Communities throughout Massachusetts face the potential effects of climate change, ranging from more extreme rainfall to more pronounced and frequent droughts. Understanding the effects of climate change on hydrology is important to State and community officials to evaluate the potential effects on infrastructure and water systems. To better understand the effects of climate change on hydrology, the U.S. Geological Survey, in partnership with Cornell University and Tufts University, conducted a study in cooperation with the Massachusetts Executive Office of Energy and Environmental Affairs to develop tools for projecting 21st-century climate and hydrologic characteristics in Massachusetts.
A stochastic weather generator was developed to project future climatic characteristics for Massachusetts. The stochastic weather generator estimates daily precipitation, minimum temperature, and maximum temperature for 17 warming scenarios (from 0 to 8 degrees Celsius, in 0.5-degree increments). To project future hydrologic characteristics, the stochastic weather generator output data were input to the Precipitation-Watershed Modeling System deterministic watershed model for the Squannacook River watershed, which is the watershed selected as the pilot study location for investigating future hydrologic characteristics. Hydrologic data output from the deterministic watershed model were then input to a stochastic watershed model developed for this study to correct model errors (model errors are often observed in the output from deterministic models at the high- and low-flow extremes). The output from the stochastic watershed model was then used to characterize hydrology for the 17 warming scenarios. For the Squannacook River watershed, the results project more extreme flood and low streamflows under the warming scenarios.
Output from the tools allows the characterization of future streamflows for the years 2030, 2050, 2070, and 2090, which expands our understanding of 21st-century climatic and hydrologic risk in Massachusetts. These tools could improve Federal, State, and community officials’ ability to mitigate the effects of climate change over the next several decades.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Watershed nutrient management often focuses on actions that reduce the movement of nitrogen (N) and phosphorus (P) from agricultural lands into streams. One area of management focus is the buffer of land adjacent to streams. Wetlands and forests in this buffer can intercept and retain N and P from the landscape. In addition to directly intercepting agricultural nutrients, natural habitats in the buffer can alter stream geomorphology and influence the in-stream processing and transformation of N and P to less labile and mobile forms. Here, we assess the influence of buffer land cover on in-stream processing of N and P. We measured nutrient dynamics in the water column and sediments of agricultural streams in the Fox River and Duck Creek watersheds (WI, USA) during the growing season. In these streams, water column processing was low, possibly due to a lack of primary producers in the water column. Water column P processing was weakly associated with wetland land cover in the buffer, but buffer land cover had no clear effect on inorganic N processing. On the other hand, sediments were almost always a source of inorganic P and a sink for inorganic N. Sediment P release was higher in streams with more agricultural land cover in the buffer. Sediments in streams with agricultural land cover in the buffer also removed more nitrate, even after accounting for the greater availability of nitrate in those streams. The buffer land cover conditions we quantified occupy a very small portion of the overall watershed (100 m wide, for 1 km upstream of the study site) but nevertheless appear to influence in-stream cycling of N and P. For P management, reducing agricultural land cover in buffers is already a priority due to the ability of wetlands and forests to intercept nutrients, but this study suggests there may be some additional benefit due to changes in in-stream P processing.
The U.S. Geological Survey (USGS) collected data on the vertical distribution of hydraulic head, specific capacity, and water quality using aquifer-interval-isolation tests and other vertical profiling methods in 15 boreholes completed in fractured sedimentary bedrock in Northampton, Warminster, and Warwick Townships, Bucks County, Pennsylvania during 2018–19. This work was done, in cooperation with the U.S. Navy, to support detailed investigations at and near the former Naval Air Warfare Center (NAWC) Warminster, where groundwater contamination with per- and polyfluoroalkyl substances (PFAS) had become a concern since 2014. Two PFAS compounds, perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA), have been measured in groundwater samples from supply and monitoring wells at or near NAWC Warminster in concentrations above U.S. Environmental Protection Agency health advisory levels for drinking water. The area is underlain by the Triassic Stockton Formation, which predominantly consists of sandstone interbedded with shale and siltstone beds and forms a layered fractured-rock aquifer used for private, industrial, and public drinking water supply.
