As a first step toward understanding the feasibility of using arbuscular mycorrhizal fungi (AMF) in reconstruction practice, we addressed four objectives: (1) compare root-associated AMF communities of plants between high-quality remnant prairies and reconstructed prairies, (2) compare root-associated AMF communities between plant species that declined in reconstructions and species that were thriving, (3) compare AMF communities collected from roots of plants in geographically separate parts of Minnesota and Iowa, and (4) assess the relationship between AMF communities and soil abiotic factors. We collected soil and root samples in 8 prairies reconstructed in 2005 (and monitored through 2015) and 6 remnant prairies, and the samples were separated into 6 geographically determined clusters, each containing 1–2 reconstructions and 1 remnant. Sequencing was completed on 1,188 deoxyribonucleic acid extracts from individual plant root samples, and fungal sequences were clustered to operational taxonomic units at 97-percent identity. Nonmetric multidimensional scaling was used to visualize differences in species composition of AMF communities among plant species and field sites. Permutational analysis of variance was completed to test for differences in AMF community composition between the 2 types of sites (remnants and reconstructions), among plant species, and among the 6 site clusters. AMF communities differed between remnant and reconstructed prairies, with one exception, and AMF associated with individual plant species also tended to differ, depending on whether the plant species’ roots were collected from remnant or reconstructed prairie. On the other hand, we did not determine that, as a group, species in decline in the reconstructions we had monitored were more likely to harbor different AMF communities compared to species not in decline in the reconstructions. Significant interactions between site type and clusters indicate geographic variation in AMF communities. Total carbon and nitrogen, and organic matter, were higher in remnant soils, whereas phosphorus, which at high concentrations reduces the value of AMF to plants, was much higher in soils collected from reconstructions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221055","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service, North Dakota State University, University of Minnesota, and University of Groningen","programNote":"Land Management Research Program","usgsCitation":"Vink, S.N., Aldrich-Wolfe, L., Huerd, S.C., Larson, J.L., Vacek, S.C., Drobney, P.M., Barnes, M., Viste-Sparkman, K., Jordan, N.R., and Larson, D.L., 2022, Belowground mutualisms to support prairie reconstruction—Improving prairie habitat using mycorrhizal inoculum: U.S. Geological Survey Open-File Report 2022–1055, 18 p., https://doi.org/10.3133/ofr20221055.","productDescription":"Report: vi, 18 p.; 2 Data Releases","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-138435","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":402010,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221055/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":402009,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95R5UNN","text":"USGS data release","linkHelpText":"Arbuscular mycorrhizal fungi in remnant and reconstructed prairies in Minnesota and Iowa, 2019 (ver. 2.0, April 2022)"},{"id":402007,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1055/images"},{"id":402006,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1055/ofr20221055.XML"},{"id":402005,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1055/ofr20221055.pdf","text":"Report","size":"1.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1055"},{"id":402004,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1055/coverthb.jpg"},{"id":402008,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N8X0ZY","text":"USGS data release","linkHelpText":"Management of remnant tallgrass prairie by grazing or fire in western Minnesota, 2016–2017"}],"country":"United States","state":"Iowa, Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.26220703125,\n 46.37725420510028\n ],\n [\n -94.81201171875,\n 46.37725420510028\n ],\n [\n -94.81201171875,\n 47.931066347509784\n ],\n [\n -96.26220703125,\n 47.931066347509784\n ],\n [\n -96.26220703125,\n 46.37725420510028\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.767578125,\n 44.5278427984555\n ],\n [\n -94.28466796874999,\n 44.5278427984555\n ],\n [\n -94.28466796874999,\n 45.706179285330855\n ],\n [\n -96.767578125,\n 45.706179285330855\n ],\n [\n -96.767578125,\n 44.5278427984555\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.93310546874999,\n 40.9964840143779\n ],\n [\n -91.91162109374997,\n 40.9964840143779\n ],\n [\n -91.91162109374997,\n 41.78769700539063\n ],\n [\n -93.93310546874999,\n 41.78769700539063\n ],\n [\n -93.93310546874999,\n 40.9964840143779\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Ratings are used for a variety of reasons in water-resources investigations. The simplest rating relates discharge to the stage of the river. From a pure hydrodynamics perspective, all rivers and streams have some form of hysteresis in the relation between stage and discharge because of unsteady flow as a flood wave passes. Simple ratings are unable to represent hysteresis in a stage/discharge relation. A dynamic rating method is capable of capturing hysteresis owing to the variable energy slope caused by unsteady momentum and pressure.
A dynamic rating method developed to compute discharge from stage for compact channel geometry, referred to as DYNMOD, previously has been developed through a simplification of the one-dimensional Saint-Venant equations. A dynamic rating method, which accommodates compound and compact channel geometry, referred to as DYNPOUND, has been developed through a similar simplification as a part of this study. The DYNMOD and DYNPOUND methods were implemented in the Python programming language. Discharge time series computed with the dynamic rating method implementations were then compared to simulated discharge time series and discrete discharge measurements made at U.S. Geological Survey streamgage sites.
Four sets of stage and discharge time series were created using one-dimensional unsteady simulation software with compound channel geometry to compare the results of both dynamic rating methods to results from the full one-dimensional shallow water equations. Discharge time series were computed from stage time series using DYNMOD and DYNPOUND. DYNPOUND outperformed DYNMOD in all four scenarios. The minimum and maximum mean squared logarithmic error (MSLE) for the DYNMOD results were 2.75×10−2 and 3.40×10−2, respectively. The minimum and maximum MSLE for the DYNPOUND results were 2.51×10−7 and 1.91×10−4, respectively.
The dynamic rating methods were calibrated for six U.S. Geological Survey streamgage sites using observed discharge data collected at the sites. The calibration objective for each site was to minimize the MSLE of the discharge computed with the rating method with respect to observed discharge. For each site, the calibration included all field measurements within a selected water year. The DYNMOD method failed to compute discharge for the full calibration time series for three sites. A method fails to compute when the implementation returns a nonfinite value at a time step. Because the values computed for following time steps are dependent on the previous time step, a nonfinite value results in nonfinite values that follow. For the three sites for which DYNMOD computed the complete discharge time series, the minimum MSLE for calibration was 2.19×10−3 and the maximum was 9.77×10−3. The MSLE of the DYNPOUND computed discharge calibration time series for the six sites ranged from 3.70×10−3 to 1.25. For each site, an event-based time period was selected to compare the discharge time series computed with the dynamic rating methods to discrete discharge field measurements made at the streamgage sites. The DYNMOD-computed discharge time series for the three sites had an MSLE range of 2.76×10−3 to 3.14×10−2. The range of MSLE for the six DYNPOUND sites was 3.64×10−3 to 7.23×10−2. Although the DYNMOD method outperforms the DYNPOUND method when calibrated streamgage sites are under consideration, the DYNMOD method failed to compute a discharge time series at three of the six sites. The DYNPOUND method, therefore, was more robust than the DYNMOD method. Improvements to the implementation of the DYNPOUND method may improve the accuracy of the method.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221031","programNote":"Groundwater and Streamflow Information Program","usgsCitation":"Domanski, M., Holmes, R.R., Jr., and Heal, E.N., 2022, Dynamic rating method for computing discharge from time-series stage data: U.S. Geological Survey Open-File Report 2022–1031, 48 p., https://doi.org/10.3133/ofr20221031.","productDescription":"Report: vii, 48 p.; 2 Data Releases; Dataset","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-128037","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501770,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113161.htm","linkFileType":{"id":5,"text":"html"}},{"id":400457,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":400459,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YUV9DG","text":"USGS data release","linkHelpText":"Dynamic stage to discharge rating model archive"},{"id":400458,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P955QRPQ","text":"USGS data release","linkHelpText":"Dynamic rating method for computing discharge from time series stage data—Site datasets"},{"id":400454,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1031/ofr20221031.XML"},{"id":400455,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1031/images"},{"id":400453,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1031/ofr20221031.pdf","text":"Report","size":"2.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1031"},{"id":400452,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1031/coverthb.jpg"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Mercury is a global contaminant that can be detrimental to wildlife and human health. Anthropogenic emissions and point sources are primarily responsible for elevated mercury concentrations in sediments and waters. Mercury can physically move and chemically transform in the environment, resulting in biomagnification of mercury, in the form of methylmercury, in the food web and causing elevated mercury concentrations in upper trophic levels. The ability to measure total mercury concentrations in the environment has existed for several decades and makes it possible to detect hotspots that might exist because of ongoing or previous anthropogenic activity. However, recent (within the past 15 years) developments in mass spectrometry have made it possible to complete low level stable isotope analysis allowing for the determination of mercury sources—natural and anthropogenic—in the environment through “fingerprinting.” Grubers Grove Bay in Lake Wisconsin, the focus area of this study, was determined to have elevated mercury levels even after multiple remediation efforts, resulting in its listing on the Federal list of impaired waters pursuant to the Clean Water Act. Adjacent to the bay is the former Badger Army Ammunition Plant, which manufactured ammunition for the U.S. Army during the early and middle 20th century, after which it was put on standby before being fully decommissioned. This study assesses mercury concentrations in the sediments and suspended particulate matter of Grubers Grove Bay, Wiegands Bay, and upstream sites, and in adjacent soils on the former Badger Army Ammunition Plant site. This study confirmed that mercury contamination exists in the sediments of Grubers Grove Bay even after dredging attempts by the U.S. Army. Additionally, using isotope ratios and a two-endmember mixing model, it was determined that soil from within Badger Army Ammunition Plant’s former site contributed a substantial amount of mercury to the bay. This result was supported by an observed gradient of high to low mercury concentrations from the innermost (nearest Badger Army Ammunition Plant) to the outermost (farthest from Badger Army Ammunition Plant) part of the bay.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221051","collaboration":"Prepared in cooperation with U.S. Army Environmental Command","usgsCitation":"Routhier, E.J., Janssen, S.E., Tate, M.T., Ogorek, J.M., DeWild, J.F., and Krabbenhoft, D.P., 2022, Assessment of mercury in sediments and waters of Grubers Grove Bay, Wisconsin: U.S. Geological Survey Open-File Report 2022–1051, 20 p., https://doi.org/10.3133/ofr20221051.","productDescription":"Report: vii, 20 p.; Data release","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-133343","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501779,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113162.htm","linkFileType":{"id":5,"text":"html"}},{"id":401822,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P990MFHU","text":"USGS data release","linkHelpText":"Gruber's Grove Bay mercury site assessment"},{"id":401821,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1051/images"},{"id":401819,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1051/ofr20221051.pdf","text":"Report","size":"2.56 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1051"},{"id":401818,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1051/coverthb.jpg"},{"id":401820,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1051/ofr20221051.XML"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Grubers Grove Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.74800109863281,\n 43.32467816302811\n ],\n [\n -89.64569091796874,\n 43.32467816302811\n ],\n [\n -89.64569091796874,\n 43.393572674883146\n ],\n [\n -89.74800109863281,\n 43.393572674883146\n ],\n [\n -89.74800109863281,\n 43.32467816302811\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Mechanistic river models capable of simulating hydrodynamics and stream temperature are valuable tools for investigating thermal conditions and their relation to streamflow in river basins where upstream water storage and management decisions have an important influence on river reaches with threatened fish populations. In the Willamette River Basin in northwestern Oregon, a two-dimensional, hydrodynamic water-quality model (CE‑QUAL‑W2) has been used to investigate the downstream effects of dam operations and other anthropogenic influences on stream temperature. By simulating the managed releases of water and various temperatures from the large Willamette Valley Project dams upstream of the modeling domain, these models can be used to investigate riverine temperature conditions and their relation to streamflow to determine where and when conditions are most challenging for threatened fish populations and how dam operations and flow management can affect and optimize thermal conditions in the river.
