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At least 1,369 records · Page 76Linked to original sources
USGS2013

Modeling for the SAFRR Tsunami Scenario-generation, propagation, inundation, and currents in ports and harbors: Chapter D in The SAFRR (Science Application for Risk Reduction) Tsunami Scenario

This U.S. Geological Survey (USGS) Open-File report presents a compilation of tsunami modeling studies for the Science Application for Risk Reduction (SAFRR) tsunami scenario. These modeling studies are based on an earthquake source specified by the SAFRR tsunami source working group (Kirby and others, 2013). The modeling studies in this report are organized into three groups. The first group relates to tsunami generation. The effects that source discretization and horizontal displacement have on tsunami initial conditions are examined in section 1 (Whitmore and others). In section 2 (Ryan and others), dynamic earthquake rupture models are explored in modeling tsunami generation. These models calculate slip distribution and vertical displacement of the seafloor as a result of realistic fault friction, physical properties of rocks surrounding the fault, and dynamic stresses resolved on the fault. The second group of papers relates to tsunami propagation and inundation modeling. Section 3 (Thio) presents a modeling study for the entire California coast that includes runup and inundation modeling where there is significant exposure and estimates of maximum velocity and momentum flux at the shoreline. In section 4 (Borrero and others), modeling of tsunami propagation and high-resolution inundation of critical locations in southern California is performed using the National Oceanic and Atmospheric Administration’s (NOAA) Method of Splitting Tsunami (MOST) model and NOAA’s Community Model Interface for Tsunamis (ComMIT) modeling tool. Adjustments to the inundation line owing to fine-scale structures such as levees are described in section 5 (Wilson). The third group of papers relates to modeling of hydrodynamics in ports and harbors. Section 6 (Nicolsky and Suleimani) presents results of the model used at the Alaska Earthquake Information Center for the Ports of Los Angeles and Long Beach, as well as synthetic time series of the modeled tsunami for other selected locales in southern California. Importantly, section 6 provides a comparison of the effect of including horizontal displacements at the source described in section 1 and differences in bottom friction on wave heights and inundation in the Ports of Los Angeles and Long Beach. Modeling described in section 7 (Lynett and Son) uses a higher order physical model to determine variations of currents during the tsunami and complex flow structures such as jets and eddies. Section 7 also uses sediment transport models to estimate scour and deposition of sediment in ports and harbors—a significant effect that was observed in southern California following the 2011 Tohoku tsunami. Together, all of the sections in this report form the basis for damage, impact, and emergency preparedness aspects of the SAFRR tsunami scenario. Three sections of this report independently calculate wave height and inundation results using the source specified by Kirby and others (2013). Refer to figure 29 in section 3, figure 52 in section 4, and figure 62 in section 6. All of these results are relative to a mean high water (MHW) vertical datum. Slight differences in the results are observed in East Basin of the Port of Los Angeles, Alamitos Bay, and the Seal Beach National Wildlife Refuge. However, given that these three modeling efforts involved different implementations of the source, different numerical wave propagation and runup models, and slight differences in the digital elevation models (DEMs), the similarity among the results is remarkable.

California↗
USGS2013

Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE; Version 1.0): web-based tools to assess the impact of sea level rise in south Florida

South Florida's Greater Everglades area is particularly vulnerable to sea level rise, due to its rich endowment of animal and plant species and its heavily populated urban areas along the coast. Rising sea levels are expected to have substantial impacts on inland flooding, the depth and extent of surge from coastal storms, the degradation of water supplies by saltwater intrusion, and the integrity of plant and animal habitats. Planners and managers responsible for mitigating these impacts require advanced tools to help them more effectively identify areas at risk. The U.S. Geological Survey's (USGS) Internet-based Modeling, Mapping, and Analysis for the Greater Everglades (IMMAGE) Web site has been developed to address these needs by providing more convenient access to projections from models that forecast the effects of sea level rise on surface water and groundwater, the extent of surge and resulting economic losses from coastal storms, and the distribution of habitats. IMMAGE not only provides an advanced geographic information system (GIS) interface to support decision making, but also includes topic-based modules that explain and illustrate key concepts for nontechnical users. The purpose of this report is to familiarize both technical and nontechnical users with the IMMAGE Web site and its various applications.

Florida↗
USGS2013

Native Prairie Adaptive Management: a multi region adaptive approach to invasive plant management on Fish and Wildlife Service owned native prairies

