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At least 1,639 records · Page 91Linked to original sources
USGS2022

Undeveloped and developed phases in the centennial evolution of a barrier-marsh-lagoon system: The case of Long Beach Island, New Jersey

Barrier islands and their associated backbarrier environments protect mainland population centers and infrastructure from storm impacts, support biodiversity, and provide long-term carbon storage, among other ecosystem services. Despite their socio-economic and ecological importance, the response of coupled barrier-marsh-lagoon environments to sea-level rise is poorly understood. Undeveloped barrier-marsh-lagoon systems typically respond to sea-level rise through the process of landward migration, driven by storm overwash and landward mainland marsh expansion. Such response, however, can be affected by human development and engineering activities such as lagoon dredging and shoreline stabilization. To better understand the difference in the response between developed and undeveloped barrier-marsh-lagoon environments to sea-level rise, we perform a local morphologic analysis that describes the evolution of Long Beach Island (LBI), New Jersey, over the last 182 years. We find that between 1840 and 1934 the LBI system experienced landward migration of all five boundaries, including 171 meters of shoreline retreat. Between the 1920s and 1950s, however, there was a significant shift in system behavior that coincided with the onset of groin construction, which was enhanced by beach nourishment and lagoon dredging practices. From 1934 to 2022 the LBI system experienced ~22 meters of shoreline progradation and a rapid decline in marsh platform extent. Additionally, we extend a morphodynamic model to describe the evolution of the system in terms of five geomorphic boundaries: the ocean shoreline and backbarrier-marsh interface, the seaward and landward lagoon-marsh boundaries, and the landward limit of the inland marsh. We couple this numerical modeling effort with the map analysis during the undeveloped phase of LBI evolution, between 1840 and 1934. Despite its simplicity, the modeling framework can describe the average cross-shore evolution of the barrier-marsh-lagoon system during this period without accounting for human landscape modifications, supporting the premise that natural processes were the key drivers of morphological change. Overall, these results suggest that anthropogenic effects have played a major role in the evolution of LBI over the past century by altering overwash fluxes and marsh-lagoon geometry; this is likely the case for other barrier-marsh-lagoon environments around the world.

New Jersey↗
USGS2026

Facilitating water resilience in wildfire affected communities: Lessons learned from rapid response research

Wildland–urban interface fires (WUI fires) can pose a significant threat to water resources, including drinking water supplies, water treatment infrastructure, ecosystem function, and agricultural irrigation. Wildfires, especially WUI fires, are expected to increase in frequency and severity. Despite the need for effective mitigation and response strategies for wildfires, rapid research co-production to support decision-making for water incident response and water management is generally limited. This manuscript draws on five U.S. wildfire case studies to highlight how research co-production between scientists, water agencies, and managers supports more effective decision-making for water resilience and recovery. The case studies demonstrate the importance of rapid response activities, coordinating collaborative response, pre-wildfire preparation, and knowledge co-production among agencies, researchers, and managers in addressing the impacts of wildfires on water supply and quality. The lessons learned emphasize opportunities to pivot wildfire-water research and operations from reactive to proactive, focusing on mutually beneficial activities such as understanding watershed health, fostering collaboration, embracing new discoveries and tools, and enabling pre-wildfire research through table-top activities, workshops, pre-fire data collection and analysis, and appointing a central water response lead. These outcomes inform the development of a research-to-operations and operations-to-research (R2O2R) co-production framework and future opportunities to guide proactive response and management efforts before, during, and after wildfire.

California, Hawaii, New Mexico↗
USGS2024

Using systematic conservation planning to recover climate resilient habitat for threatened and endangered species while retaining areas of cultural importance

The effective management of at-risk species often requires fine-scale actions by natural resource managers. However, balancing these actions with concurrent land uses is challenging, particularly when compounded by the interplay of climate shifts, and escalating wildland–urban interface conflicts. We used spatial prioritization tools designed for biodiversity conservation to help resource managers on the Island of Lānaʻi prioritize mutually exclusive land use objectives: endangered species recovery and subsistence and recreational hunting. We weighed the current and anticipated future distributions of threatened and endangered plant species against the distribution of non-native game mammals to plan for species recovery more effectively. Prioritization results identified multiple footprints that could support recovery of all endangered species targets in climate resilient areas while retaining the majority of existing hunting areas. However, very little native vegetation was retained in conservation footprints without deliberate inclusion, which increased footprint area by 268%. Scenarios which prioritized contiguous conservation areas also dramatically increased conservation footprint area, although these scenarios may reduce associated fencing costs. This work demonstrates how spatial prioritization may guide localized species recovery efforts by supporting long-term conservation planning that addresses anticipated climate-driven increases in conflict between conservation and other land uses, with clear applicability beyond Lānaʻi.

