{"pageNumber":"195","pageRowStart":"4850","pageSize":"25","recordCount":46674,"records":[{"id":70222518,"text":"70222518 - 2021 - Geologic and geophysical maps of the Newfoundland Mountains and part of the adjacent Wells 30' x 60' quadrangles, Box Elder County, Utah","interactions":[],"lastModifiedDate":"2021-08-02T16:10:26.071312","indexId":"70222518","displayToPublicDate":"2021-06-30T11:01:45","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5437,"text":"Utah Geological Survey Miscellaneous Publication","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"MP-173DM","title":"Geologic and geophysical maps of the Newfoundland Mountains and part of the adjacent Wells 30' x 60' quadrangles, Box Elder County, Utah","docAbstract":"

The Newfoundland Mountains map area (Newfoundland Mountains and adjacent part of Wells 30' x 60' quadrangles) is located in Box Elder County, northwestern Utah. The map encompasses broad expanses of the Great Salt Lake Desert as well as several picturesque mountain ranges (figures 1, 2, and 3). The geology of the area was last mapped and summarized by Doelling (1980). Since that landmark study, much of the area has been mapped in greater detail and new paleontologic, geochronologic, and structural data provide for an updated view of the geology. In addition, new geophysical studies (Langenheim and others, 2013; Langenheim, 2016) provide key data for improved interpretation of subsurface geology.

The geologic map (plate 1) was compiled from fifteen 7.5' quadrangles mapped at a scale of 1:24,000 (mostly in the western part of the area), one map covering the Newfoundland Mountains at a scale of 1:31,680 (Allmendinger and Jordan, 1989), unpublished geologic mapping at scales from 1:24,000 to 1:50,000 (most by Miller; Bovine Mountain by T.E. Jordan), and reconnaissance mapping and aerial photo interpretation in intervening areas by Miller. Some published maps were remapped or reinterpreted by the authors in light of more recent studies north of the map area. Geologic mapping conducted as part of several theses/dissertations and a few published papers also were used (plate 2, index to geologic mapping). Concealed faults under valley bottoms were interpreted from gravity and aeromagnetic data.

Our approach for this map was to integrate across the main themes of the mapped geology by generalizing units, structures, and polygons. This has the aim of illustrating the principal tectonic and stratigraphic packages, as well as illustrating the patterns of surficial units and geomorphology. Cross sections (plate 2) were constructed to coincide with representative cross sections for several detailed geologic maps. This approach required large bends across valleys. Basin geometry shown in the cross sections was constrained by gravity data and a seismic line since few deep drill holes are available.

","language":"English","publisher":"Utah Geological Survey","usgsCitation":"Miller, D., Felger, T.J., and Langenheim, V., 2021, Geologic and geophysical maps of the Newfoundland Mountains and part of the adjacent Wells 30' x 60' quadrangles, Box Elder County, Utah: Utah Geological Survey Miscellaneous Publication MP-173DM, 34 p.","productDescription":"34 p.","ipdsId":"IP-021940","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":387633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":387618,"type":{"id":11,"text":"Document"},"url":"https://ugspub.nr.utah.gov/publications/misc_pubs/mp-173/mp-173.pdf"}],"country":"United States","state":"Utah","county":"Box Elder County","otherGeospatial":"Newfoundland Mountains, Wells 30' x 60' 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Elder\",\"state\":\"UT\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, David M. 0000-0003-3711-0441 dmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":140769,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","email":"dmiller@usgs.gov","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":820417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Felger, Tracey J. 0000-0003-0841-4235 tfelger@usgs.gov","orcid":"https://orcid.org/0000-0003-0841-4235","contributorId":1117,"corporation":false,"usgs":true,"family":"Felger","given":"Tracey","email":"tfelger@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":820418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langenheim, Victoria E. 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":206978,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":820419,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223832,"text":"70223832 - 2021 - Toward improved decision-support tools for Delta Smelt management actions","interactions":[],"lastModifiedDate":"2021-09-09T16:00:10.030009","indexId":"70223832","displayToPublicDate":"2021-06-30T10:48:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":419,"text":"White Paper","active":false,"publicationSubtype":{"id":9}},"title":"Toward improved decision-support tools for Delta Smelt management actions","docAbstract":"

The Collaborative Science and Adaptive Management Program (CSAMP) has endorsed a goal of reversing the recent downward trajectory of the Delta Smelt population within 5-10 generations, with the long-term aim of establishing a self-sustaining population. An ambitious agenda of management actions is planned, and more management actions are being considered. This White Paper furthers one of the recommendations in the 2019 Delta Smelt Science Plan – the need to predict the potential ecological effects of taking a management action. Existing statistical models can be highly informative in assessing the response of Delta Smelt to changing system conditions and management actions. However, management actions can shift or alter conditions in ways that models based on analysis of historical data may not be able to represent, and short-term or localized effects may be missed with models designed to assess effects at the population level.

Decision support tools (DSTs) are computer-based tools developed to assist decision-making, often combining computationally intensive analysis and spatial mapping of environmental relationships. DSTs can be used in planning processes that evaluate an array of actions, such as in Structured Decision Making (SDM), where DSTs are needed to compare among alternatives. DSTs can also be used to explore the potential effects of different approaches to implementing management actions. The goal of this White Paper is to identify plausible options for DSTs that could be developed for future use to evaluate management actions that seek to either reverse the decline of Delta Smelt or minimize or mitigate the effects of other water management actions.

Different types of management actions lead to different needs for DSTs. This White Paper was developed using three types of actions currently being considered to enhance the Delta Smelt population: Supplementation with Hatchery Fish, Summer-Fall Habitat, and Food Enhancement actions. These three management actions target different parts of the estuary and different processes, with a variety of possible metrics to gauge performance.

Three DSTs are proposed that collectively address management questions related to the management actions considered, with each requiring a slightly different set of processes to be included and producing an array of outputs at varying spatial and temporal scales: DST 1. Modeling Fish Movement, Survival, and Reproduction Across Their Range. This DST can address management questions that require information about Delta Smelt spatial distribution and movement.

• DST 1 could be used to compare conditions with and without management actions in place, how the management action performs among different types of water years (with varied flow and associated abiotic conditions), and to assess relative change with different variations and strategies of the management actions.
• DST 2. Changes in Habitat Conditions and Delta Smelt Response. This DST is intended to evaluate combinations of conditions that are considered to provide suitable habitat for Delta Smelt, and Delta Smelt response. Delta Smelt habitat is generally described as open water with low salinity (0 to 6), turbidity of at least 12 NTU, suitable temperature conditions, and sufficient food availability to support growth.
• DST 3. Regional Effects of Food Subsidy. This DSTs seeks to evaluate effectiveness of food enhancement actions by providing information on responses of the immediate targets of the action (i.e., phytoplankton or zooplankton) and tracing those to projected growth responses of Delta Smelt.

There is not a single DST that adequately addresses management questions relevant to all management actions, although there is some overlap in the management questions each of the three DSTs can address.

For each of the DSTs a substantial foundation of models and approaches already exists and modeling has already been applied to several of the management actions described. However, a number of outstanding issues remain for further development of the proposed DSTs. These are summarized in this White Paper together with potential approaches that could be applied or tested. Some components for the DSTs are already available and thus development could be relatively easy. However, for several of the topics identified there are gaps in knowledge that currently limit formulation of model structure and process representations. This presents challenges to readily incorporate some needed mechanisms into the models.

Eleven next steps, aligned with relevant DSTs, are outlined. The next steps vary in their complexity or technical ‘lift’ required. Many build on existing work, or methods and approaches that have already been developed or are underway, while others require additional thinking to establish a viable approach. Some interim utility for decisions could be gained during initial development of the DSTs with further features added over time.

Development of a DST requires engagement of both managers and scientists. Identifying the outputs and resolution needed for management purposes early in development of any DST is essential for effective pursuit of next steps and suitable approaches to address challenges. Dialog between managers and technical experts also informs what process-based simulation can do, and what tradeoffs are acceptable to meet a given purpose. To further develop the DSTs outlined here for application in the estuary requires:

- Engagement of a committed group of technical experts with appropriate expertise.
- The development of a coordinated workplan including appropriate project management and tracking.
- Dialog between potential users (i.e., managers and policy makers) and technical experts.
- Resources to pursue DST development including personnel and computational resources.

This White Paper demonstrates the potential for moving toward DSTs for a variety of management actions in support of Delta Smelt that include mechanistic representations of physical and biological processes. Through focused effort from technical experts, managers and policy makers, DSTs can be developed to provide quantitative predictions of management effects on the ecosystem, targeting the changes the management actions seek to achieve, how these effects compare to ambient conditions, and how the effects vary among water year types or with timing and location of actions. Importantly, solid foundations exist which can be leveraged, refined, and built upon to specifically inform current and future management decisions.

","language":"English","publisher":"Collaborative Adaptive Management Team","usgsCitation":"Reed, D., Acuna, S., Ateljevich, E., Brown, L.R., Geske, B., Gross, E., Hobbs, J., Kimmerer, W.J., Lucas, L., Nobriga, M., and Rose, K.A., 2021, Toward improved decision-support tools for Delta Smelt management actions: White Paper, v, 34 p.","productDescription":"v, 34 p.","ipdsId":"IP-127826","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":389005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389004,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.baydeltalive.com/CSAMP/docs/24756"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Denise","contributorId":215697,"corporation":false,"usgs":false,"family":"Reed","given":"Denise","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":822849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acuna, Shawn","contributorId":257756,"corporation":false,"usgs":false,"family":"Acuna","given":"Shawn","email":"","affiliations":[{"id":52106,"text":"Metropolitan Water District of Southern California","active":true,"usgs":false}],"preferred":false,"id":822850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ateljevich, Eli","contributorId":187437,"corporation":false,"usgs":false,"family":"Ateljevich","given":"Eli","email":"","affiliations":[],"preferred":false,"id":822851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Geske, Ben","contributorId":265520,"corporation":false,"usgs":false,"family":"Geske","given":"Ben","email":"","affiliations":[{"id":54715,"text":"Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":822853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gross, Edward","contributorId":264402,"corporation":false,"usgs":false,"family":"Gross","given":"Edward","affiliations":[{"id":28024,"text":"UCDavis","active":true,"usgs":false}],"preferred":false,"id":822854,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hobbs, Jim","contributorId":200389,"corporation":false,"usgs":false,"family":"Hobbs","given":"Jim","email":"","affiliations":[],"preferred":false,"id":822855,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kimmerer, Wim J.","contributorId":59169,"corporation":false,"usgs":false,"family":"Kimmerer","given":"Wim","email":"","middleInitial":"J.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":822856,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":822857,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nobriga, Matthew","contributorId":139247,"corporation":false,"usgs":false,"family":"Nobriga","given":"Matthew","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":822858,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rose, Kenneth A","contributorId":147274,"corporation":false,"usgs":false,"family":"Rose","given":"Kenneth","email":"","middleInitial":"A","affiliations":[{"id":16815,"text":"Dept. of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge","active":true,"usgs":false}],"preferred":false,"id":822859,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70223729,"text":"70223729 - 2021 - Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading","interactions":[],"lastModifiedDate":"2021-09-16T15:12:14.688708","indexId":"70223729","displayToPublicDate":"2021-06-30T09:26:46","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading","docAbstract":"

Green Lake is the deepest natural inland lake in Wisconsin, USA, with a maximum depth of about 72 meters (m). In the early 1900’s, the lake was believed to have very good water quality (low nutrient concentrations and good water clarity), with low dissolved oxygen (DO) concentrations only in the deepest part of the lake. Because of increased phosphorus (P) inputs from anthropogenic activities in its watershed, total phosphorus (TP) concentrations in the lake increased, which led to increased algal production and low DO concentrations not only occurring in its deepest areas but also in the middle of the water column (metalimnion). Routine monitoring of the lake and its tributaries has been conducted by the U.S. Geological Survey since 2004 and 1988, respectively. Results from this monitoring led to the Wisconsin Department of Natural Resources (WDNR) listing the lake as impaired because of low DO concentrations in the metalimnion, with elevated TP concentrations identified as the cause of impairment.

As part of this study, comprehensive sampling of the lake and its tributaries was conducted in 2017–2018 to augment ongoing monitoring and further describe the low DO concentrations in the lake (especially in the metalimnion). Empirical and process-driven water quality models were then used to determine the causes of the low DO concentrations and the magnitude of P load reductions needed to improve the water quality of the lake to meet multiple water-quality goals, including the WDNR criteria for TP and DO.

