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407 records · Page 23Linked to original sources
USGS2004

Estimating the Magnitude and Frequency of Floods in Small Urban Streams in South Carolina, 2001

The magnitude and frequency of floods at 20 streamflowgaging stations on small, unregulated urban streams in or near South Carolina were estimated by fitting the measured wateryear peak flows to a log-Pearson Type-III distribution. The period of record (through September 30, 2001) for the measured water-year peak flows ranged from 11 to 25 years with a mean and median length of 16 years. The drainage areas of the streamflow-gaging stations ranged from 0.18 to 41 square miles. Based on the flood-frequency estimates from the 20 streamflow-gaging stations (13 in South Carolina; 4 in North Carolina; and 3 in Georgia), generalized least-squares regression was used to develop regional regression equations. These equations can be used to estimate the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence-interval flows for small urban streams in the Piedmont, upper Coastal Plain, and lower Coastal Plain physiographic provinces of South Carolina. The most significant explanatory variables from this analysis were mainchannel length, percent impervious area, and basin development factor. Mean standard errors of prediction for the regression equations ranged from -25 to 33 percent for the 10-year recurrence-interval flows and from -35 to 54 percent for the 100-year recurrence-interval flows. The U.S. Geological Survey has developed a Geographic Information System application called StreamStats that makes the process of computing streamflow statistics at ungaged sites faster and more consistent than manual methods. This application was developed in the Massachusetts District and ongoing work is being done in other districts to develop a similar application using streamflow statistics relative to those respective States. Considering the future possibility of implementing StreamStats in South Carolina, an alternative set of regional regression equations was developed using only main channel length and impervious area. This was done because no digital coverages are currently available for basin development factor and, therefore, it could not be included in the StreamStats application. The average mean standard error of prediction for the alternative equations was 2 to 5 percent larger than the standard errors for the equations that contained basin development factor. For the urban streamflow-gaging stations in South Carolina, measured water-year peak flows were compared with those from an earlier urban flood-frequency investigation. The peak flows from the earlier investigation were computed using a rainfall-runoff model. At many of the sites, graphical comparisons indicated that the variance of the measured data was much less than the variance of the simulated data. Several statistical tests were applied to compare the variances and the means of the measured and simulated data for each site. The results indicated that the variances were significantly different for 11 of the 13 South Carolina streamflow-gaging stations. For one streamflow-gaging station, the test for normality, which is one of the assumptions of the data when comparing variances, indicated that neither the measured data nor the simulated data were distributed normally; therefore, the test for differences in the variances was not used for that streamflow-gaging station. Another statistical test was used to test for statistically significant differences in the means of the measured and simulated data. The results indicated that for 5 of the 13 urban streamflowgaging stations in South Carolina there was a statistically significant difference in the means of the two data sets. For comparison purposes and to test the hypothesis that there may have been climatic differences between the period in which the measured peak-flow data were measured and the period for which historic rainfall data were used to compute the simulated peak flows, 16 rural streamflow-gaging stations with long-term records were reviewed using similar techniques as those used for the measured an

South Carolina↗
USGS2012

Data resources for range-wide assessment of livestock grazing across the sagebrush biome

