We develop a remote wave gauging technique to estimate wave height and period from imagery of waves in the surf zone. In this proof-of-concept study, we apply the same framework to three datasets: the first, a set of close-range monochrome infrared (IR) images of individual nearshore waves at Duck, NC, USA; the second, a set of visible (i.e. RGB) band orthomosaics of a larger nearshore area near Santa Cruz, CA, USA; and the third, a set of oblique (unrectified) images from the same site. The network is trained using coincident images and in situ wave measurements. The optical wave gauge (OWG) consists of a deep convolutional neural network (CNN) to extract features from imagery — called a ‘base model’, with additional layers to distill the feature information into lower dimensional spaces, and a final layer of dense neurons to predict continuously varying quantities. Four base models are compared. The OWG is trained for both individual wave height and period, and statistical quantities like significant wave height and peak wave period. The best performing OWG on the IR dataset achieved RMS errors of 0.14 m and 0.41 s for height and period, respectively, capturing up to 98% of the variance in these quantities. The best performing OWG on the visible band rectified dataset achieved RMS errors of 0.08 m and 0.79 s, respectively, for height and period. The same values for the oblique RGB imagery were 0.11 m and 0.81 s for height and period, respectively. Overall, wave height and period accuracy is sensitive to choice of base model; OWGs built upon MobilenetV2 tend to perform worst and those built on Inception-ResnetV2 have the smallest RMS error. The presence or otherwise of residual layers in the model makes little systematic difference to the final OWG accuracy. Smaller batch sizes used in model training tend to result in more accurate OWGs. An out-of-calibration validation, using images associated with wave heights or periods outside the range of values represented in the training data, showed that the ability for OWGs to predict the bottom 5% of low wave heights and the top 5% of high wave heights was reasonably good, but the same was not generally true of wave period. The same framework, not optimized for either dataset, predicts both quantities with high accuracy when trained on imagery, despite the differences in electromagnetic band, perspective, and scale. The OWG estimates wave properties from an image in less than 100 ms on a modestly sized CPU, allowing for the possibility of continuous real-time wave estimates.
Large-scale assessments of rangeland runoff and erosion require methods to extend plot-scale parameterizations to large areas. In this study, Rangeland Hydrology and Erosion Model (RHEM) parameters were developed from plot-scale foliar and ground-cover transect data for an arid, grass-shrub rangeland in southern New Mexico, and a method was assessed to upscale transect-plot parameters to a large landscape. The transect-plot data compared favorably to corresponding cell data generated from publicly available geospatial data for total foliar cover but less favorably for litter cover and poorly for rock cover. The RHEM effective hydraulic conductivity (Ke) parameter was comparable between transect-plot and geospatial-cell methods, but the splash and sheet erosion factor (Kss) had poor agreement between the two methods. Simulated runoff and erosion reflected differences in transect-plot and geospatial-cell-based RHEM parameterizations, with low error and very good agreement for runoff but high error and poor agreement for soil loss. These results demonstrate that Ke parameters developed using geospatial data calibrated to plot data can be extrapolated to large spatial areas and provide reasonable simulation of runoff using RHEM. However, these same geospatial methods do not provide reasonable estimation of Kss or simulation of soil loss. Poor representation of litter and rock cover variables, which are highly spatially heterogeneous at the plot scale, was inadequate to accurately represent Kss or soil loss using RHEM. High resolution ground cover data, such as from unmanned aerial systems, may improve parameterization of Kss, and, ultimately, arid rangeland soil erosion simulation.
","language":"English","publisher":"American Society of Agricultural and Biological Engineers","doi":"10.13031/aea.13275","usgsCitation":"Ball, G., and Douglas-Mankin, K., 2019, Geospatial scaling of runoff and erosion modeling in the Chihuahuan Desert: Applied Engineering in Agriculture, v. 5, no. 35, p. 733-743, https://doi.org/10.13031/aea.13275.","productDescription":"11 p.","startPage":"733","endPage":"743","ipdsId":"IP-104120","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":369346,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Chihuahuan Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.732421875,\n 34.88593094075317\n ],\n [\n -105.13916015625,\n 33.60546961227188\n ],\n [\n -105.2490234375,\n 32.861132322810946\n ],\n [\n -105.75439453125,\n 32.491230287947594\n ],\n [\n -106.9189453125,\n 34.34343606848294\n ],\n [\n -107.1826171875,\n 33.970697997361626\n ],\n [\n -107.698974609375,\n 32.80574473290688\n ],\n [\n -109.09423828125,\n 33.19273094190692\n ],\n [\n -109.10522460937499,\n 31.325486676506983\n ],\n [\n -108.226318359375,\n 31.325486676506983\n ],\n [\n -108.204345703125,\n 31.774877618507386\n ],\n [\n -106.578369140625,\n 31.765537409484374\n ],\n [\n -106.644287109375,\n 31.970803930433096\n ],\n [\n -103.11767578124999,\n 32.01739159980399\n ],\n [\n -103.084716796875,\n 34.994003757575776\n ],\n [\n -105.732421875,\n 34.88593094075317\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"5","issue":"35","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ball, Grady 0000-0003-3030-055X","orcid":"https://orcid.org/0000-0003-3030-055X","contributorId":220746,"corporation":false,"usgs":true,"family":"Ball","given":"Grady","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":775597,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas-Mankin, Kyle R. 0000-0002-3155-3666","orcid":"https://orcid.org/0000-0002-3155-3666","contributorId":200849,"corporation":false,"usgs":false,"family":"Douglas-Mankin","given":"Kyle R.","affiliations":[],"preferred":false,"id":775598,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70206053,"text":"sim3444 - 2019 - Potentiometric surface of groundwater-level altitudes near the planned Highway 270 bypass, east of Hot Springs, Arkansas, July–August 2017","interactions":[],"lastModifiedDate":"2019-11-19T17:19:14","indexId":"sim3444","displayToPublicDate":"2019-11-19T13:49:10","publicationYear":"2019","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":"3444","displayTitle":"Potentiometric Surface of Groundwater-Level Altitudes Near the Planned Highway 270 Bypass, East of Hot Springs, Arkansas, July–August 2017","title":"Potentiometric surface of groundwater-level altitudes near the planned Highway 270 bypass, east of Hot Springs, Arkansas, July–August 2017","docAbstract":"The Ouachita Mountains aquifer system potentiometric-surface map is one component of the Hot Springs Bypass Groundwater Monitoring Project. The potentiometric-surface map provides a baseline assessment of shallow groundwater levels and flow directions before the construction of the Arkansas Department of Transportation planned extension of the Highway 270 bypass, east of Hot Springs, Arkansas. The map provides data regarding status of groundwater levels and potential effects on the recharge area in the Hot Springs National Park and to groundwater that supplies water to domestic users near the Highway 270 bypass.
Groundwater levels from 66 wells were measured in July–August 2017. Fifty nine of the 66 groundwater-level altitudes measured, along with select surface-water features and springs, were used to construct the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric surface, a two-dimensional representation, shows groundwater-level altitudes ranging from a maximum of 766 ft above the North American Vertical Datum of 1988 (NAVD 88) to a minimum of 443 ft NAVD 88. The spring altitudes on the potentiometric-surface map range from 534 ft to 927 ft above NAVD 88. The study area, located in the Ouachita Mountains physiographic section of the Ouachita physiographic province, comprises narrow valleys and high ridges of Stanley Shale, Hot Springs Sandstone, Arkansas Novaculite, Missouri Mountain-Polk Creek Shale, and Bigfork Chert. The highest groundwater-level altitudes observed were in the Hot Springs Sandstone, Arkansas Novaculite, and Missouri Mountain-Polk Creek Shale. The springs discharge in outcrop areas of the Stanley Shale, Bigfork Chert, and Arkansas novaculite. The planned Highway 270 bypass will cut across ridges and valleys comprising these formations and, very importantly, across areas with elevations above 660 ft above NAVD 88 that define the hot springs recharge zone.
This potentiometric-surface map defines the status of the shallow groundwater potentiometric surface near the Highway 270 bypass prior to initiation of construction activities. A post-construction potentiometric map is planned. It must be noted that shallow groundwater levels are also subject to climatic effects including changes in amount and timing of precipitation and changes in temperature.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3444","collaboration":"Prepared in cooperation with the Arkansas Department of Transportation and the National Park Service","usgsCitation":"Nottmeier, A.M., and Hays, P.D., 2019, Potentiometric surface of groundwater-level altitudes near the planned Highway 270 bypass, east of Hot Springs, Arkansas, July–August 2017: U.S. Geological Survey Scientific Investigations Map 3444, 13 p., 1 sheet, https://doi.org/10.3133/sim3444.","productDescription":"Pamphlet: v, 13 p.; Sheet: 22 x 28 inches; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-092242","costCenters":[{"id":129,"text":"Arkansas Water Science Center","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":369331,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TD9WK0","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Datasets of the Potentiometric Surface of Groundwater-Level Altitudes Near the Planned Highway 270 Bypass, East of Hot Springs, Arkansas, July–August 2017"},{"id":369328,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3444/coverthb.jpg"},{"id":369329,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3444/sim3444.pdf","text":"Sheet ","size":"2.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3444 ","linkHelpText":"– Potentiometric-surface map for the Ouachita Mountains aquifer, July–August 2017"},{"id":369330,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3444/sim3444_pamphlet.pdf","text":"Pamphlet","size":"3.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3444 Pamphlet"}],"country":"United States","state":"Arkansas","county":"Garland County","city":"Hot Springs","otherGeospatial":"Highway 270 Bypass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.01935195922852,\n 34.50542493789137\n ],\n [\n -92.94931411743164,\n 34.50542493789137\n ],\n [\n -92.94931411743164,\n 34.5710371883746\n ],\n [\n -93.01935195922852,\n 34.5710371883746\n ],\n [\n -93.01935195922852,\n 34.50542493789137\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The precise estimation of process effects in hydrological models requires applying models to large scales with extensive spatial variability in controlling factors. Despite progress in large‐scale applications of hydrological models in conterminous United States (CONUS) river basins, spatial constraints in model parameters have prevented the interbasin sharing of data, complicating quantification of process effects and limiting the accuracy of model predictions and uncertainties. Hierarchical Bayesian methods enable data sharing between basins and the identification of the causes of model uncertainties, which can improve model accuracy and interpretability; however, computational inefficiencies have been an obstacle to their large‐scale application. We used a new generation of Bayesian methods to develop a hierarchical version of a previous hybrid (statistical‐mechanistic) SPAtially Referenced Regression On Watershed attributes model of long‐term mean annual streamflow in the CONUS. We identified hierarchical (regional) variations in model coefficients and uncertainties and evaluated their effects on model accuracy and interpretability across diverse environments in 16 major CONUS regions. Hierarchical coefficients significantly improved spatial accuracy of model predictions, with the largest improvements in humid eastern regions, where uncertainties were approximately one third of those in arid western regions. Half of the coefficients varied regionally, with the largest variations in coefficients associated with water losses in streams and reservoirs. Our unraveling of the causes of model uncertainties identified a small latent process component of runoff that varies inversely with river size in most CONUS regions. Our study advances the use of hierarchical Bayesian methods to improve the predictive capabilities of hydrological models.