Data from previous studies showed that DO concentrations in the metalimnion decreased slightly as summer progressed in the early 1900’s, but since the late 1970s have typically dropped below 5 milligrams per liter (mg/L), which is the WDNR criterion for impairment. During 2014–2018 (baseline period for this study), the near-surface geometric-mean TP concentration during June–September in the east side of the lake was 0.020 mg/L and in the west side was 0.016 mg/L (both were below the 0.015 mg/L WDNR criterion for the lake), and the minimum metalimnetic DO concentrations measured in August ranged from 1.0 to 4.7 mg/L. It was believed that the degradation in water quality was caused by excessive P inputs to the lake; therefore, the total P inputs to the lake were estimated. The mean annual external P load during 2014–2018 was estimated to be 8,980 kilograms per year (kg/yr), of which monitored and unmonitored tributary inputs contributed 84 percent, atmospheric inputs contributed 8 percent, waterfowl contributed 7 percent, and septic systems contributed 1 percent. At fall turnover, internal sediment recycling contributed an additional 7,040 kg that increased TP concentrations in shallow areas of the lake by about 0.020 mg/L. The elevated TP concentrations then persisted until the following spring. On an annual basis, however, there is a net deposition of P to the bottom sediments.

Empirical models were used to describe how the near-surface water quality of Green Lake would be expected to respond to changes in external P loading. Predictions from the models showed a relatively linear response between P loading and TP and chlorophyll-a (Chl-a) concentrations in the lake, with the changes in TP and Chl-a concentrations being less on a percentage basis (50–60 percent for TP and 30–70 percent for Chl-a) than the changes in P loading. Mean summer water clarity, indicated by Secchi disk depths, had a larger response to decreases in P loading than to increases in loading. Based on these relations, external P loading to the lake would need to be decreased from 8,980 kg/yr to about 5,460 kg/yr for the geometric mean June–September TP concentration on the east side of the lake, with higher TP concentrations than the west side, to reach the WDNR criterion of 0.015 mg/L. This reduction of 3,520 kg/yr equates to a 46-percent reduction in the potentially controllable external P sources (all external sources except precipitation, atmospheric deposition, and waterfowl) from that measured during water years (WYs) 2014–2018. The total external P loading would need to be decreased to 7,680 kg/yr (17-percent reduction in potentially controllable external P sources) for near-surface June–September TP concentrations in the west side of the lake to reach 0.015 mg/L. Total external P loading would need to be decreased to 3,870–5,320 kg/yr for the lake to be classified as oligotrophic, with a near-surface June-September TP concentration of 0.012 mg/L.

Results from the hydrodynamic water-quality model GLM-AED (General Lake Model coupled to the Aquatic Ecodynamics modeling library) indicated that metalimnetic DO minima are driven by external P loading and internal sediment recycling that lead to high TP concentrations during spring and early summer, which in turn lead to high phytoplankton production, high metabolism and respiration, and ultimately DO consumption in the upper, warmer areas of the metalimnion. GLM-AED results indicated that settling of organic material during summer may be slowed by the colder, denser, and more viscous water in the metalimnion and increase DO consumption. Based on empirical evidence comparing minimum metalimnetic DO concentrations with various meteorological, hydrologic, water quality, and in-lake physical factors, lower metalimnetic DO concentrations occurred when there was warmer metalimnetic water temperatures, higher near-surface Chl-a and TP concentrations, and lower Secchi depths during summer. GLM-AED results indicated that the external P load would need to be reduced to about 4,010 kg/yr, a 57-percent reduction from that measured in 2014–2018, to eliminate the occurrence of metalimnetic DO minima of less than 5 mg/L in over 75 percent of the years (the target provided by the WDNR).

Large reductions in external P loading are expected to have an immediate effect on the near-surface TP concentrations and metalimnetic DO concentrations in Green Lake. However, it may take several years for the full effects of the external load reduction to be observed because internal sediment recycling is an important source of P for the following spring.

","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Diagnostic and feasibility study findings: Water quality improvements for Green Lake, Wisconsin","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Green Lake Association","usgsCitation":"Robertson, D., Siebers, B.J., Ladwig, R., Hamilton, D., Reneau, P., McDonald, C.P., Prellwitz, S., and Lathrop, R.C., 2021, Appendix E. Water quality and hydrology of Green Lake, Wisconsin, and the response in its near-surface water-quality and metalimnetic dissolved oxygen minima to changes in phosphorus loading, vii, 115 p.","productDescription":"vii, 115 p.","ipdsId":"IP-129488","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":389346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":388824,"type":{"id":15,"text":"Index Page"},"url":"https://www.greenlakeassociation.org/research/"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.07920837402344,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.75894467245554\n ],\n [\n -88.9133834838867,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.864485327996704\n ],\n [\n -89.07920837402344,\n 43.75894467245554\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":217258,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Siebers, Benjamin J. 0000-0002-2900-5169","orcid":"https://orcid.org/0000-0002-2900-5169","contributorId":206518,"corporation":false,"usgs":true,"family":"Siebers","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ladwig, Robert","contributorId":265278,"corporation":false,"usgs":false,"family":"Ladwig","given":"Robert","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":822505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":822506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reneau, Paul 0000-0002-1335-7573","orcid":"https://orcid.org/0000-0002-1335-7573","contributorId":217293,"corporation":false,"usgs":true,"family":"Reneau","given":"Paul","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822507,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McDonald, Cory P. 0000-0002-1208-8471","orcid":"https://orcid.org/0000-0002-1208-8471","contributorId":261754,"corporation":false,"usgs":false,"family":"McDonald","given":"Cory","email":"","middleInitial":"P.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":822508,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prellwitz, Stephanie","contributorId":265281,"corporation":false,"usgs":false,"family":"Prellwitz","given":"Stephanie","email":"","affiliations":[{"id":54642,"text":"Green Lake Association","active":true,"usgs":false}],"preferred":false,"id":822509,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lathrop, Richard C","contributorId":172075,"corporation":false,"usgs":false,"family":"Lathrop","given":"Richard","email":"","middleInitial":"C","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":822510,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221867,"text":"70221867 - 2021 - Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups","interactions":[],"lastModifiedDate":"2022-04-13T20:17:41.090462","indexId":"70221867","displayToPublicDate":"2021-06-30T09:14:48","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups","docAbstract":"

Plant communities represent the integration of ecological and biological processes and they serve as an important component for the protection of biological diversity. To measure progress towards protection of ecosystems in the United States for various stated conservation targets we need datasets at the appropriate thematic, spatial, and temporal resolution. The recent release of the LANDFIRE Existing Vegetation Data Products (2016 Remap) with a legend based on U.S. National Vegetation Classification allowed us to assess the conservation status of plant communities of the U.S. The map legend is based on the Group level of the USNVC, which characterizes the regional differences in plant communities based on dominant and diagnostic plant species. By combining the Group level map with the Protected Areas Database of the United States (PAD-US Ver 2.1), we quantified the representation of each Group. If the mapped vegetation is assumed to be 100% accurate, using the Aichi Biodiversity target (17% land in protection by 2020) we found that 159 of the 265 natural Groups have less than 17% in GAP Status 1 & 2 lands and 216 of the 265 Groups fail to meet a 30% representation target. Only four of the twenty ecoregions have >17% of their extent in Status 1 & 2 lands. Sixteen ecoregions are dominated by Groups that are under-represented. Most ecoregions have many hectares of natural or ruderal vegetation that could contribute to future conservation efforts and this analysis helps identify specific targets and opportunities for conservation across the U.S. 

","language":"English","publisher":"MDPI","doi":"10.3390/f12070864","usgsCitation":"McKerrow, A., Davidson, A., Rubino, M., Faber-Langendoen, D., and Dockter, D., 2021, Quantifying the representation of plant communities in the protected areas of the U.S.: An analysis based on the U.S. National Vegetation Classification Groups: Forests, v. 12, no. 7, 864, 15 p., https://doi.org/10.3390/f12070864.","productDescription":"864, 15 p.","ipdsId":"IP-129762","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":451704,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/f12070864","text":"Publisher Index Page"},{"id":387107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":819084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davidson, Anne","contributorId":197967,"corporation":false,"usgs":false,"family":"Davidson","given":"Anne","email":"","affiliations":[],"preferred":false,"id":819085,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rubino, Matthew J. 0000-0003-0651-3053","orcid":"https://orcid.org/0000-0003-0651-3053","contributorId":215500,"corporation":false,"usgs":false,"family":"Rubino","given":"Matthew J.","affiliations":[{"id":39268,"text":"North Carolina State University, NC Cooperative Fish & Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":819086,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faber-Langendoen, Don","contributorId":260895,"corporation":false,"usgs":false,"family":"Faber-Langendoen","given":"Don","affiliations":[{"id":17658,"text":"NatureServe","active":true,"usgs":false}],"preferred":false,"id":819087,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dockter, Daryn 0000-0003-1914-8657","orcid":"https://orcid.org/0000-0003-1914-8657","contributorId":216814,"corporation":false,"usgs":true,"family":"Dockter","given":"Daryn","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":819088,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70222579,"text":"70222579 - 2021 - Identifying elusive piercing points along the North American transform margin using mixture modeling of detrital zircon data from sedimentary units and their crystalline sources","interactions":[],"lastModifiedDate":"2021-08-05T13:08:29.47498","indexId":"70222579","displayToPublicDate":"2021-06-30T08:03:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9134,"text":"The Sedimentary Record","active":true,"publicationSubtype":{"id":10}},"title":"Identifying elusive piercing points along the North American transform margin using mixture modeling of detrital zircon data from sedimentary units and their crystalline sources","docAbstract":"The San Gabriel and Canton faults represent early stages in the development of the San Andreas fault system. However, questions of timing of initiation and magnitude of slip on these structures remain unresolved, with published estimates ranging from 42-75 km and likely starting in the Miocene. This uncertainty in slip history reflects an absence of appropriate piercing points. We attempt to better constrain the slip history on these faults by quantifying the changing proportions of source terranes contributing sediment to the Ventura Basin, California, through the Cenozoic, including refining data for a key piercing point.\nVentura Basin sediments show an increase in detrital zircon U-Pb dates and mineral abundances associated with crystalline sources in the northern San Gabriel Mountains through time, which we interpret to record the basin’s northwest translation by dextral strike-slip faulting. In particular, an Oligocene unit mapped as part of the extra-regional Sespe Formation instead has greater affinity to the Vasquez Formation. Specifically, the presence of a unimodal population of ~1180 Ma zircon, high (57%) plagioclase content, and proximal alluvial fan facies indicate that the basin was adjacent to the San Gabriel anorthosite during deposition of the Vasquez Formation, requiring 35-60 km of slip on the San Gabriel-Canton fault system. Mixture modeling of detrital zircon data supported by automated mineralogy highlights the importance of this piercing point along the San Gabriel-Canton fault system and suggests that fault slip began during the late Oligocene to early Miocene, which is earlier than published models. These two lines of evidence disagree with recent models that estimate >60 km of offset, requiring a reappraisal of the slip history of an early strand of the San Andreas transform zone.","language":"English","publisher":"Society for Sedimentary Geology","doi":"10.2110/sedred.2021.2.3","usgsCitation":"Gilbert, C., Jobe, Z.R., Johnstone, S., and Sharman, G.R., 2021, Identifying elusive piercing points along the North American transform margin using mixture modeling of detrital zircon data from sedimentary units and their crystalline sources: The Sedimentary Record, v. 19, no. 2, p. 12-21, https://doi.org/10.2110/sedred.2021.2.3.","productDescription":"10 p.","startPage":"12","endPage":"21","ipdsId":"IP-126612","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":451706,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2110/sedred.2021.2.3","text":"Publisher Index Page"},{"id":387714,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ventura","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.38568115234374,\n 34.24813554589752\n ],\n [\n -119.1851806640625,\n 34.24813554589752\n ],\n [\n -119.1851806640625,\n 34.44315867450577\n ],\n [\n -119.38568115234374,\n 34.44315867450577\n ],\n [\n -119.38568115234374,\n 34.24813554589752\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Gilbert, Clark","contributorId":261777,"corporation":false,"usgs":false,"family":"Gilbert","given":"Clark","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":820621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jobe, Zane R.","contributorId":207547,"corporation":false,"usgs":false,"family":"Jobe","given":"Zane","email":"","middleInitial":"R.","affiliations":[{"id":37560,"text":"Department of Geology and Geological Engineering, Colorado School of Mines, Golden, Colorado 80401, USA","active":true,"usgs":false}],"preferred":false,"id":820622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnstone, Samuel 0000-0002-3945-2499","orcid":"https://orcid.org/0000-0002-3945-2499","contributorId":207545,"corporation":false,"usgs":true,"family":"Johnstone","given":"Samuel","email":"","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":820623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sharman, Glenn R.","contributorId":196537,"corporation":false,"usgs":false,"family":"Sharman","given":"Glenn","email":"","middleInitial":"R.","affiliations":[{"id":34621,"text":"Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA","active":true,"usgs":false}],"preferred":false,"id":820624,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221769,"text":"70221769 - 2021 - Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada","interactions":[],"lastModifiedDate":"2021-07-16T11:49:32.681303","indexId":"70221769","displayToPublicDate":"2021-06-30T06:52:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5804,"text":"Geothermal Energy – Science, Society and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada","docAbstract":"

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fluid-flow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fluid-flow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes.