Much of the native prairie managed by the U.S. Fish and Wildlife Service (FWS) in the Prairie Pothole Region (PPR) of the northern Great Plains is extensively invaded by the introduced cool-season grasses, smooth brome ( Bromus inermis ) and Kentucky bluegrass ( Poa pratensis ). Management to suppress these invasive plants has had poor to inconsistent success. The central challenge to managers is selecting appropriate management actions in the face of biological and environmental uncertainties. In partnership with the FWS, the U.S. Geological Survey (USGS) developed an adaptive decision support framework to assist managers in selecting management actions under uncertainty and maximizing learning from management outcomes. This joint partnership is known as the Native Prairie Adaptive Management (NPAM) initiative. The NPAM decision framework is built around practical constraints faced by FWS refuge managers and includes identification of the management objective and strategies, analysis of uncertainty and construction of competing decision models, monitoring, and mechanisms for model feedback and decision selection. Nineteen FWS field stations, spanning four states of the PPR, have participated in the initiative. These FWS cooperators share a common management objective, available management strategies, and biological uncertainties. Though the scope is broad, the initiative interfaces with individual land managers who provide site-specific information and receive updated decision guidance that incorporates understanding gained from the collective experience of all cooperators. We describe the technical components of this approach, how the components integrate and inform each other, how data feedback from individual cooperators serves to reduce uncertainty across the whole region, and how a successful adaptive management project is coordinated and maintained on a large scale. During an initial scoping workshop, FWS cooperators developed a consensus management objective: increase the composition of native grasses and forbs on native sod while minimizing cost. Cooperators agreed that decision guidance should be provided annually and should account for local, real-time vegetation conditions observed on the ground. Over the course of development, two prototypes of the decision framework were considered. The final framework recognized four alternative actions that managers could take in any given year: (1) Graze—targeted use of grazing ungulates to achieve defoliation, (2) Burn—application of prescribed fire as the single form of defoliation, (3) Burn/Graze—a combination treatment, and (4) Rest—no action. The study area included northern mixed-grass and tallgrass prairie. Native vegetation in mixed–grass prairie has a strong cool-season component and thus the dominant native species have a phenology similar to that of smooth brome and Kentucky bluegrass, making management of those species challenging. In contrast, tallgrass prairie has a strong warm-season native component, leading to an existence of cool-season windows, periods of time in the fall and spring when cool‐season invasive grass species are actively growing and vulnerable to damage via select management actions, but warm‐season grass species are not active and are thus less susceptible to damage via the same actions. This dichotomy between prairie types necessitated the development of separate but parallel decision support systems for mixed-grass and tallgrass biomes. Management units are parcels of native prairie that receive a single management treatment at any one time over their entire extent. At any particular time, the vegetation state of each management unit is characterized by the amount of cover of native grasses and forbs and the type of invasive grass that is dominant. In addition, each unit has a defoliation state which reflects the number of years since the last defoliation event and an index to how intensively the unit was managed during the previous 7 years. State-transition models are used to predict the state of a management unit in year t+1 from its state in year t and a prescribed management action that was applied between the two monitoring events. Alternative models are built around key uncertainties that make choice of a management action difficult. Three uncertainties revolve around whether the effect of management actions depends on (1) type of dominant invader, (2) past defoliation history, and (3) level of invasion. Two additional uncertainties are considered when choosing a management action for tallgrass units: (4) the effectiveness of grazing within the cool-season window as a surrogate for burning when smooth brome is the dominant invader, and (5) the differential effect of active management outside the window as compared to rest. Because data on the probability of transitioning from one state to another under the various models were lacking, expert opinion and elicitation were used to parameterize the models. In addition, cooperators participated in elicitation exercises to extract their beliefs regarding the value of having native prairie compared to the cost of achieving it. Quantifying the subjective expression of utility in this way allowed for mathematical representation of the management objective into an objective function. By maximizing the objective function, cumulative utility is maximized, leading to the identification of a sequence of decisions that will achieve the management objective. The NPAM system adopted a vegetation monitoring protocol that was rapid, inexpensive, and familiar to many of the cooperators. The monitoring protocol served three purposes: (1) determining current vegetation and defoliation states of each unit, (2) evaluating progress toward the management objective, and (3) assessing predictive performance of the alternative models. The management year runs from September 1 to August 31. Management can be applied anytime during that period and monitoring takes places from late June to mid-August. Cooperators enter vegetation data and management information into a centralized database by August 25 of each year. Given the current state of the system (vegetation and defoliation states) and the current understanding of the system (or the belief state), identifying the current best management decision is a matter of looking up the combination (that is, system state and belief state) in the appropriate (mixed-grass or tallgrass) optimal decision table. Given complete uncertainty at the outset of decision-making, initial assignment of equal belief weights to each model was believed reasonable. The decisions in the optimal decision table that correspond to the current belief state constitute the current optimal decision policy. By August 31 of each year, individual cooperators are provided with a recommended management action for each of their management units for the upcoming management year. Upon receiving the management recommendations for their units, managers consider the recommendation, along with other relevant information, and at some point during the year one of the management alternatives is carried out. This iterative cycle of making and implementing a management decision, predicting the response, monitoring the outcome, comparing predicted and observed outcomes, updating model weights, and recommending a management action for the next cycle is expected to result in an accumulation of weight on a representative model of system dynamics, thereby increasing understanding needed to effectively manage native prairies. The NPAM system is now entering its second full year of complete operation, and represents one of only a few fully implemented applications of adaptive management within the U.S. Fish and Wildlife Service. NPAM is truly unique in that it originated from the ground up as a result of the leadership and steadfastness of several refuge biologists and managers confronted with a common problem. These biologists recognized that working together across a large landscape presented perhaps the best opportunity for halting and reversing the invasion of native grasslands by non-native cool-season grasses. Importantly, the NPAM system encapsulates the collective thinking and experience of tens if not hundreds of individuals who have battled this vexing problem for much of their careers. The NPAM initiative is rooted in principles of adaptive management, thereby affording the opportunity for grassland managers to pursue management objectives while acquiring information to reduce uncertainty and improve future management. The project introduced a number of technical innovations that will serve as templates for conservation efforts throughout and beyond the U.S. Fish and Wildlife Service. First, NPAM is an on-the-ground implementation of active adaptive management—possibly the first of its kind in conservation management—in which recommended management actions result from a prospective analysis of future learning (Williams, 1996). Second, by the use of dynamic optimization, NPAM demonstrates how decisions can be made that take into account possible future transitions of the system. Third, NPAM demonstrates how models of partial controllability are an effective means of accommodating unpredictable circumstances that cause a manager to follow a different course than was intended. Finally, the database developed for NPAM is an unparalleled system that enables the rapid integration of data from the field for the generation of ‘just-in-time’ management recommendations. In all, NPAM provides an example of how a science-management partnership can be forged to achieve large-scale conservation objectives.

