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At least 55 records · Page 3Linked to original sources

Roles of inflammatory caspases during processing of zebrafish interleukin-1β in Francisella noatunensis infection

The interleukin-1 family of cytokines are essential for the control of pathogenic microbes but are also responsible for devastating autoimmune pathologies. Consequently, tight regulation of inflammatory processes is essential for maintaining homeostasis. In mammals, interleukin-1 beta (IL-1β) is primarily regulated at two levels, transcription and processing. The main pathway for processing IL-1β is the inflammasome, a multiprotein complex that forms in the cytosol and which results in the activation of inflammatory caspase (caspase 1) and the subsequent cleavage and secretion of active IL-1β. Although zebrafish encode orthologs of IL-1β and inflammatory caspases, the processing of IL-1β by activated caspase(s) has never been examined. Here, we demonstrate that in response to infection with the fish-specific bacterial pathogen Francisella noatunensis , primary leukocytes from adult zebrafish display caspase-1-like activity that results in IL-1β processing. Addition of caspase 1 or pancaspase inhibitors considerably abrogates IL-1β processing. As in mammals, this processing event is concurrent with the secretion of cleaved IL-1β into the culture medium. Furthermore, two putative zebrafish inflammatory caspase orthologs, caspase A and caspase B, are both able to cleave IL-1β, but with different specificities. These results represent the first demonstration of processing and secretion of zebrafish IL-1β in response to a pathogen, contributing to our understanding of the evolutionary processes governing the regulation of inflammation.

Infection and Immunity↗

PRISM Software: Processing and Review Interface for Strong‐Motion Data

A continually increasing number of high‐quality digital strong‐motion records from stations of the National Strong Motion Project (NSMP) of the U.S. Geological Survey, as well as data from regional seismic networks within the United States, calls for automated processing of strong‐motion records with human review limited to selected significant or flagged records. The NSMP has developed the Processing and Review Interface for Strong Motion data (PRISM) software to meet this need. In combination with the Advanced National Seismic System Quake Monitoring System (AQMS), PRISM automates the processing of strong‐motion records. When used without AQMS, PRISM provides batch‐processing capabilities. The PRISM software is platform independent (coded in Java), open source, and does not depend on any closed‐source or proprietary software. The software consists of two major components: a record processing engine composed of modules for each processing step, and a review tool, which is a graphical user interface for manual review, edit, and processing. To facilitate use by non‐NSMP earthquake engineers and scientists, PRISM (both its processing engine and review tool) is easy to install and run as a stand‐alone system on common operating systems such as Linux, OS X, and Windows. PRISM was designed to be flexible and extensible to accommodate implementation of new processing techniques. All the computing features have been thoroughly tested.

Seismological Research Letters↗

SPECtrum Processing Routines User's Manual Version 3 (program SPECPR)

Introduction The SPECtrum Processing Routines (SPECPR) is a large-scale interactive program for general one-dimensional array processing and optimized for reflectance spectroscopy data and analysis. The program processes one-dimensional arrays up to 4852 data points and the operations include addition, subtraction, multiplication, division, trigonometric functions, logarithmic and exponential functions, and many more specialized routines. The Specpr user interface is a menu driven, character command system with all user input entered as ascii characters to the program. User commands are thoroughly checked for the context in which they will be used. This provides for essentially all user input mistakes to be caught and appropriate error messages to be issued. The user interface also allows for command aliasing, variable substitutions, command history and batch command processing to occur at any point in the program. The user can also control his or her own variables, increment and decrement them, and monitor certain internal variables. This flexibility allows for sophisticated programming, e.g. from specific computations, to management of a database. All arrays are treated as one dimensional lists of numbers, each with an appropriate header for identification and history. Any array can be plotted versus another array, and each axis labeled appropriately. For example, you may plot a reflectance spectrum versus wavelength or wavelength versus reflectance. The array type is simply a label, so data like temperature versus time can be treated just as easily as a reflectance spectrum. Some routines are specific to certain purposes, however. For example, the Planck black body generator is specific to intensity versus wavelength, whereas a smoothing routine could operate equally on reflectance versus wavelength or temperature versus time. Each array element can have an error bar associated with it and errors are propagated through all appropriate routines. Data points can be marked deleted and deleted points are tracked appropriately. These features allow the program to be effectively used on real world data. This philosophy has resulted in a flexible system for which a user can manipulate data arrays efficiently. The program was designed for analysis of laboratory, field, telescopic, and spacecraft spectroscopic data, and although general in nature, there are biases built in. For example, horizontal axis labels default to wavelength in micrometers, but the defaults can be changed. Because of the orientation in Specpr to process planetary and terrestrial data, and the need to access such data, Specpr has been extended to have access to non-Specpr default file types. In particular, Specpr can "skewer" an 3-dimensional (3D) data file along any of the three axes. Further, block skewers can be done with the standard deviation of the mean computed for each channel in the block. Currently, Specpr can access any of the standard file types common in the terrestrial and planetary remote sensing communities. Details on 3D file I/O are given in Chapter 6. Specpr has multiple record types available within a single Specpr data file. Currently defined record types are data and text. In a data record, a standard one-dimensional array is held, along with its header information. The header information includes a title, history, dates and time of data acquisition and when the data were last processed, the user who processed the data, information typical of a spectrum like temperature and viewing geometry, as well as pointers to wavelengths, resolution, and text. If a spectrum is more than 256 channels in length, then the data gets put in succeeding records in the file, and the following records are continuation records. The second record type is the text, where a title and a block of text (up to 19 kbytes) can be stored. This record type is typically used for a description of samples, experiments, instruments, or data processing for a particular data set. It could also store the actual commands used to create a spectrum. The commands for a complicated plot for a publication might be stored in a text record. That way, you can easily regenerate the plot, or even write the commands to a non-Specpr file, modify them with any editor and re-execute them. The file types, text and command processing, combined with the math and special functions, provide for very powerful and general analysis tool. These facilities also provide the framework for database management.

