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At least 1,243 records · Page 69Linked to original sources

A model of Precambrian geology of Kansas derived from gravity and magnetic data

The fabric of the Precambrian geology of Kansas is revealed through inversion of gravity and magnetic data to pseudo-lithology. There are five main steps in the inversion process: (1) reduction of potential-field data to a horizontal plane in the wavenumber domain; (2) separation of the residual anomaly of interest from the regional background, where an assumption is made that the regional anomaly could be represented by some order of polynomial; (3) subtraction of the signal due to the known topography on the Phanerozoic/Precambrian boundary from the residual anomaly (we assume what is left at this stage are the signals due to lateral variation in the Precambrian lithology); (4) inversion of the residual anomaly in the wavenumber domain to density and magnetization distribution in the top part of the Precambrian constrained by the known geologic information; (5) derivation of pseudo-lithology by characterization of density and magnetization. The boundary between the older Central Plains Province to the north and the Southern Granite-Rhyolite Province to the south is clearly delineated. The Midcontinent Rift System appears to widen in central Kansas and involve a considerable portion of southern Kansas. Lithologies in southwestern Kansas appear to change over fairly small areas and include mafic rocks which have not been encountered in drill holes. The texture of the potential field data from southwestern Kansas suggests a history of continental growth by broad extension. Copyright ?? 1996 Elsevier Science Ltd.

Computers & Geosciences↗

Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning

Most environmental data come from a minority of well-monitored sites. An ongoing challenge in the environmental sciences is transferring knowledge from monitored sites to unmonitored sites. Here, we demonstrate a novel transfer-learning framework that accurately predicts depth-specific temperature in unmonitored lakes (targets) by borrowing models from well-monitored lakes (sources). This method, meta-transfer learning (MTL), builds a meta-learning model to predict transfer performance from candidate source models to targets using lake attributes and candidates' past performance. We constructed source models at 145 well-monitored lakes using calibrated process-based (PB) modeling and a recently developed approach called process-guided deep learning (PGDL). We applied MTL to either PB or PGDL source models (PB-MTL or PGDL-MTL, respectively) to predict temperatures in 305 target lakes treated as unmonitored in the Upper Midwestern United States. We show significantly improved performance relative to the uncalibrated PB General Lake Model, where the median root mean squared error (RMSE) for the target lakes is 2.52°C. PB-MTL yielded a median RMSE of 2.43°C; PGDL-MTL yielded 2.16°C; and a PGDL-MTL ensemble of nine sources per target yielded 1.88°C. For sparsely monitored target lakes, PGDL-MTL often outperformed PGDL models trained on the target lakes themselves. Differences in maximum depth between the source and target were consistently the most important predictors. Our approach readily scales to thousands of lakes in the Midwestern United States, demonstrating that MTL with meaningful predictor variables and high-quality source models is a promising approach for many kinds of unmonitored systems and environmental variables.

Water Resources Research↗

An orbital comparison of a late mantling unit on Aeolis Mons with other erosion-resistant strata explored by MSL in Gale crater, Mars

Previous investigations along Curiosity 's traverse in Gale crater have explored the relationship between orbital and in situ observations. This work aims to better understand the geologic environment of units only observable from orbit and compare them to the properties of units examined by Curiosity . Here, we map an erosion-resistant dark-toned mantling unit that overlies the modern topography of Aeolis Mons (informally known as Mt. Sharp) and compare this unit to two other previously mapped dark-toned resistant units, the marker band and the mound skirting unit (MSU), that have been inferred to represent different geologic environments (lacustrine and aeolian, respectively). Visible to short wave infrared spectra from the Compact Reconnaissance Imaging Spectrometer for Mars and visual images from the High Resolution Imaging Science Experiment and Context Cameras aboard the Mars Reconnaissance Orbiter are used for this comparison. Spectral data suggest a mafic composition with minor alteration, although the composition varies more with location around Mt. Sharp rather than between units. Morphologically, the mantling unit has strong similarities to the marker band based on their consistent low-albedo, erosion-resistance, and smooth appearance, contrasting with the highly variable surface texture of the MSU. We hypothesize that all three units had a similar sediment source but experienced aqueous alteration at different times: early ubiquitous cementation in a surface aqueous environment in the mantling unit and marker band versus patchy late diagenesis in the MSU. If true, these results suggest that water activity continued within the Gale crater long after the erosion of Mt. Sharp.

