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At least 451 records · Page 25Linked to original sources

Economics, helium, and the U.S. Federal Helium Reserve: Summary and outlook

In 2017, disruptions in the global supply of helium reminded consumers, distributors, and policy makers that the global helium supply chain lacks flexibility, and that attempts to increase production from the U.S. Federal Helium Reserve (the FHR) may not be able to compensate for the loss of one of the few major producers in the world. Issues with U.S. and global markets for helium include inelastic demand, economic availability of helium only as a byproduct, only 4–5 major producers, helium’s propensity to escape earth’s crust, an ongoing absence of storage facilities comparable to the FHR, and a lack of consequences for the venting of helium. The complex combination of these economic, physical, and regulatory issues is unique to helium, and determining helium’s practical availability goes far beyond estimating the technically accessible volume of underground resources. Although most of these issues have been analyzed since helium was recognized to be a valuable mineral commodity in the early 1900s, very few economic models have been developed that adequately consider the unique characteristics of helium and helium markets. In particular, there is a notable lack of recent empirical work to estimate the responsiveness of helium demand, supply, prices, and trade patterns to the ongoing drawdown and sale of helium reserves stored in the FHR. In general, existing models of helium either do not account for an oligopoly controlling supply, or they do not evaluate potential helium extraction and storage programs based on an intertemporal maximization of the value of the resource. Such models could be of very limited use to decision makers. This review found only one working paper with a helium market model that has incorporated both of these vital considerations. That and other economic studies along similar lines could be very useful in helping inform current helium policy discussions and decisions.

Natural Resources Research↗

The 3D Elevation Program: summary for Utah

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Utah, elevation data are critical for infrastructure and construction management, natural resources conservation, geologic resource assessment and hazard mitigation, flood risk management, agriculture and precision farming, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

Utah↗

Optimizing management of invasions in an uncertain world using dynamic spatial models

Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions.

Ecological Applications↗

Development and evaluation of a reservoir model for the Chain of Lakes in Illinois

Forecasts of flows entering and leaving the Chain of Lakes reservoir on the Fox River in northeastern Illinois are critical information to water-resource managers who determine the optimal operation of the dam at McHenry, Illinois, to help minimize damages to property and loss of life because of flooding on the Fox River. In 2014, the U.S. Geological Survey; the Illinois Department of Natural Resources, Office of Water Resources; and National Weather Service, North Central River Forecast Center began a cooperative study to develop a system to enable engineers and planners to simulate and communicate flows and to prepare proactively for precipitation events in near real time in the upper Fox River watershed. The purpose of this report is to document the development and evaluation of the Chain of Lakes reservoir model developed in this study. The reservoir model for the Chain of Lakes was developed using the Hydrologic Engineering Center–Reservoir System Simulation program. Because of the complex relation between the dam headwater and reservoir pool elevations, the reservoir model uses a linear regression model that relates dam headwater elevation to reservoir pool elevation. The linear regression model was developed using 17 U.S. Geological Survey streamflow measurements, along with the gage height in the reservoir pool and the gage height at the dam headwater. The Nash-Sutcliffe model efficiency coefficients for all three linear regression model variables ranged from 0.90 to 0.98. The reservoir model performance was evaluated by graphically comparing simulated and observed reservoir pool elevation time series during nine periods of high pool elevation. In addition, the peak elevations during these time periods were graphically compared to the closest-in-time observed pool elevation peak. The mean difference in the simulated and observed peak elevations was -0.03 feet, with a standard deviation of 0.19 feet. The Nash-Sutcliffe coefficient for peak prediction was calculated as 0.94. Evaluation of the model based on accuracy of peak prediction and the ability to simulate an elevation time series showed the performance of the model was satisfactory.

Illinois↗

The 3D Elevation Program: summary for Puerto Rico

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, scientific research, national security, recreation, and many others. For the Commonwealth of Puerto Rico, elevation data are critical for flood risk management, landslide mitigation, natural resources conservation, sea level rise and subsidence, coastal zone management, infrastructure and construction management, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, U.S. territorial, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States, Hawaii, and selected U.S. territories, and quality level 5 interferometric synthetic aperture radar (IfSAR) data for Alaska, all with a 6- to 10-year acquisition cycle, provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A‒16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other three-dimensional (3D) representations of the Nation’s natural and constructed features.

