Chronic wasting disease (CWD) is the only transmissible spongiform encephalopathy, a class of invariably fatal neurodegenerative mammalian diseases associated with a misfolded cellular prion protein found in wild free-ranging animals. Because it has a long incubation period, affected animals in Cervidae (the deer family; referred to as “cervids”) may not show signs of disease for several years. While signs are not specific to CWD, affected cervids (deer, elk, moose, and reindeer) show changes in appearance (such as progressive weight loss) and changes in behavior such as stumbling, tremors, and teeth grinding. CWD can be transmitted by direct contact or through a contaminated environment. The causative prion agent is highly resistant to degradation.
In recent decades, CWD has transitioned from a novel, obscure prion disease of cervids with limited geographical distribution, to a disease that poses substantial ecological, agricultural, and economic risks across large regions of North America. Since its discovery in free-ranging elk and deer populations in the western United States in the 1980s, CWD has been reported in captive or free-ranging cervid populations in 26 States, 3 Canadian Provinces, the Republic of South Korea, Finland, Sweden, and Norway. In addition, the proportion of CWD-infected animals is increasing in many areas where the disease is already established. In some heavily affected areas, total cervid numbers have decreased over time due to CWD, which suggests that these cervid populations may not be sustainable in the long-term.
The U.S. Geological Survey (USGS) conducts wildlife disease surveillance and research to support management of CWD-affected species and their habitats. The scientific information is relevant to governmental agencies that manage wildlife and their habitats including the U.S. Fish and Wildlife Service, the National Park Service, the U.S. Department of Agriculture, and other Federal, State, and Tribal agencies as well as conservation partners (non-governmental organizations, businesses, and private landowners). Each project description in this report (1–30) includes the non-USGS collaborators (Federal, State, Tribal agencies, universities) and a USGS point of contact (principal investigator). If there are USGS publications associated with the project, a publication list is provided at the end of each project description.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191109","usgsCitation":"Hopkins, M.C., Carlson, C.M., Cross, P.C., Johnson, C.J., Richards, B.J., Russell, R.E., Samuel, M.D., Sargeant, G.A., Walsh, D.P., and Walter, W.D., 2019, Chronic wasting disease—Research by the U.S. Geological Survey and partners (ver. 2.0, November 2019): U.S. Geological Survey Open-File Report 2019–1109, 29 p., https://doi.org/10.3133/ofr20191109.","productDescription":"viii, 29 p.","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-074357","costCenters":[{"id":189,"text":"Colorado Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":382939,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://usgs.maps.arcgis.com/apps/MapJournal/index.html?appid=a8a1a7a97aaa4aa7b50499a755eb854d","text":"Geonarrative: Chronic Wasting Disease Research by the U.S. Geological Survey & Partners"},{"id":369205,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1109/ofr20191109.pdf","text":"Report","size":"1.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1109"},{"id":368674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1109/coverthb2.jpg"},{"id":369204,"rank":2,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1109/versionhist.txt","size":"4.32 KB","linkFileType":{"id":2,"text":"txt"}}],"country":"Canada, United States","state":"Alberta, California, Colorado, Idaho, Iowa, Kansas, 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U.S. Geological Survey
954 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Identifying sources of sediment to streams in the Sprague River Basin, in south-central Oregon, is important for restoration efforts that are focused on reducing sediment erosion and transport. Reducing sediment loads in these streams also contributes to compliance with the total maximum daily load reduction requirements for total phosphorus in this basin. In the Sprague River Basin, phosphorus occurs in surface waters in both dissolved phase and particulate phase, and particulate phosphorus is readily transported in streams on fine-grained suspended sediments, which eventually deposit in Upper Klamath Lake. The lake has seasonal blooms of cyanobacteria that require phosphorus for growth and degrade water-quality conditions, violating State water-quality standards and creating conditions that are stressful to two endangered suckers that reside in the lake. Identifying sources of sediment to the Sprague River could help inform restoration actions by determining the principal locations in the basin contributing fine sediment to the river. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, conducted a proof-of-concept study to determine if sediment fingerprinting can differentiate sources of bank erosion by source material, basin, river reach, and soil horizon. The sediment fingerprinting approach uses properties of streambank and streambed sediment to differentiate between multiple sediment sources by determining a composite signature, or fingerprint. The composite fingerprint is established by combining fingerprint properties from laboratory results of elemental analysis, stable isotopes, and total carbon and nitrogen. The methods for differentiating sediment samples for this study include grouping bank and bed samples by basin, river reach, and soil horizon, and using non-parametric statistics to determine which fingerprint properties could be used to differentiate the sample groups. Results indicate that fingerprint properties differentiated source material, river reach, and basin, and were more successful at differentiating samples grouped by geographic location (basin and reach) compared to source material. Source material (banks, bed, levees) were differentiated with three fingerprint properties—Antimony (Sb), copper (Cu), and manganese (Mn). The basin category (South Fork and main-stem Sprague River) differentiated the South Fork and main stem with stable nitrogen isotopes (δ15N), aluminum (Al), silicon (Si), and vanadium (V). Specific river reaches within the study area were differentiated with 11 different fingerprint properties. These results can be used for apportionment studies using suspended sediment samples and mixing models to determine sediment source contributions within the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191120","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service","usgsCitation":"Schenk, L.N., Harden, T.M., and Kelson, J.K., 2019, Differentiating sediment sources using sediment fingerprinting techniques, in the Sprague River Basin, south-central Oregon: U.S. Geological Survey Open-File Report 2019-1120, 25 p., https://doi.org/10.3133/ofr20191120.","productDescription":"Report: vi, 25 p.; 2 Tables; Appendix","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106755","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":369299,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1120/ofr20191120.pdf","text":"Report","size":"7.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1120"},{"id":369302,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1120/ofr20191120_appendix1.xlsx","text":"Appendix 1 –","size":"41 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1120 Appendix 1","linkHelpText":" Analytical Results and Site Characteristics"},{"id":369298,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1120/coverthb.jpg"},{"id":369300,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1120/ofr20191120_table03.xlsx","text":"Table 3","size":"21 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1120 Table 3"},{"id":369301,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2019/1120/ofr20191120_table05.xlsx","text":"Table 5","size":"28 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1120 Table 5"}],"country":"United States","state":"Oregon","otherGeospatial":"Sprague River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.947998046875,\n 41.95949009892467\n ],\n [\n -119.21264648437499,\n 41.95949009892467\n ],\n [\n -119.21264648437499,\n 44.04811573082351\n ],\n [\n -122.947998046875,\n 44.04811573082351\n ],\n [\n -122.947998046875,\n 41.95949009892467\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
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The California Department of Water Resources (DWR) requested analysis of juvenile Chinook salmon survival in the Sacramento-San Joaquin River Delta (henceforth identified as “the Delta”) as part of an effects analysis that will be included in an Incidental Take Permit (ITP) Application. This application is in compliance with the California Endangered Species Act (CESA) and Environmental Impact Report (EIR), which is itself in compliance with California Environmental Quality Act (CEQA). DWR is seeking an ITP and preparing CEQA compliance documentation for long-term operation of the State Water Project (SWP). DWR requested assistance from the U.S. Geological Survey to aid in determining the effect of two proposed projects on juvenile Chinook salmon (Oncorhynchus tshawytscha) populations migrating through the Delta. Therefore, in this report we analyzed an 82-year time series of simulated river flows and Delta Cross Channel (DCC) gate operations under three scenarios constructed for the ITP: the proposed project (PP), the second proposed project (PP2b) and the existing (EX) scenarios.
To evaluate the proposed projects (PP and PP2b), we used the STARS model (Survival, Travel time, And Routing Simulation model), a stochastic, individual-based simulation model designed to predict survival of a cohort of fish that experience variable daily river flows during migration through the Delta. The STARS model uses parameter estimates from a Bayesian mark-recapture model that jointly estimates travel time and survival in eight discrete reaches of the Delta and migration routing at two key river junctions.
