients and pesticides in streams of the eastern Iowa basins, 1970-95

Water-quality data from 17 surface-water monitoring sites were compiled for 1970 through 1995 and analyzed to determine historical waterquality conditions and possible trends in the Eastern Iowa Basins study unit as part of the U.S. Geological Survey's National Water-Quality Assessment Program. The Eastern Iowa Basins encompasses the Wapsipinicon, Cedar, Iowa, and Skunk River Basins and covers about 19,500 square miles. Seven of the monitoring sites were sampled by the Iowa Department of Natural Resources, three sites by the Minnesota Pollution Control Agency, three sites by the University of Iowa Institute for Hydraulic Research, and four sites by the U.S. Geological Survey. Water-quality analyses typically consisted of nitrate, ammonia, total nitrogen, and total phosphorus, with limited analyses available for organic nitrogen, dissolved phosphorus, dissolved orthophosphate, and water-soluble pesticides. Long-term historical nutrient and pesticide data were not available for the Wapsipinicon River Basin. Median concentrations for total nitrogen ranged from 4.6 to 9.4 milligrams per liter, and maximum concentrations of total nitrogen ranged from 4.6 to 31 milligrams per liter. The majority of nitrogen transported in surface waters of the Eastern Iowa Basins study unit is in the form of nitrate (nitrogen). Median concentrations of total phosphorus ranged from less than 0.10 to 0.66 milligram per liter, and maximum concentrations of total phosphorus ranged from less than 0.10 to 5.4 milligrams per liter. Nitrate varied seasonally. Median concentrations of nitrate were largest during the spring and the winter (6.0 to 7.0 milligrams per liter) compared to the summer and fall (2.0 to 4.0 milligrams per liter). Concentrations of nitrate greater than 10 milligrams per liter typically occurred during spring runoff. Median ammonia concentrations generally were highest during the winter (approximately 0.3-0.5 milligram per liter) compared to the spring and summer when ammonia concentrations were often close to the detection limit (0.01 milligram per liter). In general, the median concentrations of total phosphorus varied less than 0.1 milligram per liter between seasons. The statistical analysis of the nutrient data typically indicated a strong positive correlation of nitrate with streamflow. Total phosphorus concentrations with streamflow showed greater variability than nitrate, perhaps reflecting the greater potential of transport of phosphorus on sediment rather than in the dissolved phase as with nitrate. Ammonia and ammonia plus organic nitrogen showed no correlation with streamflow or a weak positive correlation. Seasonal variations and the relations of nutrients and pesticides to streamflow generally corresponded with nonpoint‑source loadings, although possible point sources for nutrients were indicated by the data at selected monitoring sites. Statistical trend tests for concentrations and loads were computed for nitrate, ammonia, and total phosphorus. Trend analysis indicated decreases for ammonia and total phosphorus concentrations at several sites and increases for nitrate concentrations at other sites in the study unit.

Iowa, Minnesota↗

USGS2018

Time-dependent pore filling

Capillarity traps fluids in porous media during immiscible fluid displacement. Most field situations involve relatively long time scales, such as hydrocarbon migration into reservoirs, resource recovery, nonaqueous phase liquid remediation, geological CO 2 storage, and sediment‐atmosphere interactions. Yet laboratory studies and numerical simulations of capillary phenomena rarely consider the impact of time on these processes. We use time‐lapse microphotography to record the evolution of saturation in air‐ or hydrocarbon‐filled capillary tubes submerged in water to investigate long‐term pore filling phenomena beyond imbibition. Microphotographic sequences capture a lively pore filling history where various concurrent physical phenomena coexist. Dissolution and diffusion play a central role. Observations indicate preferential transport of the wetting liquid along corners, vapor condensation, capillary flow induced by asymmetrical interfaces, and interface pinning that defines the diffusion length. Other processes include internal snap‐offs, fluid redistribution, and changes in wettability as fluids dissolve into each other. Overall, the rate of pore filling is diffusion‐controlled for a given interfacial configuration; diffusive transport takes place at a constant rate for pinned interfaces and is proportional to the square root of time for free interfaces where the diffusion length increases with time.