Conservation↗
USGS2024

Recognition of artificial gases formed during drill-bit metamorphism using advanced mud gas

Drill-bit metamorphism (DBM) is the process of thermal degradation of drilling fluid at the interface of the bit and rock due to the overheating of the bit. The heat generated by the drill when drilling into a rock formation promotes the generation of artificial hydrocarbon and non-hydrocarbon gas, changing the composition of the gas. The objective of this work is to recognize and evaluate artificial gases originating from DBM in wells targeting oil accumulations in pre-salt carbonates in the Santos Basin, Brazil. For the evaluation, chromatographic data from advanced mud gas equipment, drilling parameters, drill type, and lithology were used. The molar concentrations of gases and gas ratios (especially ethene/ethene+ethane and dryness) were analyzed, which identified the occurrence of DBM. DBM is most severe when wells penetrate igneous and carbonate rocks with diamond-impregnated drill bits. The rate of penetration, weight on bit, and rotation per minute were evaluated together with gas data but did not present good correlations to assist in identifying DBM. The depth intervals over which artificial gases formed during DBM are recognized should not be used to infer pay zones or predict the composition and properties of reservoir fluids because the gas composition is completely changed.

Santos Basin↗
USGS2017

Assessment of forest degradation in Vietnam using Landsat time series data

Landsat time series data were used to characterize forest degradation in Lam Dong Province, Vietnam. We conducted three types of image change analyses using Landsat time series data to characterize the land cover changes. Our analyses concentrated on the timeframe of 1973–2014, with much emphasis on the latter part of that range. We conducted a field trip through Lam Dong Province to develop a better understanding of the ground conditions of the region, during which we obtained many photographs of representative forest sites with Global Positioning System locations to assist us in our image interpretations. High-resolution Google Earth imagery and Landsat data of the region were used to validate results. In general, our analyses indicated that many land-use changes have occurred throughout Lam Dong Province, including gradual forest to non-forest transitions. Recent changes are most marked along the relatively narrow interfaces between agricultural and forest areas that occur towards the boundaries of the province. One important observation is that the most highly protected national reserves in the region have not changed much over the entire Landsat timeframe (1972–present). Spectral changes within these regions have not occurred at the same levels as those areas adjacent to the reserves.

Forests↗
USGS2017

Differences in human versus lightning fires between urban and rural areas of the boreal forest in interior Alaska

In western North America, the carbon-rich boreal forest is experiencing warmer temperatures, drier conditions and larger and more frequent wildfires. However, the fire regime is also affected by direct human activities through suppression, ignition, and land use changes. Models are important predictive tools for understanding future conditions but they are based on regional generalizations of wildfire behavior and weather that do not adequately account for the complexity of human–fire interactions. To achieve a better understanding of the intensity of human influence on fires in this sparsely populated area and to quantify differences between human and lightning fires, we analyzed fires by both ignition types in regard to human proximity in urban (the Fairbanks subregion) and rural areas of interior Alaska using spatial (Geographic Information Systems) and quantitative analysis methods. We found substantial differences in drivers of wildfire: while increases in fire ignitions and area burned were caused by lightning in rural interior Alaska, in the Fairbanks subregion these increases were due to human fires, especially in the wildland urban interface. Lightning fires are starting earlier and fires are burning longer, which is much more pronounced in the Fairbanks subregion than in rural areas. Human fires differed from lightning fires in several ways: they started closer to settlements and highways, burned for a shorter duration, were concentrated in the Fairbanks subregion, and often occurred outside the brief seasonal window for lightning fires. This study provides important insights that improve our understanding of the direct human influence on recently observed changes in wildfire regime with implications for both fire modeling and fire management.

