Autonomous, robotic environmental (e)DNA samplers now make it possible for biological observations to match the scale and quality of abiotic measurements collected by automated sensor networks. Merging these automated data streams may allow for improved insight into biotic responses to environmental change and stressors. Here, we merged eDNA data collected by robotic samplers installed at three U.S. Geological Survey (USGS) streamgages with gridded daily weather data, and daily water quality and quantity data into a cloud-hosted database. The eDNA targets were a rare fish parasite and a more common salmonid fish. We then used computationally expedient Bayesian hierarchical occupancy models to evaluate associations between abiotic conditions and eDNA detections and to simulate how uncertainty in result interpretation changes with the frequency of autonomous robotic eDNA sample collection. We developed scripts to automate data merging, cleaning and analysis steps into a chained-step, workflow. We found that inclusion of abiotic covariates only provided improved insight for the more common salmonid fish since its DNA was more frequently detected. Rare fish parasite DNA was infrequently detected, which caused occupancy parameter estimates and covariate associations to have high uncertainty. Our simulations found that collecting samples at least once per day resulted in more detections and less parameter uncertainty than less frequent sampling. Our occupancy and simulation results together demonstrate the advantages of robotic eDNA samplers and how these samples can be combined with easy to acquire, publicly available data to foster real-time biosurveillance and forecasting.
","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2021.620715","usgsCitation":"Sepulveda, A., Hoegh, A.B., Gage, J.A., Caldwell Eldridge, S.L., Birch, J.M., Stratton, C., Hutchins, P.R., and Barnhart, E.P., 2021, Integrating environmental DNA results with diverse data sets to improve biosurveillance of river health: Frontiers in Ecology and Evolution, v. 9, 13 p., https://doi.org/10.3389/fevo.2021.620715.","productDescription":"13 p.","ipdsId":"IP-123750","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":453078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2021.620715","text":"Publisher Index Page"},{"id":384620,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming, Montana","otherGeospatial":"Yellowstone River, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.400390625,\n 43.89789239125797\n ],\n [\n -107.666015625,\n 43.89789239125797\n ],\n [\n -107.666015625,\n 46.49839225859763\n ],\n [\n -115.400390625,\n 46.49839225859763\n ],\n [\n -115.400390625,\n 43.89789239125797\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Sepulveda, Adam 0000-0001-7621-7028 asepulveda@usgs.gov","orcid":"https://orcid.org/0000-0001-7621-7028","contributorId":4187,"corporation":false,"usgs":true,"family":"Sepulveda","given":"Adam","email":"asepulveda@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812861,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoegh, Andrew B.","contributorId":166684,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":812862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gage, Joshua A.","contributorId":255726,"corporation":false,"usgs":false,"family":"Gage","given":"Joshua","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":812863,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caldwell Eldridge, Sara L. 0000-0001-8838-8940 seldridge@usgs.gov","orcid":"https://orcid.org/0000-0001-8838-8940","contributorId":4981,"corporation":false,"usgs":true,"family":"Caldwell Eldridge","given":"Sara","email":"seldridge@usgs.gov","middleInitial":"L.","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":812864,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Birch, James M.","contributorId":255728,"corporation":false,"usgs":false,"family":"Birch","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":812865,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stratton, Christian","contributorId":217711,"corporation":false,"usgs":false,"family":"Stratton","given":"Christian","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":812866,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hutchins, Patrick R. 0000-0001-5232-0821 phutchins@usgs.gov","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":198337,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick","email":"phutchins@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":812867,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812868,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70218751,"text":"sir20205149 - 2021 - Assessment of groundwater trends near Crex Meadows, Wisconsin","interactions":[],"lastModifiedDate":"2021-12-01T15:54:43.723114","indexId":"sir20205149","displayToPublicDate":"2021-03-15T08:01:04","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5149","displayTitle":"Assessment of Groundwater Trends near Crex Meadows, Wisconsin","title":"Assessment of groundwater trends near Crex Meadows, Wisconsin","docAbstract":"Crex Meadows Wildlife Area (Crex) is a 30,000-acre property in Burnett County, Wisconsin. Crex is managed by the Wisconsin Department of Natural Resources (WDNR) with the goal of providing public recreation opportunities while also protecting the quality of native ecological communities and species on the property. The WDNR’s management strategy includes controlling water levels at flowages in Crex using a system of dikes, water control structures, ditches, and a diversion pump. For the past several decades there has been concern among nearby landowners that the water manage-ment strategy at Crex may be contributing to groundwater flooding in adjacent, privately held properties. This issue has been particularly contentious during periods when regional groundwater elevations are already high. This study was conducted in response to those concerns. For the study, a network of 12 monitoring wells was installed in and to the west of Crex. Groundwater elevations were recorded in the wells before, during, and after water-level changes in the western Crex flowages to assess if groundwater elevations to the west of Crex are detectably affected by the flowage water levels.
This study successfully collected groundwater elevations in 11 study wells during a 3-month period in 2019 when water elevations in the Dike 6 flowage and Erickson flowage were lowered and then raised. The data logger at a 12th location failed and no data were recorded. The groundwater elevation trends in these study wells were compared with groundwater elevation trends at a regional U.S. Geological Survey well to provide information for determining if changing the flowage elevations had a noticeable response in the study wells west of Crex Meadows. This analysis was done by (1) evaluating study well groundwater elevation trends compared to the regional well, (2) using a scatter plot of study well and regional well data during raising and lowering periods,
(3) assessing horizontal hydraulic gradient data during the study period, and (4) assessing the cumulative departure from the mean groundwater elevation for each well.
Overall, regional groundwater elevations had a down-ward trend before and during the flowage lowering period and then had an upward trend during the flowage raising period. This pattern was observed in the regional well and in all the study wells adjacent to and several miles from the flowages. The similarity in patterns indicates that precipitation and regional groundwater flow conditions were the dominant drivers of the system during the study period. The scatter plot and cumulative departure from the mean analysis showed that in addition to regional trends, wells 1, 6, and 7 were likely affected by the changes in the flowage water levels. Overall, at least on the timescale of this study, water management at Crex likely did not have detectable effects on wells outside the Crex property. Wells installed on the Crex property including the wells in the lakebeds of the flowages (wells 1 and 7) and possibly well 6 east of the flowages showed what seems to be minor affects due to water management at Crex.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205149","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources","usgsCitation":"Haserodt, M.J., and Fienen, M.N., 2020, Assessment of groundwater trends near Crex Meadows, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2020–5149, 32 p., https://doi.org/10.3133/sir20205149.","productDescription":"vi, 36 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-117629","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385958,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5149/versionHist.txt","text":"Version History","size":"1.69 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020–5149 Version History"},{"id":385957,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5149/sir20205149.pdf","text":"Report","size":"13.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5149"},{"id":384275,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5149/coverthb3.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Crex Meadows","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.51930236816406,\n 45.81540082150529\n ],\n [\n -92.51861572265625,\n 45.829756159282766\n ],\n [\n -92.51861572265625,\n 45.84506443975059\n ],\n [\n -92.52616882324219,\n 45.84506443975059\n ],\n [\n -92.52754211425781,\n 45.87853662114514\n ],\n [\n -92.55226135253906,\n 45.882360730184025\n ],\n [\n -92.55088806152344,\n 45.90768880475299\n ],\n [\n -92.60856628417967,\n 45.90386643939614\n ],\n [\n -92.67105102539061,\n 45.897654534346906\n ],\n [\n -92.68272399902344,\n 45.88618457602257\n ],\n [\n -92.68135070800781,\n 45.867062714815475\n ],\n [\n -92.69096374511719,\n 45.817315080406246\n ],\n [\n -92.691650390625,\n 45.80008438131991\n ],\n [\n -92.68753051757812,\n 45.79338211440398\n ],\n [\n -92.64770507812499,\n 45.79338211440398\n ],\n [\n -92.61543273925781,\n 45.813965084145295\n ],\n [\n -92.57972717285156,\n 45.817315080406246\n ],\n [\n -92.57492065429688,\n 45.82688538784564\n ],\n [\n -92.54539489746094,\n 45.82640691154487\n ],\n [\n -92.54539489746094,\n 45.81109349837976\n ],\n [\n -92.51930236816406,\n 45.81540082150529\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: March 15, 2021; Version 1.1: May 26, 2021","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
The Osage Nation of northeastern Oklahoma, conterminous with Osage County, covers about 2,900 square miles. The area is primarily rural with 62 percent of the land being native prairie grass, and much of the area is used for cattle ranching and extraction of petroleum and natural gas. Protection of water rights are important to the Osage Nation because of its reliance on cattle ranching and the potential for impairment of water quality by petroleum extraction. Additionally, the potential for future population increases, demands for water from neighboring areas such as the Tulsa metropolitan area, and expansion of petroleum and natural-gas extraction on water resources of this area further the need for the Osage Nation to better understand its water availability. Therefore, the U.S. Geological Survey, in cooperation with the Osage Nation, completed a hydrologic investigation to assess the status and availability of surface-water and groundwater resources in the Osage Nation.
A transient integrated hydrologic-flow model was constructed using the U.S. Geological Survey fully integrated hydrologic-flow model called the MODFLOW One-Water Hydrologic Model. The integrated hydrologic-flow model, called the Osage Nation Integrated Hydrologic Model (ONIHM), was constructed and uses an orthogonal grid of 276 rows and 289 columns, and each grid cell measures 1,312.34 feet (ft; 400 meters) per side, with eight variably thick vertical layers that represented the alluvial and bedrock aquifers within the study area, including the alluvial aquifer, the Vamoosa-Ada aquifer, and the minor Pennsylvanian bedrock aquifers, and the confining units. Landscape and groundwater-flow processes were simulated for two periods: (1) the 1950–2014 period from January 1950 through September 2014 and (2) the forecast period from October 2014 through December 2099. The 1950–2014 period ONIHM simulated past conditions using measured or estimated inputs, and the forecast-period ONIHM simulated three separate potential forecast conditions under constant dry, average, or wet climate conditions using calibrated input values from the 1950–2014 period ONIHM.