JGR Planets↗

Water-use data in the United States: Challenges and future directions

In the United States, greater attention has been given to developing water supplies and quantifying available waters than determining who uses water, how much they withdraw and consume, and how and where water use occurs. As water supplies are stressed due to an increasingly variable climate, changing land-use, and growing water needs, greater consideration of the demand side of the water balance equation is essential. Data about the spatial and temporal aspects of water use for different purposes are now critical to long-term water supply planning and resource management. We detail the current state of water-use data, the major stakeholders involved in their collection and applications, and the challenges in obtaining high-quality nationally consistent data applicable to a range of scales and purposes. Opportunities to improve access, use, and sharing of water-use data are outlined. We cast a vision for a world-class national water-use data product that is accessible, timely, and spatially detailed. Our vision will leverage the strengths of existing local, state, and federal agencies to facilitate rapid and informed decision-making, modeling, and science for water resources. To inform future decision-making regarding water supplies and uses, we must coordinate efforts to substantially improve our capacity to collect, model, and disseminate water-use data.

Journal of the American Water Resources Associatio↗

Predicting sea-level rise vulnerability of terrestrial habitat and wildlife of the Northwestern Hawaiian Islands

If current climate change trends continue, rising sea levels may inundate low-lying islands across the globe, placing island biodiversity at risk. Recent models predict a rise of approximately one meter (1 m) in global sea level by 2100, with larger increases possible in areas of the Pacific Ocean. Pacific Islands are unique ecosystems home to many endangered endemic plant and animal species. The Northwestern Hawaiian Islands (NWHI), which extend 1,930 kilometers (km) beyond the main Hawaiian Islands, are a World Heritage Site and part of the Papahanaumokuakea Marine National Monument. These NWHI support the largest tropical seabird rookery in the world, providing breeding habitat for 21 species of seabirds, 4 endemic land bird species and essential foraging, breeding, or haul-out habitat for other resident and migratory wildlife. In recent years, concern has grown about the increasing vulnerability of the NWHI and their wildlife populations to changing climatic patterns, particularly the uncertainty associated with potential impacts from global sea-level rise (SLR) and storms. In response to the need by managers to adapt future resource protection strategies to climate change variability and dynamic island ecosystems, we have synthesized and down scaled analyses for this important region. This report describes a 2-year study of a remote northwestern Pacific atoll ecosystem and identifies wildlife and habitat vulnerable to rising sea levels and changing climate conditions. A lack of high-resolution topographic data for low-lying islands of the NWHI had previously precluded an extensive quantitative model of the potential impacts of SLR on wildlife habitat. The first chapter (chapter 1) describes the vegetation and topography of 20 islands of Papahanaumokuakea Marine National Monument, the distribution and status of wildlife populations, and the predicted impacts for a range of SLR scenarios. Furthermore, this chapter explores the potential effects of SLR on wildlife breeding habitats for each island. The subsequent chapter (chapter 2) details a study of the Laysan Island ecosystem, describing a quantitative model that incorporates SLR, storm wave, and rising groundwater inundation. Wildlife, storm, and oceanographic data allowed for an assessment of the phenological and spatial vulnerability of Laysan Island's breeding bird species to SLR and storms. Using remote sensing and geospatial techniques, we estimated topography, classified vegetation, modeled SLR, and evaluated a range of climate change scenarios. On the basis of high-resolution airborne data collected during 2010-11 (root-mean-squared error = 0.05-0.18 m), we estimated the maximum elevation of 20 individual islands extending from Kure Atoll to French Frigate Shoals (range: 1.8-39.7 m) and computed the mean elevation (1.7 m, standard deviation 1.1 m) across all low-lying islands. We also analyzed general climate models to describe rainfall and temperature scenarios expected to influence adaptation of some plants and animals for this region. Outcomes for the NWHI predicted an increase in temperature of 1.8-2.6 degrees Celsius (°C) and an annual decrease in precipitation of 24.7-76.3 millimeters (mm) across the NWHI by 2100. Our models of passive SLR (excluding wave-driven effects, erosion, and accretion) showed that approximately 4 percent of the total land area in the NWHI will be lost with scenarios of +1.0 m of SLR and 26 percent will be lost with +2.0 m of SLR. Some atolls are especially vulnerable to SLR. For example, at Pearl and Hermes Atoll our analysis indicated substantial habitat losses with 43 percent of the land area inundated at +1.0 m SLR and 92 percent inundated at +2.0 m SLR. Across the NWHI, seven islands will be