The original models were initially developed to simulate conditions in spring–autumn of 2001 and 2002. This report documents (1) the upgrade of the river models to CE‑QUAL‑W2 version 4.2 and (2) the update of those models to simulate conditions that occurred from March through October of 2011, 2015, and 2016. These years were selected to represent a range of climatic and hydrologic conditions in the Willamette River Basin, including a “cool, wet” year (2011), a “hot, dry” year (2015), and a “normal” year (2016). Six submodels comprise the modeling system updated in this report; each submodel can be run independently or run with the others as a system. These models include the Coast Fork and Middle Fork Willamette River submodel, which includes the Coast Fork and Middle Fork Willamette Rivers, the Row River, and Fall Creek; the McKenzie River submodel, which includes the South Fork McKenzie River downstream of Cougar Dam and the McKenzie River from its confluence with the South Fork McKenzie River to its mouth; the South Santiam River submodel, which comprises the South Santiam River from Foster Dam to the Santiam River; the North Santiam and Santiam River submodel, which includes the Santiam River and the North Santiam River downstream of Big Cliff Dam; the Upper Willamette River submodel, which includes the Willamette River from Eugene to Salem; and the Middle Willamette River submodel, which includes the Willamette River from Salem to Willamette Falls near Oregon City.
The models included in this report were originally developed, calibrated, and documented by other researchers. As part of the model updates described here, some model parameters were adjusted to improve stability and decrease runtime. Boundary conditions including meteorological, hydrologic, and thermal parameters were developed and updated for model years 2011, 2015, and 2016. In many cases, the data sources used to drive the 2001 and 2002 models were no longer available, which required the use of new data sources, the determination of a proxy record, or the development of appropriate estimation techniques. Goodness-of-fit statistics for the updated models show a good model fit, with the models simulating subdaily water temperatures at most comparable locations with a mean absolute error of generally less than 1 °C and often nearing 0.5 °C, depending on the individual submodel, and a reasonably low bias. The subdaily mean error for the South Santiam River submodel produced the highest bias of any of the submodels. Goodness-of-fit statistics indicate that the results may be biased cool (ranging from -0.43 °C in 2016 to -0.80 °C in 2011 for subdaily results), but the only water temperature data available for comparison on the South Santiam River is itself estimated, and those estimates are known to be too high in summer. Depending on future modeling needs, that submodel may warrant further refinement, along with additional data collection to properly define and minimize any model bias.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221017","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Stratton Garvin, L.E., Rounds, S.A., and Buccola, N.L., 2022, Updates to models of streamflow and water temperature for 2011, 2015, and 2016 in rivers of the Willamette River Basin, Oregon: U.S. Geological Survey Open-File Report 2022–1017, 73 p., https://doi.org/10.3133/ofr20221017.","productDescription":"Report: x, 73 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119723","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":401872,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1017/ofr20221017.XML"},{"id":401871,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1017/images"},{"id":401815,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225035","text":"SIR 2022-5035 —","linkHelpText":"The thermal landscape of the Willamette River—Patterns and controls on stream temperature and implications for flow management and cold-water salmonids"},{"id":401814,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225034","text":"SIR 2022-5034 —","linkHelpText":"Assessment of habitat availability for juvenile Chinook salmon (Oncorhynchus tshawytscha) and steelhead (O. mykiss) in the Willamette River, Oregon"},{"id":501762,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113157.htm","linkFileType":{"id":5,"text":"html"}},{"id":401754,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1017/coverthb.jpg"},{"id":401755,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1017/ofr20221017.pdf","text":"Report","size":"10.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1017"},{"id":401756,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P908DXKH","text":"USGS data release","description":"USGS data release","linkHelpText":"CE-QUAL-W2 models for the Willamette River and major tributaries below U.S. Army Corps of Engineers dams—2011, 2015, and 2016"},{"id":401813,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225006","text":"SIR 2022-5006 —","linkHelpText":"Tracking heat in the Willamette River system, Oregon"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.134765625,\n 42.779275360241904\n ],\n [\n -120.673828125,\n 42.779275360241904\n ],\n [\n -120.673828125,\n 45.9511496866914\n ],\n [\n -123.134765625,\n 45.9511496866914\n ],\n [\n -123.134765625,\n 42.779275360241904\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Low-streamflow statistics, such as the annual minimum 7-day streamflow (which is the 7-day streamflow likely to be exceeded in 9 out of 10 years on average [7Q10]), that are computed by using the full historical streamflow record may not accurately represent current conditions at sites with statistically significant trends in low streamflow over time. Recent research suggests that using a contemporary subset of the historical streamflow record (specifically, the most recent 30 years) to compute an estimate of 7Q10 more accurately represents current streamflow conditions when a statistically significant trend in the streamflow record is present. This report presents the results of a Monte Carlo simulation experiment on artificial low-streamflow records, derived from the characteristics of streamflows at 174 U.S. Geological Survey streamgages, to test whether a similar approach is appropriate for the computation of the annual minimum 7-day streamflow exceeded in 1 out of 2 years on average (7Q2) and the annual minimum 7-day streamflow exceeded in 19 out of 20 years on average (7Q20). The results indicate that using the most recent 30-year subset of the low-streamflow record also may be the best approach when computing 7Q2 and 7Q20 at sites where a statistically significant trend in low streamflows is detected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211111","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Schalk, L., Dudley, R.W., and Blum, A.G., 2022, Methods for computing 7Q2 and 7Q20 low-streamflow statistics to account for possible trends: U.S. Geological Survey Open-File Report 2021–1111, 15 p., https://doi.org/10.3133/ofr20211111.","productDescription":"iv, 15 p.","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119807","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":401572,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1111/images/"},{"id":401570,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1111/ofr20211111.pdf","text":"Report","size":"996 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1111"},{"id":401569,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1111/coverthb.jpg"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Regional groundwater recharge commonly is estimated using a threshold-type water-budget approach in which groundwater recharge is assumed to occur when water in the plant-root zone exceeds the soil’s moisture storage capacity. A water budget of the plant-soil system accounts for water inputs (rainfall, fog interception, irrigation, septic-system leachate, and other inputs), water outputs (runoff, evaporation, transpiration, and recharge), and changes in stored water during a specified time interval. Water budgets can be computed on any desired interval, including annual, monthly, daily, and subdaily intervals. In general, uncertainty in recharge estimates is expected to be lower using daily or subdaily intervals relative to monthly and annual intervals. Average recharge rates computed over a period of a year or multiple years are commonly determined from water budgets computed using a daily computation interval capable of capturing rainfall and land-cover changes during the period.
