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The Best Management Practices Statistical Estimator (BMPSE) was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021a,b). The BMPSE was used to calculate statistics and create input files for fitting the trapezoidal distribution to data from studies documenting the performance of individual structural stormwater...
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Inventories of landslides and liquefaction triggered by major earthquakes are key research tools that can be used to develop and test hazard models. To eliminate redundant effort, we created a centralized and interactive repository of ground failure inventories that currently hosts 32 inventories generated by USGS and non-USGS authors and designed a pipeline for adding more as they become available. The repository consists of (1) a ScienceBase community page where the data are available for download and (2) an accompanying web application that allows users to browse and visualize the available datasets. We anticipate that easier access to these key datasets will accelerate progress in earthquake-triggered ground...
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Deep learning is a computer analysis technique inspired by the human brain’s ability to learn. It involves several layers of artificial neural networks to learn and subsequently recognize patterns in data, forming the basis of many state-of-the-art applications from self-driving cars to drug discovery and cancer detection. Deep neural networks are capable of learning many levels of abstraction, and thus outperform many other types of automated classification algorithms. This project developed software tools, resources, and two training workshops that will allow USGS scientists to apply deep learning to remotely sensed imagery and to better understand natural hazards and habitats across the Nation. The tools and...
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This is a macro-embedded EXCEL program that calculates and displays indicators representing valued characteristics or processes in Black Canyon of the Gunnison National Park based on input of daily flows of the Gunnison River. The program is designed to easily accept input from downloaded stream gage records or output from the RIVERWARE reservoir operations model being used for the upstream Aspinall Unit. The decision support system is structured to compare as many as eight alternative flow regimes, where each alternative is represented by a daily sequence of at least 20 calendar years of streamflow. Indicators include selected flow statistics, riparian plant community distribution, clearing of box elder by inundation...
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Insect pests cost billions of dollars per year globally, negatively impacting food crops and infrastructure and contributing to the spread of disease. Timely information regarding developmental stages of pests can facilitate early detection and control, increasing efficiency and effectiveness. To address this need, the USA National Phenology Network (USA-NPN) created a suite of “Pheno Forecast” map products relevant to science and management. Pheno Forecasts indicate, for a specified day, the status of the insect’s target life cycle stage in real time across the contiguous United States. These risk maps enhance decision-making and short-term planning by both natural resource managers and members of the public. ...
2012 Updates (from the FY12 Annual Review) The NWIS Web Services Snapshot represents the next generation of data retrieval and management. The newest Snapshot tool allows instant access to NWIS data from four different web services through ArcGIS, software available to all USGS scientists in all mission areas. Increased data retrieval efficiency reduces the steps required to retrieve and compile water data from multiple sites from what can be more than 30 steps to just a few clicks. As an end-user education tool, it promotes use of NWIS data from both web services and the NWIS database, which increases the production of scientific research and analysis that uses NWIS data. The Snapshot database design enables efficient...
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Landscape Conservation Cooperatives (LCCs) have a critical need for information management processes that facilitate science product (i.e., data, analysis and decision tools, documents) sharing; data storage, security, and dissemination; and project tracking, communication and collaboration tools (Arctic LCC 2010, North Atlantic LCC 2011, Southern Rockies LCC 2011, California LCC 2011). LCCs and their partners are already developing and using a wide variety of data management systems to address LCC and project needs, but the LCC Network and all partners need better coordination across these efforts to develop and share common standards and services, improving the intellectual exchange needed to achieve the LCC mission....
Artificial Intelligence (AI) is revolutionizing ecology and conservation by enabling species recognition from photos and videos. Our project evaluates the capacity to expand AI for individual fish recognition for population assessment. The success of this effort would facilitate fisheries analysis at an unprecedented scale by engaging anglers and citizen scientists in imagery collection.This project is one of the first attempts to apply AI towards fish population assessment with citizen science.
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Geotagged photographs have become a useful medium for recording, analyzing, and communicating Earth science phenomena. Despite their utility, many field photographs are not published or preserved in a spatial or accessible format—oftentimes because of confusion about photograph metadata, a lack of stability, or user customization in free photo sharing platforms. After receiving a request to release about 1,210 geotagged geological field photographs of the Grand Canyon region, we set out to publish and preserve the collection in the most robust (and expedient) manner possible (fig. 6). We leveraged and reworked existing metadata, JavaScript, and Python tools and developed a toolkit and proposed workflow to display...
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Advances in information technology now provide large volume, high-frequency data collection which may improve real-time biosurveillance and forecasting. But, big data streams present challenges for data management and timely analysis. As a first step in creating a data science pipeline for translating large datasets into meaningful interpretations, we created a cloud-hosted PostgreSQL database that collates climate data served from PRISM (https://climatedataguide.ucar.edu/climate-data) and water-quality data from the National Water Quality Portal (https://www.waterqualitydata.us/) and NWIS (https://waterdata.usgs.gov/nwis; fig 1). Using Python-based code, these data streams are queried and updated every 24 hours,...