Simultaneous autoregressive (SAR) models are useful for accommodating various forms of dependence among data that have discrete support in a space of interest. These models are often specified hierarchically as mixed-effects regression models with first-moment structure controlled by a conventional linear regression term and second-moment structure induced by correlated random effects. In their general form, SAR models resemble conditional autoregressive (CAR) models, and can be made equivalent but are often parameterized differently. Importantly, SAR models can be specified by simultaneously regressing a discrete spatial process on itself. Thus, they allow one to construct statistical models for processes with directional graphical properties that pertain to data generating mechanisms. Most commonly SAR models have been used to account for structure among data with areal spatial support in applications involving ecology, epidemiology, sociology, and environmental science.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Wiley StatsRef: Statistics reference online","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Wiley","doi":"10.1002/9781118445112.stat08208","usgsCitation":"Hooten, M., Ver Hoef, J.M., and Hanks, E., 2019, Simultaneous autoregressive (SAR) model, chap. of Wiley StatsRef: Statistics reference online, HTML Document, https://doi.org/10.1002/9781118445112.stat08208.","productDescription":"HTML Document","ipdsId":"IP-105139","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":388871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2019-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":822560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ver Hoef, Jay M.","contributorId":265330,"corporation":false,"usgs":false,"family":"Ver Hoef","given":"Jay","email":"","middleInitial":"M.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":822561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanks, Ephraim M.","contributorId":265331,"corporation":false,"usgs":false,"family":"Hanks","given":"Ephraim M.","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":822562,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236884,"text":"70236884 - 2019 - On the utilization of synthetic and measured earthquake ground motions for designing building monitoring systems in the near-field of major faults","interactions":[],"lastModifiedDate":"2022-09-21T13:32:08.495923","indexId":"70236884","displayToPublicDate":"2019-11-15T08:25:34","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"On the utilization of synthetic and measured earthquake ground motions for designing building monitoring systems in the near-field of major faults","docAbstract":"Agencies and research groups engaged in studying measures for enhancing the resiliency of communities have recently placed emphasis on the need for extensive implementation of monitoring systems for rapid post-event assessment of structural integrity. Designing a monitoring system for a building requires a thorough knowledge of its potential nonlinear dynamic behavior with an associated localization of interstory drift. Extending this task across a regional scale becomes even more challenging because of the heterogeneity of the buildings inventory and the limited knowledge of the characteristics of the demand especially for sites located in the near-field of a major fault.\nThe existing observational database of near-field ground motion records is in fact too limited to constitute a comprehensive basis for full understanding of the potential range of structural response variability at different locations near a major fault. In addition, current insight into monitoring system design typically relies on linear structural models and sensors deployed on a limited number of floors.\nIn this context, this paper presents first results of a study that combines synthetic earthquake ground motions generated from a massively parallel regional-scale geophysics wave propagation model at frequencies of engineering interest (0-5 Hz) with nonlinear tall building models. The objective is to gain new insight into the potential impact of localization of nonlinearities in structures subjected to realistic near-field earthquakes and develop a methodology that optimizes the deployment of sensors at the building and site level. In addition to the large database of synthetic motions, available real records are also employed to compare and contrast with the trends observed using synthetic ground motions.\nPreliminary results confirm a tendency of the demand to localize in specific portions of the structure, especially when nonlinearities occur. The building analyses provide guidance for various sensor deployment configurations associated with different probability of error in measuring structural drifts.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Structural health monitoring 2019: Enabling intelligent life-cycle health management for industry internet of things (IIOT)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Structural Health Monitoring 2019","conferenceDate":"September 10-12, 2019","language":"English","publisher":"DEStech Publications Inc.","doi":"10.12783/shm2019/32124","usgsCitation":"Petrone, F., McCallen, D., and Celebi, M., 2019, On the utilization of synthetic and measured earthquake ground motions for designing building monitoring systems in the near-field of major faults, in Structural health monitoring 2019: Enabling intelligent life-cycle health management for industry internet of things (IIOT), September 10-12, 2019, https://doi.org/10.12783/shm2019/32124.","ipdsId":"IP-107991","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":407131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2019-11-15","publicationStatus":"PW","contributors":{"editors":[{"text":"Miah, Mamun","contributorId":296778,"corporation":false,"usgs":false,"family":"Miah","given":"Mamun","email":"","affiliations":[{"id":64169,"text":"Lawrance Berkeley Lab","active":true,"usgs":false}],"preferred":false,"id":852463,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Petrone, Floriana","contributorId":296776,"corporation":false,"usgs":false,"family":"Petrone","given":"Floriana","email":"","affiliations":[{"id":64168,"text":"Larance Berkeley Lab","active":true,"usgs":false}],"preferred":false,"id":852460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCallen, David","contributorId":296777,"corporation":false,"usgs":false,"family":"McCallen","given":"David","affiliations":[{"id":64169,"text":"Lawrance Berkeley Lab","active":true,"usgs":false}],"preferred":false,"id":852461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":852462,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202385,"text":"70202385 - 2019 - The U. S. Geological Survey’s approach to analysis ready data","interactions":[],"lastModifiedDate":"2020-05-27T17:14:07.429313","indexId":"70202385","displayToPublicDate":"2019-11-14T11:59:57","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"The U. S. Geological Survey’s approach to analysis ready data","docAbstract":"Analysis Ready Data (ARD) is a recent concept in Earth observing remote sensing which encompasses many different initiatives by individual imagery providers and collaborative international organizations working towards easing/minimizing data preprocessing required by users. This allows users to spend more time on analysis and less time on downloading, formatting, and ingesting. The U. S. Geological Survey (USGS), the primary provider of Landsat image data, has been making internal strides to provide ARD: moving towards Level-2 surface reflectance and surface temperature as standard products. External cooperation, working toward a common ARD definition, has also been a focus of the USGS by working directly with other governmental or commercial providers, both national and international, or through organizations such as the Committee on Earth Observation Satellites (CEOS) and the Joint Agency Commercial Imagery Evaluation (JACIE) workshop. The USGS is determined to provide users with the most accurate and easy to use data.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"Jul 28-Aug 2, 2019","conferenceLocation":"Yokohama, Japan","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS.2019.8899216","usgsCitation":"Anderson, C., Labahn, S., Helder, D., Stensaas, G.L., Engebretson, C., Crawford, C., Jenkerson, C.B., and Barnes, C., 2019, The U. S. Geological Survey’s approach to analysis ready data, in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, Jul 28-Aug 2, 2019, p. 5541-5544, https://doi.org/10.1109/IGARSS.2019.8899216.","productDescription":"3 p.","startPage":"5541","endPage":"5544","ipdsId":"IP-105685","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":375094,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":758132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Labahn, Steven 0000-0002-9258-2890","orcid":"https://orcid.org/0000-0002-9258-2890","contributorId":213605,"corporation":false,"usgs":true,"family":"Labahn","given":"Steven","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":758133,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Helder, Dennis 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":213606,"corporation":false,"usgs":true,"family":"Helder","given":"Dennis","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":758134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stensaas, Gregory L. 0000-0001-6679-2416 stensaas@usgs.gov","orcid":"https://orcid.org/0000-0001-6679-2416","contributorId":2551,"corporation":false,"usgs":true,"family":"Stensaas","given":"Gregory","email":"stensaas@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":758135,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Engebretson, Christopher 0000-0003-1012-8684","orcid":"https://orcid.org/0000-0003-1012-8684","contributorId":224985,"corporation":false,"usgs":true,"family":"Engebretson","given":"Christopher","email":"","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":758136,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crawford, Christopher J. 0000-0002-7145-0709 cjcrawford@usgs.gov","orcid":"https://orcid.org/0000-0002-7145-0709","contributorId":213607,"corporation":false,"usgs":true,"family":"Crawford","given":"Christopher J.","email":"cjcrawford@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":758137,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jenkerson, Calli B. 0000-0002-3780-9175 jenkerson@usgs.gov","orcid":"https://orcid.org/0000-0002-3780-9175","contributorId":469,"corporation":false,"usgs":true,"family":"Jenkerson","given":"Calli","email":"jenkerson@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":758138,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barnes, Christopher 0000-0002-4608-4364 christopher.barnes.ctr@usgs.gov","orcid":"https://orcid.org/0000-0002-4608-4364","contributorId":198908,"corporation":false,"usgs":true,"family":"Barnes","given":"Christopher","email":"christopher.barnes.ctr@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":758139,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70207116,"text":"70207116 - 2019 - Using stream-side groundwater discharge for geochemical exploration in mountainous terrain","interactions":[],"lastModifiedDate":"2019-12-07T09:27:29","indexId":"70207116","displayToPublicDate":"2019-11-14T09:24:22","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2302,"text":"Journal of Geochemical Exploration","active":true,"publicationSubtype":{"id":10}},"title":"Using stream-side groundwater discharge for geochemical exploration in mountainous terrain","docAbstract":"Groundwater chemistry has been predominantly used in geochemical exploration studies to identify mineralized targets concealed under transported cover in areas with gentle topography. Another potentially valuable ap-plication that has received little attention is using groundwater chemistry to identify deposits concealed within mountain ridges. A number of geochemical exploration studies have employed surface water chemistry in hilly and mountainous terrain and have demonstrated mixed success. However, groundwater chemistry is potentially a more refined and powerful tool because groundwater sample locations can be selected to target a specific hillslope zone, whereas surface water integrates the geochemical signature of the entire watershed area above the sample site. In this study, we propose an approach that utilizes shallow groundwater samples collected from near-stream, hand-installed sampling points. We tested this approach in Handcart Gulch, an unmined catchment in the Montezuma Mining District, Colorado, USA, where previous studies suggested the possible presence of a concealed mineral occurrence. Seventeen near-stream mini-boreholes were drilled in a stream-parallel 3.3-km-long transect to depths of ~2 m in ferricrete (iron-oxide cemented colluvium and alluvium) using a handheld rock-coring drill. Groundwater in the mini-boreholes was sampled for major and trace element chemistry, as well as environmental tracers including stable water isotopes, dissolved noble gases, helium isotopes, and tritium, to identify possible variations in groundwater age and recharge conditions/sources. Chemistry data delineate a well-defined 900-m-wide zone of elevated SO4, Cu, and other metal concentrations on the east side of the stream. In this zone, Cu concentrations up to 2670 μg/L are 1–3 orders of magnitude greater than concentrations in upstream and downstream areas. Environmental tracer data show no corresponding anomalies in groundwater age or recharge conditions/sources within the zone, suggesting that the source of the elevated metals is a change in bedrock mineralogy as opposed to other hydrologic factors. The groundwater samples therefore successfully identify and refine the location of a potential concealed high-Cu mineral occurrence in the watershed, de-monstrating that near-stream groundwater discharge may be a valuable medium for geochemical exploration in mountainous areas.