The 1950–2014 period ONIHM was calibrated by linking the Parameter Estimation software (PEST) with the MODFLOW One-Water Hydrologic Model. PEST uses statistical parameter estimation techniques to identify the best set of parameter values to minimize the difference between measured or estimated calibration targets and their simulated equivalent values (residuals). Tikhonov regularization and singular-value decomposition-assist features of PEST were used during the calibration process. The 1950–2014 period ONIHM was calibrated to 713 measured groundwater levels at 195 wells; 95,636 estimated monthly mean groundwater levels at 124 wells; 5,307 measured streamflows at 13 streamgages; and 8,679 simulated mean monthly streamflows at 10 streamgages extracted from a surface-water model by adjusting 231 parameters. The estimated groundwater-level observations and streamflows were included as observations to improve the spatial and temporal density of observation targets during calibration. The best set of parameter values obtained during the calibration process of the 1950–2014 model was then used as the input parameter values for the forecast model simulations. A comparison of the calibration targets to their corresponding simulated values indicated that the model adequately reproduced streamflows and groundwater levels for some streamgages and wells and underestimated streamflows and groundwater levels at other locations. Measured and simulated streamflows correlated adequately with a coefficient of determination of 0.938, as did water levels with a coefficient of determination of 0.795. The 1950–2014 period ONIHM underestimated certain groundwater levels and streamflows, but generally measured or estimated calibration targets correlated well with simulated equivalents, which indicated that the model can adequately simulate the response of the hydrologic system to stresses in the 1950–2014 and forecast periods.
In the 1950–2014 period ONIHM, the calibrated mean horizontal hydraulic conductivity for layer 1 alluvial aquifer was 30.7 feet per day, and the seven lower layers had a calibrated mean horizontal hydraulic conductivity of less than 3.3 feet per day. The mean calibrated groundwater-level residual was 16.6 ft, and the mean calibrated streamflow residual of the Arkansas River at Ralston, Oklahoma, streamgage (U.S. Geological Survey station 07152500) was within 6 percent (373 cubic feet per second) of mean measured streamflow for the 1950–2014 period ONIHM.
The ONIHM simulated landscape fluxes of precipitation; groundwater applied by irrigation wells; evapotranspiration from precipitation, groundwater, and irrigation; runoff from precipitation; and deep percolation from precipitation. The largest loss of water from the landscape was evapotranspiration from precipitation with a calibrated mean annual outflow of 32 inches (in.): mean annual precipitation was about 36 in. Calibrated mean annual runoff and deep percolation (recharge to the water table) rates were 4.7 inches per year (in/yr) and 0.70 in/yr, respectively, for the 1950–2014 period ONIHM.
The calibrated 1950–2014 period ONIHM groundwater fluxes included net farm net recharge (calculated as the difference between the inflow of recharge to the water table and the outflow of evapotranspiration from the water table such that negative values indicate that evapotranspiration from the water table was greater than deep percolation [recharge to the water table] and vice versa). Net farm net recharge was the largest flux from the groundwater system with a mean annual net outflow of 153.4 cubic feet per second. Stream leakage was the largest flux to the groundwater system with a mean annual net inflow of 152.5 cubic feet per second, indicating that, on average, the groundwater/surface-water interaction was a “losing” system where stream water leaked into the subsurface and recharged the water table. Simulated monthly trends demonstrated that net stream leakage was the largest inflow to the groundwater-flow system for 10 of the 12 months; for the other 2 months (January and March), farm net recharge (January) and net storage (March) were the largest inflow to the groundwater-flow system.
A saline groundwater interface map was created for the study and compared to the water levels from the final stress period of the 1950–2014 model to identify the presence of fresh/marginal groundwater throughout the study area. Fresh/marginal groundwater was characterized as groundwater with less than 1,500 milligrams per liter of total dissolved solids. Fresh/marginal groundwater thickness ranged from 0 to 438.2 ft within the study area. The thickest regions of fresh/marginal groundwater were in the eastern part of the study area near Sand Creek, Bird Creek, and Hominy Creek and in the Arkansas River alluvial aquifer in the region downstream from the Arkansas River at Ralston, Okla.
Like the 1950–2014 model, forecast model results for the landscape indicated that transpiration from precipitation was the largest flux out of the landscape for all three forecasts, constituting 77, 73, and 58 percent of precipitation for the dry, average, and wet forecasts, respectively. The dry and average forecast landscape fluxes demonstrated similar trends and magnitudes, whereas the wet forecast landscape fluxes indicated the largest changes compared to the average forecast fluxes. Most notably, runoff increased from a mean of 1.1 and 1.6 in/yr for the dry and average forecasts, respectively, to 10 in/yr for the wet forecast. Similar changes occurred for the other wet forecast landscape fluxes.
The calibrated 1950–2014 period ONIHM simulated three forecasts to assess the effects of potential climatic changes on the hydrologic system from October 2014 to December 2099. The three forecasts simulated theoretical dry, average, and wet conditions using precipitation and potential evapotranspiration datasets from selected years in the calibrated 1950–2014 period ONIHM. Annual precipitation amounts were 26.89, 35.47, and 50.73 in. for the dry, average, and wet forecasts, respectively. Groundwater-flow component forecast results indicated that stream leakage is always a net inflow to the groundwater-flow system for dry, average, and wet conditions, meaning the study area stream network is always predominantly a “losing” regime where stream water infiltrates into the underlying aquifer. Storage was only a net outflow from the groundwater-flow system and indicated a replenishment to groundwater storage that resulted in an increase in groundwater levels only during the wet forecast. Further, these gains in groundwater storage for the wet forecast occurred only during February through June.
Mean fresh/marginal groundwater saturated thicknesses were 125 and 126 ft for the dry and average forecast conditions, respectively, and wet forecast average thickness was 145 ft and ranged from 0 to 443 ft. The spatial extents of fresh/marginal groundwater at the end of the dry, average, and wet forecast model periods (December 2099) did not change substantially from the end of the 1950–2014 model period (September 2014).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205141","collaboration":"Prepared in cooperation with the Osage Nation","usgsCitation":"Traylor, J.P., Mashburn, S.L., Hanson, R.T., and Peterson, S.M., 2021, Assessment of water availability in the Osage Nation using an integrated hydrologic-flow model: U.S. Geological Survey Scientific Investigations Report 2020–5141, 96 p., https://doi.org/10.3133/sir20205141.","productDescription":"Report: xiii, 96 p.; 2 Interactive Figures; Data Release; Dataset","numberOfPages":"114","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102662","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":384320,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5141/coverthb.jpg"},{"id":384321,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141.pdf","text":"Report","size":"9.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141"},{"id":384322,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure8.pdf","text":"Figure 8 (layered)","size":"626 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 8","linkHelpText":"— Supergroups for the Osage Nation Integrated Hydrologic Model (note: some supergroups are hidden; in order to see a given supergroup, the reader may need to turn off layers for the overlying supergroups)."},{"id":384324,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91OKQ2C","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-One Water Hydrologic Model integrated hydrologic-flow model used to evaluate water availability in the Osage Nation"},{"id":384323,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure14.pdf","text":"Figure 14 (layered)","size":"711 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 14","linkHelpText":"— Simulated groundwater-level altitude contours for the final stress period of the calibrated Osage Nation Integrated Hydrologic Model (September 30, 2014), dry forecast (December 31, 2099), average forecast (December 31, 2099), and wet forecast (December 31, 2099). This figure is a layered PDF."},{"id":384325,"rank":6,"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"}],"country":"United States","state":"Kansas, Oklahoma","otherGeospatial":"Osage Nation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.99578857421875,\n 36.13565654678543\n ],\n [\n -95.99853515625,\n 37.00035919622158\n ],\n [\n -95.97930908203125,\n 37.081475648860525\n ],\n [\n -96.29241943359375,\n 37.13623498442895\n ],\n [\n -96.48193359375,\n 36.96306042436515\n ],\n [\n -96.9873046875,\n 36.94989178681327\n ],\n [\n -97.12188720703125,\n 36.6992553955527\n ],\n [\n -97.14385986328125,\n 36.36822190085111\n ],\n [\n -96.6412353515625,\n 36.213255233061844\n ],\n [\n -96.26220703125,\n 36.11125252076156\n ],\n [\n -95.99578857421875,\n 36.13565654678543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
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Biophysical processes that affect subsurface water clarity play a key role in ecosystem function. However, subsurface water clarity is poorly monitored in marine ecosystems because doing so requires in-situ sampling that is logistically difficult to conduct and sustain. Novel solutions are thus needed to improve monitoring of subsurface water clarity. To that end, we developed a sampling method and data processing algorithm that enable the use of bottom trawl fishing gear as a platform for conducting subsurface water clarity monitoring using trawl-mounted irradiance sensors without disruption to fishing operations. The algorithm applies quality control checks to irradiance measurements and calculates the downwelling diffuse attenuation coefficient, Kd, and optical depth, ζ– apparent optical properties (AOPs) that characterize the rate of decrease in downwelling irradiance and relative irradiance transmission to depth, respectively. We applied our algorithm to irradiance measurements, obtained using bottom-trawl-mounted archival tags equipped with a photodiode collected during NOAA’s Alaska Fisheries Science Center annual summer bottom trawl surveys of the eastern Bering Sea continental shelf from 2004 to 2018. We validated our AOPs by quantitatively comparing surface-weighted Kd from tags to the multi-sensor Kd(490) product from the Ocean Colour Climate Change Initiative project (OC-CCI) and qualitatively evaluating whether tag Kd was consistent with patterns of subsurface chlorophyll-a concentrations predicted by a coupled regional physical-biological model (Bering10K-BESTNPZ). We additionally examined patterns and trends in water clarity in the eastern Bering Sea. Key findings are: 1) water clarity decreased significantly from 2004 to 2018; 2) a recurrent, pycnocline-associated, maximum in Kd occurred over much of the northwestern shelf, putatively due to a subsurface chlorophyll maximum; and 3) a turbid bottom layer (nepheloid layer) was present over a large portion of the eastern Bering Sea shelf. Our study demonstrates that bottom trawls can provide a useful platform for monitoring water clarity, especially when trawling is conducted as part of a systematic stock assessment survey.
Regionally scaled assessments of hydrologic alteration for small streams and its effects on freshwater taxa are often inhibited by a low number of stream gages. To overcome this limitation, we paired modeled estimates of hydrologic alteration to a benthic macroinvertebrate index of biotic integrity data for 4522 stream reaches across the Chesapeake Bay watershed. Using separate random-forest models, we predicted flow status (inflated, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize the main components of flow regimes. We used these models to predict each HM status for each stream reach in the watershed, and linked predictions to macroinvertebrate condition samples collected from streams with drainage areas less than 200 km2. Flow alteration was calculated as the number of HMs with inflated or diminished status and ranged from 0 (no HM inflated or diminished) to 12 (all 12 HMs inflated or diminished). When focused solely on the stream condition and flow-alteration relationship, degraded macroinvertebrate condition was, depending on the number of HMs used, 3.8–4.7 times more likely in a flow-altered site; this likelihood was over twofold higher in the urban-focused dataset (8.7–10.8), and was never significant in the agriculture-focused dataset. Logistic regression analysis using the entire dataset showed for every unit increase in flow-alteration intensity, the odds of a degraded condition increased 3.7%. Our results provide an indication of whether altered streamflow is a possible driver of degraded biological conditions, information that could help managers prioritize management actions and lead to more effective restoration efforts.