completely submerged with +2.0 m SLR. The limited global ranges of some tropical nesting birds make them particularly vulnerable to climate change impacts in the NWHI. Climate change scenarios and potential SLR impacts presented here emphasize the need for early climate change adaptation and mitigation planning, especially for species with limited breeding distributions and/or ranges restricted primarily to the low-lying NWHI: Cyperus pennatiformis var. bryanii , Black-footed Albatross ( Phoebastria nigripes ), Laysan Albatross ( P. immutabilis ), Bonin Petrel ( Pterodroma hypoleuca ), Gray-backed Tern ( Onychoprion lunatus ), Laysan Teal ( Anas laysanensis ), Laysan Finch ( Telespiza cantans ), and Hawaiian monk seal ( Monachus schauinslandi ). Furthermore, SLR scenarios that include the effects of wave dynamics and groundwater rise may indicate amplified vulnerability to climate change driven habitat loss on low-lying islands. In chapter 2, we incorporated the combined effects of SLR, dynamic wave-driven inundation, and rising groundwater in a quantitative study specifically for the Laysan Island ecosystem. This is the first hydrodynamic model to simulate the combined impacts of SLR and wave-driven inundation in the NWHI. We developed a high-resolution digital elevation model (mean vertical accuracy of 0.32 m) for the island. Then using recent satellite imagery, geospatial models, and historical oceanographic, storm, and biological data we estimated potential inundation extent, habitat loss, and wildlife population impacts for a range of potential SLR scenarios (0.00, +0.50, +1.00, +1.50, and +2.00 m) that may occur over the next century. Additionally, we estimated the carrying capacity of Laysan Island for five species based on the available population monitoring data and described how potential losses in nesting habitat could influence population dynamics for Black-footed Albatross, Laysan Albatross, Red-footed Booby (Sula sula), Laysan Teal, and Laysan Finch. For some other seabird populations (Masked Booby, S. dactylatra ; Brown Booby, S. leucogaster ; Great Frigatebird, Fregata minor ; and Sooty Tern, Onychoprion fuscata ), we used recent colony distribution data, land cover maps, and nesting behavior to estimate potential losses of nesting habitat from SLR and wave-driven inundation. We observed far greater potential impacts of SLR to wildlife with the dynamic wave-driven modeling approach than with the passive modeling approach. Depending on SLR scenario and coastal orientation, during storms under a +2.00 m SLR scenario, the wave-driven inundation model predicted three times more inundation than the passive model (17.2 percent of total terrestrial area versus 4.6 percent, respectively). Large-wave events generally added 1 m of water height to passive inundation surfaces, therefore our dynamic models (during storm events) forecasted comparable inundation extents earlier than passive models. Although wave-driven water levels were highest in the northwest quadrant of Laysan Island, the greatest extent of inundation occurred in the southeast where coastal dunes less than 3 m above mean sea level provide little protection from wave-driven inundation. When wave-driven inundation was included in the SLR model for Laysan Island greater nesting habitat loss and potential impacts on wildlife population dynamics were predicted. The consequences of habitat loss due to SLR may be worse for species with colonies in the wave-exposed coastal zones (for example, Black-footed Albatross) and for populations already near the island's carrying capacity (for example, Laysan Teal). Species whose peak incubation and chick-rearing periods coincide with seasonally high wave heights also will be increasingly vulnerable, especially those species nesting on the ground in areas vulnerable to inundation, such as Gray-backed Tern and Black-footed Albatross. Other species that have space for population growth, or are not restricted to a narrow range of habitat types on Laysan (for instance, Sooty Terns), may be less sensitive to habitat loss from SLR over the next century. Our assessments of inundation risk, habitat loss, and wildlife species vulnerability synthesize current knowledge about individual islands and contribute to a broader understanding of the impacts of inundation from SLR and storm-induced waves. Yet, most NWHI and their bird populations lack monitoring data to evaluate adaptations to and impacts of climate change. Exceptions include some data sets from long-term monitoring of wildlife populations, tides, or weather at French Frigate Shoals, Laysan Island, and Midway Atoll. These data sets are potentially valuable baselines, which could be informative for adaptive learning (integrating management and science) to predict, adapt, and mitigate the effects of climate change on NWHI wildlife in the future. This study provides the first quantitative vulnerability assessment for all of the low-lying NWHI, and results identify biological communities, locales, and resident endangered species of Papahanaumokuakea Marine National Monument expected to be at risk from SLR. This report is also intended as a reference for managers and conservation planners, a tool to identify and potentially reduce uncertainty, and a starting place for developing climate change monitoring priorities and future scientific studies.

