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At least 415 records · Page 23Linked to original sources
USGS2024

The National Ocean Biodiversity Strategy

President Biden has been clear that the ocean is central to life on Earth. As he has proclaimed, “the ocean powers millions of jobs; feeds and sustains us; and is a rejuvenating source of inspiration, exploration, and recreation.” The Biden-Harris Administration has worked hard to fulfill the President’s goal to protect and conserve at least 30% of U.S. waters by 2030. The ocean faces increased threats from warming, overfishing, increased acidity, and loss of biodiversity. It is now more important than ever to sustain the many benefits that the ocean, coasts, and Great Lakes provide, including food, a favorable climate, recreation, physical and mental health, and for many, a sense of cultural identity. Ocean life represents an irreplaceable heritage, the foundation of a habitable planet, and a vast trove of resources. Keeping our ocean healthy requires reliable information on the changing status of these living organisms, the drivers of biodiversity change, and options for effectively addressing those drivers. Over 2 million species are estimated to live in the ocean, yet only about 240,000 species have been described by scientists. Most of the ocean’s benefits result from those diverse species interacting with one another and the environment they create. To protect and conserve the ocean, we as a nation need to make better use of existing knowledge and prioritize acquiring new biodiversity knowledge to enable better policy and management decisions. The ability to monitor ocean species and habitats has expanded dramatically over the past decade, with innovations in technology, genomics, taxonomy, big data management and sharing, artificial intelligence, and machine learning. Yet large fractions of the U.S. ocean remain almost unknown. The National Ocean Biodiversity Strategy (strategy) reflects the urgent need to leverage these advances. The goals of this strategy must be guided by the nation’s diverse voices and ways of knowing, in order to maximize effective and equitable stewardship of the ocean’s diverse life and its benefits to people. The strategy is intended as a guiding document for government to advance three overarching goals: ● Goal 1: Drive delivery of ocean biodiversity knowledge at the national scale. Objectives include developing an Implementation Plan for achieving the strategy’s three goals; establishing a coordination mechanism to manage the implementation; and documenting gaps in biodiversity knowledge and the benefits of ocean biodiversity to people and economies. ● Goal 2: Strengthen tools and institutions to deliver ocean biodiversity knowledge. Objectives include establishing a robust information pipeline to support indicators and dynamic maps of ocean biodiversity, from the coasts to the deep sea. This pipeline should include expanded observing systems and comprehensive data management; science and technology solutions to accelerate the availability of biodiversity information; and plans to leverage previous investments to rebuild and expand the nation’s human capital and infrastructure to sustain foundational taxonomy and biodiversity science. ● Goal 3: Protect, conserve, restore, and sustainably use ocean biodiversity. Objectives include expanding the collection, delivery, and use of biodiversity knowledge to inform actions that advance ocean protection, conservation, restoration, and sustainable development. Government should lead in establishing and incentivizing diverse partnerships across scales and sectors to implement those actions and should educate and involve the public to discover and value the nation’s diverse ocean life. Achieving these goals will require commitments across society: new federal and private investments, coordination across sectors to address climate and equity challenges, and engagement of Indigenous Knowledge holders and frontline communities as full partners throughout planning and implementation. The Subcommittee on Ocean Science and Technology (SOST) IWG-Biodiversity will begin developing an Implementation Plan to describe and direct specific actions to implement the strategy. Successful implementation of the strategy will harmonize and expand collection and delivery of timely knowledge on ocean life to all of society. The strategy will also enable evidence-based management and protection of the ocean. Advancing the strategy will build human and institutional capital and partnerships that support both existing mandates and new needs to rebuild and sustain biodiversity, achieve healthy ocean ecosystems, and manage living resources. Implementing the strategy will deliver knowledge for monitoring, modeling, forecasting, and assessments that support food security, public health, and cultural values, and that more effectively protect, conserve, and restore nature.

Report↗
USGS2025

Geologic models underpinning the 2024 U.S. Geological Survey assessment of undiscovered oil and gas resources in the Hosston and Travis Peak Formations of the onshore Gulf Coast region, U.S.A.

