Intense geoelectric fields during geomagnetic storms drive geomagnetically induced currents in power grids and other infrastructure, yet there are limited direct measurements of these storm-time geoelectric fields. Moreover, most previous studies examining storm-time geoelectric fields focused on single events or small geographic regions, making it difficult to determine the typical source(s) of intense geoelectric fields. We perform the first comparative analysis of (a) the sources of intense geoelectric fields over multiple geomagnetic storms, (b) using 1-s cadence geoelectric field measurements made at (c) magnetotelluric survey sites distributed widely across the United States. Temporally localized intense perturbations in measured geoelectric fields with prominences (a measure of the relative amplitude of geoelectric field enhancement above the surrounding signal) of at least 500 mV/km were detected during geomagnetic storms with Dst minima (Dstmin) of less than −100 nT from 2006 to 2019. Most of the intense geoelectric fields were observed in resistive regions with magnetic latitudes greater than 55° even though we have 167 sites located at lower latitudes during geomagnetic storms of −200 nT ≤ Dstmin < −100 nT. Our study indicates intense short-lived (<1 min) and geoelectric field perturbations with periods on the order of 1–2 min are common. Most of these perturbations cannot be resolved with 1-min data because they correspond to higher frequency or impulsive phenomena that vary on timescales shorter than that sampling interval. The sources of geomagnetic perturbations inducing these intense geoelectric fields include interplanetary shocks, interplanetary magnetic field turnings, substorms, and ultralow frequency waves.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021SW002967","usgsCitation":"Shi, X., Hartinger, M.D., Baker, J.B., Murphy, B.S., Bedrosian, P.A., Kelbert, A., and Rigler, E., 2022, Characteristics and sources of intense geoelectric fields in the United States: Comparative analysis of multiple geomagnetic storms: Space Weather, v. 20, no. 4, e2021SW002967, 18 p., https://doi.org/10.1029/2021SW002967.","productDescription":"e2021SW002967, 18 p.","ipdsId":"IP-137255","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":448523,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021sw002967","text":"Publisher Index Page"},{"id":406954,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.158203125,\n 32.47269502206151\n ],\n [\n -114.169921875,\n 32.76880048488168\n ],\n [\n -114.169921875,\n 34.52466147177172\n ],\n [\n -119.35546875000001,\n 38.75408327579141\n ],\n [\n -119.794921875,\n 39.30029918615029\n ],\n [\n -101.953125,\n 39.027718840211605\n ],\n [\n -98.26171875,\n 38.8225909761771\n ],\n [\n -97.646484375,\n 37.37015718405753\n ],\n [\n -94.130859375,\n 36.73888412439431\n ],\n [\n -90,\n 36.94989178681327\n ],\n [\n -88.857421875,\n 35.817813158696616\n ],\n [\n -87.451171875,\n 33.063924198120645\n ],\n [\n -84.90234375,\n 29.916852233070173\n ],\n [\n -81.03515625,\n 30.44867367928756\n ],\n [\n -76.37695312499999,\n 35.17380831799959\n ],\n [\n -73.037109375,\n 40.713955826286046\n ],\n [\n -70.3125,\n 41.64007838467894\n ],\n [\n -70.48828125,\n 43.004647127794435\n ],\n [\n -66.884765625,\n 44.84029065139799\n ],\n [\n -67.587890625,\n 46.92025531537451\n ],\n [\n -68.37890625,\n 47.45780853075031\n ],\n [\n -69.9609375,\n 47.45780853075031\n ],\n [\n -70.751953125,\n 45.521743896993634\n ],\n [\n -75.05859375,\n 44.77793589631623\n ],\n [\n -77.080078125,\n 43.32517767999296\n ],\n [\n -79.013671875,\n 43.197167282501276\n ],\n [\n -81.298828125,\n 41.83682786072714\n ],\n [\n -82.96875,\n 41.376808565702355\n ],\n [\n -82.79296874999999,\n 42.68243539838623\n ],\n [\n -82.79296874999999,\n 44.84029065139799\n ],\n [\n -84.638671875,\n 46.73986059969267\n ],\n [\n -90,\n 48.10743118848039\n ],\n [\n -92.021484375,\n 48.16608541901253\n ],\n [\n -95.537109375,\n 48.748945343432936\n ],\n [\n -95.712890625,\n 49.095452162534826\n ],\n [\n -101.6015625,\n 48.980216985374994\n ],\n [\n -109.77539062499999,\n 49.38237278700955\n ],\n [\n -110.0390625,\n 58.12431960569374\n ],\n [\n -119.970703125,\n 59.84481485969105\n ],\n [\n -120.234375,\n 53.64463782485651\n ],\n [\n -121.28906250000001,\n 50.17689812200107\n ],\n [\n -123.22265625000001,\n 49.095452162534826\n ],\n [\n -123.22265625000001,\n 48.10743118848039\n ],\n [\n -125.068359375,\n 48.40003249610685\n ],\n [\n -124.18945312500001,\n 46.437856895024204\n ],\n [\n -124.541015625,\n 43.26120612479979\n ],\n [\n -124.8046875,\n 40.84706035607122\n ],\n [\n -123.662109375,\n 37.71859032558816\n ],\n [\n -120.673828125,\n 34.52466147177172\n ],\n [\n -117.158203125,\n 32.47269502206151\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Xueling 0000-0001-8425-8241","orcid":"https://orcid.org/0000-0001-8425-8241","contributorId":296644,"corporation":false,"usgs":false,"family":"Shi","given":"Xueling","email":"","affiliations":[{"id":64114,"text":"Virginia Tech; NCAR High Altitude Observatory","active":true,"usgs":false}],"preferred":false,"id":852080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartinger, Michael D 0000-0002-2643-2202","orcid":"https://orcid.org/0000-0002-2643-2202","contributorId":296645,"corporation":false,"usgs":false,"family":"Hartinger","given":"Michael","email":"","middleInitial":"D","affiliations":[{"id":48422,"text":"Space Science Institute","active":true,"usgs":false}],"preferred":false,"id":852081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baker, Joseph B. 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The RCWD goals of this stream restoration were to reduce water quality impairments, improve aquatic habitat, and reduce associated costs of dredging a sedimentation pond. During the study there were several factors that introduced uncertainty in the sampling results; however, the sampling results indicated there was an increase in the post-stream restoration sediment data because of higher streamflows during the post-stream than the pre-stream restoration monitoring period. The negative relation between suspended fines and streamflow was explained by a reduction in the supply of fines with increasing streamflows. The positive relation among suspended sand, bedload, and streamflow was because of those constituents having a functional relation with the hydraulic properties of flow and a consistent supply of sand. Two-dimensional flow modeling simulations indicated the downstream restored section had less shear stress, more pools, and could access the floodplain at a lower streamflow than the original channel. Overall, the uncertainty of the sampling results indicates the complexity of sediment transport in a river and suggests a need for multisite, multifaceted, multiyear data, and tools to simulate those data to effectively evaluate river restorations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225004","collaboration":"Prepared in cooperation with Rice Creek Watershed District","usgsCitation":"Groten, J.T., Livdahl, C.T., DeLong, S.B., Lund, J.W., Nelson, J.M., Coenen, E.N., Ziegeweid, J.R., and Kocian, M.J., 2022, Sediment monitoring and streamflow modeling before and after a stream restoration in Rice Creek, Minnesota, 2010–2019: U.S. Geological Survey Scientific Investigations Report 2022–5004, 40 p., https://doi.org/10.3133/sir20225004.","productDescription":"Report: viii, 40 p.; Data Release; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-126710","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":396980,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SJIY32","text":"USGS data release","linkHelpText":"Suspended sediment and bedload data, simple linear regression models, loads, elevation data, and FaSTMECH models for Rice Creek, Minnesota, 2010-2019"},{"id":396979,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":396978,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5004/images"},{"id":502292,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112549.htm","linkFileType":{"id":5,"text":"html"}},{"id":396976,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5004/sir20225004.pdf","text":"Report","size":"19.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5004"},{"id":396975,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5004/coverthb.jpg"},{"id":396977,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5004/sir20225004.XML"}],"country":"United States","state":"Minnesota","otherGeospatial":"Rice Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.21710586547852,\n 45.08236949749694\n ],\n [\n -93.18449020385742,\n 45.08236949749694\n ],\n [\n -93.18449020385742,\n 45.09957848291159\n ],\n [\n -93.21710586547852,\n 45.09957848291159\n ],\n [\n -93.21710586547852,\n 45.08236949749694\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Green Lake is the deepest natural inland lake in Wisconsin, with a maximum depth of about 72 meters. In the early 1900s, the lake was believed to have very good water quality (low nutrient concentrations and good water clarity) with low dissolved oxygen (DO) concentrations occurring in only the deepest part of the lake. Because of increased phosphorus (P) inputs from anthropogenic activities in its watershed, total phosphorus (TP) concentrations in the lake have increased; these changes have led to increased algal production and low DO concentrations not only in the deepest areas but also in the middle of the water column (metalimnion). The U.S. Geological Survey has routinely monitored the lake since 2004 and its tributaries since 1988. Results from this monitoring led the Wisconsin Department of Natural Resources (WDNR) to list the lake as impaired because of low DO concentrations in the metalimnion, and they identified elevated TP concentrations as the cause of impairment.
As part of this study by the U.S. Geological Survey, in cooperation with the Green Lake Sanitary District, the lake and its tributaries were comprehensively sampled in 2017–18 to augment ongoing monitoring that would further describe the low DO concentrations in the lake (especially in the metalimnion). Empirical and process-driven water-quality models were then used to determine the causes of the low DO concentrations and the magnitudes of P-load reductions needed to improve the water quality of the lake enough to meet multiple water-quality goals, including the WDNR’s criteria for TP and DO.