By applying the STARS model to the three 82-year scenarios, we found that both proposed projects had negative effects on survival, travel time, and routing in November but slightly positive effects in October, December, and May, and in June for only the PP. In November, there was a high probability that survival for PP and PP2b were less than EX and that travel time and routing to the Interior Delta for PP and PP2b were greater than for EX. We found that the magnitude of the difference in survival between scenarios was large in some years. For example, survival under both the PP and PP2b scenarios were 10 percent lower than EX in 25 percent of the water years in November. During this period, inflow to the Delta tended to be lower under the PP and PP2b scenarios, and the DCC gate was open more frequently under the PP and PP2b scenarios relative to the EX scenario. Lower inflow reduces survival, and more frequent operation of the DCC gate 1) increases the proportion of fish entering the Interior Delta, where survival is low, and thus 2) reduces survival in the Sacramento River in reaches downstream of the DCC. In contrast, during October, December, May (both PP and PP2b), and June (PP only), survival was slightly higher, travel times were lower, and routing to the Interior Delta was lower under the PP and PP2b relative to the EX scenario in the same time period, although the magnitude of the increase was relatively small in most years (less than two percent). This difference between scenarios was driven by higher river flows in some years under the PP and PP2b relative to the EX scenario. Overall, the differences in survival, travel time, and routing distance between the three operational scenarios were primarily driven by the timing and magnitude of the annual high river flows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191127","collaboration":"Prepared in cooperation with California Department of Water Resources","usgsCitation":"Perry, R.W., Hansen, A.C., Evans, S.D., and Kock, T.J., 2019, Using the STARS Model to evaluate the effects of two proposed projects for the long-term operation of State Water Project Incidental Take Permit Application and CEQA compliance (ver. 2.0, February 2020): U.S. Geological Survey Open-File Report 2019-1127, 39 p. plus appendixes, https://doi.org/10.3133/ofr20191127.","productDescription":"Report: vii, 31 p.; Appendixes 1-8","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112215","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":372670,"rank":9,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix7.pdf","text":"Appendix 7","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 7","linkHelpText":"– Simulated Daily Routing by Year, Existing Conditions Compared to Proposed Project 2b Scenarios, 1922–2003"},{"id":369242,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix2.pdf","text":"Appendix 2","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 2","linkHelpText":"– Simulated Daily Travel Time by Year, Existing Conditions Compared to Proposed Project Scenarios, 1922–2003"},{"id":372669,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix6.pdf","text":"Appendix 6","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 6","linkHelpText":"– Simulated Daily Travel Time by Year, Existing Conditions Compared to Proposed Project 2b Scenarios, 1922–2003"},{"id":372668,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix5.pdf","text":"Appendix 5","size":"1.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 5","linkHelpText":"– Simulated Daily Survival by Year, Existing Conditions Compared to Proposed Project 2b Scenarios, 1922–2003"},{"id":369244,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix4.pdf","text":"Appendix 4","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 4","linkHelpText":"– Simulated Proportion of Fish Entering the Interior Delta by Year, Existing Conditions Compared to Proposed Project Scenarios, 1922–2003"},{"id":369243,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix3.pdf","text":"Appendix 3","size":"1.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 3","linkHelpText":"– Simulated Daily Routing by Year, Existing Conditions Compared to Proposed Project Scenarios, 1922–2003"},{"id":369239,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1127/coverthb2.jpg"},{"id":369240,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127"},{"id":369241,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix1.pdf","text":"Appendix 1","size":"1.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 1","linkHelpText":"– Simulated Daily Survival by Year, Existing Conditions Compared to Proposed Project Scenarios, 1922–2003"},{"id":372672,"rank":11,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1127/versionHist.txt","size":"8 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version History"},{"id":372671,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1127/ofr20191127_Appendix8.pdf","text":"Appendix 8","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1127 Appendix 8","linkHelpText":"– Simulated Proportion of Fish Entering the Interior Delta by Year, Existing Conditions Compared to Proposed Project 2b Scenarios, 1922–2003"}],"country":"United States","state":"California ","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.9150390625,\n 38.46219172306828\n ],\n [\n -122.1240234375,\n 38.46219172306828\n ],\n [\n -122.1240234375,\n 38.993572058209466\n ],\n [\n -122.9150390625,\n 38.993572058209466\n ],\n [\n -122.9150390625,\n 38.46219172306828\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: November 2019; Version 2.0: February 2020","contact":"Director, Western Fisheries Research Center
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6505 NE 65th Street
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Understanding the optimal time needed to purge a well while pumping to collect a representative groundwater sample requires an understanding of groundwater flow in wells (in-well flow). Parameters that affect in-well flow include the hydraulic properties of the aquifer, well construction, drawdown from pumping, and pump rate. The time of travel relative to in-well flow is affected by the pump’s intake location. The Purge Analyzer Tool (PAT) incorporates hydraulic calculations to help assess the optimal purge times required to vertically transport groundwater in the well to the pump intake (Harte, 2017). Harte (2017) includes a discussion on the rationale for determining in-well groundwater flow and time of travel and also discusses the limitations inherent in the PAT; an understanding of the limitations is important to ensure proper use.
The PAT calculates flow by use of the Dupuit-Theim equation (Lohman, 1979) that assumes steady-state radial flow and a total inflow from the well opening or screen equal to the pumping rate (eq. 1). A bulk average hydraulic conductivity (Kavg) is derived from this relationship. Once Kavg is calculated, the program calculates incremental (layered) horizontal radial inflow into the well over user defined increments (layers). These defined increments represent the screen or well opening as a fraction of the total inflow. The amount of inflow per layer is proportional to the user-defined layered distribution of hydraulic conductivity (Klayer) because drawdown is assumed to be uniformly distributed in the well. The water budget equation that guides the solution of the PAT (eq. 1) is specified as:
Qp = Qv + QH + Qw (1)
where
QP is pumping rate,
Qv is vertical flow entering the boundary of the mixing zone (Mz) from the summation of layered radial flow (∑Qhl-n) where l-n denotes number of layers,
QH is horizontal radial flow into the mixing zone (Mz), and
Qw is flow from wellbore storage effects.
The in-well flow is computed from the convergence of incremental (layered) radial inflows (Qhl-n) summed to the total vertical flow (QV) entering the adjacent zone to the pump intake (called mixing zone [Mz]) as shown in figure 1. The Qv is transported as one-dimensional piston flow. Within the Mz, it's assumed that flow to the pump is dominated by horizontal radial flow (QH) when the pump is in the open interval of the well. Flow from the wellbore storage (Qw) is computed from the volume of water pumped from the well at the time of the drawdown (s) measurement(s). Aquifer storage effects are unaccounted for but are likely to be problematic when (1) dewatering within the well opening occurs or (2) when the water table is close to the top of the well screen or open interval where additional flow into the upper portion of the well opening may occur. For fully saturated wells tens of feet below the water table, storage effects are likely to be more uniformly distributed across the well screen or open interval (regardless of confined or unconfined conditions). Therefore, radial inflow from storage will be less prominent under pump rates commonly used in groundwater sampling either for volumetric sampling (<3 gallons per minute) or low-flow sampling (<0.5 liters per minute).
A major benefit of the use of the PAT is the understanding of time-varying, vertical integration of captured pump water. The analytical model computes aquifer (formation) capture intervals relative to the open interval of the well. This information is displayed graphically (called aquifer fraction graphs) and can be used to assess the likely formation intervals contributing water to the sample at any time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191104","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Harte, P.T., Huffman, B.J., Perina, T., Levine, H., and Rojas-Mickelson, D., 2019, Instructions for running the analytical code PAT (Purge Analyzer Tool) for computation of in-well time of travel of groundwater under pumping conditions: U.S. Geological Survey Open-File Report 2019–1104, 23 p., https://doi.org/10.3133/ofr20191104.","productDescription":"Report: vii, 23 p.; Application Site","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102617","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437282,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93EF0GM","text":"USGS data release","linkHelpText":"Purge Analyzer Tool - For computation of in-well time of travel of groundwater under pumping conditions"},{"id":368709,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1104/ofr20191104.pdf","text":"Report","size":"1.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1104"},{"id":368708,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://code.usgs.gov/ptharte/pat","text":"USGS Official Source Code Archive","linkFileType":{"id":5,"text":"html"}},{"id":368706,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1104/coverthb_3.jpg"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
In 2016, the U.S. Bureau of Reclamation (USBR) and California Department of Water Resources requested a reinitiation of consultation under Section 7 of the Endangered Species Act on the coordinated long-term operations of the Central Valley and State Water Projects. This resulted in a Biological Assessment released by USBR in 2019. In its analysis of the Biological Assessment for its Biological Opinion on the proposed action, the National Marine Fisheries Service (NMFS) requested assistance from the U.S. Geological Survey to describe the effect of the proposed action on juvenile Chinook salmon (Oncorhynchus tshawytscha) populations migrating through the Sacramento-San Joaquin River Delta (henceforth called “the Delta”). Therefore, in this report we analyzed an 82-year time series of simulated river flows and Delta Cross Channel (DCC) gate operations under two scenarios constructed for the Biological Assessment: the proposed-action (PA) scenario and the continuing-operations scenario (COS).
To evaluate the proposed action, we used the STARS model (Survival, Travel time, And Routing Simulation model), a stochastic, individual-based simulation model designed to predict survival of a cohort of fish that experiences variable daily river flows as the fish migrate through the Delta. The STARS model uses parameter estimates from a Bayesian mark-recapture model that jointly estimates travel time and survival in eight discrete reaches of the Delta and migration routing at two key river junctions.