","language":"English","publisher":"Springer","doi":"10.1186/s40517-021-00199-8","usgsCitation":"Siler, D.L., Pepin, J.D., Vesselinov, V.V., Mudunuru, M.K., and Ahmmed, B., 2021, Machine learning to identify geologic factors associated with production in geothermal fields: A case-study using 3D geologic data, Brady geothermal field, Nevada: Geothermal Energy – Science, Society and Technology, v. 9, 17, 17 p., https://doi.org/10.1186/s40517-021-00199-8.","productDescription":"17, 17 p.","ipdsId":"IP-125602","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":451713,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40517-021-00199-8","text":"Publisher Index Page"},{"id":386929,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Brady geothermal field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.70703125,\n 39.16414104768742\n ],\n [\n -118.38867187499999,\n 39.16414104768742\n ],\n [\n -118.38867187499999,\n 40.212440718286466\n ],\n [\n -119.70703125,\n 40.212440718286466\n ],\n [\n -119.70703125,\n 39.16414104768742\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-06-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":818672,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pepin, Jeff D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeff","email":"","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818673,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vesselinov, Velimir V.","contributorId":260765,"corporation":false,"usgs":false,"family":"Vesselinov","given":"Velimir","email":"","middleInitial":"V.","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":818674,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mudunuru, Maruti K.","contributorId":260766,"corporation":false,"usgs":false,"family":"Mudunuru","given":"Maruti","email":"","middleInitial":"K.","affiliations":[{"id":52195,"text":"Pacific Northwest National Lab","active":true,"usgs":false}],"preferred":false,"id":818675,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ahmmed, Bulbul","contributorId":260767,"corporation":false,"usgs":false,"family":"Ahmmed","given":"Bulbul","email":"","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":818676,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221893,"text":"70221893 - 2021 - Determination of burn severity models ranging from regional to continental scales for the conterminous United States","interactions":[],"lastModifiedDate":"2021-07-13T18:43:48.420401","indexId":"70221893","displayToPublicDate":"2021-06-29T13:35:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Determination of burn severity models ranging from regional to continental scales for the conterminous United States","docAbstract":"

Identifying meaningful measures of ecological change over large areas is dependent on the quantification of robust relationships between ecological metrics and remote sensing products. Over the past several decades, ground observations of wildfire and prescribed fire severity have been acquired across hundreds of wildland fires in the United States, primarily utilizing the Composite Burn Index (CBI) plot protocol. These observations have been coupled to spaceborne passive spectral reflectance indices (e.g. Landsat-derived variations of the Normalized Burn Ratio [NBR]) to produce regression models describing their relationship. Here we develop regression models by vegetation type for multiple vegetation classification systems representing a range of spatial scales, and a decision tree framework for evaluating these regression models. Our overall goals were to determine which scale of ecological classifications provided the best estimate of burn severity from Landsat data and how to choose the best regression model. We aggregated a total of 6280 CBI plots for 234 wildland fires that burned between 1994 and 2017 and produced Landsat-derived NBR and differenced NBR (dNBR) values for each plot. We then calculated best fit linear or higher order regression equations between CBI and NBR/dNBR for each landcover classification system from smallest to largest scale: LANDFIRE Biophysical Settings (BPS), National Vegetation Classification macrogroup (NVC) landcover classifications, Omernick III, II, and I ecoregions, LANDFIRE Fire Regime Groups (FRG), and the entire conterminous United States (CONUS) dataset. The CONUS regression model goodness of fit was moderate (R2 = 0.55, P < 0.001) for dNBR and poor (R2 = 0.30, P < 0.001) for NBR. Within landcover classifications, CBI was better fit by dNBR than NBR. Finer scale regional regression models including BPS (dNBR R2&#xAF;\">R2¯ = 0.56 and 0.00–0.83 R2 range; NBR R2&#xAF;\">R(bar)2 = 0.43 and 0.00–0.82 R2 range) and NVC (dNBR R2&#xAF;\">R(bar)2 = 0.55 and 0.15–0.78 R2 range; NBR R2&#xAF;\">R(bar)2 = 0.41 and 0.00–0.79 R2 range) were on average the same or better than the CONUS models for dNBR and NBR, with the strongest fit models exhibiting R2 ≥ 0.70, whereas larger scale regional models R2&#xAF;\">R(bar)2 ranged from 0.28 to 0.5. However, variation in accuracy among landcover types indicate that dNBR and NBR regression models could be used to effectively estimate CBI for future fires in certain regions, while for other regions models may require additional field observations or alternative spectral transformations. Our decision tree schema can be used to help users determine which scale is likely to produce the most accurate results using our models. The CBI regression models developed here, paired with the decision tree, provide users with a simple method to estimate burn severity in units of CBI for any fire within CONUS with moderate to high levels of confidence and provide a template for further development of models with new data going forward.

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California has, in recent years, become a hotspot of interannual climatic variability, recording devastating climate-related disturbances with severe effects on tree resources. Understanding the patterns of tree cover change associated with these events is vital for developing strategies to sustain critical habitats of endemic and threatened vegetation communities. We assessed patterns of tree cover change, especially the effects of the 2012–2016 drought within the distribution range of blue oak (Quercus douglasii), an endemic tree species to California with a narrow geographic extent. We utilized multiple, annual land-cover and land-surface change products from the U.S. Geological Survey (USGS) Land Change Monitoring, Assessment and Projection (LCMAP) project along with climate and wildfire datasets to monitor changes in tree cover state and condition and examine their relationships with interannual climate variability between 1985 and 2016. Here, we refer to a change in tree cover class without a land-cover change to another class as “conditional change.” The unusual drought of 2012–2016, accompanied by anomalously high temperatures and vapor pressure deficit, was associated with exceptional spikes in the amount of both fire and non-fire induced tree cover loss and tree cover conditional change, especially in 2015 and 2016. Approximately 1,266 km2 of tree cover loss and 617 km2 of tree cover conditional change were recorded during that drought. Tree cover loss through medium to high severity fires was especially large in exceptionally dry and hot years. Our study demonstrates the usefulness of the LCMAP products for monitoring the effects of climatic extremes and disturbance events on both thematic and conditional land-cover change over a multi-decadal period. Our results signify that blue oak woodlands may be vulnerable to extreme climate events and changing wildfire regimes. Here, we present early evidence that frequent droughts associated with climate warming may continue to affect tree cover in this region, while drought interaction with wildfires and the resulting feedbacks may have substantial influence as well. Consequently, efforts to conserve the blue oak woodlands, and potentially other vegetation communities in the Western United States, may benefit from consideration of climate risks as well as the potential for climate-fire and vegetation feedbacks.

","language":"English","publisher":"Frontiers Media","doi":"10.3389/fclim.2021.689945","usgsCitation":"Dwomoh, F.K., Brown, J.F., Tollerud, H.J., and Auch, R.F., 2021, Hotter drought escalates tree cover declines in blue oak woodlands of California: Frontiers in Climate, v. 3, 689945, 15 p., https://doi.org/10.3389/fclim.2021.689945.","productDescription":"689945, 15 p.","ipdsId":"IP-129607","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":451715,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fclim.2021.689945","text":"Publisher Index Page"},{"id":387137,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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(Geography)","active":false,"usgs":true}],"preferred":true,"id":818999,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223414,"text":"70223414 - 2021 - Paths to computational fluency for natural resource educators, researchers, and managers","interactions":[],"lastModifiedDate":"2021-08-26T16:21:15.327269","indexId":"70223414","displayToPublicDate":"2021-06-29T11:21:04","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9155,"text":"Natural Resource Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Paths to computational fluency for natural resource educators, researchers, and managers","docAbstract":"

Natural resource management and supporting research teams need computational fluency in the data and model-rich 21st century. Computational fluency describes the ability of practitioners and scientists to conduct research and represent natural systems within the computer's environment. Advancement in information synthesis for natural resource management requires more sophisticated computational approaches, as well as reproducible, reusable, extensible, and transferable methods. Despite this importance, many new and current natural resource practitioners lack computational fluency and no common set of recommended resources and practices exist for learning these skills. Broadly, attaining computational fluency entails moving beyond the simple use of computers to applying sound computational principles and methods and including computational experts (such as computer scientists) on research teams. Our path for computational fluency includes using open-source tools when possible; reproducible data management, statistics, and modeling; understanding and applying the benefits of basic computer programming to carry out more complex procedures; tracking code with version control; working in controlled computer environments; and using advanced computing resources.

","language":"English","publisher":"Wiley","doi":"10.1111/nrm.12318","usgsCitation":"Erickson, R.A., Burnett, J.L., Wiltermuth, M.T., Bulliner, E.A., and Hsu, L., 2021, Paths to computational fluency for natural resource educators, researchers, and managers: Natural Resource Modelling, v. 34, no. 3, e12318, 21 p., https://doi.org/10.1111/nrm.12318.","productDescription":"e12318, 21 p.","ipdsId":"IP-124147","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":489797,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/nrm.12318","text":"Publisher Index Page"},{"id":388550,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burnett, Jessica Leigh 0000-0002-0896-5099","orcid":"https://orcid.org/0000-0002-0896-5099","contributorId":248195,"corporation":false,"usgs":true,"family":"Burnett","given":"Jessica","email":"","middleInitial":"Leigh","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":821997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiltermuth, Mark T. 0000-0002-8871-2816 mwiltermuth@usgs.gov","orcid":"https://orcid.org/0000-0002-8871-2816","contributorId":708,"corporation":false,"usgs":true,"family":"Wiltermuth","given":"Mark","email":"mwiltermuth@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821998,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bulliner, Edward A. 0000-0002-2774-9295 ebulliner@usgs.gov","orcid":"https://orcid.org/0000-0002-2774-9295","contributorId":4983,"corporation":false,"usgs":true,"family":"Bulliner","given":"Edward","email":"ebulliner@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":821999,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hsu, Leslie 0000-0002-5353-807X lhsu@usgs.gov","orcid":"https://orcid.org/0000-0002-5353-807X","contributorId":191745,"corporation":false,"usgs":true,"family":"Hsu","given":"Leslie","email":"lhsu@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":822000,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221692,"text":"ofr20211041 - 2021 - GIS-based identification of areas that have resource potential for lode gold in Alaska","interactions":[],"lastModifiedDate":"2022-05-18T16:22:14.173807","indexId":"ofr20211041","displayToPublicDate":"2021-06-29T09:43:20","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1041","displayTitle":"GIS-Based Identification of Areas that have Resource Potential for Lode Gold in Alaska","title":"GIS-based identification of areas that have resource potential for lode gold in Alaska","docAbstract":"

Several comprehensive, data-driven geographic information system (GIS) analyses were conducted to assess prospectivity for lode gold in Alaska. These analyses use available geospatial datasets of lithologic, geochemical, mineral occurrence, and geophysical data to build models for recognizing different types of gold deposits within physiographic units defined by stream drainage basins that are approximately 100 square kilometers in area. The analytical methods successfully delineated areas in the State that contain known lode gold deposits and occurrences, providing some measure of confidence in their ability to predict gold prospectivity in areas of unknown lode gold potential. The results of our analyses indicate high prospectivity in a few areas scattered around the State that are not known to contain lode gold deposits.