Alaska↗
USGS2020

Developing behavioral and evidence-based programs for wildfire risk mitigation

The actions of residents in the wildland–urban interface can influence the private and social costs of wildfire. Wildfire programs that encourage residents to take action are often delivered without evidence of effects on behavior. Research from the field of behavioral science shows that simple, often low-cost changes to program design and delivery can influence socially desirable behaviors. In this research report, we highlight how behavioral science and experimental design may advance efforts to increase wildfire risk mitigation on private property. We offer an example in which we tested changes in outreach messaging on property owners’ interest in wildfire risk information. In partnership with a regional wildfire organization, we mailed 4564 letters directing property owners to visit personalized wildfire risk webpages. By tracking visitation, we observed that 590 letter recipients (12%) sought information about their wildfire risk and response varied by community. This research–practice collaboration has three benefits: innovation in outreach, evidence of innovation through experimental design, and real impacts on interest in wildfire mitigation among property owners. Future collaborations may inform behavioral and evidence-based programs to better serve residents and the public interest as the risks from wildfires are projected to grow.

Colorado↗
USGS2022

What makes wildfires destructive in California?

As human impacts from wildfires mount, there is a pressing need to understand why structures are lost in destructive fires. Despite growing research on factors contributing to structure loss, fewer studies have focused on why some fires are destructive and others are not. We characterized overall differences between fires that resulted in structure loss (“destructive fires”) and those that did not (“non-destructive wildfires”) across three California regions. Then, we performed statistical analyses on large fires only (≥100 ha) to distinguish the primary differences between large destructive large fires and large non-destructive fires. Overall, destructive fires were at least an order of magnitude larger than non-destructive fires, with the largest area burned varying by season in different regions. Fire severity was also significantly higher in destructive than non-destructive fires. The statistical analysis showed that, in the San Francisco Bay Area and the northern Sierra Nevada foothills, proximity to the Wildland Urban Interface (WUI) was by far the most important factor differentiating destructive and non-destructive wildfires, followed by different combinations of short-term weather, seasonal climate, topography, and vegetation productivity. In Southern California, wind velocity on the day of the fire ignition was the top factor, which is consistent with previous assumptions that wind-driven fires tend to be most destructive and most of the destruction occurs within the first 24 h. Additionally, Southern California’s high population density increases the odds that a human-caused wildfire may occur during a severe fire-weather event. The geographical differences among regions and the variation of factors explaining the differences between large destructive and large non-destructive fires reflects the complexity inherent in decision-making for reducing wildfire risk. Land use planning to reduce future exposure of housing development to fire and increased focus on wildfire ignition prevention emerge as two approaches with substantial potential.

California↗
USGS2019

Analysis and visualization of coastal ocean model data in the cloud

The traditional flow of coastal ocean model data is from High Performance Computing (HPC) centers to the local desktop, or to a file server where just the data needed can be extracted via services such as OPeNDAP. Analysis and visualization is then conducted using local hardware and software. This requires moving large amounts of data across the internet as well as acquiring and maintaining local hardware, software and support personnel. Further, as data sets increase in size, the traditional workflow may not be scalable. Alternatively, recent advances make it possible to move data from HPC to the Cloud and perform interactive, scalable, data-proximate analysis and visualization, with simply a web browser user interface. We use the framework advanced by the NSF-funded Pangeo project, a free, open-source Python system which provides multi-user login via JupyterHub and parallel analysis via Dask, both running in Docker containers orchestrated by Kubernetes. Data is stored in the Zarr format, a Cloud-friendly ndarray format that allows performant extraction of data by anyone without relying on data services like OPeNDAP. Interactive visual exploration of data on massive model grids is made possible by new tools in the Python PyViz ecosystem, which can render maps at screen resolution, dynamically updating on pan and zoom operations. Two example are given: (1) calculating the maximum water level at each grid cell from a 53GB, 720 time step, 9 million node triangular mesh ADCIRC simulation of Hurricane Ike; (2) creating a dashboard for visualizing data from the curvilinear orthogonal COAWST/ROMS forecast model.

Journal of Marine Science and Engineering↗
USGS2017

A land product characterization system for comparative analysis of satellite data and products

A Land Product Characterization System (LPCS) has been developed to provide land data and products to the community of individuals interested in validating space-based land products by comparing them with similar products available from other sensors or surface-based observations. The LPCS facilitates the application of global multi-satellite and in situ data for characterization and validation of higher-level, satellite-derived, land surface products (e.g., surface reflectance, normalized difference vegetation index, and land surface temperature). The LPCS includes data search, inventory, access, and analysis functions that will permit data to be easily identified, retrieved, co-registered, and compared statistically through a single interface. The system currently includes data and products available from Landsat 4 through 8, Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua, Suomi National Polar-Orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS), and simulated data for the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). In addition to the future inclusion of in situ data, higher-level land products from the European Space Agency (ESA) Sentinel-2 and -3 series of satellites, and other high and medium resolution spatial sensors, will be included as available. When fully implemented, any of the sensor data or products included in the LPCS would be available for comparative analysis.