Water Resources Research↗

USGS2017

Coastal single-beam bathymetry data collected in 2015 from Raccoon Point to Point Au Fer Island, Louisiana

As part of the Barrier Island Comprehensive Monitoring Program (BICM), scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a nearshore single-beam bathymetry survey along the south-central coast of Louisiana, from Raccoon Point to Point Au Fer Island, in July 2015. The goal of the BICM program is to provide long-term data on Louisiana’s coastline and use this data to plan, design, evaluate, and maintain current and future barrier island restoration projects. The data described in this report will provide baseline bathymetric information for future research investigating island evolution, sediment transport, and recent and long-term geomorphic change, and will support modeling of future changes in response to restoration and storm impacts. The survey area encompasses more than 300 square kilometers of nearshore environment from Raccoon Point to Point Au Fer Island. This data series serves as an archive of processed single-beam bathymetry data, collected from July 22–29, 2015, under USGS Field Activity Number 2015-320-FA. Geographic information system data products include a 200-meter-cell-size interpolated bathymetry grid, trackline maps, and point data files. Additional files include error analysis maps, Field Activity Collection System logs, and formal Federal Geographic Data Committee metadata.

Louisiana↗

USGS1975

Dynamics of turbidity plumes in Lake Ontario

Large-turbidity features along the 275-kilometre long south shore of Lake Ontario were analyzed using LANDSAT-1 images. The ESIAC system developed by the Stanford Research Institute, was used to obtain enlargements and false-color renditions of turbidity plumes. After projection on a video screen, individual turbidity features were analyzed, mapped, and photographed. The Niagara River plume, as much as 500 square kilometres in area, is by far, the largest turbidity feature in the lake. Based on image tonal comparisons, turbidity in the Welland Canal is usually higher than that in any other watercourse discharging into the lake throughout the shipping season. Somewhat less turbid water enters the lake from the Port Dalhousie diversion channel and the Genesee River. Relatively clear water resulting from the deposition of suspended matter in numerous upstream lakes is discharged by the Niagara and Oswego rivers. Plume analysis corroborates the presence of a prevailing eastward flowing longshore current along the entire south shore. This current is most persistent at the Oswego River outlet but is quite variable in the Rochester embayment, where rapid shifts in water movement were occasionally detected in LANDSAT images. The position of the spring thermal bar, a zone, of maximum density water corresponding to the 4°C isotherm, was approximately located in images obtained during April 1973. Although eastward moving currents were detected on the inshore side of the thermal bar, westward moving counter currents seem to be dominant along its offshore side. Plumes generated by beach erosion were readily detected in the images. Such areas are identified by light to very light, long, narrow plumes paralleling the coastline. Extensive areas of the south shore are subject to erosion, but the most severely affected beaches are situated between Fifty Mile Pt., Ontario and Thirty Mile Pt., N.Y., along the Rochester embayment, and between Sodus Bay and Nine Mile Pt. Color illustrations of figure accession numbers EDC-010077 to EDC-010106 are available for purchase from the EROS Data Center, Sioux Falls, South Dakota 57198.

Lake Ontario↗

USGS2021

A machine learning approach to modeling streamflow with sparse data in ungaged watersheds on the Wyoming Range, Wyoming, 2012–17