The Early Cretaceous (Berriasian–Hauterivian) Hosston Formation in Louisiana and eastward is time correlative to the Travis Peak Formation of Texas and southern Arkansas. The formation is a first-order clastic sequence with a regional carbonate transgressive surface as an upper contact. The Hosston and Travis Peak formations contain conventional natural gas and oil accumulations that have been produced for nearly a century. These mature reservoirs contain terrigenous fluvial-deltaic, shore-zone, and paralic deposits across the productive trend; organic-lean mudstone and siltstone lithologies are found outboard of the Lower Cretaceous shelf margin. Producing reservoirs exhibit normal pressure gradients from 0.43 to 0.55 psi/ft (9.7 to 12.4 kpa/m), depths from 4000 to over 20,000 ft (1220 to 6100 m), and temperatures from 150 to 385°F (65 to 196°C). Wells are primarily vertical completions. The number of new field wildcats has been declining since the late 1990s. This paper presents comprehensive geologic models, which include lithofacies maps, structure and isopach maps, burial history models, regional seismic interpretations, and events charts that underpin the recently completed U.S. Geological Survey assessment of undiscovered, technically recoverable hydrocarbons within the Hosston and Travis Peak formations. This study also provides geographic and stratigraphic distributions of Hosston–Travis Peak reservoir properties, including geopressure, reservoir temperature, porosity, permeability, API gravity, and gas-oil ratios. Results indicate estimated undiscovered, technically recoverable mean resources of 28 million barrels of oil and 35.8 trillion cubic ft of gas in conventional and continuous accumulations within the Lower Cretaceous Hosston and Travis Peak formations of the onshore U.S. Gulf Coast region. Quantitative assessment results are detailed in U.S. Geological Survey Fact Sheet 2025–3021 and associated Data Release.

GCAGS Journal↗
USGS2025

High resolution mapping of submerged sediment size and suitable salmon spawning habitat using topo-bathymetric Lidar in the Santiam Basin, Oregon

The distribution of river-bed grain sizes plays a foundational role in river morphology and ecology. River-bed grain size is a key driver of channel form and process, and has first order effects on aquatic macroinvertebrate assemblages, fish nesting, and biogeochemical processes. Despite this importance, tools to spatially quantify grain-size distributions, particularly submerged grain-size distributions, are lacking. Efforts to address this knowledge gap include developing optical and sonographic tools, however, these approaches have limitations, especially in shallow rivers and over large spatial extents. This study quantifies submerged grain size at high resolution (1 m 2 ) across 260 km of geomorphically diverse river corridors in the Santiam River Basin, Oregon, by pairing bathymetric Lidar point clouds with georeferenced pebble counts. Results suggest that derivatives of Lidar point clouds are able to accurately estimate measured median grain size across seven of the eight river reaches investigated, including reaches above and below high-head dams. Spatial analysis of predicted grain-sizes in the context of Chinook salmon spawning habitat suggests that suitable size sediment patches in the upper, unregulated reaches the study basin is typically small and unorganized. In contrast, the larger rivers downstream of high-head dams typically have larger areas of suitable spawning gravels. This method may be useful for quantification of fish and macroinvertebrates habitats, surface grain-size metrics for sediment transport models, and monitoring of natural and anthropogenic changes in river systems.

Oregon↗
USGS2014

Optimization in the utility maximization framework for conservation planning: a comparison of solution procedures in a study of multifunctional agriculture