The data contained in this series were compiled, modified, and analyzed for the U.S. Geological Survey (USGS) report "Range-Wide Assessment of Livestock Grazing Across the Sagebrush Biome." This report can be accessed through the USGS Publications Warehouse (online linkage: http://pubs.usgs.gov/of/2011/1263/). The dataset contains spatial and tabular data related to Bureau of Land Management (BLM) Grazing Allotments. We reviewed the BLM national grazing allotment spatial dataset available from the GeoCommunicator National Integrated Land System (NILS) website in 2007 (http://www.geocommunicator.gov). We identified several limitations in those data and learned that some BLM State and/or field offices had updated their spatial data to rectify these limitations, but maintained the data outside of NILS. We contacted appropriate BLM offices (State or field, 25 in all) to obtain the most recent data, assessed the data, established a data development protocol, and compiled data into a topologically enforced dataset throughout the area of interest for this project (that is, the pre-settlement distribution of Greater Sage-Grouse in the Western United States). The final database includes three spatial datasets: Allotments (BLM Grazing Allotments), OUT_Polygons (nonallotment polygons used to ensure topology), and Duplicate_Polygon_Allotments. See Appendix 1 of the aforementioned report for complete methods. The tabular data presented here consists of information synthesized by the Land Health Standard (LHS) analysis (Appendix 2), and data obtained from the BLM Rangeland Administration System (http://www.blm.gov/ras/). In 2008, available LHS data for all allotments in all regions were compiled by BLM in response to a Freedom of Information Act (FOIA) request made by a private organization. The BLM provided us with a copy of these data. These data provided three major types of information that were of interest: (1) date(s) (if any) of the most recent LHS evaluation for each allotment; (2) whether if evaluated, each region-specific standard (3–8 LHS depending on region) had been met on a given allotment; and (3) whether livestock contributed to any of these standards not being met. A description of how we processed the original data to prepare for analysis is described in Appendix 2, and the synthesized dataset can be found in the table "lhs_x_walk." Permitted use dates, livestock type (horse, sheep or cattle), number of livestock, and Animal Unit Months [the number of animal units (1,000-pound animal equivalents) that can be grazed for 31 days with the available forage in a sustainable manner] are the legal maximum grazing amounts for a given allotment, and legal adjustments to these numbers occur infrequently. We summarized permitted use by BLM allotment in the table "Permitted_Use." Billed use records are used for calculations of permittees' annual grazing bills. We summarized billed use by allotment for BLM grazing year in the table "Billed_Use." All three tables can be joined with the allotment spatial data in a geographic information system (GIS) environment, using the IDENT attribute as the primary key.

Data Series↗
USGS1987

A review of circulation and mixing studies of San Francisco Bay, California

A description of the major characteristics and remaining unknowns of circulation and mixing in San Francisco Bay has been constructed from a review of published studies. From a broad perspective San Francisco Bay is an ocean-river mixing zone with a seaward flow equal to the sum of the river inflows less evaporation. Understanding of circulation and mixing within the bay requires quantification of freshwater inflows and ocean-bay exchanges, characterization of source-water variations, and separation of the within-bay components of circulation and mixing processes. Description of net circulation and mixing over a few days to a few months illustrates best the interactions of major components. Quantification of tidal circulation and mixing is also necessary because net circulation and mixing contain a large tide-induced component, and because tidal variations are dominant in measurements of stage, currents, and salinity. The discharge of the Sacramento-San Joaquin Delta into Suisun Bay is approximately 90 percent of the freshwater inflow to San Francisco Bay. Annual delta discharge is characterized by a winter season of high runoff and a summer season of low runoff. For the period 1956 to 1985 the mean of monthly discharges exceeded 1,000 cubic meters per second (35,000 cubic feet per second) for the months of December through April, whereas for July through October, it was less than 400 cubic meters per second (14,000 cubic feet per second). The months of November, May, and June commonly were transition months between these seasons. Large year-to-year deviations from this annual pattern have occurred frequently. Much less is known about the ocean-bay exchange process. Net exchanges depend on net seaward flow in the bay, tidal amplitude, and longshore coastal currents, but exchanges have not yet been measured successfully. Source-water variations are ignored by limiting discussion of mixing to salinity. The bay is composed of a northern reach, which is strongly influenced by delta discharge, and South Bay, a tributary estuary which responds to conditions in Central Bay. In the northern reach net circulation is characterized by the river-induced seaward, flow and a resulting gravitational circulation in the channels, and by a tide- and wind-induced net horizontal circulation. A surface layer of relatively fresh water in Central Bay generated by high delta discharges can induce gravitational circulation in South Bay. During low delta discharges South Bay has nearly the same salinity as Central Bay and is characterized by tide- and wind-induced net horizontal circulation. Several factors control the patterns of circulation and mixing in San Francisco Bay. Viewing circulation and mixing over different time-periods and at different geographic scales causes the influences of different factors to be emphasized. The exchange between the bay and coastal ocean and freshwater inflows determine the year-to-year behavior of San Francisco Bay as a freshwater-saltwater mixing zone. Within the bay, exchanges between the embayments control variations over a season. Circulation and mixing patterns within the embayments and the magnitude of river-induced seaward flow influence the between-bay exchanges. The within-bay patterns are in turn determined by tides, winds, and freshwater inflows. Because freshwater inflow is the only factor that can be managed, a major study focus is estimation of inflow-related effects. Most questions relate to the patterns of freshwater inflow necessary to protect valuable resources whose welfare is dependent on conditions in the bay. Among the important questions being addressed are: --What quantity of freshwater inflow is necessary to prevent salt intrusion into the Sacramento-San Joaquin Delta, and what salinity distributions in the bay would result from various inflow patterns? --What quantity of freshwater inflow is sufficient to flush pollutants through the bay? Knowledge of circul