In addition to assessing the potential for lode gold deposits in Alaska, we designed analyses to distinguish different lode gold deposit types, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. These can primarily be differentiated using their unique trace element geochemical fingerprints and elemental enrichments, which reflect the characteristics of the geologic environment and chemistry of the ore-forming fluids. We identified multiple parameters that would discriminate the different types of gold deposits, but owing to the limits of available data, the compositional similarity of ore-forming fluids among some types of lode gold deposits, and overlapping geologic environments, distinguishing deposit types at the state scale in Alaska remains problematic. These limitations resulted in overlapping areas of prospectivity for different deposit types, highlighting the challenges for targeted gold exploration in Alaska. Adjustment of some scoring parameters and recharacterization at smaller scales to highlight individual mineral systems for application of prospectivity analyses may be helpful at a district scale. At a regional scale, the aerial overlap of individual deposit type analyses reinforces confidence in prospectivity for a lode gold resource in a drainage basin. Our analysis for undivided lode gold deposits will be the most practical analysis for landuse decisions in which delineation of areas that have confident potential for gold deposits in general is the primary goal.

Data-driven GIS analysis for lode gold potential in Alaska, although limited by the size and uneven coverage of available datasets, objectively indicates prospectivity in areas where exposure is good as well as in areas under cover. The results of our analyses show medium to high prospectivity in areas that surround known deposits, indicating an overall expansion of areas that have the potential to contain gold deposits. Exploration in these areas may help improve the balance between the volume of gold produced in placer districts statewide and the relatively low volume of identified lode resources that contribute to these placer deposits. The results of our analyses can help focus future investigations in areas that show prospectivity but are not known to contain gold deposits, as well as in areas where data are lacking and the geology is poorly understood, and acquisition of additional data may help better define and constrain gold prospectivity.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211041","collaboration":"Prepared in cooperation with the Alaska Division of Geological & Geophysical Surveys and the Bureau of Land Management","usgsCitation":"Karl, S.M., Kreiner, D.C., Case, G.N.D., Labay, K.A., Shew, N.B., Granitto, M., Wang, B., and Anderson, E.D., 2021, GIS-based identification of areas that have resource potential for lode gold in Alaska (ver. 1.1, October 2021): U.S. Geological Survey Open-File Report 2021–1041, 75 p., 9 plates, https://doi.org/10.3133/ofr20211041.","productDescription":"Report: x, 75 p.; 9 Plates: 11.00 x 17.00 inches or smaller; Data Release; 3 Appendixes","numberOfPages":"75","additionalOnlineFiles":"Y","ipdsId":"IP-099538","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":400764,"rank":18,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20223008","text":"Fact Sheet 2022-3008","description":"Karl, S.M., Kreiner, D.C., Case, G.N.D., and Labay, K., 2022, Geospatial analysis delineates lode gold prospectivity in Alaska: U.S. Geological Survey Fact Sheet 2022–3008, 4 p., https://doi.org/10.3133/fs202230008","linkHelpText":"- Geospatial Analyses Delineate Lode Gold Prospectivity in Alaska"},{"id":391052,"rank":17,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_appendix3.xlsx","text":"Appendix 3","size":"250 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Lithology-Keyword Search Terms for the “Geologic Map of Alaska”"},{"id":391050,"rank":15,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_appendix1.xlsx","text":"Appendix 1","size":"30 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Statistical Calculations of Levels of Background Values for Sediment and Rock Geochemical Data"},{"id":391049,"rank":14,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1041/versionHist.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}},{"id":386835,"rank":13,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CAM3F9","linkHelpText":"Data and results for GIS-based identification of areas that have resource potential for lode gold in Alaska"},{"id":386834,"rank":12,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_plate9.pdf","text":"Plate 9","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map Showing Overlap of Gold-bearing Porphyry-Epithermal Gold and Reduced Intrusion-related-Orogenic Gold Deposit Prospectivity Maps"},{"id":391051,"rank":16,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_appendix2.xlsx","text":"Appendix 2","size":"300 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Alaska Resource Data File (ARDF) Mineral-Deposit-Keyword-and-Scoring Templates"},{"id":386829,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_plate4.pdf","text":"Plate 4","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Estimated Resource Potential and Certainty for Reduced Intrusion-related Gold Deposits"},{"id":386823,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1041/ofr20211041_v1.1.pdf","text":"Report","size":"2.5 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1.0: Jun 2021; Version 1.1: October 2021","contact":"

Director,
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-06-29","revisedDate":"2021-10-28","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Karl, Susan M. 0000-0003-1559-7826 skarl@usgs.gov","orcid":"https://orcid.org/0000-0003-1559-7826","contributorId":502,"corporation":false,"usgs":true,"family":"Karl","given":"Susan","email":"skarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":818435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kreiner, Douglas C. 0000-0002-4405-1403","orcid":"https://orcid.org/0000-0002-4405-1403","contributorId":220474,"corporation":false,"usgs":true,"family":"Kreiner","given":"Douglas","email":"","middleInitial":"C.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":818436,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Case, George N.D. 0000-0001-9826-5661 gcase@usgs.gov","orcid":"https://orcid.org/0000-0001-9826-5661","contributorId":224941,"corporation":false,"usgs":true,"family":"Case","given":"George","email":"gcase@usgs.gov","middleInitial":"N.D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":818437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Labay, Keith A. 0000-0002-6763-3190 klabay@usgs.gov","orcid":"https://orcid.org/0000-0002-6763-3190","contributorId":217714,"corporation":false,"usgs":true,"family":"Labay","given":"Keith","email":"klabay@usgs.gov","middleInitial":"A.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":818438,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shew, Nora B. 0000-0003-0025-7220 nshew@usgs.gov","orcid":"https://orcid.org/0000-0003-0025-7220","contributorId":3382,"corporation":false,"usgs":true,"family":"Shew","given":"Nora","email":"nshew@usgs.gov","middleInitial":"B.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":818439,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Granitto, Matthew 0000-0003-3445-4863 granitto@usgs.gov","orcid":"https://orcid.org/0000-0003-3445-4863","contributorId":1224,"corporation":false,"usgs":true,"family":"Granitto","given":"Matthew","email":"granitto@usgs.gov","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":818440,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wang, Bronwen 0000-0003-1044-2227 bwang@usgs.gov","orcid":"https://orcid.org/0000-0003-1044-2227","contributorId":2351,"corporation":false,"usgs":true,"family":"Wang","given":"Bronwen","email":"bwang@usgs.gov","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":818441,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anderson, Eric D. 0000-0002-0138-6166 ericanderson@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-6166","contributorId":1733,"corporation":false,"usgs":true,"family":"Anderson","given":"Eric","email":"ericanderson@usgs.gov","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":818442,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70239000,"text":"70239000 - 2021 - Improving ChemCam LIBS long-distance elemental compositions using empirical abundance trends","interactions":[],"lastModifiedDate":"2022-12-20T13:42:34.83873","indexId":"70239000","displayToPublicDate":"2021-06-29T07:39:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3464,"text":"Spectrochimica Acta Part B: Atomic Spectroscopy","active":true,"publicationSubtype":{"id":10}},"title":"Improving ChemCam LIBS long-distance elemental compositions using empirical abundance trends","docAbstract":"

The ChemCam instrument on the Curiosity rover provides chemical compositions of Martian rocks and soils using remote laser-induced breakdown spectroscopy (LIBS). The elemental calibration is stable as a function of distance for Ti, Fe, Mg, and Ca. The calibration shows small, systematically increasing abundance trends as a function of distance for Al, Na, K, and to some extent, Si. The distance effect is known to be due to a dependence with distance on the relative strengths of atomic transition lines. Emission lines representing transitions from relatively low energy levels remain intense at longer distances while emission lines representing transitions from higher energy levels decrease in intensity more rapidly as a function of distance. The multivariate algorithms used to determine elemental compositions rely on a large number of emission lines in many cases, so rather than trying to correct all emission lines, a study was made of the predicted compositions as a function of distance, in order to determine an empirical correction. Abundance trends can be well approximated by a linear trend with distance within the ranges of abundances and distances observed up to ~6 m. Data from 11 distinct geological members and data groups of the Murray formation in Gale crater, Mars, were used to form the model, selecting the members and data groups yielding the best statistics. The model was tested using data from several targets observed from two different distances, and using data from the Kimberley formation, the composition of which is significantly different from the Murray formation, showing that the model works on other compositions beyond those used to build the model. For long-distance observations up to ~6 m, corrections can be made back to an equivalent composition at the median distance of ChemCam observations (2.6 m). The model has been validated up to 6.2 m, although ChemCam is able to observe bedrock targets to >7 m, and iron meteorites to distances of >9 m.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.sab.2021.106247","usgsCitation":"Wiens, R.C., Blazon-Brown, A.J., Melikechi, N., Frydenvang, J., Dehouck, E., Clegg, S.M., Delapp, D., Anderson, R.B., Cousin, A., and Maurice, S., 2021, Improving ChemCam LIBS long-distance elemental compositions using empirical abundance trends: Spectrochimica Acta Part B: Atomic Spectroscopy, v. 182, 106247, https://doi.org/10.1016/j.sab.2021.106247.","productDescription":"106247","ipdsId":"IP-127439","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":451723,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://insu.hal.science/insu-03672412","text":"Publisher Index Page"},{"id":410791,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"182","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wiens, Roger C.","contributorId":140330,"corporation":false,"usgs":false,"family":"Wiens","given":"Roger","email":"","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":859635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blazon-Brown, A. J.","contributorId":300205,"corporation":false,"usgs":false,"family":"Blazon-Brown","given":"A.","email":"","middleInitial":"J.","affiliations":[{"id":65042,"text":"LANL, U. Mass.","active":true,"usgs":false}],"preferred":false,"id":859636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Melikechi, N.","contributorId":300206,"corporation":false,"usgs":false,"family":"Melikechi","given":"N.","affiliations":[{"id":65043,"text":"U. Mass.","active":true,"usgs":false}],"preferred":false,"id":859637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frydenvang, J.","contributorId":181927,"corporation":false,"usgs":false,"family":"Frydenvang","given":"J.","affiliations":[],"preferred":false,"id":859638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dehouck, E.","contributorId":290073,"corporation":false,"usgs":false,"family":"Dehouck","given":"E.","affiliations":[{"id":62330,"text":"Univ. Lyon, Univ. Lyon 1, ENSL, CNRS","active":true,"usgs":false}],"preferred":false,"id":859639,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clegg, S. M.","contributorId":300207,"corporation":false,"usgs":false,"family":"Clegg","given":"S.","email":"","middleInitial":"M.","affiliations":[{"id":27196,"text":"LANL","active":true,"usgs":false}],"preferred":false,"id":859640,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Delapp, D.","contributorId":290074,"corporation":false,"usgs":false,"family":"Delapp","given":"D.","affiliations":[{"id":62306,"text":"Space and Planetary Exploration Team, Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":859641,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Anderson, Ryan B. 0000-0003-4465-2871 rbanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-4465-2871","contributorId":170054,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","email":"rbanderson@usgs.gov","middleInitial":"B.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":859642,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cousin, A.","contributorId":290035,"corporation":false,"usgs":false,"family":"Cousin","given":"A.","affiliations":[{"id":62314,"text":"Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":859643,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Maurice, S.","contributorId":296856,"corporation":false,"usgs":false,"family":"Maurice","given":"S.","affiliations":[{"id":64219,"text":"Institut de Recherche en Astrophysique et Planetologie, Universite de Toulouse 3 Paul Sabatier, CNRS, CNES","active":true,"usgs":false}],"preferred":false,"id":859644,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70221750,"text":"70221750 - 2021 - Increasing hydroperiod in a karst-depression wetland based on 165 years of simulated daily water levels","interactions":[],"lastModifiedDate":"2021-07-01T12:27:24.261014","indexId":"70221750","displayToPublicDate":"2021-06-29T07:25:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Increasing hydroperiod in a karst-depression wetland based on 165 years of simulated daily water levels","docAbstract":"

The hydrology of seasonally inundated depression wetlands can be highly sensitive to climatic fluctuations. Hydroperiod—the number of days per year that a wetland is inundated—is often of primary ecological importance in these systems and can vary interannually depending on climate conditions. In this study we re-examined an existing hydrologic model to simulate daily water levels in Sinking Pond, a 35-hectare seasonally inundated karst-depression wetland in Tennessee, USA. We recalibrated the model using 22 years of climate and water-level observations and used the recalibrated model to reconstruct (hindcast) daily water levels over a 165-year period from 1855 to 2019. A trend analysis of the climatic data and reconstructed water levels over the hindcasting period indicated substantial increases in pond hydroperiod over time, apparently related to increasing regional precipitation. Wetland hydroperiod increased on average by 5.9 days per decade between 1920 and 2019, with a breakpoint around the year 1970. Hydroperiod changes of this magnitude may have profound consequences for wetland ecology, such as a transition from a forested wetland to a mostly open-water pond at the Sinking Pond site. More broadly, this study illustrates the needs for robust hydrologic models of depression wetlands and for consideration of model transferability in time (i.e., hindcasting and forecasting) under non-stationary hydroclimatic conditions. As climate change is expected to influence water cycles, hydrologic processes, and wetland ecohydrology in the coming decades, hydrologic model projections may become increasingly important to detect, anticipate, and potentially mitigate ecological impacts in depression wetland ecosystems.