Quantitative methods of spatial conservation prioritization have traditionally been applied to issues in conservation biology and reserve design, though their use in other types of natural resource management is growing. The utility maximization problem is one form of a covering problem where multiple criteria can represent the expected social benefits of conservation action. This approach allows flexibility with a problem formulation that is more general than typical reserve design problems, though the solution methods are very similar. However, few studies have addressed optimization in utility maximization problems for conservation planning, and the effect of solution procedure is largely unquantified. Therefore, this study mapped five criteria describing elements of multifunctional agriculture to determine a hypothetical conservation resource allocation plan for agricultural land conservation in the Central Valley of CA, USA. We compared solution procedures within the utility maximization framework to determine the difference between an open source integer programming approach and a greedy heuristic, and find gains from optimization of up to 12%. We also model land availability for conservation action as a stochastic process and determine the decline in total utility compared to the globally optimal set using both solution algorithms. Our results are comparable to other studies illustrating the benefits of optimization for different conservation planning problems, and highlight the importance of maximizing the effectiveness of limited funding for conservation and natural resource management.

California↗
USGS2013

Computationally efficient statistical differential equation modeling using homogenization

Statistical models using partial differential equations (PDEs) to describe dynamically evolving natural systems are appearing in the scientific literature with some regularity in recent years. Often such studies seek to characterize the dynamics of temporal or spatio-temporal phenomena such as invasive species, consumer-resource interactions, community evolution, and resource selection. Specifically, in the spatial setting, data are often available at varying spatial and temporal scales. Additionally, the necessary numerical integration of a PDE may be computationally infeasible over the spatial support of interest. We present an approach to impose computationally advantageous changes of support in statistical implementations of PDE models and demonstrate its utility through simulation using a form of PDE known as “ecological diffusion.” We also apply a statistical ecological diffusion model to a data set involving the spread of mountain pine beetle (Dendroctonus ponderosae) in Idaho, USA.

Journal of Agricultural, Biological, and Environme↗
USGS2015

The 3D Elevation Program: summary for Delaware

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 Delaware, elevation data are critical for agriculture and precision farming, river and stream resource management, natural resources conservation, flood risk management, coastal zone management, geologic resource assessment and hazard mitigation, 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 publicly available 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.

Delaware↗
USGS2022

Weakly supervised spatial deep learning for Earth image segmentation based on imperfect polyline labels

In recent years, deep learning has achieved tremendous success in image segmentation for computer vision applications. The performance of these models heavily relies on the availability of large-scale high-quality training labels (e.g., PASCAL VOC 2012). Unfortunately, such large-scale high-quality training data are often unavailable in many real-world spatial or spatiotemporal problems in earth science and remote sensing (e.g., mapping the nationwide river streams for water resource management). Although extensive efforts have been made to reduce the reliance on labeled data (e.g., semi-supervised or unsupervised learning, few-shot learning), the complex nature of geographic data such as spatial heterogeneity still requires sufficient training labels when transferring a pre-trained model from one region to another. On the other hand, it is often much easier to collect lower-quality training labels with imperfect alignment with earth imagery pixels (e.g., through interpreting coarse imagery by non-expert volunteers). However, directly training a deep neural network on imperfect labels with geometric annotation errors could significantly impact model performance. Existing research that overcomes imperfect training labels either focuses on errors in label class semantics or characterizes label location errors at the pixel level. These methods do not fully incorporate the geometric properties of label location errors in the vector representation. To fill the gap, this article proposes a weakly supervised learning framework to simultaneously update deep learning model parameters and infer hidden true vector label locations. Specifically, we model label location errors in the vector representation to partially reserve geometric properties (e.g., spatial contiguity within line segments). Evaluations on real-world datasets in the National Hydrography Dataset (NHD) refinement application illustrate that the proposed framework outperforms baseline methods in classification accuracy.