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saustin@usgs.gov","orcid":"https://orcid.org/0000-0001-5626-023X","contributorId":153,"corporation":false,"usgs":true,"family":"Austin","given":"Samuel","email":"saustin@usgs.gov","middleInitial":"H.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":843237,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cashman, Matthew J. 0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":843238,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Young, John A. 0000-0002-4500-3673 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sediment toxicity testing","docAbstract":"The benthic foraminifers Bulimina denudata and Eggerelloides advenus are commonly abundant in offshore regions in the Pacific Ocean, especially in waste-discharge sites. The relationship between their abundance and standard macrofaunal sediment toxicity tests (amphipod survival and sea urchin fertilization) as well as sediment chemistry analyte measurements were determined for sediments collected in 1997 in Santa Monica Bay, California, USA, an area impacted by historical sewage input from the Hyperion Outfall primarily since the late 1950s. Very few surface samples proved to be contaminated based on either toxicity or chemistry tests and the abundance of B. denudata did not correlate with any of these. The abundance of E. advenus also did not correlate with toxicity, but positively correlated with total solids and negatively correlated with arsenic, beryllium, chromium, lead, mercury, nickel, zinc, iron, and TOC. In contrast, several downcore samples proved to be contaminated as indicated by both toxicity and chemistry data. The abundance of B. denudata positively correlated with amphipod survival and negatively correlated with arsenic, cadmium, unionized ammonia, and TOC; E. advenus negatively correlated with sea urchin fertilization success as well as beryllium, cadmium, and total PCBs. As B. denudata and E. advenus are tolerant of polluted sediments and their relative abundances appear to track those of macrofaunal toxicity tests, their use as cost- and time-effective marine sediment toxicity tests may have validity and should be further investigated.
","language":"English","publisher":"MDPI","doi":"10.3390/w13060775","usgsCitation":"McGann, M., 2021, Potential use of the benthic foraminifers Bulimina denudata and Eggerelloides advenus in marine sediment toxicity testing: Water, v. 13, no. 6, 775, 33 p., https://doi.org/10.3390/w13060775.","productDescription":"775, 33 p.","ipdsId":"IP-117658","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453114,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13060775","text":"Publisher Index Page"},{"id":385443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Los Angeles coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.58343505859374,\n 34.14590795200977\n ],\n [\n -118.85009765625,\n 33.708347493688414\n ],\n [\n -118.23486328125,\n 33.458942753687644\n ],\n [\n -117.77618408203124,\n 33.56199537293026\n ],\n [\n -118.28979492187499,\n 33.916013113401696\n ],\n [\n -118.98468017578125,\n 34.15045403191448\n ],\n [\n -119.34997558593749,\n 34.35023911062779\n ],\n [\n -119.58343505859374,\n 34.14590795200977\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":815133,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70222571,"text":"70222571 - 2021 - Extreme-event magnetic storm probabilities derived from rank statistics of historical Dst intensities for solar cycles 14-24","interactions":[],"lastModifiedDate":"2021-08-05T12:07:42.766418","indexId":"70222571","displayToPublicDate":"2021-03-12T07:04:10","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3456,"text":"Space Weather","active":true,"publicationSubtype":{"id":10}},"title":"Extreme-event magnetic storm probabilities derived from rank statistics of historical Dst intensities for solar cycles 14-24","docAbstract":"A compilation is made of the largest and second-largest magnetic-storm-maximum intensities, −Dst1 and −Dst2, for solar cycles 14–24 (1902–2016) by sampling Oulu Dcx for cycles 19–24, using published −Dstm values for 4 intense storms in cycles 14, 15, and 18 (1903, 1909, 1921, 1946), and calculating 15 new storm-maximum −Dstm values (reported here) for cycles 14–18. Three different models are fitted to the cycle-ranked −Dst1 and −Dst2 values using a maximum-likelihood algorithm: A Gumbel model, an unconstrained Generalized-Extreme-Value model, and a Weibull model constrained to have a physically justified maximum storm intensity of −Dstm = 2500 nT. All three models are good descriptions of the data. Since the best model is not clearly revealed with standard statistical tests, inference is precluded of the source process giving rise to storm-maximum −Dstm values. Of the three candidate models, the constrained Weibull gives the lowest superstorm occurrence probabilities. Using the compiled data and the constrained Weibull model, a once-per-century storm intensity is estimated to be −Dst1 = 663 nT, with a bootstrap 68% confidence interval of [497, 694] nT. Similarly, the probability that a future storm will have an intensity exceeding that of the March 1989 superstorm, −Dstm > 565 nT, is 0.246 per cycle with a 68% confidence interval of [0.140, 0.311] per cycle. Noting (possibly slight) ambiguity in the rankings of storm intensities, using the same methods, but storms more intense than those identified for cycles 14–16, would yield a higher once-per-century intensity and a higher probability for a −Dstm > 565 nT storm.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020SW002579","usgsCitation":"Love, J.J., 2021, Extreme-event magnetic storm probabilities derived from rank statistics of historical Dst intensities for solar cycles 14-24: Space Weather, v. 19, no. 4, e2020SW002579, 25 p., https://doi.org/10.1029/2020SW002579.","productDescription":"e2020SW002579, 25 p.","ipdsId":"IP-124185","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":490073,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020sw002579","text":"Publisher Index Page"},{"id":387701,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820608,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237213,"text":"70237213 - 2021 - Piping plovers demonstrate regional differences in nesting habitat selection patterns along the U.S. Atlantic coast","interactions":[],"lastModifiedDate":"2022-10-04T13:27:41.941655","indexId":"70237213","displayToPublicDate":"2021-03-11T08:15:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Piping plovers demonstrate regional differences in nesting habitat selection patterns along the U.S. Atlantic coast","docAbstract":"Habitat studies that encompass a large portion of a species’ geographic distribution can explain characteristics that are either consistent or variable, further informing inference from more localized studies and improving management successes throughout the range. We identified landscape characteristics at Piping Plover nests at 21 sites distributed from Massachusetts to North Carolina and compared habitat selection patterns among the three designated U.S. recovery units (New England, New York–New Jersey, and Southern). Geomorphic setting, substrate type, and vegetation type and density were determined in situ at 928 Piping Plover nests (hereafter, used resource units) and 641 random points (available resource units). Elevation, beach width, Euclidean distance to ocean shoreline, and least-cost path distance to low-energy shorelines with moist substrates (commonly used as foraging habitat) were associated with used and available resource units using remotely sensed spatial data. We evaluated multivariate differences in habitat selection patterns by comparing recovery unit-specific Bayesian networks. We then further explored individual variables that drove disparities among Bayesian networks using resource selection ratios for categorical variables and Welch’s unequal variances t-tests for continuous variables. We found that relationships among variables and their connections to habitat selection were similar among recovery units, as seen in commonalities in Bayesian network structures. Furthermore, nesting Piping Plovers consistently selected mixed sand and shell, gravel, or cobble substrates as well as areas with sparse or no vegetation, irrespective of recovery unit. However, we observed significant differences among recovery units in the elevations, distances to ocean, and distances to low-energy shorelines of used resource units. Birds also exhibited increased selectivity for overwash habitats and for areas with access to low-energy shorelines along a latitudinal gradient from north to south. These results have important implications for conservation and management, including assessment of shoreline stabilization and habitat restoration planning as well as forecasting effects of climate change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3418","usgsCitation":"Zeigler, S.L., Gutierrez, B.T., Hecht, A., Plant, N., and Sturdivant, E., 2021, Piping plovers demonstrate regional differences in nesting habitat selection patterns along the U.S. Atlantic coast: Ecosphere, v. 12, no. 3, e03418, 21 p., https://doi.org/10.1002/ecs2.3418.","productDescription":"e03418, 21 p.","ipdsId":"IP-123170","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":453120,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3418","text":"Publisher Index Page"},{"id":407856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, Maryland, Massachusetts, new Jersey, New York, North Carolina, Rhode Island, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.06884765624999,\n 33.7243396617476\n ],\n [\n -75.47607421875,\n 34.92197103616377\n ],\n [\n -75.6298828125,\n 37.07271048132943\n ],\n [\n -73.95996093749999,\n 39.87601941962116\n ],\n [\n -73.54248046875,\n 40.54720023441049\n ],\n [\n -71.52099609375,\n 40.93011520598305\n ],\n [\n -70.02685546875,\n 41.47566020027821\n ],\n [\n -69.80712890625,\n 42.16340342422401\n ],\n [\n -70.81787109374999,\n 42.601619944327965\n ],\n [\n -71.21337890625,\n 42.439674178149424\n ],\n [\n -71.74072265625,\n 41.590796851056005\n ],\n [\n -73.98193359375,\n 41.04621681452063\n ],\n [\n -74.5751953125,\n 40.53050177574321\n ],\n [\n -74.37744140625,\n 40.027614437486655\n ],\n [\n -74.77294921875,\n 39.45316112807394\n ],\n [\n -75.38818359375,\n 39.85915479295669\n ],\n [\n -76.7724609375,\n 39.57182223734374\n ],\n [\n -77.255859375,\n 38.54816542304656\n ],\n [\n -77.2998046875,\n 36.89719446989036\n ],\n [\n -78.37646484375,\n 34.43409789359469\n ],\n [\n -78.06884765624999,\n 33.7243396617476\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeigler, Sara Lynn 0000-0002-5472-769X szeigler@usgs.gov","orcid":"https://orcid.org/0000-0002-5472-769X","contributorId":297200,"corporation":false,"usgs":true,"family":"Zeigler","given":"Sara","email":"szeigler@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":853647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gutierrez, Benjamin T. 0000-0002-1879-7893 bgutierrez@usgs.gov","orcid":"https://orcid.org/0000-0002-1879-7893","contributorId":2924,"corporation":false,"usgs":true,"family":"Gutierrez","given":"Benjamin","email":"bgutierrez@usgs.gov","middleInitial":"T.