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gexplo.2019.106415","usgsCitation":"Manning, A.H., Morrison, J.M., Wanty, R., and Mills, C., 2019, Using stream-side groundwater discharge for geochemical exploration in mountainous terrain: Journal of Geochemical Exploration, v. 209, 106415, https://doi.org/10.1016/j.gexplo.2019.106415.","productDescription":"106415","ipdsId":"IP-109321","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":459186,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gexplo.2019.106415","text":"Publisher Index Page"},{"id":370077,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Montezuma Mining District","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-106.190554,40.997607],[-106.061181,40.996999],[-105.730421,40.996886],[-105.724804,40.99691],[-105.277138,40.998173],[-105.27686,40.998173],[-105.256527,40.998191],[-105.254779,40.99821],[-104.943371,40.998084],[-104.855273,40.998048],[-104.829504,40.99927],[-104.675999,41.000957],[-104.497149,41.001828],[-104.497058,41.001805],[-104.467672,41.001473],[-104.214692,41.001657],[-104.214191,41.001568],[-104.211473,41.001591],[-104.123586,41.001626],[-104.10459,41.001543],[-104.086068,41.001563],[-104.066961,41.001504],[-104.053249,41.001406],[-104.039238,41.001502],[-104.023383,41.001887],[-104.018223,41.001617],[-103.972642,41.001615],[-103.971373,41.001524],[-103.953525,41.001596],[-103.906324,41.001387],[-103.896207,41.00175],[-103.877967,41.001673],[-103.858449,41.001681],[-103.750498,41.002054],[-103.574522,41.001721],[-103.497447,41.001635],[-103.486697,41.001914],[-103.421975,41.002007],[-103.421925,41.001969],[-103.396991,41.002558],[-103.382492,41.002232],[-103.365314,41.001846],[-103.362979,41.001844],[-103.077804,41.002298],[-103.076536,41.002253],[-103.059538,41.002368],[-103.057998,41.002368],[-103.043444,41.002344],[-103.038704,41.002251],[-103.002026,41.002486],[-103.000102,41.0024],[-102.98269,41.002157],[-102.981483,41.002112],[-102.963669,41.002186],[-102.962522,41.002072],[-102.960706,41.002059],[-102.959624,41.002095],[-102.94483,41.002303],[-102.943109,41.002051],[-102.925568,41.00228],[-102.924029,41.002142],[-102.906547,41.002276],[-102.904796,41.002207],[-102.887407,41.002178],[-102.885746,41.002131],[-102.867822,41.002183],[-102.865784,41.001988],[-102.849263,41.002301],[-102.846455,41.002256],[-102.830303,41.002351],[-102.82728,41.002143],[-102.773546,41.002414],[-102.766723,41.002275],[-102.754617,41.002361],[-102.739624,41.00223],[-102.653463,41.002332],[-102.621033,41.002597],[-102.578696,41.002291],[-102.575738,41.002268],[-102.575496,41.0022],[-102.566048,41.0022],[-102.556789,41.002219],[-102.487955,41.002445],[-102.470537,41.002382],[-102.469223,41.002424],[-102.379593,41.002301],[-102.364066,41.002174],[-102.292833,41.002207],[-102.292622,41.00223],[-102.292553,41.002207],[-102.291354,41.002207],[-102.2721,41.002245],[-102.267812,41.002383],[-102.231931,41.002327],[-102.2122,41.002462],[-102.209361,41.002442],[-102.19121,41.002326],[-102.124972,41.002338],[-102.070598,41.002423],[-102.051718,41.002377],[-102.051614,41.002377],[-102.051292,40.749591],[-102.051292,40.749586],[-102.051398,40.697542],[-102.051725,40.537839],[-102.051519,40.520094],[-102.051465,40.440008],[-102.05184,40.396396],[-102.051572,40.39308],[-102.051798,40.360069],[-102.051553,40.349214],[-102.051309,40.338381],[-102.051922,40.235344],[-102.051894,40.229193],[-102.051909,40.162674],[-102.052001,40.148359],[-102.051744,40.003078],[-102.051569,39.849805],[-102.051363,39.843471],[-102.051318,39.833311],[-102.051254,39.818992],[-102.050594,39.675594],[-102.050099,39.653812],[-102.050422,39.646048],[-102.049954,39.592331],[-102.049806,39.574058],[-102.049764,39.56818],[-102.049554,39.538932],[-102.049673,39.536691],[-102.049679,39.506183],[-102.049369,39.423333],[-102.04937,39.41821],[-102.049167,39.403597],[-102.04896,39.373712],[-102.048449,39.303138],[-102.04725,39.13702],[-102.047189,39.133147],[-102.047134,39.129701],[-102.046571,39.047038],[-102.045388,38.813392],[-102.045334,38.799463],[-102.045448,38.783453],[-102.045371,38.770064],[-102.045287,38.755528],[-102.045375,38.754339],[-102.045212,38.697567],[-102.045156,38.688555],[-102.045127,38.686725],[-102.04516,38.675221],[-102.045102,38.674946],[-102.045074,38.669617],[-102.045288,38.615249],[-102.045288,38.615168],[-102.045211,38.581609],[-102.045189,38.558732],[-102.045223,38.543797],[-102.045112,38.523784],[-102.045262,38.505532],[-102.045263,38.505395],[-102.045324,38.453647],[-102.044936,38.41968],[-102.044442,38.415802],[-102.044944,38.384419],[-102.044613,38.312324],[-102.044568,38.268819],[-102.044567,38.268749],[-102.04451,38.262412],[-102.044398,38.250015],[-102.044251,38.141778],[-102.044589,38.125013],[-102.044255,38.113011],[-102.044644,38.045532],[-102.043844,37.928102],[-102.043845,37.926135],[-102.043219,37.867929],[-102.043033,37.824146],[-102.042953,37.803535],[-102.042668,37.788758],[-102.042158,37.760164],[-102.04199,37.738541],[-102.041876,37.723875],[-102.041574,37.680436],[-102.041694,37.665681],[-102.041582,37.654495],[-102.041585,37.644282],[-102.041618,37.607868],[-102.041894,37.557977],[-102.041899,37.541186],[-102.042016,37.535261],[-102.041786,37.506066],[-102.041801,37.469488],[-102.041755,37.434855],[-102.041669,37.43474],[-102.041676,37.409898],[-102.041586,37.38919],[-102.041524,37.375018],[-102.042089,37.352819],[-102.041974,37.352613],[-102.041817,37.30949],[-102.041664,37.29765],[-102.041963,37.258164],[-102.042002,37.141744],[-102.042135,37.125021],[-102.042092,37.125021],[-102.041809,37.111973],[-102.041983,37.106551],[-102.04192,37.035083],[-102.041749,37.034397],[-102.041921,37.032178],[-102.04195,37.030805],[-102.041952,37.024742],[-102.04224,36.993083],[-102.054503,36.993109],[-102.184271,36.993593],[-102.208316,36.99373],[-102.260789,36.994388],[-102.355288,36.994506],[-102.355367,36.994575],[-102.698142,36.995149],[-102.74206,36.997689],[-102.75986,37.000019],[-102.778569,36.999242],[-102.806762,37.000019],[-102.814616,37.000783],[-102.841989,36.999598],[-102.979613,36.998549],[-102.985807,36.998571],[-102.986976,36.998524],[-103.002199,37.000104],[-103.086106,37.000174],[-103.155922,37.000232],[-103.733247,36.998016],[-103.734364,36.998041],[-104.007855,36.996239],[-104.250536,36.994644],[-104.338833,36.993535],[-104.519257,36.993766],[-104.624556,36.994377],[-104.625545,36.993599],[-104.645029,36.993378],[-104.732031,36.993447],[-104.73212,36.993484],[-105.000554,36.993264],[-105.029228,36.992729],[-105.1208,36.995428],[-105.155042,36.995339],[-105.220613,36.995169],[-105.251296,36.995605],[-105.41931,36.995856],[-105.442459,36.995994],[-105.447255,36.996017],[-105.465182,36.995991],[-105.508836,36.995895],[-105.512485,36.995777],[-105.533922,36.995875],[-105.62747,36.995679],[-105.66472,36.995874],[-105.716471,36.995849],[-105.71847,36.995846],[-105.996159,36.995418],[-105.997472,36.995417],[-106.006634,36.995343],[-106.201469,36.994122],[-106.247705,36.994266],[-106.248675,36.994288],[-106.293279,36.99389],[-106.343139,36.99423],[-106.47628,36.993839],[-106.500589,36.993768],[-106.617159,36.992967],[-106.617125,36.993004],[-106.628652,36.993175],[-106.628733,36.993161],[-106.661344,36.993243],[-106.675626,36.993123],[-106.750591,36.992461],[-106.869796,36.992426],[-106.877292,37.000139],[-107.420913,37.000005],[-107.420915,37.000005],[-107.481737,37.000005],[-108.000623,37.000001],[-108.249358,36.999015],[-108.250635,36.999561],[-108.288086,36.999555],[-108.2884,36.99952],[-108.320464,36.999499],[-108.320721,36.99951],[-108.379203,36.999459],[-108.619689,36.999249],[-108.620309,36.999287],[-108.954404,36.998906],[-108.958868,36.998913],[-109.045223,36.999084],[-109.045166,37.072742],[-109.045058,37.074661],[-109.044995,37.086429],[-109.045189,37.096271],[-109.045173,37.109464],[-109.045203,37.111958],[-109.045156,37.112064],[-109.045995,37.177279],[-109.045978,37.201831],[-109.045487,37.210844],[-109.045584,37.249351],[-109.046039,37.249993],[-109.04581,37.374993],[-109.043464,37.484711],[-109.043137,37.499992],[-109.041915,37.530653],[-109.041865,37.530726],[-109.041806,37.604171],[-109.042131,37.617662],[-109.042089,37.623795],[-109.042269,37.666067],[-109.041732,37.711214],[-109.04176,37.713182],[-109.041636,37.74021],[-109.042098,37.74999],[-109.041461,37.800105],[-109.041754,37.835826],[-109.041723,37.842051],[-109.041844,37.872788],[-109.041653,37.88117],[-109.041058,37.907236],[-109.043121,37.97426],[-109.042819,37.997068],[-109.04282,37.999301],[-109.041837,38.153022],[-109.041762,38.16469],[-109.054648,38.244921],[-109.060062,38.275489],[-109.059962,38.499987],[-109.060253,38.599328],[-109.059541,38.719888],[-109.057388,38.795456],[-109.054189,38.874984],[-109.053943,38.904414],[-109.053797,38.905284],[-109.053233,38.942467],[-109.053292,38.942878],[-109.052436,38.999985],[-109.051512,39.126095],[-109.050765,39.366677],[-109.051363,39.497674],[-109.05104,39.660472],[-109.050615,39.87497],[-109.050873,40.058915],[-109.050813,40.059579],[-109.050944,40.180712],[-109.050973,40.180849],[-109.050969,40.222662],[-109.050946,40.444368],[-109.050314,40.495092],[-109.050698,40.499963],[-109.049955,40.539901],[-109.050074,40.540358],[-109.048044,40.619231],[-109.048249,40.653601],[-109.048373,40.662602],[-109.049088,40.714562],[-109.048455,40.826081],[-109.050076,41.000659],[-108.884138,41.000094],[-108.631108,41.000156],[-108.526667,40.999608],[-108.500659,41.000112],[-108.250649,41.000114],[-108.181227,41.000455],[-108.046539,41.002064],[-107.918421,41.002036],[-107.625624,41.002124],[-107.367443,41.003073],[-107.317794,41.002967],[-107.241194,41.002804],[-107.000606,41.003444],[-106.857773,41.002663],[-106.453859,41.002057],[-106.439563,41.001978],[-106.437419,41.001795],[-106.43095,41.001752],[-106.391852,41.001176],[-106.386356,41.001144],[-106.321165,40.999123],[-106.217573,40.997734],[-106.190554,40.997607]]]},\"properties\":{\"name\":\"Colorado\",\"nation\":\"USA \"}}]}","volume":"209","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":776879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morrison, Jean M. 0000-0002-6614-8783 jmorrison@usgs.gov","orcid":"https://orcid.org/0000-0002-6614-8783","contributorId":994,"corporation":false,"usgs":true,"family":"Morrison","given":"Jean","email":"jmorrison@usgs.gov","middleInitial":"M.