","language":"English","publisher":"Springer","doi":"10.1007/s13157-021-01474-x","usgsCitation":"Cartwright, J.M., and Wolfe, W., 2021, Increasing hydroperiod in a karst-depression wetland based on 165 years of simulated daily water levels: Wetlands, v. 41, 75, 18 p., https://doi.org/10.1007/s13157-021-01474-x.","productDescription":"75, 18 p.","ipdsId":"IP-122342","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":451725,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13157-021-01474-x","text":"Publisher Index Page"},{"id":386916,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Tennessee","otherGeospatial":"Arnold Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.09230041503906,\n 35.35601619488275\n ],\n [\n -86.03187561035156,\n 35.35601619488275\n ],\n [\n -86.03187561035156,\n 35.4019238757293\n ],\n [\n -86.09230041503906,\n 35.4019238757293\n ],\n [\n -86.09230041503906,\n 35.35601619488275\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wolfe, William J. 0000-0002-3292-051X","orcid":"https://orcid.org/0000-0002-3292-051X","contributorId":224729,"corporation":false,"usgs":false,"family":"Wolfe","given":"William J.","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":818610,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221689,"text":"sir20215054 - 2021 - Estimating flow-duration statistics and low-flow frequencies for selected streams and the implementation of a StreamStats web-based tool in Puerto Rico","interactions":[],"lastModifiedDate":"2021-06-29T14:33:40.229468","indexId":"sir20215054","displayToPublicDate":"2021-06-28T16:46:42","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5054","displayTitle":"Estimating Flow-Duration Statistics and Low-Flow Frequencies for Selected Streams and the Implementation of a StreamStats Web-Based Tool in Puerto Rico","title":"Estimating flow-duration statistics and low-flow frequencies for selected streams and the implementation of a StreamStats web-based tool in Puerto Rico","docAbstract":"

Daily mean streamflow data from 28 U.S. Geological Survey streamflow-gaging stations in Puerto Rico with 10 or more years of unregulated or minimally affected flow record through water year 2018 were used to develop regression equations for flow duration and annual n-day low-flow statistics. Ordinary least-squares and generalized least-squares regression techniques were used to develop regional regression equations for flow-duration statistics at the 99th, 98th, 95th, 90th, 80th, 70th, 60th, and 50th percent exceedance probabilities and annual n-day low-flow frequency statistics for the 1-, 7-, 14-, and 30-day mean low flows with the 2-year (0.5 nonexceedance probability), 5-year (0.2 nonexceedance probability), and 10-year (0.1 nonexceedance probability) recurrence intervals. A StreamStats web application was developed to estimate basin and climatic characteristics for the regional regression equation analysis. Basin and climatic characteristics determined to be significant explanatory variables in one or more regression equations included drainage area, mean total annual reference evapotranspiration, and minimum basin elevation. The adjusted coefficient of determination for the flow-duration regression equations ranged from 57.7 to 81.4 percent. The pseudo coefficient of determination for the annual n-day low-flow regression equations ranged from 64.6 to 70.7 percent. The StreamStats web application incorporates the flow duration, and annual n-day low-flow regression equations and can provide streamflow estimates for most ungaged sites in the island.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215054","collaboration":"Prepared in cooperation with the Puerto Rico Environmental Quality Board","usgsCitation":"Williams-Sether, T., 2021, Estimating flow-duration statistics and low-flow frequencies for selected streams and the implementation of a StreamStats web-based tool in Puerto Rico: U.S. Geological Survey Scientific Investigations Report 2021–5054, 18 p., https://doi.org/10.3133/sir20215054.","productDescription":"Report: v, 17 p.; Data Release; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-118184","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386816,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5054/coverthb.jpg"},{"id":386817,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5054/sir20215054.pdf","text":"Report","size":"5.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5054"},{"id":386819,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":386818,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y2QVJ6","text":"USGS data release","linkHelpText":"Data files for the development of regression equations for flow-duration statistics and n-day low-flow frequencies for ungaged streams in Puerto Rico through water year 2018"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3187255859375,\n 17.85590441431915\n ],\n [\n -65.5828857421875,\n 17.85590441431915\n ],\n [\n -65.5828857421875,\n 18.557739984085266\n ],\n [\n -67.3187255859375,\n 18.557739984085266\n ],\n [\n -67.3187255859375,\n 17.85590441431915\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-06-28","noUsgsAuthors":false,"publicationDate":"2021-06-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams-Sether, Tara 0000-0001-6515-9416 tjsether@usgs.gov","orcid":"https://orcid.org/0000-0001-6515-9416","contributorId":152247,"corporation":false,"usgs":true,"family":"Williams-Sether","given":"Tara","email":"tjsether@usgs.gov","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818431,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70221888,"text":"70221888 - 2021 - Further adventures in Mars DTM quality: Smoothing errors, sharpening details","interactions":[],"lastModifiedDate":"2021-07-14T11:49:43.972121","indexId":"70221888","displayToPublicDate":"2021-06-28T14:01:34","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Further adventures in Mars DTM quality: Smoothing errors, sharpening details","docAbstract":"We have used high-precision, high-resolution digital terrain models (DTMs) of the NASA Mars Science Laboratory (MSL) and Mars 2020 rover landing sites based on mosaicked images from the Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment (MRO HiRISE) camera as a reference data set to evaluate DTMs based on Mars Express High Resolution Stereo Camera (MEX HRSC) images. The Next Generation Automatic Terrain Extraction (NGATE) matcher in the SOCET SET/GXP † commercial photogrammetric system produces DTMs with relatively good (small) horizontal resolution but high error, and results are terrain dependent, with poorer resolution and smaller errors on smoother surfaces. Multiple approaches to smoothing the NGATE DTMs give very similar tradeoffs between resolution and error. Smoothing the NGATE DTMs with a single pass of an area-based matcher, which has been the standard approach to generating planetary DTMs at the U.S. Geological Survey (USGS) to date is probably near-optimal in terms of both combined resolution-error performance and local slope estimation, but smoothing with a 5x5 lowpass filter performs as well or better. DTMs from the HRSC team processing pipeline fall within this same trade space but are less sensitive to terrain roughness. DTMs produced with the Ames Stereo Pipeline also fall in this space at resolutions intermediate between NGATE and the team pipeline. Although DTM resolution and error each vary by a factor of two, their product is much more consistent, varying by <20% across multiple image sets and matching algorithms. Refinement of the stereo DTM by photoclinometry can yield significant quantitative improvement in resolution and some improvement in error (improving their product by as much as a factor of two), provided that albedo variations over distances smaller than the stereo DTM resolution are not too severe.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"International Archives of Photogrammetry, Remote Sensing, and Spatial Information Science","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"XXIV ISPRS Congress Imaging today, foreseeing tomorrow, Commission III","conferenceDate":"July 5-9, 2021","language":"English","publisher":"ISPRS","doi":"10.5194/isprs-archives-XLIII-B3-2021-659-2021","usgsCitation":"Kirk, R.L., Mayer, D., Redding, B.L., Galuszka, D.M., Fergason, R.L., Hare, T.M., and Gwinner, K., 2021, Further adventures in Mars DTM quality: Smoothing errors, sharpening details, in International Archives of Photogrammetry, Remote Sensing, and Spatial Information Science, v. 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The California Department of Transportation, commonly known as CalTrans, and other municipal separate storm sewer system permittees in California as well as other State departments of transportation nationwide need information about potential loads and yields (loads per unit area) of constituents of concern in stormwater runoff and discharges from stormwater best management practices (BMPs). Although its National Pollution Discharge Elimination System stormwater permit is focused on areas subject to total maximum daily load (TMDL) regulations, CalTrans builds and maintains BMPs to minimize the adverse effects of roadway runoff on receiving waters throughout the State. This report describes approaches used by the U.S. Geological Survey in cooperation with CalTrans for using the Stochastic Empirical Loading and Dilution Model (SELDM) to assess long-term annual yields of highway and urban runoff in selected areas of California. In this study, a series of regional and local yields were simulated to provide statewide planning-level estimates and more refined TMDL-specific yield values. SELDM was used to analyze 368 State roadway and urban runoff yields for 53 runoff quality constituents. The analyses included 222 random-seed analyses, 60 regional State roadway-runoff analyses, 24 regional urban roadway-runoff analyses, and 62 focused TMDL-area analyses.

This report describes approaches and statistics used to analyze available hydrologic and runoff quality data in all analyses. Results for all analyses are provided in the model archive, but only a selected subset of results are presented as examples in this report. State roadway runoff, urban runoff, and BMP discharge yields for total suspended solids, total nitrogen, total phosphorus, and total zinc were selected as examples because they are widespread constituents of concern with substantial amounts of State roadway and urban runoff monitoring data. In this report, a hypothetical basin was specified by using available geographic information to demonstrate use of the State roadway and urban runoff yields to estimate long-term annual stormwater loads from developed areas. Application of these yields to the hypothetical basin indicates that although State-roadway yields may be higher than urban-runoff yields for some constituents, State-roadway loads may be a small proportion of total stormwater loads because State roadways themselves are a small fraction of the total impervious area in such basins. Although application of results from this study may have considerable uncertainty for any particular stormwater outfall, the study does provide robust estimates to support basin-scale runoff-load analyses in California. These analyses also provide estimates for the 12 U.S. Environmental Protection Agency level III ecoregions that are completely or partially within the boundaries of the State of California.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215043","collaboration":"Prepared in cooperation with the California Department of Transportation","usgsCitation":"Granato, G.E., and Friesz, P.J., 2021, Approaches for assessing long-term annual yields of highway and urban runoff in selected areas of California with the Stochastic Empirical Loading and Dilution Model (SELDM): U.S. Geological Survey Scientific Investigations Report 2021–5043, 37 p., https://doi.org/10.3133/sir20215043.","productDescription":"Report: vii, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124902","costCenters":[{"id":466,"text":"New England Water Science 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\"}}]}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-06-28","noUsgsAuthors":false,"publicationDate":"2021-06-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Granato, Gregory E. 0000-0002-2561-9913 ggranato@usgs.gov","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":197631,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory","email":"ggranato@usgs.gov","middleInitial":"E.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818157,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friesz, Paul J. 0000-0002-4660-2336 pfriesz@usgs.gov","orcid":"https://orcid.org/0000-0002-4660-2336","contributorId":1075,"corporation":false,"usgs":true,"family":"Friesz","given":"Paul","email":"pfriesz@usgs.gov","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818158,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221490,"text":"ofr20201105 - 2021 - Distribution of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in monitoring wells at the former Naval Air Warfare Center, West Trenton, New Jersey, 2014–17","interactions":[],"lastModifiedDate":"2021-06-28T14:54:40.661083","indexId":"ofr20201105","displayToPublicDate":"2021-06-28T09:30:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1105","displayTitle":"Distribution of Chlorinated Volatile Organic Compounds and Per- and Polyfluoroalkyl Substances in Monitoring Wells at the Former Naval Air Warfare Center, West Trenton, New Jersey, 2014–17","title":"Distribution of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in monitoring wells at the former Naval Air Warfare Center, West Trenton, New Jersey, 2014–17","docAbstract":"

A study was conducted by the U.S. Geological Survey in cooperation with the U.S. Navy (the Navy) to determine the status of volatile organic compounds (VOCs) and per- and polyfluoroalkyl substances (PFASs) in groundwater at the former Naval Air Warfare Center (NAWC) in West Trenton, New Jersey. Wells contaminated with VOCs were sampled in 2014, 2015, 2016, and 2017 as part of the Navy’s long-term monitoring program. The results for trichloroethene (TCE), cis-1,2-dichloroethene (cisDCE), and vinyl chloride (VC) were plotted in map view to determine whether the areal extent of the contamination had changed over the 4-year period. TCE, cisDCE, and VC concentrations were plotted along nine lines of section across the former NAWC site to determine whether the vertical distribution of VOCs had changed during 2014–17. TCE, cisDCE, and VC concentrations over time were plotted on graphs for each well to determine long-term trends and changes in VOC concentrations. Data from 1990 to 2017 were used, if available, to make these graphs.