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":853648,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hecht, Anne","contributorId":297201,"corporation":false,"usgs":false,"family":"Hecht","given":"Anne","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":853649,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Plant, Nathaniel 0000-0002-5703-5672","orcid":"https://orcid.org/0000-0002-5703-5672","contributorId":81234,"corporation":false,"usgs":true,"family":"Plant","given":"Nathaniel","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":853650,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sturdivant, Emily J.","contributorId":297196,"corporation":false,"usgs":false,"family":"Sturdivant","given":"Emily J.","affiliations":[{"id":56085,"text":"Woodwell Climate Research Center","active":true,"usgs":false}],"preferred":false,"id":853651,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231209,"text":"70231209 - 2021 - A study of marine temperature variations in the northern Gulf of Alaska across years of marine heatwaves and cold spells","interactions":[],"lastModifiedDate":"2022-05-03T13:37:01.222443","indexId":"70231209","displayToPublicDate":"2021-03-11T08:10:39","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":10741,"text":"Gulf Watch Alaska Long-Term Monitoring Program Synthesis Report","active":true,"publicationSubtype":{"id":3}},"chapter":"1","title":"A study of marine temperature variations in the northern Gulf of Alaska across years of marine heatwaves and cold spells","docAbstract":"We use over 100 in situ and remotely sensed temperature datasets to investigate thermal variability within and across the intertidal nearshore, coastal and offshore waters of the northern Gulf of Alaska. For the years 1970 through 2019 we document a warming trend of 0.24±0.10 °C per decade for the coastal northern shelf (0-250 m depth average) and a Gulf-wide sea surface temperature (SST) trend of 0.25±0.11 °C per decade. The Gulf-wide SST trend in the last halfcentury is more than twice that of the 0.11±0.003 °C warming rate computed for 1900-2019. Decorrelation length scales vary regionally and correlation of synoptic scale fluctuations (less than one month) between two stations rapidly degrades with increasing station distance, accounting for less than 10% of the covariance for separations exceeding 100 km. In contrast, stations separated by as much as 500 km retain 50% of their covariance in common for seasonal and sub-seasonal fluctuations. While satellite-based measures often capture most of the daily SST anomaly in coastal and offshore waters, a significant portion of the variance (30-40%) can remain unresolved, even exceeding 75% in the nearshore realm. Similarly, the North Pacific and Gulf of Alaska leading modes of SST variability leave large fractions (25-50%) of the subseasonal thermal variance unresolved. These evaluations show the importance of in situ temperature records for studies that seek to understand mechanistic responses of marine organisms to habitat variability at biologically important time and space scales. We find that near-bottom temperature anomalies on the outer shelf vary inversely with surface temperatures and with near-bottom salinity, suggesting that thermal anomalies are also linked with nutrient flux anomalies. A case study of the recent Pacific marine heatwave and transition out of preceding cool years shows that the northern Gulf of Alaska surface temperatures (0-50 m) were elevated from 2014 to 2019 relative to the long-term record. Coastal temperatures warmed contemporaneously with offshore waters through the 2013 calendar year. In contrast, deep inner shelf waters (200-250 m) exhibited delayed warming relative to the surface and relative to deep waters offshore at the same depth. While offshore surface waters cooled from early 2014 into 1-2 Science Synthesis Final Report Gulf Watch Alaska, 2021 early 2016, the shelf continued to warm over this time as the effects of local air-sea and advective heat fluxes continued to permeate across the northern Gulf. These results highlight the importance of different heating mechanisms for surface and near-bottom waters across the northern Gulf of Alaska.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"The Pacific marine heatwave: Monotoring during a major perturbation in the Gulf of Alaska","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Exxon Valdez Oil Spill Trustee Council","usgsCitation":"Danielson, S.L., Hennon, T.D., Monson, D., Suryan, R.M., Campbell, R.W., Baird, S.J., Holderied, K., and Weingartner, T., 2021, A study of marine temperature variations in the northern Gulf of Alaska across years of marine heatwaves and cold spells: Gulf Watch Alaska Long-Term Monitoring Program Synthesis Report, 56 p.","productDescription":"56 p.","startPage":"1-1","endPage":"1-56","ipdsId":"IP-119985","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":400044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":400013,"type":{"id":15,"text":"Index Page"},"url":"https://gulfwatchalaska.org/resources/reports/science-synthesis-reports/"}],"country":"United States","state":"Alaska","otherGeospatial":"Gulf of Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.95703125,\n 54.87660665410869\n ],\n [\n -134.208984375,\n 58.401711667608\n ],\n [\n -140.712890625,\n 60.50052541051131\n ],\n [\n -147.568359375,\n 62.186013857194226\n ],\n [\n -153.017578125,\n 61.3546135846894\n ],\n [\n -161.89453125,\n 55.57834467218206\n ],\n [\n -164.443359375,\n 54.316523240258256\n ],\n [\n -162.24609375,\n 52.74959372674114\n ],\n [\n -143.7890625,\n 51.01375465718821\n ],\n [\n -132.626953125,\n 52.908902047770255\n ],\n [\n -130.95703125,\n 54.87660665410869\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Danielson, Seth L.","contributorId":256682,"corporation":false,"usgs":false,"family":"Danielson","given":"Seth","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":842031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hennon, Tyler D.","contributorId":291317,"corporation":false,"usgs":false,"family":"Hennon","given":"Tyler","email":"","middleInitial":"D.","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":842032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":842033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Suryan, Rob M.","contributorId":291318,"corporation":false,"usgs":false,"family":"Suryan","given":"Rob","email":"","middleInitial":"M.","affiliations":[{"id":62685,"text":"Alaska Fisheries Science Center, NOAA","active":true,"usgs":false}],"preferred":false,"id":842034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Campbell, Rob W.","contributorId":251805,"corporation":false,"usgs":false,"family":"Campbell","given":"Rob","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":842035,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baird, Steven J.","contributorId":12375,"corporation":false,"usgs":false,"family":"Baird","given":"Steven","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":842036,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holderied, Kristine","contributorId":291319,"corporation":false,"usgs":false,"family":"Holderied","given":"Kristine","affiliations":[{"id":62686,"text":"Kasitsna Bay Laboratory, NOAA","active":true,"usgs":false}],"preferred":false,"id":842037,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Weingartner, Thomas","contributorId":291321,"corporation":false,"usgs":false,"family":"Weingartner","given":"Thomas","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":842038,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70223211,"text":"70223211 - 2021 - Remote sensing inventory and geospatial analysis of brick kilns and clay quarrying in Kabul, Afghanistan","interactions":[],"lastModifiedDate":"2021-08-18T12:56:55.65441","indexId":"70223211","displayToPublicDate":"2021-03-11T07:54:53","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing inventory and geospatial analysis of brick kilns and clay quarrying in Kabul, Afghanistan","docAbstract":"ABSTRACT: The ‘bet hedging’ life history strategy of long-lived iteroparous species reduces short-term reproductive output to minimize the risk of reproductive failure over a lifetime. For desert-dwelling ectotherms living in variable and unpredictable environments, reproductive output is further influenced by precipitation and temperature via effects on food availability and limits on activity. We assembled multiple (n = 12) data sets on egg production for the threatened Agassiz’s desert tortoise Gopherus agassizii across its range and used these data to build a range-wide predictive model of annual reproductive output as a function of annual weather variation and individual-level attributes (body size and prior-year reproductive status). Climate variables were more robust predictors of reproductive output than individual-level attributes, with overall reproductive output positively related to prior-year precipitation and an earlier start to the spring activity season, and negatively related to spring temperature extremes (monthly temperature range in March-April). Reproductive output was highest for individuals with larger body sizes that reproduced in the previous year. Expected annual reproductive output from 1990-2018 varied from 2-5 to 6-12 eggs female-1 yr-1 , with a weak decline in expected reproductive output over this time (p = 0.02). Climate-driven environmental variation in expected reproductive output was highly correlated across all 5 Recovery Units for this species (Pearson’s r > 0.9). Overall, our model suggests that climate change could strongly impact the reproductive output of Agassiz’s desert tortoise, and could have a negative population-level effect if precipitation is significantly reduced across the species’ range as predicted under some climate models.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01103","usgsCitation":"Mitchell, C.I., Friend, D., Phillips, L.T., Hunter, E., Lovich, J.E., Agha, M., Puffer, S., Cummings, K.L., Medica, P.A., Esque, T., Nussear, K.E., and Shoemaker, K.T., 2021, ‘Unscrambling’ the drivers of egg production in Agassiz’s desert tortoise: Climate and individual attributes predict reproductive output: Endangered Species Research, v. 44, p. 217-230, https://doi.org/10.3354/esr01103.","productDescription":"14 p.","startPage":"217","endPage":"230","ipdsId":"IP-121127","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":453130,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01103","text":"Publisher Index Page"},{"id":436463,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97WD6AH","text":"USGS data release","linkHelpText":"Mojave Desert Tortoise (Gopherus agassizii) Morphometrics and Egg Data from Seven Sites across the Mojave, (1997-2002)"},{"id":436462,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97XT7HF","text":"USGS data release","linkHelpText":"Agassiz's desert tortoise and egg data from the Sonoran Desert of California (1997-2000, 2015-2018)"},{"id":384375,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Arizona, Nevada, Utah","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 33.211116472416855\n ],\n [\n -112.763671875,\n 33.211116472416855\n ],\n [\n -112.763671875,\n 37.16031654673677\n ],\n [\n 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0000-0003-4957-0963","orcid":"https://orcid.org/0000-0003-4957-0963","contributorId":193099,"corporation":false,"usgs":true,"family":"Puffer","given":"Shellie R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":812088,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cummings, Kristy L. 0000-0002-8316-5059","orcid":"https://orcid.org/0000-0002-8316-5059","contributorId":202061,"corporation":false,"usgs":true,"family":"Cummings","given":"Kristy","email":"","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":812089,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Medica, Philip A.","contributorId":55780,"corporation":false,"usgs":true,"family":"Medica","given":"Philip","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":812090,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":812168,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nussear, Kenneth E.","contributorId":117361,"corporation":false,"usgs":false,"family":"Nussear","given":"Kenneth","email":"","middleInitial":"E.","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":812092,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shoemaker, Kevin T. 0000-0002-3789-3856","orcid":"https://orcid.org/0000-0002-3789-3856","contributorId":255290,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin","email":"","middleInitial":"T.","affiliations":[{"id":51513,"text":"Department of Natural Resources and Environmental Science, University of Nevada, Reno. 1664 N Virginia St, Reno, NV 89557, USA","active":true,"usgs":false}],"preferred":false,"id":812093,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70217783,"text":"70217783 - 2021 - Performance of the GenEst Mortality Estimator Compared to The Huso and Shoenfeld Estimators","interactions":[],"lastModifiedDate":"2021-04-19T15:43:33.892853","indexId":"70217783","displayToPublicDate":"2021-03-10T10:41:13","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":8561,"text":"AWWI Technical Report","active":true,"publicationSubtype":{"id":4}},"title":"Performance of the GenEst Mortality Estimator Compared to The Huso and Shoenfeld Estimators","docAbstract":"The impacts of wind power development on bat and bird populations are commonly assessed by estimating the number of fatalities at wind power facilities through post-construction monitoring (PCM) studies. Standard methodology involves periodic carcass searches on plots beneath turbines (Strickland et al. 2011, US Fish and Wildlife Service 2012). The resulting counts are adjusted to compensate for bias due to imperfect carcass detection by searchers, removal of carcasses by scavengers or other processes (Korner-Nievergelt et al. 2011), and carcasses that may have fallen outside of searched areas. To account for the bias in counts due to imperfect detection and carcass removal, investigators typically conduct bias trial experiments to inform models of carcass detection probability. Many different estimators have been proposed that combine information about the bias trial experiments to estimate a detection probability for carcasses (g) and ultimately obtain an estimate of total mortality (M). The two estimators that have seen the most widespread use in North America recently are the Huso (Huso 2011, Huso et al. 2012) and Shoenfeld (Shoenfeld 2004; also called the Erickson estimator) estimators. GenEst (Dalthorp et al. 2018a, 2018b, 2018c) is the newest statistical estimator to become available and was designed to improve upon the Huso and Shoenfeld estimators by generalizing the key assumptions in both, and to improve comparability among new PCM studies. In addition to relaxing some of the assumptions inherent to the Huso and Shoenfeld estimators, GenEst uses a parametric bootstrap applied to a novel approach to variance estimation (Madsen et al. 2019).