This report documents the Water-budget Accounting for Tropical Regions Model, or WATRMod, code that can be used to estimate spatially variable, daily water-budget components in tropical-island and other appropriate settings. The purpose of this report is to provide descriptions of WATRMod’s (1) approach to computing a daily water budget, (2) represented processes, (3) limitations, and (4) execution procedure, input requirements, output files, and example files. The model computes a daily water budget for each hydrologically independent subarea within the overall study area. A subarea is defined by its climatic, soil, land-cover, and human-related (for example, adding irrigation or other water) characteristics. The water-budget model can represent processes including rainfall, fog interception, irrigation, septic-system leachate, direct recharge that bypasses the plant-soil system, runoff, canopy evaporation in forested areas, evapotranspiration, and groundwater recharge. The water-budget model can represent either one of the following different accounting orders: (1) accounting for loss of water by evapotranspiration before accounting for recharge, and (2) accounting for recharge before accounting for evapotranspiration. WATRMod’s limitations include: (1) uncharacterized, subdaily transient changes in water inputs and outputs from the plant-soil system, (2) unrepresented precipitation in the form of snow and sublimation, and (3) routing runoff from one subarea to an adjacent subarea that is not directly represented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221013","usgsCitation":"Oki, D.S., 2022, Water-budget accounting for tropical regions model (WATRMod) documentation: U.S. Geological Survey Open-File Report 2022-1013, 77 p., https://doi.org/10.3133/ofr20221013.","productDescription":"Report: viii, 77 p.; Data Release","numberOfPages":"77","onlineOnly":"Y","ipdsId":"IP-126805","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":501758,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113077.htm","linkFileType":{"id":5,"text":"html"}},{"id":401381,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VPAY41","text":"WATRMod, a Water-budget accounting for tropical regions model—source code, executable file, and example files","description":"Oki, D.S., 2022, WATRMod, a Water-budget accounting for tropical regions model—source code, executable file, and example files: U.S. Geological Survey data release, https://doi.org/10.5066/P9VPAY41."},{"id":401379,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1013/covrthb.jpg"},{"id":401380,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1013/ofr20221013.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1013"}],"country":"United States","state":"Hawaii","otherGeospatial":"Island of Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.3848876953125,\n 20.555652403773365\n ],\n [\n -156.0003662109375,\n 20.6379249854131\n ],\n [\n -155.9454345703125,\n 20.776659051878816\n ],\n [\n -156.26678466796875,\n 20.964004409178308\n ],\n [\n -156.47003173828125,\n 20.925527866647226\n ],\n [\n -156.610107421875,\n 21.056307701901847\n ],\n [\n -156.72271728515625,\n 20.94604992010052\n ],\n [\n -156.67327880859375,\n 20.822875478868443\n ],\n [\n -156.55792236328122,\n 20.761250430919652\n ],\n [\n -156.48651123046875,\n 20.771523019513364\n ],\n [\n -156.4617919921875,\n 20.622502259344817\n ],\n [\n -156.3848876953125,\n 20.555652403773365\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
On January 18, 2022, groundwater levels were measured in selected wells in the Hālawa area, O‘ahu, Hawai‘i, constituting a synoptic groundwater-level survey (shortened herein to “synoptic survey”) of the area. Groundwater levels were measured mainly from 9:00 a.m. to 12:00 p.m. (times listed in Hawai‘i standard time) and provide a snapshot of groundwater levels during the survey period. Following a reported fuel release that affected groundwater quality in the Red Hill area, several production wells were shut down in the weeks prior to the synoptic survey. These wells include the Red Hill Shaft (shut down on November 28, 2021) and the Hālawa Shaft (shut down on December 3, 2021, except for weekly, short-duration operations for water-quality sampling). Groundwater levels measured in wells during the synoptic survey ranged from 16.81 to 20.19 feet above mean sea level. The groundwater levels measured on January 18, 2022, were about 0.3 to 0.6 feet higher than those measured at common sites during a synoptic groundwater-level survey on December 23, 2021.
The groundwater levels collected during the multiagency synoptic survey contain uncertainty because of several potential sources of error associated with (1) the accuracy of the measuring tapes used, (2) the accuracy of the measuring-point altitude at the top of each well, (3) well plumbness and alignment, (4) human error, and (5) changing conditions during the survey period. Because of these potential sources of error, comparability of groundwater-level measurements may be affected. Some of the sources of uncertainty can be addressed and lead to improved accuracy and comparability of the groundwater levels. For example, uncertainty associated with the measuring-point altitudes can be addressed by resurveying measuring-point altitudes to a common vertical datum using consistent surveying methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221048","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, January 18, 2022, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1048, 11 p., https://doi.org/10.3133/ofr20221048.","productDescription":"Report: v, 11 p.; Data Release","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-138445","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":401209,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1048/covrthb.jpg"},{"id":401210,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1048/ofr20221048.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":401211,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the nation","description":"U.S. Geological Survey, 2022, USGS water data for the nation: U.S. Geological Survey National Water Information System database, https://doi.org/10.5066/F7P55KJN."},{"id":401230,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221018","text":"Open-File Report 2022–1018","description":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, December 23, 2021, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1018, 10 p., Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, December 23, 2021, Red Hill synoptic groundwater-level survey, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1018, 10 p., https://doi.org/10.3133/ofr20221018..","linkHelpText":"- December 23, 2021, Red Hill Synoptic Groundwater-Level Survey, Hālawa Area, O‘ahu, Hawai‘i"},{"id":404437,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221069","text":"Open-File Report 2022-1069","description":"Nakama, R.K., Mitchell, J.N., and Oki, D.S., 2022, Groundwater-level monitoring from January 17 to March 3, 2022, Hālawa area, O‘ahu, Hawai‘i: U.S. Geological Survey Open-File Report 2022–1069, 29 p., https://doi.org/10.3133/ofr20221069.","linkHelpText":"- Groundwater-Level Monitoring from January 17 to March 3, 2022, Hālawa Area, O‘ahu, Hawai‘i"},{"id":501778,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113078.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Hālawa Area, O‘ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.97103881835938,\n 21.317522325157526\n ],\n [\n -157.84194946289062,\n 21.317522325157526\n ],\n [\n -157.84194946289062,\n 21.410883719938866\n ],\n [\n -157.97103881835938,\n 21.410883719938866\n ],\n [\n -157.97103881835938,\n 21.317522325157526\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Anthropogenic barriers to main-stem and tributary passage are one of the primary threats associated with declining populations of Pacific Lamprey (Entosphenus tridentatus) in the Columbia River Basin. Juvenile lamprey are of special interest because their downstream migration to the ocean may be affected by barriers such as dams or water diversions. Telemetry studies that describe the movement and passage of juvenile lamprey have not been possible until the recent development of a micro-transmitter specifically for use in juvenile lamprey and eels. Through a collaborative research approach, we used these prototype transmitters and acoustic monitoring arrays installed for a juvenile salmon (Oncorhynchus spp.) migration study to evaluate juvenile lamprey movements in the Yakima River (river kilometer 179 to the river mouth) in 2019 and 2020. We tagged and released 152 juvenile lamprey from April 30 to June 5, 2019, and on June 9, 2020. Lamprey were released 6.9 kilometers (km) upstream from Wapato Dam, 1.2 km upstream from Prosser Dam, and into the canal and tailrace at Prosser Dam. Most tagged lamprey did not initiate downstream movements within the 18 days of tag life, as evidenced by our detections of lamprey in the highest numbers at the first monitoring site downstream from their release site, with limited or no detections at sites farther downstream. There was no evidence of missed detections (lamprey detected at a downstream site without corresponding detections upstream). Overall detections of tagged lamprey were low: 27.0 percent in 2019 and 48.0 percent in 2020. River flows were less than the 10-year average during the monitoring period and water temperatures were variable. Lamprey arrived at detections sites predominantly during periods of darkness (85.3–96.6 percent) following daytime releases. Travel rates through the study area ranged from 0.2 to 45.3 kilometers per day, and lamprey generally remained at each detection station for less than about 20 minutes. Groups of lamprey released together generally had similar travel rates with a small number of fish that moved more quickly or slowly than the remainder of the group. In addition to monitoring the migration and behavior of juvenile lamprey, we also assessed some assumptions of survival models (determining downstream drift of purposely killed fish and empirically measuring transmitter operating life) to benefit future evaluations focused on migration survival of juvenile lamprey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221052","collaboration":"Prepared in cooperation with the Bureau of Reclamation, Yakama Nation Fisheries, McNary Fisheries Compensation Committee, Bonneville Power Administration, and the Pacific Northwest National Laboratory","usgsCitation":"Liedtke, T.L., Lampman, R.T., Monk, P., Hansen, A.C., Kock, T.J., Beals, T.E., Deng, D.Z., and Porter, M.S., 2022, Monitoring the movements of juvenile Pacific Lamprey (Entosphenus tridentatus) in the Yakima River, Washington, using acoustic telemetry, 2019–20: U.S. Geological Survey Open-File Report 2022–1052, 28 p., https://doi.org/10.3133/ofr20221052.","productDescription":"Report: viii, 28 p.; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-133893","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":401158,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1052/images"},{"id":401157,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://app.streamnet.org/files/822/","text":"Pacific States Marine Fisheries Commission, StreamNet—Fish Data for the Northwest data files"},{"id":401156,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1052/ofr20221052.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1052"},{"id":401155,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1052/covrthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.684814453125,\n 46.01985337287631\n ],\n [\n -118.94622802734374,\n 46.01985337287631\n ],\n [\n -118.94622802734374,\n 46.71161922789268\n ],\n [\n -120.684814453125,\n 46.71161922789268\n ],\n [\n -120.684814453125,\n 46.01985337287631\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The delta smelt (Hypomesus transpacificus) is a small, euryhaline fish species endemic to the Sacramento–San Joaquin Delta; it is protected under the U.S. and California Endangered Species Acts, and because of declines in population abundance, the delta smelt may be vulnerable to extinction. The California Department of Water Resources (DWR) is conducting studies to test the viability of using domesticated fish to supplement the wild population of delta smelt. These studies have focused on examining the health and survival of domesticated delta smelt placed inside enclosures (circular cages that are approximately 1.5 meters tall by 1 meter in diameter) into the wild. We completed two parts within this study using underwater cameras inside the enclosures to observe fish behavior and their responses to certain stimuli. In both parts of the study, delta smelt behaviors were broadly categorized into two basic categories: (1) normal and (2) alarm. Normal behavior was characterized as calm, non-polarized, and docile swimming behavior. Alarm behavior was characterized by sudden and rapid darting, polarized frantic swimming activity, and tighter schooling polarization of individuals.
The first part of the study took place in a semi-controlled agricultural pond on the campus of the University of California, Davis. At this agricultural pond, we developed methods of observation and documented how fish behaved in response to enclosure disturbances associated with routine cleaning and service that is required during extended field deployments of the enclosures. We observed that delta smelt behavior changed from normal to alarm at the onset of an enclosure service and from alarm to normal within about 2 minutes after the service ended.