Open-File Report↗

The future of nearshore processes research

The nearshore is the transition region between land and the continental shelf including (from onshore to offshore) coastal plains, wetlands, estuaries, coastal cliffs, dunes, beaches, surf zones (regions of wave breaking), and the inner shelf (Figure ES-1). Nearshore regions are vital to the national economy, security, commerce, and recreation. The nearshore is dynamically evolving, is often densely populated, and is under increasing threat from sea level rise, long-term erosion, extreme storms, and anthropogenic influences. Worldwide, almost one billion people live at elevations within 10 m of present sea level. Long-term erosion threatens communities, infrastructure, ecosystems, and habitat. Extreme storms can cause billions of dollars of damage. Degraded water quality impacts ecosystem and human health. Nearshore processes, the complex interactions between water, sediment, biota, and humans, must be understood and predicted to manage this often highly developed yet vulnerable nearshore environment. Over the past three decades, the understanding of nearshore processes has improved. However, societal needs are growing with increased coastal urbanization and threats of future climate change, and significant scientific challenges remain. To address these challenges, members of academia, industry, and federal agencies (USGS, USACE, NPS, NOAA, FEMA, ONR) met at the “The Past and Future of Nearshore Processes Research: Reflections on the Sallenger Years and a New Vision for the Future” workshop to develop a nearshore processes research vision where societal needs and science challenges intersect. The resulting vision is comprised of three broad research themes: Long-term coastal evolution due to natural and anthropogenic processes: As global climate change alters the rates of sea level rise and potentially storm patterns and coastal urbanization increases over the coming decades, an understanding of coastal evolution is critical. Improved knowledge of long-term morphological, ecological, and societal processes and their interactions will result in an improved ability to simulate coastal change. This will enable proactive solutions for resilient coasts and better guidance for reducing coastal vulnerability. Extreme Events: Flooding, erosion, and the subsequent recovery: Hurricane Sandy caused flooding and erosion along hundreds of miles of shoreline, flooded New York City, and impacted communities and infrastructure. Overall U.S. coastal extreme event related economic losses have increased substantially. Furthermore, climate change may cause an increase in coastal extreme events and rising sea levels could increase the occurrence of extreme events. Addressing this research theme will result in an improved understanding of the physical processes during extreme events, leading to improved models of flooding, erosion, and recovery. The resulting societal benefit will be more resilient coastal communities. The physical, biological and chemical processes impacting human and ecosystem health: Nearshore regions are used for recreation, tourism, and human habitation, and provide habitat and valuable ecosystem services. These areas must be sustained for future generations, however overall coastal water quality is declining due to microbial pathogens, fertilizers, pesticides, and heavy metal contamination, threatening ecosystem and human health. To ensure sustainable nearshore regions, predictive real-time water- and sediment-based based pollutant modeling capabilities must be developed, which requires expanding our knowledge of the physics, chemistry, and biology of the nearshore. The resulting societal benefits will include better beach safety, healthier ecosystems, and improved mitigation and regulatory policies. The scientists and engineers of the U.S. nearshore community are poised to make significant progress on these research themes, which have significant societal impact. The U.S. nearshore community, including academic, government, and industry colleagues, recommends multi-agency investment into a coordinated development of observational and modeling research infrastructure to address these themes, as discussed in the whitepaper. The observational infrastructure should include development of new sensors and methods, focused observational programs, and expanded nearshore observing systems. The modeling infrastructure should include improved process representation, better model coupling, incorporation of data assimilation techniques, and testing of real-time models. The observations will provide test beds to compare and improve models.