Minnesota;Montana;North Dakota;South Dakota↗
USGS2015

Database of mineral deposits in the Islamic Republic of Mauritania (phase V, deliverables 90 and 91)

Three ore deposits databases from previous studies were evaluated and combined with new known mineral occurrences into one database, which can now be used to manage information about the known mineral occurrences of Mauritania. The Microsoft Access 2010 database opens with the list of tables and forms held within the database and a Switchboard control panel from which to easily navigate through the existing mineral deposit data and to enter data for new deposit locations. The database is a helpful tool for the organization of the basic information about the mineral occurrences of Mauritania. It is suggested the database be administered by a single operator in order to avoid data overlap and override that can result from shared real time data entry. It is proposed that the mineral occurrence database be used in concert with the geologic maps, geophysics and geochemistry datasets, as a publicly advertised interface for the abundant geospatial information that the Mauritanian government can provide to interested parties.

Open-File Report↗
USGS2014

Sediment and water chemistry of the San Juan River and Escalante River deltas of Lake Powell, Utah, 2010-2011

Recent studies have documented the presence of trace elements, organic compounds including polycyclic aromatic hydrocarbons, and radionuclides in sediment from the Colorado River delta and from sediment in some side canyons in Lake Powell, Utah and Arizona. The fate of many of these contaminants is of significant concern to the resource managers of the National Park Service Glen Canyon National Recreation Area because of potential health impacts to humans and aquatic and terrestrial species. In 2010, the U.S. Geological Survey began a sediment-core sampling and analysis program in the San Juan River and Escalante River deltas in Lake Powell, Utah, to help the National Park Service further document the presence or absence of contaminants in deltaic sediment. Three sediment cores were collected from the San Juan River delta in August 2010 and three sediment cores and an additional replicate core were collected from the Escalante River delta in September 2011. Sediment from the cores was subsampled and composited for analysis of major and trace elements. Fifty-five major and trace elements were analyzed in 116 subsamples and 7 composited samples for the San Juan River delta cores, and in 75 subsamples and 9 composited samples for the Escalante River delta cores. Six composited sediment samples from the San Juan River delta cores and eight from the Escalante River delta cores also were analyzed for 55 low-level organochlorine pesticides and polychlorinated biphenyls, 61 polycyclic aromatic hydrocarbon compounds, gross alpha and gross beta radionuclides, and sediment-particle size. Additionally, water samples were collected from the sediment-water interface overlying each of the three cores collected from the San Juan River and Escalante River deltas. Each water sample was analyzed for 57 major and trace elements. Most of the major and trace elements analyzed were detected at concentrations greater than reporting levels for the sediment-core subsamples and composited samples. Low-level organochlorine pesticides and polychlorinated biphenyls were not detected in any of the samples. Only one polycyclic aromatic hydrocarbon compound was detected at a concentration greater than the reporting level for one San Juan composited sample. Gross alpha and gross beta radionuclides were detected at concentrations greater than reporting levels for all samples. Most of the major and trace elements analyzed were detected at concentrations greater than reporting levels for water samples.

Utah↗
USGS2014

Users' guide to system dynamics model describing Coho salmon survival in Olema Creek, Point Reyes National Seashore, Marin County, California