ACM Transactions on Intelligent Systems and Techno↗
USGS2012

Watershed modeling applications in south Texas

Watershed models can be used to simulate natural and human-altered processes including the flow of water and associated transport of sediment, chemicals, nutrients, and microbial organisms within a watershed. Simulation of these processes is useful for addressing a wide range of water-resource challenges, such as quantifying changes in water availability over time, understanding the effects of development and land-use changes on water resources, quantifying changes in constituent loads and yields over time, and quantifying aquifer recharge temporally and spatially throughout a watershed. The U.S. Geological Survey (USGS), in cooperation with State and Federal agency partners, developed simulation models for several watersheds in south Texas. These models provide the capability to simulate scenarios of possible future conditions and management alternatives to help water-resource professionals with planning decisions. The program used for creating these Texas watershed models is the Hydrological Simulation Program - FORTRAN (HSPF). HSPF is one of the most comprehensive watershed modeling programs because it can simulate a variety of stream and watershed conditions with reasonable accuracy and enables flexibility in adjusting the model to simulate alternative conditions or scenarios. The HSPF model provides time-series data simulating water movement (runoff from land surfaces, infiltration of water through soil layers, flow in stream channels) and water-quality parameter values and constituent concentrations associated with the water movement at any selected location in the watershed. Time-series outputs from an HSPF simulation are continuous (for example, hourly or daily). Continuous models provide the advantage of simulating watershed processes for a full range of streamflow conditions. Continuous models can illustrate how processes that appreciably affect water-quality conditions during low flows might have relatively minor effects on water-quality conditions during high flows. This fact sheet presents an overview of six selected watershed modeling studies by the USGS and partners that address a variety of water-resource issues in south Texas. These studies provide examples of modeling applications and demonstrate the usefulness and versatility of watershed models in aiding the understanding of hydrologic systems.

Texas↗
USGS2011

The USA National Phenology Network; taking the pulse of our planet

People have tracked phenology for centuries and for the most practical reasons: it helped them know when to hunt and fish, when to plant and harvest crops, and when to navigate waterways. Now phenology is being used as a tool to assess climate change and its effects on both natural and modified ecosystems. How is the timing of events in plant and animal life cycles, like flowering or migration, responding to climate change? And how are those responses, in turn, affecting people and ecosystems? The USA National Phenology Network (the Network) is working to answer these questions for science and society by promoting a broad understanding of plant and animal phenology and their relationship to environmental change. The Network is a consortium of organizations and individuals that collect, share, and use phenology data, models, and related information to enable scientists, resource managers, and the public to adapt in response to changing climates and environments. In addition, the Network encourages people of all ages and backgrounds to observe and record phenology as a way to discover and explore the nature and pace of our dynamic world. The National Coordinating Office (NCO) of the Network is a resource center that facilitates and encourages widespread collection, integration, and sharing of phenology data and related information (for example, meteorological and hydrological data). The NCO develops and promotes standardized methods for field data collection and maintains several online user interfaces for data upload and download, as well as data exploration, visualization, and analysis. The NCO also facilitates basic and applied research related to phenology, the development of decision-support tools for resource managers and planners, and the design of educational and outreach materials

Fact Sheet↗
USGS2023

Assessment of resource potential from mine tailings using geostatistical modeling for compositions: A methodology and application to Katherine Mine site, Arizona, USA

The mining industry, in most cases, targets a specific valuable commodity that is present in small quantities within large volumes of extracted material. After milling and processing, most of the extracted material and the effluents are stored as waste (tailings) in impoundments, such as dams or waste dumps, or are backfilled into underground mines. In time, tailing materials may become an issue of environmental and health concern due to the hazardous elements, ions, and oxides contained within the waste material. In addition, handling and storage of such waste in dams may pose the risk of dam failure with catastrophic consequences to nature and nearby communities. On the other hand, tailings may offer potential as secondary sources of critical elements (CEs), including rare earth elements (REEs), which may have been overlooked during primary production and processing. Therefore, treating mine tailings as a resource has economic and environmental benefits by reducing the waste from new and historical mine sites through remining. One of the critical steps for taking advantage of these benefits is to spatially quantify the resources and the pollutants, which require the application of adequate data analysis and modeling methods, often to compositional geochemical data. Utilizing adequate methods is especially important for correctly quantifying resource potential, as the quantities will often be at low concentrations. This work presents quantification of resource potential (Au, Ag, Cu, Zn, Pb) and elements of environmental concern (Hg and As) from the tailings of a historic mine site, Katherine Mine, AZ, USA. Data reported by the U.S. Bureau of Mines (USBM) after extensive field campaigns in the 1990s, including sampling from tailing impoundment and surrounding areas for geochemical characterization and geophysical surveys, were used. First, compositional data (CoDa) analysis was employed to explore associations of sampling locations, geochemical parts, and the clustering of samples. Next, sequential Gaussian simulation (SGSIM) was applied to samples that showed a genetic link to tailing material after isometric log-ratio transformation (ilr) and mix/max autocorrelation factor (MAF) transformation for spatial modeling and uncertainty evaluation. Geostatistical results revealed spatial variability of concentrations within the tailing area. Uncertainty evaluation based on realizations indicated that Cu (14.27–20.01 t), Zn (44.23–76.23 t), and Pb (22.56–38.28 t) are the most abundant elements within a 5 %–95 % interval, followed by Ag and Au (~5.3 and 0.18 t, at 50th percentile), respectively. Of the elements of health concern, As was found to be ~4.8 t (50th percentile) in the tailing area. The work also showed that ~0.51 t As, 0.005 t Hg, 0.020 t of Au, and 0.62 t of Ag were carried to Lake Mohave by an ephemeral stream called Katherine Wash, which transects the tailings.