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":776880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wanty, Richard B. 0000-0002-2063-6423","orcid":"https://orcid.org/0000-0002-2063-6423","contributorId":209899,"corporation":false,"usgs":true,"family":"Wanty","given":"Richard","middleInitial":"B.","affiliations":[],"preferred":true,"id":776881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mills, Christopher T. 0000-0001-8414-1414 cmills@usgs.gov","orcid":"https://orcid.org/0000-0001-8414-1414","contributorId":150137,"corporation":false,"usgs":true,"family":"Mills","given":"Christopher T.","email":"cmills@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":776882,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206789,"text":"70206789 - 2019 - Adult monarch (Danaus plexippus) abundance is higher in burned sites than in grazed sites","interactions":[],"lastModifiedDate":"2019-11-22T09:07:44","indexId":"70206789","displayToPublicDate":"2019-11-14T09:06:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Adult monarch (Danaus plexippus) abundance is higher in burned sites than in grazed sites","docAbstract":"Much of the remaining suitable habitat for monarchs (Danaus plexippus) in Minnesota is found in tallgrass prairies. We studied the association of adult monarch abundance with use of fire or grazing to manage prairies. Sites (n=20) ranged in size from 1 to 145 hectares and included land owned and managed by the Minnesota DNR, U.S. Fish and Wildlife Service, The Nature Conservancy, and private landowners. We measured Asclepias spp. (milkweeds, monarch host plants) and forb frequency in 0.5 x 2-m plots located along randomly-placed transects that were stratified to sample wet, mesic, and dry prairie types at each site. Adult butterfly surveys took place three times at each site during the summers of 2016 and 2017, using a standardized Pollard Walk (400 meters). Data were analyzed using mixed effects models. Monarchs were more abundant at sites managed with prescribed fire than with grazing. We found no difference in milkweed and forb frequency between burned and grazed prairies. There was no relationship between monarch abundance and the other predictor variables tested: milkweed frequency, site area, forb frequency, and percent prairie in a 1.5 km buffer area surrounding each site. Monarch abundance was lowest at grazed sites with high stocking rates. Our findings suggest that the use of burning or grazing for prairie management is not associated with milkweed or forb frequency, at least for sites that have not been burned in several years. They also suggest that heavy grazing may have negative impacts on monarchs.","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2019.00435","usgsCitation":"Leone, J.B., Larson, D.L., Larson, J.L., Pennarola, P., and Oberhauser, K., 2019, Adult monarch (Danaus plexippus) abundance is higher in burned sites than in grazed sites: Frontiers in Ecology and Evolution, v. 7, 435, https://doi.org/10.3389/fevo.2019.00435.","productDescription":"435","ipdsId":"IP-106587","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":459188,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00435","text":"Publisher Index Page"},{"id":437283,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P940ICLS","text":"USGS data release","linkHelpText":"Monarch densities in burned or grazed Minnesota remnant prairie, 2016-2017"},{"id":369456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-92.204691,46.704041],[-92.205192,46.698341],[-92.183091,46.695241],[-92.176091,46.686341],[-92.204092,46.666941],[-92.201592,46.656641],[-92.207092,46.651941],[-92.242493,46.649241],[-92.256592,46.658741],[-92.270592,46.650741],[-92.274392,46.657441],[-92.286192,46.660342],[-92.287392,46.667342],[-92.291292,46.668142],[-92.292192,46.663308],[-92.294033,46.074377],[-92.332912,46.062697],[-92.35176,46.015685],[-92.372717,46.014198],[-92.410649,46.027259],[-92.428555,46.024241],[-92.442259,46.016177],[-92.453373,45.992913],[-92.464512,45.985038],[-92.461138,45.980216],[-92.469354,45.973811],[-92.527052,45.983245],[-92.548459,45.969056],[-92.551186,45.95224],[-92.60246,45.940815],[-92.614314,45.934529],[-92.638824,45.934166],[-92.638474,45.925971],[-92.659549,45.922937],[-92.676167,45.912072],[-92.675737,45.907478],[-92.707702,45.894901],[-92.734039,45.868108],[-92.739278,45.84758],[-92.765146,45.830183],[-92.757815,45.806574],[-92.776496,45.790014],[-92.784621,45.764196],[-92.809837,45.744172],[-92.869193,45.717568],[-92.870025,45.697272],[-92.875488,45.689014],[-92.887929,45.639006],[-92.882529,45.610216],[-92.886442,45.598679],[-92.883749,45.575483],[-92.871082,45.567581],[-92.823309,45.560934],[-92.770223,45.566939],[-92.726082,45.541112],[-92.726677,45.514462],[-92.702224,45.493046],[-92.680234,45.464344],[-92.653549,45.455346],[-92.646602,45.441635],[-92.650422,45.398507],[-92.664102,45.393309],[-92.676961,45.380137],[-92.678223,45.373604],[-92.70272,45.358472],[-92.698967,45.336374],[-92.709968,45.321302],[-92.737122,45.300459],[-92.761013,45.289028],[-92.760615,45.278827],[-92.751659,45.26591],[-92.760249,45.2496],[-92.751708,45.218666],[-92.763908,45.204866],[-92.767408,45.190166],[-92.764872,45.182812],[-92.752404,45.173916],[-92.757707,45.155466],[-92.739584,45.115598],[-92.744938,45.108309],[-92.791528,45.079647],[-92.803079,45.060978],[-92.793282,45.047178],[-92.770362,45.033803],[-92.76206,45.02432],[-92.771231,45.001378],[-92.769445,44.97215],[-92.754603,44.955767],[-92.750645,44.937299],[-92.758701,44.908979],[-92.774571,44.898084],[-92.773946,44.889997],[-92.764133,44.875905],[-92.769102,44.862167],[-92.765278,44.837186],[-92.78043,44.812589],[-92.785206,44.792303],[-92.805287,44.768361],[-92.807988,44.75147],[-92.787906,44.737432],[-92.737259,44.717155],[-92.700948,44.693751],[-92.660988,44.660884],[-92.632105,44.649027],[-92.619779,44.634195],[-92.621456,44.615017],[-92.601516,44.612052],[-92.586216,44.600088],[-92.569434,44.603539],[-92.549777,44.58113],[-92.549957,44.568988],[-92.540551,44.567258],[-92.518358,44.575183],[-92.493808,44.566063],[-92.481001,44.568276],[-92.455105,44.561886],[-92.433256,44.5655],[-92.399281,44.558292],[-92.361518,44.558935],[-92.336114,44.554004],[-92.314071,44.538014],[-92.302466,44.516487],[-92.302215,44.500298],[-92.291005,44.485464],[-92.232472,44.445434],[-92.195378,44.433792],[-92.124513,44.422115],[-92.111085,44.413948],[-92.078605,44.404869],[-92.056486,44.402729],[-92.038147,44.388731],[-91.970266,44.365842],[-91.941311,44.340978],[-91.92559,44.333548],[-91.918625,44.322671],[-91.913534,44.311392],[-91.924613,44.291815],[-91.896388,44.27469],[-91.896008,44.262871],[-91.88704,44.251772],[-91.892698,44.231105],[-91.877429,44.212921],[-91.872369,44.199167],[-91.829167,44.17835],[-91.808064,44.159262],[-91.751747,44.134786],[-91.721552,44.130342],[-91.710597,44.12048],[-91.708207,44.105186],[-91.69531,44.09857],[-91.68153,44.0974],[-91.667006,44.086964],[-91.647873,44.064109],[-91.638115,44.063285],[-91.610487,44.04931],[-91.59207,44.031372],[-91.507121,44.01898],[-91.48087,44.008145],[-91.463515,44.009041],[-91.432522,43.996827],[-91.407395,43.965148],[-91.385785,43.954239],[-91.366642,43.937463],[-91.357426,43.917231],[-91.347741,43.911964],[-91.338141,43.897664],[-91.320605,43.888491],[-91.310991,43.867381],[-91.284138,43.847065],[-91.262436,43.792166],[-91.244135,43.774667],[-91.255431,43.744876],[-91.255932,43.729849],[-91.268455,43.709824],[-91.273252,43.666623],[-91.271749,43.654929],[-91.262397,43.64176],[-91.268748,43.615348],[-91.232707,43.583533],[-91.232812,43.564842],[-91.243214,43.550722],[-91.243183,43.540309],[-91.232941,43.523967],[-91.218292,43.514434],[-91.217706,43.50055],[-96.453049,43.500415],[-96.453067,45.298115],[-96.489065,45.357071],[-96.521787,45.375645],[-96.562142,45.38609],[-96.617726,45.408092],[-96.680454,45.410499],[-96.692541,45.417338],[-96.731396,45.45702],[-96.76528,45.521414],[-96.857751,45.605962],[-96.844211,45.639583],[-96.835769,45.649648],[-96.760866,45.687518],[-96.745086,45.701576],[-96.662595,45.738682],[-96.641941,45.759871],[-96.627778,45.786239],[-96.583085,45.820024],[-96.574517,45.843098],[-96.561334,45.945655],[-96.57035,45.963595],[-96.57794,46.026874],[-96.559271,46.058272],[-96.554507,46.083978],[-96.557952,46.102442],[-96.56692,46.11475],[-96.563043,46.119512],[-96.571439,46.12572],[-96.56926,46.133686],[-96.579453,46.147601],[-96.577952,46.165843],[-96.587408,46.178164],[-96.584372,46.204155],[-96.59755,46.227733],[-96.598645,46.241626],[-96.590942,46.250183],[-96.59887,46.26069],[-96.595014,46.275135],[-96.60136,46.30413],[-96.599761,46.330386],[-96.619991,46.340135],[-96.618147,46.344295],[-96.629211,46.352654],[-96.644335,46.351908],[-96.646341,46.360982],[-96.655206,46.365964],[-96.658436,46.373391],[-96.666028,46.374566],[-96.669132,46.390037],[-96.680687,46.407383],[-96.688082,46.40788],[-96.701358,46.420584],[-96.703078,46.429467],[-96.718074,46.438255],[-96.715557,46.463232],[-96.73627,46.48138],[-96.737798,46.489785],[-96.733612,46.497224],[-96.737702,46.50077],[-96.738475,46.525793],[-96.744341,46.533006],[-96.743003,46.54294],[-96.74883,46.558127],[-96.744436,46.56596],[-96.746442,46.574078],[-96.772446,46.600129],[-96.774094,46.613288],[-96.78995,46.631531],[-96.790663,46.649112],[-96.798823,46.658071],[-96.792958,46.677427],[-96.784339,46.685054],[-96.790906,46.70297],[-96.779252,46.727429],[-96.784279,46.732993],[-96.781216,46.740944],[-96.787466,46.756753],[-96.784314,46.766973],[-96.796195,46.789881],[-96.795756,46.807795],[-96.801446,46.810401],[-96.80016,46.819664],[-96.787657,46.827817],[-96.789663,46.832306],[-96.779347,46.843672],[-96.781358,46.879363],[-96.768458,46.879563],[-96.767358,46.883663],[-96.773558,46.884763],[-96.776558,46.895663],[-96.759241,46.918223],[-96.761757,46.934663],[-96.78312,46.925482],[-96.79038,46.929398],[-96.791558,46.944464],[-96.797734,46.9464],[-96.798737,46.962399],[-96.821852,46.969372],[-96.82318,46.999965],[-96.834221,47.006671],[-96.829499,47.021537],[-96.818557,47.02778],[-96.821422,47.032842],[-96.819321,47.0529],[-96.824479,47.059682],[-96.818175,47.104193],[-96.827344,47.120144],[-96.824807,47.124968],[-96.831547,47.142017],[-96.822377,47.162744],[-96.829637,47.17497],[-96.826962,47.182802],[-96.838806,47.197894],[-96.832789,47.203911],[-96.838806,47.22502],[-96.832946,47.237588],[-96.83766,47.240876],[-96.835368,47.250428],[-96.841672,47.258164],[-96.838997,47.267716],[-96.842531,47.269531],[-96.844088,47.289981],[-96.832884,47.30449],[-96.841958,47.316907],[-96.835845,47.321014],[-96.835845,47.335914],[-96.852417,47.366241],[-96.848907,47.370565],[-96.852676,47.374973],[-96.846925,47.376891],[-96.840621,47.389881],[-96.845492,47.394179],[-96.844919,47.399815],[-96.863593,47.418775],[-96.85748,47.440457],[-96.859868,47.470926],[-96.85471,47.478281],[-96.85853,47.489934],[-96.851653,47.497098],[-96.851367,47.509037],[-96.866363,47.524893],[-96.85471,47.535973],[-96.859153,47.566355],[-96.853689,47.570381],[-96.856373,47.575749],[-96.851293,47.589264],[-96.856903,47.602329],[-96.855421,47.60875],[-96.873671,47.613654],[-96.871005,47.616832],[-96.879496,47.620576],[-96.882393,47.633489],[-96.888573,47.63845],[-96.882376,47.649025],[-96.88697,47.653049],[-96.887126,47.666369],[-96.895271,47.67357],[-96.899352,47.689473],[-96.908928,47.688722],[-96.907266,47.693976],[-96.920119,47.710383],[-96.923544,47.718201],[-96.919471,47.722515],[-96.932809,47.737139],[-96.928505,47.748037],[-96.934173,47.752412],[-96.939179,47.768397],[-96.9644,47.782995],[-96.957283,47.790147],[-96.966068,47.797297],[-96.975131,47.798326],[-96.980579,47.805614],[-96.979327,47.824533],[-96.986685,47.837639],[-96.998295,47.841724],[-96.998144,47.858882],[-97.005557,47.863977],[-97.002456,47.868677],[-97.023156,47.874978],[-97.019355,47.880278],[-97.024955,47.886878],[-97.019155,47.889778],[-97.024955,47.894978],[-97.020155,47.900478],[-97.024955,47.908178],[-97.017254,47.905678],[-97.015354,47.910278],[-97.023754,47.915878],[-97.018054,47.918078],[-97.035754,47.930179],[-97.036054,47.939379],[-97.054554,47.946279],[-97.052454,47.957179],[-97.061454,47.96358],[-97.053553,47.991612],[-97.064289,47.998508],[-97.066762,48.009558],[-97.063012,48.013179],[-97.072239,48.019107],[-97.068987,48.026267],[-97.072257,48.048068],[-97.097772,48.07108],[-97.103052,48.071669],[-97.099431,48.082106],[-97.105226,48.09044],[-97.104872,48.097851],[-97.109535,48.104723],[-97.123205,48.106648],[-97.120702,48.114987],[-97.131956,48.139563],[-97.141401,48.14359],[-97.138911,48.157793],[-97.146745,48.168556],[-97.141474,48.179099],[-97.146233,48.186054],[-97.134372,48.210434],[-97.136304,48.228984],[-97.141254,48.234668],[-97.135763,48.237596],[-97.138765,48.244991],[-97.127276,48.253323],[-97.131846,48.267589],[-97.11657,48.279661],[-97.12216,48.290056],[-97.128862,48.292882],[-97.122072,48.300865],[-97.132443,48.315489],[-97.127601,48.323319],[-97.134854,48.331314],[-97.131145,48.339722],[-97.147748,48.359905],[-97.140106,48.380479],[-97.145592,48.394195],[-97.135012,48.406735],[-97.142849,48.419471],[-97.1356,48.424369],[-97.139173,48.430528],[-97.134229,48.439797],[-97.137689,48.447583],[-97.132746,48.459942],[-97.144116,48.469212],[-97.141397,48.476256],[-97.144981,48.481571],[-97.140291,48.484722],[-97.138864,48.494362],[-97.148133,48.503384],[-97.153076,48.524148],[-97.150481,48.536877],[-97.163105,48.543855],[-97.160863,48.549236],[-97.152459,48.552326],[-97.158638,48.564067],[-97.149616,48.569876],[-97.14974,48.579516],[-97.142915,48.583733],[-97.143684,48.597066],[-97.137504,48.612268],[-97.132