By applying the STARS model to the two 82-year scenarios, we found that the proposed action had negative effects on survival, travel time, and routing in October–December but positive effects in April–June. In October–December, there was a high probability that survival in the PA scenario was less than that in the COS, and that travel time and routing to the Interior Delta for the PA scenario was greater than that for the COS. The magnitude of the difference in survival between scenarios was larger in some years than in others. For example, we quantified that survival under the PA scenario was 10 percent lower than under the COS in 25 percent of the water years from October through December. During this period, inflow to the Delta tended to be lower under the PA scenario, and the DCC gate was open more frequently under the PA scenario than during the COS. Lower inflow reduces survival, and more frequent operation of the DCC gate 1) increases the proportion of fish entering the Interior Delta, where survival is low, and thus 2) reduces survival in the Sacramento River in reaches downstream of the DCC. In contrast, during the period April–June, survival was higher, travel times were lower, and routing to the Interior Delta was lower under the PA scenario relative to the COS, although the magnitude of the increase in survival was relatively small in most years (less than a 3-percent difference in survival). This difference between scenarios was driven by higher river flows in some years under the PA scenario relative to the COS. Overall, the differences in survival, travel time, and routing distance between the two operational scenarios were primarily driven by the timing and magnitude of the annual high river flows.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191125","collaboration":"Prepared in cooperation with National Oceanic and Atmospheric Administration, National Marine Fisheries Service","usgsCitation":"Perry, R.W., Pope, A.C., and Sridharan, V.K., 2019, Using the STARS model to evaluate the effects of the proposed action for the reinitiation of consultation on the coordinated long-term operation of the Central Valley and State Water Project: U.S. Geological Survey Open-File Report 2019–1125, 31 p. plus appendixes, https://doi.org/10.3133/ofr20191125.","productDescription":"Report: vii, 31 p.; Appendixes 1–4","numberOfPages":"43","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-108833","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":369157,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1125/ofr20191125_Appendix3.pdf","text":"Appendix 3","size":"1.74 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1125 Appendix 3","linkHelpText":"– Simulated Daily Routing by Year, Continuing Operations Compared to Proposed Action Scenarios, 1922–2003"},{"id":369158,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1125/ofr20191125_Appendix4.pdf","text":"Appendix 4","size":"1.02 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1125 Appendix 4","linkHelpText":"– Simulated Proportion of Fish Entering the Interior Delta by Year Continuing Operations Compared to Proposed Action Scenarios, 1922–2003"},{"id":369153,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1125/coverthb.jpg"},{"id":369154,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1125/ofr20191125.pdf","text":"Report","size":"3.41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1125"},{"id":369155,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1125/ofr20191125_Appendix1.pdf","text":"Appendix 1","size":"1.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1125 Appendix 1","linkHelpText":"– Simulated Daily Survival by Year, Continuing Operations Compared to Proposed Action Scenarios, 1922–2003"},{"id":369156,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1125/ofr20191125_Appendix2.pdf","text":"Appendix 2","size":"1.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1125 Appendix 2","linkHelpText":"– Simulated Daily Travel Time by Year, Continuing Operations Compared to Proposed Action Scenarios, 1922–2003"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.684326171875,\n 37.56199695314352\n ],\n [\n -119.59716796875,\n 37.56199695314352\n ],\n [\n -119.59716796875,\n 39.41922073655956\n ],\n [\n -122.684326171875,\n 39.41922073655956\n ],\n [\n -122.684326171875,\n 37.56199695314352\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 Full Equations Model Graphical Data Inspector (FEQ–GDI) is a menu-driven utility program that enables users to visualize and check the geometric and hydraulic properties of channel cross sections, selected control structures, and stream profiles in the input files for the Full Equations (FEQ) Model and the Full Equations Utilities (FEQUTL) Model. The FEQ Model is a computer program for the simulation of one-dimensional, unsteady flow in open channels and through control structures using the full, dynamic equations of motion. The input to FEQ Model includes the output from the FEQUTL Model, which computes tables relating the hydraulic properties of channel cross sections and control structures to depth, flow, and (or) other specified parameters. FEQ–GDI can be used to help users quickly detect anomalies in the data that may indicate errors in the input files.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191113","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Ern, J.L., Ortel, T., Ishii, A.L., and Bera, M., 2019, Full Equations Model Graphical Data Inspector (FEQ–GDI) user guide: U.S. Geological Survey Open-File Report 2019–1113, 11 p., https://doi.org/10.3133/ofr20191113.","productDescription":"iv, 11 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-111050","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":369032,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1113/coverthb.jpg"},{"id":369033,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1113/ofr20191113.pdf","text":"Report","size":"4.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1113"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The lithologic logs described in this open-file report are from holes augered in the South Carolina Low Country in parts of Charleston, Dorchester, and Colleton Counties from 1998 through 2010. Lithologic units described here include not only surficial Pleistocene units but also subsurface stratigraphic units ranging as far back in age as late Eocene. This region comprises the southernmost and westernmost portions of the area included in the 1:100,000 Charleston region geologic map, which lies east of 80°30′ west and south of 33°15′ north. Logs of the remainder of that map area were published prior to the release of that map. The present report completes the lithologic log record from which the 1:100,000 Charleston region geologic map largely was compiled.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191119","collaboration":"Prepared in cooperation with the South Carolina Geological Survey","usgsCitation":"Weems, R.E., and Lewis, W.C., 2019, Detailed lithologic logs from auger holes in southern Charleston County, southwestern Dorchester County, and eastern Colleton County, South Carolina: U.S. Geological Survey Open-File Report 2019–1119, 129 p., https://doi.org/10.3133/ofr20191119.","productDescription":"iv, 129 p.","numberOfPages":"136","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-105009","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":368507,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1119/coverthb2.jpg"},{"id":369044,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1119/ofr20191119.pdf","text":"Report","size":"2.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1119"}],"country":"United States","state":"South Carolina","county":"Charleston County, Colleton County, Dorchester County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.8758544921875,\n 32.20350534542368\n ],\n [\n -79.8870849609375,\n 32.20350534542368\n ],\n [\n -79.8870849609375,\n 33.02248191961359\n ],\n [\n -80.8758544921875,\n 33.02248191961359\n ],\n [\n -80.8758544921875,\n 32.20350534542368\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
U.S. Geological Survey
MS 926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Trace-metal concentrations in sediment and in the clam Macoma petalum (formerly reported as Macoma balthica), clam reproductive activity, and benthic macroinvertebrate community structure were investigated in a mudflat 1 kilometer south of the discharge of the Palo Alto Regional Water Quality Control Plant (PARWQCP) in south San Francisco Bay, Calif. This report includes the data collected by U.S. Geological Survey (USGS) scientists for the period January 2018 to December 2018. These append to long-term datasets extending back to 1974. A major focus of the report is an integrated description of the 2018 data within the context of the longer, multi-decadal dataset. This dataset supports the City of Palo Alto’s Near-Field Receiving-Water Monitoring Program, initiated in 1994.
Significant reductions in silver and copper concentrations in both sediment and M. petalum occurred at the site in the 1980s following the implementation by PARWQCP of advanced wastewater treatment and source control measures. Since the 1990s, concentrations of these elements appear to have stabilized at concentrations somewhat above (silver [Ag]) or near (copper [Cu]) regional background concentrations. Data for other metals, including chromium (Cr), mercury (Hg), nickel (Ni), selenium (Se), and zinc (Zn), have been collected since 1994. Over this period, concentrations of these elements have remained relatively constant, aside from seasonal variation that is common to all elements. In 2018, concentrations of silver and copper in M. petalum varied seasonally in response to a combination of site-specific metal exposures and annual growth and reproduction, as reported previously. Seasonal patterns for other elements, including Cr, Ni, Zn, Hg, and Se, were generally similar in timing and magnitude as those for Ag and Cu. This record suggests that legacy contamination and regional-scale factors now largely control sedimentary and bioavailable concentrations of silver and copper, as well as other elements of regulatory interest, at the Palo Alto site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191084","collaboration":"Prepared in cooperation with the City of Palo Alto, California","usgsCitation":"Cain, D.J., Thompson, J.K., Parchaso, F., Pearson, S., Stewart, R., Turner, M., Shrader, K.H., Zierdt Smith, E.L., and Luoma, S.N., 2019, Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2018: U.S. Geological Survey Open-File Report 2019–1084, 41 p., https://doi.org/10.3133/ofr20191084.","productDescription":"vi, 41 p.","numberOfPages":"41","onlineOnly":"Y","ipdsId":"IP-109149","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":416180,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20161118","text":"Open-File Report 2016-1118","linkHelpText":"- Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2015"},{"id":416181,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20171135","text":"Open-File Report 2017-1135","linkHelpText":"- Near-field receiving water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California; 2016"},{"id":416182,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181107","text":"Open-File Report 2018-1107","linkHelpText":"- Near-field receiving-water monitoring of trace metals and a benthic community near the Palo Alto Regional Water Quality Control Plant in south San Francisco Bay, California—2017"},{"id":416184,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20211079","text":"Open-File Report 2021-1079","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2019"},{"id":368964,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1084/ofr20191084.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-FIle Report 2019-1084"},{"id":368963,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1084/coverthb.jpg"},{"id":416185,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20231017","text":"Open-File Report 2023-1017","linkHelpText":"- Near-Field Receiving-Water Monitoring of Trace Metals and a Benthic Community Near the Palo Alto Regional Water Quality Control Plant in South San Francisco Bay, California—2020"}],"country":"United States","state":"California","otherGeospatial":"Palo Alto Regional Water Quality Control Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.14187622070311,\n 37.43179575348695\n ],\n [\n -122.08419799804689,\n 37.43179575348695\n ],\n [\n -122.08419799804689,\n 37.48085213924346\n ],\n [\n -122.14187622070311,\n 37.48085213924346\n ],\n [\n -122.14187622070311,\n 37.43179575348695\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Earth System Processes Division
U.S. Geological Survey
411 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing salt marsh management decisions at the Rhode Island National Wildlife Refuge Complex. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of nine salt marsh management units within the refuge complex and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that would be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per salt marsh management unit, that would maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to approximately $150,000, but that further expenditures may yield diminishing return on investment. Management actions in optimal portfolios at total costs less than $150,000 included digging runnels (by hand or machine) on the marsh surface to improve drainage in eight management units, applying sediment to the marsh surface (thin layer deposition) in one management unit, constructing islands for use by tidal marsh obligate birds in two management units, and controlling Phragmites australis in one management unit. The management benefits were derived from expected improvements in the capacity for marsh elevation to keep pace with sea-level rise and increases in numbers of spiders (as an indicator of trophic health) and tidal marsh obligate birds. The prototype presented here provides a framework for decision making at the Rhode Island National Wildlife Refuge Complex that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191103","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., and Ernst, N.T., 2019, Optimization of salt marsh management at the Rhode Island National Wildlife Refuge Complex through use of structured decision making: U.S. Geological Survey Open-File Report 2019–1103, 39 p., https://doi.org/10.3133/ofr20191103.","productDescription":"vi, 39 p.","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-102061","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":368643,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1103/ofr20191103.pdf","text":"Report","size":"3.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1103"},{"id":368642,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1103/coverthb.jpg"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Rhode Island National Wildlife Refuge Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.57524108886719,\n 41.3757780692323\n ],\n [\n -71.42074584960938,\n 41.3757780692323\n ],\n [\n -71.42074584960938,\n 41.49674964110098\n ],\n [\n -71.57524108886719,\n 41.49674964110098\n ],\n [\n -71.57524108886719,\n 41.3757780692323\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708-4039
The U.S. Fish and Wildlife Service (FWS) manages wetlands and grasslands for wildlife habitat throughout the central North American Prairie Pothole Region (PPR). PPR wetlands, or potholes, are widely recognized as critical habitats for North American migratory waterfowl, waterbirds, and other wildlife. Potholes also provide other ecosystem services such as carbon sequestration, flood mitigation, filtration of pollutants, groundwater recharge, nutrient retention, and recreational opportunities. Wetland condition assessments have been completed nationally at coarse scales, but focused, regionwide assessments of the biological condition of potholes managed by the FWS are lacking. Therefore, FWS personnel require information pertaining to the biological condition and status of wetlands on FWS fee-title lands in the PPR to support management, restoration, and acquisition efforts. The biological condition of wetlands typically is reflected by their plant communities, and these communities correspond to past and current management and anthropogenic disturbances; thus, plant communities are a suitable surrogate of wetland condition.