Results show that the areas of VOC concentrations greater than or equal to 1 microgram per liter decreased slightly on the northwestern side and the northeastern side of the NAWC site from 2014 to 2017 under the influence of a pump-and-treat system, natural attenuation processes, and engineered bioaugmentation experiments ongoing at the site. The pump-and-treat system continued to hydraulically contain the VOC contamination and kept it from moving offsite to the south and west of NAWC. One well northeast of the NAWC site, 50BR, was found to have detectable TCE and cisDCE concentrations. These detections indicated that VOC contamination had migrated offsite and that the pump-and-treat system was not containing the VOC contamination on the eastern side of the facility. Detectable VOC concentrations were present in wells as deep as 200 and 221 feet on the eastern and western sides of the NAWC site. TCE concentrations in most wells were found to be stable or to have slowly decreased since the facility closed in 1999. Only 7 wells, including 3 pump-and-treat extraction wells, showed substantial increases in TCE concentration from 2014 to 2017. Continuing sources of TCE to the system are desorption of TCE from organic materials in the aquifer, back diffusion of TCE from the contaminated bedrock matrix, and dissolution of remaining dense nonaqueous phase TCE in the aquifer.

Wells at the former NAWC site were sampled for PFASs in 2015, 2016, and 2017. Perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), and perfluorononanoic acid (PFNA) results were plotted in map and cross-section views to determine the areal and vertical extent of the PFAS contamination at the site. PFOS, PFOA, and PFNA concentrations greater than their established maximum contaminant levels were detected in 25, 24, and 21 of the 26 wells sampled, respectively, on the eastern side of NAWC in 2017. Vertically, the highest PFAS concentrations were present in shallow wells along the fence near the firehouse and along the railroad tracks where the aqueous film-forming foam discharge reportedly occurred back in 1990. PFAS concentrations were detected in one well (54BR) as deep as 200 feet on the eastern side of the NAWC site. PFASs were present in wells east of the railroad tracks, indicating that PFAS-contaminated groundwater had moved offsite. In a limited test of five wells, samples collected with regenerated cellulose dialysis membrane (RCDM) passive samplers contained PFAS concentrations equal to those in samples from low-flow purging.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201105","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Imbrigiotta, T.E., and Fiore, A.R., 2021, Distribution of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in monitoring wells at the former Naval Air Warfare Center, West Trenton, New Jersey, 2014–17: U.S. Geological Survey Open-File Report 2020–1105, 107 p., https://doi.org/10.3133/ofr20201105.","productDescription":"Report: xii, 107 p.; Data Release; 4 Appendixes","numberOfPages":"107","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-110205","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":386575,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix2.xlsx","text":"Appendix 2","size":"288 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Appendix 2. Volatile organic compounds, per- and polyfluoroalkyl substances, and 1,4-dioxane concentrations measured in samples from wells at the former Naval Air Warfare Center site, West Trenton, New Jersey, 1990–2017"},{"id":386577,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix2.csv","text":"Appendix 2 as CSV file","size":"187 KB","linkFileType":{"id":7,"text":"csv"}},{"id":386576,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix1.csv","text":"Appendix 1 as CSV file","size":"22.9 KB","linkFileType":{"id":7,"text":"csv"}},{"id":386573,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCAQ5N","text":"USGS data release","linkHelpText":"Concentrations of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in groundwater and surface water, former Naval Air Warfare Center, West Trenton, New Jersey"},{"id":386572,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105.pdf","text":"Report","size":"9.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1105"},{"id":386571,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1105/coverthb.jpg"},{"id":386574,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix1.xlsx","text":"Appendix 1","size":"43.7 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Appendix 1. Descriptions of boreholes, well locations, and well construction at the former Naval Air Warfare Center, West Trenton, New Jersey"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.80979204177856,\n 40.26746805544402\n ],\n [\n -74.80759263038635,\n 40.27155298671227\n ],\n [\n -74.8130750656128,\n 40.27224060619094\n ],\n [\n -74.81433033943176,\n 40.26832763061523\n ],\n [\n -74.81412649154663,\n 40.268139343654944\n ],\n [\n -74.80979204177856,\n 40.26746805544402\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike Ste 110
Lawrenceville, New Jersey, 08648

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2021-06-28","noUsgsAuthors":false,"publicationDate":"2021-06-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Imbrigiotta, Thomas E. 0000-0003-1716-4768 timbrig@usgs.gov","orcid":"https://orcid.org/0000-0003-1716-4768","contributorId":152114,"corporation":false,"usgs":true,"family":"Imbrigiotta","given":"Thomas","email":"timbrig@usgs.gov","middleInitial":"E.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817836,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fiore, Alex R. 0000-0002-0986-5225 afiore@usgs.gov","orcid":"https://orcid.org/0000-0002-0986-5225","contributorId":4977,"corporation":false,"usgs":true,"family":"Fiore","given":"Alex","email":"afiore@usgs.gov","middleInitial":"R.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":817837,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70223146,"text":"70223146 - 2021 - Fully accounting for nest age reduces bias when quantifying nest survival","interactions":[],"lastModifiedDate":"2024-04-17T18:34:37.961408","indexId":"70223146","displayToPublicDate":"2021-06-28T07:42:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Fully accounting for nest age reduces bias when quantifying nest survival","docAbstract":"

Accurately measuring nest survival is challenging because nests must be discovered to be monitored, but nests are typically not found on the first day of the nesting interval. Studies of nest survival therefore often monitor a sample that overrepresents older nests. To account for this sampling bias, a daily survival rate (DSR) is estimated and then used to calculate nest survival to the end of the interval. However, estimates of DSR (and thus nest survival) can still be biased if DSR changes with nest age and nests are not found at age 0. Including nest age as a covariate of DSR and carefully considering the method of estimating nest survival can prevent such biases, but many published studies have not fully accounted for changes in DSR with nest age. I used a simulation study to quantify biases in estimates of nest survival resulting from changes in DSR with nest age under a variety of scenarios. I tested four methods of estimating nest survival from the simulated datasets and evaluated the bias and variance of each estimate. Nest survival estimates were often strongly biased when DSR varied with age but DSR was assumed to be constant, as well as when the model included age as a covariate but calculated nest survival from DSR at the mean monitored nest age (the method typically used in previous studies). In contrast, biases were usually avoided when nest survival was calculated as the product of age-specific estimates of DSR across the full nesting interval. However, the unbiased estimates often showed large variance, especially when few nests were found at young ages. Future field studies can maximize the accuracy and precision of nest survival estimates by aiming to find nests at young ages, including age as a covariate in the DSR model, and calculating nest survival as the product of age-specific estimates of DSR when DSR changes with nest age.

","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duab030","usgsCitation":"Weiser, E.L., 2021, Fully accounting for nest age reduces bias when quantifying nest survival: Ornithological Applications, v. 123, no. 3, duab030, 23 p., https://doi.org/10.1093/ornithapp/duab030.","productDescription":"duab030, 23 p.","ipdsId":"IP-123574","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":451735,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duab030","text":"Publisher Index Page"},{"id":436286,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9THD5GK","text":"USGS data release","linkHelpText":"Nest Survival Bias Analysis"},{"id":387895,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"123","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-06-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":821108,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70221577,"text":"sim3474 - 2021 - Delineating the Pierre Shale from geophysical surveys within and near Ellsworth Air Force Base, South Dakota, 2019","interactions":[],"lastModifiedDate":"2022-04-14T16:06:18.123963","indexId":"sim3474","displayToPublicDate":"2021-06-28T07:21:51","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3474","displayTitle":"Delineating the Pierre Shale from Geophysical Surveys Within and Near Ellsworth Air Force Base, South Dakota, 2019","title":"Delineating the Pierre Shale from geophysical surveys within and near Ellsworth Air Force Base, South Dakota, 2019","docAbstract":"

The U.S. Geological Survey, in cooperation with the U.S. Air Force Civil Engineering Center, investigated the use of surface geophysical methods to delineate the top of the Cretaceous Pierre Shale along survey transects in selected areas within and near Ellsworth Air Force Base, South Dakota. Two complementary geophysical methods—electrical resistivity and passive seismic—were used along 26 co-located transect surveys within and near Ellsworth Air Force Base for a total of 12.7 line-kilometers. Electrical resistivity results were analyzed using EarthImager2D electrical resistivity tomography processing and inversion software. Two-dimensional earth models showing the electrical properties of the subsurface were evaluated by directly comparing the high and low subsurface resistivity values to a surficial geologic map and nearby wells with driller logs. Passive seismic data were analyzed using the horizontal-to-vertical spectral ratio method to determine the depth to the Pierre Shale at each survey point. The electrical resistivity and passive seismic results were compared to driller logs from nearby wells to delineate the top of the Pierre Shale. The depth to the Pierre Shale along the transects ranged from about 2.4 to 20.3 meters, and mean and median depths were about 9.2 and 9.0 meters, respectively. The elevation of the Pierre Shale and thickness of unconsolidated deposits generally increased with land-surface elevation from south to north; however, some transects displayed topographically high and low areas that sometimes did not correlate with land-surface topography and may affect local groundwater flow.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3474","collaboration":"Prepared in cooperation with U.S. Air Force Civil Engineering Center","usgsCitation":"Medler, C.J., and Anderson, T.M., 2021, Delineating the Pierre Shale from geophysical surveys within and near Ellsworth Air Force Base, South Dakota, 2019: U.S. Geological Survey Scientific Investigations Map 3474, 3 sheets, 16-p. pamphlet, https://doi.org/10.3133/sim3474.","productDescription":"Pamphlet: ix,16 p.; 3 Sheets: 48.00 x 40.00 inches or smaller; Data Release; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-126004","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_pamphlet.pdf","text":"Pamphlet","size":"2.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Pamphlet"},{"id":386682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3474/coverthb2.jpg"},{"id":398136,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3474/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"}},{"id":398000,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3474/images"},{"id":386688,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":386687,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJH17","text":"USGS data release","linkHelpText":"Electrical Resistivity Tomography (ERT) and Horizontal-to-Vertical Spectral Ratio (HVSR) data collected within and near Ellsworth Air Force Base, South Dakota, from 2014 to 2019"},{"id":386686,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_sheet03.pdf","text":"Sheet 3","size":"9.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Sheet 3","linkHelpText":"— Electrical resistivity tomography inversion results with depth to Pierre Shale from horizontal-to-vertical spectral ratio results for transects 9A, 9B, 9C, 11, 8A, 8B, 8C, 10, and 12, Ellsworth Air Force Base, South Dakota"},{"id":386685,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_sheet02.pdf","text":"Sheet 2","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Sheet 2","linkHelpText":"— Electrical resistivity tomography inversion results with depth to Pierre Shale from horizontal-to-vertical spectral ratio results for transects 4A1, 4A2, 2, 3A, 3B, 3C, and 5, Ellsworth Air Force Base, South Dakota"},{"id":386684,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_sheet01.pdf","text":"Sheet 1","size":"8.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Sheet 1","linkHelpText":"— Electrical resistivity tomography inversion results with depth to Pierre Shale from horizontal-to-vertical spectral ratio results for transects 1C1, 1C2, 14, 15, 13A, 13B, 1A, 1B, 4B, and 4C, Ellsworth Air Force Base, South Dakota"},{"id":397999,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3474/sim3474.XML"}],"country":"United States","state":"South Dakota","otherGeospatial":"Ellsworth Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.14857482910155,\n 44.10977494207831\n ],\n [\n -103.04145812988281,\n 44.10977494207831\n ],\n [\n -103.04145812988281,\n 44.17136989600329\n ],\n [\n -103.14857482910155,\n 44.17136989600329\n ],\n [\n -103.14857482910155,\n 44.10977494207831\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503

1608 Mountain View Road
Rapid City, SD 57702

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-06-28","noUsgsAuthors":false,"publicationDate":"2021-06-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Medler, Colton J. 0000-0001-6119-5065","orcid":"https://orcid.org/0000-0001-6119-5065","contributorId":201463,"corporation":false,"usgs":true,"family":"Medler","given":"Colton","email":"","middleInitial":"J.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Todd M. 0000-0001-8971-9502","orcid":"https://orcid.org/0000-0001-8971-9502","contributorId":218978,"corporation":false,"usgs":true,"family":"Anderson","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818151,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70224967,"text":"70224967 - 2021 - Identifying policy-relevant indicators for assessing landscape vegetation patterns to inform planning and management on multiple use public lands","interactions":[],"lastModifiedDate":"2021-10-11T15:27:03.245825","indexId":"70224967","displayToPublicDate":"2021-06-26T10:23:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Identifying policy-relevant indicators for assessing landscape vegetation patterns to inform planning and management on multiple use public lands","docAbstract":"

Understanding the structure and composition of landscapes can empower agencies to effectively manage public lands for multiple uses while sustaining land health. Many landscape metrics exist, but they are not often used in public land decision-making. Our objectives were to (1) develop and (2) apply a process for identifying a core set of indicators that public land managers can use to understand landscape-level resource patterns on and around public lands. We first developed a process for identifying indicators that are grounded in policy, feasible to quantify using existing data and resources, and useful for managers. We surveyed landscape monitoring efforts by other agencies, gathered science and agency input on monitoring goals, and quantified the prevalence of potential indicators in agency land health standards to identify five landscape indicators: amount, distribution, patch size, structural connectivity, and diversity of vegetation types. We then conducted pilot applications in four bureau of land management (BLM) field offices in Arizona, California, and Colorado to refine procedures for quantifying the indicators and assess the utility of the indicators for managers. Results highlighted the dominance of upland and the limited extent of riparian/wetland vegetation communities, moderate connectivity of priority vegetation patches, and lower diversity of native vegetation types on BLM compared to non-BLM lands. Agency staff can use the indicators to inform the development of quantitative resource management objectives in land use plans, evaluate progress in meeting those objectives, quantify potential impacts of proposed actions, and as a foundation for an all-lands approach to landscape-level management across public lands.