Report↗

Fluvial processes and vegetation - Glimpses of the past, the present, and perhaps the future.

"Most research before 1960 into interactions among fluvial processes, resulting landforms, and vegetation was descriptive. Since then, however, research has become more detailed and quantitative permitting numerical modeling and applications including agricultural-erosion abatement and rehabilitation of altered bottomlands. Although progress was largely observational, the empiricism increasingly yielded to objective recognition of how vegetation interacts with and influences geomorphic process. A review of advances relating fluvial processes and vegetation during the last 50 years centers on hydrologic reconstructions from tree rings, plant indicators of flow- and flood-frequency parameters, hydrologic controls on plant species, regulation of sediment movement by vegetation, vegetative controls on mass movement, and relations between plant cover and sediment movement. Extension of present studies of vegetation as a regulator of bottomland hydrologic and geomorphic processes may become markedly more sophisticated and widespread than at present. Research emphases that are likely to continue include vegetative considerations for erosion modeling, response of riparian-zone forests to disturbance such as dams and water diversion, the effect of vegetation on channel and bottomland dynamics, and rehabilitation of stream corridors. Research topics that presently are receiving attention are the effect of woody vegetation on the roughness of stream corridors and, hence, processes of flood conveyance and flood-plain sedimentation, the development of a theoretical basis for rehabilitation projects as opposed to fully empirical approaches, the effect of invasive plant species on the dynamics of bottomland vegetation, the quantification of below-surface biomass and related soil-stability factors for use in erosion prediction models, and the effect of impoundments on downstream narrowing of channels and accompanying encroachment of vegetation. Bottomland vegetation partially controls and is controlled by fluvial-geomorphic processes. The purposes of this paper are to identify and review investigations that have related vegetation to bottomland features and processes, to distinguish the present status of these investigations, and to anticipate future research into how hydrologic and fluvial-geomorphic processes of bottomlands interact with vegetation."

Geomorphology↗

Automated methods for processing camera trap video data for distance sampling

Context Population monitoring is an essential need for tracking biodiversity and judging efficacy of conservation management actions, both globally and in the Pacific. However, population monitoring efforts are often temporally inconsistent and limited to small scales. Motion-activated cameras (‘camera traps’) offer a way to cost-effectively monitor populations, but they also generate large amounts of data that are time intensive to process. Aims To develop an automated pipeline for processing videos of ungulates (Philippine deer, Rusa marianna ; and pigs, Sus scrofa ) on Andersen Air Force Base in Guam. Methods We processed camera videos with a machine learning model for object detection and classification. To estimate density using distance sampling methods, we used a separate machine learning model to estimate the distance of target animals from the camera. We compared density estimates generated using manual versus automated methods and assessed accuracy and processing time saved. Key results The object detection and classification model achieved an overall accuracy >80% and F1 score ≥0.9 and saved 36.9 h of processing time. The automated distance estimation was fairly accurate, with a 1.1 m (±1.4 m) difference from manual distance estimates, and saved 16.8 h of processing time. Density estimates did not differ substantially between manual and automated distance estimation. Conclusions Machine learning models accurately processed camera videos, allowing efficient estimates of density from camera data. Implications Further adoption of motion-activated cameras coupled with automated processing could lead to continuous, large-scale monitoring of populations, helping to understand and address changes in biodiversity.