Circular↗
USGS2022

A comparison of orbital-resolution, Late Pleistocene Alkenone and foraminiferal assemblage-based sea surface temperature reconstructions from the Southwest Pacific

Global and regional reconstructions of past climate conditions often incorporate sea surface temperature (SST) estimates from multiple proxies because not every paleotemperature proxy is applicable in all geographic locations. This practice of assimilating estimates from different proxies in global or regional temperature syntheses makes the implicit assumption that estimates derived from different proxies can be meaningfully intercompared. However, evidence to support the validity of this assumption is limited. Using paleotemperature data from sediments collected from ODP Site 1125 in the Southwest Pacific, we conduct a ∼1 Myr, orbital-scale SST proxy comparison of a recently published alkenone-derived SST record with a previously published foraminiferal assemblage-based SST record. These alkenone and foraminiferal assemblage SST datasets show strong structural similarity and yield remarkably similar estimates for basic climate metrics, including mean, median, standard deviation, and range. Statistical analysis indicates that the correlation between the two SST records is highly significant. In the spectral domain, the records share the same dominant 100 kyr beat, are coherent and in phase with each other at this frequency, and have the same coherence and phase relationship with benthic foraminiferal δ 18 O. Results from this work demonstrate that these two proxies would yield very similar estimates for the paleoclimate metrics most commonly used in empirical paleoclimate reconstructions that seek to document the evolution of climate over this interval. However, significant disparities between SST estimates derived from the two proxies exist for some time periods, particularly during glacial and interglacial extrema. This comparison suggests that treating estimates from these proxies as equivalent in studies that focus on short time windows (e.g. a few thousand to tens-of-thousands of years), particularly in investigations that seek to characterize glacial or interglacial extrema, could be potentially problematic. However, the sensitive location of Site 1125, just north of the Subtropical Front, likely accentuates the difference between temperature estimates from these proxies, which may be attenuated in other oceanographic settings. We attribute the discrepancies between the two SST records to two main causes: seasonal leakages of cold water across the Subtropical Front during glacial extrema and differential seasonality of maximum alkenone and foraminiferal production.

Quaternary Science Reviews↗
USGS2024

Dust in the Critical Zone: North American case studies

The dust cycle facilitates the exchange of particles among Earth's major systems, enabling dust to traverse ecosystems, cross geographic boundaries, and even move uphill against the natural flow of gravity. Dust in the atmosphere is composed of a complex and ever-changing mixture that reflects the evolving human footprint on the landscape. The emission, transport, and deposition of dust interacts with and connects Critical Zone processes at all spatial and temporal scales. Landscape properties, land use, and climatic factors influence the wind erosion of soil and nutrient loss, which alters the long-term ecological dynamics at erosional locations. Once in the atmosphere, dust particles influence the amount of solar radiation reaching Earth, and interact with longwave (terrestrial) radiation, with cascading effects on the climate system. Finally, the wet and dry deposition of particles influences ecosystem structure, composition, and function over both short and long-term scales. Tracking dust particles from source to sink relies on monitoring and measurement of the geochemical composition and size distribution of the particles, space-borne and ground-based remote sensing, and dust modeling. Dust is linked to human systems via land use and policies that contribute to dust emissions and the health-related consequences of particulate loads and composition. Despite the significant influence dust has in shaping coupled natural-human systems, it has not been considered a key component of the Critical Zone. Here, we demonstrate that dust particles should be included as a key component of the Critical Zone by outlining how dust interacts with and shapes Earth System processes from generation, through transport, to deposition. We synthesize current understanding from global research and identify critical data and knowledge gaps while showcasing case studies from North America.