The system dynamics model described in this report is the result of a collaboration between U.S. Geological Survey (USGS) scientists and National Park Service (NPS) San Francisco Bay Area Network (SFAN) staff, whose goal was to develop a methodology to integrate inventory and monitoring data to better understand ecosystem dynamics and trends using salmon in Olema Creek, Marin County, California, as an example case. The SFAN began monitoring multiple life stages of coho salmon (Oncorhynchus kisutch) in Olema Creek during 2003 (Carlisle and others, 2013), building on previous monitoring of spawning fish and redds. They initiated water-quality and habitat monitoring, and had access to flow and weather data from other sources. This system dynamics model of the freshwater portion of the coho salmon life cycle in Olema Creek integrated 8 years of existing monitoring data, literature values, and expert opinion to investigate potential factors limiting survival and production, identify data gaps, and improve monitoring and restoration prescriptions. A system dynamics model is particularly effective when (1) data are insufficient in time series length and/or measured parameters for a statistical or mechanistic model, and (2) the model must be easily accessible by users who are not modelers. These characteristics helped us meet the following overarching goals for this model: Summarize and synthesize NPS monitoring data with data and information from other sources to describe factors and processes affecting freshwater survival of coho salmon in Olema Creek. Provide a model that can be easily manipulated to experiment with alternative values of model parameters and novel scenarios of environmental drivers. Although the model describes the ecological dynamics of Olema Creek, these dynamics are structurally similar to numerous other coastal streams along the California coast that also contain anadromous fish populations. The model developed for Olema can be used, at least as a starting point, for other watersheds. This report describes each of the model elements with sufficient detail to guide the primary target audience, the NPS resource specialist, to run the model, interpret the results, change the input data to explore hypotheses, and ultimately modify and improve the model. Running the model and interpreting the results does not require modeling expertise on the part of the user. Additional companion publications will highlight other aspects of the model, such as its development, the rationale behind the methodological approach, scenario testing, and discussions of its use. System dynamics models consist of three basic elements: stocks , flows , and converters . Stocks are measurable quantities that can change over time, such as animal populations. Flows are any processes or conditions that change the quantity in a stock over time (Ford, 1999), are expressed in the model as a rate of change, and are diagrammed as arrows to or from stocks. Converters are processes or conditions that change the rate of flows. A converter is connected to a flow with an arrow indicating that it alters the rate of change. Anything that influences the rate of change (such as different environmental conditions, other external factors, or feedbacks from other stocks or flows) is modeled as a converter. For example, the number of fish in a population is appropriately modeled as a stock. Mortality is modeled as a flow because it is a rate of change over time used to determine the number of fish in the population. The density-dependent effect on mortality is modeled as a converter because it influences the rate of morality. Together, the flow and converter change the number, or stock, of juvenile coho. The instructions embedded in the stocks, flows, converters, and the sequence in which they are linked are processed by the simulation software with each completed sequence composing a model run. At each modeled time step within the model run, the stock counts will go up, down, or stay the same based on the modeled flows and the influence of converters on those flows. The model includes a user-friendly interface to change model parameters, which allows park staff and others to conduct sensitivity analyses, incorporate future knowledge, and implement scenarios for various future conditions. The model structure incorporates place holders for relationships that we hypothesize are significant but data are currently lacking. Future climate scenarios project stream temperatures higher than any that have ever been recorded at Olema Creek. Exploring climate change impacts on coho survival is a high priority for park staff, therefore the model provides the user with the option to experiment with hypothesized effects and to incorporate effects based on future observations.

California↗
USGS2014

Metadata Wizard: an easy-to-use tool for creating FGDC-CSDGM metadata for geospatial datasets in ESRI ArcGIS Desktop

Creating compliant metadata for scientific data products is mandated for all federal Geographic Information Systems professionals and is a best practice for members of the geospatial data community. However, the complexity of the The Federal Geographic Data Committee’s Content Standards for Digital Geospatial Metadata, the limited availability of easy-to-use tools, and recent changes in the ESRI software environment continue to make metadata creation a challenge. Staff at the U.S. Geological Survey Fort Collins Science Center have developed a Python toolbox for ESRI ArcDesktop to facilitate a semi-automated workflow to create and update metadata records in ESRI’s 10.x software. The U.S. Geological Survey Metadata Wizard tool automatically populates several metadata elements: the spatial reference, spatial extent, geospatial presentation format, vector feature count or raster column/row count, native system/processing environment, and the metadata creation date. Once the software auto-populates these elements, users can easily add attribute definitions and other relevant information in a simple Graphical User Interface. The tool, which offers a simple design free of esoteric metadata language, has the potential to save many government and non-government organizations a significant amount of time and costs by facilitating the development of The Federal Geographic Data Committee’s Content Standards for Digital Geospatial Metadata compliant metadata for ESRI software users. A working version of the tool is now available for ESRI ArcDesktop, version 10.0, 10.1, and 10.2 (downloadable at http:/www.sciencebase.gov/metadatawizard ).

Open-File Report↗
USGS2014

Spatially explicit modeling of greater sage-grouse ( Centrocercus urophasianus ) habitat in Nevada and northeastern California: a decision-support tool for management