Data from previous studies showed that DO concentrations in the metalimnion decreased slightly as summer progressed in the early 1900s but, since the late 1970s, have typically dropped below 5 milligrams per liter (mg/L), which is the WDNR criterion for impairment. During 2014–18 (the baseline period for this study), the near-surface geometric mean TP concentration during June–September in the east side of the lake was 0.020 mg/L and in the west side was 0.016 mg/L (both were above the 0.015-mg/L WDNR criterion for the lake), and the metalimnetic DO minimum concentrations (MOMs) measured in August ranged from 1.0 to 4.7 mg/L. The degradation in water quality was assumed to have been caused by excessive P inputs to the lake; therefore, the TP inputs to the lake were estimated. The mean annual external P load during 2014–18 was estimated to be 8,980 kilograms per year (kg/yr), of which monitored and unmonitored tributary inputs contributed 84 percent, atmospheric inputs contributed 8 percent, waterfowl contributed 7 percent, and septic systems contributed 1 percent. During fall turnover, internal sediment recycling contributed an additional 7,040 kilograms that increased TP concentrations in shallow areas of the lake by about 0.020 mg/L. The elevated TP concentrations then persisted until the following spring. On an annual basis, however, there was a net deposition of P to the bottom sediments.
Empirical models were used to describe how the near-surface water quality of Green Lake would be expected to respond to changes in external P loading. Predictions from the models showed a relatively linear response between P loading and TP and chlorophyll-a (Chl-a) concentrations in the lake, with the changes in TP and Chl-a concentrations being less on a percentage basis (50–60 percent for TP and 30–70 percent for Chl-a) than the changes in P loading. Mean summer water clarity, quantified by Secchi disk depths, had a greater response to decreases in P loading than to increases in P loading. Based on these relations, external P loading to the lake would need to be decreased from 8,980 kg/yr to about 5,460 kg/yr for the geometric mean June–September TP concentration in the east side of the lake, with higher TP concentrations than in the west side, to reach the WDNR criterion of 0.015 mg/L. This reduction of 3,520 kg/yr is equivalent to a 46-percent reduction in the potentially controllable external P sources (all external sources except for precipitation, atmospheric deposition, and waterfowl) from those measured during water years 2014–18. The total external P loading would need to decrease to 7,680 kg/yr (a 17-percent reduction in potentially controllable external P sources) for near-surface June–September TP concentrations in the west side of the lake to reach 0.015 mg/L. Total external P loading would need to decrease to 3,870–5,320 kg/yr for the lake to be classified as oligotrophic, with a near-surface June–September TP concentration of 0.012 mg/L.
Results from the hydrodynamic water-quality model GLM–AED (General Lake Model coupled to the Aquatic Ecodynamics modeling library) indicated that MOMs are driven by external P loading and internal sediment recycling that lead to high TP concentrations during spring and early summer, which in turn lead to high phytoplankton production, high metabolism and respiration, and ultimately DO consumption in the upper, warmer areas of the metalimnion. GLM–AED results indicated that settling of organic material during summer might be slowed by the colder, denser, and more viscous water in the metalimnion and thus increase DO consumption. Based on empirical evidence from a comparison of MOMs with various meteorological, hydrologic, water quality, and in-lake physical factors, MOMs were lower during summers, when metalimnetic water temperatures were warmer, near-surface Chl-a and TP concentrations were higher, and Secchi depths were lower. GLM–AED results indicated that the external P load would need to be reduced to about 4,060 kg/yr, a 57-percent reduction from that measured in 2014–18, to eliminate the occurrence of MOMs less than 5 mg/L during more than 75 percent of the years (the target provided by the WDNR).
Large reductions in external P loading are expected to have an immediate effect on the near-surface TP concentrations and metalimnetic DO concentrations in Green Lake; however, it may take several years for the full effects of the external-load reduction to be observed because internal sediment recycling is an important source of P for the following spring.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225003","collaboration":"Prepared in cooperation with the Green Lake Sanitary District","usgsCitation":"Robertson, D.M., Siebers, B.J., Ladwig, R., Hamilton, D.P., Reneau, P.C., McDonald, C.P., Prellwitz, S., and Lathrop, R.C., 2022, Response of Green Lake, Wisconsin, to changes in phosphorus loading, with special emphasis on near-surface total phosphorus concentrations and metalimnetic dissolved oxygen minima: U.S. Geological Survey Scientific Investigations Report 2022–5003, 77 p., https://doi.org/10.3133/sir20225003.","productDescription":"Report: xi, 77 p.; Data Release","numberOfPages":"77","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-123380","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":502291,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112545.htm","linkFileType":{"id":5,"text":"html"}},{"id":396912,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5003/images/"},{"id":396910,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H85BK0","text":"USGS data release","linkHelpText":"Eutrophication models to simulate changes in the water quality of Green Lake, Wisconsin in response to changes in phosphorus loading, with supporting water-quality data for the lake, its tributaries, and atmospheric deposition"},{"id":396911,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5003/sir20225003.XML"},{"id":396909,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5003/sir20225003.pdf","text":"Report","size":"8.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5003"},{"id":396908,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5003/coverthb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Green Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.09225463867188,\n 43.756712928570245\n ],\n [\n -88.86428833007814,\n 43.756712928570245\n ],\n [\n -88.86428833007814,\n 43.85384062624276\n ],\n [\n -89.09225463867188,\n 43.85384062624276\n ],\n [\n -89.09225463867188,\n 43.756712928570245\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Villa Angela Beach, on the Lake Erie lakeshore near Cleveland, Ohio, is just west of the mouth of Euclid Creek, a small, flashy stream that drains approximately 23 square miles and is susceptible to periodic contamination from combined sewer overflows (CSOs; 190 and 189 events in 2018 and 2019, respectively). Concerns about high concentrations of Escherichia coli (E. coli) in water samples collected along this beach and subsequent frequent beach closures led to the collection of water-quality and water-velocity data in the nearshore area to gain insights into nearshore mixing processes, circulation, and the potential for transport of bacteria and other CSO-related contaminants from nearby sources to the beach. Synoptic surveys were completed by the U.S. Geological Survey on June 10–12, 2019, and August 19–21, 2019, to observe conditions during early and late periods of the summer season. This study follows several studies in this area. Data-collection methods for this study included deployment of an autonomous underwater vehicle and use of a manned boat equipped with an acoustic Doppler current profiler and a multiparameter sonde. Spatial distributions of water-quality constituents and nearshore currents indicated that the mixing zone near the mouth of Euclid Creek and Villa Angela Beach is dynamic and highly variable in spatial extent. Similar observations around the Easterly Wastewater Treatment Plant 1.5 miles to the southwest of Villa Angela Beach indicated a mixing zone that was likewise dynamic and highly variable in spatial extent. Observed circulation patterns during synoptic surveys in summer 2019 indicated that contaminants from CSOs in Euclid Creek and at CSO discharge points along the Lake Erie lakefront (as traced using specific conductance as a surrogate) tended to be transported differently depending on the magnitude and direction of winds and longshore currents. The southwesterly longshore current that was responsible for driving a recirculation pattern along the beach during a previous study in summer 2012 was not observed during the summer 2019 synoptic surveys. That was not surprising because continuous velocity data collected near Villa Angela Beach indicated that longshore currents with a northeasterly component occurred most (65 percent) of the time from June 12 to August 28, 2019.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215122","collaboration":"Prepared in cooperation with the Northeast Ohio Regional Sewer District","usgsCitation":"Boldt, J.A., and Jackson, P.R., 2022, Circulation, mixing, and transport in nearshore Lake Erie in the vicinity of Villa Angela Beach and Euclid Creek, Cleveland, Ohio, June 10–12, 2019, and August 19–21, 2019: U.S. Geological Survey Scientific Investigations Report 2021–5122, 78 p., https://doi.org/10.3133/sir20215122.","productDescription":"Report: x, 77 p.; Data Release","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-122040","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":502125,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112548.htm","linkFileType":{"id":5,"text":"html"}},{"id":396926,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P963OH6M","text":"USGS data release","linkHelpText":"Velocity surveys and three-dimensional point measurements of basic water-quality constituents in nearshore Lake Erie in the vicinity of Villa Angela Beach and Euclid Creek, Cleveland, Ohio, June 10–12, 2019, and August 19–21, 2019"},{"id":396925,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5122/images"},{"id":396924,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5122/sir20215122.XML"},{"id":396923,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5122/sir20215122.pdf","text":"Report","size":"56.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5122"},{"id":396922,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5122/coverthb.jpg"}],"country":"United States","state":"Ohio","city":"Cleveland","otherGeospatial":"Villa Angela Beach, Euclid Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.61949157714844,\n 41.55381099217959\n ],\n [\n -81.59923553466797,\n 41.54327642327762\n ],\n [\n -81.5346908569336,\n 41.58463401188338\n ],\n [\n -81.56044006347656,\n 41.603377487685165\n ],\n [\n -81.61949157714844,\n 41.55381099217959\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
High-quality rangeland data are critical to supporting adaptive management. However, concrete, cost-saving steps to ensure data quality are often poorly defined and understood.
Data quality is more than data management. Ensuring data quality requires 1) clear communication among team members; 2) appropriate sample design; 3) training of data collectors, data managers, and data users; 4) observer and sensor calibration; and 5) active data management. Quality assurance and quality control are ongoing processes to help rangeland managers and scientists identify, prevent, and correct errors in past, current, and future monitoring data.