This report describes the study design, selection of sample sites, and field survey methods for a wetland condition assessment for FWS fee-title lands in the PPR of North Dakota, South Dakota, and Montana. Various spatial databases were gathered (for example, National Wetlands Inventory) to identify and assess potholes on FWS fee-title lands and to facilitate the selection of study sites. A spatially balanced, site-selection process resulted in the inclusion of 125 temporarily and 125 seasonally ponded potholes distributed across the area of interest; the first 100 for each classification were considered the primary study sites, whereas the remaining 25 were considered an oversample to replace those deemed not appropriate for sampling by field crews. Study sites were within native prairie and reseeded grasslands on FWS National Wildlife Refuges and Waterfowl Production Areas and are distributed among the primary physiographic subregions of the PPR: the Glaciated Plains, Missouri Coteau, and Prairie Coteau; a small number of sites also are within the Lake Agassiz Plain and Turtle Mountains. Site assessment protocols, vegetation survey methods, data analyses, and condition categories (for example, poor, good, very good) for the wetland assessment are based on the North Dakota Rapid Assessment Method and an Index of Plant Community Integrity developed for potholes. Results of the wetland condition assessment will aid FWS staff in assessing past and current management and help to identify priority areas for future management and acquisition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191118","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and in collaboration with North Dakota State University","usgsCitation":"Tangen, B.A., Bansal, S., Fern, R.R., DeKeyser, E.S., Hargiss, C.L.M., Mushet, D.M., and Dixon, C.S., 2019, Study design and methods for a wetland condition assessment on U.S. Fish and Wildlife Service fee-title lands in the Prairie Pothole Region of North Dakota, South Dakota, and Montana, USA: U.S. Geological Survey Open-File Report 2019–1118, 24 p., https://doi.org/10.3133/ofr20191118.","productDescription":"Report: vi, 24 p.; Appendix Figure 3.1","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-111056","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":368679,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1118/ofr20191118.pdf","text":"Report","size":"973 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1118"},{"id":368680,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1118/ofr20191118_appendix_fig_3.1.pdf","text":"Appendix figure 3.1","size":"176 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1118 Appendix Figure 1.3","linkHelpText":"– North Dakota Wetland Rapid Assessment Form"},{"id":368678,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1118/coverthb.jpg"}],"country":"United States","state":"North Dakota, South Dakota, Montana","otherGeospatial":"Prairie Pothole region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.1630859375,\n 48.60385760823255\n ],\n [\n -97.0751953125,\n 48.80686346108517\n ],\n [\n -96.9873046875,\n 49.35375571830993\n ],\n [\n -101.6015625,\n 49.35375571830993\n ],\n [\n -106.5234375,\n 49.03786794532644\n ],\n [\n -106.6552734375,\n 48.63290858589535\n ],\n [\n -105.380859375,\n 47.69497434186282\n ],\n [\n -105.29296874999999,\n 46.01222384063236\n ],\n [\n -104.32617187499999,\n 43.03677585761058\n ],\n [\n -102.48046875,\n 42.90816007196054\n ],\n [\n -96.1083984375,\n 42.52069952914966\n ],\n [\n -97.1630859375,\n 48.60385760823255\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
Policy makers, individuals from government agencies, and natural resource managers face increasing demands to manage coastal areas in a way that meets economic, social, and ecological needs as sea levels rise. Scientific knowledge of how coastal processes drive beach and barrier island changes and how those changes affect habitat use can support decision makers as they balance sometimes conflicting human and ecological needs. However, uncertainties in the knowledge of the cumulative results of coastal processes make it challenging to forecast specific changes for a particular location and time. The U.S. Geological Survey is developing tools for identifying and forecasting barrier island characteristics as well as suitable coastal habitats for species of concern (such as piping plovers, Charadrius melodus) given ongoing sea-level rise. As part of this effort, we use three Bayesian networks to calculate probabilities of shoreline change rates, changes in barrier island biogeomorphic characteristics, and piping plover habitat availability, which together forecast the effects of different sea-level-rise rates and storm regimes. This report details the methodology used to derive geospatial biogeomorphic datasets that are used as inputs for two of these Bayesian networks, which forecast barrier island geomorphology and piping plover habitat availability at sites along the U.S. Atlantic coast (Maine to North Carolina). Further information about the project, including specific study sites, can be found at https://woodshole.er.usgs.gov/project-pages/beach-dependent-shorebirds/.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191071","usgsCitation":"Zeigler, S.L., Sturdivant, E.J., and Gutierrez, B.T., 2019, Evaluating barrier island characteristics and piping plover (Charadrius melodus) habitat availability along the U.S. Atlantic coast—Geospatial approaches and methodology (ver. 1.1, October 2019): U.S. Geological Survey Open-File Report 2019–1071, 34 p., https://doi.org/10.3133/ofr20191071.","productDescription":"Report: vii, 34 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095609","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":437293,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V7F6UX","text":"USGS data release","linkHelpText":"Barrier island geomorphology and shorebird habitat metrics: 16 sites on the U.S. Atlantic Coast, 2013-2014"},{"id":365872,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1071/ofr20191071.pdf","text":"Report","size":"4.60 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1071"},{"id":365871,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1071/coverthb2.jpg"},{"id":368509,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1071/versionhist.txt","text":"Version History","size":"5.73 KB","linkFileType":{"id":2,"text":"txt"}},{"id":365873,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P944FPA4","text":"USGS data release","description":"USGS data release","linkHelpText":"Barrier Island Geomorphology and Shorebird Habitat Metrics—Four Sites in New York, New Jersey, and Virginia, 2010–2014"}],"country":"United States","state":"Connecticut, Maine, Maryland, Massachusetts, North Carolina, New Jersey, Rhode Island, Virginia","otherGeospatial":"Mid-Atlantic Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.4228515625,\n 44.84029065139799\n ],\n [\n -70.9716796875,\n 43.61221676817573\n ],\n [\n -71.3232421875,\n 42.52069952914966\n ],\n [\n -71.015625,\n 41.902277040963696\n ],\n [\n -72.333984375,\n 41.705728515237524\n ],\n [\n -74.267578125,\n 41.0130657870063\n ],\n [\n -74.70703125,\n 40.212440718286466\n ],\n [\n -75.849609375,\n 38.37611542403604\n ],\n [\n -76.37695312499999,\n 36.4566360115962\n ],\n [\n -76.1572265625,\n 35.60371874069731\n ],\n [\n -76.025390625,\n 34.84987503195418\n ],\n [\n -75.1025390625,\n 35.10193405724606\n ],\n [\n -75.498046875,\n 37.16031654673677\n ],\n [\n -74.7509765625,\n 38.58252615935333\n ],\n [\n -74.091796875,\n 39.50404070558415\n ],\n [\n -72.4658203125,\n 40.613952441166596\n ],\n [\n -69.697265625,\n 41.04621681452063\n ],\n [\n -69.78515625,\n 41.80407814427234\n ],\n [\n -70.57617187499999,\n 43.004647127794435\n ],\n [\n -69.4775390625,\n 43.644025847699496\n ],\n [\n -68.115234375,\n 44.11914151643737\n ],\n [\n -68.4228515625,\n 44.84029065139799\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: October 23, 2019; Version 1.0: July 25, 2019","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543
Water-withdrawal, water-use, and water-return information have been collected and compiled for each county in Georgia every 5 years since 1980 using data obtained from various Federal, State, and private agencies, as well as additional online sources. For 2015, water use, water withdrawal, and water returns were estimated for each county, water-planning region, major river basin, and principal aquifer in Georgia. Offstream water use in 2015 is estimated for the categories of domestic, commercial, industrial processing, mining, irrigation (subdivided into crop and golf course irrigation), livestock, aquaculture, and thermoelectric power cooling.
According to the U.S. Census Bureau, approximately 10.2 million people in Georgia needed water resources to meet their personal, commercial, and recreational needs in 2015. Public water suppliers provided water to about 85 percent of the population of Georgia. Estimated total water withdrawals from both surface-water and groundwater sources were about 3,384 million gallons per day (Mgal/d) in 2015, which is a 27-percent reduction from 2010, a 48.1-percent reduction from 2000, and a 49.7-percent reduction from 1980. In 2015, surface-water withdrawals were greatest for thermoelectric power cooling (839.8 Mgal/d), and groundwater withdrawals were greatest for irrigating crops (547.9 Mgal/d). Water needs in northern Georgia are typically met by withdrawing a larger percentage of water from surface-water than groundwater sources; conversely, counties in southern Georgia withdraw more water from groundwater sources. About 1,571 Mgal/d of water were returned to Georgia streams and lakes in 2015, which represents about 46 percent of the total water withdrawn from all sources in 2015.