Hawai'i↗

U.S. Department of the Interior Climate Science Centers and U.S. Geological Survey National Climate Change and Wildlife Science Center—Annual report for 2016

Introduction 2016 was an exciting year for the Department of the Interior (DOI) Climate Science Centers (CSCs) and the U.S. Geological Survey (USGS) National Climate Change and Wildlife Science Center (NCCWSC). In recognition of our ongoing efforts to raise awareness and provide the scientific data and tools needed to address the impacts of climate change on fish, wildlife, ecosystems, and people, NCCWSC and the CSCs received an honorable mention in the first ever Climate Adaptation Leadership Award for Natural Resources sponsored by the National Fish, Wildlife, and Plant Climate Adaptation Strategy’s Joint Implementation Working Group. The recognition is a reflection of our contribution to numerous scientific workshops and publications, provision of training for students and early career professionals, and work with Tribes and indigenous communities to improve climate change resilience across the Nation. In this report, we highlight some of the activities that took place throughout the NCCWSC and CSC network in 2016.

Open-File Report↗

Earthquake probabilities in the San Francisco Bay region: 2000 to 2030 - A summary of findings

The San Francisco Bay region sits astride a dangerous “earthquake machine,” the tectonic boundary between the Pacific and North American Plates. The region has experienced major and destructive earthquakes in 1838, 1868, 1906, and 1989, and future large earthquakes are a certainty. The ability to prepare for large earthquakes is critical to saving lives and reducing damage to property and infrastructure. An increased understanding of the timing, size, location, and effects of these likely earthquakes is a necessary component in any effective program of preparedness. This study reports on the probabilities of occurrence of major earthquakes in the San Francisco Bay region (SFBR) for the three decades 2000 to 2030. The SFBR extends from Healdsberg on the northwest to Salinas on the southeast and encloses the entire metropolitan area, including its most rapidly expanding urban and suburban areas. In this study a “major” earthquake is defined as one with M≥6.7 (where M is moment magnitude). As experience from the Northridge, California (M6.7, 1994) and Kobe, Japan (M6.9, 1995) earthquakes has shown us, earthquakes of this size can have a disastrous impact on the social and economic fabric of densely urbanized areas. To reevaluate the probability of large earthquakes striking the SFBR, the U.S. Geological Survey solicited data, interpretations, and analyses from dozens of scientists representing a wide crosssection of the Earth-science community (Appendix A). The primary approach of this new Working Group (WG99) was to develop a comprehensive, regional model for the long-term occurrence of earthquakes, founded on geologic and geophysical observations and constrained by plate tectonics. The model considers a broad range of observations and their possible interpretations. Using this model, we estimate the rates of occurrence of earthquakes and 30-year earthquake probabilities. Our study considers a range of magnitudes for earthquakes on the major faults in the region—an innovation over previous studies of the SFBR that considered only a small number of potential earthquakes of fixed magnitude.

California↗

Geoinformatics 2006--Abstracts

In order to facilitate the discovery, integration, and analysis of distributed data, geoscientists-in partnership with information technologists and computer scientists-have established the emerging science of geoinformatics. Geoinformatics 2006, an international conference that was attended by over 300 participants between May 10 and 12, 2006, represents the first nationally organized meeting whose primary goal was to provide a forum for the exchange of the most current research in geoinformatics.