Puerto Rico↗

Markov decision processes in natural resources management: observability and uncertainty

The breadth and complexity of stochastic decision processes in natural resources presents a challenge to analysts who need to understand and use these approaches. The objective of this paper is to describe a class of decision processes that are germane to natural resources conservation and management, namely Markov decision processes, and to discuss applications and computing algorithms under different conditions of observability and uncertainty. A number of important similarities are developed in the framing and evaluation of different decision processes, which can be useful in their applications in natural resources management. The challenges attendant to partial observability are highlighted, and possible approaches for dealing with it are discussed.

Ecological Modelling↗

Managing the rippling stream: decisionmaking in natural resource administration

This article addresses the conflict which exists within the water resources decisionmaking arena over the allocation of water for instream uses. The discussion reviews the literature on public administration regarding decisionmaking, and is based on research performed by the authors which synthesizes a model of decisionmaking. This model can be used as both a description of agency behavior, and as the basis for developing a prescription for strategy formulation.

Journal of the American Water Resources Associatio↗

The 3D Elevation Program: summary for Georgia

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Georgia, elevation data are critical for infrastructure and construction management, natural resources conservation, flood risk management, agriculture and precision farming, forest resources management, water supply and quality, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios.The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

Georgia↗

An interactive program for computer-aided map design, display, and query: EMAPKGS2

EMAPKGS2 is a user-friendly, PC-based electronic mapping tool for use in hydrogeologic exploration and appraisal. EMAPKGS2 allows the analyst to construct maps interactively from data stored in a relational database, perform point-oriented spatial queries such as locating all wells within a specified radius, perform geographic overlays, and export the data to other programs for further analysis. EMAPKGS2 runs under Microsoft?? Windows??? 3.1 and compatible operating systems. EMAPKGS2 is a public domain program available from the Kansas Geological Survey. EMAPKGS2 is the centerpiece of WHEAT, the Windows-based Hydrogeologic Exploration and Appraisal Toolkit, a suite of user-friendly Microsoft?? Windows??? programs for natural resource exploration and management. The principal goals in development of WHEAT have been ease of use, hardware independence, low cost, and end-user extensibility. WHEAT'S native data format is a Microsoft?? Access?? database. WHEAT stores a feature's geographic coordinates as attributes so they can be accessed easily by the user. The WHEAT programs are designed to be used in conjunction with other Microsoft?? Windows??? software to allow the natural resource scientist to perform work easily and effectively. WHEAT and EMAPKGS have been used at several of Kansas' Groundwater Management Districts and the Kansas Geological Survey on groundwater management operations, groundwater modeling projects, and geologic exploration projects. ?? 1997 Elsevier Science Ltd.

Computers & Geosciences↗

Fast forward modeling of Titan's infrared spectra to invert VIMS/Cassini hyperspectral images

The surface of Titan, the largest icy moon of Saturn, is veiled by a very thick and hazy atmosphere. The Visual and Infrared Mapping Spectrometer onboard the Cassini spacecraft, in orbit around Saturn since July 2004, conduct an intensive survey of Titan with the objective to understand the complex nature of the atmosphere and surface of the mysterious moon and the way they interact. Accurate radiative transfer modeling is necessary to analyze Titan's infrared spectra, but are often very computer resources demanding. As Cassini has gathered hitherto millions of spectra of Titan and will still observe it until at least 2010, we report here on the development of a new rapid, simple and versatile radiative transfer model specially designed to invert VIMS datacubes. ?? 2009 IEEE.

Conference Paper↗

Measuring and mapping the topography of the Florida Everglades for ecosystem restoration

One of the major issues facing ecosystem restoration and management of the Greater Everglades is the availability and distribution of clean, fresh water. The South Florida ecosystem encompasses an area of approximately 28,000 square kilometers and supports a human population that exceeds 5 million and is continuing to grow. The natural systems of the Kissimmee-Okeechobee-Everglades watershed compete for water resources primarily with the region's human population and urbanization, and with the agricultural and tourism industries. Surface water flow modeling and ecological modeling studies are important means of providing scientific information needed for ecosystem restoration planning and modeling. Hydrologic and ecological models provide much-needed predictive capabilities for evaluating management options for parks, refuges, and land acquisition and for understanding the impacts of land management practices in surrounding areas. These models require various input data, including elevation data that very accurately define the topography of the Florida Everglades.