931,48.61338],[-97.130089,48.621166],[-97.125639,48.620919],[-97.125269,48.629694],[-97.108466,48.632658],[-97.111921,48.642918],[-97.100551,48.658614],[-97.102652,48.664793],[-97.097708,48.68395],[-97.118286,48.700573],[-97.116185,48.709348],[-97.136083,48.727763],[-97.139488,48.746611],[-97.151289,48.757428],[-97.147478,48.763698],[-97.154854,48.774515],[-97.157093,48.790024],[-97.163535,48.79507],[-97.165624,48.809627],[-97.180028,48.81845],[-97.177747,48.824815],[-97.181116,48.832741],[-97.173811,48.838309],[-97.175618,48.853105],[-97.187362,48.867598],[-97.185738,48.87222],[-97.197982,48.880341],[-97.197982,48.898332],[-97.210541,48.90439],[-97.211161,48.916649],[-97.217992,48.919735],[-97.218666,48.931781],[-97.224505,48.9341],[-97.232147,48.948955],[-97.230859,48.960891],[-97.239209,48.968684],[-97.237297,48.985696],[-97.230833,48.991303],[-97.229039,49.000687],[-95.153711,48.998903],[-95.15335,49.383079],[-95.126467,49.369439],[-95.058404,49.35317],[-95.014415,49.356405],[-94.988908,49.368897],[-94.957465,49.370186],[-94.854245,49.324154],[-94.816222,49.320987],[-94.824291,49.308834],[-94.82516,49.294283],[-94.797244,49.214284],[-94.797527,49.197791],[-94.773223,49.120733],[-94.750221,49.099763],[-94.750218,48.999992],[-94.718932,48.999991],[-94.683069,48.883929],[-94.684217,48.872399],[-94.692527,48.86895],[-94.693044,48.853392],[-94.685681,48.840119],[-94.701968,48.831778],[-94.704284,48.824284],[-94.694974,48.809206],[-94.694312,48.789352],[-94.690889,48.778066],[-94.651765,48.755913],[-94.645164,48.749975],[-94.645083,48.744143],[-94.61901,48.737374],[-94.58715,48.717599],[-94.549069,48.714653],[-94.533057,48.701262],[-94.452332,48.692444],[-94.438701,48.694889],[-94.416191,48.710948],[-94.384221,48.711806],[-94.342758,48.703382],[-94.308446,48.710239],[-94.290737,48.707747],[-94.260541,48.696381],[-94.251169,48.683514],[-94.254643,48.663888],[-94.250497,48.656654],[-94.224276,48.649527],[-94.091244,48.643669],[-94.065775,48.646104],[-94.035616,48.641018],[-94.006933,48.643193],[-93.944221,48.632294],[-93.91153,48.634673],[-93.840754,48.628548],[-93.824144,48.610724],[-93.806763,48.577616],[-93.811201,48.542385],[-93.818253,48.530046],[-93.794454,48.516021],[-93.656652,48.515731],[-93.643091,48.518294],[-93.628865,48.53121],[-93.612844,48.521876],[-93.60587,48.522472],[-93.594379,48.528793],[-93.547191,48.528684],[-93.467504,48.545664],[-93.460798,48.550552],[-93.456675,48.561834],[-93.465199,48.590659],[-93.438494,48.59338],[-93.405269,48.609344],[-93.395022,48.603303],[-93.371156,48.605085],[-93.362132,48.613832],[-93.35324,48.613378],[-93.349095,48.624935],[-93.254854,48.642784],[-93.207398,48.642474],[-93.178095,48.623339],[-93.088438,48.627597],[-92.984963,48.623731],[-92.954876,48.631493],[-92.95012,48.630419],[-92.949839,48.608269],[-92.929614,48.606874],[-92.909947,48.596313],[-92.894687,48.594915],[-92.728046,48.53929],[-92.657881,48.546263],[-92.634931,48.542873],[-92.625739,48.518189],[-92.631117,48.508252],[-92.627237,48.503383],[-92.636696,48.499428],[-92.654039,48.501635],[-92.661418,48.496557],[-92.698824,48.494892],[-92.712562,48.463013],[-92.687998,48.443889],[-92.656027,48.436709],[-92.507285,48.447875],[-92.475585,48.418793],[-92.456325,48.414204],[-92.456389,48.401134],[-92.47675,48.37176],[-92.469948,48.351836],[-92.437825,48.309839],[-92.416285,48.295463],[-92.369174,48.220268],[-92.336831,48.235383],[-92.269742,48.248241],[-92.273706,48.256747],[-92.294541,48.27156],[-92.292999,48.276404],[-92.301451,48.288608],[-92.294527,48.306454],[-92.306309,48.316442],[-92.304561,48.322977],[-92.295412,48.323957],[-92.288994,48.342991],[-92.26228,48.354933],[-92.222813,48.349203],[-92.216983,48.345114],[-92.206803,48.345596],[-92.203684,48.352063],[-92.178418,48.351881],[-92.177354,48.357228],[-92.145049,48.365651],[-92.143583,48.356121],[-92.083513,48.353865],[-92.077961,48.358253],[-92.055228,48.359213],[-92.045734,48.347901],[-92.046562,48.33474],[-92.037721,48.333183],[-92.030872,48.325824],[-92.000133,48.321355],[-92.01298,48.297391],[-92.006577,48.265421],[-91.989545,48.260214],[-91.976903,48.244626],[-91.971056,48.247667],[-91.971779,48.252977],[-91.954432,48.251678],[-91.952209,48.244394],[-91.957683,48.242683],[-91.957798,48.232989],[-91.941838,48.230602],[-91.915772,48.238871],[-91.89347,48.237699],[-91.884691,48.227321],[-91.867882,48.219095],[-91.864382,48.207031],[-91.815772,48.211748],[-91.809038,48.206013],[-91.79181,48.202492],[-91.789011,48.196549],[-91.756637,48.205022],[-91.749075,48.198844],[-91.741932,48.199122],[-91.742313,48.204491],[-91.714931,48.19913],[-91.711611,48.1891],[-91.721413,48.180255],[-91.724584,48.170657],[-91.705318,48.170775],[-91.70726,48.153661],[-91.698174,48.141643],[-91.699981,48.13184],[-91.712226,48.116883],[-91.703524,48.113548],[-91.682845,48.122118],[-91.687623,48.111698],[-91.676876,48.107264],[-91.665208,48.107011],[-91.653261,48.114137],[-91.653571,48.109567],[-91.640175,48.096926],[-91.559272,48.108268],[-91.552962,48.103012],[-91.569746,48.093348],[-91.575471,48.066294],[-91.575672,48.048791],[-91.567254,48.043719],[-91.488646,48.068065],[-91.45033,48.068806],[-91.437582,48.049248],[-91.429642,48.048608],[-91.391128,48.057075],[-91.370872,48.06941],[-91.365143,48.066968],[-91.340159,48.073236],[-91.332589,48.069331],[-91.26638,48.078713],[-91.214428,48.10294],[-91.190461,48.124891],[-91.183207,48.122235],[-91.176181,48.125811],[-91.137733,48.14915],[-91.139402,48.154738],[-91.092258,48.173101],[-91.082731,48.180756],[-91.024208,48.190072],[-90.976955,48.219452],[-90.914971,48.230603],[-90.88548,48.245784],[-90.875107,48.237784],[-90.847352,48.244443],[-90.839176,48.239511],[-90.836313,48.176963],[-90.832589,48.173765],[-90.821115,48.184709],[-90.817698,48.179569],[-90.804207,48.177833],[-90.796596,48.159373],[-90.777917,48.163801],[-90.778031,48.148723],[-90.79797,48.136894],[-90.787305,48.134196],[-90.789919,48.129902],[-90.76911,48.116585],[-90.761555,48.100133],[-90.751608,48.090968],[-90.641596,48.103515],[-90.626886,48.111846],[-90.59146,48.117546],[-90.582217,48.123784],[-90.55929,48.121683],[-90.555845,48.117069],[-90.569763,48.106951],[-90.567482,48.101178],[-90.556838,48.096008],[-90.487077,48.099082],[-90.467712,48.108818],[-90.438449,48.098747],[-90.403219,48.105114],[-90.374542,48.090942],[-90.367658,48.094577],[-90.344234,48.094447],[-90.330052,48.102399],[-90.312386,48.1053],[-90.289337,48.098993],[-90.224692,48.108148],[-90.188679,48.107947],[-90.176605,48.112445],[-90.136191,48.112136],[-90.116259,48.104303],[-90.073873,48.101138],[-90.023595,48.084708],[-90.015057,48.067188],[-90.008446,48.068396],[-89.997852,48.057567],[-89.99305,48.028404],[-89.97718,48.023501],[-89.968255,48.014482],[-89.954605,48.011516],[-89.95059,48.015901],[-89.934489,48.015628],[-89.915341,47.994866],[-89.897414,47.987599],[-89.873286,47.985419],[-89.868153,47.989898],[-89.847571,47.992442],[-89.842568,48.001368],[-89.830385,48.000284],[-89.820483,48.014665],[-89.797744,48.014505],[-89.763967,48.022969],[-89.724048,48.018996],[-89.721038,48.017965],[-89.724044,48.013675],[-89.716114,48.016441],[-89.716417,48.010251],[-89.702528,48.006325],[-89.673798,48.01151],[-89.667128,48.007421],[-89.657051,48.009954],[-89.649057,48.003853],[-89.617867,48.010947],[-89.611678,48.017529],[-89.607821,48.006566],[-89.594749,48.004332],[-89.582117,47.996314],[-89.564288,48.00293],[-89.489226,48.014528],[-89.495344,48.002356],[-89.541521,47.992841],[-89.551555,47.987305],[-89.555015,47.974849],[-89.572315,47.967238],[-89.58823,47.9662],[-89.611412,47.980731],[-89.624559,47.983153],[-89.631825,47.980039],[-89.640129,47.96793],[-89.638285,47.954275],[-89.697619,47.941288],[-89.793539,47.891358],[-89.85396,47.873997],[-89.87158,47.874194],[-89.923649,47.862062],[-89.930844,47.857723],[-89.92752,47.850825],[-89.933899,47.84676],[-89.974296,47.830514],[-90.072025,47.811105],[-90.075559,47.803303],[-90.1168,47.79538],[-90.16079,47.792807],[-90.178755,47.786414],[-90.187636,47.77813],[-90.248794,47.772763],[-90.323446,47.753771],[-90.332686,47.746387],[-90.437712,47.731612],[-90.441912,47.726404],[-90.458365,47.7214],[-90.537105,47.703055],[-90.551291,47.690266],[-90.735927,47.624343],[-90.86827,47.5569],[-90.907494,47.532873],[-90.914247,47.522639],[-90.939072,47.514532],[-91.032945,47.458236],[-91.045646,47.456525],[-91.097569,47.413888],[-91.128131,47.399619],[-91.146958,47.381464],[-91.156513,47.378816],[-91.188772,47.340082],[-91.238658,47.304976],[-91.262512,47.27929],[-91.288478,47.26596],[-91.326019,47.238993],[-91.357803,47.206743],[-91.418805,47.172152],[-91.477351,47.125667],[-91.497902,47.122579],[-91.518793,47.108121],[-91.573817,47.089917],[-91.591508,47.068684],[-91.626824,47.049953],[-91.644564,47.026491],[-91.666477,47.014297],[-91.704649,47.005246],[-91.780675,46.945881],[-91.806851,46.933727],[-91.841349,46.925215],[-91.883238,46.905728],[-91.914984,46.883836],[-91.952985,46.867037],[-92.094089,46.787839],[-92.088289,46.773639],[-92.06449,46.745439],[-92.025789,46.710839],[-92.01529,46.706469],[-92.020289,46.704039],[-92.03399,46.708939],[-92.08949,46.74924],[-92.10819,46.74914],[-92.13789,46.73954],[-92.14329,46.73464],[-92.141291,46.72524],[-92.146291,46.71594],[-92.167291,46.719941],[-92.189091,46.717541],[-92.204691,46.704041]]]},\"properties\":{\"name\":\"Minnesota\",\"nation\":\"USA \"}}]}","volume":"7","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Leone, Julia B.","contributorId":216121,"corporation":false,"usgs":false,"family":"Leone","given":"Julia","email":"","middleInitial":"B.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":775752,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, Diane L. 0000-0001-5202-0634 dlarson@usgs.gov","orcid":"https://orcid.org/0000-0001-5202-0634","contributorId":2120,"corporation":false,"usgs":true,"family":"Larson","given":"Diane","email":"dlarson@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":775751,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larson, Jennifer L.","contributorId":178444,"corporation":false,"usgs":false,"family":"Larson","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":775753,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pennarola, Patrick","contributorId":216123,"corporation":false,"usgs":false,"family":"Pennarola","given":"Patrick","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":775754,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oberhauser, Karen","contributorId":191431,"corporation":false,"usgs":false,"family":"Oberhauser","given":"Karen","affiliations":[],"preferred":false,"id":775755,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208872,"text":"70208872 - 2019 - The power, potential, and pitfalls of open access biodiversity data in range size assessments: Lessons from the fishes","interactions":[],"lastModifiedDate":"2020-03-04T06:30:58","indexId":"70208872","displayToPublicDate":"2019-11-14T06:29:19","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"The power, potential, and pitfalls of open access biodiversity data in range size assessments: Lessons from the fishes","docAbstract":"Geographic rarity is a driver of a species’ intrinsic risk of extinction. It encompasses multiple key components including range size, which is one of the most commonly measured estimates of geographic rarity. Range size estimates are often used to prioritize conservation efforts when there are multiple candidate species, because data for other components of rarity such as population size are sparse, or do not exist for species of interest. Range size estimates can provide rankings of species vulnerability to changing environments or threats, identifying rare species for future study or conservation initiatives. However, range sizes can be estimated by several different metrics, and the degree of overlap in the identification of the rarest or most common species across methodologies is not well understood. This knowledge gap compromises our ability to prioritize correctly rare species, and presents a particularly difficult challenge for stream-dwelling organisms with distributions constrained to river networks. We evaluated the relationship of multiple range size estimates of a subset of freshwater fishes native to the United States to determine the degree of overlap in rarity rankings using different data sources and grain sizes. We used publicly available, open access data from the Global Biodiversity Information Facility (GBIF) to calculate extent of occurrence (minimum convex polygons) and area of occupancy (total area occupied, measured across various grain sizes). We compared range sizes estimated using GBIF data with the best available estimates of current distributions described by publicly available digital maps (NatureServe) to evaluate the efficacy of GBIF data in assessments of range size. We found strong correlations between range size estimates across analytical approaches and data sources with no detectable bias of taxonomy. We found that variation among rarity rankings was highest for species with intermediate range sizes indicating that the approaches considered here generally converge when used to identify the rarest or the most common species. Importantly, our results show that the rarest, and perhaps the most vulnerable, species are consistently identified across common methodological approaches. More broadly, our results support the use of open access biodiversity data that include opportunistically collated and collected point occurrence records as a complement to coarse-grain (e.g., whole range map) approaches, as we observed no systematic bias or deviation across data sources in our analyses. This indicates databases such as the GBIF may help fill important fundamental and applied knowledge gaps for many poorly understood species, particularly in a broad-scale, multispecies framework.