Scientific Investigations Report↗

ChemCam results from the Shaler Outcrop in Gale Crater, Mars

The "Shaler" outcrop in Gale crater is approximately 0.7 m thick and >20 m long, and exhibits multiple well-exposed platy and cross-stratified facies [1] interpreted to be primarily fluvial sandstone deposits. The outcrop is a part of the upper Glenelg member in the Yellowknife Bay (YKB) stratigraphic section [2]. Curiosity first encountered the "Shaler" outcrop on sol 121 of the mission, and returned to the outcrop on sols 309- 324. The rugged nature of the outcrop and short time available for analysis limited opportunities for contact science, but ChemCam’s ability to remotely collect compositional and textural observations resulted in a large data set from Shaler. ChemCam conducted analyses of 29 non-soil targets at Shaler, 26 of which used laser-induced breakdown spectroscopy (LIBS) for a total of 9,180 spectra. Three observations used only the remote micro-imager (RMI). Each of the 26 LIBS targets were analyzed at between 5 and 25 points, providing a measure of the target homogeneity and in some cases transecting fine strata.

Conference Paper↗

High throughput computing: a solution for scientific analysis

Public land management agencies continually face resource management problems that are exacerbated by climate warming, land-use change, and other human activities. As the U.S. Geological Survey (USGS) Fort Collins Science Center (FORT) works with managers in U.S. Department of the Interior (DOI) agencies and other federal, state, and private entities, researchers are finding that the science needed to address these complex ecological questions across time and space produces substantial amounts of data. The additional data and the volume of computations needed to analyze it require expanded computing resources well beyond single- or even multiple-computer workstations. To meet this need for greater computational capacity, FORT investigated how to resolve the many computational shortfalls previously encountered when analyzing data for such projects. Our objectives included finding a solution that would: harness existing Computer Processing Units (CPUs) when they're idle to run multiple jobs concurrently, which reduces the overall processing time without requiring additional hardware; offer an effective, centralized job-management system; handle job failures due to hardware, software, or network interruptions (obviating the need to manually resubmit the job after each stoppage); be affordable; and most importantly, allow us to complete very large, complex analyses that otherwise would not even be possible. In short, we envisioned a job-management system that would take advantage of unused FORT CPUs within a local area network (LAN) to effectively distribute and run highly complex analytical processes. What we found was a solution that uses High Throughput Computing (HTC) and High Performance Computing (HPC) systems to do exactly that (Figure 1).

Report↗

User guide to the FireCLIME Vulnerability Assessment (VA) Tool: A rapid and flexible system for assessing ecosystem vulnerability to climate-fire interactions

Decisionmakers need better methods for identifying critical ecosystem vulnerabilities to changing climate and fire regimes. Climate-wildfire-vegetation interactions are complex and hinder classification and projection necessary for development of management strategies. One such vulnerability assessment (VA) is FireCLIME VA, which allows users to compare management strategies under various climate scenarios and gauge the potential effectiveness of those strategies for reducing undesirable impacts of climate on wildfire regimes and resulting impacts of wildfire on natural ecosystems. Developed as part of the SW FireCLIME science-management partnership, FireCLIME is meant to be quick, flexible, and amendable to a range of data inputs (literature review, expert, and modeling or monitoring activities), allowing users to easily compare various fire-climate outcomes for one or more ecosystems of interest. Users can use literature, hypothetical scenarios, or quantitative data to implement the FireCLIME VA tool. This tool, unlike other vulnerability assessment, is best used iteratively to explore a range of possible scenarios and management strategies.

Report↗

Examining the mechanisms of species responses to climate change: Are there biological thresholds?