Earth-Science Reviews↗
USGS2020

Foreward: Geology Field Trips in and around the U.S. Capital

The first annual meeting of the Geological Society of America (GSA) was held in 1888 in Ithaca, New York (Fairchild, 1932), but official Sections of GSA formed much later. During the spring of 1949, a symposium in Knoxville, Tennessee, on mineral resources of the southeastern United States became the catalyst for the creation of the Southeastern Section of the Geological Society of America (King, 1964), and the first annual meeting of the Southeastern Section was held in 1952 in Roanoke, Virginia (Wilson, 1954). The Northeastern Section formed much later, and its first annual meeting was held in 1966 in Philadelphia, Pennsylvania (Socolow, 1968). At all of these section meetings, field trips have been important venues for geologists and especially students to gather together, examine rocks in the field, and discuss ideas. These field trips have been especially important at combined section meetings because they provide settings for geologists who are experienced in one geographic region to examine and compare the geology of other regions. The first combined meeting of the Southeastern and Northeastern sections occurred in 1976 in Arlington, Virginia. Since then, the Southeastern and Northeastern sections have met together on numerous occasions, including 1982 in Washington, DC; 1991 in Baltimore, Maryland; 2004 in Tysons Corner, Virginia; and 2010 in Baltimore, Maryland. Since the first combined section meeting in 1976, there has been a gradual increase in the role of technology in geology field studies. In fact, during the past several decades there has been an increase in emphasis in our society on the instrumental component of science, the goal of which is operational techniques to do or control things, and a corresponding decrease in emphasis on the natural philosophy component of science, the goal of which is a greater understanding of the natural world (Dear, 2006). The modern education acronym STEM (Science, Technology, Engineering, and Mathematics), for example, is often used as a catch-all term that implies that science and technology are relatively synonymous, and implies that greater technology leads automatically to greater understanding of the natural world. This assumption, however, is not always valid (Dear, 2006), and technology should not be promoted as a substitute for field experiences. Technology can be a tool that leads to greater understanding of the natural world, but not all Science uses technology as a means of providing greater understanding. The benefits of new technologies include: (1) data of greater resolution; and (2) greater efficiency of capturing, storing, and visualizing data. The risks of new technologies include: (1) an overabundance of data, some of which may be of little value; (2) less time available for analysis of data, if geologists become occupied primarily with capturing and storing data; and (3) errors that arise from complacency and the perception that field-checking may not be necessary. In other words, there is a risk that a glut of data and vast amounts of time devoted to the capturing and storing of data may result in a reduced interest and (or) willingness to field-check data. In the spirit of the early GSA section meetings, we feel that there are still enormous advantages to conducting geology field trips in conjunction with traditional meeting presentations and posters. In 2020, with this current combined Southeastern and Northeastern section meeting in Reston, Virginia, we have assembled eight different field trips that cover a wide range of territory in and around the Nation’s capital. These field trip localities include the immediate vicinity of Washington, DC, as well as various locations in nearby areas of Virginia, Maryland, and West Virginia. The physiographic provinces include Mesozoic Rift Basins, the Piedmont, the Blue Ridge, the Valley and Ridge, and the Allegheny Plateau of the Appalachian Basin. The field trip sites exhibit a wide range of igneous, metamorphic, and sedimentary rocks, as well as rocks with a wide range of geologic ages from the Mesoproterozoic to the Holocene. We hope that this guidebook provides new motivation for geologists to examine rocks in the field, to discuss ideas with colleagues in the field, and to avoid becoming complacent. The editors of this volume would like to thank the authors of the different field trip guides, the field trip leaders, and all of the reviewers who made suggestions for improving the field trip manuscripts. The editors would also like to thank Elle Derwent of GSA for her logistical help and guidance regarding the field trips, and April Leo and the staff of the GSA Publications Department for seeing this book through to publication.

Maryland, Virginia, West Virginia↗
USGS2007

Scale-dependent approaches to modeling spatial epidemiology of chronic wasting disease.