Greater sage-grouse ( Centrocercus urophasianus , hereafter referred to as “sage-grouse”) populations are declining throughout the sagebrush ( Artemisia spp.) ecosystem, including millions of acres of potential habitat across the West. Habitat maps derived from empirical data are needed given impending listing decisions that will affect both sage-grouse population dynamics and human land-use restrictions. This report presents the process for developing spatially explicit maps describing relative habitat suitability for sage-grouse in Nevada and northeastern California. Maps depicting habitat suitability indices (HSI) values were generated based on model-averaged resource selection functions informed by more than 31,000 independent telemetry locations from more than 1,500 radio-marked sage-grouse across 12 project areas in Nevada and northeastern California collected during a 15-year period (1998–2013). Modeled habitat covariates included land cover composition, water resources, habitat configuration, elevation, and topography, each at multiple spatial scales that were relevant to empirically observed sage-grouse movement patterns. We then present an example of how the HSI can be delineated into categories. Specifically, we demonstrate that the deviation from the mean can be used to classify habitat suitability into three categories of habitat quality (high, moderate, and low) and one non-habitat category. The classification resulted in an agreement of 93–97 percent for habitat versus non-habitat across a suite of independent validation datasets. Lastly, we provide an example of how space use models can be integrated with habitat models to help inform conservation planning. In this example, we combined probabilistic breeding density with a non-linear probability of occurrence relative to distance to nearest lek (traditional breeding ground) using count data to calculate a composite space use index (SUI). The SUI was then classified into two categories of use (high and low-to-no) and intersected with the HSI categories to create potential management prioritization scenarios based oninformation about sage-grouse occupancy coupled with habitat suitability. This provided an example of a conservation planning application that uses the intersection of the spatially-explicit HSI and empirically-based SUI to identify potential spatially explicit strategies for sage-grouse management. Importantly, the reported categories for the HSI and SUI can be reclassified relatively easily to employ alternative conservation thresholds that may be identified through decision-making processes with stake-holders, managers, and biologists. Moreover, the HSI/SUI interface map can be updated readily as new data become available.

California;Nevada↗
USGS2014

National Oceanic and Atmospheric Administration hydrographic survey data used in a U.S. Geological Survey regional geologic framework study along the Delmarva Peninsula

The U.S. Geological Survey initiated a research effort in 2014 to define the geologic framework of the Delmarva Peninsula inner continental shelf, which included new data collection and assembly of relevant extant datasets. Between 2006 and 2011, Science Applications International Corporation, under contract to the National Oceanic and Atmospheric Administration National Ocean Service, carried out 23 hydrographic surveys covering more than 4,100 square kilometers of the continental shelf using Reson multibeam echosounders and Klein towed sidescan sonars to update nautical charts along the Delmarva Peninsula. Acoustic backscatter data from these instruments are valuable for characterizing aspects of shallow geologic framework, including seafloor geology, sediment transport pathways, and marine resources. The data cover an area that extends from the entrance of Delaware Bay, Delaware, south to Parramore Island, Virginia, in water depths of about 3 to 35 meters below mean lower low water. Data were collected along lines spaced 40 meters apart, resulting in 40 to 100 percent seafloor coverage for multibeam bathymetry. Processed bathymetric data within the Delmarva Peninsula study area are available through a National Ocean Service interactive map interface, but towed sidescan data products are limited, and multibeam backscatter data products have not been available in the past. The U.S. Geological Survey obtained raw Reson multibeam data files from Science Applications International Corporation and the National Oceanic and Atmospheric Administration for 20 hydrographic surveys and extracted backscatter data using the Fledermaus Geocoder Toolbox from Quality Positioning Service. The backscatter mosaics produced by the U.S. Geological Survey for the inner continental shelf of the Delmarva Peninsula using National Oceanic and Atmospheric Administration data increased regional geophysical surveying efficiency, collaboration among government agencies, and the area over which geologic data can be interpreted by the U.S. Geological Survey. This report describes the methods by which the backscatter data were extracted and processed and includes backscatter mosaics and interpolated bathymetric surfaces.

Delaware, Maryland, Virginia↗
USGS2015

A case study of data integration for aquatic resources using semantic web technologies

Use cases, information modeling, and linked data techniques are Semantic Web technologies used to develop a prototype system that integrates scientific observations from four independent USGS and cooperator data systems. The techniques were tested with a use case goal of creating a data set for use in exploring potential relationships among freshwater fish populations and environmental factors. The resulting prototype extracts data from the BioData Retrieval System, the Multistate Aquatic Resource Information System, the National Geochemical Survey, and the National Hydrography Dataset. A prototype user interface allows a scientist to select observations from these data systems and combine them into a single data set in RDF format that includes explicitly defined relationships and data definitions. The project was funded by the USGS Community for Data Integration and undertaken by the Community for Data Integration Semantic Web Working Group in order to demonstrate use of Semantic Web technologies by scientists. This allows scientists to simultaneously explore data that are available in multiple, disparate systems beyond those they traditionally have used.

Open-File Report↗
USGS2015

National assessment of shoreline change: historical change along the north coast of Alaska, U.S.-Canadian border to Icy Cape