Guam↗

Diapiric transfer of melt in Kilauea Iki lava lake, Hawaii: A quick, efficient process of igneous differentiation

Kilauea Iki lava lake, formed in 1959, is a large pond of picritic basalt (average MgO content = 15.34% by weight), which has cooled and crystallized as a small, self-roofed magma chamber. Repeated drilling of the upper crust of the lake, down to its molten core, and more recent (1981) drilling, through the thermal maximum and part way through the lower crust, have made it possible to monitor the differentiation processes in the lake in detail. Differentiation processes recognized as active in the lake include rather inefficient settling of the larger (2-10 mm) olivine phenocrysts, formation of segregation veins, and formation of diapir-like vertical olivine-rich bodies, all processes which occur in one or more of the other Kilauean lava lakes as well. In addition, most of the central part of Kilauea Iki has been affected by diapiric melt transfer. In this process, relatively low-density liquid, present at 1145-1160 °C, rose from within the loose crystal mush at the base of the lens of melt and intruded the equivalent thermal horizon at the top of the lens of melt, passing through the hotter but denser melt in the core of the lake without mixing. The source volume from which the low-density liquid was extracted is depleted in TiO 2 and other incompatible elements and enriched in FeO and CaO. The upper part of the lake shows the opposite effects. The crystalline assemblage present was olivine + augite + minor plagioclase. The crystallinity of both the source and receiving layers was low enough that no obvious textural imprint was left by the transfer process; the principal evidence for its occurrence is the chemical zonation of the lake seen in core from depths of 13 to 80 m. Diapiric melt transfer was active from 1960 to 1971 and has affected most of the central part of the lake from 13 m to at least 80 m. The process ran simultaneously with the other three main differentia tion processes but started and stopped independently of the others. Calculations suggest that between 21 and 42 wt % liquid has been extracted from the depleted zone at 56-78 m in the center of the lake, making this a very efficient process of chemical differentiation.

Hawaii↗

User's manual for the National Water Information System of the U.S. Geological Survey: Automated Data Processing System (ADAPS)

The Automated Data Processing System (ADAPS) was developed for the processing, storage, and retrieval of water data, and is part of the National Water Information System (NWIS) developed by the U.S. Geological Survey. NWIS is a distributed water database in which data can be processed over a network of computers at U.S. Geological Survey offices throughout the United States. NWIS comprises four subsystems: ADAPS, the Ground-Water Site Inventory System (GWSI), the Water-Quality System (QWDATA), and the Site-Specific Water-Use Data System (SWUDS). This section of the NWIS User's Manual describes the automated data processing of continuously recorded water data, which primarily are surface-water data; however, the system also allows for the processing of water-quality and ground-water data. This manual describes various components and features of the ADAPS, and provides an overview of the data processing system and a description of the system framework. The components and features included are: (1) data collection and processing, (2) ADAPS menus and programs, (3) command line functions, (4) steps for processing station records, (5) postprocessor programs control files, (6) the standard format for transferring and entering unit and daily values, and (7) relational database (RDB) formats.

Open-File Report↗

Hydratools manual version 1.0, documentation for a MATLAB ® -based post-processing package for the Sontek Hydra

The Sediment Transport Instrumentation Group (STG) at the U.S. Geological Survey (USGS) Woods Hole Science Center has a long-standing comitment to providing scientists with high quality oceanographic data. To meet this commitment, STG personnel are vigilant in checking data as well as hardware for signs of instrument malfunction. STG data sets are accompanied by processing histories to detail data processing procedures that may have modified the natural data signal while removing noise from the data. The history also allows the data to be reprocessed in the ligth of new insight into instrument function and moored conditions. This toolbox was compiled to meet these data quality commitments for data generated by Sontek Hydra systems using both ADV and PCADP probes. In the mid 1900's, the USGS Coastal and Marine Program began frequent deployments of Sontek Hydra systems in support of projects in estuaries, coastal, and continental shelf regions nationwide. Hydra data sets are large and complex in structure, and existing processing and editing tools consisted of fragments of MATLAB code written by USGS scientists to satisfy personal research needs. This code did not meet STG quality control criteria. This toolbox permits engineers and scientists to monitor data quality by: 1. processing data with interactive critical review; 2. preserving data quality indicators; 3. preserving minimally processed and partially processed versions of data sets. STG usually deploys ADV and PCADP probes configured as downward looking, mounted on bottom tripods, with the objective of measuring high-resolution near-bed currents. The velocity profiles are recorded with minimal internal data processing. Also recorded are parameters such as temperature, conductivity, optical backscatter, light transmission, and high frequency pressure. Sampling consists of high-frequency bursts(1–10 Hz) bursts of long duration (5–30 minutes) at regular and recurring intervals for a duration of 1 to 6 months. The result is very large data files, often 500 MB per Hydra, per deployment, in Sontek's compressed binary format. This section introduces the Hydratools toolbox and provides information about the history of the system's development. The USGS philosophy regarding data quality is discussed to provide an understating of the motivation for creating the system. General information about the following topics will also be discussed: hardware and software required for the system, basic processing steps, limitations of program usage, and features that are unique to the programs.

