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Results for “Natural Resource Modelling”

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At least 397 records · Page 22Linked to original sources

USGS2003

Modeling hyporheic zone processes

Stream biogeochemistry is influenced by the physical and chemical processes that occur in the surrounding watershed. These processes include the mass loading of solutes from terrestrial and atmospheric sources, the physical transport of solutes within the watershed, and the transformation of solutes due to biogeochemical reactions. Research over the last two decades has identified the hyporheic zone as an important part of the stream system in which these processes occur. The hyporheic zone may be loosely defined as the porous areas of the stream bed and stream bank in which stream water mixes with shallow groundwater. Exchange of water and solutes between the stream proper and the hyporheic zone has many biogeochemical implications, due to differences in the chemical composition of surface and groundwater. For example, surface waters are typically oxidized environments with relatively high dissolved oxygen concentrations. In contrast, reducing conditions are often present in groundwater systems leading to low dissolved oxygen concentrations. Further, microbial oxidation of organic materials in groundwater leads to supersaturated concentrations of dissolved carbon dioxide relative to the atmosphere. Differences in surface and groundwater pH and temperature are also common. The hyporheic zone is therefore a mixing zone in which there are gradients in the concentrations of dissolved gasses, the concentrations of oxidized and reduced species, pH, and temperature. These gradients lead to biogeochemical reactions that ultimately affect stream water quality. Due to the complexity of these natural systems, modeling techniques are frequently employed to quantify process dynamics.

Advances in Water Resources↗

USGS2006

Panel: Eco-informatics and decision making managing our natural resources

This panel responds to the December 2004 workshop on Eco-Informatics and Decision Making [1], which addressed how informatics tools can help with better management of natural resources and policy making. The workshop was jointly sponsored by the NSF, NBII, NASA, and EPA. Workshop participants recommended that informatics research in four IT areas be funded: modeling and simulation, data quality, information integration and ontologies, and social and human aspects. Additionally, they recommend that funding agencies provide infrastructure and some changes in funding habits to assure cycles of innovation in the domain were addressed. This panel brings issues raised in that workshop to the attention of digital government researchers.

Conference Paper↗

USGS2014

Adaptive management and the value of information: learning via intervention in epidemiology

Optimal intervention for disease outbreaks is often impeded by severe scientific uncertainty. Adaptive management (AM), long-used in natural resource management, is a structured decision-making approach to solving dynamic problems that accounts for the value of resolving uncertainty via real-time evaluation of alternative models. We propose an AM approach to design and evaluate intervention strategies in epidemiology, using real-time surveillance to resolve model uncertainty as management proceeds, with foot-and-mouth disease (FMD) culling and measles vaccination as case studies. We use simulations of alternative intervention strategies under competing models to quantify the effect of model uncertainty on decision making, in terms of the value of information, and quantify the benefit of adaptive versus static intervention strategies. Culling decisions during the 2001 UK FMD outbreak were contentious due to uncertainty about the spatial scale of transmission. The expected benefit of resolving this uncertainty prior to a new outbreak on a UK-like landscape would be £45–£60 million relative to the strategy that minimizes livestock losses averaged over alternate transmission models. AM during the outbreak would be expected to recover up to £20.1 million of this expected benefit. AM would also recommend a more conservative initial approach (culling of infected premises and dangerous contact farms) than would a fixed strategy (which would additionally require culling of contiguous premises). For optimal targeting of measles vaccination, based on an outbreak in Malawi in 2010, AM allows better distribution of resources across the affected region; its utility depends on uncertainty about both the at-risk population and logistical capacity. When daily vaccination rates are highly constrained, the optimal initial strategy is to conduct a small, quick campaign; a reduction in expected burden of approximately 10,000 cases could result if campaign targets can be updated on the basis of the true susceptible population. Formal incorporation of a policy to update future management actions in response to information gained in the course of an outbreak can change the optimal initial response and result in significant cost savings. AM provides a framework for using multiple models to facilitate public-health decision making and an objective basis for updating management actions in response to improved scientific understanding.