This e-book is the product of a second workshop that was funded and promoted by the United States Geological Survey to enhance cooperation between states for the management of chronic wasting disease (CWD). The first workshop addressed issues surrounding the statistical design and collection of surveillance data for CWD. The second workshop, from which this document arose, followed logically from the first workshop and focused on appropriate methods for analysis, interpretation, and use of CWD surveillance and related epidemiology data. Consequently, the emphasis of this e-book is on modeling approaches to describe and gain insight of the spatial epidemiology of CWD. We designed this e-book for wildlife managers and biologists who are responsible for the surveillance of CWD in their state or agency. We chose spatial methods that are popular or common in the spatial epidemiology literature and evaluated them for their relevance to modeling CWD. Our opinion of the usefulness and relevance of each method was based on the type of field data commonly collected as part of CWD surveillance programs and what we know about CWD biology, ecology, and epidemiology. Specifically, we expected the field data to consist primarily of the infection status of a harvested or culled sample along with its date of collection (not date of infection), location, and demographic status. We evaluated methods in light of the fact that CWD does not appear to spread rapidly through wild populations, relative to more highly contagious viruses, and can be spread directly from animal to animal or indirectly through environmental contamination. We discovered that many of the wellpublished methods were developed for fast-spreading human diseases, such as influenza and measles. While these methods are applicable to fast spreading wildlife diseases, such as foot-and-mouth disease or West Nile virus, many are not likely to work well for CWD. Only limited data exist to evaluate geographic and spatial spread because many locations where we find CWD tend to be locations where samples have just been taken or sample sizes have just become large enough to have a high probability of detecting a low prevalence. Consequently, methods that work well to describe or predict the spread of foot-and-mouth disease throughout England, which occurred within a year, do not work well for describing or predicting CWD spread. We did not exclude methods that we regarded as inappropriate; rather, we included methods that are commonly used for disease epidemiology and then discussed their applicability for modeling the spatial epidemiology of CWD. We hope including inappropriate methods with an explanation of why they are ill-suited for CWD will make it easier to drop them from consideration and explain to others why they were not recommended for spatial modeling of CWD. We organized the three chapters by scale and extent for which each method was developed or best suited. The first chapter covers methods appropriate to multi-jurisdictional or multi-state modeling, which we call “regional” scale. The second chapter covers methods appropriate for within state areas such as wildlife management units or metapopulations, which we call “landscape” scale. The third chapter covers methods appropriate for population or individual-based modeling, which we call “fine” scale. We know this rubric is somewhat artificial because many methods work at multiple scales. We hope, however, that this structure addresses some of the challenges faced by managers that work at local, regional, state, and national scales. Further, the resolution of empirical data often changes with spatial scale, which affects the utility of different modeling approaches. For example, individual-based models work best at modeling spread within populations, while risk analysis is most useful for summarizing data over larger scales such as a region. Because some methods are applicable at several scales, however, we included a graphic at the beginning of each method that indicates the range of scales for which it applies. For example, the graphic to the right indicates that the method is most applicable for regional-scale modeling. There is also a question of resolution as well as scale and extent for each method. CWD surveillance data have been collected over large areas, such as a wildlife management unit or state, but the resolution of the data may be fine scale with GPS locations for many samples. For each method, we described the required resolution of the data and describe the type of data required, as well as what questions the method could answer and how useful the method is, given typical CWD data. For each scale, we presented a focal approach that would be useful for understanding the spatial pattern and epidemiology of CWD, as well as being a useful tool for CWD management. The focal approaches include risk analysis and micromaps for the regional scale, cluster analysis for the landscape scale, and individual based modeling for the fine scale of within population. For each of these methods, we used simulated data and walked through the method step by step to fully illustrate the “how to”, with specifics about what is input and output, as well as what questions the method addresses. We also provided a summary table to, at a glance, describe the scale, questions that can be addressed, and general data required for each method described in this e-book. We hope that this review will be helpful to biologists and managers by increasing the utility of their surveillance data, and ultimately be useful for increasing our understanding of CWD and allowing wildlife biologists and managers to move beyond retroactive fire-fighting to proactive preventative action.