The second part of the study was completed in cooperation with the DWR. In October 2019, DWR deployed three enclosures in the Sacramento River near Rio Vista, California. To monitor survival rate of delta smelt, DWR permitted us to deploy cameras in one enclosure to document the frequency and duration of alarm behaviors exhibited by delta smelt and the frequency, duration, and intensity of three types of disturbances: (1) noise generated from passing boats, (2) noise generated from the enclosure moving in response to wave energy, and (3) vertical movements of the enclosure generated from wave energy. Alarm behaviors averaged about 2 minutes in duration and occurred most frequently during the evening compared to midday or morning. Each disturbance variable exhibited substantial variability in duration and intensity and occurred least frequently during the morning and evening compared to midday. Alarm behaviors appeared to be most associated with high intensity enclosure noises and vertical movements; however, limited replicate samples prohibited developing a statistical relation. Alarm behaviors did not directly contribute to injury or mortality of individual delta smelt; however, indirect or sublethal effects of alarm behaviors were not examined.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221028","collaboration":"Prepared in cooperation with California Department of Water Resources","programNote":"Water Availability and Use Science Program","usgsCitation":"Enos, E., Patton, O., and Feyrer, F., 2022, Underwater videographic observations of domesticated Delta smelt in field enclosures: U.S. Geological Survey Open-File Report 2022–1028, 17 p., https://doi.org/10.3133/ofr20221028.","productDescription":"Report: vii, 17 p.; Data Release","numberOfPages":"17","onlineOnly":"Y","ipdsId":"IP-120423","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":401000,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221028/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Open-File Report 2022-1028"},{"id":400874,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CY39ZG","text":"Underwater videographic observations of cultured Delta Smelt in field enclosures—Video clips and summary data","description":"Enos, E.R., Patton, O.J., and Feyrer, F.V., 2020, Underwater videographic observations of cultured Delta Smelt in field enclosures—Video clips and summary data: U.S. Geological Survey data release, https://doi.org/10.5066/P9CY39ZG."},{"id":400873,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1028/images"},{"id":400872,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1028/ofr20221028.xml"},{"id":400870,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1028/covrthb.jpg"},{"id":400871,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1028/ofr20221028.pdf","text":"Report","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2022-1028"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.1514892578125,\n 37.896530447543\n ],\n [\n -120.311279296875,\n 37.896530447543\n ],\n [\n -120.311279296875,\n 39.01064750994083\n ],\n [\n -122.1514892578125,\n 39.01064750994083\n ],\n [\n -122.1514892578125,\n 37.896530447543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Yuma Ridgway’s rail (Rallus obsoletus yumanensis, hereafter, rail) is an endangered species for which patches of emergent marsh within the Salton Sea watershed comprise a substantial part of habitat for the species’ disjointed range in the southwestern United States. These areas of emergent marsh include (1) marshes managed by federal (particularly the U.S. Fish and Wildlife Service’s Sonny Bono Salton Sea National Wildlife Refuge), state (California Department of Fish and Wildlife), and local (Imperial Irrigation District) resource agencies that are sustained by direct deliveries of Colorado River water and (2) unmanaged marshes sustained by agricultural drainage water. Management of rail habitat in this arid environment is complicated by increasingly limited availability of unimpaired freshwater owing to water management decisions associated with the Quantification Settlement Agreement and risks posed by potentially harmful concentrations of selenium found in agricultural drainage water that can readily bioaccumulate in aquatic food webs.
To provide timely science for managers, herein we report summary statistics for managed and unmanaged emergent marshes sampled at the Salton Sea during the rail breeding season of 2016 pertaining to (1) selenium concentrations in food webs representing dietary pathways of selenium exposure and (2) patterns of rail occupancy and inter-marsh movements, estimated abundance, and regional population size of rail. For selenium-specific objectives, we sampled unfiltered surface water, midge larvae (Chironomidae), water boatmen (Corixidae), mosquitofish (Gambusia spp.), and crayfish (Astacidae). Selenium samples were collected from 15 fixed sampling points, each in managed and unmanaged marshes, during late February, April, and June 2016, which corresponded to rail pre-nesting, nesting, and fledgling reproductive life-stages, respectively. Two areas within the two treatment types (managed versus unmanaged marsh) were of particular interest to help assess risks associated with changing sea dynamics and different water-management strategies: (1) a large unmanaged marsh (Morton Bay) unintentionally created in approximately 2008 when it became separated from the Salton Sea as water inflows began to drop and a berm formed from accumulated sediment and (2) a restored marsh (HZ9A) managed by the Sonny Bono Salton Sea National Wildlife Refuge, which is currently supplied with Colorado River water but may be sustained in the future by a blend of clean (that is, low selenium) Colorado River and agricultural drainage water with higher selenium from the Alamo River. Hence, baseline data for these marshes are important for future management decisions. We also report selenium concentrations in rail blood, head feathers, and breast feathers from rails captured as part of the movement study. Results indicated relatively higher risks from dietary selenium exposure for rails occupying unmanaged marshes compared to managed marshes and similar risks among unmanaged marshes. However, risks also were potentially elevated for rails occupying some managed marshes (that is, the Hazard Marshes), where relatively high proportions of Chironomidae and mosquitofish exceeded dietary thresholds for selenium effects on avian reproduction.
For rail-specific objectives, we quantified occupancy and spatial distribution using call count data analyzed with imperfect detection models. Imperfect detection models allowed us to jointly estimate detection probability and abundance of detected rails in association with habitats. We then used estimates of detection probability and abundance at the habitat level to extrapolate rail population abundance for the Salton Sea region. Inter- and intra-marsh movements were described from over 5,000 locations obtained from 15 radio-marked rails. Resultant space use patterns indicated that, in general, selenium risk to individuals is not equally shared because of high levels of territoriality and very limited movement throughout the landscape. Moreover, the largest contiguous blocks of habitat are associated with unmanaged marshlands located on the former southeastern shoreline and outside traditional management areas and authorities. Thus, a substantial proportion of the rail population that is using unmanaged marsh on the southeastern shoreline may have disproportionate risk of elevated selenium exposure, yet how that risk translates to population-level effects remains unknown.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221045","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Ricca, M.A., Overton, C.T., Anderson, T.W., Merritt, A., Harrity, E., Matchett, E., and Casazza, M.L., 2022, Yuma Ridgway’s rail selenium exposure and occupancy within managed and unmanaged emergent marshes at the Salton Sea: U.S. Geological Survey Open-File Report 2022–1045, 49 p., https://doi.org/10.3133/ofr20221045.","productDescription":"Report: x, 49 p.; 2 Data Releases","numberOfPages":"49","onlineOnly":"Y","ipdsId":"IP-115651","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":400780,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1045/ofr20221045.xml"},{"id":501775,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113059.htm","linkFileType":{"id":5,"text":"html"}},{"id":400770,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R39F33","text":"Selenium concentrations in Yuma Ridgway's Rails occupying managed and unmanaged emergent marshes at the Salton Sea","description":"Ricca, M.A, Overton, C.T., Anderson, T.W., Merritt, A., Harrity, E. Matchett, E., and Casazza, M.L., 2022, Selenium concentrations in Yuma Ridgway’s Rails occupying managed and unmanaged emergent marshes at the Salton Sea: U.S. Geological Survey data release, https://doi.org/10.5066/P9R39F33."},{"id":400769,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JRP0L6","text":"Yuma Ridgway’s Rail (Rallus obsoletus yumanensis) Population Surveys, Rail Movement, and Potential Habitat at the Salton Sea of California","description":"Overton, C.T., Ricca, M.A., Anderson, T.W., Merritt, A.M., Harrity, E., Matchett, E.L., Casazza, M.L., 2022, Yuma Ridgway’s rail (Rallus obsoletus yumanensis) population surveys, rail movement, and potential habitat at the Salton Sea of California: U.S. Geological Survey data release, https://doi.org/10.5066/P9JRP0L6."},{"id":400768,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1045/images"},{"id":400767,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221045/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400766,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1045/ofr20221045.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":400765,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1045/covrthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Salton Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.6365966796875,\n 33.128351191631566\n ],\n [\n -115.51849365234374,\n 33.128351191631566\n ],\n [\n -115.51849365234374,\n 33.30298618122413\n ],\n [\n -115.6365966796875,\n 33.30298618122413\n ],\n [\n -115.6365966796875,\n 33.128351191631566\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The U.S. Army Corps of Engineers, Jacksonville District, is deepening the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the fifth annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project. The report contains information pertinent to data collection during the 2020 water year, from October 2019 to September 2020. The addition of water-quality data collection at St. Johns River at Buffalo Bluff near Satsuma was the only modification to the previously installed network.
Discharge and salinity varied widely during the data collection period, which included above-average rainfall for 3 of the 5 counties in the study area. Total annual rainfall for all counties ranked third among the annual totals computed for the 5 years considered for this study. Annual mean discharge at Clapboard Creek was highest among the tributaries, followed by Ortega River, Durbin Creek, Pottsburg Creek at U.S. 90, Cedar River, Trout River, Julington Creek, Pottsburg Creek near South Jacksonville, Dunn Creek, and Broward River, whose annual mean was lowest. Annual mean discharge at 8 of the 10 tributary monitoring sites was higher for the 2020 water year than for the 2019 water year, and the computed annual mean flow at Clapboard Creek was the highest over the 5 years considered for this study. The annual mean discharge for each of the main-stem sites was higher for the 2020 water year than for the 2019 water year except for Buffalo Bluff, which remained the same.
Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and was lowest at Durbin Creek, the site farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which was expected. Relative to annual mean salinity calculated for the 2019 water year, annual mean salinity at all monitoring locations was higher for the 2020 water year except at the tributary sites of Trout River, Dunn Creek, and Clapboard Creek, which were lower, and Durbin Creek, which remained the same. The 2020 annual mean salinity on the main-stem of the St. Johns River was the highest since the beginning of the study in 2016 at Dancy Point, Racy Point, Shands Bridge, below Shands Bridge, above Buckman Bridge, and Jacksonville (Acosta Bridge). Among the tributary sites, annual mean salinity rankings for 2020 were highest for Julington Creek and Ortega River, which were the second-highest on record for those sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221024","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2022, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2020: U.S. Geological Survey Open-File Report 2022–1024, 48 p., https://doi.org/10.3133/ofr20221024.","productDescription":"Report: ix, 48 p.; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-133884","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":400657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1024/coverthb.jpg"},{"id":400658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1024/ofr20221024.pdf","text":"Report","size":"3.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1024"},{"id":400659,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1024/ofr20221024.XML"},{"id":400660,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1024/images"},{"id":400661,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":401171,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221024/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":501767,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113057.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","otherGeospatial":"St. Johns River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.27935791015625,\n 29.14736383122664\n ],\n [\n -80.38970947265625,\n 29.14736383122664\n ],\n [\n -80.38970947265625,\n 30.56226095049944\n ],\n [\n -82.27935791015625,\n 30.56226095049944\n ],\n [\n -82.27935791015625,\n 29.14736383122664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
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A cooperative study between the National Park Service (NPS) and the U.S. Geological Survey (USGS) characterized groundwater quality and hydrogeology in parts of Pinnacles National Park. The water-quality investigation assessed the geochemistry of springs, wells, surface water, and precipitation and analyzed geochemistry of rock formations that affect the water chemistry through water-rock interaction. The hydrogeology investigation used geophysical and groundwater level data to characterize groundwater-flow processes in the alluvial deposits of Bear Valley and the Chalone Creek watershed.
Analysis of aqueous geochemical parameters in water samples from perennial springs, water-supply wells, and surface waters was conducted for samples collected after the dry season (autumnal) and after the wet season (vernal) to assess changes in geochemistry due to changes in groundwater levels or flow resulting from precipitation. The chemistry of bulk precipitation collected during the wet season was also analyzed. Bedrock samples were analyzed for geochemical parameters to help constrain groundwater sources, flow paths, and weathering. The geochemical investigations show a correspondence between the source rock and the spring-water chemistry that can be attributed to the mineralogy of the source rock. The narrow range of strontium isotopes in water samples, sourced in geochemically and mineralogically disparate rocks, indicates that the bedrock groundwater is relatively old and has reached quasi-steady state with respect to weathering of susceptible minerals.
Groundwater-level monitoring indicated that the water table is shallow—from 0 to 10 meters (m) below land surface. In southern Bear Valley and in the Chalone Creek alluvium, water levels rose and declined by several meters over each annual cycle of this study. In northern Bear Valley, water levels rose modestly over two wet seasons but declined during a third wet season. In Bear Valley, groundwater/surface-water interaction occurs along the perennial reach of Sandy Creek. Groundwater discharges to the upstream part of the reach, becomes surface water and is partly consumed by evapotranspiration, and infiltrates farther downstream. In the Chalone Creek alluvium, runoff-generated surface-water flow in intermittent stream reaches is a major component of groundwater recharge. After the onset of significant streamflow, creek water rapidly recharges groundwater until water levels rise to nearly the creek level. Groundwater levels generally remain high throughout the wet season, then gradually decline after the creek becomes dry.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221026","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Scheiderich, K., Tiedeman, C.R., Hsieh, P.A., 2022, Aqueous geochemistry of waters and hydrogeology of alluvial deposits, Pinnacles National Park, California: U.S. Geological Survey Open-File Report 2022-1026, 39 p., https://doi.org/10.3133/ofr20221026.","productDescription":"Report: viii, 39 p.; 3 Data Releases","numberOfPages":"39","onlineOnly":"Y","ipdsId":"IP-129434","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":400733,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IZXRC0","text":"Streamflow data collected by the wading method, Pinnacles National Park, California, 2018","description":"Tiedeman, C.R., Ingebritsen, S.E., and Hsieh, P.A., 2021, Streamflow data collected by the wading method, Pinnacles National Park, California, 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9IZXRC0."},{"id":400732,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AMDH71","text":"Passive Seismic Data Collected for the Horizontal-to-Vertical Spectral Ratio (HVSR) Method, Pinnacles National Park, California, 2018-2020","description":"Tiedeman, C.R., and Hsieh, P.A., 2021, Passive Seismic Data Collected for the Horizontal-to-Vertical Spectral Ratio (HVSR) Method, Pinnacles National Park, California, 2018-2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9AMDH71."},{"id":400435,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1026/covrthb.jpg"},{"id":400731,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BMM0XG","text":"Geochemistry of rocks, precipitation, and water sources from Pinnacles National Park, California, 2016-2017","description":"Scheiderich, K.D., 2021, Geochemistry of rocks, precipitation, and water sources from Pinnacles National Park, California, 2016-2017: U.S. Geological Survey data release, https://doi.org/10.5066/P9BMM0XG."},{"id":400436,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1026/ofr20221026.pdf","text":"Report","size":"8 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"Pinnacles National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.20666503906249,\n 36.4729263733008\n ],\n [\n -121.2070083618164,\n 36.458430880233415\n ],\n [\n -121.20237350463867,\n 36.45856894533104\n ],\n [\n -121.20254516601562,\n 36.444761218880885\n ],\n [\n -121.2070083618164,\n 36.44448503928196\n ],\n [\n -121.20769500732422,\n 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WMA- Laboratory & Analytical Services Division
U.S. Geological Survey
USGS Headquarters
12201 Sunrise Valley Drive
Reston, VA 20192
Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
A study was conducted to compile and evaluate data used to identify groundwater sources that are under the direct influence of surface water (GUDI) in Pennsylvania. In the early 1990s, the Pennsylvania Department of Environmental Protection (PADEP) implemented the Surface Water Identification Protocol (SWIP) for the identification of GUDI sources. Since the establishment of the SWIP, PADEP has classified more than 500 individual sources across Pennsylvania as GUDI, but Pennsylvania’s complex geology and physiography provide a challenge for a uniform method of GUDI determination. Components used in this study to compile and evaluate data associated with GUDI determination include: (1) a preliminary review of file information for 43 public water-supply wells, (2) quality control and addition of data to PADEP’s database for public water-supply systems to prepare data for analysis, and (3) exploratory evaluation of existing GUDI sources in the database with respect to hydrogeologic and source-construction characteristics that are currently utilized in the assessment methodology.
Case files for 43 wells from PADEP’s Northcentral and Southcentral regions were reviewed to: (1) provide a better understanding of how the SWIP was applied in practice, (2) verify and compile missing data, and (3) find additional attributes not previously available that might explain a well’s categorization as GUDI. Review of file information showed that the SWIP outlined in PADEP technical guidance was usually followed, but for some sources, the GUDI determination was more complex and could not be easily summarized.
Data compiled for study analyses provided by PADEP include source data derived from public water-supply system case files, a source-information database for public water-supply systems, and Microscopic Particulate Analysis (MPA) results and associated water-quality data for public water-supply system groundwater sources. Data from the Pennsylvania Drinking Water Information System (PADWIS), which is PADEP’s database for public water-supply systems, were also used for this study. The PADWIS database originally included data for 12,147 groundwater sources (11,812 groundwater sources not under the direct influence of surface water (non-GUDI) wells and 335 GUDI wells). A subset (4,018 wells consisting of 3,842 non-GUDI wells and 175 GUDI wells) of the PADWIS database was created for an analysis and includes only community wells evaluated in accordance with the SWIP. MPA results for 631 community and noncommunity wells were compiled, along with associated water-quality data (alkalinity, chloride, Escherichia coli, fecal coliform, nitrate, pH, sodium, specific conductance, sulfate, total coliform, total dissolved solids, total residue, and turbidity) populated from the PADEP Bureau of Laboratories Sample Information System. Data compiled from sources other than PADEP include spatial data, both naturogenic (for example, average precipitation or distance to closest hydrologic feature) and anthropogenic (for example, percentage of developed or agricultural land cover within a specific vicinity of a public water-supply system well) data representing spatially derived variables.
Comparison among wells in the PADWIS dataset subset using the nonparametric Kruskal-Wallis test showed that GUDI wells had significantly older median construction years, shallower depths, and static water levels closer to the land surface than non-GUDI wells and that carbonate aquifers had the highest percentages of wells designated as GUDI (12 percent; 57 wells). Further comparison of wells in the PADWIS database subset using the Spearman’s rho monotonic correlation test illustrated that public water-supply wells designated as GUDI largely occur in unconfined aquifers and have high average yield and shallow static water levels. Assessment of the MPA database subset using the Kruskal-Wallis test showed wells with MPA total risk-factor scores that exceeded zero had older median construction years and shallower casing depths than wells with MPA total risk-factor scores of zero and that carbonate aquifers had the highest percentages of wells with MPA total risk-factor scores exceeding zero (30 percent; 63 wells). Spearman’s rho correlations showed that wells completed in aquifers with depths to major water-bearing zones closer to the land-surface had higher total risk-factor scores resulting from MPA samples.
Based on the results of the analyses described in this report, broad conclusions can be drawn regarding site-specific well characteristics as well as anthropogenic and naturogenic factors that could be responsible for a well being designated as GUDI, but the accuracy of these results is dependent on the quality of the data being analyzed. Ultimately, study results serve as an added resource for initial desktop screening of wells to determine if additional site-specific investigation is warranted and underscore the need for field evaluation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221023","collaboration":"Prepared in cooperation with the Pennsylvania Department of Environmental Protection, Bureau of Safe Drinking Water","usgsCitation":"Gross, E.L., Conlon, M.D., Risser, D.W., and Reisch, C.E., 2022, Compilation and evaluation of data used to identify groundwater sources under the direct influence of surface water in Pennsylvania (ver. 2.0, June 2023): U.S. Geological Survey Open-File Report 2022–1023, 41 p., https://doi.org/10.3133/ofr20221023.","productDescription":"Report: viii, 38 p.; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101611","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":417972,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2022/1023/versionHist.txt","size":"49.8 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\"}}]}","edition":"Version 1.0: May 2022; Version 2.0: June 2023","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Collection of juvenile salmonids at Round Butte Dam is a critical part of the effort to enhance populations of anadromous fish species in the upper Deschutes River because fish that are not collected at the dam may either incur increased mortality during dam passage or remain landlocked and lost to the anadromous fish population. Adaptive resolution imaging sonar systems were used to assess the behavior, abundance, and timing of fish at the entrance to the Selective Water Withdrawal (SWW) intake and fish collection structure located in the forebay of Round Butte Dam during the spring of 2020. The purpose of the SWW is to direct surface currents in the forebay to attract and collect downriver migrating juvenile salmonid smolts (Chinook salmon [Oncorhynchus tshawytscha], sockeye salmon [O. nerka], and steelhead [O. mykiss]) from Lake Billy Chinook and to enable operators of the SWW to withdraw water from surface and benthic elevations in the reservoir to manage downriver water temperatures. The objective of this study was to assess the abundance and behaviors of smolt-size fish (95–300 millimeters) observed near the SWW and to determine if the presence of bull trout (Salvelinus confluentus; >350 millimeters), the predominant predator of juvenile salmonids, influenced the behavior of downriver migrants.