Climate-change-driven shifts in distribution and abundance have been documented in many species. However, in order to better predict species responses, managers are seeking to understand the mechanisms that are driving these changes, including any thresholds that might soon be crossed. Leveraging the research that has already been supported by the Northeast Climate Adaptation Science Center and its partners, this project used the latest modeling techniques combined with robust field data to examine the impact of specific climate variables, land use change, and species interactions on the future distribution and abundance of species of conservation concern. Moreover, this project documented biological thresholds related to climate variability and change for critical species in the Northeastern and Midwestern U.S. Specifically, our objectives were to identify the primary drivers (climate change vs. urban growth) of species distribution changes in the Northeast; examine the nature of species landscape capability change over time to identify potential thresholds; determine how changing temperatures and snowpack characteristics will drive species interactions; analyze the sensitivity of tree and bird responses to the magnitude, variability, periodicity, and seasonality of temperature and precipitation under climate change in the eastern U.S.; and identify how discrete climate triggers such as extreme events will correlate with known biological thresholds. Major outcomes included 1) refining the understanding of the mechanisms that drive projected changes in the distribution of vulnerable populations; and 2) improving how these results are conveyed to stakeholders by identifying understandable responses in the form of thresholds.

Connecticut, Delaware, Illinois, Indiana, Iowa, Ke↗

Estimating visitor use and economic contributions of National Park visitor spending

This chapter provides an overview of the National Park Service (NPS) methods for estimating visitor spending and calculating economic contributions of visitor spending in terms of jobs supported, wage and labor income, and total economic activity. The Visitor Spending Effects model combines visitor spending patterns and trip characteristic data with visitor use data to estimate total visitor spending. Economic contributions measure the total economic activity within a regional economy stemming from visitor spending, and include the effects of spending by both local visitors who live within gateway regions and non-local visitors who travel to NPS sites from outside of gateway regions. The Social Science Program collaborates with individual parks to develop visitor counting instructions that contain the procedures for measuring, compiling, and recording required visitor use data. Visitor surveys are used to collect the essential visitor spending and trip characteristic data necessary for developing spending profiles to represent distinct visitor spending patterns for each park.

Book chapter↗

Hydrogeologic framework and groundwater conditions of the Ararat Basin in Armenia

Armenia is a landlocked country located in the mountainous Caucasus region between Asia and Europe. It shares borders with the countries of Georgia on the north, Azerbaijan on the east, Iran on the south, and Turkey and Azerbaijan on the west. The Ararat Basin is a transboundary basin in Armenia and Turkey. The Ararat Basin (or Ararat Valley) is an intermountain depression that contains the Aras River and its tributaries, which also form the border between Armenia and Turkey and divide the basin into northern and southern regions. The Ararat Basin also contains Armenia’s largest agricultural and fish farming zone that is supplied by high-quality water from wells completed in the artesian aquifers that underlie the basin. Groundwater constitutes about 40 percent of all water use, and groundwater provides 96 percent of the water used for drinking purposes in Armenia. Since 2000, groundwater withdrawals and consumption in the Ararat Basin of Armenia have increased because of the growth of aquaculture and other uses. Increased groundwater withdrawals caused decreased springflow, reduced well discharges, falling water levels, and a reduction of the number of flowing artesian wells in the southern part of Ararat Basin in Armenia. In 2016, the U.S. Geological Survey and the U.S. Agency for International Development (USAID) began a cooperative study in Armenia to share science and field techniques to increase the country’s capabilities for groundwater study and modeling. The purpose of this report is to describe the hydrogeologic framework and groundwater conditions of the Ararat Basin in Armenia based on data collected in 2016 and previous hydrogeologic studies. The study area includes the Ararat Basin in Armenia. This report was completed through a partnership with USAID/Armenia in the implementation of its Science, Technology, Innovation, and Partnerships effort through the Advanced Science and Partnerships for Integrated Resource Development program and associated partners, including the Government of Armenia, Armenia’s Hydrogeological Monitoring Center, and the USAID Global Development Lab and its GeoCenter. The hydrogeologic framework of the Ararat Basin includes several basin-fill stratigraphic units consisting of interbedded dense clays, gravels, sands, volcanic basalts, and andesite deposits. Previously published cross sections and well lithologic logs were used to map nine general hydrogeologic units. Hydrogeologic units were mapped based on lithology and water-bearing potential. Water-level data measured in the water-bearing hydrogeologic units 2, 4, 6, and 8 in 2016 were used to create potentiometric surface maps. In hydrogeologic unit 2, the estimated direction of groundwater flow is from the west to north in the western part of the basin (away from the Aras River) and from north to south (toward the Aras River) in the eastern part of the basin. In hydrogeologic unit 4, the direction of groundwater flow is generally from west to east and north to south (toward the Aras River) except in the western part of the basin where groundwater flow is toward the north or northwest. Hydrogeologic unit 6 has the same general pattern of groundwater flow as unit 4. Hydrogeologic unit 8 is the deepest of the water-bearing units and is confined in the basin. Groundwater flow generally is from the south to north (away from the Aras River) in the western part of the basin and from west to east and north to south (toward the Aras River) elsewhere in the basin. In addition to water levels, personnel from Armenia’s Hydrogeological Monitoring Center also measured specific conductance at 540 wells and temperature at 2,470 wells in the Ararat Basin using U.S. Geological Survey protocols in 2016. The minimum specific conductance was 377 microsiemens per centimeter (μS/cm), the maximum value was 4,000 μS/cm, and the mean was 998 μS/cm. The maximum water temperature was 24.2 degrees Celsius. An analysis between water temperature and well depth indicated no relation; however, spatially, most wells with cooler water temperatures were within the 2016 pressure boundary or in the western part of the basin. Wells with generally warmer water temperatures were in the eastern part of the basin. Samples were collected from four groundwater sites and one surface-water site by the U.S. Geological Survey in 2016. The stable-isotope values were similar for all five sites, indicating similar recharge sources for the sampled wells. The Hrazdan River sample was consistent with the groundwater samples, indicating the river could serve as a source of recharge to the Ararat artesian aquifer.