Arizona↗
USGS2014

The 3D Elevation Program: Summary for Massachusetts

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 Commonwealth of Massachusetts, elevation data are critical for flood risk management, natural resources conservation, agriculture and precision farming, 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.

Massachusetts↗
USGS2014

The 3D Elevation Program: summary for Ohio

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

Ohio↗
USGS2015

3D Elevation Program: summary for Vermont

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 Vermont, elevation data are critical for hazard mitigation, geologic resource assessment, natural resources conservation, agriculture and precision farming, flood risk 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, 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.

Vermont↗
USGS2022

Fuel reduction treatments reduce modeled fire intensity in the sagebrush steppe

Increased fire size and frequency coupled with annual grass invasion pose major challenges to sagebrush ( Artemisia spp.) ecosystem conservation, which is currently focused on protecting sagebrush community composition and structure. A common strategy for mitigating potential fire is to use fuel treatments that alter the structure and amount of burnable material, thus reducing fire behavior and creating access points for fire suppression resources. While there is some recent information on the impacts of fuel treatments on ecological communities, we have little information on fuel treatment effectiveness at modifying fire behavior in sagebrush ecosystems. We present 10 years of data on fuel accumulation and the resultant modeled fire behavior in prescribed fire, mowed, herbicide (tebuthiuron or imazapic), and untreated control plots in the Sagebrush Treatment Evaluation Project (SageSTEP) network in the Great Basin, USA. Fuel data (i.e., aboveground burnable live and dead biomass) were collected in each treatment plot at Years 0 (pretreatment), 1, 2, 3, 6, and 10 posttreatment. We used the Fuel and Fire Tool fire behavior modeling program to test whether treatments impacted potential fire behavior. Prescribed fire initially removed 49% of the total fuel load and 75% of shrubs, and fuel loads remained reduced through Year 10. Mowing shifted fuels from the shrub canopy to the ground surface but did not change the total fuel amount. Prescribed fire and mowing increased herbaceous fuel by the second posttreatment year and that trend persisted through Year 10. Tebuthiuron treatments were ineffective at altering fuel loads. Imazapic suppressed herbaceous vegetation by 30% in Years 2 and 3 following treatment. The modified fuel beds in fire and mow treatments resulted in modeled flame lengths that were significantly lower than untreated control plots for the duration of the study, with shorter term reductions in reaction intensity and rate of spread. Understanding fuel treatment effectiveness will allow natural resource managers to evaluate trade-offs between protecting wildlife habitat and reducing the potential for high-intensity wildfire.