Remote Sensing↗
USGS2019

Modeling barrier island habitats using landscape position information

Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.

Remote Sensing↗
USGS2021

Digital elevation models: Terminology and definitions

Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earth’s surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background.

Remote Sensing↗
USGS2024

Mitigating disparate elevation differences between adjacent topobathymetric data models using binary code

Integrating coastal topographic and bathymetric data for creating regional seamless topobathymetric digital elevation models of the land/water interface presents a complex challenge due to the spatial and temporal gaps in data acquisitions. The Coastal National Elevation Database (CoNED) Applications Project develops topographic (land elevation) and bathymetric (water depth) regional scale digital elevation models by integrating multiple sourced disparate topographic and bathymetric data models. These integrated regional models are broadly used in coastal and climate science applications, such as sediment transport, storm impact, and sea-level rise modeling. However, CoNED’s current integration method does not address the occurrence of measurable vertical discrepancies between adjacent near-shore topographic and bathymetric data sources, which often create artificial barriers and sinks along their intersections. To tackle this issue, the CoNED project has developed an additional step in its integration process that collectively assesses the input data to define how to transition between these disparate datasets. This new step defines two zones: a micro blending zone for near-shore transitions and a macro blending zone for the transition between high-resolution (3 m or less) to moderate-resolution (between 3 m and 10 m) bathymetric datasets. These zones and input data sources are reduced to a multidimensional array of zeros and ones. This array is compiled into a 16-bit integer representing a vertical assessment for each pixel. This assessed value provides the means for dynamic pixel-level blending between disparate datasets by leveraging the 16-bit binary notation. Sample site RMSE assessments demonstrate improved accuracy, with values decreasing from 0.203–0.241 using the previous method to 0.126–0.147 using the new method. This paper introduces CoNED’s unique approach of using binary code to improve the integration of coastal topobathymetric data.

Remote Sensing↗
USGS2025

RUSH: Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models

Coastal vegetated ecosystems, including tidal marshes, vegetated dunes, and shrub- and forest-dominated wetlands, can mitigate hurricane impacts such as coastal flooding and erosion by increasing surface roughness and reducing wave energy. Land cover maps can be used as input to improve simulations of surface roughness in advanced hydro-morphological models. Consequently, there is a need for efficient tools to develop up-to-date land cover maps that include the accurate distribution of vegetation types prior to an extreme storm. In response, we developed the RUSH tool (Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models). RUSH delivers high-resolution maps of coastal vegetation for near-real-time or historical conditions via a Jupyter Notebook application and a graphical user interface (GUI). The application generates 3 m spatial resolution land cover maps with classes relevant to coastal settings, especially along mainland beaches, headlands, and barrier islands, as follows: (1) open water; (2) emergent wetlands; (3) dune grass; (4) woody wetlands; and (5) bare ground. These maps are developed by applying one of two seasonal random-forest machine learning models to Planet Labs SuperDove multispectral imagery. Cool Season and Warm Season Models were trained on 665 and 594 reference points, respectively, located across study regions in the North Carolina Outer Banks, the Mississippi Delta in Louisiana, and a portion of the Florida Gulf Coast near Apalachicola. Cool Season and Warm Season Models were tested with 666 and 595 independent points, with an overall accuracy of 93% and 94%, respectively. The Jupyter Notebook application provides users with a flexible platform for customization for advanced users, whereas the GUI, designed with user-experience feedback, provides non-experts access to remote sensing capabilities. This application can also be used for long-term coastal geomorphic and ecosystem change assessments.