We present 10 guiding data quality questions to help managers and scientists identify appropriate workflows to improve data quality by 1) describing the data ecosystem, 2) creating a data quality plan, 3) identifying roles and responsibilities, 4) building data collection and data management workflows, 5) training and calibrating data collectors, 6) detecting and correcting errors, and 7) describing sources of variability.
Iteratively improving rangeland data quality is a key part of adaptive monitoring and rangeland data collection. All members of the rangeland community are invited to participate in ensuring rangeland data quality.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rala.2021.07.006","usgsCitation":"McCord, S.E., Welty, J.L., Courtwright, J., Dillon, C., Laurence-Traynor, A., Burnett, S.H., Courtright, E., Fults, G., Karl, J.W., Van Zee, J., Webb, N.P., and Tweedie, C.E., 2022, Ten practical questions to improve data quality: Rangelands, v. 44, no. 1, p. 17-28, https://doi.org/10.1016/j.rala.2021.07.006.","productDescription":"12 p.","startPage":"17","endPage":"28","ipdsId":"IP-123539","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":448539,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rala.2021.07.006","text":"Publisher Index Page"},{"id":396928,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCord, Sarah E.","contributorId":195931,"corporation":false,"usgs":false,"family":"McCord","given":"Sarah","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":837507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Welty, Justin L. 0000-0001-7829-7324 jwelty@usgs.gov","orcid":"https://orcid.org/0000-0001-7829-7324","contributorId":4206,"corporation":false,"usgs":true,"family":"Welty","given":"Justin","email":"jwelty@usgs.gov","middleInitial":"L.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":837508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Courtwright, Jennifer 0000-0002-9864-8547","orcid":"https://orcid.org/0000-0002-9864-8547","contributorId":288137,"corporation":false,"usgs":false,"family":"Courtwright","given":"Jennifer","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":837509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dillon, Catherine","contributorId":288138,"corporation":false,"usgs":false,"family":"Dillon","given":"Catherine","email":"","affiliations":[],"preferred":false,"id":837510,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Laurence-Traynor, Alexander","contributorId":288139,"corporation":false,"usgs":false,"family":"Laurence-Traynor","given":"Alexander","email":"","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":true,"id":837511,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Burnett, Sarah H.","contributorId":288140,"corporation":false,"usgs":false,"family":"Burnett","given":"Sarah","email":"","middleInitial":"H.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":837512,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Courtright, Ericha M.","contributorId":169759,"corporation":false,"usgs":false,"family":"Courtright","given":"Ericha M.","affiliations":[{"id":25579,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM 88003","active":true,"usgs":false}],"preferred":false,"id":837513,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fults, Gene","contributorId":288305,"corporation":false,"usgs":false,"family":"Fults","given":"Gene","email":"","affiliations":[{"id":39979,"text":"USDA Natural Resources Conservation Service, Portland, OR","active":true,"usgs":false}],"preferred":false,"id":837669,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Karl, Jason W.","contributorId":191703,"corporation":false,"usgs":false,"family":"Karl","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":7045,"text":"USDA-ARS Jornada Experimental Range ","active":true,"usgs":false}],"preferred":false,"id":837514,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Van Zee, Justin W.","contributorId":169758,"corporation":false,"usgs":false,"family":"Van Zee","given":"Justin W.","affiliations":[{"id":25579,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM 88003","active":true,"usgs":false}],"preferred":false,"id":837515,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Webb, Nicholas P.","contributorId":195924,"corporation":false,"usgs":false,"family":"Webb","given":"Nicholas","email":"","middleInitial":"P.","affiliations":[{"id":6973,"text":"USDA-ARS Jornada Experimental Range and Jornada Basin LTER, Las Cruces, NM; New Mexico State University, Dept. of Plant and Environmental Sciences, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":837516,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Tweedie, Craig E.","contributorId":200176,"corporation":false,"usgs":false,"family":"Tweedie","given":"Craig","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":837517,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70255284,"text":"70255284 - 2022 - Greater than the sum of its parts: Computationally flexible Bayesian hierarchical modeling","interactions":[],"lastModifiedDate":"2024-06-14T13:43:23.197776","indexId":"70255284","displayToPublicDate":"2022-03-09T08:39:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9352,"text":"Journal of Agricultural, Biological and Environmental Statistics","active":true,"publicationSubtype":{"id":10}},"title":"Greater than the sum of its parts: Computationally flexible Bayesian hierarchical modeling","docAbstract":"We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computations to take place in parallel form using software that is most appropriate for each data partition. The full hierarchical model is then approximated by the product of independent normal distributions for the data component of the model. In the second stage, the Bayesian maximum a posteriori (MAP) estimator is found by maximizing the approximated posterior density with respect to the parameters. If the parameters of the model can be represented as normally distributed random effects, then the second-stage optimization is equivalent to fitting a multivariate normal linear mixed model. We consider a third stage that updates the estimates of distinct parameters for each data partition based on the results of the second stage. The method is demonstrated with two ecological data sets and models, a generalized linear mixed effects model (GLMM) and an integrated population model (IPM). The multistage results were compared to estimates from models fit in single stages to the entire data set. In both cases, multistage results were very similar to a full MCMC analysis. Supplementary materials accompanying this paper appear online.
","language":"English","publisher":"Springer","doi":"10.1007/s13253-021-00485-9","usgsCitation":"Johnson, D., Brost, B., and Hooten, M., 2022, Greater than the sum of its parts: Computationally flexible Bayesian hierarchical modeling: Journal of Agricultural, Biological and Environmental Statistics, v. 27, https://doi.org/10.1007/s13253-021-00485-9.","productDescription":"19 p.","startPage":"400","ipdsId":"IP-123441","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":448547,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13253-021-00485-9","text":"Publisher Index Page"},{"id":430203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","edition":"382","noUsgsAuthors":false,"publicationDate":"2022-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Devin S.","contributorId":337626,"corporation":false,"usgs":false,"family":"Johnson","given":"Devin S.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":904099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brost, Brian M.","contributorId":244504,"corporation":false,"usgs":false,"family":"Brost","given":"Brian M.","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":904100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":904098,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230166,"text":"70230166 - 2022 - Maximizing species distribution model performance when using historical occurrences and variables of varying persistency","interactions":[],"lastModifiedDate":"2022-04-01T22:02:19.08828","indexId":"70230166","displayToPublicDate":"2022-03-09T08:07:23","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Maximizing species distribution model performance when using historical occurrences and variables of varying persistency","docAbstract":"Occurrence data used to build species distribution models often include historical records from locations in which the species no longer exists. When these records are paired with contemporary environmental values that no longer represent the conditions the species experienced, the model creates false associations that hurt predictive performance. The extent of mismatching increases with the number of historical occurrences and with inclusion of environmental variables that are prone to change over time. Indeed, the mismatch between occurrence data and contemporaneous environmental variables is a common dilemma when modeling rare or cryptic species, especially those of conservation concern that were once more abundant. Herein, we assess (1) the impact of historical occurrences on model performance across three sets of environmental variables of increasing persistency and (2) the performance of models built using selected-historical occurrences from locations that showed evidence of limited environmental change over time. Concepts are tested on federally listed flatwoods salamanders, reflecting real-world conservation management efforts. We predicted that, compared to other occurrence sets, (1) historical occurrences would perform best with environmental variables that were more persistent, (2) recent occurrences would perform best when the environmental variables were more impersistent, and that (3) our selected-historical occurrences would perform best with a combination of persistent and impersistent variables. Our results showed the expected inversion of model performance of recent and historical occurrences across environmental variables of increasing persistency when evaluated by correct predictions. However, the inversion was not seen in area under the curve performance, in which historical occurrences outperformed recent occurrence models across all variable sets. Selected-historical occurrences did not notably improve performance over all-historical occurrences in any metric or variable set. To maximize utility and performance, modelers could acknowledge potential trade-offs from inclusion of historical occurrences and consider number and age of recent and historical occurrences available, the persistency of environmental variables considered, and how their conservation goals are reflected in model design and evaluation, particularly with respect to sensitivity versus specificity. Our study lends support for inclusion of historical occurrences, with the potential exception of mostly impersistent variables when sensitivity is the highest priority.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3951","usgsCitation":"Bracken, J.T., Davis, A., O’Donnell, K., Barichivich, W., Walls, S., and Jezkova, T., 2022, Maximizing species distribution model performance when using historical occurrences and variables of varying persistency: Ecosphere, v. 13, no. 3, e3951, 13 p., https://doi.org/10.1002/ecs2.3951.","productDescription":"e3951, 13 p.","ipdsId":"IP-126905","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":489148,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3951","text":"Publisher Index Page"},{"id":397930,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Georgia, South Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.4619140625,\n 30.391830328088137\n ],\n [\n -87.62695312499999,\n 30.107117887092357\n ],\n [\n -86.8359375,\n 30.240086360983426\n ],\n [\n -85.97900390625,\n 30.06909396443887\n ],\n [\n -85.517578125,\n 29.592565403314087\n ],\n [\n -84.9462890625,\n 29.516110386062277\n ],\n [\n -84.111328125,\n 29.954934549656144\n ],\n [\n -83.27636718749999,\n 29.209713225868185\n ],\n [\n -82.79296874999999,\n 28.786918085420226\n ],\n [\n -83.07861328125,\n 28.013801376380712\n ],\n [\n -82.96875,\n 27.702983735525862\n ],\n [\n -80.52978515625,\n 28.265682390146477\n ],\n [\n -80.419921875,\n 28.555576049185973\n ],\n [\n -80.79345703125,\n 28.92163128242129\n ],\n [\n -81.474609375,\n 30.80791068136646\n ],\n [\n -80.88134765625,\n 31.98944183792288\n ],\n [\n -79.89257812499999,\n 32.54681317351514\n ],\n [\n -79.189453125,\n 33.063924198120645\n ],\n [\n -78.59619140625,\n 33.90689555128866\n ],\n [\n -88.41796875,\n 32.93492866908233\n ],\n [\n -88.52783203125,\n 31.82156451492074\n ],\n [\n -88.4619140625,\n 30.391830328088137\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Bracken, Jason T.","contributorId":289570,"corporation":false,"usgs":false,"family":"Bracken","given":"Jason","email":"","middleInitial":"T.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":839350,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Davis, Amelie Y.","contributorId":289572,"corporation":false,"usgs":false,"family":"Davis","given":"Amelie Y.","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":839351,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Donnell, Katherine M. 0000-0001-9023-174X kmodonnell@usgs.gov","orcid":"https://orcid.org/0000-0001-9023-174X","contributorId":176897,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Katherine M.","email":"kmodonnell@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839352,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barichivich, William 0000-0003-1103-6861","orcid":"https://orcid.org/0000-0003-1103-6861","contributorId":215988,"corporation":false,"usgs":true,"family":"Barichivich","given":"William","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":839353,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walls, Susan C. 0000-0001-7391-9155","orcid":"https://orcid.org/0000-0001-7391-9155","contributorId":3055,"corporation":false,"usgs":true,"family":"Walls","given":"Susan C.","affiliations":[],"preferred":true,"id":839354,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jezkova, Tereza","contributorId":209721,"corporation":false,"usgs":false,"family":"Jezkova","given":"Tereza","email":"","affiliations":[{"id":16608,"text":"Miami University","active":true,"usgs":false}],"preferred":false,"id":839355,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229522,"text":"70229522 - 2022 - Linkages between land-use change and groundwater management foster long-term resilience of water supply in California","interactions":[],"lastModifiedDate":"2022-03-11T13:00:26.592646","indexId":"70229522","displayToPublicDate":"2022-03-09T06:56:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Linkages between land-use change and groundwater management foster long-term resilience of water supply in California","docAbstract":"We created a 270-m coupled model of land-use and groundwater conditions, LUCAS-W[ater], for California’s Central Coast. This groundwater-dependent region is undergoing a dramatic reorganization of groundwater management under California’s 2014 Sustainable Groundwater Management Act (SGMA).