","language":"English","publisher":"Springer Link","doi":"10.1007/s00267-021-01493-8","usgsCitation":"Carter, S.K., Burris, L., Domschke, C., Garman, S.L., Haby, T., Harms, B., Kachergis, E., Litschert, S.E., and Miller, K., 2021, Identifying policy-relevant indicators for assessing landscape vegetation patterns to inform planning and management on multiple use public lands: Environmental Management, v. 68, p. 426-443, https://doi.org/10.1007/s00267-021-01493-8.","productDescription":"18 p.","startPage":"426","endPage":"443","ipdsId":"IP-120719","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":451737,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00267-021-01493-8","text":"Publisher Index Page"},{"id":390386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"68","noUsgsAuthors":false,"publicationDate":"2021-06-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":824906,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burris, Lucy","contributorId":267280,"corporation":false,"usgs":false,"family":"Burris","given":"Lucy","affiliations":[],"preferred":false,"id":824907,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Domschke, Chris","contributorId":267281,"corporation":false,"usgs":false,"family":"Domschke","given":"Chris","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":824908,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garman, Steven L 0000-0002-9032-9074","orcid":"https://orcid.org/0000-0002-9032-9074","contributorId":267282,"corporation":false,"usgs":false,"family":"Garman","given":"Steven","email":"","middleInitial":"L","affiliations":[],"preferred":false,"id":824909,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haby, Travis","contributorId":202409,"corporation":false,"usgs":false,"family":"Haby","given":"Travis","affiliations":[{"id":36421,"text":"Bureau of Land Management National Operations Center","active":true,"usgs":false}],"preferred":false,"id":824996,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harms, Benjamin R","contributorId":267283,"corporation":false,"usgs":false,"family":"Harms","given":"Benjamin R","affiliations":[],"preferred":false,"id":824910,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kachergis, Emily","contributorId":195930,"corporation":false,"usgs":false,"family":"Kachergis","given":"Emily","affiliations":[],"preferred":false,"id":824911,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Miller, Kevin","contributorId":178815,"corporation":false,"usgs":false,"family":"Miller","given":"Kevin","affiliations":[],"preferred":false,"id":824913,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Litschert, S. E.","contributorId":267284,"corporation":false,"usgs":false,"family":"Litschert","given":"S.","email":"","middleInitial":"E.","affiliations":[{"id":55460,"text":"Quantum Spatial, Inc.","active":true,"usgs":false}],"preferred":false,"id":824912,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70230358,"text":"70230358 - 2021 - Identification of low-frequency earthquakes on the San Andreas fault with deep learning","interactions":[],"lastModifiedDate":"2022-04-08T11:58:40.972488","indexId":"70230358","displayToPublicDate":"2021-06-26T06:56:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Identification of low-frequency earthquakes on the San Andreas fault with deep learning","docAbstract":"

Low-frequency earthquakes are a seismic manifestation of slow fault slip. Their emergent onsets, low amplitudes, and unique frequency characteristics make these events difficult to detect in continuous seismic data. Here, we train a convolutional neural network to detect low-frequency earthquakes near Parkfield, CA using the catalog of Shelly (2017), https://doi.org/10.1002/2017jb014047 as training data. We explore how varying model size and targets influence the performance of the resulting network. Our preferred network has a peak accuracy of 85% and can reliably pick low-frequency earthquake (LFE) S-wave arrival times on single station records. We demonstrate the abilities of the network using data from permanent and temporary stations near Parkfield, and show that it detects new LFEs that are not part of the Shelly (2017), https://doi.org/10.1002/2017jb014047 catalog. Overall, machine-learning approaches show great promise for identifying additional low-frequency earthquake sources. The technique is fast, generalizable, and does not require sources to repeat.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GL093157","usgsCitation":"Thomas, A., Inbal, A., Searcy, J., Shelly, D.R., and Bürgmann, R., 2021, Identification of low-frequency earthquakes on the San Andreas fault with deep learning: Geophysical Research Letters, v. 48, no. 13, e2021GL093157, 10 p., https://doi.org/10.1029/2021GL093157.","productDescription":"e2021GL093157, 10 p.","ipdsId":"IP-128858","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":451740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gl093157","text":"Publisher Index Page"},{"id":398379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"48","issue":"13","noUsgsAuthors":false,"publicationDate":"2021-07-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, A. M.","contributorId":289920,"corporation":false,"usgs":false,"family":"Thomas","given":"A. M.","affiliations":[{"id":62287,"text":"Department of Earth Sciences, University of Oregon, Eugene, Oregon, USA","active":true,"usgs":false}],"preferred":false,"id":840066,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Inbal, A.","contributorId":289921,"corporation":false,"usgs":false,"family":"Inbal","given":"A.","email":"","affiliations":[{"id":62288,"text":"Department of Geophysics, Tel Aviv University, Tel Aviv, Israel","active":true,"usgs":false}],"preferred":false,"id":840067,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Searcy, J.","contributorId":289922,"corporation":false,"usgs":false,"family":"Searcy","given":"J.","email":"","affiliations":[{"id":62289,"text":"Research Advanced Computing Services, University of Oregon, Eugene, Oregon, USA","active":true,"usgs":false}],"preferred":false,"id":840068,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":840069,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bürgmann, R.","contributorId":289923,"corporation":false,"usgs":false,"family":"Bürgmann","given":"R.","affiliations":[{"id":62290,"text":"Department of Earth and Planetary Science, University of California, Berkeley, Berkeley, California, USA","active":true,"usgs":false}],"preferred":false,"id":840070,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221579,"text":"ofr20211031 - 2021 - Annotated bibliography of scientific research on Ventenata dubia published from 2010 to 2020","interactions":[],"lastModifiedDate":"2021-06-25T19:53:40.817282","indexId":"ofr20211031","displayToPublicDate":"2021-06-25T12:45:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1031","displayTitle":"Annotated Bibliography of Scientific Research on Ventenata dubia Published from 2010 to 2020","title":"Annotated bibliography of scientific research on Ventenata dubia published from 2010 to 2020","docAbstract":"

Integrating recent science into management decisions supports effective natural resource management and can lead to better resource outcomes. However, finding and accessing science information can be time consuming and costly. To assist in this process, the U.S. Geological Survey (USGS) is creating a series of annotated bibliographies on topics of management concern for western lands. Previously published reports introduced a methodology for preparing annotated bibliographies to facilitate the integration of recent, peer-reviewed science into resource management decisions. Therefore, relevant text from those efforts is reproduced here to frame the presentation. Invasive annual grasses are widely distributed throughout the western United States and threaten native ecosystems by altering fire regimes, replacing native plants, and altering grazing patterns, often with tremendous associated costs. One invasive annual grass, Ventenata dubia (hereafter, ventenata), was first introduced to the United States in the 1950s and has recently been identified as a management concern. Ventenata has a wide native geographic range, from Africa to northern Europe, and could thus potentially spread widely in the United States if left unmanaged. We compiled and summarized peer-reviewed journal articles, government reports, and data products on ventenata published between January 1, 2010, and August 27, 2020. We first conducted a systematic search of three reference databases and three government databases using the search phrase: “ventenata” OR “Ventenata dubia.” We refined the initial list of products by removing (1) duplicates, (2) products not written in English, (3) publications that were not focused in North America, (4) publications that were not published as research, data products, or scientific review articles in peer-reviewed journals or as formal technical reports, and (5) products for which ventenata was not a research focus or for which the study did not present new data or findings about ventenata. We summarized each product using a consistent structure (background, objectives, methods, location, findings, and implications) and identified the management topics (for example, species and population characteristics, habitat, control and management efforts) addressed by each product. We also noted which publications included new geospatial data. The review process for this annotated bibliography included an initial internal colleague review of each summary, requesting input on each summary from an author of the original publication, and a formal peer review. Our initial searches resulted in 505 total products, of which 29 met our criteria for inclusion. Nonnative invasive plants; weed management; behavior or demographics; dispersal, spread, vectors and pathways; site-scale habitat characteristics; survival; and weed management subtopic: herbicides were the management topics most commonly addressed. The online version of this annotated bibliography will be searchable by topic, location, and year; it will also include links to each original publication, where available. The studies compiled and summarized here may inform planning and management actions that seek to maintain and restore landscapes and control nonnative invasive species across the western United States.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20211031","usgsCitation":"Poor, E.E., Kleist, N.J., Bencin, H.L., Foster, A.C., and Carter, S.K., 2021, Annotated bibliography of scientific research on Ventenata dubia published from 2010 to 2020: U.S. Geological Survey Open-File Report 2021–1031, 26 p., https://doi.org/10.3133/ofr20211031.","productDescription":"iv, 26 p.","onlineOnly":"Y","ipdsId":"IP-121527","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":386678,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1031/ofr20211031.pdf","text":"Report","size":"1.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1031"},{"id":386677,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1031/coverthb.jpg"}],"contact":"

Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118

","tableOfContents":"","publishedDate":"2021-06-25","noUsgsAuthors":false,"publicationDate":"2021-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Poor, Erin E. 0000-0002-8799-3193","orcid":"https://orcid.org/0000-0002-8799-3193","contributorId":260597,"corporation":false,"usgs":false,"family":"Poor","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":818152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kleist, Nathan J. 0000-0002-2468-4318","orcid":"https://orcid.org/0000-0002-2468-4318","contributorId":260598,"corporation":false,"usgs":true,"family":"Kleist","given":"Nathan","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":818153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bencin, Heidi L. 0000-0002-0879-5392","orcid":"https://orcid.org/0000-0002-0879-5392","contributorId":222412,"corporation":false,"usgs":true,"family":"Bencin","given":"Heidi","email":"","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":818154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foster, Alison C. 0000-0002-6659-2120","orcid":"https://orcid.org/0000-0002-6659-2120","contributorId":260599,"corporation":false,"usgs":true,"family":"Foster","given":"Alison","email":"","middleInitial":"C.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":818155,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carter, Sarah K. 0000-0003-3778-8615","orcid":"https://orcid.org/0000-0003-3778-8615","contributorId":192418,"corporation":false,"usgs":true,"family":"Carter","given":"Sarah","email":"","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":818156,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229781,"text":"70229781 - 2021 - Proposed standard weight (Ws) equation and length categories for Utah Chub","interactions":[],"lastModifiedDate":"2022-03-17T16:07:09.637674","indexId":"70229781","displayToPublicDate":"2021-06-25T11:01:28","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Proposed standard weight (Ws) equation and length categories for Utah Chub","title":"Proposed standard weight (Ws) equation and length categories for Utah Chub","docAbstract":"