Open-File Report↗

GWM-a ground-water management process for the U.S. Geological Survey modular ground-water model (MODFLOW-2000)

GWM is a Ground?Water Management Process for the U.S. Geological Survey modular three?dimensional ground?water model, MODFLOW?2000. GWM uses a response?matrix approach to solve several types of linear, nonlinear, and mixed?binary linear ground?water management formulations. Each management formulation consists of a set of decision variables, an objective function, and a set of constraints. Three types of decision variables are supported by GWM: flow?rate decision variables, which are withdrawal or injection rates at well sites; external decision variables, which are sources or sinks of water that are external to the flow model and do not directly affect the state variables of the simulated ground?water system (heads, streamflows, and so forth); and binary variables, which have values of 0 or 1 and are used to define the status of flow?rate or external decision variables. Flow?rate decision variables can represent wells that extend over one or more model cells and be active during one or more model stress periods; external variables also can be active during one or more stress periods. A single objective function is supported by GWM, which can be specified to either minimize or maximize the weighted sum of the three types of decision variables. Four types of constraints can be specified in a GWM formulation: upper and lower bounds on the flow?rate and external decision variables; linear summations of the three types of decision variables; hydraulic?head based constraints, including drawdowns, head differences, and head gradients; and streamflow and streamflow?depletion constraints. The Response Matrix Solution (RMS) Package of GWM uses the Ground?Water Flow Process of MODFLOW to calculate the change in head at each constraint location that results from a perturbation of a flow?rate variable; these changes are used to calculate the response coefficients. For linear management formulations, the resulting matrix of response coefficients is then combined with other components of the linear management formulation to form a complete linear formulation; the formulation is then solved by use of the simplex algorithm, which is incorporated into the RMS Package. Nonlinear formulations arise for simulated conditions that include water?table (unconfined) aquifers or head?dependent boundary conditions (such as streams, drains, or evapotranspiration from the water table). Nonlinear formulations are solved by sequential linear programming; that is, repeated linearization of the nonlinear features of the management problem. In this approach, response coefficients are recalculated for each iteration of the solution process. Mixed?binary linear (or mildly nonlinear) formulations are solved by use of the branch and bound algorithm, which is also incorporated into the RMS Package. Three sample problems are provided to demonstrate the use of GWM for typical ground?water flow management problems. These sample problems provide examples of how GWM input files are constructed to specify the decision variables, objective function, constraints, and solution process for a GWM run. The GWM Process runs with the MODFLOW?2000 Global and Ground?Water Flow Processes, but in its current form GWM cannot be used with the Observation, Sensitivity, Parameter?Estimation, or Ground?Water Transport Processes. The GWM Process is written with a modular structure so that new objective functions, constraint types, and solution algorithms can be added.

Open-File Report↗

Systematic comparisons between PRISM version 1.0.0, BAP, and CSMIP ground-motion processing

A series of benchmark tests was run by comparing results of the Processing and Review Interface for Strong Motion data (PRISM) software version 1.0.0 to Basic Strong-Motion Accelerogram Processing Software (BAP; Converse and Brady, 1992), and to California Strong Motion Instrumentation Program (CSMIP) processing (Shakal and others, 2003, 2004). These tests were performed by using the MatLAB implementation of PRISM, which is equivalent to its public release version in Java language. Systematic comparisons were made in time and frequency domains of records processed in PRISM and BAP, and in CSMIP, by using a set of representative input motions with varying resolutions, frequency content, and amplitudes. Although the details of strong-motion records vary among the processing procedures, there are only minor differences among the waveforms for each component and within the frequency passband common to these procedures. A comprehensive statistical evaluation considering more than 1,800 ground-motion components demonstrates that differences in peak amplitudes of acceleration, velocity, and displacement time series obtained from PRISM and CSMIP processing are equal to or less than 4 percent for 99 percent of the data, and equal to or less than 2 percent for 96 percent of the data. Other statistical measures, including the Euclidian distance (L 2 norm) and the windowed root mean square level of processed time series, also indicate that both processing schemes produce statistically similar products.