Understanding land-use and land-cover change supports long-term sustainable water management. Anthropogenic water demand has depleted groundwater aquifers worldwide, while future water shortages will likely affect land-use change, creating system feedbacks. Our novel participatory approach fused changes in land-use and associated water use from county-scale data to local water agencies’ estimates of total sustainable supply, scaling up local hydro-geologic knowledge from heterogeneous aquifers and diverse management approaches to a regional level. We assessed five stakeholder-driven scenarios with the same historic rates of urban and agricultural land-use change, but different water and land-use management, analyzing how management strategies altered both the spatial pattern of development and subsequent water sustainability from 2001 to 2061.
Transformative strategies using demand-side interventions that coupled water availability to land-use more effectively achieved long-term sustainability than adaptive strategies using supply-side interventions to increase water supplies. Limiting water withdrawals within SGMA regulated basins resulted in leakage of development into unregulated basins, increasing groundwater pumping there. Protecting ecosystems, farmlands, and recharge areas from development reduced leakage into undeveloped basins without negatively affecting water sustainability.
Gas hydrates are found in significant quantities on the North Slope of Alaska in subpermafrost sand units and intermixed in lower portions of permafrost within the hydrate stability window. While conventional surface seismic data and established imaging methods can indicate the presence of gas hydrate reservoirs, producing high-resolution images of (seismically) thin layers remains challenging due to the preferential attenuation of the higher-frequency data components. An alternative strategy is to use distributed acoustic sensing (DAS) involving cementing optical fibers into boreholes to measure seismic wavefield energy closer to the strata of interest using vertical seismic profiling (VSP). DAS VSP imaging takes advantage of the shorter travel paths and reduced attenuation to generate higher-resolution near-well images. We illustrate these benefits on a DAS VSP data set acquired at the Hydrate-01 stratigraphic test well located in the Prudhoe Bay Unit of Alaska where significant gas hydrate deposits have been detected in two subpermafrost sand layers that are intended for long-duration production testing. Our DAS data preprocessing workflow effectively isolates the upgoing compressional-wave (P-wave) reflections required for subsurface acoustic imaging. After applying three-dimensional (3-D) tomography to improve the quality of the 3-D migration velocity model, we use 3-D reverse-time migration (RTM) to develop high-quality images of the two target sands and minor near-well faulting. We validate our RTM images through highly accurate well-ties with previously acquired petrophysical log data. This study demonstrates that combining 3-D RTM imaging with DAS VSP data provides significant value to gas hydrate and similar projects, and it suggests that more advanced inversion approaches such as (elastic) least-squares RTM could recover higher-resolution and more quantitative estimates of subsurface reflectivity, which would be valuable for refining the understanding of gas hydrate systems.
Idaho may be popular for potatoes, but the State’s richness also lies in its scenery and natural resources. Its terrain varies from mountains, rivers, and waterfalls to forests, volcanic rock, and hot springs. A growing population gives Idaho even more reason to use the best information available to serve the needs of its residents while wisely managing its environment and natural resources.
Soon after the first Landsat satellite launched, Idaho recognized how useful its data could be. In 1975, the Idaho Department of Water Resources started using data from Landsat 1 and has since used data from every subsequent Landsat satellite.
Here is a closer look at Landsat as a key tool for monitoring water, along with other examples of Landsat’s value to Idaho.
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\"}}]}","edition":"Version 1.0: March 8, 2022; Version 1.1: January 23, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Stocking of rare native fishes for conservation purposes is a common practice in the southwestern United States. Monitoring typically occurs after hatchery-reared fish are released to assess post-stocking movement and survival. We conducted a two-year study, in which tow-barge electrofishing and portable, flat-bed passive integrated transponder (PIT) antennas were used to monitor PIT-tagged, hatchery-reared roundtail chub (Gila robusta) following release into the upper Verde River in Arizona. Specifically, our study aimed to compare the performance of PIT antennas and electrofishing in detecting PIT tagged fish released in a small desert river and to examine the behavioral response of hatchery-reared roundtail chub after stocking. In both years, more fish were detected by antenna arrays (84%) than by electrofishing (30%). roundtail chub were significantly more likely to be detected by antennas than electrofishing each year; however, when antenna data were evaluated only during the few days in which electrofishing took place, there was no significant difference (Year 1, p=0.1784; Year 2, p=0.6295) in detection between gear types for the same time interval, suggesting that electrofishing and antennas are equally likely to detect fish during 48-72 hour time frames. Within 72 hours of release, antennas detected 100% of fish that moved upstream and 93.8% of fish that moved downstream from the stocking location. Overall, less than half (45.6% in Year 1; 41.1% in Year 2) of the stocked roundtail chub were detected using both methods in both years. Utilization of both active capture gear (electrofishing) and passive gear (antennae) had advantages over monitoring with a single method. PIT antennae can be especially useful for managers who lack the personnel or time to implement more intensive methods of capture but want to monitor post-stocking movement and survival of stocked fish.