The current study was undertaken to document the performance of GenEst relative to the Huso and Shoenfeld estimators. We took a simulation approach to the study because simulation data provides the basis to compare mortality estimators under conditions where the “truth” is known. The estimators were compared on three metrics: 1) bias—the tendency of an estimator to over- or under-estimate actual mortality, 2) precision—the ability of an estimator to constrain an estimate to a narrow range (measured here as the width of a 90% confidence interval [CI] around the point estimate divided by the true, known mortality), and 3) CI coverage—the probability a CI with a specified level of confidence actually includes the true level of mortality.
Although our simulations were conceived and designed—and are discussed—with respect to wind power facilities, it is important to note that the estimators and results discussed here are relevant to any post-construction fatality monitoring study that may occur (such as at solar facilities) where detection is imperfect. Although our study treats the problem of mortality estimation when detection is imperfect, it is also important to note that all of the estimators considered here are Horvitz-Thompson (Horvitz and Thompson 1952) style estimators, that is, none are designed to estimate the mortality of rare species as might be necessary under an Incidental Take Permit. The Evidence of Absence estimator (Dalthorp et al. 2017) is still the most appropriate statistical tool for rare event estimation.
The simulations cover a broad range of conditions that may occur in field studies and complete results are presented without commentary in the appendix. The main body of this report does not provide a comprehensive treatment of our results; rather, we try to identify some of the more important differences among the estimators and some conditions under which reliable mortality estimates are especially challenging.
","language":"English","publisher":"American Wind Wildlife Institute","usgsCitation":"Rabie, P., Riser-Espinoza, D., Studyvin, J., Dalthorp, D., and Huso, M., 2021, Performance of the GenEst Mortality Estimator Compared to The Huso and Shoenfeld Estimators: AWWI Technical Report, 29 p.","productDescription":"29 p.","ipdsId":"IP-119710","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":385197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385196,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://awwi.org/resources/genest/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rabie, Paul","contributorId":248699,"corporation":false,"usgs":false,"family":"Rabie","given":"Paul","affiliations":[{"id":49982,"text":"WEST, Inc.","active":true,"usgs":false}],"preferred":false,"id":809635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riser-Espinoza, Daniel","contributorId":248700,"corporation":false,"usgs":false,"family":"Riser-Espinoza","given":"Daniel","email":"","affiliations":[{"id":49982,"text":"WEST, Inc.","active":true,"usgs":false}],"preferred":false,"id":809636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Studyvin, Jared","contributorId":248701,"corporation":false,"usgs":false,"family":"Studyvin","given":"Jared","affiliations":[{"id":49982,"text":"WEST, Inc.","active":true,"usgs":false}],"preferred":false,"id":809637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dalthorp, Daniel 0000-0002-4815-6309 ddalthorp@usgs.gov","orcid":"https://orcid.org/0000-0002-4815-6309","contributorId":4902,"corporation":false,"usgs":true,"family":"Dalthorp","given":"Daniel","email":"ddalthorp@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":809638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huso, Manuela 0000-0003-4687-6625 mhuso@usgs.gov","orcid":"https://orcid.org/0000-0003-4687-6625","contributorId":223969,"corporation":false,"usgs":true,"family":"Huso","given":"Manuela","email":"mhuso@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":809639,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219076,"text":"70219076 - 2021 - Inclusion of pesticide transformation products is key to estimating pesticide exposures and effects in small U.S. streams","interactions":[],"lastModifiedDate":"2021-05-27T13:21:52.551307","indexId":"70219076","displayToPublicDate":"2021-03-10T10:18:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Inclusion of pesticide transformation products is key to estimating pesticide exposures and effects in small U.S. streams","docAbstract":"Improved analytical methods can quantify hundreds of pesticide transformation products (TPs), but understanding of TP occurrence and potential toxicity in aquatic ecosystems remains limited. We quantified 108 parent pesticides and 116 TPs in more than 3 700 samples from 442 small streams in mostly urban basins across five major regions of the United States. TPs were detected nearly as frequently as parents (90 and 95% of streams, respectively); 102 TPs were detected at least once and 28 were detected in >20% samples in at least one region—TPs of 9 herbicides, 2 fungicides (chlorothalonil and thiophanate-methyl), and 1 insecticide (fipronil) were the most frequently detected. TPs occurred commonly during baseflow conditions, indicating chronic environmental TP exposures to aquatic organisms and the likely importance of groundwater as a TP source. Hazard quotients based on acute aquatic-life benchmarks for invertebrates and nonvascular plants and vertebrate-centric molecular endpoints (sublethal effects) quantify the range of the potential contribution of TPs to environmental risk and highlight several TP exposure–response data gaps. A precautionary approach using equimolar substitution of parent benchmarks or endpoints for missing TP benchmarks indicates that potential aquatic effects of pesticide TPs could be underestimated by an order of magnitude or more.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c06625","usgsCitation":"Mahler, B., Nowell, L.H., Sandstrom, M.W., Bradley, P., Romanok, K., Konrad, C., and Van Metre, P., 2021, Inclusion of pesticide transformation products is key to estimating pesticide exposures and effects in small U.S. streams: Environmental Science and Technology, v. 55, no. 8, p. 4740-4752, https://doi.org/10.1021/acs.est.0c06625.","productDescription":"13 p.","startPage":"4740","endPage":"4752","ipdsId":"IP-122426","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science 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bjmahler@usgs.gov","orcid":"https://orcid.org/0000-0002-9150-9552","contributorId":1249,"corporation":false,"usgs":true,"family":"Mahler","given":"Barbara","email":"bjmahler@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":812672,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nowell, Lisa H. 0000-0001-5417-7264 lhnowell@usgs.gov","orcid":"https://orcid.org/0000-0001-5417-7264","contributorId":490,"corporation":false,"usgs":true,"family":"Nowell","given":"Lisa","email":"lhnowell@usgs.gov","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes 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M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812675,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Romanok, Kristin M. 0000-0002-8472-8765","orcid":"https://orcid.org/0000-0002-8472-8765","contributorId":221227,"corporation":false,"usgs":true,"family":"Romanok","given":"Kristin M.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812676,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Konrad, Christopher 0000-0002-7354-547X","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":220231,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812677,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Van Metre, Peter 0000-0001-7564-9814","orcid":"https://orcid.org/0000-0001-7564-9814","contributorId":255624,"corporation":false,"usgs":false,"family":"Van Metre","given":"Peter","affiliations":[{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":812678,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237781,"text":"70237781 - 2021 - Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","interactions":[],"lastModifiedDate":"2022-10-24T14:38:38.267353","indexId":"70237781","displayToPublicDate":"2021-03-09T09:28:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","title":"Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","docAbstract":"African elephants (Loxodonta africana) are well-studied and inhabit diverse landscapes that are being transformed by both humans and natural forces. Most tools currently in use are limited in their ability to predict how elephants will respond to novel changes in the environment. Individual-, or agent-based modeling (ABM), may extend current methods in addressing and predicting spatial responses to environmental conditions over time. We developed a spatially explicit agent-based model to simulate elephant space use and validated the model with movement data from elephants in Kruger National Park (KNP) and Chobe National Park (CNP). We simulated movement at an hourly scale, as this scale can reflect switches in elephant behavior due to changes in internal states and short-term responses to the local availability and distribution of critical resources, including forage, water, and shade. Known internal drivers of elephant movement, including perceived temperature and the time since an individual last visited a water source, were linked to the external environment through behavior-based movement rules. Simulations were run on model landscapes representing the wet season and the hot, dry season for both parks. The model outputs, including home range size, daily displacement distance, net displacement distance, and maximum distance traveled from a permanent water source, were evaluated through qualitative and quantitative comparisons to actual elephant movement data from both KNP and CNP. The ABM was successful in reproducing the differences in daily displacements between seasons in each park, and in distances traveled from a permanent water source between parks and seasons. Other movement characteristics, including differences in home range sizes and net daily displacements, were partially reproduced. Out of the all the statistical comparisons made between the empirical and simulated movement patterns, the majority were classified as discrepancies of medium or small effect size. We have shown that a resource-driven model with relatively simple decision rules generates trajectories with movement characteristics that are mostly comparable to those calculated from empirical data. Simulating hourly movement (as our model does) may be useful in predicting how finer-scale patterns of space use, such as those created by foraging movements, are influenced by finer spatio-temporal changes in the environment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2021.109499","usgsCitation":"Diaz, S.G., DeAngelis, D.L., Gaines, M.S., Purdon, A., Mole, M.A., and van Aarde, R.J., 2021, Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement: Ecological Modelling, v. 447, 109499, 27 p., https://doi.org/10.1016/j.ecolmodel.2021.109499.","productDescription":"109499, 27 p.","ipdsId":"IP-124073","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":408643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Botswana, Mozambique, South Africa","otherGeospatial":"Chobe National Park, Kruger National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 25.26908811865576,\n -17.811236528794907\n ],\n [\n 23.696375701212872,\n -17.811236528794907\n ],\n [\n 23.696375701212872,\n -19.291477668581805\n ],\n [\n 25.26908811865576,\n -19.291477668581805\n ],\n [\n 25.26908811865576,\n -17.811236528794907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 30.337765815705552,\n -22.153479707969097\n ],\n [\n 30.337765815705552,\n -25.725433227433996\n ],\n [\n 33.00624860561675,\n -25.725433227433996\n ],\n [\n 33.00624860561675,\n -22.153479707969097\n ],\n [\n 30.337765815705552,\n -22.153479707969097\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"447","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diaz, Stephanie G.","contributorId":212228,"corporation":false,"usgs":false,"family":"Diaz","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":855617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":855618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaines, Michael S.","contributorId":298435,"corporation":false,"usgs":false,"family":"Gaines","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":855619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Purdon, Andrew","contributorId":298436,"corporation":false,"usgs":false,"family":"Purdon","given":"Andrew","email":"","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855620,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mole, Michael