The vertical distribution of aquifer properties and water quality was assessed through hydraulic tests and sampling of aquifer intervals using a straddle-packer system (13 boreholes) or depth-discrete point sampling under known borehole-flow conditions (2 boreholes). Geophysical and video logs collected by USGS during 2017–19 were used to identify potential water-bearing fractures in 15 boreholes, which ranged in depth from 210 to 604 feet (ft) and included 6 boreholes drilled in 2018 and 9 existing wells on or near the former NAWC Warminster. Measured borehole flow was predominantly downward in most of the deepest boreholes (greater than 400 ft), which were commonly located at the highest land-surface elevations, with inflow from fractures at relatively shallow depths and outflow through fractures near or below depths of 500 ft below land surface. Hydraulic head differences measured during packer tests were up to about 60 ft between shallow and deep intervals. Borehole flow was predominantly upward in most boreholes less than 400 ft in depth and farther from, and at lower land-surface elevations than, the former NAWC Warminster. Total borehole specific capacity ranged from about 0.07 to 41 gallons per minute per foot [(gal/min)/ft]. Specific-capacity values for individual intervals ranged from 0.02 to 40.0 (gal/min)/ft, with a median of 1.14 (gal/min)/ft and a large range in values at most depths.
Differences in water quality of samples as indicated by field properties (pH, dissolved oxygen, and specific conductance) and concentrations of dissolved major ions, PFOA, and PFOS were apparent among isolated intervals in the boreholes. Summed concentrations of PFOA and PFOS ranged from about 11 to 10,780 nanograms per liter (ng/L) and were greater than the 2016 U.S. Environmental Protection Agency health advisory of 70 ng/L for summed PFOA and PFOS concentrations in 62 of 104 intervals and discrete depths tested. The mass ratio of PFOS to PFOA was generally higher than 1.0 in samples with summed PFOA and PFOS concentrations greater than 70 ng/L, with ratio values as high as 8.7. In many boreholes, summed concentrations of PFOA and PFOS were positively related to chloride concentrations, which were elevated above natural-background values [less than 10 milligrams per liter] in most samples and as high as 717 milligrams per liter. Sources of the elevated chloride other than, or in addition to, common rock salt (sodium chloride) were indicated by chloride to sodium molar ratios greater than 1.0. Water-quality data indicated that sampled water from some intervals with lower hydraulic heads may be affected by water from intervals with higher hydraulic heads because of vertical flow in open boreholes; samples from these intervals with lower hydraulic heads may not be fully representative due to some component of cross contamination and should be interpreted with caution.
Through a preliminary correlation of natural gamma and resistivity logs of boreholes drilled at and near the former NAWC Warminster, 11 lithologic units were identified and interpreted to strike northeast and dip to the northwest. Hydraulic heads were generally highest in isolated intervals that intercepted beds which, when projected up dip, crop out at the highest land-surface elevation on the former NAWC Warminster, indicating that the dipping-bed structure and topography are factors affecting the distribution of hydraulic head in the aquifer. The hydrogeologic framework in conjunction with the vertical distribution of hydraulic heads and water quality may assist in evaluating the locations of various PFAS sources and potential migration pathways of PFAS in groundwater at and near NAWC Warminster.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241007","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Senior, L.A., and Fiore, A.R., 2024, Results of 2018–19 water-quality and hydraulic characterization of aquifer intervals using packer tests and preliminary geophysical-log correlations for selected boreholes at and near the former Naval Air Warfare Center Warminster, Bucks County, Pennsylvania (ver. 1.1, January 2025): U.S. Geological Survey Open-File Report 2024–1007, 136 p., https://doi.org/10.3133/ofr20241007.","productDescription":"Report: xv, 136 p.; 5 Plates; Data Release","numberOfPages":"136","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-138405","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":426405,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241007/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1007 HTML"},{"id":426406,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1007/ofr20241007.XML","description":"OFR 2024-1007 XML"},{"id":426407,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1007/images/"},{"id":426403,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1007/coverthb2.jpg"},{"id":426404,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1007/ofr20241007.pdf","text":"Report","size":"9.