","language":"English","publisher":"Arizona-Nevada Academy of Sciences","doi":"10.2181/036.049.0209","usgsCitation":"Tennant, L.A., Ward, D., and Gibb, A.C., 2022, Comparison of electrofishing and PIT antennas for detection of hatchery-reared Roundtail Chub (Gila robusta) stocked into a desert stream: Journal of the Arizona-Nevada Academy of Science, v. 49, no. 2, p. 116-126, https://doi.org/10.2181/036.049.0209.","productDescription":"11 p.","startPage":"116","endPage":"126","ipdsId":"IP-099559","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":448570,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2181/036.049.0209","text":"Publisher Index Page"},{"id":435935,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PGQGL","text":"USGS data release","linkHelpText":"Hatchery-reared Roundtail Chub Data, Arizona USA"},{"id":396785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Verde River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.47227668762206,\n 34.85346724741666\n ],\n [\n -112.39751815795898,\n 34.85346724741666\n ],\n [\n -112.39751815795898,\n 34.87565098440711\n ],\n [\n -112.47227668762206,\n 34.87565098440711\n ],\n [\n -112.47227668762206,\n 34.85346724741666\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tennant, Laura A. 0000-0003-0062-7287 ltennant@usgs.gov","orcid":"https://orcid.org/0000-0003-0062-7287","contributorId":5984,"corporation":false,"usgs":true,"family":"Tennant","given":"Laura","email":"ltennant@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":837338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ward, David 0000-0002-3355-0637","orcid":"https://orcid.org/0000-0002-3355-0637","contributorId":216231,"corporation":false,"usgs":true,"family":"Ward","given":"David","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":837339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gibb, Alice C.","contributorId":207521,"corporation":false,"usgs":false,"family":"Gibb","given":"Alice","email":"","middleInitial":"C.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":837340,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229393,"text":"70229393 - 2022 - Deep learning detection and recognition of spot elevations on historic topographic maps","interactions":[],"lastModifiedDate":"2022-03-07T14:39:01.222238","indexId":"70229393","displayToPublicDate":"2022-03-07T08:33:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Deep learning detection and recognition of spot elevations on historic topographic maps","docAbstract":"Some information contained in historical topographic maps has yet to be captured digitally, which limits the ability to automatically query such data. For example, U.S. Geological Survey’s historical topographic map collection (HTMC) displays millions of spot elevations at locations that were carefully chosen to best represent the terrain at the time. Although research has attempted to reproduce these data points, it has proven inadequate to automatically detect and recognize spot elevations in the HTMC. We propose a deep learning workflow pretrained using large benchmark text datasets. To these datasets we add manually crafted training image/label pairs, and test how many are required to improve prediction accuracy. We find that the initial model, pretrained solely with benchmark data, fails to predict any HTMC spot elevations correctly, whereas the addition of just 50 custom image/label pairs increases the predictive ability by ~50%, and the inclusion of 350 data pairs increased performance by ~80%. Data augmentation in the form of rotation, scaling and translation (offset) expanded the size and diversity of the training dataset and vastly improved recognition accuracy up to ~95%. Visualization methods, such as heat map generation and salient feature detection are recommended to better understand why some predictions fail.","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvs.2022.804155","usgsCitation":"Arundel, S., Morgan, T.P., and Thiem, P.T., 2022, Deep learning detection and recognition of spot elevations on historic topographic maps: Frontiers in Environmental Science, v. 10, p. 1-10, https://doi.org/10.3389/fenvs.2022.804155.","productDescription":"804155, 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-129409","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":448574,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2022.804155","text":"Publisher Index Page"},{"id":396784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-02-18","publicationStatus":"PW","contributors":{"editors":[{"text":"Chiang, Yao-Yi","contributorId":288084,"corporation":false,"usgs":false,"family":"Chiang","given":"Yao-Yi","email":"","affiliations":[{"id":13249,"text":"University of Southern California","active":true,"usgs":false}],"preferred":false,"id":837350,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true}],"preferred":true,"id":837265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morgan, Trenton P.","contributorId":287989,"corporation":false,"usgs":false,"family":"Morgan","given":"Trenton","email":"","middleInitial":"P.","affiliations":[{"id":61682,"text":"Rolla, MO","active":true,"usgs":false}],"preferred":false,"id":837341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thiem, Philip T. 0000-0002-3324-2589","orcid":"https://orcid.org/0000-0002-3324-2589","contributorId":287990,"corporation":false,"usgs":true,"family":"Thiem","given":"Philip","email":"","middleInitial":"T.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":837342,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229369,"text":"70229369 - 2022 - Leveraging rangeland monitoring data for wildlife: From concept to practice","interactions":[],"lastModifiedDate":"2022-03-04T15:45:24.688257","indexId":"70229369","displayToPublicDate":"2022-03-04T09:31:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Leveraging rangeland monitoring data for wildlife: From concept to practice","docAbstract":"Available rangeland data, from field-measured plots to remotely sensed landscapes, provide much needed information for mapping and modeling wildlife habitats.
Better integration of wildlife habitat characteristics into rangeland monitoring schemes is needed for most rangeland wildlife species at varying spatial and temporal scales.
Here, we aim to stimulate use of and inspire ideas about rangeland monitoring data in the context of wildlife habitat modeling and species conservation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rala.2021.09.005","usgsCitation":"Pilliod, D., Beck, J.L., Duchardt, C.J., Rachlow, J.L., and Veblen, K.E., 2022, Leveraging rangeland monitoring data for wildlife: From concept to practice: Rangelands, v. 44, no. 1, p. 87-98, https://doi.org/10.1016/j.rala.2021.09.005.","productDescription":"12 p.","startPage":"87","endPage":"98","ipdsId":"IP-125490","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":448591,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rala.2021.09.005","text":"Publisher Index Page"},{"id":396752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Kansas, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Utah, Wyoming","otherGeospatial":"Great 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0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":837217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beck, Jeffrey L.","contributorId":287806,"corporation":false,"usgs":false,"family":"Beck","given":"Jeffrey","middleInitial":"L.","affiliations":[{"id":12729,"text":"UW","active":true,"usgs":false}],"preferred":false,"id":837218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duchardt, Courtney Jean 0000-0003-4563-0199","orcid":"https://orcid.org/0000-0003-4563-0199","contributorId":264471,"corporation":false,"usgs":true,"family":"Duchardt","given":"Courtney","email":"","middleInitial":"Jean","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":837219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rachlow, Janet L.","contributorId":69298,"corporation":false,"usgs":true,"family":"Rachlow","given":"Janet","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":837220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Veblen, Kari E.","contributorId":76872,"corporation":false,"usgs":false,"family":"Veblen","given":"Kari","email":"","middleInitial":"E.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":837221,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255537,"text":"70255537 - 2022 - Stage-specific environmental correlates of reproductive success in Boreal Toads (Anaxyrus boreas boreas)","interactions":[],"lastModifiedDate":"2024-06-21T11:53:57.84801","indexId":"70255537","displayToPublicDate":"2022-03-04T06:51:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"title":"Stage-specific environmental correlates of reproductive success in Boreal Toads (Anaxyrus boreas boreas)","docAbstract":"Compensatory recruitment can facilitate the persistence of populations experiencing high adult mortality. Because early life-stages of many taxa, including amphibians, are difficult to mark and recapture, sources of variation in survival at these stages often are unknown, which creates barriers to improving in situ recruitment rates. We leveraged count data and open N-mixture models to examine the environmental factors associated with the hatching of egg clutches, tadpole survival, and probability of metamorphosis in Boreal Toads (Anaxyrus boreas boreas) that inhabit pastures leased for cattle grazing in western Wyoming, USA. We conducted weekly surveys and measured a suite of environmental variables at 20 breeding ponds during May–September 2018. The hatching of egg clutches was most strongly related to pond surface area, as clutches often desiccated at smaller ponds. Weekly tadpole survival was lowest in ponds with high abundance of aquatic predators. Predation did not preclude metamorphosis, which was more strongly associated with higher dissolved oxygen and vegetation cover. Cattle grazing reduced vegetation cover in and around breeding ponds, which resulted in lower levels of dissolved oxygen. Grazing-induced habitat changes are therefore likely to negatively influence tadpole metamorphosis both via indirect effects on dissolved oxygen, and direct effects on vegetation cover, which also serves as feeding sites and escape cover from predators. We demonstrate the success of three critical phases in early life-stage development (egg hatching, tadpole survival, metamorphosis) was associated with different environmental factors. The inclusion of stage-specific responses in demographic analyses is therefore critical for a thorough understanding of what limits populations.