Beach erosion is a persistent problem along most open-ocean shores of the United States. Along the Arctic coast of Alaska, coastal erosion is widespread, may be accelerating, and is threatening defense and energy-related infrastructure, coastal habitats, and Native communities. As coastal populations continue to expand and infrastructure and habitat are increasingly threatened by erosion, there is increased demand for accurate information regarding past and present trends and rates of shoreline movement. There also is a need for a comprehensive analysis of shoreline change with metrics that are consistent from one coastal region to another. To meet these national needs, the U.S. Geological Survey is conducting an analysis of historical shoreline changes along the open-ocean sandy shores of the conterminous United States and parts of Hawaii, Alaska, and the Great Lakes. One purpose of this work is to develop standard, repeatable methods for mapping and analyzing shoreline change so that periodic, systematic, and internally consistent updates regarding coastal erosion and land loss can be made nationally. This report on shoreline change along the north coast of Alaska, between the U.S.-Canadian border and Icy Cape, is one in a series of regionally focused reports on historical shoreline change. Previous investigations include analyses and descriptive reports for the coasts of the U.S. Gulf of Mexico, the Southeast Atlantic, California, the New England and Mid-Atlantic, portions of Hawaii, and the Pacific Northwest coasts of Oregon and Washington. Similar to the earlier reports in this series, this report summarizes the methods of analysis, documents and describes the results of the analysis, and explains historical trends and rates of shoreline change. This Alaska shoreline change assessment differs from previously published shoreline change assessments in that: (1) only two historical shorelines (from the 1940s and 2000s eras) were available for the Alaska study area whereas four or more shorelines (from 1850 to 2002) were available for the other assessments and, thus, only end-point rates for one long-term analysis period are reported here, compared to a combination of long-term and short-term rates as reported in other studies; (2) modern (2000s era) shorelines in this study represent a visually derived land-water interface position versus an elevation based, tidally referenced shoreline position; and (3) both exposed open-ocean and sheltered mainland-lagoon shorelines and rates of change are included in this study compared to other locations where only exposed open-ocean sandy shorelines or bluff edges were evaluated. No distinction was made between sand or gravel beaches, and the base of the unconsolidated coastal bluff was considered the shoreline where no fronting beach existed.

Alaska↗
USGS2015

csa2sac—A program for computing discharge from continuous slope-area stage data

Introduction Continuous Slope-Area (CSA) gages were developed by the Arizona Water Science Center to enable the estimation of hydrographs when direct measurements of discharge cannot be made (Smith and others, 2010). CSA gages extend standard U.S. Geological Survey (USGS) methods for determining peak discharges to mid and high flows over a hydrograph computed at regular intervals with indirect measurement methods (Benson and Dalrymple, 1967; Dalrymple and Benson, 1967). CSA gages combine continuous stage records at two or more (typically three or four) cross sections with crosssection surveys and estimates of channel roughness to compute discharge over a range of flows. With standard indirect methods of determining peak discharge, water-surface elevation in the study reach at the peak flow is estimated from surveys of debris associated with the peak-flow water line. With CSA gages, stages are continuously measured at the cross sections, at regular and synchronized intervals (typically 5 minutes) over a flow event, and discharge can be calculated at each interval. Calculation of discharge using indirect methods has been automated with the slope-area computation (SAC) program (Fulford, 1994). SAC is a widely used program within the USGS; it is easily run and displays output in a clear and convenient format, which includes flags that alert the user to shortcomings in the calculation. Use of SAC has been facilitated by SACGUI (Bradley, 2012; SACGUI uses a version of SAC called SAC7), a user interface that directly reads and displays survey data, allows for specification of water-surface slope and channel roughness, writes the input file for SAC7, runs SAC7, and displays SAC7 output. csa2sac is a program (appendix 1) that repeatedly runs SAC7 using stage data and a SAC7 input template file to compute the discharge at CSA gages. It is written in the C programming language, and is compatible with 64-bit Windows operating systems. The program reads a SAC7 input file and a file containing stage-data time series. It writes a new version of the SAC7 input file with the stage data for one time step, runs SAC7, then extracts computed discharges from the SAC7 output file and collates the discharges and stages to a separate file. It repeats these steps for each time interval in the stage file to produce a discharge time series from the stage data. csa2sac has been tested with two, three, four, and six cross sections and found to operate successfully. By running SAC7, csa2sac maintains consistency and comparability of both discharges calculated from CSA gages and of standard USGS methods for computing discharges indirectly. Brown and Metcalfe (2014) have made available alternative software for producing CSA discharges. In addition to csa2sac, the SAC7 program is required. It is the same as the original SAC program, except that it is compiled for 64-bit Windows operating systems and has a slightly different command line input. It is available online (http://water.usgs.gov/software/SAC/) as part of the SACGUI installation program. The program name, “SAC7.exe,” is coded into csa2sac, and must not be changed.

Open-File Report↗
USGS2016

Visual Basic, Excel-based fish population modeling tool - The pallid sturgeon example

The model presented in this report is a spreadsheet-based model using Visual Basic for Applications within Microsoft Excel ( http://dx.doi.org/10.5066/F7057D0Z ) prepared in cooperation with the U.S. Army Corps of Engineers and U.S. Fish and Wildlife Service. It uses the same model structure and, initially, parameters as used by Wildhaber and others (2015) for pallid sturgeon. The difference between the model structure used for this report and that used by Wildhaber and others (2015) is that variance is not partitioned. For the model of this report, all variance is applied at the iteration and time-step levels of the model. Wildhaber and others (2015) partition variance into parameter variance (uncertainty about the value of a parameter itself) applied at the iteration level and temporal variance (uncertainty caused by random environmental fluctuations with time) applied at the time-step level. They included implicit individual variance (uncertainty caused by differences between individuals) within the time-step level. The interface developed for the model of this report is designed to allow the user the flexibility to change population model structure and parameter values and uncertainty separately for every component of the model. This flexibility makes the modeling tool potentially applicable to any fish species; however, the flexibility inherent in this modeling tool makes it possible for the user to obtain spurious outputs. The value and reliability of the model outputs are only as good as the model inputs. Using this modeling tool with improper or inaccurate parameter values, or for species for which the structure of the model is inappropriate, could lead to untenable management decisions. By facilitating fish population modeling, this modeling tool allows the user to evaluate a range of management options and implications. The goal of this modeling tool is to be a user-friendly modeling tool for developing fish population models useful to natural resource managers to inform their decision-making processes; however, as with all population models, caution is needed, and a full understanding of the limitations of a model and the veracity of user-supplied parameters should always be considered when using such model output in the management of any species.