PLoS Biology↗

USGS2015

Model averaging and muddled multimodel inferences

Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse ( Centrocercus urophasianus ) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

Ecology↗

USGS2023

The 3D Elevation Program—Supporting Arkansas's economy

Introduction In recent years, Arkansas has coordinated with Federal and local partners to improve the quality and availability of high-resolution elevation data for the State. With high-quality elevation data, Arkansas can improve services offered to the public and within government, resulting in better quality of life, improved public safety, and higher return on investments. Elevation data are beneficial in numerous business activities, including agriculture and precision farming, flood risk management, geologic resource assessment and hazard mitigation, infrastructure and construction management, urban and regional planning, and natural resources conservation. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features. The 3D Elevation Program (3DEP) is managed by the U.S. Geological Survey in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage at quality level 2 or better to meet the many needs of the Nation and Arkansas. This fact sheet shows the status of available and in-progress 3DEP baseline lidar data in Arkansas.

Arkansas↗

USGS2005

Middle Mississippi River decision support system: user's manual

This user's manual describes the Middle Mississippi River Decision Support System (MMRDSS) and gives detailed examples on its use. The MMRDSS provides a framework to assist decision makers regarding natural resource issues in the Middle Mississippi River floodplain. The MMRDSS is designed to provide users with a spatially explicit tool for tasks, such as inventorying existing knowledge, developing models to investigate the potential effects of management decisions, generating hypotheses to advance scientific understanding, and developing scientifically defensible studies and monitoring. The MMRDSS also includes advanced tools to assist users in evaluating differences in complexity, connectivity, and structure of aquatic habitats among river reaches. The Environmental Systems Research Institute ArcView 3.x platform was used to create and package the data and tools of the MMRDSS.

Techniques and Methods↗

USGS2015

The 3D Elevation Program: summary for Connecticut

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 Connecticut, elevation data are critical for coastal zone management, flood risk management, natural resources conservation, agriculture and precision farming, sea level rise and subsidence, 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.

Connecticut↗

USGS2019

Grounding simulation models with qualitative case studies: Toward a holistic framework to make climate science usable for US public land management

Policies directing agencies and public land managers to incorporate climate change into management face several barriers. These stem, in part, from a disconnect between the information that is produced and the information needs of local resource managers. A disproportionate focus on the natural and physical sciences in climate vulnerability and adaptation assessment obscure understandings of complex social systems and the interactions and feedbacks in social-ecological systems. We use a qualitative case study of bison management on Department of the Interior-managed and tribal lands to explore how a social-science driven Determinants and Analogue Vulnerability Assessment (DAVA) can inform ecological response models, specifically simulation models that account for multiple drivers of change. First, we illustrate how a DAVA approach can help to: 1) identify key processes, entities, and interactions across scales; 2) document local impacts, indicators, and monitoring efforts of drought and climate; and 3) identify major tradeoffs and uncertainties. We then demonstrate how qualitative narratives can inform simulation models by: 1) prioritizing model components included in modeling efforts; 2) framing joint management and climate scenarios; and 3) parameterizing and evaluating model performance. We do this by presenting a conceptual joint agent-based/state-and-transition simulation modeling framework. Simulation models can represent multiple interacting variables and can identify surprising, emergent outcomes that might not be evident from qualitative analysis alone, and we argue that qualitative case studies can ground simulation models in local contexts and help make them more structurally realistic and useful. Together, these can provide a step toward developing actionable climate change adaptation strategies.

Climate Risk Management↗

USGS2010

Filtering NetCDF Files by Using the EverVIEW Slice and Dice Tool

Network Common Data Form (NetCDF) is a self-describing, machine-independent file format for storing array-oriented scientific data. It was created to provide a common interface between applications and real-time meteorological and other scientific data. Over the past few years, there has been a growing movement within the community of natural resource managers in The Everglades, Fla., to use NetCDF as the standard data container for datasets based on multidimensional arrays. As a consequence, a need surfaced for additional tools to view and manipulate NetCDF datasets, specifically to filter the files by creating subsets of large NetCDF files. The U.S. Geological Survey (USGS) and the Joint Ecosystem Modeling (JEM) group are working to address these needs with applications like the EverVIEW Slice and Dice Tool, which allows users to filter grid-based NetCDF files, thus targeting those data most important to them. The major functions of this tool are as follows: (1) to create subsets of NetCDF files temporally, spatially, and by data value; (2) to view the NetCDF data in table form; and (3) to export the filtered data to a comma-separated value (CSV) file format. The USGS and JEM will continue to work with scientists and natural resource managers across The Everglades to solve complex restoration problems through technological advances.