The goal of the Louisiana Barrier Island Comprehensive Monitoring (BICM) program is to provide long-term data on coastal Louisiana for monitoring change and assisting in coastal management. This study (carried out under Coastal Protection and Restoration Authority contract number 2000339324, BICM2—Chenier TopoBathy DEM) builds upon the previous BICM physical assessment of the Chenier Plain region using bathymetric data from three periods (1930, 2007, and 2017) to develop digital elevation models for historical and current periods. In addition to bathymetric datasets, the study includes light detection and ranging elevation measurements along the coastline to produce elevation datasets for the 2007 and 2017 periods. This report describes the methods used to acquire, process, and produce these products.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221014","collaboration":"Prepared in cooperation with Coastal Protection and Restoration Authority of Louisiana","programNote":"Louisiana Barrier Island Comprehensive Monitoring Program 2015–2020","usgsCitation":"Flocks, J.G., Forde, A.S., and Bernier, J.C., 2022, Chenier Plain region bathymetric and topographic datasets: Methodology report: U.S. Geological Survey Open-File Report 2022–1014, 21 p., https://doi.org/10.3133/ofr20221014.","productDescription":"vii, 21 p.","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-122902","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":501759,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112530.htm","linkFileType":{"id":5,"text":"html"}},{"id":396683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1014/ofr20221014.pdf","text":"Report","size":"6.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1014"},{"id":396682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1014/coverthb.jpg"},{"id":396685,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1014/images/"},{"id":396684,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1014/ofr20221014.XML"},{"id":396704,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221014/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Louisiana","otherGeospatial":"Chenier Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.85620117187499,\n 29.439597566602902\n ],\n [\n -92.1258544921875,\n 29.439597566602902\n ],\n [\n -92.1258544921875,\n 29.8\n ],\n [\n -93.85620117187499,\n 29.8\n ],\n [\n -93.85620117187499,\n 29.439597566602902\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Conservation translocations are increasingly used to help recover imperiled species. However, success of establishing populations remains low, especially for amphibians. Identifying factors associated with translocation success can help increase efficiency and efficacy of recovery efforts. Since the 1990s, several captive and semi-captive facilities have produced Chiricahua Leopard Frogs (Rana chiricahuensis) to establish or augment wild populations in Arizona and New Mexico, USA. During this same time, personnel associated with several programs surveyed translocation and non-translocation sites for presence of amphibians. We used 25 years (1995–2019) of survey and translocation data for the federally threatened Chiricahua Leopard Frog to identify factors linked with population persistence. Our dataset included approximately 40,642 egg masses or animals translocated in 314 events to 115 distinct sites and > 5800 visual encounter surveys from 641 sites; 120 of these sites were also surveyed with environmental DNA methods in 2018. We used a hierarchical dynamic occupancy model that accounted for imperfect detection to identify patch- and landscape-level attributes associated with site occupancy, and then used predictions from that model to evaluate factors associated with population persistence at translocation sites. Across all sites, extinction probability for Chiricahua Leopard Frogs was higher in lotic (stream) than lentic (pond) habitats and when Western Tiger Salamanders (Ambystoma mavortium) were present. Restoration of sites specifically for frog conservation reduced extinction probability. Colonization of unoccupied sites increased moderately with increasing numbers of translocation sites within 2 km, indicating a benefit of translocation efforts beyond sites where frogs were stocked. At translocation sites, persistence was greater in lentic than lotic habitats and was negatively correlated with the proportion of years tiger salamanders were present. Increasing numbers of translocation events, especially of late-stage larvae, increased persistence. There was little difference in population persistence based on whether stock was from captive, semi-captive, or wild sources, but translocations during the dry season (January—July) succeeded more than those after the typical arrival of summer rains (August—December). Based on the number of years translocation sites were predicted to be occupied, 2 or more translocations produced, on average, a > 4-yr increase in predicted occupancy compared to sites without translocations. While translocations have increased the number of populations across the landscape, continued management of water availability and threats such as invasive predators and disease remain critical to recovery of the Chiricahua Leopard Frog.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02078","usgsCitation":"Hossack, B., Howell, P., Owens, A., Cobos, C., Goldberg, C.S., Hall, D.L., Hedwall, S., MacVean, S., McCaffery, M., McCall, A.H., Mosley, C., Oja, E.B., Rorabaugh, J.C., Sigafus, B., and Sredl, M.J., 2022, Identifying factors linked with persistence of reintroduced populations: Lessons learned from 25 years of amphibian translocations: Global Ecology and Conservation, v. 35, e02078, 22 p., https://doi.org/10.1016/j.gecco.2022.e02078.","productDescription":"e02078, 22 p.","ipdsId":"IP-135486","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":448603,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02078","text":"Publisher Index Page"},{"id":397306,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.35717773437499,\n 31.325486676506983\n ],\n [\n -106.0400390625,\n 31.325486676506983\n ],\n [\n -106.0400390625,\n 35.10193405724606\n ],\n [\n -112.35717773437499,\n 35.10193405724606\n ],\n [\n -112.35717773437499,\n 31.325486676506983\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":838451,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howell, Paige E.","contributorId":173495,"corporation":false,"usgs":false,"family":"Howell","given":"Paige E.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":838452,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Owens, Audrey K","contributorId":288932,"corporation":false,"usgs":false,"family":"Owens","given":"Audrey K","affiliations":[{"id":61907,"text":"AGFD","active":true,"usgs":false}],"preferred":false,"id":838453,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cobos, C","contributorId":288933,"corporation":false,"usgs":false,"family":"Cobos","given":"C","email":"","affiliations":[{"id":38107,"text":"Turner Endangered Species Fund","active":true,"usgs":false}],"preferred":false,"id":838454,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goldberg, Caren S.","contributorId":76879,"corporation":false,"usgs":false,"family":"Goldberg","given":"Caren","email":"","middleInitial":"S.","affiliations":[{"id":5132,"text":"Washington State University, Pullman","active":true,"usgs":false}],"preferred":false,"id":838455,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hall, David L.","contributorId":222395,"corporation":false,"usgs":false,"family":"Hall","given":"David","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":838456,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hedwall, Shaula","contributorId":288934,"corporation":false,"usgs":false,"family":"Hedwall","given":"Shaula","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":838457,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"MacVean, Susi","contributorId":288935,"corporation":false,"usgs":false,"family":"MacVean","given":"Susi","email":"","affiliations":[{"id":61907,"text":"AGFD","active":true,"usgs":false}],"preferred":false,"id":838458,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McCaffery, Magnus","contributorId":288936,"corporation":false,"usgs":false,"family":"McCaffery","given":"Magnus","email":"","affiliations":[{"id":38107,"text":"Turner Endangered Species Fund","active":true,"usgs":false}],"preferred":false,"id":838459,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"McCall, A. Hunter","contributorId":288937,"corporation":false,"usgs":false,"family":"McCall","given":"A.","email":"","middleInitial":"Hunter","affiliations":[{"id":48661,"text":"Private","active":true,"usgs":false}],"preferred":false,"id":838460,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mosley, C","contributorId":288938,"corporation":false,"usgs":false,"family":"Mosley","given":"C","email":"","affiliations":[{"id":61907,"text":"AGFD","active":true,"usgs":false}],"preferred":false,"id":838461,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Oja, Emily Bea 0000-0002-8621-9665","orcid":"https://orcid.org/0000-0002-8621-9665","contributorId":261164,"corporation":false,"usgs":true,"family":"Oja","given":"Emily","email":"","middleInitial":"Bea","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":838462,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rorabaugh, James C.","contributorId":191978,"corporation":false,"usgs":false,"family":"Rorabaugh","given":"James","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":838463,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Sigafus, Brent H. 0000-0002-7422-8927","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":264740,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":838464,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Sredl, Michael J","contributorId":288939,"corporation":false,"usgs":false,"family":"Sredl","given":"Michael","email":"","middleInitial":"J","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":838465,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70229162,"text":"70229162 - 2022 - A novel application of hierarchical modelling to decouple sampling artifacts from socio-ecological effects on poaching intensity","interactions":[],"lastModifiedDate":"2022-03-02T17:54:50.469801","indexId":"70229162","displayToPublicDate":"2022-03-02T11:42:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"A novel application of hierarchical modelling to decouple sampling artifacts from socio-ecological effects on poaching intensity","docAbstract":"Poaching is a global driver of wildlife population decline, including inside protected areas (PAs). Reducing poaching requires an understanding of its cryptic drivers and accurately quantifying poaching scales and intensity. There is little quantification of how poaching is affected by law enforcement intensity (e.g., ranger stations) versus economic factors (e.g., unemployment), while simultaneously accounting for imperfect detection. Using extensive data of poaching events (i.e., seizures) and censuses of nine ungulate species across the PAs and unprotected lands of Iran from 2010 to 2018, we developed a single-visit hierarchical (N-mixture) model to accurately estimate annual poaching of Iranian ungulates and to differentiate between social and ecological effects on annual poaching intensity. We found that poaching detectability increased with numbers of ranger stations. A recent surge in poaching (2013–2018) coincides with rising unemployment rate. We estimated that 19,727 ungulates (95% confidence interval 11,178–36,195) were poached across the country during 2010–2018. Poaching intensity was positively related to unemployment rate, road density, and ungulate abundance. Our simulations demonstrated that the Poisson and Negative binomial N-mixture models had adequate performance when the conditions of Sólymos et al. (2012) were satisfied, in particular, when at least one covariate is unique to both the detection and abundance parts of the model. Overall, we suggest that single-visit models offer unique insights into understanding the link between poaching intensity, economic conditions, and law enforcement in large-scale landscapes while accounting for imperfect detection of poaching events.","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2022.109488","usgsCitation":"Soofi, M., Qashqaei, A.T., Trei, J., Shokri, S., Selyari, J., Ghasemi, B., Sepahvand, P., Egli, L., Nezami, B., Zamani, N., Yusefi, G.H., Kiabi, B.H., Balkenhol, N., Royle, A., Pavey, C.R., Redpath, S.M., and Waltert, M., 2022, A novel application of hierarchical modelling to decouple sampling artifacts from socio-ecological effects on poaching intensity: Biological Conservation, v. 267, p. 1-12, https://doi.org/10.1016/j.biocon.2022.109488.","productDescription":"109488, 12 p.","startPage":"1","endPage":"12","ipdsId":"IP-127985","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":487970,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://resolver.sub.uni-goettingen.de/purl?gro-2/108655","text":"External Repository"},{"id":396659,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Iran","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[53.9216,37.19892],[54.8003,37.39242],[55.51158,37.96412],[56.18037,37.93513],[56.61937,38.12139],[57.33043,38.02923],[58.43615,37.52231],[59.23476,37.41299],[60.37764,36.52738],[61.12307,36.4916],[61.21082,35.65007],[60.80319,34.4041],[60.52843,33.67645],[60.9637,33.52883],[60.53608,32.98127],[60.86365,32.18292],[60.94194,31.54807],[61.69931,31.37951],[61.78122,30.73585],[60.87425,29.82924],[61.36931,29.30328],[61.77187,28.69933],[62.72783,28.25964],[62.75543,27.37892],[63.2339,27.21705],[63.31663,26.75653],[61.87419,26.23997],[61.49736,25.07824],[59.61613,25.38016],[58.52576,25.60996],[57.39725,25.7399],[56.97077,26.96611],[56.49214,27.1433],[55.72371,26.96463],[54.71509,26.48066],[53.4931,26.81237],[52.4836,27.58085],[51.52076,27.86569],[50.85295,28.81452],[50.11501,30.14777],[49.57685,29.98572],[48.94133,30.31709],[48.56797,29.92678],[48.01457,30.45246],[48.0047,30.98514],[47.68529,30.98485],[47.8492,31.70918],[47.33466,32.46916],[46.10936,33.01729],[45.41669,33.9678],[45.64846,34.74814],[46.15179,35.09326],[46.07634,35.67738],[45.42062,35.97755],[44.77267,37.17045],[44.22576,37.97158],[44.4214,38.28128],[44.10923,39.42814],[44.79399,39.713],[44.95269,39.33576],[45.45772,38.87414],[46.14362,38.7412],[46.50572,38.77061],[47.68508,39.50836],[48.0601,39.58224],[48.35553,39.28876],[48.01074,38.79401],[48.63438,38.27038],[48.88325,38.32025],[49.19961,37.58287],[50.14777,37.37457],[50.84235,36.87281],[52.26402,36.70042],[53.82579,36.96503],[53.9216,37.19892]]]},\"properties\":{\"name\":\"Iran\"}}]}","volume":"267","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Soofi, Mahmood","contributorId":287507,"corporation":false,"usgs":false,"family":"Soofi","given":"Mahmood","affiliations":[{"id":61590,"text":"School of Biological Sciences, University of Aberdeen","active":true,"usgs":false}],"preferred":false,"id":836831,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qashqaei, Ali T.","contributorId":287508,"corporation":false,"usgs":false,"family":"Qashqaei","given":"Ali","email":"","middleInitial":"T.","affiliations":[{"id":61592,"text":"Sahel Square, Parsia Complex, Tehran","active":true,"usgs":false}],"preferred":false,"id":836832,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trei, Jan-Niklas","contributorId":287509,"corporation":false,"usgs":false,"family":"Trei","given":"Jan-Niklas","email":"","affiliations":[{"id":61593,"text":"Workgroup on Endangered Species, J. 