Open-File Report↗
USGS2016

QRev—Software for computation and quality assurance of acoustic doppler current profiler moving-boat streamflow measurements—User’s manual for version 2.8

The software program, QRev computes the discharge from moving-boat acoustic Doppler current profiler measurements using data collected with any of the Teledyne RD Instrument or SonTek bottom tracking acoustic Doppler current profilers. The computation of discharge is independent of the manufacturer of the acoustic Doppler current profiler because QRev applies consistent algorithms independent of the data source. In addition, QRev automates filtering and quality checking of the collected data and provides feedback to the user of potential quality issues with the measurement. Various statistics and characteristics of the measurement, in addition to a simple uncertainty assessment are provided to the user to assist them in properly rating the measurement. QRev saves an extensible markup language file that can be imported into databases or electronic field notes software. The user interacts with QRev through a tablet-friendly graphical user interface. This report is the manual for version 2.8 of QRev.

Open-File Report↗
USGS2016

Construction, calibration, and validation of the RBM10 water temperature model for the Trinity River, northern California

We constructed a one-dimensional daily averaged water-temperature model to simulate Trinity River temperatures for 1980–2013. The purpose of this model is to assess effects of water-management actions on water temperature and to provide water temperature inputs for a salmon population dynamics model. Simulated meteorological data, observed streamflow data, and observed water temperatures were used as model inputs to simulate a continuous 34-year time series of historical daily mean water temperature at eight locations along 112.2 river miles from Lewiston Dam near Weaverville, California, downstream to the Klamath River confluence. To demonstrate the utility of the model to inform management actions, we simulated three management alternatives to assess the effects of bypass flow augmentation in a drought year, 1994, and compared those results to the simulated historical baseline, referred to as the “No Action” alternative scenario. Augmentation flows from the Lewiston Dam bypass consist of temperature-controlled releases capable of cooling downstream water temperatures in hot times of the year, which can reduce the probability of disease outbreaks in fish populations. Outputs from the Trinity River water-temperature model were then used as inputs to an existing water-temperature model of the Klamath River to evaluate the effect of augmentation flow releases on water temperatures in the lower Klamath River. We structured the Trinity River water-temperature model in River Basin Model-10 (RBM10), which uses a simple equilibrium flow model, assuming discharge in each river segment on each day is transmitted downstream instantaneously. The model uses a heat-budget formulation to quantify heat flux at the air-water interface. Inputs for the heat budget are calculated from daily mean meteorological data, including net shortwave solar radiation, net longwave atmospheric radiation, air temperature, wind speed, vapor pressure, and a psychrometric constant needed to calculate the Bowen ratio. The modeling domain was divided into eight reaches ranging in length from 8.8 to 20.6 miles, which were calibrated and validated separately with observed water temperature data collected irregularly from 1980 to 2013. Root mean square errors of observed and simulated water temperatures for the eight reaches ranged from 0.25 to 1.12 degrees Celsius (°C). Mean absolute errors ranged from 0.18 to 0.89 °C. For model validation, a k -fold cross-validation technique was used. Validation root mean square error and mean absolute error for the eight reaches ranged from 0.24 to 1.11 °C and from 0.18 to 0.89 °C, respectively. Augmentation scenarios were based on historical hydrological and meteorological data, combined with prescribed flow and temperature releases from Lewiston Dam provided by the Bureau of Reclamation. Water releases were scheduled to achieve targeted flows of 2,500, 2,800, and 3,200 cubic feet per second in the lower Klamath River from mid-August through late September, coinciding with the upstream migration of adult fall-run Chinook salmon ( Oncorhynchus tshawytscha ). Water temperatures simulated at river mile 5.7 on the Klamath River showed a 5 °C decrease from the No Action historical baseline, which was near or greater than 23 °C when augmentation began in mid-August. Thereafter, an approximate 1 °C difference among augmentation scenarios emerged, with the decrease in water temperature commensurate to the level of augmentation. All augmentation scenarios simulated water temperatures equal to or less than 21 °C from mid-August through late September. Water temperatures equal to or greater than 23 °C are of particular interest because of a thermal threshold known to inhibit upstream migration of salmon. When temperatures exceed this approximate 23 °C threshold, Chinook salmon are known to congregate in high densities in thermal refugias and show extended residence times, which can potentially trigger epizootic outbreaks such as of Ichthyophthirius multifiliis (“Ich”) and Flavobacterium columnare (“Columnaris”) that were the causative factors of the Klamath River fish kill in 2002. A model with the ability to simulate water temperatures in response to management actions at the basin scale is a valuable asset for water managers who must make decisions about how best to use limited water resources, which directly affect the state of fisheries in the Klamath Basin.