Fact Sheet↗

USGS2012

Developing spatially explicit footprints of plausible land-use scenarios in the Santa Cruz Watershed, Arizona and Sonora

The SLEUTH urban growth model is applied to a binational dryland watershed to envision and evaluate plausible future scenarios of land use change into the year 2050. Our objective was to create a suite of geospatial footprints portraying potential land use change that can be used to aid binational decision-makers in assessing the impacts relative to sustainability of natural resources and potential socio-ecological consequences of proposed land-use management. Three alternatives are designed to simulate different conditions: (i) a Current Trends Scenario of unmanaged exponential growth, (ii) a Conservation Scenario with managed growth to protect the environment, and (iii) a Megalopolis Scenario in which growth is accentuated around a defined international trade corridor. The model was calibrated with historical data extracted from a time series of satellite images. Model materials, methodology, and results are presented. Our Current Trends Scenario predicts the footprint of urban growth to approximately triple from 2009 to 2050, which is corroborated by local population estimates. The Conservation Scenario results in protecting 46% more of the Evergreen class (more than 150,000 acres) than the Current Trends Scenario and approximately 95,000 acres of Barren Land, Crops, Deciduous Forest (Mesquite Bosque), Grassland/Herbaceous, Urban/Recreational Grasses, and Wetlands classes combined. The Megalopolis Scenario results also depict the preservation of some of these land-use classes compared to the Current Trends Scenario, most notably in the environmentally important headwaters region. Connectivity and areal extent of land cover types that provide wildlife habitat were preserved under the alternative scenarios when compared to Current Trends.

Arizona;Sonora↗

USGS2014

Hydraulic modeling development and application in water resources engineering

The use of modeling has become widespread in water resources engineering and science to study rivers, lakes, estuaries, and coastal regions. For example, computer models are commonly used to forecast anthropogenic effects on the environment, and to help provide advanced mitigation measures against catastrophic events such as natural and dam-break floods. Linking hydraulic models to vegetation and habitat models has expanded their use in multidisciplinary applications to the riparian corridor. Implementation of these models in software packages on personal desktop computers has made them accessible to the general engineering community, and their use has been popularized by the need of minimal training due to intuitive graphical user interface front ends. Models are, however, complex and nontrivial, to the extent that even common terminology is sometimes ambiguous and often applied incorrectly. In fact, many efforts are currently under way in order to standardize terminology and offer guidelines for good practice, but none has yet reached unanimous acceptance. This chapter provides a view of the elements involved in modeling surface flows for the application in environmental water resources engineering. It presents the concepts and steps necessary for rational model development and use by starting with the exploration of the ideas involved in defining a model. Tangible form of those ideas is provided by the development of a mathematical and corresponding numerical hydraulic model, which is given with a substantial amount of detail. The issues of model deployment in a practical and productive work environment are also addressed. The chapter ends by presenting a few model applications highlighting the need for good quality control in model validation.

Book chapter↗

USGS2015

The 3D Elevation Program: summary for South Carolina

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 South Carolina, elevation data are critical for flood risk management, natural resources conservation, agriculture and precision farming, infrastructure and construction management, 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 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.

South Carolina↗

USGS2014

The 3D Elevation Program: summary for Tennessee

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 Tennessee, elevation data are critical for agriculture and precision farming, flood risk management, natural resources conservation, infrastructure and construction management, forest resources management, aviation navigation and safety, 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 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 (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.

Tennessee↗

USGS2013

The 3D Elevation Program: summary for Minnesota

Minnesota↗

USGS2014

The 3D Elevation Program: summary for North Carolina

Elevation data are essential to a broad range of applications, including forest resources management, wildlife and habitat management, national security, and recreation. For the State of North Carolina, elevation data are critical for flood risk management, natural resources conservation, agriculture and precision farming, infrastructure and construction management, 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 (NEEA; Dewberry, 2011) 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 use 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.