Ararat Basin↗

Actionable social science can guide community level wildfire solutions. An illustration from North Central Washington, US

In this study we illustrate the value of social data compiled at the community scale to guide a local wildfire mitigation and education effort. The four contiguous fire-prone study communities in North Central Washington, US, fall within the same jurisdictional fire service boundary and within one US census block group. Across the four communities, similar attitudes toward wildfire were observed. However, significant differences were found on the measures critical to tailoring wildfire preparation and mitigation programs to the local context such as risk mitigation behaviors, reported barriers to mitigation, and communication preferences across the four communities.

Washington↗

Humid tropical forest clearing from 2000 to 2005 quantified by using multitemporal and multiresolution remotely sensed data

Forest cover is an important input variable for assessing changes to carbon stocks, climate and hydrological systems, biodiversity richness, and other sustainability science disciplines. Despite incremental improvements in our ability to quantify rates of forest clearing, there is still no definitive understanding on global trends. Without timely and accurate forest monitoring methods, policy responses will be uninformed concerning the most basic facts of forest cover change. Results of a feasible and cost-effective monitoring strategy are presented that enable timely, precise, and internally consistent estimates of forest clearing within the humid tropics. A probability-based sampling approach that synergistically employs low and high spatial resolution satellite datasets was used to quantify humid tropical forest clearing from 2000 to 2005. Forest clearing is estimated to be 1.39% (SE 0.084%) of the total biome area. This translates to an estimated forest area cleared of 27.2 million hectares (SE 2.28 million hectares), and represents a 2.36% reduction in area of humid tropical forest. Fifty-five percent of total biome clearing occurs within only 6% of the biome area, emphasizing the presence of forest clearing “hotspots.” Forest loss in Brazil accounts for 47.8% of total biome clearing, nearly four times that of the next highest country, Indonesia, which accounts for 12.8%. Over three-fifths of clearing occurs in Latin America and over one-third in Asia. Africa contributes 5.4% to the estimated loss of humid tropical forest cover, reflecting the absence of current agro-industrial scale clearing in humid tropical Africa.

Proceedings of the National Academy of Sciences of↗

Volunteer map data collection at the USGS

Since 1994, citizen volunteers have helped the U.S. Geological Survey (USGS) improve its topographic maps. Through the Earth Science Corps program, citizens were able to "adopt a quad" and collect new information and update existing map features. Until its conclusion in 2001, as many as 300 volunteers annotated paper maps which were incorporated into the USGS topographic-map revision process.