Ecosphere↗
USGS2001

The Upper Pennsylvanian Pittsburgh coal bed: Resources and mine models

The U.S. Geological Survey recently completed a digital coal resource assessment model of the Upper Pennsylvanian Pittsburgh coal bed, which indicates that after subtracting mined-out coal, 16 billion short tons (14 billion tonnes) remain of the original 34 billion short tons (31 billion tonnes) of coal. When technical, environmental, and social restrictions are applied to the remaining Pittsburgh coal model, only 12 billion short tons (11 billion tonnes) are available for mining. Our assessment models estimate that up to 0.61 billion short tons (0.55 billion tonnes), 2.7 billion short tons (2.4 billion tonnes), and 8.5 billion short tons (7.7 billion tonnes) could be available for surface mining, continuous mining, and longwall mining, respectively. This analysis is an example of a second-generation regional coal availability study designed to model recoverability characteristics for all the major coal beds in the United States.

Maryland, Ohio, Pennsylvania, West Virginia↗
USGS2019

Development and implementation of an empirical habitat change model and decision support tool for estuarine ecosystems

Widespread land use change in coastal ecosystems has led to a decline in the amount of habitat available for fish and wildlife, lower production of ecosystem goods and services, and loss of recreational and aesthetic value. This has prompted global efforts to restore the natural hydrologic regimes of developed shorelines, especially resource-rich estuaries, but the resilience of these restored ecosystems in the face of accelerated sea-level rise (SLR) remains uncertain. We implemented a Mo nitoring-based S imulation of A ccretion i n C oastal E s tuaries (MOSAICS) in R statistical software to address uncertainty in the resilience of modified estuarine habitats, using the Nisqually River Delta in the Pacific Northwest USA as a case study. MOSAICS is a spatially explicit model with a numerical foundation that uses empirical monitoring datasets to forecast habitat change in response to rising tidal levels. Because it accounts for the crucial ecomorphodynamic feedbacks between tidal inundation, vegetative growth, and sediment accretion, MOSAICS can be used to determine whether alternative management scenarios, such as enhanced sediment inputs, will bolster estuarine resilience to SLR. Under moderate SLR (0.62 m), the model predicted that a two-fold increase in mean daily suspended sediment during the rainy season was sufficient to maintain Nisqually’s emergent marshes through 2100, but under high SLR (1.35 m) MOSAICS indicated that greater sediment additions would be necessary to prevent submergence. A comparison between a restored marsh with subsided and high-elevation areas and a relict marsh demonstrated that the subsided restoration area was highly susceptible to SLR. Findings from the MOSAICS model highlight the importance of a site’s initial elevation, capacity for producing above and belowground biomass, and suspended sediment availability when considering management actions in estuaries and other coastal ecosystems.

Washington↗
USGS2020

Units recovery methods in compositional data analysis

Compositional data carry relative information. Hence, their statistical analysis has to be performed on coordinates with respect to a log-ratio basis. Frequently, the modeler is required to back-transform the estimates obtained with the modeling to have them in the original units such as euros, kg or mg/liter. Approaches for recovering original units need to be formally introduced and its properties explored. Here, we formulate and analyze the properties of two procedures: a simple approach consisting of adding a residual part to the composition and an approach based on the use of an auxiliary variable. Both procedures are illustrated using a geochemical data set where the original units are recovered when spatial models are applied.

Natural Resources Research↗
USGS2009

Introduction to special section on impacts of land use change on water resources

Changes in land use have potentially large impacts on water resources, yet quantifying these impacts remains among the more challenging problems in hydrology. Water, food, energy, and climate are linked through complex webs of direct and indirect effects and feedbacks. Land use is undergoing major changes due not only to pressures for more efficient food, feed, and fiber production to support growing populations but also due to policy shifts that are creating markets for biofuel and agricultural carbon sequestration. Hydrologic systems embody flows of water, solutes, sediments, and energy that vary even in the absence of human activity. Understanding land use impacts thus necessitates integrated scientific approaches. Field measurements, remote sensing, and modeling studies are shedding new light on the modes and mechanisms by which land use changes impact water resources. Such studies can help deconflate the interconnected influences of human actions and natural variations on the quantity and quality of soil water, surface water, and groundwater, past, present, and future.

Water Resources Research↗