Two imaging sonar units were deployed during the spring of 2020 smolt out-migration period. One unit monitored fish movements near the entrances and one unit monitored in one of the collection flumes of the SWW. The imaging sonar technology was informative for assessing abundance and spatial and temporal behaviors of smolt and bull trout-size fish. Smolt and bull trout-size fish were regularly observed near the entrance to and in the collection flume. Increased abundances were observed during the night, with corresponding increased discharge through the SWW, compared to during the day when discharge was reduced. Behavioral differences also were observed at different discharge rates, with smolt-size fish exhibiting more directed movement toward the collector during periods of increased discharge. Additionally, the presence of bull trout-size fish may have affected the behavior of smolt-size fish because a greater percentage of smolt-size fish were observed traveling away from the SWW when bull trout-size fish were present than when bull trout-size fish were absent. Increased counts of bull trout-size fish coincided with the increased abundances of smolt-size fish. Overall, the results indicate that smolt-size fish are more abundant near the entrance and in the flume of the SWW during periods of increased discharge, and bull trout-size fish were present at the SWW and may have affected smolt collection.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221038","collaboration":"Prepared in cooperation with Portland General Electric","usgsCitation":"Smith, C.D., Hatton, T.W., and Adams, N.S., 2022, Monitoring fish abundance and behavior, using multi-beam acoustic imaging sonar, at a Selective Water Withdrawal structure in Lake Billy Chinook, Deschutes River, Oregon, 2020: U.S. Geological Survey Open-File Report 2022–1038, 31 p., https://doi.org/10.3133/ofr20221038.","productDescription":"viii, 31 p.","onlineOnly":"Y","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":400087,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1038/coverthb.jpg"},{"id":400088,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1038/ofr20221038.pdf","text":"Report","size":"12.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1038"},{"id":400089,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1038/images"},{"id":400090,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1038/ofr20221038.XML"}],"country":"United States","state":"Oregon","otherGeospatial":"Lake Billy Chinook, Round Butte Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.56990051269533,\n 44.43623132529392\n ],\n [\n -121.07414245605469,\n 44.43623132529392\n ],\n [\n -121.07414245605469,\n 44.73454012555642\n ],\n [\n -121.56990051269533,\n 44.73454012555642\n ],\n [\n -121.56990051269533,\n 44.43623132529392\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
The Kalispel Tribe of Indians (KT), U. S. Fish and Wildlife Service, and Washington Department of Fish and Wildlife are engaged in conservation of bull trout (Salvelinus confluentus) in the Lake Pend Oreille (LPO) Core Area. The LPO is a complex habitat core area which falls within three states (Montana, Idaho, and Washington) and a tribal entity. As part of the conservation process, KT worked in cooperation with the U. S. Geological Survey (USGS) to complete a risk assessment for introduction of bull trout into Sullivan Lake/Harvey Creek, northeastern, Washington. The risk assessment was designed to evaluate potential risks to resident fish species, to bull trout introduced into Sullivan Lake, and to bull trout donor source populations. This risk assessment describes the potential risks associated with pathogens (introduction of pathogens and increased pathogen burden), genetics (such as risk to donor sources, straying and breeding with native bull trout, and introduction of bull-brook hybrids), and ecological interactions (such as predation and competition). Potential donor source populations were identified and evaluated using a qualitative approach based on expert opinion and a decision framework.
Literature reviews were completed for fish species composition and abundance in Sullivan Lake basin to assess potential ecological interactions and risks to these populations and to the introduced bull trout. The USGS assessed pathogen risks through two major questions: (1) whether introduced bull trout might bring pathogens into the Sullivan Lake basin that were not previously present and (2) whether the health of introduced bull trout could be adversely affected by pathogens already present in the basin. Assessment of genetic risks included demographic risks to donor source populations, potential for hybridization with native bull trout, and the risk of introducing bull-brook hybrids. Literature reviews were used in conjunction with discussions among regional biologists to identify potential donor source populations and their population attributes. A decision framework was developed by USGS in collaboration with KT biologists that identified desirable population attributes (life history behavior, abundance, population viability, feasibility of collection, and environmental match) associated with donor source populations and established ranking criteria. The population attribute information was used with the (1) decision framework, (2) established ranking criteria, and (3) expert opinion of regional biologists, to assign scores for overall ranking of donor source populations.
The LPO source population was the highest ranked and is considered a robust and stable population. The risk of introducing pathogens from LPO into Sullivan Lake via a bull trout introduction program seems low, and indirect pathogen burden risks to resident species can be mitigated using established pathogen surveillance methods. The likelihood that bull trout, introduced into Sullivan Lake, stray and spawn with native bull trout is low. Nearest-neighbor donor source populations, such as LPO, could minimize negative fitness impacts that might occur from straying and interbreeding of individuals that become entrained and help maintain natural patterns of genetic diversity in native populations. The ecological risk that a bull trout introduction presents to resident species seems to be low but with some uncertainty. Pygmy whitefish, a Washington State Sensitive species, is likely most vulnerable to extirpation with increased predation pressure with introduction of an additional piscivore into the ecosystem. The status of the pygmy whitefish in Sullivan Lake is unknown. The ecological risks most likely to reduce the viability of introduced bull trout are predation by burbot and an adequate forage base in Sullivan Lake. Prior fish surveys provided data on resident species abundance, provided an established baseline for effective monitoring, and identifying ecosystem changes post-bull trout introduction to inform future adaptive management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221032","collaboration":"Prepared in cooperation with Kalispel Tribe of Indians","usgsCitation":"Hardiman, J.M., Breyta, R.B., and Ostberg, C.O., 2022, Risk assessment for bull trout introduction into Sullivan Lake and Harvey Creek, northeastern Washington: U.S. Geological Survey Open-File Report 2022–1032, 26 p., https://doi.org/10.3133/ofr20221032.","productDescription":"Report: vii, 26 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-128540","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":400079,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1032/images"},{"id":400080,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1032/ofr20221032.XML"},{"id":400074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1032/coverthb.jpg"},{"id":400075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1032/ofr20221032.pdf","text":"Report","size":"8.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1032"},{"id":400076,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCQEZ1","text":"USGS data release","description":"USGS data release","linkHelpText":"Information tables associated with a risk assessment for bull trout introduction into Sullivan Lake, northeastern, Washington including population donor sources and resident species, April 2021"}],"country":"United States","state":"Washington","otherGeospatial":"Harvey Creek, Sullivan Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.49191284179688,\n 48.67736049788919\n ],\n [\n -117.03598022460938,\n 48.67736049788919\n ],\n [\n -117.03598022460938,\n 48.89632393659644\n ],\n [\n -117.49191284179688,\n 48.89632393659644\n ],\n [\n -117.49191284179688,\n 48.67736049788919\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Bull trout (Salvelinus confluentus) in the Klamath Basin are on the southernmost border of the range of the species, where threats are most severe and where bull trout are most imperiled. In their recovery plan the U.S. Fish and Wildlife Service (2015, https://ecos.fws.gov/ecp/report/species-with-recovery-plans) suggested that Klamath Basin bull trout are at increased risk of extirpation due to habitat fragmentation, degradation of habitat complexity, and introduction of non-native trout species that often outcompete bull trout. The goals of this study were to determine if there was a lack of connectivity between habitat areas impeding migration, habitat differences, or interference by non-native species affecting bull trout distribution in the Klamath Basin. This study examined three populations of bull trout in conjunction with a concurrent native species (redband trout [Oncorhynchus mykiss gairdnerii]), and a concurrent non-native species (brown trout [Salmo trutta]) in tributaries of the upper Sprague River within the Klamath Basin. Culverts present at the beginning of the study may have impeded migration of bull trout, but culvert upgrades made during the study appeared to eliminate the impediments to migration. The presence of non-native brown trout appeared to cause bull trout to use a smaller portion of Leonard Creek, whereas the low numbers of brown trout in the studied portion of Brownsworth Creek did not appear to interfere with the local distribution of bull trout. Downstream migration of bull trout may have been impeded if there were increased numbers of brown trout or increased temperatures in the lower portions of the creeks outside of the study area. Although habitat complexity was not examined in detail during this study, there was an attempt to enhance the habitat for bull trout by introducing large woody debris into treatment sections of the creeks. We compared bull trout numbers between the treatment sections and nearby control sections prior to and after introduction of the large woody debris. The introduction of large woody debris did not appear to enhance the use of those areas by bull trout, but the large woody debris may not have been of suitable size to enhance the habitat for bull trout.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221022","usgsCitation":"Martin, B.A., Banish, N., Hewitt, D.A., Hayes, B.S., Dolan-Caret, A., Harris, A.C., and Kelsey, C., 2022, Distribution of bull trout (Salvelinus confluentus) in conjunction with habitat and trout assemblages in creeks within the Klamath Basin, Oregon 2010–16: U.S. Geological Survey Open-File Report 2022–1022, 28 p., https://doi.org/10.3133/ofr20221022.","productDescription":"vi, 28 p.","onlineOnly":"Y","ipdsId":"IP-133280","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":399925,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1022/ofr20221022.pdf","text":"Report","size":"7.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1022"},{"id":399924,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1022/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Klamath Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.80517578125,\n 42.00848901572399\n ],\n [\n -120.838623046875,\n 42.00848901572399\n ],\n [\n -120.838623046875,\n 42.924251753870685\n ],\n [\n -122.80517578125,\n 42.924251753870685\n ],\n [\n -122.80517578125,\n 42.00848901572399\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Large lahars pose substantial threats to people and property downstream from Mount Rainier volcano in Washington State. Geologic evidence indicates that these threats exist even during the absence of volcanic activity and that the threats are highest in the densely populated Puyallup and Nisqually River valleys on the west side of the volcano. However, the precise character of these threats can be difficult to anticipate.