Florida, Louisiana, North Carolina↗
USGS2026

Compilation of a nationwide river image dataset for identifying river channels and river rapids via deep learning

Remote sensing enables large-scale, image-based assessments of river dynamics, offering new opportunities for hydrological monitoring. We present a publicly available dataset consisting of 281,024 satellite and aerial images of U.S. rivers, constructed using an Application Programming Interface (API) and the U.S. Geological Survey’s National Hydrography Dataset. The dataset includes images, primary keys, and ancillary geospatial information. We use a manually labeled subset of the images to train models for detecting rapids, defined as areas where high velocity and turbulence lead to a wavy, rough, or even broken water surface visible in the imagery. To demonstrate the utility of this dataset, we develop an image segmentation model to identify rivers within images. This model achieved a mean test intersection-over-union ( 𝐼 𝑜 𝑈 ) of 0.57, with performance rising to an actual 𝐼 𝑜 𝑈 of 0.89 on the subset of predictions with high confidence (predicted 𝐼 𝑜 𝑈 > 0.9). Following this initial segmentation of river channels within the images, we trained several convolutional neural network (CNN) architectures to classify the presence or absence of rapids. Our selected model reached an accuracy and F1 score of 0.93, indicating strong performance for the classification of rapids that could support consistent, efficient inventory and monitoring of rapids. These data provide new resources for recreation planning, habitat assessment, and discharge estimation. Overall, the dataset and tools offer a foundation for scalable, automated identification of geomorphic features to support riverine science and resource management.

Remote Sensing↗
USGS2023

Integration of distributed streamflow measurement metadata for improved water resource decision-making

Streamflow data are critical for monitoring and managing water resources, yet there are significant spatial gaps in our federal monitoring networks with biases toward large perennial rivers. In some cases, streamflow monitoring exists in these spatial gaps, but information about these monitoring locations is challenging to obtain. Here, we present a streamflow catalog for the United States Pacific Northwest that includes current and historical streamflow monitoring location information obtained from 32 organizations (other than the U.S. Geological Survey), which includes 2661 continuous streamflow gaging locations (22% are currently active) and 30,557 discrete streamflow measurements. A stakeholder advisory board with representatives from organizations that operate streamflow monitoring networks identified metadata requirements and provided feedback on the Streamflow Data Catalog user interface. Engagement with the water resources community through this effort highlighted challenges that water professionals face in collecting and managing streamflow data so that data are findable, accessible, interoperable, and reusable (FAIR). Over 60% of the streamflow monitoring locations in the Streamflow Data Catalog are not available online and are thus not findable through web search engines. Providing organizations technical assistance with standard measurement procedures, metadata collection, and web accessibility could substantially increase the availability and utility of streamflow information to water resources communities.

Water↗
USGS2023

Snow surface roughness across spatio-temporal scales

The snow surface is at the interface between the atmosphere and Earth. The surface of the snowpack changes due to its interaction with precipitation, wind, humidity, short- and long-wave radiation, underlying terrain characteristics, and land cover. These connections create a dynamic snow surface that impacts the energy and mass balance of the snowpack, blowing snow potential, and other snowpack processes. Despite this, the snow surface is generally considered a constant parameter in many Earth system models. Data from the National Aeronautics and Space Administration (NASA) Cold Land Processes Experiment (CLPX) collected in 2002 and 2003 across northern Colorado were used to investigate the spatial and temporal variability of snow surface roughness. The random roughness (RR) and fractal dimension (D) metrics used in this investigation are well correlated. However, roughness is not correlated across scales, computed here from snow roughness boards at a millimeter resolution and airborne lidar at a meter resolution. Process scale differences were found based on land cover at each of the two measurement scales, as appraised through measurements in the forest and alpine.

Colorado↗
USGS2023

Reproducibility starts at the source: R, Python, and Julia Packages for retrieving USGS hydrologic data

Much of modern science takes place in a computational environment, and, increasingly, that environment is programmed using R, Python, or Julia. Furthermore, most scientific data now live on the cloud, so the first step in many workflows is to query a cloud database and load the response into a computational environment for further analysis. Thus, tools that facilitate programmatic data retrieval represent a critical component in reproducible scientific workflows. Earth science is no different in this regard. To fulfill that basic need, we developed R, Python, and Julia packages providing programmatic access to the U.S. Geological Survey’s National Water Information System database and the multi-agency Water Quality Portal. Together, these packages create a common interface for retrieving hydrologic data in the Jupyter ecosystem, which is widely used in water research, operations, and teaching. Source code, documentation, and tutorials for the packages are available on GitHub. Users can go there to learn, raise issues, or contribute improvements within a single platform, which helps foster better engagement and collaboration between data providers and their users.

Water↗