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Blumenbach Institute of Zoology and Anthropology, University of Goettingen","active":true,"usgs":false}],"preferred":false,"id":836833,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shokri, Shirko","contributorId":287510,"corporation":false,"usgs":false,"family":"Shokri","given":"Shirko","email":"","affiliations":[{"id":61594,"text":"Department of Environmental Sciences, Faculty of Natural Resources, University of Tehran","active":true,"usgs":false}],"preferred":false,"id":836834,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Selyari, Javad","contributorId":287511,"corporation":false,"usgs":false,"family":"Selyari","given":"Javad","email":"","affiliations":[{"id":61596,"text":"Department of Environmental Sciences, Faculty of Natural Resources and Environment Islamic Azad University","active":true,"usgs":false}],"preferred":false,"id":836835,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ghasemi, Benjamin","contributorId":287512,"corporation":false,"usgs":false,"family":"Ghasemi","given":"Benjamin","email":"","affiliations":[{"id":61597,"text":"Department of Rangeland, Wildlife & Fisheries Management, Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":836836,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sepahvand, Poorya","contributorId":287513,"corporation":false,"usgs":false,"family":"Sepahvand","given":"Poorya","email":"","affiliations":[{"id":61598,"text":"Kooch Foundation for communities and Biodiversity Conservation","active":true,"usgs":false}],"preferred":false,"id":836837,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Egli, Lukas","contributorId":287514,"corporation":false,"usgs":false,"family":"Egli","given":"Lukas","email":"","affiliations":[{"id":61599,"text":"UFZ, Permoserstr. 15, 04318, Leipzig, Germany","active":true,"usgs":false}],"preferred":false,"id":836838,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nezami, Bagher","contributorId":287515,"corporation":false,"usgs":false,"family":"Nezami","given":"Bagher","email":"","affiliations":[{"id":61600,"text":"Department of Natural Environment and Biodiversity, College of Environment, Karaj, Iran","active":true,"usgs":false}],"preferred":false,"id":836839,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zamani, Navid","contributorId":287516,"corporation":false,"usgs":false,"family":"Zamani","given":"Navid","email":"","affiliations":[{"id":61601,"text":"Department of Environment, Faculty of Natural Resources, University of Kurdistan","active":true,"usgs":false}],"preferred":false,"id":836840,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Yusefi, Gholam Hosein","contributorId":287517,"corporation":false,"usgs":false,"family":"Yusefi","given":"Gholam","email":"","middleInitial":"Hosein","affiliations":[{"id":61602,"text":"CIBIO/InBIO, Research Centre in Biodiversity and Genetic Resources. University of Porto","active":true,"usgs":false}],"preferred":false,"id":836841,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kiabi, Bahram H.","contributorId":287518,"corporation":false,"usgs":false,"family":"Kiabi","given":"Bahram","email":"","middleInitial":"H.","affiliations":[{"id":61603,"text":"Eskandari 14, PO. Box 14195149, Tehran, Iran.","active":true,"usgs":false}],"preferred":false,"id":836842,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Balkenhol, Niko","contributorId":287519,"corporation":false,"usgs":false,"family":"Balkenhol","given":"Niko","affiliations":[{"id":61604,"text":"Wildlife Sciences, University of Goettingen","active":true,"usgs":false}],"preferred":false,"id":836843,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":836844,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Pavey, Chris R.","contributorId":287520,"corporation":false,"usgs":false,"family":"Pavey","given":"Chris","email":"","middleInitial":"R.","affiliations":[{"id":39017,"text":"CSIRO Land and Water","active":true,"usgs":false}],"preferred":false,"id":836845,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Redpath, Steve M.","contributorId":287596,"corporation":false,"usgs":false,"family":"Redpath","given":"Steve","email":"","middleInitial":"M.","affiliations":[{"id":7165,"text":"University of Aberdeen","active":true,"usgs":false}],"preferred":false,"id":836926,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Waltert, Matthias","contributorId":287597,"corporation":false,"usgs":false,"family":"Waltert","given":"Matthias","affiliations":[{"id":37650,"text":"University of Goettingen, Goettingen, Germany","active":true,"usgs":false}],"preferred":false,"id":836927,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70221284,"text":"70221284 - 2022 - Multi-task deep learning of daily streamflow and water temperature","interactions":[],"lastModifiedDate":"2022-07-06T16:36:20.299415","indexId":"70221284","displayToPublicDate":"2022-03-02T11:35:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Multi-task deep learning of daily streamflow and water temperature","docAbstract":"Deep learning (DL) models can accurately predict many hydrologic variables including streamflow and water temperature; however, these models have typically predicted hydrologic variables independently. This study explored the benefits of modeling two interdependent variables, daily average streamflow and daily average stream water temperature, together using multi-task DL. A multi-task scaling factor controlled the relative contribution of the auxiliary variable's error to the overall loss during training. Our experiments examined the improvement in prediction accuracy of the multi-task approach using paired streamflow and water temperature data from sites across the conterminous United States. Our results showed that for 56 out of 101 sites, the best performing multi-task models performed better overall than the single-task models in terms of Nash-Sutcliffe efficiency for predicting streamflow with single-site models. For 43 sites, the best multi-task, single-site models made no significant difference in predicting streamflow. The multi-task approach had a smaller effect when applied to a model trained with data from 101 sites together, significantly improving performance for only 17 sites. The multi-task scaling factor was consequential in determining to what extent the multi-task approach was beneficial. A naïve selection of this factor led to significantly worse-performing models for 3 of 101 sites when predicting streamflow as the primary variable, and 47 of 53 sites when predicting stream temperature as the primary variable. We conclude that a multi-task approach can make more accurate predictions by leveraging information from interdependent hydrologic variables, but only for some sites, variables, and model configurations.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021WR030138","usgsCitation":"Sadler, J.M., Appling, A.P., Read, J., Oliver, S.K., Jia, X., Zwart, J.A., and Kumar, V., 2022, Multi-task deep learning of daily streamflow and water temperature: Water Resources Research, v. 58, no. 4, e2021WR030138, 18 p., https://doi.org/10.1029/2021WR030138.","productDescription":"e2021WR030138, 18 p.","ipdsId":"IP-129032","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":448611,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021wr030138","text":"Publisher Index Page"},{"id":386338,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":817231,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":817232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Read, Jordan 0000-0002-3888-6631","orcid":"https://orcid.org/0000-0002-3888-6631","contributorId":221385,"corporation":false,"usgs":true,"family":"Read","given":"Jordan","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":817233,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":817234,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":817235,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":817236,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":817237,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229180,"text":"70229180 - 2022 - Assessing mineral supply concentration from different perspectives through a case study of zinc","interactions":[],"lastModifiedDate":"2022-10-31T14:03:16.051554","indexId":"70229180","displayToPublicDate":"2022-03-02T11:25:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5502,"text":"Mineral Economics","onlineIssn":"2191-2211","printIssn":"2191-2203","active":true,"publicationSubtype":{"id":10}},"title":"Assessing mineral supply concentration from different perspectives through a case study of zinc","docAbstract":"Increasing demand for nonfuel mineral commodities has increased concerns regarding the reliability of their supplies. “Criticality” assessments over the past decade have attempted to capture this concern through a set of indicators, the most common of which quantifies the risk associated with market concentration by applying the Herfindahl-Hirschman Index (HHI) to the world production of a given commodity by country in a given year. Although this approach is useful, it inherently assumes that all of world production is available to the market and is thus potentially at risk. In this analysis, the HHI, as well as HHI weighted by country governance, is calculated for mined and refined zinc using the standard approach of using all world production data and comparing that to the HHI when using the best estimate of what is available to the world market. The results indicate that although the HHI of both mined and refined zinc world production has increased markedly over the past decade, the HHI for what is available to the market for mined and refined zinc has remained relatively constant and low, which is indicative of minimal supply risk. This is mainly owing to the fact that a large and increasing share of the world’s mined and refined zinc production comes from China, but that production supplies domestic consumption as well as small amounts of exports. As a result, the zinc materials that are available to the world market outside of China are produced by a relatively large and diverse set of countries. Although these analyses are specific to zinc, they are likely to be comparable for other commodities of which the largest producers are also the largest consumers and highlight the importance of examining different perspectives in criticality assessments.","language":"English","publisher":"Springer","doi":"10.1007/s13563-021-00291-2","usgsCitation":"Thomas, C.L., Nassar, N.T., and DeYoung, J., 2022, Assessing mineral supply concentration from different perspectives through a case study of zinc: Mineral Economics, v. 35, p. 6067-616, https://doi.org/10.1007/s13563-021-00291-2.","productDescription":"10 p.","startPage":"6067","endPage":"616","ipdsId":"IP-101409","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":448614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13563-021-00291-2","text":"Publisher Index