A.","contributorId":298438,"corporation":false,"usgs":false,"family":"Mole","given":"Michael","email":"","middleInitial":"A.","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855621,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Aarde, Rudi J.","contributorId":298440,"corporation":false,"usgs":false,"family":"van Aarde","given":"Rudi","email":"","middleInitial":"J.","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855622,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219424,"text":"70219424 - 2021 - UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence","interactions":[],"lastModifiedDate":"2021-04-05T13:40:13.157647","indexId":"70219424","displayToPublicDate":"2021-03-09T08:18:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence","docAbstract":"Seasonal snow cover in the dry forests of the American West provides essential water resources to both human and natural systems. The structure of trees and their arrangement across the landscape are important drivers of snow cover distribution across these forests, varying widely in both space and time. We used unmanned aerial vehicle (UAV) multispectral imagery and Structure-from-Motion (SfM) models to quantify rapidly melting snow cover dynamics and examine the effects of forest structure shading on persistent snow cover in a recently thinned ponderosa pine forest. Using repeat UAV multispectral imagery (n = 11 dates) across the 76 ha forest, we first developed a rapid and effective method for identifying persistent snow cover with 90.2% overall accuracy. The SfM model correctly identified 98% (n = 1280) of the trees, when compared with terrestrial laser scanner validation data. Using the SfM-derived forest structure variables, we then found that canopy shading associated with the vertical and horizontal metrics was a significant driver of persistent snow cover patches (R2 = 0.70). The results indicate that UAV image-derived forest structure metrics can be used to accurately predict snow patch size and persistence. Our results provide insight into the importance of forest structure, specifically canopy shading, in the amount and distribution of persistent seasonal snow cover in a typical dry forest environment. An operational understanding of forest structure effects on snow cover will help drive forest management that can target snow cover dynamics in addition to forest health.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13051036","usgsCitation":"Belmonte, A., Sankey, T.T., Biedermann, J., Bradford, J., Goetz, S.J., and Kolb, T., 2021, UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence: Remote Sensing, v. 13, no. 5, 1036, 20 p., https://doi.org/10.3390/rs13051036.","productDescription":"1036, 20 p.","ipdsId":"IP-126824","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13051036","text":"Publisher Index Page"},{"id":384871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.78863525390625,\n 34.5235300339023\n ],\n [\n -111.23382568359374,\n 34.5235300339023\n ],\n [\n -111.23382568359374,\n 35.15135442846945\n ],\n [\n -111.78863525390625,\n 35.15135442846945\n ],\n [\n -111.78863525390625,\n 34.5235300339023\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Belmonte, Adam","contributorId":222546,"corporation":false,"usgs":false,"family":"Belmonte","given":"Adam","email":"","affiliations":[{"id":40559,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":813495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":813496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biedermann, Joel","contributorId":256936,"corporation":false,"usgs":false,"family":"Biedermann","given":"Joel","email":"","affiliations":[{"id":51904,"text":"USDA Agricultural Research Service Southwest Watershed Research Center, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":813497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goetz, Scott J 0000-0002-6326-4308","orcid":"https://orcid.org/0000-0002-6326-4308","contributorId":210734,"corporation":false,"usgs":false,"family":"Goetz","given":"Scott","email":"","middleInitial":"J","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":813499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolb, Thomas","contributorId":174381,"corporation":false,"usgs":false,"family":"Kolb","given":"Thomas","affiliations":[],"preferred":false,"id":813500,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219558,"text":"70219558 - 2021 - Fish habitat use and food web structure following pond and plug restoration of a Montane Meadow in the Sierra Nevada, California","interactions":[],"lastModifiedDate":"2021-04-13T12:43:12.368334","indexId":"70219558","displayToPublicDate":"2021-03-09T07:34:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8119,"text":"Northwest Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Fish habitat use and food web structure following pond and plug restoration of a Montane Meadow in the Sierra Nevada, California","docAbstract":"Montane meadows are areas of high biodiversity and provide many important ecosystem services; however, degradation of 40–60% of these habitats in the Sierra Nevada region of California has left many of these areas impaired. The “pond-and-plug” meadow-restoration technique is 1 type of treatment implemented to restore montane meadows. The objectives of this technique are to re-water the meadow and promote downstream flow by increasing the water-table elevation and providing additional water storage that will promote the growth of mesic and hydric vegetation that maintains and stabilizes stream channels. However, aquatic habitat and the composition and functioning of aquatic communities in these systems post-treatment are poorly documented or understood. We evaluated: (1) fish habitat, community composition, and relative abundance among recently created ponds spanning the range of pond habitats; (2) seasonal movement and survival of fish within and among ponds; and (3) food web structure in ponds. We documented over-summer and winter survival in the fish community and short-distance movement by 1 species occupying the ponds. Mark-recapture data suggest that all fish species present are capable of surviving both summer and winter conditions when pond conditions could be most limiting. Food web structure among intensively sampled ponds was similar, with overlapping isotopic niche width for dominant taxa. However, basal resource diversity (BRD) varied among ponds, with those having higher macrophyte cover also showing greater BRD. Our findings suggest that pond-and-plug techniques can provide habitat for native fishes that are able to tolerate departures from the species thermal and dissolved oxygen optima. Future meadow treatments could benefit from short-term restoration techniques such as pond-and-plug to allow for longer-term processes to influence meadow condition over time.
Regional flood frequency analysis (RFFA) methods are essential tools to assess flood hazard and plan interventions for its mitigation. They are used to estimate flood quantiles when the at‐site record of streamflow data is not available or limited. One commonly used RFFA method is the index flood method (IFM), which assumes that peak floods satisfy the simple scaling hypothesis.
In this work we present an integrated approach to assess the spatial scaling behavior of floods in the United Kingdom (UK) for 540 catchments, where the IFM is currently used operationally. This assessment employs product moments, probability weighted moments, and quantile analysis, and is applied to two different types of “hydrologically homogeneous” UK regions: geographical regions as defined in the Flood Studies Report (NERC, 1975) and pooling‐groups as defined in the updated Flood Estimation Handbook (FEH; Institute of Hydrology, 1999). To understand which variables play a significant role in the flood‐peak generating mechanism, the assessment approach considers scaling not only of drainage area alone but also of other hydro‐geomorphological variables. Results provided by the different methodologies consistently showed that only part (ranging from 30% to 70%) of the peak flow variability is explained by drainage area alone; this fraction increases (up to 80%–95%) when multiple regression is used. Supported by the peak flow spatial scaling assessment, we compared the proposed approach for peak flow quantile estimation with the current FEH method in ungauged catchments. The quantile regression method based on the pooling‐group outperforms the current FEH‐ungauged method, providing a 14% relative improvement in root mean square error over the entire country.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028076","usgsCitation":"Formetta, G., Over, T.M., and Stewart, E., 2021, Assessment of peak flow scaling and Its effect on flood quantile estimation in the United Kingdom: Water Resources Research, v. 57, no. 4, e2020WR028076, 21 p., https://doi.org/10.1029/2020WR028076.","productDescription":"e2020WR028076, 21 p.","ipdsId":"IP-119682","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":453168,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://nora.nerc.ac.uk/id/eprint/529960/1/N529960PP.pdf","text":"External Repository"},{"id":384966,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United Kingdom","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -5.712890625,\n 49.61070993807422\n ],\n [\n -2.28515625,\n 50.064191736659104\n ],\n [\n 1.669921875,\n 50.84757295365389\n ],\n [\n 2.3291015625,\n 52.32191088594773\n ],\n [\n 0.9228515625,\n 54.826007999094955\n ],\n [\n -0.2197265625,\n 55.85064987433714\n ],\n [\n -0.791015625,\n 57.231502991478926\n ],\n [\n -1.142578125,\n 57.938183012205315\n ],\n [\n -2.548828125,\n 58.63121664342478\n ],\n [\n -4.130859375,\n 59.153403092050375\n ],\n [\n -6.767578125,\n 58.97266715450153\n ],\n [\n -8.1298828125,\n 56.24334992410525\n ],\n [\n -7.9541015625,\n 54.521081495443596\n ],\n [\n -7.0751953125,\n 54.059387886623576\n ],\n [\n -5.888671875,\n 53.409531853086435\n ],\n [\n -5.6689453125,\n 51.56341232867588\n ],\n [\n -6.1083984375,\n 50.233151832472245\n ],\n [\n -6.064453125,\n 49.55372551347579\n ],\n [\n -5.712890625,\n 49.61070993807422\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Formetta, Giuseppe 0000-0002-0252-1462","orcid":"https://orcid.org/0000-0002-0252-1462","contributorId":210296,"corporation":false,"usgs":false,"family":"Formetta","given":"Giuseppe","email":"","affiliations":[{"id":38100,"text":"Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO","active":true,"usgs":false}],"preferred":false,"id":813730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stewart, Elizabeth","contributorId":257050,"corporation":false,"usgs":false,"family":"Stewart","given":"Elizabeth","email":"","affiliations":[{"id":51971,"text":"UK Centre for Ecology & Hydrology","active":true,"usgs":false}],"preferred":false,"id":813732,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218721,"text":"70218721 - 2021 - Prioritizing landscapes for grassland bird conservation with hierarchical community models","interactions":[],"lastModifiedDate":"2021-04-08T15:11:47.181072","indexId":"70218721","displayToPublicDate":"2021-03-06T07:56:20","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Prioritizing landscapes for grassland bird conservation with hierarchical community models","docAbstract":"Given widespread population declines of birds breeding in North American grasslands, management that sustains wildlife while supporting rancher livelihoods is needed. However, management effects vary across landscapes, and identifying areas with the greatest potential bird response to conservation is a pressing research need.
We developed a hierarchical modeling approach to study grassland bird response to habitat factors at multiple scales and levels. We then identified areas to prioritize for implementing a bird-friendly ranching program.
Using bird survey data from grassland passerine species and 175 sites (2009–2018) across northeast Wyoming, USA, we fit hierarchical community distance sampling models and evaluated drivers of site-level density and regional-level distribution. We then created spatially-explicit predictions of bird density and distribution for the study area and predicted outcomes from pasture-scale management scenarios.
Cumulative overlap of species distributions revealed areas with greater potential community response to management. Within each species’ potential regional-level distribution, the grassland bird community generally responded negatively to cropland cover and vegetation productivity at local scales (up to 10 km of survey sites). Multiple species declined with increasing bare ground and litter cover, shrub cover, and grass height measured within sites.