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PHREEQCI is a widely-used geochemical computer program that can be used to calculate chemical speciation and specific conductance of a natural water sample from its chemical composition (Charlton and Parkhurst, 2002; Parkhurst and Appelo, 1999). The specific conductance of a natural water calculated with PHREEQCI (Appelo, 2010) is reliable for pH greater than 4 and temperatures less than 35 °C (McCleskey and others, 2012b). An alternative method for calculating the specific conductance of natural waters is accurate over a large range of ionic strength (0.0004–0.7 mol/kg), pH (1–10), temperature (0–95 °C), and specific conductance (30–70,000 μS/cm) (McCleskey and others, 2012a). PHREEQCI input files for calculating...
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Executive Summary Traditionally in the USGS, data is processed and analyzed on local researcher computers, then moved to centralized, remote computers for preservation and publishing (ScienceBase, Pubs Warehouse). This approach requires each researcher to have the necessary hardware and software for processing and analysis, and also to bring all external data required for the workflow over the internet to their local computer. To explore a more efficient and effective scientific workflow, we explored an alternate model: storing scientific data remotely, and performing data analysis and visualization close to the data, using only a local web browser as an interface. Although this environment was not a good fit...
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Land-use researchers need the ability to rapidly compare multiple land-use scenarios over a range of spatial and temporal scales, and to visualize spatial and nonspatial data; however, land-use datasets are often distributed in the form of large tabular files and spatial files. These formats are not ideal for the way land-use researchers interact with and share these datasets. The size of these land-use datasets can quickly balloon in size. For example, land-use simulations for the Pacific Northwest, at 1-kilometer resolution, across 20 Monte Carlo realizations, can produce over 17,000 tabular and spatial outputs. A more robust management strategy is to store scenario-based, land-use datasets within a generalized...
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USGS scientists often face computationally intensive tasks that require high-throughput computing capabilities. Several USGS facilities use HTCondor to run their computational pools but are not necessarily connected to the larger USGS pool. This project demonstrated how to connect HTCondor pools by flocking, or coordinating, within the USGS. In addition to flocking the Upper Midwest Environmental Science Center and the Wisconsin Water Science Center, we have flocked with the USGS Advanced Research Computing Yeti supercomputing cluster and other water science centers. We also developed tutorials on how to sandbox code using Docker within the USGS environment for use with high-throughput computing. A main accomplishment...
Wildfires are increasing across the western U.S., causing damage to ecosystems and communities. Addressing the fire problem requires understanding the trends and drivers of fire, yet most fire data is limited only to recent decades. Tree-ring fire scars provide fire records spanning 300-500 years, yet these data are largely inaccessible to potential users. Our project will deliver the newly compiled North American Fire Scar Network — 2,592 sites, 35,602 trees, and > 300,000 fire records — to fire scientists, managers and the public through an online application that will provide tools to explore, visualize, and analyze fire history data. The app will provide raw and derived data products, graphics, statistical summaries,...
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We developed an Internet of Things (IoT) prototype and associated cloud infrastructure for camera-based data collection and initial processing of river streamflow using the cloud (fig. 1). This pilot successfully created a hardware and cloud infrastructure to collect and upload video from a camera gage at San Pedro Creek in San Antonio, Texas. Using a ThingLogix Foundry instance in the Amazon Webservices Cloud, we have created a cloud framework that can auto-provision new camera-based gaging equipment, as well as process incoming videos into image frames for the computation of streamflow. Additionally, we began testing of serving timeseries data from a camera gage (water level and CPU temperature) using real-time...
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U.S. Geological Survey (USGS) scientists are at the forefront of research that is critical for decision-making, particularly through the development of models (Bayesian networks, or BNs) that forecast coastal change. The utility of these tools outside the scientific community has been limited because they rely on expensive, technical software and a moderate understanding of statistical analyses. We proposed to convert one of our models from proprietary to freely available open-source software, resulting in a portable interactive web-interface. The resulting product will serve as a prototype to demonstrate how interdisciplinary USGS science and models can be transformed into an approachable format for decision-makers....
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The purpose of this project was to support the enhanced search, access, and visualization capability for disaster maps and other contributed products on the public USGS Hazards Data Distribution System (HDDS) (U.S. Geological Survey, 2015). These products are often provided to USGS by collaborators for sharing across the response community during the course of an emergency event response; however, in the past, they were not easy for users to discover or access. This project involved the design, testing, and delivery of a new capability for HDDS to ingest, catalog, and display informational or value-added products when provided in a variety of formats. As a result of this work, the user community will be able to...


map background search result map search result map Black Canyon of the Gunnison Decision Support System (BCG_DSS) Integrated Data Management Network for LCCs and Partners: A Framework for Coordinated Data Discovery, Access, Analysis, Visualization, Project Tracking and Coordination Best Management Practices Statistical Estimator (BMPSE) Version 1.2.0 Black Canyon of the Gunnison Decision Support System (BCG_DSS) Integrated Data Management Network for LCCs and Partners: A Framework for Coordinated Data Discovery, Access, Analysis, Visualization, Project Tracking and Coordination Best Management Practices Statistical Estimator (BMPSE) Version 1.2.0