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USGS2016

Tree Cover Mapping Tool—Documentation and user manual

The Tree Cover Mapping (TCM) tool was developed by scientists at the U.S. Geological Survey Earth Resources Observation and Science Center to allow a user to quickly map tree cover density over large areas using visual interpretation of high resolution imagery within a geographic information system interface. The TCM tool uses a systematic sample grid to produce maps of tree cover. The TCM tool allows the user to define sampling parameters to estimate tree cover within each sample unit. This mapping method generated the first on-farm tree cover maps of vast regions of Niger and Burkina Faso. The approach contributes to implementing integrated landscape management to scale up re-greening and restore degraded land in the drylands of Africa. The TCM tool is easy to operate, practical, and can be adapted to many other applications such as crop mapping, settlements mapping, or other features. This user manual provides step-by-step instructions for installing and using the tool, and creating tree cover maps. Familiarity with ArcMap tools and concepts is helpful for using the tool.

Open-File Report↗
USGS2016

Building groundwater modeling capacity in Mongolia

Ulaanbaatar, the capital city of Mongolia (fig. 1), is dependent on groundwater for its municipal and industrial water supply. The population of Mongolia is about 3 million people, with about one-half the population residing in or near Ulaanbaatar (World Population Review, 2016). Groundwater is drawn from a network of shallow wells in an alluvial aquifer along the Tuul River. Evidence indicates that current water use may not be sustainable from existing water sources, especially when factoring the projected water demand from a rapidly growing urban population (Ministry of Environment and Green Development, 2013). In response, the Government of Mongolia Ministry of Environment, Green Development, and Tourism (MEGDT) and the Freshwater Institute, Mongolia, requested technical assistance on groundwater modeling through the U.S. Army Corps of Engineers (USACE) to the U.S. Geological Survey (USGS). Scientists from the USGS and USACE provided two workshops in 2015 to Mongolian hydrology experts on basic principles of groundwater modeling using the USGS groundwater modeling program MODFLOW-2005 (Harbaugh, 2005). The purpose of the workshops was to bring together representatives from the Government of Mongolia, local universities, technical experts, and other key stakeholders to build in-country capacity in hydrogeology and groundwater modeling. A preliminary steady-state groundwater-flow model was developed as part of the workshops to demonstrate groundwater modeling techniques to simulate groundwater conditions in alluvial deposits along the Tuul River in the vicinity of Ulaanbaatar. ModelMuse (Winston, 2009) was used as the graphical user interface for MODFLOW for training purposes during the workshops. Basic and advanced groundwater modeling concepts included in the workshops were groundwater principles; estimating hydraulic properties; developing model grids, data sets, and MODFLOW input files; and viewing and evaluating MODFLOW output files. A key to success was developing in-country technical capacity and partnerships with the Mongolian University of Science and Technology; Freshwater Institute, Mongolia, a non-profit organization; United Nations Educational, Scientific and Cultural Organization (UNESCO); the Government of Mongolia; and the USACE.

Open-File Report↗
USGS2016

U.S. Geological Survey science strategy for highly pathogenic avian influenza in wildlife and the environment (2016–2020)

Introduction Through the Science Strategy for Highly Pathogenic Avian Influenza (HPAI) in Wildlife and the Environment, the USGS will assess avian influenza (AI) dynamics in an ecological context to inform decisions made by resource managers and policymakers from the local to national level. Through collection of unbiased scientific information on the ecology of AI viruses and wildlife hosts in a changing world, the U.S. Geological Survey (USGS) will enhance the development of AI forecasting tools and ensure this information is integrated with a quality decision process for managing HPAI. The overall goal of this USGS Science Strategy for HPAI in Wildlife and the Environment goes beyond document­ing the occurrence and distribution of AI viruses in wild birds. The USGS aims to understand the epidemiological processes and environmental factors that influence HPAI distribution and describe the mechanisms of transmission between wild birds and poultry. USGS scientists developed a conceptual model describing the process linking HPAI dispersal in wild waterfowl to the outbreaks in poul­try. This strategy focuses on five long-term science goals, which include: Science Goal 1—Augment the National HPAI Surveillance Plan; Science Goal 2—Determine mechanisms of HPAI disease spread in wildlife and the environment; Science Goal 3—Characterize HPAI viruses circulating in wildlife; Science Goal 4—Understand implications of avian ecol­ogy on HPAI spread; and Science Goal 5—Develop HPAI forecasting and decision-making tools. These goals will help define and describe the processes outlined in the conceptual model with the ultimate goal of facilitating biosecurity and minimizing transfer of diseases across the wildlife-poultry interface. The first four science goals are focused on scientific discovery and the fifth goal is application-based. Decision analyses in the fifth goal will guide prioritization of proposed actions in the first four goals.

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