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2019.105896","usgsCitation":"Benson, A., 2019, The power, potential, and pitfalls of open access biodiversity data in range size assessments: Lessons from the fishes: Ecological Indicators, v. 110, 105896, 12 p., https://doi.org/10.1016/j.ecolind.2019.105896.","productDescription":"105896, 12 p.","ipdsId":"IP-106411","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":459190,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2019.105896","text":"Publisher Index Page"},{"id":372880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"110","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Benson, Abigail 0000-0002-4391-107X","orcid":"https://orcid.org/0000-0002-4391-107X","contributorId":202078,"corporation":false,"usgs":true,"family":"Benson","given":"Abigail","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":783790,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70205604,"text":"ds1119 - 2019 - Quality of surface water in Missouri, water year 2018","interactions":[],"lastModifiedDate":"2019-11-13T14:36:33","indexId":"ds1119","displayToPublicDate":"2019-11-13T12:21:38","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1119","displayTitle":"Quality of Surface Water in Missouri, Water Year 2018","title":"Quality of surface water in Missouri, water year 2018","docAbstract":"The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a network of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2018 (October 1, 2017, through September 30, 2018), water-quality data were collected at 76 stations: 74 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Among the 76 stations in this report, 4 stations have data presented from additional sampling performed in cooperation with the U.S. Army Corps of Engineers. Summaries of the concentrations of dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and selected pesticide compounds are presented. Most of the stations have been classified based on the physiographic province or primary land use in the watershed monitored by the station. Some stations have been classified based on the unique hydrologic characteristics of the waterbodies (springs, large rivers) they monitor. A summary of hydrologic conditions including peak streamflows, monthly mean streamflows, and 7-day low flows also are presented for representative streamflow-gaging stations in the State.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1119","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Kay, R.T., 2019, Quality of surface water in Missouri, water year 2018: U.S. Geological Survey Data Series 1119, 25 p., https://doi.org/10.3133/ds1119.","productDescription":"v, 25 p.","numberOfPages":"35","onlineOnly":"Y","ipdsId":"IP-107435","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":369064,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1119/ds1119.pdf","text":"Report","size":"1.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1119"},{"id":369063,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1119/coverthb.jpg"}],"country":"United States","state":"Missouri","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-89.545006,36.336809],[-89.605668,36.342234],[-89.615841,36.336085],[-89.620255,36.323006],[-89.611819,36.309088],[-89.578492,36.288317],[-89.554289,36.277751],[-89.539487,36.277368],[-89.534507,36.261802],[-89.539229,36.248821],[-89.562206,36.250909],[-89.577544,36.242262],[-89.602374,36.238106],[-89.642182,36.249486],[-89.678046,36.248284],[-89.695235,36.252766],[-89.705328,36.239898],[-89.69263,36.224959],[-89.607004,36.171179],[-89.591605,36.144096],[-89.59307,36.129699],[-89.601936,36.11947],[-89.666598,36.095802],[-89.678821,36.084636],[-89.688577,36.029238],[-89.706932,36.000981],[-90.37789,35.995683],[-90.351732,36.025347],[-90.34909,36.040131],[-90.339343,36.047112],[-90.333261,36.067504],[-90.320746,36.071326],[-90.320662,36.087138],[-90.29991,36.098236],[-90.294492,36.112949],[-90.266256,36.120559],[-90.235585,36.139474],[-90.231386,36.147348],[-90.23537,36.159153],[-90.220425,36.184764],[-90.21128,36.183392],[-90.188189,36.20536],[-90.152497,36.215582],[-90.14224,36.227522],[-90.126366,36.229367],[-90.130114,36.240307],[-90.118219,36.253491],[-90.114922,36.265595],[-90.086471,36.271531],[-90.06398,36.303038],[-90.081961,36.322097],[-90.074074,36.342895],[-90.077695,36.348478],[-90.066297,36.3593],[-90.064514,36.382085],[-90.078671,36.399116],[-90.138512,36.413952],[-90.134231,36.422827],[-90.143743,36.424433],[-90.143798,36.428483],[-90.134136,36.436602],[-90.137323,36.455411],[-90.141101,36.461791],[-90.155804,36.463555],[-90.152888,36.47093],[-90.142222,36.470554],[-90.143683,36.476029],[-90.158838,36.479558],[-90.159305,36.492446],[-90.152481,36.497952],[-94.617919,36.499414],[-94.617975,37.722176],[-94.607354,39.113444],[-94.589933,39.140403],[-94.591933,39.155003],[-94.608834,39.160503],[-94.640035,39.153103],[-94.662435,39.157603],[-94.663835,39.179103],[-94.680336,39.184303],[-94.714137,39.170403],[-94.741938,39.170203],[-94.763138,39.179903],[-94.781518,39.206146],[-94.811663,39.206594],[-94.831679,39.215938],[-94.835056,39.220658],[-94.825663,39.241729],[-94.831471,39.256273],[-94.84632,39.268481],[-94.887056,39.28648],[-94.905329,39.311952],[-94.910017,39.352543],[-94.88136,39.370383],[-94.879281,39.37978],[-94.885026,39.389801],[-94.901823,39.392798],[-94.92311,39.384492],[-94.942039,39.389499],[-94.946293,39.405646],[-94.972952,39.421705],[-94.982144,39.440552],[-95.0375,39.463689],[-95.045716,39.472459],[-95.052177,39.499996],[-95.082714,39.516712],[-95.109304,39.542285],[-95.113077,39.559133],[-95.103228,39.577783],[-95.089515,39.581028],[-95.064519,39.577115],[-95.049277,39.589583],[-95.046361,39.599557],[-95.055152,39.621657],[-95.053367,39.630347],[-95.027644,39.665454],[-95.018318,39.672869],[-94.984149,39.67785],[-94.971317,39.68641],[-94.971206,39.729305],[-94.965318,39.739065],[-94.948726,39.745593],[-94.902612,39.724202],[-94.875643,39.730494],[-94.862943,39.742994],[-94.860743,39.763094],[-94.869644,39.772894],[-94.912293,39.759338],[-94.934262,39.773642],[-94.935206,39.78313],[-94.929654,39.788282],[-94.884084,39.794234],[-94.875944,39.813294],[-94.878677,39.826522],[-94.886933,39.833098],[-94.916918,39.836138],[-94.942567,39.856602],[-94.928466,39.876344],[-94.929574,39.888754],[-94.95154,39.900533],[-94.986975,39.89667],[-95.00844,39.900596],[-95.024389,39.891202],[-95.027931,39.871522],[-95.037767,39.865542],[-95.085003,39.861883],[-95.128166,39.874165],[-95.140601,39.881688],[-95.143802,39.901918],[-95.149657,39.905948],[-95.179453,39.900062],[-95.199347,39.902709],[-95.206326,39.912121],[-95.20069,39.928155],[-95.204428,39.938949],[-95.250254,39.948644],[-95.269886,39.969396],[-95.302507,39.984357],[-95.315271,40.01207],[-95.356876,40.031522],[-95.387195,40.02677],[-95.40726,40.033112],[-95.416824,40.043235],[-95.42164,40.058952],[-95.409856,40.07432],[-95.407591,40.09803],[-95.394216,40.108263],[-95.39284,40.115887],[-95.398667,40.126419],[-95.428749,40.135577],[-95.436348,40.15872],[-95.460746,40.169173],[-95.479193,40.185652],[-95.482757,40.197346],[-95.469718,40.227908],[-95.477501,40.24272],[-95.490333,40.248966],[-95.521925,40.24947],[-95.552473,40.261904],[-95.556325,40.267714],[-95.550966,40.285947],[-95.562157,40.297359],[-95.581787,40.29958],[-95.610439,40.31397],[-95.642262,40.306025],[-95.657328,40.310856],[-95.653729,40.322582],[-95.625204,40.334288],[-95.623728,40.346567],[-95.641027,40.366399],[-95.643934,40.386849],[-95.659134,40.40869],[-95.65819,40.44188],[-95.693133,40.469396],[-95.699969,40.505275],[-95.661687,40.517309],[-95.652262,40.538114],[-95.655848,40.546609],[-95.671754,40.562626],[-95.678718,40.56256],[-95.694147,40.556942],[-95.69505,40.533124],[-95.708591,40.521551],[-95.722444,40.528118],[-95.75711,40.52599],[-95.769281,40.536656],[-95.763366,40.550797],[-95.773549,40.578205],[-95.765645,40.585208],[-94.632035,40.571186],[-94.080463,40.572899],[-92.689854,40.589884],[-91.729115,40.61364],[-91.716769,40.59853],[-91.686357,40.580875],[-91.690804,40.559893],[-91.681714,40.553035],[-91.6219,40.542292],[-91.618028,40.53403],[-91.621353,40.510072],[-91.590817,40.492292],[-91.574746,40.465664],[-91.52509,40.457845],[-91.524053,40.448437],[-91.533623,40.43832],[-91.519935,40.433673],[-91.526555,40.419872],[-91.522333,40.409648],[-91.498093,40.401926],[-91.489816,40.404317],[-91.484507,40.3839],[-91.465116,40.385257],[-91.465009,40.376223],[-91.452458,40.375501],[-91.441243,40.386255],[-91.419422,40.378264],[-91.444833,40.36317],[-91.46214,40.342414],[-91.492727,40.278217],[-91.490524,40.259498],[-91.505828,40.238839],[-91.505495,40.195606],[-91.512974,40.181062],[-91.508224,40.157665],[-91.510322,40.127994],[-91.489606,40.057435],[-91.494878,40.036453],[-91.465315,39.983995],[-91.41936,39.927717],[-91.41988,39.916533],[-91.443513,39.893583],[-91.446922,39.883034],[-91.436051,39.84551],[-91.377971,39.811273],[-91.361571,39.787548],[-91.370009,39.732524],[-91.3453,39.709402],[-91.27614,39.665759],[-91.229317,39.620853],[-91.181936,39.602677],[-91.174651,39.593313],[-91.168419,39.564928],[-91.153628,39.548248],[-91.100307,39.538695],[-91.079769,39.507728],[-91.064305,39.494643],[-91.059439,39.46886],[-91.03827,39.448436],[-90.993789,39.422959],[-90.940766,39.403984],[-90.928745,39.387544],[-90.904862,39.379403],[-90.893777,39.367343],[-90.8475,39.345272],[-90.816851,39.320496],[-90.793461,39.309498],[-90.751599,39.265432],[-90.72996,39.255894],[-90.717113,39.213912],[-90.707902,39.15086],[-90.686051,39.117785],[-90.681086,39.10059],[-90.681994,39.090066],[-90.712541,39.057064],[-90.71158,39.046798],[-90.678193,38.991851],[-90.675949,38.96214],[-90.657254,38.92027],[-90.639917,38.908272],[-90.625122,38.888654],[-90.583388,38.86903],[-90.555693,38.870785],[-90.500117,38.910408],[-90.486974,38.925982],[-90.482419,38.94446],[-90.472122,38.958838],[-90.440078,38.967364],[-90.395816,38.960037],[-90.309454,38.92412],[-90.250248,38.919344],[-90.109407,38.843548],[-90.123107,38.798048],[-90.166409,38.772649],[-90.176309,38.754449],[-90.20991,38.72605],[-90.20921,38.70275],[-90.18641,38.67475],[-90.181325,38.660381],[-90.17801,38.63375],[-90.18451,38.611551],[-90.196011,38.594451],[-90.222112,38.576451],[-90.260314,38.528352],[-90.285215,38.443453],[-90.295316,38.426753],[-90.349743,38.377609],[-90.368219,38.340254],[-90.373929,38.281853],[-90.353902,38.213855],[-90.331554,38.18758],[-90.290765,38.170453],[-90.274928,38.157615],[-90.243116,38.112669],[-90.218708,38.094365],[-90.17222,38.069636],[-90.158533,38.074735],[-90.130788,38.062341],[-90.126612,38.043981],[-90.11052,38.026547],[-90.08826,38.015772],[-90.059367,38.015543],[-90.051357,38.003584],[-90.03241,37.995258],[-90.00011,37.964563],[-89.978919,37.962791],[-89.942099,37.970121],[-89.933797,37.959143],[-89.925085,37.960021],[-89.932467,37.947497],[-89.959646,37.940196],[-89.974918,37.926719],[-89.952499,37.883218],[-89.923185,37.870672],[-89.901832,37.869822],[-89.844786,37.905572],[-89.799333,37.881517],[-89.796087,37.859505],[-89.786369,37.851734],[-89.782035,37.855092],[-89.739873,37.84693],[-89.71748,37.825724],[-89.669644,37.799922],[-89.660227,37.781032],[-89.667993,37.759484],[-89.665546,37.752095],[-89.64953,37.745498],[-89.617278,37.74972],[-89.612478,37.740036],[-89.596566,37.732886],[-89.583316,37.713261],[-89.516685,37.692762],[-89.51204,37.680985],[-89.517718,37.641217],[-89.478399,37.598869],[-89.47603,37.590226],[-89.486062,37.580853],[-89.519808,37.582748],[-89.521