We demonstrated a novel approach to multi-scale and multi-level prioritization for grassland bird conservation based on hierarchical community models and extensive population monitoring. Pasture-scale management scenarios also suggested the examined community may benefit from less bare ground cover and shorter grass height. Our approach could be extended to other bird guilds in this region and beyond.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-021-01211-z","usgsCitation":"Monroe, A.P., Edmunds, D.R., Aldridge, C.L., Holloran, M.J., Assal, T.J., and Holloran, A., 2021, Prioritizing landscapes for grassland bird conservation with hierarchical community models: Landscape Ecology, v. 36, p. 1023-1038, https://doi.org/10.1007/s10980-021-01211-z.","productDescription":"16 p.","startPage":"1023","endPage":"1038","ipdsId":"IP-122010","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":453170,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-021-01211-z","text":"Publisher Index Page"},{"id":384245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bird Conservation Region 17","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.5341796875,\n 45.02695045318546\n ],\n [\n -107.05078125,\n 44.715513732021336\n ],\n [\n -106.69921875,\n 44.37098696297173\n ],\n [\n -106.61132812499999,\n 43.78695837311561\n ],\n [\n -106.435546875,\n 43.052833917627936\n ],\n [\n -105.57861328125,\n 42.79540065303723\n ],\n [\n -104.83154296875,\n 42.4234565179383\n ],\n [\n -104.0185546875,\n 42.52069952914966\n ],\n [\n -104.0625,\n 45.042478050891546\n ],\n [\n -107.5341796875,\n 45.02695045318546\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","noUsgsAuthors":false,"publicationDate":"2021-03-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Monroe, Adrian Pierre-Frederic 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":254952,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian","email":"amonroe@usgs.gov","middleInitial":"Pierre-Frederic","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":811526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holloran, Matthew J 0000-0001-5244-770X","orcid":"https://orcid.org/0000-0001-5244-770X","contributorId":254954,"corporation":false,"usgs":false,"family":"Holloran","given":"Matthew","email":"","middleInitial":"J","affiliations":[{"id":51367,"text":"Operational Conservation LLC","active":true,"usgs":false}],"preferred":false,"id":811527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Assal, Timothy J","contributorId":238085,"corporation":false,"usgs":false,"family":"Assal","given":"Timothy","email":"","middleInitial":"J","affiliations":[{"id":18142,"text":"Kent State University","active":true,"usgs":false}],"preferred":false,"id":811528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holloran, Alison G","contributorId":254955,"corporation":false,"usgs":false,"family":"Holloran","given":"Alison G","affiliations":[{"id":51369,"text":"Audubon Rockies","active":true,"usgs":false}],"preferred":false,"id":811529,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70222570,"text":"70222570 - 2021 - Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data","interactions":[],"lastModifiedDate":"2021-08-05T12:13:48.433134","indexId":"70222570","displayToPublicDate":"2021-03-06T07:09:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3071,"text":"Physics of the Earth and Planetary Interiors","active":true,"publicationSubtype":{"id":10}},"title":"Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data","docAbstract":"We use combined teleseismic and strong motion data sets to investigate finite-fault slip models for a double of earthquakes that occurred on August 11, 2012, in northwestern Iran near the cities of Ahar and Varzaghan. The data include teleseismic P-waveforms retrieved from broadband seismic stations located between 30°–94° from the earthquakes and local strong motion data recorded by the Iran Strong Motion Network, installed and operated by the Building and Housing Research Centre. We first invert teleseismic P-waveforms and local strong motion data separately. For the first event (12:23 UTC), the teleseismic broadband inversion yields a somewhat deeper and simpler distribution of slip than the local strong motion inversion. The strong motion inversion results in a more complex distribution because of higher frequency content but can also be influenced by complexities in the propagation path. For the second event (12:34 UTC), the slip distribution from strong motion data is more similar to the teleseismic result and shows a simple slip area with a small relative movement to the west. To resolve the differences between the results of these two data sets and obtain a better constrained slip model, we perform a joint inversion of teleseismic broadband and local strong motion data.
The joint inversion for the first event shows two asperities with a maximum slip of 3.9 m up- dip from the hypocenter and extending to the west between depths of 1 and 5 km. A second narrower high-slip area is seen just above the hypocenter from 6 to 10 km depth. The total moment for this earthquake is calculated to be Mo = 3.8 × 1025 dyn-cm (3.8 × 1018 N.m) (Mw 6.4). For the second event, the results of the joint inversion show a simple slip distribution that is mainly confined in a single patch around the hypocenter with a depth range from about 10 to 13 km and maximum slip of 1.9 m. We compute a total seismic moment of Mo = 1.6 × 1025 dyn-cm (1.6 × 1018 N.m) (Mw 6.1) for the second event. The largest stress drops for the first event occur above the hypocenter with an average stress drop over the rupture area of 120 bar (12 Mpa). For the second event, the maximum stress drop occurs at the reported focal depth with an average stress drop over the rupture area of 80 bar (8 Mpa).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.pepi.2021.106688","usgsCitation":"Saltanatpouri, A., Hartzell, S.H., Rahimi, H., Rouhollahi, R., and Amiri Fard, R., 2021, Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data: Physics of the Earth and Planetary Interiors, v. 313, 106688, 15 p., https://doi.org/10.1016/j.pepi.2021.106688.","productDescription":"106688, 15 p.","ipdsId":"IP-124948","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":387702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","otherGeospatial":"East Anatolian Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 42.626953125,\n 39.65011210186371\n ],\n [\n 43.1982421875,\n 39.65011210186371\n ],\n [\n 43.1982421875,\n 39.94975340768179\n ],\n [\n 42.626953125,\n 39.94975340768179\n ],\n [\n 42.626953125,\n 39.65011210186371\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"313","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Saltanatpouri, Atefeh","contributorId":261761,"corporation":false,"usgs":false,"family":"Saltanatpouri","given":"Atefeh","email":"","affiliations":[{"id":52998,"text":"Institute of Geophysics, University of Tehran, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rahimi, Habib","contributorId":261762,"corporation":false,"usgs":false,"family":"Rahimi","given":"Habib","email":"","affiliations":[{"id":52998,"text":"Institute of Geophysics, University of Tehran, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rouhollahi, Rahmatollah","contributorId":261763,"corporation":false,"usgs":false,"family":"Rouhollahi","given":"Rahmatollah","email":"","affiliations":[{"id":53001,"text":"Babol Noshirvani University of Technology, Babol, Iran","active":true,"usgs":false}],"preferred":false,"id":820606,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Amiri Fard, Rouholla","contributorId":261764,"corporation":false,"usgs":false,"family":"Amiri Fard","given":"Rouholla","email":"","affiliations":[{"id":53002,"text":"International Institute of Earthquake Engineering and Seismology, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820607,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219437,"text":"70219437 - 2021 - Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk","interactions":[],"lastModifiedDate":"2021-04-06T11:58:58.749804","indexId":"70219437","displayToPublicDate":"2021-03-06T06:53:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk","docAbstract":"Toxic cyanobacterial blooms, often containing multiple toxins, are a serious public health issue. However, there are no known models that predict a cyanotoxin mixture (anatoxin-a, microcystin, saxitoxin). This paper presents two cyanotoxin mixture models (MIX) and compares them to two microcystin (MC) models from data collected in 2016–2017 from three recurring cyanobacterial bloom locations in Kabetogama Lake, Voyageurs National Park (Minnesota, USA). Models include those using near-real-time environmental variables (readily available) and those using additional comprehensive variables (based on laboratory analyses). Comprehensive models (R2 = 0.87 MC; R2 = 0.86 MIX) explained more variability than the environmental models (R2 = 0.58 MC; R2 = 0.57 MIX). Although neither MIX model was a better fit than the MC models, the MIX models produced no false negatives in the calibration dataset, indicating that all observations above regulatory guidelines were simulated by the MIX models. This is the first known use of Virtual Beach software for a cyanotoxin mixture model, and the methods used in this paper may be applicable to other lakes or beaches.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2021.125560","usgsCitation":"Christensen, V., Stelzer, E., Eikenberry, B., Olds, H., LeDuc, J.F., Maki, R., Norland, J.E., and Khan, E., 2021, Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk: Journal of Hazardous Materials, v. 415, 125560, 13 p., https://doi.org/10.1016/j.jhazmat.2021.125560.","productDescription":"125560, 13 p.","ipdsId":"IP-123013","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436472,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X7EO1K","text":"USGS data release","linkHelpText":"Data and model archive for multiple linear regression models for prediction of weighted cyanotoxin mixture concentrations and microcystin concentrations at three recurring bloom sites in Kabetogama Lake in Minnesota"},{"id":384883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Kabetogama Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.3453369140625,\n 48.21735290928554\n ],\n [\n -92.48291015625,\n 48.21735290928554\n ],\n [\n -92.48291015625,\n 48.622016428468385\n ],\n [\n -93.3453369140625,\n 48.622016428468385\n ],\n [\n -93.3453369140625,\n 48.21735290928554\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"415","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stelzer, Erin A. 0000-0001-7645-7603","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":220549,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813549,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eikenberry, Barbara C. Scudder 0000-0001-8058-1201 beikenberry@usgs.gov","orcid":"https://orcid.org/0000-0001-8058-1201","contributorId":172148,"corporation":false,"usgs":true,"family":"Eikenberry","given":"Barbara C. Scudder","email":"beikenberry@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":813550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olds, Hayley T. 0000-0002-6701-6459 htemplar@usgs.gov","orcid":"https://orcid.org/0000-0002-6701-6459","contributorId":5002,"corporation":false,"usgs":true,"family":"Olds","given":"Hayley T.","email":"htemplar@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":813551,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LeDuc, Jaime F.","contributorId":190132,"corporation":false,"usgs":false,"family":"LeDuc","given":"Jaime","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":813552,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maki, Ryan P.","contributorId":190131,"corporation":false,"usgs":false,"family":"Maki","given":"Ryan P.","affiliations":[],"preferred":false,"id":813553,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Norland, Jack E.","contributorId":214257,"corporation":false,"usgs":false,"family":"Norland","given":"Jack","email":"","middleInitial":"E.","affiliations":[{"id":39001,"text":"School of Natural Resources Sciences, North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":813554,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Khan, Eakalak","contributorId":220550,"corporation":false,"usgs":false,"family":"Khan","given":"Eakalak","email":"","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":813555,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70218517,"text":"ofr20201145 - 2021 - Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018","interactions":[],"lastModifiedDate":"2021-03-05T12:53:46.034292","indexId":"ofr20201145","displayToPublicDate":"2021-03-04T15:39:22","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1145","displayTitle":"Estimated Total Phosphorus Loads for Selected Sites on Great Lakes Tributaries, Water Years 2014–2018","title":"Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018","docAbstract":"Monthly and annual total phosphorus loads were estimated for water years 2014 through 2018 for 23 streamgaged (gaged) sites on tributaries to the Great Lakes. Processing and regression methods described by Robertson and others (2018) were used with discrete and continuous data collected during water years 2011 and 2018 to update regression models for estimating instantaneous flux with the same form of equations as published by Robertson and others (2018). Monthly and water year average fluxes for all but two of the 23 gage sites were estimated using a weighted combination of results from surrogate models (which have streamflow, turbidity, and seasonal indicators as explanatory variables) and unit-value (UV)-flow models which have only UV streamflow and seasonal indicators as explanatory variables. Two of the gage sites had extensive periods of missing turbidity records, so average flux estimates for those stations were based solely on results from UV-flow models.