Open-File Report↗

Standards for the Analysis and Processing of Surface-Water Data and Information Using Electronic Methods

Surface-water computation methods and procedures are described in this report to provide standards from which a completely automated electronic processing system can be developed. To the greatest extent possible, the traditional U. S. Geological Survey (USGS) methodology and standards for streamflow data collection and analysis have been incorporated into these standards. Although USGS methodology and standards are the basis for this report, the report is applicable to other organizations doing similar work. The proposed electronic processing system allows field measurement data, including data stored on automatic field recording devices and data recorded by the field hydrographer (a person who collects streamflow and other surface-water data) in electronic field notebooks, to be input easily and automatically. A user of the electronic processing system easily can monitor the incoming data and verify and edit the data, if necessary. Input of the computational procedures, rating curves, shift requirements, and other special methods are interactive processes between the user and the electronic processing system, with much of this processing being automatic. Special computation procedures are provided for complex stations such as velocity-index, slope, control structures, and unsteady-flow models, such as the Branch-Network Dynamic Flow Model (BRANCH). Navigation paths are designed to lead the user through the computational steps for each type of gaging station (stage-only, stagedischarge, velocity-index, slope, rate-of-change in stage, reservoir, tide, structure, and hydraulic model stations). The proposed electronic processing system emphasizes the use of interactive graphics to provide good visual tools for unit values editing, rating curve and shift analysis, hydrograph comparisons, data-estimation procedures, data review, and other needs. Documentation, review, finalization, and publication of records are provided for with the electronic processing system, as well as archiving, quality assurance, and quality control.

Water-Resources Investigations Report↗

The spatially adaptable filter for error reduction (SAFER) process: Remote sensing-based LANDFIRE disturbance mapping updates

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.

Fire↗

Towards simplification of hydrologic modeling: Identification of dominant processes

The Precipitation–Runoff Modeling System (PRMS), a distributed-parameter hydrologic model, has been applied to the conterminous US (CONUS). Parameter sensitivity analysis was used to identify: (1) the sensitive input parameters and (2) particular model output variables that could be associated with the dominant hydrologic process(es). Sensitivity values of 35 PRMS calibration parameters were computed using the Fourier amplitude sensitivity test procedure on 110 000 independent hydrologically based spatial modeling units covering the CONUS and then summarized to process (snowmelt, surface runoff, infiltration, soil moisture, evapotranspiration, interflow, baseflow, and runoff) and model performance statistic (mean, coefficient of variation, and autoregressive lag 1). Identified parameters and processes provide insight into model performance at the location of each unit and allow the modeler to identify the most dominant process on the basis of which processes are associated with the most sensitive parameters. The results of this study indicate that: (1) the choice of performance statistic and output variables has a strong influence on parameter sensitivity, (2) the apparent model complexity to the modeler can be reduced by focusing on those processes that are associated with sensitive parameters and disregarding those that are not, (3) different processes require different numbers of parameters for simulation, and (4) some sensitive parameters influence only one hydrologic process, while others may influence many

Hydrology and Earth System Sciences↗

Continental-scale prediction of hydrologic signatures and processes

Understanding how dominant hydrologic processes and their drivers vary across diverse continental-scale landscapes is critical for hydrologic modeling and water management applications. Our research addresses this question by synthesizing large-sample watershed datasets, Caravan and GAGES-II, and developing random forest models to identify patterns in hydrologic function. We assessed dominant processes by examining hydrologic signatures – summary indicators of watershed function derived from hydroclimatic time series and random forest models across 14 146 gauged United States watersheds. The results reveal clear continental-scale gradients in hydrologic processes, including baseflow, overland flow, storage, and water balance losses. Our map of dominant processes highlights, for example, the transition from baseflow to fast responses and back to baseflow along the elevation gradient from the Appalachian spine, through the Piedmont, to the Eastern Coastal Plain; a distinct outer ring around the Great Lakes region; and sharp contrasts between coastal and inland processes in the West. Variable importance analysis from random forest models show that processes in the western U.S. are primarily controlled by climate, whereas in the eastern U.S., soil, geology, and topography play larger roles, with distinct human influences apparent in urban areas. Our approach of estimating dominant processes and their drivers facilitates extending process knowledge from research watersheds to the continental scale, assessing current hydrological understanding, and evaluating hydrological model structures.

conterminous United States↗

Aeolian and fluvial processes in dryland regions: The need for integrated studies