925,37.560735],[-89.517051,37.537278],[-89.475525,37.471388],[-89.439769,37.4372],[-89.421054,37.387668],[-89.432836,37.347056],[-89.489005,37.333368],[-89.511842,37.310825],[-89.51834,37.285497],[-89.489915,37.251315],[-89.470525,37.253357],[-89.458827,37.248661],[-89.467631,37.2182],[-89.456105,37.18812],[-89.42558,37.138235],[-89.37871,37.094586],[-89.375712,37.080505],[-89.384681,37.048251],[-89.362397,37.030156],[-89.322982,37.01609],[-89.29213,36.992189],[-89.278628,36.98867],[-89.263527,37.00005],[-89.257608,37.015496],[-89.260003,37.023288],[-89.304752,37.047565],[-89.310819,37.057897],[-89.30829,37.068371],[-89.259936,37.064071],[-89.25493,37.072014],[-89.234053,37.037277],[-89.200793,37.016164],[-89.192097,36.979995],[-89.185491,36.973518],[-89.170008,36.970298],[-89.125069,36.983499],[-89.109498,36.976563],[-89.099594,36.964543],[-89.100762,36.944002],[-89.117567,36.887356],[-89.131944,36.857437],[-89.137969,36.847349],[-89.1704,36.841522],[-89.178888,36.831368],[-89.179229,36.812915],[-89.171069,36.798119],[-89.155891,36.789126],[-89.12353,36.785309],[-89.116563,36.767557],[-89.126134,36.751735],[-89.166888,36.759633],[-89.184523,36.753638],[-89.197808,36.739412],[-89.19948,36.716045],[-89.169522,36.688878],[-89.169467,36.674596],[-89.15908,36.666352],[-89.197654,36.628936],[-89.202607,36.601576],[-89.217447,36.576159],[-89.236542,36.566824],[-89.258318,36.564948],[-89.278935,36.577699],[-89.326731,36.632186],[-89.365548,36.625059],[-89.375453,36.615719],[-89.382762,36.583603],[-89.41977,36.493896],[-89.448468,36.46442],[-89.464153,36.457189],[-89.486215,36.46162],[-89.494248,36.475972],[-89.465888,36.529946],[-89.467761,36.546847],[-89.479093,36.568206],[-89.500076,36.576305],[-89.542459,36.580566],[-89.566817,36.564216],[-89.571241,36.547343],[-89.560344,36.525436],[-89.519501,36.475419],[-89.523427,36.456572],[-89.543406,36.43877],[-89.545255,36.427079],[-89.509722,36.373626],[-89.519,36.3486],[-89.545006,36.336809]]]},\"properties\":{\"name\":\"Missouri\",\"nation\":\"USA \"}}]}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Conservation of at‐risk species is aided by reliable forecasts of the consequences of environmental change and management actions on population viability. Forecasts from conventional population viability analysis (PVA) are made using a two‐step procedure in which parameters are estimated, or elicited from expert opinion, and then plugged into a stochastic population model without accounting for parameter uncertainty. Recently developed statistical PVAs differ because forecasts are made conditional on models fitted to empirical data. The statistical forecasting approach allows for uncertainty about parameters, but it has rarely been applied in metapopulation contexts where spatially explicit inference is needed about colonization and extinction dynamics and other forms of stochasticity that influence metapopulation viability. We conducted a statistical metapopulation viability analysis (MPVA) using 11 yr of data on the federally threatened Chiricahua leopard frog (Lithobates chiricahuensis) to forecast responses to landscape heterogeneity, drought, environmental stochasticity, and management. We evaluated several future environmental scenarios and pond restoration options designed to reduce extinction risk. Forecasts over a 50‐yr time horizon indicated that metapopulation extinction risk was <4% for all scenarios, but uncertainty was high. Without pond restoration, extinction risk is forecasted to be 3.9% (95% CI 0–37%) by year 2066. Restoring six ponds by increasing their hydroperiod reduced extinction risk to <1% and greatly reduced uncertainty (95% CI 0–2%). Our results suggest that managers can mitigate the impacts of drought and environmental stochasticity on metapopulation viability by maintaining ponds that hold water throughout the year and keeping them free of invasive predators. Our study illustrates the utility of the spatially explicit statistical forecasting approach to MPVA in conservation planning efforts.
The rapid growth of data in water resources has created new opportunities to accelerate knowledge discovery with the use of advanced deep learning tools. Hybrid models that integrate theory with state‐of‐the art empirical techniques have the potential to improve predictions while remaining true to physical laws. This paper evaluates the Process‐Guided Deep Learning (PGDL) hybrid modeling framework with a use‐case of predicting depth‐specific lake water temperatures. The PGDL model has three primary components: a deep learning model with temporal awareness (long short‐term memory recurrence), theory‐based feedback (model penalties for violating conversation of energy), and model pretraining to initialize the network with synthetic data (water temperature predictions from a process‐based model). In situ water temperatures were used to train the PGDL model, a deep learning (DL) model, and a process‐based (PB) model. Model performance was evaluated in various conditions, including when training data were sparse and when predictions were made outside of the range in the training data set. The PGDL model performance (as measured by root‐mean‐square error (RMSE)) was superior to DL and PB for two detailed study lakes, but only when pretraining data included greater variability than the training period. The PGDL model also performed well when extended to 68 lakes, with a median RMSE of 1.65 °C during the test period (DL: 1.78 °C, PB: 2.03 °C; in a small number of lakes PB or DL models were more accurate). This case‐study demonstrates that integrating scientific knowledge into deep learning tools shows promise for improving predictions of many important environmental variables.
","language":"English","publisher":"Wiley","doi":"10.1029/2019WR024922","usgsCitation":"Read, J.S., Jia, X., Willard, J., Appling, A.P., Zwart, J.A., Oliver, S.K., Karpatne, A., Hansen, G., Hanson, P.C., Watkins, W., Steinbach, M., and Kumar, V., 2019, Process-guided deep learning predictions of lake water temperature: Water Resources Research, v. 55, no. 11, p. 9173-9190, https://doi.org/10.1029/2019WR024922.","productDescription":"28 p.","startPage":"9173","endPage":"9190","ipdsId":"IP-104941","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":459230,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2019wr024922","text":"External Repository"},{"id":377267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.626220703125,\n 42.50450285299051\n ],\n [\n -87.791748046875,\n 42.49640294093705\n ],\n [\n -87.73681640625,\n 42.76314586689492\n ],\n [\n -87.835693359375,\n 43.32517767999296\n ],\n [\n -87.681884765625,\n 43.88205730390537\n ],\n [\n -87.528076171875,\n 44.20583500104184\n ],\n [\n -86.824951171875,\n 45.44471679159555\n ],\n [\n -87.9345703125,\n 44.653024159812\n ],\n [\n -87.82470703125,\n 44.92591837128866\n ],\n [\n -87.62695312499999,\n 44.99588261816546\n ],\n [\n -87.550048828125,\n 45.09679146394738\n ],\n [\n -87.64892578125,\n 45.40616374516014\n ],\n [\n -87.901611328125,\n 45.390735154248894\n ],\n [\n -87.802734375,\n 45.767522962149876\n ],\n [\n -88.13232421875,\n 45.82114340079471\n ],\n [\n -88.13232421875,\n 45.920587344733654\n ],\n [\n -88.560791015625,\n 45.98169518512228\n ],\n [\n -89.56054687499999,\n 46.22545288226939\n ],\n [\n -90.1318359375,\n 46.31658418182218\n ],\n [\n -90.24169921875,\n 46.49839225859763\n ],\n [\n -90.428466796875,\n 46.558860303117164\n ],\n [\n -90.65917968749999,\n 46.649436163350245\n ],\n [\n -90.384521484375,\n 47.14489748555398\n ],\n [\n -89.49462890625,\n 48.019324184801185\n ],\n [\n -89.879150390625,\n 47.99727386804474\n ],\n [\n -90.098876953125,\n 48.100094697973795\n ],\n [\n -90.52734374999999,\n 48.09275716032736\n ],\n [\n -90.736083984375,\n 48.100094697973795\n ],\n [\n -90.9228515625,\n 48.28319289548349\n ],\n [\n -91.549072265625,\n 48.070738264258296\n ],\n [\n -91.77978515625,\n 48.21735290928554\n ],\n [\n -92.142333984375,\n 48.378145469762444\n ],\n [\n -92.26318359375,\n 48.3416461723746\n ],\n [\n -92.28515625,\n 48.26125565204099\n ],\n [\n -92.373046875,\n 48.246625590713826\n ],\n [\n -92.5048828125,\n 48.45106561953216\n ],\n [\n -92.713623046875,\n 48.45835188280866\n ],\n [\n -92.65869140625,\n 48.531157010976706\n ],\n [\n -93.306884765625,\n 48.65468584817256\n ],\n [\n -93.812255859375,\n 48.516604348867475\n ],\n [\n -93.85620117187499,\n 48.61112192003074\n ],\n [\n -94.669189453125,\n 48.785151998043155\n ],\n [\n -94.844970703125,\n 49.36806633482156\n ],\n [\n -95.16357421875,\n 49.396675075193976\n ],\n [\n -95.152587890625,\n 49.009050809382046\n ],\n [\n -97.294921875,\n 48.99463598353405\n ],\n [\n -97.119140625,\n 48.65468584817256\n ],\n [\n -97.18505859374999,\n 48.21735290928554\n ],\n [\n -97.0751953125,\n 47.989921667414194\n ],\n [\n -96.92138671875,\n 47.61356975397398\n ],\n [\n -96.78955078125,\n 46.702202151643455\n ],\n [\n -96.591796875,\n 46.17983040759436\n ],\n [\n -96.61376953125,\n 45.78284835197676\n ],\n [\n -96.866455078125,\n 45.606352077118316\n ],\n [\n -96.45996093749999,\n 45.29034662473613\n ],\n [\n -96.45996093749999,\n 43.51668853502906\n ],\n [\n -91.307373046875,\n 43.5326204268101\n ],\n [\n -91.20849609375,\n 43.34914966389313\n ],\n [\n -91.0546875,\n 43.28520334369384\n ],\n [\n -91.131591796875,\n 43.1090040242731\n ],\n [\n -91.131591796875,\n 42.88401467044253\n ],\n [\n -91.0546875,\n 42.72280375732727\n ],\n [\n -90.626220703125,\n 42.61779143282346\n ],\n [\n -90.626220703125,\n 42.50450285299051\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"11","noUsgsAuthors":false,"publicationDate":"2019-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Read, Jordan S. 0000-0002-3888-6631 jread@usgs.gov","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":4453,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","email":"jread@usgs.gov","middleInitial":"S.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true},{"id":160,"text":"Center for Integrated Data Analytics","active":false,"usgs":true}],"preferred":true,"id":795355,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":795356,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":795357,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":795358,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":795359,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":795360,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Karpatne, Anuj","contributorId":237810,"corporation":false,"usgs":false,"family":"Karpatne","given":"Anuj","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":795361,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hansen, Gretchen J. A.","contributorId":174557,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J. A.","affiliations":[{"id":27469,"text":"Wisconsin Department of Natural Resources, Madison, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":795362,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hanson, Paul C.","contributorId":35634,"corporation":false,"usgs":false,"family":"Hanson","given":"Paul","email":"","middleInitial":"C.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":795363,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Watkins, William 0000-0002-7544-0700 wwatkins@usgs.gov","orcid":"https://orcid.org/0000-0002-7544-0700","contributorId":178146,"corporation":false,"usgs":true,"family":"Watkins","given":"William","email":"wwatkins@usgs.gov","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":795364,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Steinbach, Michael","contributorId":237811,"corporation":false,"usgs":false,"family":"Steinbach","given":"Michael","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":795365,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":795366,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70205777,"text":"ofr20191113 - 2019 - Full Equations Model Graphical Data Inspector (FEQ–GDI) user guide","interactions":[],"lastModifiedDate":"2019-11-12T06:12:57","indexId":"ofr20191113","displayToPublicDate":"2019-11-07T15:32:54","publicationYear":"2019","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":"2019-1113","displayTitle":"Full Equations Model Graphical Data Inspector (FEQ–GDI) User Guide","title":"Full Equations Model Graphical Data Inspector (FEQ–GDI) user guide","docAbstract":"The Full Equations Model Graphical Data Inspector (FEQ–GDI) is a menu-driven utility program that enables users to visualize and check the geometric and hydraulic properties of channel cross sections, selected control structures, and stream profiles in the input files for the Full Equations (FEQ) Model and the Full Equations Utilities (FEQUTL) Model. The FEQ Model is a computer program for the simulation of one-dimensional, unsteady flow in open channels and through control structures using the full, dynamic equations of motion. The input to FEQ Model includes the output from the FEQUTL Model, which computes tables relating the hydraulic properties of channel cross sections and control structures to depth, flow, and (or) other specified parameters. FEQ–GDI can be used to help users quickly detect anomalies in the data that may indicate errors in the input files.