Water users in the Apalachicola River Basin, in 2015, withdrew the highest percentage of water (35 percent) and returned the highest percentage of water to surface-water bodies (almost 40 percent) compared to other major river basins in Georgia. Withdrawals in the Apalachicola River Basin are primarily extracted by public-supply systems (43 percent) and irrigation (34 percent). The aquifer from which 68 percent of statewide groundwater withdrawals were extracted was the Floridan aquifer system, and the majority of the water was used for irrigation (57 percent).
Historically, statewide water use in Georgia was highest in 1980 (6,735 Mgal/d), decreased to 5,353 Mgal/d in 1990, peaked at 6,531 Mgal/d in 2000, and has been declining since that time. The reduction in water use between 2000 and 2015 came primarily from surface-water withdrawals (90 percent of total reduction) and thermoelectric power cooling use (78 percent of total reduction). Water use for livestock and aquaculture increased between 1985 and 2015, and this increase correlates with the growth of agriculture in Georgia during that period. The driving forces behind the observed water-use changes include (1) shifts in population numbers and locations, (2) five periods of major drought, (3) water conservation efforts and education programs initiated by State and local governments and water utilities, and (4) changing water needs for thermoelectric power cooling, industry, and agricultural activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191086","collaboration":"Prepared in cooperation with the Georgia Department of Natural Resources, Environmental Protection Division","usgsCitation":"Painter, J.A., 2019, Estimated use of water in Georgia for 2015 and water-use trends, 1985–2015: U.S. Geological Survey Open-File Report 2019–1086, 216 p., https://doi.org/10.3133/ofr20191086.","productDescription":"vi, 216 p.","numberOfPages":"226","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-096369","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":437300,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V2F373","text":"USGS data release","linkHelpText":"Georgia Water Use Mapper"},{"id":368396,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1086/ofr20191086.pdf","text":"Report","size":"27.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1086"},{"id":367811,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1086/coverthb.jpg"},{"id":367813,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T6P5SM","text":"USGS data release","linkHelpText":"Georgia water-use information by county and water-use trends by water-planning region"}],"country":"United States ","state":"Georgia 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive
Suite 500
Norcross, GA 30093
Hurricane Matthew, the strongest Atlantic hurricane of the 2016 hurricane season, made land-fall south of McClellanville, S.C., around 1500 Coordinated Universal Time (UTC) on October 8, 2016. Hurricane Matthew affected the States of Florida, Georgia, South Carolina, and North Carolina along the U.S. Atlantic coastline. Numerous barrier islands were breached, and the erosion of beaches and dunes occurred along most of the South Atlantic coast. The U.S. Geological Survey (USGS) fore-casted potential coastal-change effects—including dune erosion and overwash that can threaten coastal resources and infrastructure—to assist with pre-storm management decisions. Following the storm, oblique aerial photography was collected, and lidar topographic survey missions were flown. These two datasets were used to document the changes that resulted from the storm and to validate coastal change forecasts. Comparisons of pre- and post-storm photographs were used to characterize the nature, extent, and spatial variability of hurricane-induced coastal changes. Analyses of pre- and post-storm lidar eleva-tions were used to quantify magnitudes of change in shoreline positions, dune elevations, and beach volumes. Erosion was observed along the coast from Florida to North Carolina; however, the coastal response exhibited extensive spatial variability, as would be expected over such a large region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191095","usgsCitation":"Birchler, J.J., Doran, K.S., Long, J.W., and Stockdon, H.F., 2019, Hurricane Matthew—Predictions, observations, and an analysis of coastal change: U.S. Geological Survey Open-File Report 2019–1095, 37 p., https://doi.org/10.3133/ofr20191095.","productDescription":"x, 37 p.","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103132","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":437304,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BW6CG6","text":"USGS data release","linkHelpText":"Storm-Induced Overwash Extent"},{"id":368353,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1095/ofr20191095.pdf","text":"Report","size":"9.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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\"}}]}","contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Landsat satellites have been operating since 1972, providing a continuous global record of the Earth’s land surface. The imagery is currently available at no cost through the U.S. Geological Survey (USGS). A previous USGS study estimated that Landsat imagery provided users an annual benefit of $2.19 billion in 2011, with U.S. users accounting for $1.79 billion of those benefits. That study, published in 2013, surveyed users in 2012 about Landsat imagery they retrieved in 2011. But since then, many changes have altered the demand for and supply of remotely sensed imagery and have made the analysis complex. This report updates these estimates, surveying users in 2018 about Landsat images they retrieved in 2017. The report discusses changes in the value per scene in 2017 when compared to 2011 and analyzes the potential consequences of charging fees. Landsat imagery has been available at no cost to the public since 2008, resulting in the distribution of millions of scenes each subsequent year. In addition, tens of thousands of Landsat users have registered with the USGS to access the data. Considering the number of Landsat data users worldwide and the broad range of Landsat data applications, it is difficult to quantify the cascading benefits to society provided by Landsat imagery. The value of Landsat imagery to these users was demonstrated by the substantial aggregated annual economic benefit from the imagery. Landsat imagery provided domestic and international users an estimated $3.45 billion in benefits in 2017 compared to $2.19 billion in 2011, with U.S. users accounting for $2.06 billion of those benefits. Much of the societal value of Landsat stems from the free and open data policy that allows users to access as much imagery as is necessary for their analysis at no cost. Charging even small fees would result in a loss of users and, most likely, a steep decline in the amount of imagery downloaded. It is reasonable that more than 50 percent of users will decline to pay. The consequences of charging for Landsat imagery would be felt by downstream users as well, through increased prices for value-added products as well as more intangible effects, such as reduced monitoring of environmental hazards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191112","usgsCitation":"Straub, C.L., Koontz, S.R., and Loomis, J.B., 2019, Economic valuation of Landsat imagery: U.S. Geological Survey Open-File Report 2019–1112, 13 p., https://doi.org/10.3133/ofr20191112.","productDescription":"iv, 13 p.","onlineOnly":"Y","ipdsId":"IP-110993","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":368280,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1112/coverthb.jpg"},{"id":368281,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1112/ofr20191112.pdf","text":"Report","size":"3.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1112"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Letterkenny Army Depot in Chambersburg, Pennsylvania, built an Ammonium Perchlorate Rocket Motor Destruction (ARMD) facility in 2016. The ARMD Facility was designed to centralize rocket motor destruction and contain or capture all waste during the destruction process. Ideally, there would be no contaminant transport to air, soil, or water from the facility, but the Code of Federal Regulations requires that any hazardous waste disposal facility have an environmental monitoring program in place. In a study by the U.S. Geological Survey, in cooperation with the Letterkenny Army Depot, baseline characterization of constituents in groundwater, surface water, and soil was conducted from September to December 2016 to document site conditions prior to the beginning of operations at the facility in January 2017. Groundwater wells, surface water, and soils were sampled monthly during the baseline characterization period. No sediment transport from the site occurred on days when samples were collected from surface-water sites, so no sediment was collected from the retention basin at the facility during the baseline period. Data collected during the baseline period can be compared to data collected in future years to determine whether there is any contaminant transport from the ARMD Facility to the surrounding environment.
During the baseline characterization period, monthly samples were collected from 4 groundwater wells and 9 soil sites near the ARMD Facility. The only surface-water site sampled monthly during the baseline period was upgradient from the facility. There was no streamflow at surface-water sites downgradient from the facility on days when surface-water samples were collected during the baseline characterization period.
Groundwater results for the four wells sampled near the ARMD Facility during the baseline period did not show any major water-quality issues. Mean specific conductance (SC) and pH in groundwater ranged from 220 to 771 microsiemens per centimeter at 25 degrees Celsius (μS/cm) and 6.45 to 6.98, respectively. No constituents in groundwater samples exceeded any U.S. Environmental Protection Agency (EPA) Maximum Contaminant Level (MCL). Dissolved iron (Fe) was the only groundwater constituent that exceeded a Secondary Maximum Contaminant Level (SMCL) established by the EPA. The SMCL for Fe is 300 micrograms per liter (μg/L); samples from three wells had mean dissolved Fe concentrations ranging from 1,100 to 2,600 μg/L. The only volatile organic compounds (VOCs) detected in groundwater samples were bromomethane, acetone, and chloromethane. All VOC detections in groundwater samples were less than the Reporting Detection Levels (RDLs). These three compounds also were detected in blank samples submitted for groundwater samples. Perchlorate was not detected in any groundwater sample collected during the baseline period.
Surface-water data collected during the baseline period were strictly representative of a stream reach upgradient from the ARMD Facility. Stream discharge ranged from 0.03 to 0.08 cubic feet per second during sample collection. The mean SC and pH were 310 μS/cm and 7.6, respectively. No EPA established MCLs or SMCLs were exceeded for any constituents in samples collected from this upgradient stream. Some VOCs were detected in surface water at less than the RDLs. The VOCs detected in surface water were generally the same VOCs as those detected at less than the RDLs for groundwater. Perchlorate was detected in each sample collected from the stream; the mean concentration was 0.07 μg/L. All perchlorate results were less than the RDL of 0.2 μg/L.