Fact Sheet↗

Accuracy assessment/validation methodology and results of 2010–11 land-cover/land-use data for Pools 13, 26, La Grange, and Open River South, Upper Mississippi River System

Introduction/Background The U.S. Geological Survey (USGS)-Upper Midwest Environmental Sciences Center (UMESC) was responsible for development of several land cover/land use (LCU) systemic datasets of the Upper Mississippi River System (UMRS). These efforts (1989 and 2000) were funded by the U.S. Army Corps of Engineers’ Upper Mississippi River Restoration Program (UMRR) Long Term Resource Monitoring (LTRM) element. Development of systemic datasets includes the acquisition, processing, and serving of high-resolution aerial photography and land cover/land use spatial datasets ( http://www.umesc.usgs.gov/data_library/land_cover_use/land_cover_use_data.html ). In 2008, the UMRR reached a collaborative agreement with the U.S. Fish and Wildlife Service-Region 3 to collect high-resolution digital imagery of the entire UMRS floodplain during 2010–11 for LTRM. The UMESC helped acquire, process, and serve this imagery, as well as produce and serve the 2010–11 LCU systemic dataset of the UMRS floodplain. Digital imagery for Pools 13, 26, La Grange, and Open River South was collected using an Applanix DSS 439 digital sensor system with a 40 millimeter lens and Color Infrared (CIR) filter. The imagery was collected at a resolution of 20 centimeters/pixel (8 inches/pixel) for Pool 13 and 40 centimeters/pixel (16 inches/pixel) for Pools 26, Open River South, and La Grange. All imagery was projected to Universal Transverse Mercator (UTM) Zone 15, North American Datum of 1983 (NAD 83). The General Wetland Vegetation Classification (GWVC) system used for mapping is hierarchical, and its 31 classes can be collapsed into broader categories using either a 15- or 7-class level. While the 1989 and 2000 LCU systemic datasets have not gone through a traditional thematic accuracy assessment (AA) in the past, nor have they undergone a validation analysis, the end products are of high quality. For each systemic dataset produced (1989, 2000, 2010–11), extensive field reconnaissance is performed before photointerpretation. The intent of this field reconnaissance is to learn, test, and verify image signatures as they relate to vegetation types. Questionable areas on the imagery are visited, and the plants or land features observed in the area are recorded for reference. This procedure verifies vegetation signatures on the imagery with those on the ground. In addition, once the photointerpretation is complete, the final LCU dataset undergoes extensive quality assurance/quality control to ensure the imagery is mapped correctly. Since the 2000 LCU systemic dataset was developed, there has been a growing interest in completing thematic AAs for the LTRM LCU spatial datasets. The objective of an AA is to measure the probability that a particular location has been assigned its correct vegetation class. An AA estimates thematic (map class) errors in the data, giving users information needed to determine data suitability for a particular application. At the same time, data producers are able to learn more about the nature of errors in the data. Thus, the two attributes of an AA are “producers’ accuracy,” which is the probability that an AA point has been mapped correctly (also referred to as an error of omission); and “users’ accuracy,” which is the probability that the map actually represents what was found on the ground (also referred to as error of commission). Producers’ and users’ accuracies can be obtained from the same set of data by using different analyses. Accuracy assessment is an extensive effort that requires seasonal field personnel and equipment, data entry, analyses, and post processing—tasks that are costly and time consuming. The geospatial team at the UMESC has suggested a validation process for understanding the accuracy of the spatial datasets, which will be tested on at least some areas of the UMRS. Validation is not a true verification of map-class type in the field; however, it can provide the user of the map with useful information that is similar to a field AA. Similar to an AA, validation involves generating random points based on the total area for each map class. However, instead of collecting field data, two or three individuals not involved with the photo-interpretative mapping separately review each of the points onscreen and record a best-fit vegetation type(s) for each site. Once the individual analyses are complete, results are joined together and a comparative analysis is performed. The objective of this initial analysis is to identify areas where the validation results were in agreement (matches) and areas where validation results were in disagreement (mismatches). The two or three individuals then perform an analysis, looking at each mismatched site, and agree upon a final validation class. (If two vegetation types at a specific site appear to be equally prevalent, the validation team is permitted to assign the site two best-fit vegetation types.) Following the validation team’s comparative analysis of vegetation assignments, the data are entered into a database and compared to the mappers’ vegetation assignments. Agreements and disagreements between the map and validation classes are identified, and a contingency table is produced. This document presents the AA processes/results for Pools 13 and La Grange, as well as the validation process/results for Pools 13 and 26 and Open River South.

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