Aeolian and fluvial processes play a fundamental role in dryland regions of the world and have important environmental and ecological consequences from local to global scales. Although both processes operate over similar spatial and temporal scales and are likely strongly coupled in many dryland systems, aeolian and fluvial processes have traditionally been studied separately, making it difficult to assess their relative importance in drylands, as well as their potential for synergistic interaction. Land degradation by accelerated wind and water erosion is a major problem throughout the world's drylands, and although recent studies suggest that these processes likely interact across broad spatial and temporal scales to amplify the transport of soil resources from and within drylands, many researchers and land managers continue to view them as separate and unrelated processes. Here, we illustrate how aeolian and fluvial sediment transport is coupled at multiple spatial and temporal scales and highlight the need for these interrelated processes to be studied from a more integrated perspective that crosses traditional disciplinary boundaries. Special attention is given to how the growing threat of climate change and land‐use disturbance will influence linkages between aeolian and fluvial processes in the future. We also present emerging directions for interdisciplinary needs within the aeolian and fluvial research communities that call for better integration across a broad range of traditional disciplines such as ecology, biogeochemistry, agronomy, and soil conservation.

California↗

Evolution and preservation potential of fluvial and transgressive deposits on the Louisiana inner shelf: Understanding depositional processes to support coastal management

The barrier-island systems of the Mississippi River Delta plain are currently undergoing some of the highest rates of shoreline retreat in North America (~20 m/year). Effective management of this coastal area requires an understanding of the processes involved in shoreline erosion and measures that can be enacted to reduce loss. The dominant stratigraphy of the delta plain is fluvial mud (silts and clays), delivered in suspension via a series of shallow-water delta lobes that prograded across the shelf throughout the Holocene. Abandonment of a delta lobe through avulsion leads to rapid land subsidence through compaction within the muddy framework. As the deltaic headland subsides below sea level, the marine environment transgresses the bays and wetlands, reworking the available sands into transgressive barrier shorelines. This natural process is further complicated by numerous factors: (1) global sea-level rise; (2) reduced sediment load within the Mississippi River; (3) diversion of the sediment load away from the barrier shorelines to the deep shelf; (4) storm-induced erosion; and (5) human alteration of the littoral process through the construction of hardened shorelines, canals, and other activities. This suite of factors has led to the deterioration of the barrier-island systems that protect interior wetlands and human infrastructure from normal wave activity and periodic storm impact. Interior wetland loss results in an increased tidal prism and inlet cross-sectional areas, and expanding ebb-tidal deltas, which removes sand from the littoral processes through diversion and sequestration. Shoreface erosion of the deltaic headlands does not provide sufficient sand to balance the loss, resulting in thinning and dislocation of the islands. Abatement measures include replenishing lost sediment with similar material, excavated from discrete sandy deposits within the muddy delta plain. These sand bodies were deposited by the same cyclical processes that formed the barrier islands, and understanding these processes is necessary to characterize their location, extent, and resource potential. In this paper we demonstrate the dominant fluvial and marine-transgressive depositional processes that occur on the inner shelf, and identify the preservation and resource potential of fluvio-deltaic deposits for coastal management in Louisiana.

Geo-Marine Letters↗

gTOOLS, an open-source MATLAB program for processing high precision, relative gravity data for time-lapse gravity monitoring

gTOOLS is an open-source software for the processing of relative gravity data. gTOOLS is available in MATLAB and as a compiled executable to be run under the free MATLAB Runtime Compiler. The software has been designed for time-lapse (temporal) gravity monitoring. Although programmed to read the Scintrex CG-5 and CG-6 gravimeters output data files, it can be easily modified to read data files from other gravimeters. The software binds together single-task processing modules within a very simple user interface that is based on one text file. Gravity processing involves three modules: (a) gravimeter calibration; (b) automatic processing of gravity data to find adjusted gravity differences; and (c) post processing of results. Each module is optional and runs independently from the others. Data processing includes (a) averaging out the measurements noise, and correction for solid Earth tides, and ocean loading, and residual instrumental drift, and (b) calculate the residual instrumental drift and gravity differences between the base station and monitoring sites, and their uncertainties, by a weighted least square analysis of the gravity data. The software allows the automatic processing of a gravity campaign spanning multiple days in a single run. The software is tested on gravity data from 2015 eruption at Cotopaxi volcano, Ecuador.

Computers & Geosciences↗