Soil samples collected during the baseline period did not have any constituent concentrations that exceeded any medium-specific concentrations (MSC) or soil screening levels (SSL) established by either the Commonwealth of Pennsylvania or the EPA. The Commonwealth of Pennsylvania calculates MSCs based on either a function of acceptable concentrations in groundwater or based on health concerns if the soil is directly contacted. The EPA derives acceptable concentrations of constituents (SSLs) in soil based on standardized equations combining exposure information assumptions with EPA toxicity data. The EPA calculates SSLs for residential and industrial sites. Soil sites at the ARMD Facility were considered “industrial” for comparative purposes. There was at least one order of magnitude difference between any inorganic constituent concentration detected in soil and the MSC and (or) SSL for that constituent. Four VOCs were detected in soil samples collected during the baseline period. None of the VOCs detected in the soils were within three orders of magnitude of any established MSCs or SSLs. The VOCs detected in soil were dichloromethane (also known as methylene chloride), methyl tert-butyl ether (MTBE), tetrachloroethene, and acetone (only detected once). Dichloromethane was the only VOC detected at greater than the RDLs; concentrations in all soil samples were greater than the RDLs. Dichloromethane concentrations ranged from 1.9 to 50.1 micrograms per kilogram (μg/kg). MTBE was detected in 50 percent of samples collected but all results were less than the RDLs of 1.7 to 2.6 μg/kg. Tetrachloroethene was detected in 20 percent of soil samples collected, with a maximum estimated value of 1.5 μg/kg. Inorganic constituents with the highest concentrations in soil were Fe and aluminum (Al); mean Fe and Al concentrations ranged from 28,700 to 52,400 and 10,300 to 19,800 milligrams per kilogram (mg/kg), respectively. Data collected during the baseline period in 2016 can be compared to future data to determine whether concentrations in water and soils surrounding the facility have shown any changes that could be caused by the facility operation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191094","collaboration":"Prepared in Cooperation with the Letterkenny Army Depot","usgsCitation":"Galeone, D.G., 2019, Baseline environmental monitoring of groundwater, surface water, and soil at the Ammonium Perchlorate Rocket Motor Destruction Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2016: U.S. Geological Survey Open-File Report 2019–1094, 32 p., https://doi.org/10.3133/ofr20191094.","productDescription":"Report: vii; 32 p.; Appendices 1-4","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102807","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":437309,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P973YRPL","text":"USGS data release","linkHelpText":"Quality Control and Soil Quality Data in support of Baseline Environmental Monitoring at the Ammonium Perchlorate Rocket Motor Destruction (ARMD) Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2016"},{"id":368210,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094_appendix3.xlsx","text":"Appendix 3","size":"16.8 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1094"},{"id":368211,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094_appendix4.xlsx","text":"Appendix 4","size":"32.3 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1094"},{"id":368208,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094_appendix1.xlsx","text":"Appendix 1","size":"15.5 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1094"},{"id":368212,"rank":7,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094.pdf","text":"Report","size":"20.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1094"},{"id":368107,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5be05a51e4b0b3fc5cf33502","text":"USGS data release","description":"OFR 2019-1094","linkHelpText":"Quality Control and Soil Quality Data in support of Baseline Environmental Monitoring at the Ammonium Perchlorate Rocket Motor Destruction (ARMD) Facility at the Letterkenny Army Depot, Chambersburg, Pennsylvania, 2016"},{"id":368209,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1094/ofr20191094_appendix2.xlsx","text":"Appendix 2","size":"22.1 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2019-1094"},{"id":368190,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1094/coverthb.jpg"}],"country":"United States","state":"Pennsylvania ","county":"Franklin County","city":"Chambersburg","otherGeospatial":"Letterkenny Army Depot","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.71831512451172,\n 40.0013199623656\n ],\n [\n -77.67333984375,\n 40.0013199623656\n ],\n [\n -77.67333984375,\n 40.03471220877633\n ],\n [\n -77.71831512451172,\n 40.03471220877633\n ],\n [\n -77.71831512451172,\n 40.0013199623656\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The Monte Blanco borate deposits are located along the southern margin of Death Valley’s Furnace Creek Wash, south of Twenty Mule Team Canyon road in California. Topographic and geologic mapping by S. Muessig and F.M. Byers, Jr., in 1954 documented these deposits’ geologic settings, geometries, mineralogies, and chemical characteristics. They estimated borate resources at the time to be in excess of 550,000 tons B2O3.
The borate bodies are composed of predominantly ulexite and colemanite. They lie beneath Monte Blanco itself and along a northwest-trending series of conspicuous, white hills and mounds formed by northeasterly dipping, fine-grained sedimentary beds and basaltic volcanic rocks of the Miocene and Pliocene Furnace Creek Formation.
Geologic data suggest that in Miocene and Pliocene time, fine-grained sediments, volcanic debris and flows, and volcanically associated, boron-rich fluids gradually filled a fairly flat playa-like environment. At times, thick beds of felty crystals of ulexite developed and were interlayered as lenses in a thick series of mudstones as is seen today at the Eagle Borax works. After burial, the exterior of the ulexite deposit was altered to massive colemanite by ground water, which produced the “shell” of colemanite that typically surrounds the presently outcropping ulexite bodies.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191111","usgsCitation":"Muessig, S.J., Pennell, W.M, Knott, J.R., and Calzia, J.P., 2019, Geology of the Monte Blanco borate deposits, Furnace Creek Wash, Death Valley, California: U.S. Geological Survey Open-File Report 2019–1111, 35, p., 2 plates, scales 1:2,400 and 1: 2,000, https://doi.org/10.3133/ofr20191111.","productDescription":"Report: v, 30 p.; 2 Plates: 28.00 x 29.75 and 18.11 x 24.96 inches","numberOfPages":"37","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-088268","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":368047,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1111/coverthb.jpg"},{"id":368050,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1111/ofr20191111_plate2.pdf","text":"Plate 2","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1111"},{"id":368048,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1111/ofr20191111_pamphlet.pdf","text":"Report","size":"2.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1111"},{"id":368049,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2019/1111/ofr20191111_plate1.pdf","text":"Plate 1","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1111"}],"country":"United States","state":"California","otherGeospatial":"Death Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.76245117187499,\n 35.60818490437746\n ],\n [\n -116.06506347656251,\n 35.60818490437746\n ],\n [\n -116.06506347656251,\n 37.19095471582605\n ],\n [\n -117.76245117187499,\n 37.19095471582605\n ],\n [\n -117.76245117187499,\n 35.60818490437746\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Coastal droughts have a different dynamic than upland droughts, which are typically characterized by agricultural, hydrologic, meteorological, and (or) socioeconomic effects. Drought uniquely affects coastal ecosystems because of changes in the salinity conditions of estuarine creeks and rivers. The location of the freshwater-saltwater interface in surface-water bodies is an important factor in the ecological and socioeconomic dynamics of coastal communities. To address the data and information gap for characterizing coastal drought, the Coastal Salinity Index (CSI) was developed by using salinity data. The CSI uses a computational approach similar to the Standardized Precipitation Index. The CSI can be computed for unique time intervals (for example 1-, 6-, 12-, and 24-month intervals) to characterize short- and long-term drought (saline) conditions, as well as wet (high freshwater inflow) conditions.