Scant availability of streamflow data can impede the utility of streamflow as a variable in ecological models of aquatic and terrestrial species, especially when studying small streams in watersheds that lack streamgages. Streamflow data at fine resolution and broad extent were needed by collaborators for ecological research on small streams in several ungaged watersheds of southwestern Wyoming, where streamflow data are sparse. To improve the utility of sparse streamflow data to ecological research in ungaged watersheds, we developed a machine learning approach in R for modeling spatially and temporally continuous monthly streamflow from 2012 through 2017 in three semiarid montane-steppe watersheds (with drainage areas of 26–55 square miles and mean elevations of 8,031–8,455 feet) on the Wyoming Range in the upper Green River Basin. A machine learning streamflow (MLFLOW) model was calibrated and validated with 971 discrete streamflow observations and 24 static and dynamic predictor variables derived from geospatial and time series data on climatic, physiographic, and anthropogenic characteristics affecting streamflow. The predictor variables were temporally and spatially conditioned to amplify the relation of predictor variables to monthly streamflow. The MLFLOW model had satisfactory agreement between observed and predicted streamflow (coefficient of determination [ R 2 ]=0.80, Nash-Sutcliffe efficiency [NSE]=0.79, NSE with log-transformed data [logNSE]=0.82, and percent bias [PBIAS]=0.7 percent). NSE and logNSE indicated the MLFLOW model performed equally well for high and low flows, and PBIAS indicated the MLFLOW model did not overpredict or underpredict monthly streamflow. Streamflow predictions seemed to well represent the annual hydrograph within the study area during the study period. The most important variables (statistically important in the MLFLOW model) for explaining monthly streamflow were temporally and spatially conditioned dynamic climatic variables, mostly precipitation and snow water equivalent. Importance of the static and dynamic variables did not differ substantially among the three watersheds but differed considerably among the 6 years. Monthly streamflow increased with increasing precipitation, snow water equivalent, and drainage area but decreased with increasing forest cover, elevation, evapotranspiration, and temperature. The MLFLOW model was most sensitive to selection of dynamic climatic variables. Unconditioned dynamic climatic variables alone explained 54 percent of the variance ( R 2 =0.54) in monthly streamflow, whereas adding static physiographic and anthropogenic variables only explained 12 percent more of the variance ( R 2 =0.66). Also, spatial conditioning of all variables together with temporal conditioning of dynamic variables increased the variance explained in the MLFLOW model by another 14 percent ( R 2 =0.80). The MLFLOW model also had greater sensitivity to temporal than to spatial differences in the data. For the MLFLOW model trained with observations from all watersheds and years or for models trained with observations from all except one watershed or 1 year left out sequentially, performance was better in testing on observations from each watershed than from each year separately. Also, performance was better for models fitted to fewer sites than to fewer months of observations. The greatest utility of the modeling approach is the ease of use and the speed of processing input data, running the model, and interpreting the model output, whereas the greatest limitation is the need for spatially and temporally representative streamflow observations to drive the model. Although familiarity with R is necessary, only a working knowledge of hydrology (for selecting appropriate predictor variables and evaluating the quality of streamflow observations) and a rudimentary understanding of machine learning models are needed. Therefore, this modeling approach is practicable for other scientists who work with water but who are not hydrologists.

Wyoming↗

USGS1973

Wolf numbers in the Superior National Forest of Minnesota

Seventy-seven radio-tagged wolves (Canis lupus), including members of 12 adjacent packs, were located more than 3000 times, and observed on 1337 occasions, from 1968 through mid-1973. Projected population estimates for the 4203 square-mile Forest were 405 plus or minus 20 for winter 1971-72, and 388 plus or minus 14 for winter 1972-73.

Research Paper↗

USGS2017

Single-beam bathymetry data collected in 2015 from Grand Bay, Alabama-Mississippi

As part of the Sea-level and Storm Impacts on Estuarine Environments and Shorelines (SSIEES) project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted a single-beam bathymetry survey within the estuarine, open-bay, and tidal creek environments of Grand Bay, Alabama-Mississippi, from May to June 2015. The goal of the SSIEES project is to assess the physical controls of sediment and material exchange between wetlands and estuarine environments along the northern Gulf of Mexico, specifically Grand Bay, Alabama-Mississippi; Vermilion Bay, Louisiana; and, along the east coast, within Chincoteague Bay, Virginia-Maryland. The data described in this report provide baseline bathymetric information for future research investigating wetland-marsh evolution, sediment transport, erosion, recent and long-term geomorphic change, and can also support the modeling of changes in response to restoration and storm impacts. The survey area encompasses more than 40 square kilometers of Grand Bay’s waters.

Alabama, Mississippi↗

USGS2010

Geophysical Data Collected off the South Shore of Martha's Vineyard, Massachusetts

The U.S. Geological Survey Woods Hole Science Center conducted a nearshore geophysical survey offshore of the southern coast of Martha's Vineyard, in the vicinity of the Martha's Vineyard Coastal Observatory in 2007. This mapping program was part of a larger research effort supporting the Office of Naval Research Ripples Directed-Research Initiative studies at Martha's Vineyard Coastal Observatory designed to improve our understanding of coastal sediment-transport processes. The survey was conducted aboard the Megan T. Miller August 9-13, 2007. The study area covers 35 square kilometers from about 0.2 kilometers to 5 kilometers offshore of the south shore of Martha's Vineyard, and ranges in depth from ~6 to 24 meters. The geophysical mapping utilized the following suite of high-resolution instrumentation to map the surficial sediment distribution, bathymetry, and sub-surface geology: a dual-frequency 100/500 kilohertz sidescan-sonar system, 234 kilohertz interferometric sonar, and 500 hertz -12 kilohertz chirp subbottom profiler. These geophysical data will be used to provide initial conditions for wave and circulation modeling within the study area.