Book↗
USGS2018

A practical primer on geostatistics

Introduction The Challenge —Most geological phenomena are extraordinarily complex in their interrelationships and vast in their geographical extension. Ordinarily, engineers and geoscientists are faced with corporate or scientific requirements to properly prepare geological models with measurements involving a small fraction of the entire area or volume of interest. Exact description of a system such as an oil reservoir is neither feasible nor economically possible. The results are necessarily uncertain. Note that the uncertainty is not an intrinsic property of the systems; it is the result of incomplete knowledge by the observer. The Aim of Geostatistics —The main objective of geostatistics is the characterization of spatial systems that are incompletely known, systems that are common in geology. A key difference from classical statistics is that geostatistics uses the sampling location of every measurement. Unless the measurements show spatial correlation, the application of geostatistics is pointless. Ordinarily the need for additional knowledge goes beyond a few points, which explains the display of results graphically as fishnet plots, block diagrams, and maps. Geostatistical Methods —Geostatistics is a collection of numerical techniques for the characterization of spatial attributes using primarily two tools: probabilistic models, which are used for spatial data in a manner similar to the way in which time-series analysis characterizes temporal data, or pattern recognition techniques. The probabilistic models are used as a way to handle uncertainty in results away from sampling locations, making a radical departure from alternative approaches like inverse distance estimation methods. Differences with Time Series —On dealing with time-series analysis, users frequently concentrate their attention on extrapolations for making forecasts. Although users of geostatistics may be interested in extrapolation, the methods work at their best interpolating. This simple difference has significant methodological implications. Historical Remarks —As a discipline, geostatistics was firmly established in the 1960s by the French engineer Georges Matheron, who was interested in the appraisal of ore reserves in mining. Geostatistics did not develop overnight. Like other disciplines, it has built on previous results, many of which were formulated with different objectives in various fields. Pioneers —Seminal ideas conceptually related to what today we call geostatistics or spatial statistics are found in the work of several pioneers, including: 1940s: A.N. Kolmogorov in turbulent flow and N. Wiener in stochastic processing; 1950s: D. Krige in mining; 1960s: B. Mathern in forestry and L.S. Gandin in meteorology Calculations —Serious applications of geostatistics require the use of digital computers. Although for most geostatistical techniques rudimentary implementation from scratch is fairly straightforward, coding programs from scratch is recommended only as part of a practice that may help users to gain a better grasp of the formulations. Software —For professional work, the reader should employ software packages that have been thoroughly tested to handle any sampling scheme, that run as efficiently as possible, and that offer graphic capabilities for the analysis and display of results. This primer employs primarily the package Stanford Geomodeling Software (SGeMS) - recently developed at the Energy Resources Engineering Department at Stanford University - as a way to show how to obtain results practically. This applied side of the primer should not be interpreted as the notes being a manual for the use of SGeMS. The main objective of the primer is to help the reader gain an understanding of the fundamental concepts and tools in geostatistics. Organization of the Primer —The chapters of greatest importance are those covering kriging and simulation. All other materials are peripheral and are included for better comprehension of these main geostatistical modeling tools. The choice of kriging versus simulation is often a big puzzle to the uninitiated, let alone the different variants of both of them. Chapters 14, 18, and 19 are intended to shed light on those subjects. The critical aspect of assessing and modeling spatial correlation is covered in chapter 7. Chapters 2 and 3 review relevant concepts in classical statistics. Course Objectives —This course offers stochastic solutions to common problems in the characterization of complex geological systems. At the end of the course, participants should have: an understanding of the theoretical foundations of geostatistics; a good grasp of its possibilities and limitations; and reasonable familiarity with the SGeMS software, thus opening the possibility of practically applying geostatistics.

Open-File Report↗
USGS2023

A 5500-year record of coastal dune evolution along the shores of Lake Michigan in the North American Great Lakes: The relationship of lake-level fluctuations and climate