To encourage the use of the CSI in current and future research endeavors, this investigation addressed three activities to enhance the use and application of the CSI. First, a software package was developed for the consistent computation of the CSI that includes preprocessing of salinity data, filling missing data, computing the CSI, post-processing, and generating the supporting metadata. This software package is available for download from the U.S. Geological Survey GitLab repository. Second, the CSI has been computed at sites along the southeastern Atlantic coast (Florida to North Carolina) and the Gulf of Mexico (Texas to Florida) to increase the opportunity for linking the CSI to ecological response data. Third, using telemetered salinity data, the real-time computation of the CSI has been prototyped and disseminated on the web.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191090","collaboration":"Prepared in cooperation with the National Integrated Drought Information System","usgsCitation":"Petkewich, M.D., Lackstrom, K., McCloskey, B.J., Rouen, L.F, and Conrads, P.A., 2019, Coastal Salinity Index along the southeastern Atlantic coast and the Gulf of Mexico, 1983 to 2018 (ver. 1.1, April 2023): U.S. Geological Survey\nOpen-File Report 2019–1090, 26 p., https://doi.org/10.3133/ofr20191090.","productDescription":"Report: vi, 26 p.; Appendix; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105920","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":499716,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109078.htm","linkFileType":{"id":5,"text":"html"}},{"id":415336,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2019/1090/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"}},{"id":415335,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1090/ofr20191090_appendix1.pdf","text":"Appendix 1","size":"1.68 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1090 Appendix 1","linkHelpText":"—Coastal Salinity Index User Guide"},{"id":415334,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1090/ofr20191090.pdf","text":"Report","size":"3.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1090"},{"id":367860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1090/coverthb2.jpg"},{"id":367858,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MQLNL2","text":"USGS data release","linkHelpText":"Coastal Salinity Index for Monitoring Drought"}],"country":"United States","state":"Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, Puerto Rico, South Carolina, Texas","otherGeospatial":"Gulf of Mexico Coast, South Atlantic Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.5439453125,\n 17.45547257997284\n ],\n [\n -65.21484375,\n 17.45547257997284\n ],\n [\n -65.21484375,\n 18.95824648598139\n ],\n [\n -67.5439453125,\n 18.95824648598139\n ],\n [\n -67.5439453125,\n 17.45547257997284\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 24.487148563173424\n ],\n [\n -75.0146484375,\n 24.487148563173424\n ],\n [\n -75.0146484375,\n 36.38591277287651\n ],\n [\n -98.61328125,\n 36.38591277287651\n ],\n [\n -98.61328125,\n 24.487148563173424\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: October 1, 2019; Version 1.1: April 6, 2023","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
The Anderson Ranch property (study area), located in Taos County, north-central New Mexico, was transferred from Chevron Mining, Inc. (CMI) to the Bureau of Land Management (BLM) as part of a Natural Resource Damage Assessment and Restoration (NRDAR) court-ordered settlement. The study area supports freshwater emergent wetlands and freshwater ponds. The settlement states that CMI will provide the land and a monetary settlement to support the restoration of the wetlands on the property. To best manage the study area, the BLM requires an understanding of potential effects of climate variability and groundwater withdrawals on the wetland function. This study, completed by the U.S. Geological Survey in cooperation with the BLM, provides an initial hydrologic characterization of the study area, which included literature review, collection of groundwater-level and aqueous-chemistry data, completion of a vegetation survey, and preliminary data analysis. The data compiled, collected, and analyzed as part of this study indicate that the wetlands within the study area are groundwater fed and that the water maintaining the wetlands is modern. Surface-water levels in the pond and groundwater levels in the surrounding wetland fluctuate seasonally. The hydraulic gradient in the study area is from northeast to southwest. Evapotranspiration is a main driver of water demand within the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191100","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Galanter, A.E., Shephard, Z.M., and Herrera-Olivas, P., 2019, Anderson Ranch wetlands hydrologic characterization in Taos County, New Mexico: U.S. Geological Survey Open-File Report 2019–1100, 42 p., https://doi.org/10.3133/ofr20191100. ","productDescription":"iii, 42 p. 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New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE, Suite B
Albuquerque, NM 87113
The production of Klamath River fall Chinook salmon (Oncorhynchus tshawytscha) in northern California and southern Oregon is thought to be limited by poor survival during freshwater juvenile life stages, in part a result of Ceratonova shasta—a highly infectious disease that can lead to high fish mortality. Higher flushing river flows are thought to affect the concentration of C. shasta spores, and in turn, juvenile salmon infection and mortality. The Stream Salmonid Simulator (S3) model was built to simulate the spatiotemporal dynamics of the growth, movement, and survival of juvenile salmon from spawning through migration to the Pacific Ocean in response to river flow, habitat availability, water temperature, and C. shasta spore concentrations. The S3 model has been calibrated to juvenile fall Chinook salmon abundances at a trap site within the Klamath River, and was specifically designed to provide objective predictions of juvenile salmon abundance and survival in relation to proposed flow management alternatives and resulting fish infection and mortality by C. shasta. Infection by C. shasta in the Klamath River is location specific, occurring in a “disease zone” with high spore concentrations. The spatial extent of this disease zone (from river kilometer 289.6 to 212.9) has been incorporated in the S3 model for the Klamath River, enabling the assessment of disease effects on fish at specific spatial locations such as the trap sampling sites, and for fish that were or were not exposed to the disease zone as they emigrate the Klamath River to the Pacific Ocean.
Given the information gained from field observations on spore concentrations in relation to river flow, deliberations by resource managers resulted in the incorporation of springtime flushing flows in a Proposed Action (PA) scenario developed in part to lower spore concentrations within the disease zone. A Historical (HI) scenario based on the observed flows, temperatures, and spore concentrations from 2004 to 2016 was used to compare and contrast the potential benefits to juvenile salmon from PA flows in relation to the HI conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191099","collaboration":"Prepared in cooperation with the National Oceanic and Atmospheric Administration, National Marine Fisheries Service","usgsCitation":"Plumb, J.M., Perry, R.W., Som, N.A., Alexander, J., and Hetrick, N.J., 2019, Using the stream salmonid simulator (S3) to assess juvenile Chinook salmon (Oncorhynchus tshawytscha) production under historical and proposed action flows in the Klamath River, California: U.S. Geological Survey Open-File\nReport 2019-1099, 43 p., https://doi.org/10.3133/ofr20191099.","productDescription":"vi, 43 p.","onlineOnly":"Y","ipdsId":"IP-107092","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":367843,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1099/coverthb.jpg"},{"id":367844,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1099/ofr20191099.pdf","text":"Report","size":"3.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1099"}],"country":"United States","state":"California","otherGeospatial":"Klamath River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.5247802734375,\n 41.38917324986403\n ],\n [\n -122.23114013671875,\n 41.38917324986403\n ],\n [\n -122.23114013671875,\n 41.92475971933975\n ],\n [\n -123.5247802734375,\n 41.92475971933975\n ],\n [\n -123.5247802734375,\n 41.38917324986403\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
In this report, we describe application of the Stream Salmonid Simulator (S3) to Chinook salmon (Oncorhynchus tshawytscha) in the Klamath River between Keno Dam in southern Oregon and the ocean in northern California. S3 is a deterministic life-stage-structured population model that tracks daily growth, movement, and survival of juvenile salmon. It can track different source populations or species, such as major tributary populations that enter a river like the Klamath River. A key theme of the model is that river flow affects habitat availability and capacity, which in turn drives density-dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily time series of discharge, water temperature, and useable habitat area or carrying capacity. In turn, the physical characteristics of each habitat unit and the number of fish occupying each unit affect survival and growth within each habitat unit and movement of fish among habitat units.
The physical template of the Klamath River was formed by classifying the river into 2,635 mesohabitat units composed of runs, riffles, and pools. This template enabled modeling of the unimpounded Klamath River between the Keno Dam (the uppermost of four dams) and Iron Gate Dam (the lowermost dam) to address dam-removal scenarios. However, in this report, our focus was on parameterizing and calibrating the model under existing conditions, which included 1,706 discrete habitat units over the 312-kilometer (km) section of river between Iron Gate Dam and the ocean. For each habitat unit, we developed a time series of daily flow, water temperature, and amount of available habitat (weighted usable habitat area [WUA]) for spawners, fry, and parr. WUA time series were constructed using habitat suitability criteria for Chinook salmon applied to eight two-dimensional (2-D) hydrodynamic models that represented the geomorphic variability in habitat across the Klamath River. Results from the 2-D models were then extrapolated to unmodeled habitat units by scaling WUA curves for changes in habitat unit length and width. These variables were then used to drive population dynamics such as egg development and survival and juvenile movement, growth, and survival.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191107","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Bureau of Reclamation","usgsCitation":"Perry, R.W., Plumb, J.M., Jones, E.C., Som, N.A., Hardy, T.B., and Hetrick, N.J., 2019, Application of the Stream Salmonid Simulator (S3) to Klamath River fall Chinook salmon (Oncorhynchus tshawytscha), California—Parameterization and calibration: U.S. Geological Survey Open-File Report 2019–1107, 89 p., https://doi.org/10.3133/ofr20191107.","productDescription":"Report: viii, 89p.; Appendix 1","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106890","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":367791,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1107/ofr20191107.pdf","text":"Report","size":"5.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1107"},{"id":367792,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2019/1107/ofr20191107_a1.pdf","text":"Appendix 1","size":"241 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1107 Appendix 1"},{"id":367790,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1107/coverthb.jpg"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Keno Dam, Klamath River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.4091796875,\n 41.17038447781618\n ],\n [\n -120.66284179687498,\n 41.17038447781618\n ],\n [\n -120.66284179687498,\n 42.4234565179383\n ],\n [\n -124.4091796875,\n 42.4234565179383\n ],\n [\n -124.4091796875,\n 41.17038447781618\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
In cooperation with the New Mexico County of Bernalillo, the U.S. Geological Survey characterized potential polychlorinated biphenyl (PCB) concentration and estimated loading into the Rio Grande from watersheds that are under the county’s jurisdiction. Water and sediment samples were collected in 2017–18 from six sites within four stormwater drainage basins in the Albuquerque, New Mexico, urbanized area for the analysis of PCB congeners and other water-quality constituents during dry and wet seasons. Also, the rainfall-runoff model Arid Lands Hydrologic Model (AHYMO) was used to estimate stormwater discharge at the two sample collection sites not affected by pump station operation. Along with the PCB analysis, the discharge data were used to estimate total PCB stormflow event loads for eight events in these urban Rio Grande tributaries. PCBs were detected in 34 of 36 water samples at concentrations as high as 65.8 nanograms per liter and in 12 of 13 sediment samples at concentrations as high as 163,000 nanograms per kilogram dry weight. Six of the 36 water samples exceeded the New Mexico surface-water quality standard for protection of wildlife habitat and aquatic life of 14 nanograms per liter for PCBs. None of the water samples exceeded the U.S. Environmental Protection Agency’s National Pollutant Discharge Elimination System permit level limit of 200 nanograms per liter for PCBs in stormwater systems discharging into the Rio Grande. PCB concentrations in water samples in this study were not linearly related to antecedent precipitation or measured water-quality parameters, but PCB concentrations had a statistically significant positive Kendall’s tau correlation with total suspended solids for water samples and with total organic carbon for sediment samples. The PCB congener profiles indicate that sources to stormwater drainage basins in Bernalillo County originate both from legacy sources, such as Aroclors (for example, in landfills and old building materials), and from current-use sources, such as yellow pigments (for example, in printed materials and packaging in urban litter or refuse). Total PCB stormflow event loads were calculated with average potential minimum and maximum event loads of 0.73 and 4.32 milligrams per storm event, respectively, at the Adobe Acres pump station site and 56.78 and 315.13 milligrams per storm event at the Sanchez Farms inflow at Albuquerque, N. Mex., site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191106","collaboration":"Prepared in cooperation with Bernalillo County","usgsCitation":"Shephard, Z.M., Conn, K.E., Beisner, K.R., Jornigan, A.D., and Bryant, C.F., 2019, Characterization and load estimation of polychlorinated biphenyls (PCBs) from selected Rio Grande tributary stormwater channels in the Albuquerque urbanized area, New Mexico, 2017–18: U.S. Geological Survey Open-File Report 2019–1106, 48 p., https://doi.org/10.3133/of20191106.","productDescription":"x, 48 p.","numberOfPages":"61","onlineOnly":"Y","ipdsId":"IP-109136","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":367784,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1106/coverthb.jpg"},{"id":367785,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1106/ofr20191106.pdf","size":"4.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1106"}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Rio Grande","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8255615234375,\n 34.9371707067839\n ],\n [\n -106.48223876953125,\n 34.9371707067839\n ],\n [\n -106.48223876953125,\n 35.20579439829525\n ],\n [\n -106.8255615234375,\n 35.20579439829525\n ],\n [\n -106.8255615234375,\n 34.9371707067839\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