Open-File Report↗

USGS2023

A multimodal data fusion and deep learning framework for large-scale wildfire surface fuel mapping

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels.

California↗

USGS1995

Graphical and PC-software analysis of volcano eruption precursors according to the Materials Failure Forecast Method (FFM)

The Materials Failure Forecasting Method for volcanic eruptions (FFM) analyses the rate of precursory phenomena. Time of eruption onset is derived from the time of “failure” implied by accelerating rate of deformation. The approach attempts to fit data, Ω, to the differential relationship Ω Ω ¨ = A Ω ˙ "> ¨ = AΩ˙ , where the dot superscript represents the time derivative, and the data Ω may be any of several parameters describing the accelerating deformation or energy release of the volcanic system. Rate coefficients, A and α, may be derived from appropriate data sets to provide an estimate of time to “failure”. As the method is still an experimental technique, it should be used with appropriate judgment during times of volcanic crisis. Limitations of the approach are identified and discussed. Several kinds of eruption precursory phenomena, all simulating accelerating creep during the mechanical deformation of the system, can be used with FFM. Among these are tilt data, slope-distance measurements, crater fault movements and seismicity. The use of seismic coda, seismic amplitude-derived energy release and time-integrated amplitudes or coda lengths are examined. Usage of cumulative coda length directly has some practical advantages to more rigorously derived parameters, and RSAM and SSAM technologies appear to be well suited to real-time applications. One graphical and four numerical techniques of applying FFM are discussed. The graphical technique is based on an inverse representation of rate versus time. For α = 2, the inverse rate plot is linear; it is concave upward for α < 2 and concave downward for α > 2. The eruption time is found by simple extrapolation of the data set toward the time axis. Three numerical techniques are based on linear least-squares fits to linearized data sets. The “linearized least-squares technique” is most robust and is expected to be the most practical numerical technique. This technique is based on an iterative linearization of the given rate-time series. The hindsight technique is disadvantaged by a bias favouring a too early eruption time in foresight applications. The “log rate versus log acceleration technique”, utilizing a logarithmic representation of the fundamental differential equation, is disadvantaged by large data scatter after interpolation of accelerations. One further numerical technique, a nonlinear least-squares fit to rate data, requires special and more complex software. PC-oriented computer codes were developed for data manipulation, application of the three linearizing numerical methods, and curve fitting. Separate software is required for graphing purposes. All three linearizing techniques facilitate an eruption window based on a data envelope according to the linear least-squares fit, at a specific level of confidence, and an estimated rate at time of failure.

Journal of Volcanology and Geothermal Research↗

USGS2009

Geography of Alaska lake districts: Identification, description, and analysis of lake-rich regions of a diverse and dynamic state