To help predict depths and rates of possible lahar inundation in the area, this report presents the results of simulations of hypothetical future lahars that originate high on the west side of Mount Rainier and travel downstream into the Puyallup and Nisqually River valleys. Many of the results portrayed as still images in the figures of this report are also available as animated files that can be accessed at the web address provided in the figure captions. We simulated eight scenarios, including worst-case scenarios in which the simulated lahars are similar in size and mobility to the approximately 260 million cubic meter (Mm3; 340 million cubic yard) Electron Mudflow lahar that descended from Mount Rainier and inundated the Puyallup River valley about 500 years ago. The other six scenarios place the worst-case scenarios in perspective by simulating lahars that originate from the same source areas but have smaller volumes or lesser mobilities.
We perform our simulations using an open-source software package that we developed called D-Claw. The numerical model composing the kernel of D-Claw solves a system of five hyperbolic partial differential equations that describe the depth-averaged dynamics of static or flowing grain-fluid mixtures interacting with three-dimensional topography. In D-Claw, the volume fraction occupied by solid grains is a dependent variable that can freely evolve, enabling simulation of landslide liquefaction and of lahar interaction with static bodies of water. The latter feature facilitates a seamless simulation of a lahar in the Nisqually River valley entering Alder Lake reservoir.
In the event of an approximately 260 Mm3 high-mobility lahar originating on the west side of Mount Rainier, our results point to two areas of pronounced hazard. One area, comprising the densely populated lowlands of Orting, Washington, and environs, could be inundated by lahars originating from either the Sunset Amphitheater or Tahoma Glacier headwall areas. In the worst-case scenario we consider for the Orting lowlands, which involves a 260 Mm3 high-mobility lahar originating from a landslide in the Sunset Amphitheater, a flow front approximately 4 meters deep and traveling about 4 meters per second reaches the Orting lowlands about 1 hour after the onset of slope failure. After passing through the Orting lowlands, the simulated lahar slows down and comes to rest in the valleys surrounding Sumner and Puyallup. A second area of pronounced hazard is the stretch of the Nisqually River valley beginning in Mount Rainier National Park and extending downstream to Alder Lake reservoir and Alder Dam. This area would be substantially affected in the worst-case scenario that involves a 260 Mm3 high-mobility lahar originating from the Tahoma Glacier headwall area—the locality identified by a previous study as the sector of Mount Rainier most prone to large-scale gravitational collapse. The simulated lahar passes through the area of Ashford, Washington, within about 20 minutes of the onset of slope failure and reaches the head of Alder Lake within about 50 minutes. The lahar ultimately displaces enough reservoir water to cause overtopping of the 100 meter (330 foot) tall Alder Dam, but consequences of such dam overtopping are not addressed in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211118","usgsCitation":"George, D.L., Iverson, R.M., and Cannon, C.M., 2022, Modeling the dynamics of lahars that originate as landslides on the west side of Mount Rainier, Washington: U.S. Geological Survey Open-File Report 2021–1118, 54 p., https://doi.org/10.3133/ofr20211118.","productDescription":"Report: vii, 54 p.;16 Companion Files","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-123581","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":399834,"rank":18,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig27.gif","text":"Supplemental animation for figure 27","size":"5 MB gif"},{"id":399833,"rank":17,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig26(T-52-HM).gif","text":"Supplemental animation for figure 26 (T-52-HM)","size":"24 MB gif"},{"id":399832,"rank":16,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig26(S-52-HM).gif","text":"Supplemental animation for figure 26 (S-52-HM)","size":"25 MB gif"},{"id":399831,"rank":15,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig25.gif","text":"Supplemental animation for figure 25","size":"1 MB gif"},{"id":399830,"rank":14,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig24.gif","text":"Supplemental animation for figure 24","size":"1 MB gif"},{"id":399828,"rank":12,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig22.gif","text":"Supplemental animation for figure 22","size":"6 MB gif"},{"id":399824,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig16.gif","text":"Supplemental animation for figure 16","size":"31 MB gif"},{"id":399823,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig14.gif","text":"Supplemental animation for figure 14","size":"6 MB gif"},{"id":399822,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig12.gif","text":"Supplemental animation for figure 12","size":"5 MB gif"},{"id":399821,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig11.gif","text":"Supplemental animation for figure 11","size":"4 MB gif"},{"id":399820,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig09.gif","text":"Supplemental animation for figure 09","size":"25 MB gif"},{"id":399819,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supplemental_animations.zip","text":"Supplemental animation files","size":"190 MB","linkFileType":{"id":6,"text":"zip"},"description":"Zip file containing all supplemental files","linkHelpText":"- Zip file containing all supplemental files."},{"id":399818,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118.pdf","text":"Report","size":"25 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":399817,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1118/covrthb.jpg"},{"id":501533,"rank":20,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112955.htm","linkFileType":{"id":5,"text":"html"}},{"id":399835,"rank":19,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig28.gif","text":"Supplemental animation for figure 28","size":"3 MB gif"},{"id":399827,"rank":11,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig21.gif","text":"Supplemental animation for figure 21","size":"12 MB gif"},{"id":399826,"rank":10,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig20.gif","text":"Supplemental animation for figure 20","size":"8 MB gif"},{"id":399825,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig18.gif","text":"Supplemental animation for figure 18","size":"7 MB gif"},{"id":399829,"rank":13,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1118/ofr20211118_supAni_fig23.gif","text":"Supplemental animation for figure 23","size":"36 MB gif"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Rainier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.91940307617188,\n 46.70031853924921\n ],\n [\n -121.51565551757812,\n 46.70031853924921\n ],\n [\n -121.51565551757812,\n 46.9980510299792\n ],\n [\n -121.91940307617188,\n 46.9980510299792\n ],\n [\n -121.91940307617188,\n 46.70031853924921\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center
Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683
Freshwater aquatic macroinvertebrates are key links in food webs and nutrient cycles, and thus often serve as biological indicators of ecosystem health. Macroinvertebrate investigations in research and monitoring require consistent and reliable field and laboratory procedures. Comprehensive standard operating procedures for sampling macroinvertebrates from depressional wetlands, which can range from riverine floodplain lakes to wetlands of any size and hydrologic regime, remain relatively sparse. This report provides step-by-step protocols for efficient use of time and resources while collecting and processing aquatic macroinvertebrate samples; for example, a single wetland can typically be field surveyed in less than 1 hour, and the samples can be processed in the laboratory in less than 2 hours. Samples can be collected from inside a motorboat or canoe or while wading. This procedures manual describes dip netting to collect macroinvertebrates from the wetland bottom and water column separately to facilitate investigations of habitat use by species occupying different areas of the wetland. This report also provides descriptive supplemental materials and data sheets to assist with the preparation of survey maps, the acquisition of field and laboratory equipment, and the calculation of macroinvertebrate densities from the wetland bottom and water column. These procedures can be applied to most macroinvertebrate species and communities that inhabit a variety of wetland sizes and types. Uses and applications can range from elementary and secondary environmental education to rigorous scientific evaluations of community abundance, diversity, distribution, or species-habitat relations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221029","collaboration":"Prepared in collaboration with Minnesota Department of Natural Resources and Bemidji State University","usgsCitation":"Keith, B.R., Carleen, J.D., Larson, D.M., Anteau, M.J., and Fitzpatrick, M.J., 2022, Protocols for collecting and processing macroinvertebrates from the benthos and water column in depressional wetlands: U.S. Geological Survey Open-File Report 2022–1029, 22 p., https://doi.org/10.3133/ofr20221029.","productDescription":"vi, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-127838","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":399709,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221029/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":399703,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1029/ofr20221029.pdf","text":"Report","size":"4.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1029"},{"id":399702,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1029/coverthb.jpg"},{"id":399705,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1029/images"},{"id":399704,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1029/ofr20221029.XML"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
This report addresses system characterization of the Italian Space Agency’s PRecursore IperSpettrale della Missione Applicativa (PRISMA) and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (band to band and image to image), radiometric, and spatial performances. Results of these analyses indicate that PRISMA has a band-to-band geometric performance in the range of −0.046 to 0.040 pixel; an image-to-image geometric performance (relative to the Landsat 8 Operational Land Imager) in the range of −60.791 meters (m; −2.03 pixels) to 299.541 m (9.98 pixels); a radiometric performance in the range of −0.037 to −0.001 in offset and 1.026 to 1.274 in slope; and a spatial performance with a relative edge response in the range of 0.56 to 0.63, full width at half maximum in the range of 1.84 to 1.97 pixels, and a modulation transfer function at a Nyquist frequency in the range of 0.054 to 0.096. Regarding fairly large geometric accuracy, the following explanation is provided to help the reader. The geometric accuracy required for PRISMA is a 200-m circular error at 90 percent (CE90) without ground control points (GCPs), a 15-m CE90 using GCPs is documented in the PRISMA mission overview (Agenzia Spaziale Italiana, 2021). The PRISMA images used for the current system characterization were georeferenced without using any GCPs; thus, the 200-m geometric accuracy requirement is applied. Beginning in 2022, a worldwide GCP database will be used in the PRISMA product processing chain, which will improve georeferencing accuracy to meet the 15-m CE90 requirement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030K","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2022, System characterization report on PRecursore IperSpettrale della Missione Applicativa (PRISMA), chap. K of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030K.","productDescription":"iv, 28 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-129829","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":398958,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/k/coverthb.jpg"},{"id":398959,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/k/ofr20211030k.pdf","text":"Report","size":"14.