Florida↗

The 3D Elevation Program: summary for West Virginia

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of West Virginia, elevation data are critical for natural resources conservation, flood risk management, forest resources management, infrastructure and construction management, agriculture and precision farming, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

West Virginia↗

A field and statistical modeling study to estimate irrigation water use at Benchmark Farms study sites in southwestern Georgia, 1995-96

A benchmark irrigation monitoring network of farms located in a 32-county area in southwestern Georgia was established in 1995 to improve estimates of irrigation water use. A stratified random sample of 500 permitted irrigators was selected from a data base--maintained by the Georgia Department of Natural Resources, Georgia Environmental Protection Division, Water Resources Management Branch--to obtain 180 voluntary participants in the study area. Site-specific irrigation data were collected at each farm using running-time totalizers and noninvasive flowmeters. Data were collected and compiled for 50 farms for 1995 and 130 additional farms for the 1996 growing season--a total of 180 farms. Irrigation data collected during the 1996 growing season were compiled for 180 benchmark farms and used to develop a statistical model to estimate irrigation water use in 32 counties in southwestern Georgia. The estimates derived were developed from using a statistical approach know as "bootstrap analysis" that allows for the estimation of precision. Five model components--whether-to-irrigate, acres irrigated, crop selected, seasonal-irrigation scheduling, and the amount of irrigation applied--compose the irrigation model and were developed to reflect patterns in the data collected at Benchmark Farms Study area sites. The model estimated that peak irrigation for all counties in the study area occurred during July with significant irrigation also occurring during May, June, and August. Irwin and Tift were the most irrigated and Schley and Houston were the least irrigated counties in the study area. High irrigation intensity primarily was located along the eastern border of the study area; whereas, low irrigation intensity was located in the southwestern quadrant where ground water was the dominant irrigation source. Crop-level estimates showed sizable variations across crops and considerable uncertainty for all crops other than peanuts and pecans. Counties having the most irrigated acres showed higher variations in annual irrigation than counties having the least irrigated acres. The Benchmark Farms Study model estimates were higher than previous irrigation estimates, with 20 percent of the bias a result of underestimating irrigation acreage in earlier studies. Model estimates showed evidence of an upward bias of about 15 percent with the likely cause being a misrepresented inches-applied model. A better understanding of the causes of bias in the model could be determined with a larger irrigation sample size and increased substantially by automating the reporting of monthly totalizer amounts.

Georgia↗

Modeling elk and bison carrying capacity for Great Sand Dunes National Park, Baca National Wildlife Refuge, and The Nature Conservancy's Medano Ranch, Colorado

Great Sand Dunes National Park and Preserve and the neighboring Baca National Wildlife Refuge constitute an extraordinary setting that offers a variety of opportunities for outdoor recreation and natural resource preservation in the San Luis Valley of Colorado. Adjacent to these federal lands, the Nature Conservancy (TNC) manages the historic Medano Ranch. The total land area of these three conservation properties is roughly 121,500 hectares (ha). It is a remote and rugged area in which resource managers must balance the protection of natural resources with recreation and neighboring land uses. The management of wild ungulates in this setting presents challenges, as wild ungulates move freely across public and private landscapes. The San Luis Valley was historically used for irrigated agriculture and ranching. Historically, livestock, including sheep ( Ovis aries ) and cattle ( Bos taurus ), were grazed throughout the valley. The former Luis Marie “Baca” Ranch, which makes up the northern part of Great Sand Dunes National Park (hereafter “Park”) and all of the Baca National Wildlife Refuge (hereafter “Refuge”), was actively grazed by cattle until 2004. Bison ( Bison bison ), elk ( Cervus elaphus ), mule deer ( Odocoileus hemionus ), and pronghorn ( Antilocapra americana ) were native to the area until about the 1840s, when bison, elk, and pronghorn were extirpated. Elk and pronghorn likely moved back into the area from surrounding populations to the north and south, and mule deer populations have varied through time. A population of 4,400 elk currently inhabits the area. The current bison population was established in 1986 for meat production. In 1999 TNC purchased the ranch and established a bison conservation herd, and eventually subcontracted management to a private rancher in 2005. A population of bison ranging in size from 1,200–2,000 ranges freely within the 16,100 ha Medano Ranch. Ungulate populations in the valley are regulated by hunting, with the exception of bison, which are rounded up and culled annually to maintain population levels. In an effort to create and form the basis of a multi-agency ungulate management plan for the region, the Park sought the development of an elk and bison ecological carrying capacity model to provide guidance to resource managers.