Condition indices, such as relative weight (Wr), provide a simple method for comparing length–weight relationships among populations. However, no standard weight (Ws) equation has been developed for Utah Chub Gila atraria, a species of important management focus in the Intermountain West. We obtained length–weight data for 30,541 Utah Chub from 24 populations in Idaho, Montana, Utah, and Wyoming. We used the regression line percentile (RLP), linear empirical percentile (EmP), and quadratic EmP methods to develop average (50th percentile) and above average (75th percentile) Ws equations. Additionally, Froese’s method was used to develop another Ws equation for Utah Chub. Length-related biases were detected in Ws equations developed using the RLP, 50th percentile quadratic EmP, and Froese methods. The linear EmP Ws equations did not exhibit length-related biases for the 50th and 75th percentiles. We propose using the 75th percentile linear EmP Ws equation for Utah Chub between 90 and 410 mm TL. The EmP 75th percentile equation was log10(Ws) = −4.938 + 3.031·log10(TL), where Ws is weight in grams and TL is in millimeters. The English equivalent of this equation is log10(Ws) = −3.335 + 3.031·log10(TL), where Ws is weight in pounds and TL is in inches for 4–16-in Utah Chub. Additionally, we propose that minimum TLs of 100 mm (4 in; stock), 200 mm (8 in; quality), 250 mm (10 in; preferred), 300 mm (12 in; memorable), and 380 mm (15 in; trophy) be used to calculate proportional size distribution (PSD) indices. Better understanding Utah Chub populations using Wr and PSDs will aid managers in assessing management strategies (e.g., biological controls) focused on Utah Chub.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10636","usgsCitation":"Black, A., Beard, Z., Flinders, J., and Quist, M.C., 2021, Proposed standard weight (Ws) equation and length categories for Utah Chub: North American Journal of Fisheries Management, v. 41, no. 5, p. 1299-1309, https://doi.org/10.1002/nafm.10636.","productDescription":"10 p.","startPage":"1299","endPage":"1309","ipdsId":"IP-127119","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":397255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Utah, 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\"}}]}","volume":"41","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Black, Aaron","contributorId":288737,"corporation":false,"usgs":false,"family":"Black","given":"Aaron","email":"","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":838255,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beard, Zach","contributorId":288738,"corporation":false,"usgs":false,"family":"Beard","given":"Zach","affiliations":[{"id":12922,"text":"Arizona Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":838256,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flinders, Jon","contributorId":288739,"corporation":false,"usgs":false,"family":"Flinders","given":"Jon","email":"","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":838257,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":838254,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221694,"text":"70221694 - 2021 - Local fruit availability and en route wind conditions are poor predictors of bird abundance and composition during fall migration in coastal Yucatán Peninsula","interactions":[],"lastModifiedDate":"2021-10-06T14:57:58.320227","indexId":"70221694","displayToPublicDate":"2021-06-25T09:07:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7509,"text":"The Wilson Journal of Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Local fruit availability and en route wind conditions are poor predictors of bird abundance and composition during fall migration in coastal Yucatán Peninsula","docAbstract":"

In migratory stopover habitats, bird abundance and composition change on a near daily basis. On any given day, the local bird community should reflect local environmental conditions but also the environments that birds encountered previously along their migratory route. For example, during fall migration, the coast of the Yucatán Peninsula in Mexico receives birds that have just crossed the Gulf of Mexico and their abundance and composition may be associated with regional factors such as wind conditions experienced on previous dates. Other factors, such as local fruit availability, may also influence daily variation in bird abundance and composition. Using mist net data from 2 coastal national parks in the Yucatán Peninsula during fall migration in 2016 and 2017, we did not find a strong association between daily changes in bird abundance or community composition with wind conditions and ripe fruit availability. Thus, despite wind and fruit being known to be important to individual birds (influencing stopover duration and departure decisions), their effects might not scale up to be drivers of population and community level variation. On the other hand, we found that the 2 sites shared only about half of their species and those shared species had different temporal abundance patterns at each site. Site and year differences in temporal patterns of migration might arise because populations of the same species are on different migration routes and schedules. While bird arrival is not timed to hit peaks in fruit production in our study sites, whether bird–resource mismatch is a general characteristic of tropical coastal stopover habitats requires further research. If birds on migration have adapted to seasonal variation in food availability, they might be equipped to deal with the additional variability in food supply that is expected to occur with climate change.

","language":"English","publisher":"Wilson Ornithological Society","doi":"10.1676/19-00131","usgsCitation":"Feldman, R., Celis-Murillo, A., Deppe, J.L., and Dorantes-Euan, A., 2021, Local fruit availability and en route wind conditions are poor predictors of bird abundance and composition during fall migration in coastal Yucatán Peninsula: The Wilson Journal of Ornithology, v. 132, no. 4, p. 850-867, https://doi.org/10.1676/19-00131.","productDescription":"18 p.","startPage":"850","endPage":"867","ipdsId":"IP-111815","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":502614,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":386849,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","otherGeospatial":"Yucatan Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.47216796875,\n 18.417078658661257\n ],\n [\n -87.36328125,\n 20.076570104545173\n ],\n [\n -86.77001953125,\n 21.14599216495789\n ],\n [\n -86.94580078125,\n 21.616579336740603\n ],\n [\n -88.87939453125,\n 21.596150576461426\n ],\n [\n -90.10986328125,\n 21.289374355860424\n ],\n [\n -90.8349609375,\n 20.427012814257385\n ],\n [\n -91.34033203125,\n 18.8543103618898\n ],\n [\n -91.47216796875,\n 18.417078658661257\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"132","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Feldman, Richard E","contributorId":260664,"corporation":false,"usgs":false,"family":"Feldman","given":"Richard E","affiliations":[{"id":52633,"text":"Centro de Investigacion Cientifica de Yucatan Merida, Yucatan MEXICO","active":true,"usgs":false}],"preferred":false,"id":818444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Celis-Murillo, Antonio 0000-0002-3371-6529","orcid":"https://orcid.org/0000-0002-3371-6529","contributorId":237851,"corporation":false,"usgs":true,"family":"Celis-Murillo","given":"Antonio","email":"","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":818445,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deppe, Jill L.","contributorId":173619,"corporation":false,"usgs":false,"family":"Deppe","given":"Jill","email":"","middleInitial":"L.","affiliations":[{"id":27256,"text":"Dept of Biological Sciences, Eastern Illinois University, Charleston, IL","active":true,"usgs":false}],"preferred":false,"id":818446,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dorantes-Euan, Alfredo","contributorId":260665,"corporation":false,"usgs":false,"family":"Dorantes-Euan","given":"Alfredo","email":"","affiliations":[{"id":52633,"text":"Centro de Investigacion Cientifica de Yucatan Merida, Yucatan MEXICO","active":true,"usgs":false}],"preferred":false,"id":818447,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223131,"text":"70223131 - 2021 - Maintenance of nest quality in Adélie penguins Pygoscelis adeliae: An additional benefit to life in the center","interactions":[],"lastModifiedDate":"2021-08-12T13:15:43.236439","indexId":"70223131","displayToPublicDate":"2021-06-25T08:13:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3093,"text":"Polar Biology","active":true,"publicationSubtype":{"id":10}},"title":"Maintenance of nest quality in Adélie penguins Pygoscelis adeliae: An additional benefit to life in the center","docAbstract":"

In colonial seabirds, differences in the nesting or fledging success have been associated with differences in nest position within the breeding aggregation (subcolony): less successful nests are located on the periphery, with more successful nests closer to the center. For Pygoscelid penguins, central nests tend to be larger, with nest size being an indicator of individual quality because stones must be gathered singly, so more stones reflect more individual effort. Competition for nest materials, including the collection of materials from another’s nest, has also frequently been described in penguins and other colonial seabirds. We used the data collected during the incubation stage from a total of 20 subcolonies at two separate breeding colonies of Adélie penguins (Pygoscelis adeliae) on Ross Island (Antarctica) to test the influence of nest position on breeding success. We also investigated how competition for nest stones could occur at different intensities depending on size of the subcolony, nest position, and quality within a subcolony. We found that peripheral nests experienced lower breeding success and higher number of individuals attempting to remove stones with higher removal success rates than from nests toward the center. The higher costs associated with maintaining and defending nests that incur higher removal pressure could be an additional factor involved in the lower breeding success of peripheral nests.


","language":"English","publisher":"Springer","doi":"10.1007/s00300-021-02894-5","usgsCitation":"Morandini, V., Dugger, K., Lescroël, A., Schmidt, A., and Ballard, G., 2021, Maintenance of nest quality in Adélie penguins Pygoscelis adeliae: An additional benefit to life in the center: Polar Biology, v. 44, https://doi.org/10.1007/s00300-021-02894-5.","productDescription":"10 p.","startPage":"1562","ipdsId":"IP-100363","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":387902,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","edition":"1553","noUsgsAuthors":false,"publicationDate":"2021-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Morandini, Virginia","contributorId":264177,"corporation":false,"usgs":false,"family":"Morandini","given":"Virginia","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":821076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":821075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lescroël, Amélie","contributorId":264179,"corporation":false,"usgs":false,"family":"Lescroël","given":"Amélie","affiliations":[{"id":54398,"text":"point blue conserv science","active":true,"usgs":false}],"preferred":false,"id":821078,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, Annie","contributorId":264178,"corporation":false,"usgs":false,"family":"Schmidt","given":"Annie","affiliations":[{"id":54398,"text":"point blue conserv science","active":true,"usgs":false}],"preferred":false,"id":821077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ballard, Grant","contributorId":264180,"corporation":false,"usgs":false,"family":"Ballard","given":"Grant","affiliations":[{"id":54398,"text":"point blue conserv science","active":true,"usgs":false}],"preferred":false,"id":821079,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221663,"text":"70221663 - 2021 - HydroMet: A new code for automated objective optimization of hydrometeorological thresholds for landslide initiation","interactions":[],"lastModifiedDate":"2021-06-28T13:13:22.787252","indexId":"70221663","displayToPublicDate":"2021-06-25T08:10:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"HydroMet: A new code for automated objective optimization of hydrometeorological thresholds for landslide initiation","docAbstract":"

Landslide detection and warning systems are important tools for mitigation of potential hazards in landslide prone areas. Traditionally, warning systems for shallow landslides have been informed by rainfall intensity-duration thresholds. More recent advances have introduced the concept of hydrometeorological thresholds that are informed not only by rainfall, but also by subsurface hydrological measurements. Previously, hydrometeorological thresholds have been shown to improve capabilities for forecasting shallow landslides, and they may ultimately be adapted to more generalized landslide forecasting. We present HydroMet, a code developed in Python by the U.S. Geological Survey, which allows users to guide the automated estimation of hydrometeorological thresholds for a site or area of interest, with the flexibility to select preferred threshold variables for the antecedent hydrologic conditions and the triggering meteorological conditions. Users can import hydrologic time-series data, including rainfall, soil-water content, and pore-water pressure, along with the times of known landslide occurrences, and then conduct objective optimization of warning thresholds using receiver operating characteristics. HydroMet presents many additional options, including selecting the threshold formula, the timescale of possible threshold variables, and the skill statistics used for optimization. Users can develop dual-stage thresholds for watch and warning alerts, with a lower, risk-averse threshold to avoid missed alarms and a less conservative threshold to minimize false alarms. Users may also choose to split their inventory data into calibration and evaluation subsets to independently evaluate the performance of optimized thresholds. We present output and applications of HydroMet using monitoring data from landslide-prone areas in the U.S. to demonstrate its utility and ability to produce thresholds with limited missed and false alarms for informing the next generation of reliable landslide warning systems.

","language":"English","publisher":"MDPI","doi":"10.3390/w13131752","usgsCitation":"Conrad, J.L., Morphew, M.D., Baum, R.L., and Mirus, B.B., 2021, HydroMet: A new code for automated objective optimization of hydrometeorological thresholds for landslide initiation: Water, v. 13, no. 3, 1752, 17 p., https://doi.org/10.3390/w13131752.","productDescription":"1752, 17 p.","ipdsId":"IP-129944","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":451750,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13131752","text":"Publisher Index Page"},{"id":386788,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-06-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Conrad, Jacob L. 0000-0001-8112-5355","orcid":"https://orcid.org/0000-0001-8112-5355","contributorId":260658,"corporation":false,"usgs":true,"family":"Conrad","given":"Jacob","email":"","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":818377,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morphew, Michael D. 0000-0003-0072-1652","orcid":"https://orcid.org/0000-0003-0072-1652","contributorId":207959,"corporation":false,"usgs":false,"family":"Morphew","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":37668,"text":"USGS, Student- Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":818378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":818379,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":818380,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}