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191113","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Ern, J.L., Ortel, T., Ishii, A.L., and Bera, M., 2019, Full Equations Model Graphical Data Inspector (FEQ–GDI) user guide: U.S. Geological Survey Open-File Report 2019–1113, 11 p., https://doi.org/10.3133/ofr20191113.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-111050","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":369032,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1113/coverthb.jpg"},{"id":369033,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1113/ofr20191113.pdf","text":"Report","size":"4.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1113"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Deposits of calcite coating the lower passages of Wind Cave in the southern Black Hills of South Dakota were precipitated under phreatic conditions. Data from samples associated with a new cave survey and hydrologic studies indicate that past water tables within Wind Cave reached a maximum height of 45 m above modern levels but were mostly confined to 25 m or less. Uranium-series ages for basal layers deposited on weathered wall rock indicate subaerial conditions in this part of the cave persisted between 1000 and 300 ka. Ages and elevations of wall coatings and cave rafts establish a 300,000 yr paleohydrograph indicating that water-table highstands occurred during interglacial or interstadial-to-early glacial periods and lowstands occurred during full-glacial and stadial episodes.
Isotopes of Sr, U, C, and O from dated calcite samples were obtained to evaluate potential shifts in paleo-groundwater composition. For comparison, Sr and U isotopic compositions were determined for modern groundwater from 18 sites previously classified into five hydrogeologic domains. Isotope data for different domains tend to cluster in separate fields, although several fields overlap. Compositions of Calcite Lake (informal name) water reflect modern recharge to shallow aquifers. In contrast, speleothem data indicate that paleo-groundwater highstands were not supported by increased infiltration associated with local recharge, or by upwelling from deeper Proterozoic sources. Instead, cave water was similar to deeper, warmer groundwater from the Madison Aquifer discharging at modern artesian springs flanking the southern Black Hills. Highstands were likely influenced by large-scale hydraulic processes associated with recharge to the Madison Aquifer under the Laurentide ice sheet on the northeast side of the Williston Basin, causing increased hydrostatic pressures in confined aquifers on the south side of the basin.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35312.1","usgsCitation":"Paces, J.B., Palmer, M.V., Palmer, A.N., Long, A.J., and Emmons, M.P., 2019, 300,000 yr history of water-table fluctuations at Wind Cave, South Dakota, USA—Scale, timing, and groundwater mixing in the Madison Aquifer: GSA Bulletin, v. 132, no. 7-8, p. 1447-1468, https://doi.org/10.1130/B35312.1.","productDescription":"22 p.","startPage":"1447","endPage":"1468","ipdsId":"IP-102435","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":385560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota","city":"Rapid City, Hot Springs","otherGeospatial":"southern Black Hills","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.029541015625,\n 42.98857645832184\n ],\n [\n -103.095703125,\n 42.98857645832184\n ],\n [\n -103.095703125,\n 44.33956524809713\n ],\n [\n -104.029541015625,\n 44.33956524809713\n ],\n [\n -104.029541015625,\n 42.98857645832184\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"132","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2019-11-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Paces, James B. 0000-0002-9809-8493","orcid":"https://orcid.org/0000-0002-9809-8493","contributorId":215864,"corporation":false,"usgs":true,"family":"Paces","given":"James","email":"","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":815425,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmer, Margaret V.","contributorId":257970,"corporation":false,"usgs":false,"family":"Palmer","given":"Margaret","email":"","middleInitial":"V.","affiliations":[{"id":52191,"text":"State University of New York, Oneonta","active":true,"usgs":false}],"preferred":false,"id":815426,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmer, Arthur N. 0000-0002-2770-0053","orcid":"https://orcid.org/0000-0002-2770-0053","contributorId":257971,"corporation":false,"usgs":false,"family":"Palmer","given":"Arthur","email":"","middleInitial":"N.","affiliations":[{"id":52191,"text":"State University of New York, Oneonta","active":true,"usgs":false}],"preferred":false,"id":815427,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Long, Andrew J. 0000-0001-7385-8081 ajlong@usgs.gov","orcid":"https://orcid.org/0000-0001-7385-8081","contributorId":989,"corporation":false,"usgs":true,"family":"Long","given":"Andrew","email":"ajlong@usgs.gov","middleInitial":"J.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":815428,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Emmons, Matthew P. 0000-0002-3429-396X memmons@usgs.gov","orcid":"https://orcid.org/0000-0002-3429-396X","contributorId":5023,"corporation":false,"usgs":true,"family":"Emmons","given":"Matthew","email":"memmons@usgs.gov","middleInitial":"P.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":815429,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212579,"text":"70212579 - 2019 - Assessment of the impacts of image signal-to-noise ratios in impervious surface mapping","interactions":[],"lastModifiedDate":"2020-08-21T14:56:33.711489","indexId":"70212579","displayToPublicDate":"2019-11-06T09:50:31","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of the impacts of image signal-to-noise ratios in impervious surface mapping","docAbstract":"Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.","language":"English","publisher":"Remote Sensing","doi":"10.3390/rs11222603","usgsCitation":"Xian, G.Z., Shi, H., Anderson, C., and Wu, Z., 2019, Assessment of the impacts of image signal-to-noise ratios in impervious surface mapping: Remote Sensing, v. 11, no. 22, 2603, 23 p., https://doi.org/10.3390/rs11222603.","productDescription":"2603, 23 p.","ipdsId":"IP-113657","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":459244,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11222603","text":"Publisher Index Page"},{"id":377729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"22","noUsgsAuthors":false,"publicationDate":"2019-11-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204 xian@usgs.gov","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":2263,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"xian@usgs.gov","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796912,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796913,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Cody 0000-0001-5612-1889","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":238942,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":796914,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":796915,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204516,"text":"ofr20191084 - 2019 - Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2018","interactions":[],"lastModifiedDate":"2023-04-24T21:01:23.233169","indexId":"ofr20191084","displayToPublicDate":"2019-11-06T09:48:09","publicationYear":"2019","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":"2019-1084","displayTitle":"Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2018","title":"Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2018","docAbstract":"Trace-metal concentrations in sediment and in the clam Macoma petalum (formerly reported as Macoma balthica), clam reproductive activity, and benthic macroinvertebrate community structure were investigated in a mudflat 1 kilometer south of the discharge of the Palo Alto Regional Water Quality Control Plant (PARWQCP) in south San Francisco Bay, Calif. This report includes the data collected by U.S. Geological Survey (USGS) scientists for the period January 2018 to December 2018. These append to long-term datasets extending back to 1974. A major focus of the report is an integrated description of the 2018 data within the context of the longer, multi-decadal dataset. This dataset supports the City of Palo Alto’s Near-Field Receiving-Water Monitoring Program, initiated in 1994.
Significant reductions in silver and copper concentrations in both sediment and M. petalum occurred at the site in the 1980s following the implementation by PARWQCP of advanced wastewater treatment and source control measures. Since the 1990s, concentrations of these elements appear to have stabilized at concentrations somewhat above (silver [Ag]) or near (copper [Cu]) regional background concentrations. Data for other metals, including chromium (Cr), mercury (Hg), nickel (Ni), selenium (Se), and zinc (Zn), have been collected since 1994. Over this period, concentrations of these elements have remained relatively constant, aside from seasonal variation that is common to all elements. In 2018, concentrations of silver and copper in M. petalum varied seasonally in response to a combination of site-specific metal exposures and annual growth and reproduction, as reported previously. Seasonal patterns for other elements, including Cr, Ni, Zn, Hg, and Se, were generally similar in timing and magnitude as those for Ag and Cu. This record suggests that legacy contamination and regional-scale factors now largely control sedimentary and bioavailable concentrations of silver and copper, as well as other elements of regulatory interest, at the Palo Alto site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191084","collaboration":"Prepared in cooperation with the City of Palo Alto, California","usgsCitation":"Cain, D.J., Thompson, J.K., Parchaso, F., Pearson, S., Stewart, R., Turner, M., Shrader, K.H., Zierdt Smith, E.L., and Luoma, S.N., 2019, Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2018: U.S. Geological Survey Open-File Report 2019–1084, 41 p., https://doi.org/10.3133/ofr20191084.","productDescription":"vi, 41 p.","numberOfPages":"41","onlineOnly":"Y","ipdsId":"IP-109149","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":416180,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20161118","text":"Open-File Report 2016-1118","linkHelpText":"- Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2015"},{"id":416181,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20171135","text":"Open-File Report 2017-1135","linkHelpText":"- Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2016"},{"id":416182,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181107","text":"Open-File Report 2018-1107","linkHelpText":"- Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2017"},{"id":416184,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211079","text":"Open-File Report 2021-1079","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2019"},{"id":368964,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1084/ofr20191084.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-FIle Report 2019-1084"},{"id":368963,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1084/coverthb.jpg"},{"id":416185,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231017","text":"Open-File Report 2023-1017","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2020"}],"country":"United States","state":"California","otherGeospatial":"Palo Alto Regional Water Quality Control Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.14187622070311,\n 37.43179575348695\n ],\n [\n -122.08419799804689,\n 37.43179575348695\n ],\n [\n -122.08419799804689,\n 37.48085213924346\n ],\n [\n -122.14187622070311,\n 37.48085213924346\n ],\n [\n -122.14187622070311,\n 37.43179575348695\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Earth System Processes Division
U.S. Geological Survey
411 National Center
12201 Sunrise Valley Drive
Reston, VA 20192