Dune fields line much of the ∼1750-km long shore of Lake Michigan that extends from northwestern Indiana, north along the west coast of Lower Michigan, and west across southern Upper Michigan. Numerous geomorphic studies at specific sites or defined coastal reaches have demonstrated that dune formation began during the Nipissing maximum (∼5500–4500 calendar year Before Present (cal yr BP) and continued episodically to the present. Dune formation in these geographically confined areas is linked mostly to high lake stages and periods of increased storminess. Some recent research has also invoked drought as a potential causal mechanism on a site-specific basis. Although more than 250 collective absolute carbon-14 ( 14 C) and optically stimulated luminescence (OSL) ages have been acquired in these many studies, a comprehensive statistical assessment of this robust dataset has yet to be conducted to reconstruct a full chronological reconstruction of dune evolution. This study statistically analyzes the dataset of all known OSL (118) and 14 C ages (175) acquired directly from sand dunes. Application of a kernel density estimation (KDE) algorithm function to both datasets revealed the temporal patterns in the ages. Analysis of OSL ages indicates three major periods of dune growth, beginning between ∼5.0 and 3.8 ka during the Nipissing high stand and subsequent regression. The second phase occurred between ∼3.0 and 2.0 ka during the Algoma high stand and regression. Finally, the most recent interval of dune growth transpired between ∼1.3 and 0.8 ka in the aftermath of a high lake stage at ∼1.7 ka and another, shorter peak in lake level at ∼1.0 ka. These findings indicate that major pulses of dune building mostly occur during lake regressions. They also correspond to the relatively warmer and drier conditions of the Holocene Climate Optimum, Roman Climate Optimum, and Medieval Warm Period, respectively, as well as periods of relative drought in the region. These associations indicate that drier conditions may have contributed to dune activation. Plotting of 14 C data reveals no single variable, such as stability or burial, explains the timing of the four peaks in the kernel distribution of ages derived from organic remains. Intervals of higher 14 C age frequencies occurred between ∼4500 and 3800 cal yr BP, ∼3500 and 2600 cal yr BP, ∼2300 and 1600 cal yr BP, and ∼700 and 200 cal yr BP. The peak at ∼4200 cal yr BP corresponds with a similar OSL pattern, indicating that 14 C ages derived from weakly developed Entisols during this interval reflects their burial by eolian sand. Peaks in 14 C densities within the latter intervals at ∼3200, ∼1800, and ∼400 cal Yr BP align with distinct drops in the OSL distribution. This pattern indicates that 14 C ages from these intervals reflect episodes of stability and soil formation subsequent to episodes of dune building. Although this study demonstrates the general patterns of dune growth and stability, local variations in behavior likely occurred.

Indiana, Michigan↗
USGS2012

USGS Environmental health science strategy: providing environmental health science for a changing world: Public review release