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1030-K"},{"id":398960,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1030/k/ofr20211030k.XML"},{"id":398961,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1030/k/images"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Groundwater is an important source of water for New Mexico. An estimated 48 percent of the total water used comes from groundwater sources, and groundwater levels generally are declining over large areas of New Mexico. Groundwater levels are affected by local and regional recharge or discharge processes. Groundwater hydrographs show the history of groundwater-level changes at a well. A single hydrograph is not necessarily representative of the larger regional area; however, individual hydrographs from several wells can be combined into a composite hydrograph to show average groundwater changes for a regional area. The U.S. Geological Survey, in cooperation with the New Mexico Office of the State Engineer, has been measuring groundwater levels in a network of wells since about 1925. Although groundwater levels in the statewide well network have been measured at various frequencies, most wells have been measured in 5-year cycles since about 1980. The composite hydrographs in this report were developed to show groundwater-level changes for selected principal aquifers in New Mexico. Composite hydrographs were developed using wells in the Colorado Plateaus aquifers, the High Plains aquifer, the Pecos River Basin alluvial aquifer, the Rio Grande aquifer system, and the Roswell Basin aquifer system. Statewide, groundwater levels generally have declined or remained steady over the time period in aquifers analyzed for this study. The largest water-level declines occurred in the Colorado Plateaus and High Plains aquifers and in the Rio Grande aquifer system (north-central New Mexico), where median water-level declines ranged from 17 to 40 feet and mean water-level declines ranged from 3.8 to 32 feet. Groundwater-level declines (or rises) were generally smaller in other areas of New Mexico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221008","collaboration":"Prepared in cooperation with the New Mexico Office of the State Engineer","usgsCitation":"Myers, N.C., 2022, Composite regional groundwater hydrographs for selected principal aquifers in New Mexico, 1980–2019: U.S. Geological Survey Open-File Report 2022–1008, 51 p., https://doi.org/10.3133/ofr20221008.","productDescription":"Report: vii, 51 p.; Data Release; Dataset","numberOfPages":"64","onlineOnly":"Y","ipdsId":"IP-128607","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":501755,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112848.htm","linkFileType":{"id":5,"text":"html"}},{"id":397979,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System 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Mexico\",\"nation\":\"USA \"}}]}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
This report summarizes the historical development and operations, from 2002 to 2020, of the U.S. Geological Survey’s (USGS) hydrologic monitoring and investigative programs at the Idaho National Laboratory in cooperation with the U.S. Department of Energy. The report covers the USGS’s programs for water-level monitoring, water-quality sampling, geochemical studies, geophysical logging, geologic framework development, groundwater-flow modeling, drilling, surface-water monitoring, and unsaturated zone studies. The report provides physical information about wells, information about changes and frequencies of sampling and measurements, and management decisions for changes. Brief summaries of USGS reports published from 2002 through 2020 (with U.S. Department of Energy report numbers) are provided in an appendix.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221027","collaboration":"DOE/ID-22256Director , Idaho Water Science Center
U.S. Geological Survey
230 Collins Rd
Boise, Idaho 83702-4520
The global food-security-support-analysis data at 30-meter resolution (GFSAD30) cropland-extent product is a project to provide high-resolution global cropland-extent data relating to water use. It is the first global-land-cover map focusing exclusively on agriculture with a 30-meter spatial resolution. The overarching goal of the GFSAD30 project is to produce consistent and unbiased estimates of global agricultural cropland products such as cropland extent; cropland types; irrigated versus rainfed cropland; cropping intensities; and spatial and temporal (from 2000 to 2017) changes in cropland extent.
The goal of this report is to assess and discuss the usage of the GFSAD30 project’s cropland-extent product. Since the public release of GFSAD30 in November 2017, the number of files downloaded has been tracked, as well as the total size of files downloaded, the country from which the GFSAD30 data were downloaded, and the user’s field of study. This report presents a monthly assessment of the usage of GFSAD30 from November 2017 through December 2019. During this period, about 1,900 gigabytes of data and about 225,000 files were downloaded by users in more than 100 countries. This report also includes how GFSAD30 has been cited in media, scientific journals, and other data products. The release of data was widely covered by the national and international press, and GFSAD30 products have been cited more than 200 times in scientific journals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221001","usgsCitation":"Oliphant, A., Thenkabail, P., and Teluguntla, P., 2022, Global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products—Download analysis: U.S. Geological Survey Open-File Report 2022–1001, 20 p., https://doi.org/10.3133/ofr20221001.","productDescription":"Report: vi, 20 p.; Data Release","ipdsId":"IP-119165","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":398273,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1001/covrthb.jpg"},{"id":398276,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HOIB7S","text":"Download rates of the global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products","description":"Oliphant, A.J., Thenkabail, P.S., and Teluguntla, P., 2022, Download rates of the global food-security-support-analysis data at 30-m resolution (GFSAD30) cropland-extent products: U.S. Geological Survey data release, https://doi.org/10.5066/P9HOIB7S."},{"id":398274,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1001/ofr20221001.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Director,
Western Geographic Science Center
U.S. Geological Survey
350 N. Akron Rd.
Moffett Field, CA 94035
Coastal and Marine Ecological Classification Standard (CMECS) geoform, substrate, and biotic component geographic information system (GIS) products were developed for the U.S. Exclusive Economic Zone (U.S. EEZ) of south-central California in the region of Santa Lucia Bank motivated by interest in development of offshore wind-energy capacity and infrastructure. The Bureau of Ocean Energy Management (BOEM), in coordination with the State of California and many other members of the California Task Force, issued calls for information in 2018 for the study area offshore of Morro Bay, California. The study area is in depths of 500 to 1,200 meters (m) and adjacent to a decommissioned nuclear power plant with a developed electric grid connection, and in an area of high wind resource. BOEM is the lead agency responsible for planning and leasing in the U.S. EEZ and funded this project to assess baseline conditions of, and the potential effects on, the seafloor environment. This project, carried out by the U.S. Geological Survey (USGS), resulted in three reports: one on biological analysis of seafloor video data, one on analysis of the geologic framework and hazards, and this report on seafloor habitat. The study area consists of 8,424 square kilometers (km2) of multibeam echo sounder (MBES) data acquired during five surveys from 2016 to 2019. Remotely operated vehicle (ROV) video was acquired in 2019 to supervise the classification of the MBES data into habitats. Derivatives of the MBES data were classified into 16 unique biotopes, 6 substrate types, 28 modifier groups, and 22 geoforms. The study area substrate is predominantly soft sediment (mud and fine sand) covering 7,804 km2 (92.7 percent) of the area. Mixed substrate areas on rocky banks, channel scarps, and the shelf break comprise 404 km2 (4.8 percent) of the study area. Hard substrate areas are found predominantly on the tops and flanks of banks and on bank ridges that separate canyons incising the banks. Hard substrates comprise 211 km2 of the study area (2.5 percent). After the bathymetry and backscatter raster images (rasters) were classified, manual editing was also done to remove noise artifacts. This effort was not completely successful and there are numerous erroneous small areas in the rasters that have been passed on to the CMECS polygon product. Nearly 120,000 annotations of organisms and their habitat were made from 25 video transects selected from 185 hours of ROV video. In total, 2,714 km2 of seafloor were successfully assigned to biotopes. Some biotopes were assigned to separate areas spatially distant from the transects that define the biotope. Expected relations between physical habitat and biota such as the number of species and the substrate induration and rugosity were verified. Slope is typically a predictive variable and was used in the classification of habitat, but the ground truth used for biotic component analysis included very little steeply sloping area. Ground-truth ROV operations were reduced by the sea state; additional ground truth could improve the biotic results and increase confidence in the spatial distribution of classifications reported here.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221035","collaboration":"Prepared in cooperation with Bureau of Ocean Energy Management, National Oceanic and Atmospheric Administration, and Monterey Bay Aquarium Research Institute","programNote":"Bureau of Ocean Energy Management OCS Study BOEM 2021–045","usgsCitation":"Cochrane, G.R., Kuhnz, L.A. Gilbane, L., Dartnell, P., Walton, M.A.L., and Paull, C.K., 2022, California Deepwater Investigations and Groundtruthing (Cal DIG) I, volume 3—Benthic habitat characterization offshore Morro Bay, California: U.S. Geological Survey Open-File Report 2022–1035 [also released as Bureau of Ocean Energy Management OCS Study BOEM 2021–045], 18 p., https://doi.org/10.3133/ofr20221035.","productDescription":"Report: vi, 18 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-129519","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":398057,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QQZ27U","text":"Multibeam echo sounder, video observation, and derived benthic habitat data offshore of south-central California in support of the Bureau of Ocean Energy Management Cal DIG I, offshore alternative energy project","description":"Cochrane, G.R., Kuhnz, L.A., Gilbane, L., Dartnell, P., and Walton, M.A., 2022, Multibeam echo sounder, video observation, and derived benthic habitat data offshore of south-central California in support of the Bureau of Ocean Energy Management Cal DIG I, offshore alternative energy project: U.S. Geological Survey data release, https://doi.org/10.5066/P9QQZ27U."},{"id":398055,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1035/coverthb.jpg"},{"id":398056,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1035/ofr20221035.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","city":"Morro Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.48431396484375,\n 34.863397850419524\n ],\n [\n -120.9210205078125,\n 35.574682600980914\n ],\n [\n -121.31103515625,\n 35.94243575255426\n ],\n [\n -121.45660400390624,\n 36.04465753921525\n ],\n [\n -122.36846923828125,\n 35.628279555648845\n ],\n [\n -122.09930419921876,\n 34.66032236481892\n ],\n [\n -120.48431396484375,\n 34.863397850419524\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060