North Carolina↗

USGS2025

Mammalian responses to select Type 3 Watershed Experiment prescriptions in the Olympic Experimental State Forest: A camera-based monitoring approach

The Type 3 Watershed Experiment is a landscape-scale management experiment designed to assess the ecological, economic, and social benefits of timber harvest and post-harvest management prescriptions in upland and riparian systems of the Washington Coast Range ecoregion. The experiment is being conducted on state trust lands in the Olympic Experimental State Forest (OESF), which are managed by the Washington Department of Natural Resources (WADNR). A wide variety of abiotic and biotic responses are being studied for the experiment, but no current studies include mammalian responses. The goal of this study is to use camera traps to assess patterns of medium and large mammal use of treatment blocks associated with the Type 3 Watershed Experiment, including the Complex Early Seral (CES) prescription and Variable Retention Harvest (VRH) prescription, in comparison to unharvested second growth forests. We are using a block design to assess responses of the medium and large mammal community to the CES and VRH prescriptions. Target taxa include Columbia black-tailed deer, Roosevelt elk, black bear, cougar, coyote, bobcat, fisher, and other medium sized mammals of interest. Mammalian use at the management prescription sites will be compared to use at the unharvested control sites (second growth forest stands). We will conduct an initial study in 2024─2025 for proof of concept after a pilot period of camera deployment. We anticipate that the cameras will be deployed continuously year-round for at least three years and possibly longer to assess longer-term patterns of use of these prescriptions. The project includes 48 cameras deployed evenly across CES, VRH, and control units. All our camera deployments are collocated at sites where avian monitoring is also occurring, and several also overlap with areas where the amphibian community is being surveyed, providing spatial linkages among different wildlife datasets. We will assess mammal community responses to forest management prescriptions using a variety of approaches. For the different prescriptions, we will examine the occurrence of a suite of species, measure species richness (alpha and beta diversity), estimate relative use, and quantify species persistence (i.e., the amount of time a species spends in a given prescription). We will estimate the influence of select habitat conditions on individual species use and on total species richness using mixed-effects models. The results of this study will provide estimates of initial responses of the mammalian community to the focal management prescriptions and serve as baseline data for long-term monitoring. This research will provide natural resource managers with a better understanding of how the mammalian community responds to and uses different types of habitats after timber harvest operations and will assess how those patterns change over time as vegetation develops under different silviculture prescriptions.

Washington↗

USGS2011

Adaptive management in the U.S. National Wildlife Refuge System: Science-management partnerships for conservation delivery

Adaptive management is an approach to recurrent decision making in which uncertainty about the decision is reduced over time through comparison of outcomes predicted by competing models against observed values of those outcomes. The National Wildlife Refuge System (NWRS) of the U.S. Fish and Wildlife Service is a large land management program charged with making natural resource management decisions, which often are made under considerable uncertainty, severe operational constraints, and conditions that limit ability to precisely carry out actions as intended. The NWRS presents outstanding opportunities for the application of adaptive management, but also difficult challenges. We describe two cooperative programs between the Fish and Wildlife Service and the U.S. Geological Survey to implement adaptive management at scales ranging from small, single refuge applications to large, multi-refuge, multi-region projects. Our experience to date suggests three important attributes common to successful implementation: a vigorous multi-partner collaboration , practical and informative decision framework components , and a sustained commitment to the process. Administrators in both agencies should consider these attributes when developing programs to promote the use and acceptance of adaptive management in the NWRS.

Journal of Environmental Management↗

USGS2025

Bright spots for advancing ecological understanding and conservation decision-making

A lot can be learned by studying bright spots—defined as unexpected positive outcomes. In fields like public health, education, and oncology, identifying factors behind bright spots reveals previously unknown drivers of success that can be replicated elsewhere. This concept is being applied in conservation but is hampered by variations in definitions of bright spots and confusion with hotspots—sites with high absolute values of a metric. We developed a framework to clearly define and distinguish between hotspots (e.g., a wetland with high plant diversity) and bright spots (e.g., a biodiverse wetland in a housing development), which outperform conservation expectations. The framework is an iterative cycle, consisting of setting expectations for relative comparisons, classifying systems into bright, dark, hot, and cold categories, and digging deeper to reveal hidden mechanisms and opportunities for intervention. We drew on examples from diverse fields to demonstrate how our framework can generate new knowledge, identify potential interventions, and inform management priorities. Defining conservation and management expectations, often through predictive models, is essential to understanding drivers of success and fosters hypotheses about overlooked factors. Our framework can enhance ecological understanding, guide interventions, and help prioritize actions in conservation and natural resource management.

Conservation Biology↗