6700 Edith Blvd.
Albuquerque, NM 87113
Eastern Energy Resources Science Center
12201 Sunrise Valley Drive
956 National Center
Reston, VA 20192
Although streamflow is widely recognized as a controlling factor in stream health, empirical relations between indicators of anthropogenic modification of streamflow and ecological indicators have been elusive. The objective of this report is to build upon specific findings reported in recent publications by providing a library of empirical models that describe the relations between streamflow modification and indicators of biological integrity. Biological monitoring data from 812 streams and rivers across the United States were matched with sites where daily streamflow was also monitored by the U.S. Geological Survey. Of these sites, 118 were sampled by the U.S. Geological Survey along gradients of streamflow modification within 3 regional focus studies. The integrity of invertebrate and fish communities was expressed as a binary variable, “impaired” or “unimpaired,” signifying whether or not the composition and structure of the biological community was statistically reduced relative to regional reference sites. Streamflow modification at each gaged site was quantified with 509 streamflow statistics scaled to express the ratio of observed streamflow conditions to site-specific expected conditions in the absence of human influences on watershed hydrology. For each region, generalized additive modeling was used to examine relations between each indicator of streamflow modification and indicators of biological integrity (response variable). In every region examined, statistically defensible and ecologically realistic relations were found between indicators of streamflow modification and indicators of biological integrity. These findings can aid practitioners and managers seeking to (1) propose empirically based hypotheses about the specific components of streamflow regimes that are critical to aquatic communities, which can subsequently be explored in detail in a region or river basin of interest; and (2) predict biological responses to anthropogenic modification of specific components of the streamflow regime.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191088","usgsCitation":"Carlisle, D.M., Grantham, T.E., Eng, K., Wolock, D.M., 2019, Regional-scale associations between indicators of biological integrity and indicators of streamflow modification: U.S. Geological Survey Open-File Report 2019–1088, 10 p., https://doi.org/10.3133/ofr20191088.\n","productDescription":"iv, 10 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-097828","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":367467,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O2ZV0M","linkHelpText":"Regional-scale Model Predictions of the Relation Between Biological Integrity and Streamflow Modification"},{"id":367452,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1088/ofr20191088.pdf","text":"Report","size":"12.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1088"},{"id":367451,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1088/coverthb.jpg"}],"contact":"Director, USGS Kansas Water Science Center
1217 Biltmore Drive
Lawrence, KS 66049
785-842-9909
The National Park Service (NPS) and the Bureau of Land Management (BLM) are concerned about cumulative effects of groundwater development on groundwater-dependent resources managed by, and other groundwater resources of interest to, these agencies in Snake Valley and adjacent areas, Utah and Nevada. Of particular concern to the NPS and BLM are withdrawals from all existing approved, perfected, certified, permitted, and vested groundwater rights in Snake Valley totaling about 55,272 acre-feet per year (acre-ft/yr), and from several senior water-right applications filed by the Southern Nevada Water Authority (SNWA) totaling 50,680 acre-ft/yr.
An existing groundwater-flow model of the eastern Great Basin was used to investigate where potential drawdown and capture of natural discharge is likely to result from potential groundwater withdrawals from existing groundwater rights in Snake Valley, and from groundwater withdrawals proposed in several applications filed by the SNWA. To evaluate the potential effects of the existing and proposed SNWA groundwater withdrawals, 11 withdrawal scenarios were simulated. All scenarios were run as steady state to estimate the ultimate long-term effects of the simulated withdrawals. This assessment provides a general understanding of the relative susceptibility of the groundwater resources of interest to the NPS and BLM, and the groundwater system in general, to existing and future groundwater development in the study area.
At the NPS and BLM groundwater resource sites of interest, simulated drawdown resulting from withdrawals based on existing approved, perfected, certified, permitted, and vested groundwater rights within Snake Valley ranged between 0 and 159 feet (ft) without accounting for irrigation return flow, and between 0 and 123 ft with accounting for irrigation return flow. With the addition of proposed SNWA withdrawals of 35,000 acre-ft/yr (equal to the Unallocated Groundwater portion allotted to Nevada in a draft interstate agreement), simulated drawdowns at the NPS and BLM sites of interest increased to range between 0 and 2,074 ft without irrigation return flow, and between 0 and 2,002 ft with irrigation return flow. With the addition of the proposed SNWA withdrawals of an amount equal to the full application amounts (50,680 acre-ft/yr), simulated drawdowns at the NPS and BLM sites of interest increased to range between 1 and 3,119 ft without irrigation return flow, and between 1 and 3,044 ft with irrigation return flow.
At the NPS and BLM groundwater resource sites of interest, simulated capture of natural discharge resulting from withdrawals based on existing groundwater rights in Snake Valley, both with and without irrigation return flow, ranged between 0 and 100 percent; simulated capture of 100 percent occurred at four sites. With the addition of proposed SNWA withdrawals of an amount equal to the Unallocated Groundwater portion allotted to Nevada in the draft interstate agreement, simulated capture of 100 percent occurred at nine additional sites without irrigation return flow, and at eight additional sites with irrigation return flow. With the addition of the proposed SNWA withdrawals of an amount equal to the full application amounts, simulated capture of 100 percent occurred at 11 additional sites without irrigation return flow, and at 9 additional sites with irrigation return flow.
The large simulated drawdowns produced in the scenarios that include large portions or all of the proposed SNWA withdrawals indicate that the groundwater system may not be able to support the amount of withdrawals from the proposed points of diversion (PODs) in the current SNWA water-right applications. Therefore, four additional scenarios were simulated where the withdrawal rates at the SNWA PODs were constrained by not allowing drawdowns to be deeper than the assumed depth of the PODs (about 2,000 ft). In the constrained scenarios, total withdrawals at the SNWA PODs were reduced to about 48 percent of the Unallocated Groundwater portion allotted to Nevada (35,000 acre-ft/yr reduced to 16,817 acre-ft/yr or 16,914 acre-ft/yr, without or with irrigation return flow, respectively), and about 44 percent of the full application amounts (50,680 acre-ft/yr reduced to 22,048 acre-ft/yr or 22,165 acre-ft/yr, without or with irrigation return flow, respectively). This indicates that the SNWA may need to add more PODs, or PODs in different locations, in order to withdraw large portions or all of the groundwater that has been applied for.
At the NPS and BLM groundwater resource sites of interest, simulated drawdown resulting from the addition of the constrained SNWA withdrawals applied to the Unallocated Groundwater amount ranged between 0 and 290 ft without irrigation return flow, and between 0 and 252 ft with irrigation return flow. With the addition of the constrained SNWA withdrawals applied to the full application amounts, simulated drawdowns at the NPS and BLM sites of interest ranged between 0 and 358 ft without irrigation return flow, and between 0 and 313 ft with irrigation return flow.
At the NPS and BLM groundwater resource sites of interest, with the addition of the constrained SNWA withdrawals applied to the Unallocated Groundwater amount, simulated capture of 100 percent of the natural discharge occurred at five additional sites without irrigation return flow, and at two additional sites with irrigation return flow (in addition to the four captured from existing water rights both with and without irrigation return flow). With the addition of the constrained SNWA withdrawals applied to the full application amounts, simulated capture of 100 percent occurred at six additional sites both with and without irrigation return flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191083","collaboration":"Prepared in cooperation with the National Park Service and the Bureau of Land Management","usgsCitation":"Masbruch, M.D., 2019, Numerical model simulations of potential changes in water levels and capture of natural discharge from groundwater withdrawals in Snake Valley and adjacent areas, Utah and Nevada: U.S. Geological Survey Open-File Report 2019–1083, 49 p., https://doi.org/10.3133/ofr20191083.","productDescription":"Report: vi, 49 p.; Data Release","numberOfPages":"49","onlineOnly":"Y","ipdsId":"IP-103457","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":367115,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1083/coverthb_.jpg"},{"id":367116,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1083/ofr20191083.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1083"},{"id":367119,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LQDQGM","text":"Data Release","linkHelpText":"MODFLOW-2005 files for numerical model simulations of potential changes in water levels and capture of natural discharge from groundwater withdrawals in Snake Valley and adjacent areas, Utah and Nevada"}],"country":"United States","state":"Nevada, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.48828125000001,\n 35.53222622770337\n ],\n [\n -110.302734375,\n 39.36827914916014\n ],\n [\n -110.12695312499999,\n 40.97989806962013\n ],\n [\n -111.005859375,\n 42.68243539838623\n ],\n [\n -114.78515624999999,\n 41.244772343082076\n ],\n [\n -117.59765625,\n 37.64903402157866\n ],\n [\n -115.48828125000001,\n 35.53222622770337\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
801-908-5000