For most sites, estimated loads of total phosphorus were computed and summed for water years 2014–2018. The cumulative loads were used to compute yields and flow-weighted mean concentrations for water years 2014–2018. The estimated cumulative total phosphorus loads for water years 2014–2018 ranged from 112 to 11,500 metric tons. The Maumee River site (U.S. Geological Survey gage number 04193500) had the largest estimated cumulative load for water years 2014–2018 and the third largest estimated flow-weighted mean concentration. In fact, the estimated cumulative load at the Maumee River site was more than three times larger than the second largest estimated cumulative load.
Estimated average annual total phosphorus yields and flow-weighted mean concentrations for water years 2014–2018 ranged from 0.016 metric tons per square kilometer to 0.771 metric tons per square kilometer and 0.033 milligram per liter to 0.466 milligram per liter, respectively. The Cattaraugus Creek gage site (U.S. Geological Survey gage number 04213500) had the highest estimated average annual total phosphorus yield and flow-weighted mean concentration. The average annual total phosphorus yield at the Cattaraugus Creek gage site was almost twice as large as the second largest estimated yield.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201145","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Koltun, G.F., 2021, Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018: U.S. Geological Survey Open-File Report 2020–1145, 13 p., https://doi.org/10.3133/ofr20201145.","productDescription":"Report: v, 13 p.; 2 Appendixes; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122090","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":383717,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145//ofr20201145_appendix_2.csv","text":"Appendix 2","size":"64.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2020–1145 Appendix 2","linkHelpText":"— Estimated monthly total phosphorus loads at 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U.S. Geological Survey
6460 Busch Boulevard Ste 100
Columbus, OH 43229-1737
Lake charr Salvelinus namaycush life history and population dynamics metrics were reviewed to evaluate populations inside (n = 462) and outside (n = 24) the native range. Our goals were to create a database of metrics useful for evaluating population status and to test for large-scale patterns between metrics and latitude and lake size. An average lake charr grew from a 69-mm length at age-0 (L0) at 89 mm/year early growth rate (ω) to 50% maturity at 420 mm (L50) at age 8 (t50), and then continued to grow toward a 717-mm asymptotic length (L∞). L50 was positively correlated to ω, whereas t50 was inversely correlated to ω. Lake charr grew slower toward larger size and older age in northern latitudes and larger lakes than in southern latitudes and smaller lakes. Population density (number/ha) and yield density (kg/ha) decreased with lake size, and yield and total annual mortality (A) decreased with latitude. Native populations grew slower (ω), were heavier at 500 mm (W500), matured at shorter L50, grew to a shorter L∞, and suffered lower annual mortality A than non-native populations. Our review and database should be useful to managers and researchers for quantifying lake charr population status across the species range.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The lake charr Salvelinus namaycush: Biology, ecology, distribution, and management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer Link","doi":"10.1007/978-3-030-62259-6_8","usgsCitation":"Hansen, M.J., Guy, C.S., Bronte, C.R., and Nate, N.A., 2021, Life history and population dynamics, chap. of The lake charr Salvelinus namaycush: Biology, ecology, distribution, and management, p. 253-286, https://doi.org/10.1007/978-3-030-62259-6_8.","productDescription":"34 p.","startPage":"253","endPage":"286","ipdsId":"IP-105836","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":390397,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-03-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Hansen, Michael J","contributorId":260100,"corporation":false,"usgs":false,"family":"Hansen","given":"Michael","email":"","middleInitial":"J","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":824840,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guy, Christopher S. 0000-0002-9936-4781 cguy@usgs.gov","orcid":"https://orcid.org/0000-0002-9936-4781","contributorId":2876,"corporation":false,"usgs":true,"family":"Guy","given":"Christopher","email":"cguy@usgs.gov","middleInitial":"S.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":5062,"text":"Office of the Chief Scientist for Ecosystems","active":true,"usgs":true}],"preferred":true,"id":824839,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bronte, Charles R.","contributorId":190727,"corporation":false,"usgs":false,"family":"Bronte","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":824841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nate, Nancy A.","contributorId":26626,"corporation":false,"usgs":true,"family":"Nate","given":"Nancy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":824842,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227101,"text":"70227101 - 2021 - Developing species-age cohorts from forest inventory and analysis data to parameterize a forest landscape model","interactions":[],"lastModifiedDate":"2021-12-29T14:14:01.03095","indexId":"70227101","displayToPublicDate":"2021-03-04T08:10:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2043,"text":"International Journal of Forestry Research","active":true,"publicationSubtype":{"id":10}},"title":"Developing species-age cohorts from forest inventory and analysis data to parameterize a forest landscape model","docAbstract":"Simulating long-term, landscape level changes in forest composition requires estimates of stand age to initialize succession models. Detailed stand ages are rarely available, and even general information on stand history often is lacking. We used data from USDA Forest Service Forest Inventory and Analysis (FIA) database to estimate broad age classes for a forested landscape to simulate changes in landscape composition and structure relative to climate change at Fort Drum, a 43,000 ha U.S. Army installation in northwestern New York. Using simple linear regression, we developed relationships between tree diameter and age for FIA site trees from the host and adjacent ecoregions and applied those relationships to forest stands at Fort Drum. We observed that approximately half of the variation in age was explained by diameter breast height (DBH) across all species studied (r2 = 0.42 for sugar maple Acer saccharum to 0.63 for white ash Fraxinus americana). We then used age-diameter relationships from published research on northern hardwood species to calibrate results from the FIA-based analysis. With predicted stand age, we used tree species life histories and environmental conditions represented by ecological site types to parameterize a stochastic forest landscape model (LANDIS-II) to spatially and temporally model successional changes in forest communities at Fort Drum. Forest stands modeled over 100 years without significant disturbance appeared to reflect expected patterns of increasing dominance by shade-tolerant mesophytic tree species such as sugar maple, red maple (Acer rubrum), and eastern hemlock (Tsuga canadensis) where soil moisture was sufficient. On drier sandy soils, eastern white pine (Pinus strobus), red pine (P. resinosa), northern red oak (Quercus rubra), and white oak (Q. alba) continued to be important components throughout the modeling period with no net loss at the landscape scale. Our results suggest that despite abundant precipitation and relatively low evapotranspiration rates for the region, low soil water holding capacity and fertility may be limiting factors for the spread of mesophytic species on excessively drained soils in the region. Increasing atmospheric temperatures projected for the region could alter moisture regimes for many coarse-textured soils providing a possible mechanism for expansion of xerophytic tree species.
","language":"English","publisher":"Hindawi","doi":"10.1155/2021/6650821","usgsCitation":"Odom, R.H., and Ford, W., 2021, Developing species-age cohorts from forest inventory and analysis data to parameterize a forest landscape model: International Journal of Forestry Research, v. 2021, 6650821, 16 p., https://doi.org/10.1155/2021/6650821.","productDescription":"6650821, 16 p.","ipdsId":"IP-111053","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":453196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.1155/2021/6650821","text":"Publisher Index Page"},{"id":393572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fort Drum","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.0089111328125,\n 43.95921358836687\n ],\n [\n -75.509033203125,\n 43.95921358836687\n ],\n [\n -75.509033203125,\n 44.209772586984485\n ],\n [\n -76.0089111328125,\n 44.209772586984485\n ],\n [\n -76.0089111328125,\n 43.95921358836687\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2021","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Odom, Richard H.","contributorId":171659,"corporation":false,"usgs":false,"family":"Odom","given":"Richard","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":829633,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":829632,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218590,"text":"ofr20201146 - 2021 - Practical field survey operations for flood insurance rate maps","interactions":[],"lastModifiedDate":"2021-03-05T12:41:18.272992","indexId":"ofr20201146","displayToPublicDate":"2021-03-04T08:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1146","displayTitle":"Practical Field Survey Operations for Flood Insurance Rate Maps","title":"Practical field survey operations for flood insurance rate maps","docAbstract":"The U.S. Geological Survey assists the Federal Emergency Management Agency in its mission to identify flood hazards and zones for risk premiums for communities nationwide, by creating flood insurance rate maps through updating hydraulic models that use river geometry data. The data collected consist of elevations of river channels, banks, and structures, such as bridges, dams, and weirs that can affect flow. To account for the model complexity of river structure hydraulics and the fidelity between river channel and structure geometry, two distinct standards for collecting geometry data are presented, both using global navigation satellite system real-time network surveying. This method is adapted from U.S. Geological Survey manuals and is foundational in hydraulic surveying for flood insurance rate maps.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201146","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Taylor, N.J., and Simeone, C.E., 2021, Practical field survey operations for flood insurance rate maps: U.S. Geological Survey Open-File Report 2020–1146, 8 p., https://doi.org/10.3133/ofr20201146.","productDescription":"iv, 8 p.","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114316","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":383741,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/tm11D1","text":"Techniques and Methods 11-D1","linkHelpText":"- Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey"},{"id":383723,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1146/coverthb.jpg"},{"id":383724,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1146/ofr20201146.pdf","text":"Report","size":"662 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1146"},{"id":383725,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm11D3","text":"Techniques and Methods 11-D3","linkHelpText":"- Procedures and Best Practices for Trigonometric Leveling in the U.S. Geological Survey"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
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Reservoirs in arid regions often provide critical water storage but little is known about their greenhouse gas (GHG) footprint. While there is growing appreciation of the role reservoirs play as GHG sources, there is a lack of understanding of GHG emission dynamics from reservoirs in arid regions and implications for environmental policy. Here we present initial GHG emission measurements from Lake Powell, a large water storage reservoir in the desert southwest United States. We report CO2-eq emissions from the shallow (< 15 m) littoral regions of the reservoir that are higher than the global average areal emissions from reservoirs (9.4 vs. 5.8 g CO2-eq m−2 d−1) whereas fluxes from the main reservoir were two orders of magnitude lower (0.09 g CO2-eq m−2 d−1). We then compared our measurements to modeled CO2 + CH4 emissions from the reservoir using four global scale models. Factoring these emissions into hydropower production at Lake Powell yielded low GHG emissions per MWh−1 as compared to fossil-fuel based energy sources. With the exception of one model, the estimated hydropower emissions for Lake Powell ranged from 10−32 kg CO2-eq MWh−1, compared to ∼400−1000 kg CO2-eq MWh−1 for natural gas, oil, and coal. We also estimate that reduced littoral habitat under low water levels leads to ∼50% reduction in the CO2 equivalent emissions per MWh. The sensitivity of GHG emissions to reservoir water levels suggests that the interaction will be an important policy consideration in the design and operation of arid region systems.