Colorado↗

The 3D Elevation Program: summary for New Mexico

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of New Mexico, elevation data are critical for infrastructure and construction management, natural resources conservation, flood risk management, agriculture and precision farming, geologic resource assessment and hazard mitigation, forest resources management, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 (table 1) for the conterminous United States and quality level 5 ifsar data (table 1) for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios.The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey (USGS), the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation’s natural and constructed features.

New Mexico↗

Will hunters steward wolves? A comment on Treves and Martin

As wolf conservation transitions away from federally sponsored protection and recovery toward sustainable management under state fish and game agencies, researchers and policymakers are interested to know what role hunters will play. Based upon hunters' responses to three recent surveys in Wisconsin and the northern Rockies, Treves and Martin question the assumption that hunters will steward wolves, noting that the majority of hunters that responded were unsupportive of wolf conservation. However, this conclusion largely depends upon what is meant by stewardship and what actions are required for wolves to be conserved. This article discusses the meaning of three concepts either explicitly or implicitly discussed by Treves and Martin—tolerance, acceptance, and stewardship—and offers a conceptual model of wildlife conservation behavior that clarifies the relationship among these concepts.

Society & Natural Resources: An International Jour↗

Assessment of imperfect detection of blister rust in whitebark pine within the Greater Yellowstone Ecosystem

We examined data on white pine blister rust (blister rust) collected during the monitoring of whitebark pine trees in the Greater Yellowstone Ecosystem (from 2004-2015). Summaries of repeat observations performed by multiple independent observers are reviewed and discussed. These summaries show variability among observers and the potential for errors being made in blister rust status. Based on this assessment, we utilized occupancy models to analyze blister rust prevalence while explicitly accounting for imperfect detection. Available covariates were used to model both the probability of a tree being infected with blister rust and the probability of an observer detecting the infection. The fitted model provided strong evidence that the probability of blister rust infection increases as tree diameter increases and decreases as site elevation increases. Most importantly, we found evidence of heterogeneity in detection probabilities related to tree size and average slope of a transect. These results suggested that detecting the presence of blister rust was more difficult in larger trees. Also, there was evidence that blister rust was easier to detect on transects located on steeper slopes. Our model accounted for potential impacts of observer experience on blister rust detection probabilities and also showed moderate variability among the different observers in their ability to detect blister rust. Based on these model results, we suggest that multiple observer sampling continue in future field seasons in order to allow blister rust prevalence estimates to be corrected for imperfect detection. We suggest that the multiple observer effort be spread out across many transects (instead of concentrated at a few each field season) while retaining the overall proportion of trees with multiple observers around 5-20%. Estimates of prevalence are confounded with detection unless it is explicitly accounted for in an analysis and we demonstrate how an occupancy model can be used to do account for this source of observation error.

idaho, Montana, Utah, Wyoming↗

The 3D Elevation Program: summary for Michigan

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, recreation, and many others. For the State of Michigan, elevation data are critical for agriculture and precision farming, natural resources conservation, flood risk management, water supply and quality, infrastructure and construction management, coastal zone management, and other business uses. Today, high-density light detection and ranging (lidar) data are the primary sources for deriving elevation models and other datasets. Federal, State, Tribal, and local agencies work in partnership to (1) replace data that are older and of lower quality and (2) provide coverage where publicly accessible data do not exist. A joint goal of State and Federal partners is to acquire consistent, statewide coverage to support existing and emerging applications enabled by lidar data. The National Enhanced Elevation Assessment evaluated multiple elevation data acquisition options to determine the optimal data quality and data replacement cycle relative to cost to meet the identified requirements of the user community. The evaluation demonstrated that lidar acquisition at quality level 2 for the conterminous United States and quality level 5 interferometric synthetic aperture radar (ifsar) data for Alaska with a 6- to 10-year acquisition cycle provided the highest benefit/cost ratios. The 3D Elevation Program (3DEP) initiative selected an 8-year acquisition cycle for the respective quality levels. 3DEP, managed by the U.S. Geological Survey, the Office of Management and Budget Circular A–16 lead agency for terrestrial elevation data, responds to the growing need for high-quality topographic data and a wide range of other 3D representations of the Nation's natural and constructed features. The Michigan Statewide Authoritative Imagery and Lidar (MiSAIL) program provides statewide lidar coordination with local, State, and national groups in support of 3DEP for Michigan.

Michigan↗