America has an abundance of natural resources. We have bountiful clean water, fertile soil, and unrivaled national parks, wildlife refuges, and public lands. These resources enrich our lives and preserve our health and wellbeing. These resources have been maintained because of our history of respect for their value and an enduring commitment to their vigilant protection. Awareness of the social, economic, and personal value of the health of our environment is increasing. The emergence of environmentally driven diseases caused by environmental exposure to contaminants and pathogens is a growing concern worldwide. New health threats and patterns of established threats are affected by both natural and anthropogenic changes to the environment. Human activities are key drivers of emerging (new and re-emerging) health threats. Societal demands for land and natural resources, a better quality of life, improved economic prosperity, and the environmental impacts associated with these demands will continue to increase. Natural earth processes, climate trends, and related climatic events will add to the environmental impact of human activities. These environmental drivers will influence exposure to disease agents, including viral, bacterial, prion, and fungal pathogens, parasites, natural earth materials, toxins and other biogenic compounds, and synthetic chemicals and substances. The U.S. Geological Survey (USGS) defines environmental health science broadly as the interdisciplinary study of relations among the quality of the physical environment, the health of the living environment, and human health. The interactions among these three spheres are driven by human activities, ecological processes, and natural earth processes; the interactions affect exposure to contaminants and pathogens and the severity of environmentally driven diseases in animals and people. This definition provides USGS with a framework for synthesizing natural science information from across the Bureau and providing it to environmental, natural resource, agricultural, and public-health managers. The USGS is a Federal science agency with a broad range of natural science expertise relevant to environmental health. USGS provides scientific information and tools as a scientific basis for management and policy decision making. USGS specializes in science at the environment-health interface, by characterizing the processes that affect the interaction among the physical environment, the living environment, and people, and the resulting factors that affect ecological and human exposure to disease agents. This report describes a 10-year strategy that encompasses the portfolio of USGS environmental health science. It summarizes national environmental health priorities that USGS is best suited to address, and will serve as a strategic framework for USGS environmental health science goals, actions, and outcomes for the next decade. Implementation of this strategy is intended to aid coordination of USGS environmental health activities and to provide a focal point for disseminating information to stakeholders. The "One Health" paradigm advocated by the World Health Organization (WHO, 2011), and the American Veterinary Medicine Association (AVMA, 2008), among others, is based on a general recognition that the health of humans, animals, and the environment are inextricably linked. Thus, successful efforts to protect that health will require increased interdisciplinary research and increased communication and collaboration among the broader scientific and health community. This strategy is built upon that paradigm. The vision, mission, and five cornerstone goals of the USGS Environmental Health Science Strategy were developed with significant input from a wide range of stakeholders. Vision - The USGS is a premier source of the environmental health science needed to safeguard the health of the environment, fish, wildlife, and people. Mission - The mission of USGS in environmental health science is to contribute scientific information to environmental, natural resource, agricultural, and public-health managers, who use that science to support sound decision making. USGS provides the science to: - Goal 1: Identify, prioritize, and detect contaminants and pathogens of emerging environmental concern. - Goal 2: Reduce the impact of contaminants on the environment, fish, wildlife, and people. - Goal 3: Reduce the impact of pathogens on the environment, fish, wildlife, and people. - Goal 4: Discover the complex interactions and combined effects of exposure to contaminants and pathogens. - Goal 5: Prepare for and respond to environmental impacts and related health threats of natural and anthropogenic disasters. Goals 1 through 4 are intended to provide science to address environmental health threats in a logical order, from informing prevention and preparedness, to supporting systematic management response to environmental health issues. Goal 4 addresses the interaction among contaminants and pathogens, an issue of emerging concern in environmental health science. Goal 5 acknowledges the fact that natural and anthropogenic disasters can cause immediate and prolonged adverse environmental health threats. This strategy proposes that USGS take the following strategic science actions to achieve each of the five goals of this strategy: Goal 1: Identify, prioritize, and detect contaminants and pathogens of emerging environmental concern. - Strategic Science Action 1. - Prioritize contaminants and pathogens of emerging concern to guide research, detection, and management activities. - Strategic Science Action 2. - Conduct surveillance and monitoring to provide early warning of emerging health threats. - Strategic Science Action 3. - Develop approaches and tools that identify vulnerable environmental settings, ecosystems, and species. Goal 2: Reduce the impact of contaminants on the environment, fish, wildlife, and people. - Strategic Science Action 1. - Systematically characterize the sources, occurrence, transport and fate of environmental contaminants to guide efforts to manage and mitigate contamination. - Strategic Science Action 2. - Evaluate the threats of contamination on the health of the environment, fish, wildlife, and people, and inform the associated management and protection efforts. - Strategic Science Action 3. - Characterize potential human exposure to support establishment of health-based standards or guidelines and contamination-reduction efforts. Goal 3: Reduce the impact of pathogens on the environment, fish, wildlife, and people. - Strategic Science Action 1. - Determine the biotic and abiotic factors that control the ecology of infectious diseases affecting natural populations of aquatic and terrestrial species and potential transmission to other animals and humans. - Strategic Science Action 2. - Establish how natural and anthropogenic environmental changes affect the distribution and severity of infectious diseases in natural populations of aquatic and terrestrial species and potential transmission to other animals and humans. - Strategic Science Action 3. - Develop surveillance systems to identify changing patterns of disease activity in priority geographic areas. Goal 4: Discover the complex interactions and combined effects of exposure to contaminants and pathogens. - Strategic Science Action 1. - Identify how exposure to one class of disease agents (contaminants or pathogens) can make an organism more susceptible to effects from exposure to the other class of disease agents. - Strategic Science Action 2. - Implement interdisciplinary studies that characterize the effects of combined exposure to pathogens and contaminants. Goal 5: Prepare for and respond to the environmental impacts and related health threats of natural and anthropogenic disasters. - Strategic Science Action 1. - Establish a formal interdisciplinary science capability to rapidly assess the environmental health risks associated with disasters. - Strategic Science Action 2. - Enhance methods to anticipate, prepare for, and identify environmental, ecological, and related health impacts of future disasters. This strategy is one of seven USGS science strategies developed concurrently: - Climate and Land Use Change - Core Science Systems - Ecosystems - Energy and Mineral Resources - Environmental Health - Natural Hazards - Water. This strategy describes how USGS will address the highest priority environmental health issues facing the Nation. The ultimate intended outcome of this science strategy is prevention and reduction of adverse impacts to the quality of the environment, the health of our living resources, and human health. Communication with, and receiving input from, partners and stakeholders regarding their science needs is essential for successful implementation of this strategy. It is incumbent on USGS to reach out to all stakeholders to ensure that USGS efforts are focused on the highest priority environmental health issues and that products are provided in the most timely and usable form to all those who can use them. USGS must reach out to the scientific community, internally and externally, to ensure that our efforts are integrated with and take full advantage of the activities of others.

Open-File Report↗
USGS2024

A benchmark dataset and workflow for landslide susceptibility zonation

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100 km 2 "> 4,100 km 2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.

Earth-Science Reviews↗