Page"},{"id":396657,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","noUsgsAuthors":false,"publicationDate":"2022-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, Christine L. 0000-0002-1391-6072","orcid":"https://orcid.org/0000-0002-1391-6072","contributorId":287564,"corporation":false,"usgs":false,"family":"Thomas","given":"Christine","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":836874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":836875,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeYoung, John H. Jr. jdeyoung@usgs.gov","contributorId":190728,"corporation":false,"usgs":true,"family":"DeYoung","given":"John H.","suffix":"Jr.","email":"jdeyoung@usgs.gov","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":false,"id":836925,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228689,"text":"fs20223003 - 2022 - Water priorities for the Nation—The USGS National Water Dashboard","interactions":[],"lastModifiedDate":"2026-03-24T21:06:50.95795","indexId":"fs20223003","displayToPublicDate":"2022-03-02T11:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3003","displayTitle":"Water Priorities for the Nation—The USGS National Water Dashboard","title":"Water priorities for the Nation—The USGS National Water Dashboard","docAbstract":"The U.S. Geological Survey National Water Dashboard supplies critical information to decision makers, emergency managers, and the public during extreme hydrologic events (such as droughts and floods) and during normal hydrologic conditions. It informs decision making that can help protect lives and property before and during extreme hydrologic events. The National Water Dashboard draws upon the extensive site-specific hydrologic data housed in the U.S. Geological Survey National Water Information System database (https://doi.org/10.5066/F7P55KJN) and also links to the U.S. Geological Survey WaterAlert system, which provides users with instant and customized updates about water conditions. Overall, the National Water Dashboard is part of the U.S. Geological Survey's effort to respond to 21st century science needs.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey National Water Dashboard supplies critical information to decision makers, emergency managers, and the public during extreme hydrologic events (such as droughts and floods) and during normal hydrologic conditions. It informs decision making that can help protect lives and property before and during extreme hydrologic events. The National Water Dashboard draws upon the extensive site-specific hydrologic data housed in the U.S. Geological Survey National Water Information System database (https://doi.org/10.5066/F7P55KJN) and also links to the U.S. Geological Survey WaterAlert system, which provides users with instant and customized updates about water conditions. Overall, the National Water Dashboard is part of the U.S. Geological Survey's effort to respond to 21st century science needs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip213","usgsCitation":"Miller, M.P., Burley, T.E., and McCallum, B.E., 2022, Visit the U.S. Geological Survey's National Water Dashboard: U.S. Geological Survey General Information Product 213, 2 p., https://doi.org/10.3133/gip213.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-127330","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"links":[{"id":396097,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/213/coverthb.jpg"},{"id":396098,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/213/gip213.pdf","text":"Report","size":"319 KB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 213"}],"contact":"Associate Director, Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The endangered mountain yellow-legged frog (Rana muscosa) has been reduced to <10 isolated populations in the wild. Due to frequent catastrophic events (floods, droughts, wildfires), the recent dynamics of these populations have been erratic, making the future of the species highly uncertain. In 2018, a recovery plan was developed to improve the species status by reducing the impacts of various threats (predation, disease, habitat destruction), as well as reinforcing wild populations through the reintroduction of captive-bred frogs. The short-term goal stated in this plan was to reach a minimum of 20 populations of 50 adults each (hereafter, the 20/50 target), before the species can be considered for downlisting from the U.S. Endangered Species Act. However, there is no guarantee that this 20/50 target will be sufficient to ensure the species persistence in the long run. Using 19 years of mark-recapture data, we estimated populations' demographic trends and assessed the viability of R. muscosa from a starting state of 20 populations of 50 adults each (i.e., the downlisting criteria). Our results reveal that, from this 20/50 state, the species has high chances of persistence only at a short time horizon (50 years). Moreover, >80% of populations would be extinct 50 years later. Therefore, the species will not be able to persist without implementation of the reintroduction program. We found that it is more important to increase the number of suitable sites occupied by R. muscosa than to simply reinforce or augment existing populations. Expanding the current distribution by establishing new populations at suitable sites, even after the “20 populations” mark has been reached, would increase the likelihood of the species' persistence in the longer term.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.12666","usgsCitation":"Chambert, T., Backlin, A.R., Gallegos, E., Baskerville-Bridges, B., and Fisher, R., 2022, Defining relevant conservation targets for the endangered Southern California distinct population segment of the mountain yellow-legged frog (Rana muscosa): Conservation Science and Practice, v. 4, no. 5, e12666, 10 p., https://doi.org/10.1111/csp2.12666.","productDescription":"e12666, 10 p.","ipdsId":"IP-136151","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448619,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.12666","text":"Publisher Index Page"},{"id":397864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Palomar Mountain, San Bernardino Mountains, San Gabriel Mountains, San Jacinto Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.795166015625,\n 33.95247360616282\n ],\n [\n -116.49902343749999,\n 33.95247360616282\n ],\n [\n -116.49902343749999,\n 34.45674800347809\n ],\n [\n -118.795166015625,\n 34.45674800347809\n ],\n [\n -118.795166015625,\n 33.95247360616282\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.99615478515624,\n 33.35347332342168\n ],\n [\n -116.37268066406249,\n 33.35347332342168\n ],\n [\n -116.37268066406249,\n 33.95475186857191\n ],\n [\n -116.99615478515624,\n 33.95475186857191\n ],\n [\n -116.99615478515624,\n 33.35347332342168\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.96319580078125,\n 33.169743600216165\n ],\n [\n -116.74621582031249,\n 33.169743600216165\n ],\n [\n -116.74621582031249,\n 33.35347332342168\n ],\n [\n -116.96319580078125,\n 33.35347332342168\n ],\n [\n -116.96319580078125,\n 33.169743600216165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Chambert, Thierry 0000-0002-9450-9080 tchambert@usgs.gov","orcid":"https://orcid.org/0000-0002-9450-9080","contributorId":191979,"corporation":false,"usgs":false,"family":"Chambert","given":"Thierry","email":"tchambert@usgs.gov","affiliations":[],"preferred":false,"id":839217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Backlin, Adam R. 0000-0001-5618-8426 abacklin@usgs.gov","orcid":"https://orcid.org/0000-0001-5618-8426","contributorId":3802,"corporation":false,"usgs":true,"family":"Backlin","given":"Adam","email":"abacklin@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gallegos, Elizabeth 0000-0002-8402-2631 egallegos@usgs.gov","orcid":"https://orcid.org/0000-0002-8402-2631","contributorId":1528,"corporation":false,"usgs":true,"family":"Gallegos","given":"Elizabeth","email":"egallegos@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baskerville-Bridges, Bradd","contributorId":289523,"corporation":false,"usgs":false,"family":"Baskerville-Bridges","given":"Bradd","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":839220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839221,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231154,"text":"70231154 - 2022 - Calibration of an evapotranspiration algorithm in a semiarid sagebrush steppe using a 3-ha lysimeter and Landsat normalized difference vegetation index data","interactions":[],"lastModifiedDate":"2022-05-02T11:47:41.414819","indexId":"70231154","displayToPublicDate":"2022-03-02T06:43:19","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Calibration of an evapotranspiration algorithm in a semiarid sagebrush steppe using a 3-ha lysimeter and Landsat normalized difference vegetation index data","docAbstract":"In arid and semiarid environments, evapotranspiration (ET) is the primary discharge component in the water balance, with potential ET exceeding precipitation. For this reason, reliable estimates of ET are needed to construct accurate water budgets in these environments. Remote sensing affords the ability to provide fast, accurate, field-scale ET estimates, but these methods have largely been restricted to deep rooted (phreatophytic) plant communities underlain by shallow groundwater. We used 13 years of data from a 3-ha drainage lysimeter in a semiarid sagebrush steppe and Landsat normalized difference vegetation index (NDVI) data to calibrate a generalized least squares model capable of predicting vadose zone ET in a high elevation upland ecosystem. Annual precipitation was the best predictor of annual ET, as they were nearly balanced every year analysed (mean difference = 3 mm). We incorporated reference crop ET and a linear combination of NDVI and precipitation to capably predict ET on a subannual, lag-determined interval of 48 days, with a mean error of only 9.92% across all observations. To our knowledge, this is the first vegetation index-ET algorithm calibrated in a semiarid upland plant community using field-scale lysimetry. Vadose zone ET is particularly important at waste disposal sites in the Desert Southwest, where accurate and spatially explicit ET estimates are needed for monitoring potential mobilization and transport of contaminants past the root zone into local aquifers and for monitoring and modelling effects of recharge on flow and transport of contaminants in underlying aquifers.
The National Energy Technology Laboratory, the Japan Oil, Gas and Metals National Corporation, and the U.S. Geological Survey are leading an effort to conduct an extended gas hydrate production test in northern Alaska. The proposed production test required the drilling of an initial stratigraphic test well (STW) to confirm the geologic conditions of the proposed test site. This well was completed in January 2019 in cooperation with the Prudhoe Bay Unit Working Interest Owners. The Prudhoe Bay Unit Hydrate-01 STW was spudded on 10-December-2018. Downhole data acquisition was completed on 25-December-2018, and the rig was released on 01-January-2019. The Hydrate-01 STW was drilled in two sections, including the surface hole that was drilled to a depth of 2248 ft measured depth (MD) (685 m MD) and cased, and the production hole section that was drilled to a depth of 3558 ft MD (1084 m MD) and also cased. A thermally chilled mineral-oil-based mud was used in the main (production) hole section of the well to maintain wellbore stability and quality of the wellbore acquired data. The primary wellbore data were acquired using logging-while-drilling tools. A sidewall pressure core system was also deployed to gather grain size and other data needed for the design of the future production test wells. In addition to confirming the geologic conditions at the test site, the Hydrate-01 STW was designed to serve as a monitoring well during future field operations. Therefore, two sets of fiber-optic cables, each including a bundled distributed acoustic sensor (DAS) and a distributed temperature sensor (DTS), were clamped to the outside of the production casing and cemented in place. In March 2019, the project team acquired three-dimensional (3D) DAS vertical seismic profiling data in the Hydrate-01 STW. Temperature surveys were also acquired with the DTS as deployed in the Hydrate-01 STW during the completion of the well and nearly continuously since March-2019.