Lakes are abundant landforms and important ecosystems in Alaska, but are unevenly distributed on the landscape with expansive lake-poor regions and several lake-rich regions. Such lake-rich areas are termed lake districts and have landscape characteristics that can be considered distinctive in similar respects to mountain ranges. In this report, we explore the nature of lake-rich areas by quantitatively identifying Alaska’s lake districts, describing and comparing their physical characteristics, and analyzing how Alaska lake districts are naturally organized and correspond to climatic and geophysical characteristics, as well as studied and managed by people. We use a digital dataset (National Hydrography Dataset) of lakes greater than 1 hectare, which includes 409,040 individual lakes and represents 3.3 percent of the land-surface area of Alaska. The selection criteria we used to identify lake districts were (1) a lake area (termed limnetic ratio, in percent) greater than the mean for the State, and (2) a lake density (number of lakes per unit area) greater than the mean for the State using a pixel size scaled to the area of interest and number of lakes in the census. Pixels meeting these criteria were grouped and delineated and all groups greater than 1,000 square kilometers were identified as Alaska’s lake districts. These lake districts were described according to lake size-frequency metrics, elevation distributions, geology, climate, and ecoregions to better understand their similarities and differences. We also looked at where lake research and relevant ecological monitoring has occurred in Alaska relative to lake districts and how lake district lands and waters are currently managed. We identified and delineated 20 lake districts in Alaska representing 16 percent of the State, but including 65 percent of lakes and 75 percent of lake area. The largest lake districts identified are the Yukon-Kuskokwim Delta, Arctic Coastal Plain, and Iliamna lake districts with high limnetic ratios of 19, 17, and 21 percent, respectively. The three smallest districts we considered were Tetlin in the eastern interior, Menhiskof on the Alaska Peninsula, and Matanuska–Susitna at the head of Cook Inlet with limnetic ratios of 14, 9, and 9 percent, respectively. Lake density and limnetic ratio were poorly related among lake districts, such that some districts had a few large lakes like Iliamna with Lakes Iliamna and Becharof—the two largest in the State, compared to other districts with many very small lakes like Yukon-Kuskokwim Delta with 111,130 lakes and 63 percent of these less than 10 hectares. Most lake districts are in regions with relatively low precipitation, but temperature regimes varied widely among lake districts. Approximately one-half of lake districts were glaciated during the Pleistocene and similar numbers occur in regions classified as having continuous, discontinuous, and sporadic permafrost, or perennially unfrozen soils. Most districts are at low elevations (less than 250 meters) with two important exceptions being Tetlin with a mean elevation of 530 meters and Ahtna with a mean elevation of 760 meters. These higher elevation districts, particularly Ahtna, had distinct characteristics from other lake districts such as continuous permafrost and Pleistocene glaciation. Several lake districts share similar boundaries to defined ecoregions with lake districts occurring in less than one-half of these 32 ecoregions of Alaska. Most lake districts are lands fully or partly managed by the U.S. Fish and Wildlife Service and the National Park Service, with other land management by the Bureau of Land Management and State and borough government. Much of the U.S. Geological Survey’s lake water-quality sampling efforts has been done in the Arctic Coastal Plain, Matanuska-Susitna, and Iliamna districts but no recorded collections in nine lake districts. Similarly, most lake limnological studies in Alaska were site-specific and represent only a small portion of Alaska’s lake districts. This identification, characterization, and analysis of lake-rich regions may help provide a template to guide future limnological and other scientific research for Alaska.

Alaska↗

USGS1996

Wetlands mitigation: Partnership between an electric power company and a federal wildlife refuge

Twenty-three acres of a degraded section of Patuxent Research Refuge in Laurel, Maryland were converted to wetland habitat by the Baltimore Gas and Electric Company in 1994. The wetlands were created as mitigation for 14 acres of wetlands that were impacted as part of the construction of a 5.3 mile 500kv overhead transmission line on the Refuge. The area consists of a created forested wetland (13.5 acres), a seasonally inundated green-tree reservoir (6.5 acres), and an impounded pond wetland (3 acres). Construction included the planting of 6,131 trees, 4,276 shrubs, and 15,102 emergent plants. Part of the site has been studied intensively since completion and survival of trees and shrubs after 2 years was 88%. Measurements of these transplants, have shown growth greater than on other created sites in Maryland. Grasses and other herbaceous vegetation were dominant plants in the meter-square plots in the first two years of sampling of the created forested wetland. Wildlife surveys for birds, mammals, amphibians, and reptiles have revealed diverse communities. Although these communities represent species consistent with open habitat, more typical forest species should colonize the area as it undergoes succession into a more mature forested wetland. The creation, management, and research of this mitigation site represents an excellent example of a partnership between a private electric power company and a federal wildlife refuge. This partnership has increased local biodiversity and improved regional water quality of the Patuxent River and the Chesapeake Bay.

Environmental Management↗

USGS1977

Surface deformation in part of the San Jacinto Valley, southern California

Ground fissuring and subsidence occur in a deep alluvium-filled graben between the Casa Loma and San Jacinto faults, San Jacinto Valley, southern California. These processes are attributed chiefly to compaction due to withdrawal of ground water. Subsidence due to compaction is estimated to be a maximum of 3.5 centimetres per year; tectonic subsidence of the graben is estimated to be from 0.3 to 0.6 cm per year. Ground fissuring occurs primarily on the west side of a closed depression in the graben. The area of ground fissures has expanded from 1 square kilometre in 1953 to 12 km2 in 1974 and could grow even more. Individual fissures have attained lengths of 850 metres.

California↗