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1007 PDF"},{"id":426408,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TC92B5","text":"USGS data release","linkHelpText":"Water-level data and selected field notes for aquifer-interval-isolation tests at and near the former Naval Air Warfare Center Warminster, Bucks County, Pennsylvania, 2018–19 (ver. 2.0, January 2024)"},{"id":426409,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2024/1007/ofr20241007_plates.pdf","text":"Plates 1–5","size":"921 KB"},{"id":481558,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2024/1007/ofr20241007_versionHist.txt","size":"949 B","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Naval Air Warfare Center Warminster","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.21874919403015,\n 40.292862181975266\n ],\n [\n -75.21874919403015,\n 40.12697956762551\n ],\n [\n -74.97075997042653,\n 40.12697956762551\n ],\n [\n -74.97075997042653,\n 40.292862181975266\n ],\n [\n -75.21874919403015,\n 40.292862181975266\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: March 2024; Version 1.1 January 2025","contact":"Director, Pennsylvania Water Science Center
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A massive bloom of the raphidophyte Heterosigma akashiwo occurred in summer 2022 in San Francisco Bay, causing widespread ecological impacts including events of low dissolved oxygen and mass fish kills. The rapidly evolving bloom required equally rapid management response, leading to the use of near-real-time image analysis of chlorophyll from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3. Standard algorithms failed to adequately capture the bloom, signifying a need to refine a two-band algorithm developed for coastal and inland waters that relates the red-edge part of the remote sensing reflectance spectrum to chlorophyll. While the bloom was the initial motivation for optimizing this algorithm, an extensive dataset of in-water validation measurements from both bloom and non-bloom periods was used to evaluate performance over a range of concentrations and community composition. The modified red-edge algorithm with a simplified atmospheric correction scheme outperformed existing standard products across diverse conditions, and given the modest computational requirements, was found suitable for operational use and near-real-time product generation. The final version of the algorithm successfully minimizes error for non-bloom periods when chlorophyll a is typically <30 mg m−3, while also capturing bloom periods of >100 mg m−3 chlorophyll a.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16061103","usgsCitation":"Kudela, R.M., Senn, D.B., Richardson, E.T., Bouma-Gregson, K., Bergamaschi, B.A., and Sim, L., 2024, Evaluation and refinement of chlorophyll-a algorithms for high-biomass blooms in San Francisco Bay (USA): Remote Sensing, v. 16, no. 6, 1103, 15 p., https://doi.org/10.3390/rs16061103.","productDescription":"1103, 15 p.","ipdsId":"IP-160723","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":440089,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16061103","text":"Publisher Index Page"},{"id":435018,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GXJHZ3","text":"USGS data release","linkHelpText":"Assessing spatial variability of nutrients, phytoplankton, and related water-quality constituents in the San Francisco Bay, California: 2021-2022 High-resolution mapping surveys"},{"id":427314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.07594813067364,\n 37.409521554962424\n ],\n [\n -122.01459141030173,\n 37.543473010939294\n ],\n [\n -122.3138751078345,\n 37.9439399615851\n ],\n [\n -122.2371288097209,\n 38.076947981745235\n ],\n [\n -122.4289707157553,\n 38.1493978144897\n ],\n [\n -122.49802549540809,\n 38.08298835733879\n ],\n [\n -122.5286656402067,\n 37.93788689033987\n ],\n [\n -122.4673236665621,\n 37.792501512759955\n ],\n [\n -122.35216906140471,\n 37.610380642182776\n ],\n [\n -122.07594813067364,\n 37.409521554962424\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-03-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Kudela, Raphael M.","contributorId":205181,"corporation":false,"usgs":false,"family":"Kudela","given":"Raphael","email":"","middleInitial":"M.","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":897798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Senn, David B.","contributorId":205182,"corporation":false,"usgs":false,"family":"Senn","given":"David","email":"","middleInitial":"B.","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":897799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richardson, Emily T. 0000-0003-2696-8266","orcid":"https://orcid.org/0000-0003-2696-8266","contributorId":304430,"corporation":false,"usgs":true,"family":"Richardson","given":"Emily","email":"","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897800,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bouma-Gregson, Keith 0000-0002-0304-6034","orcid":"https://orcid.org/0000-0002-0304-6034","contributorId":311235,"corporation":false,"usgs":true,"family":"Bouma-Gregson","given":"Keith","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897801,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":897802,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sim, Lawrence","contributorId":168731,"corporation":false,"usgs":false,"family":"Sim","given":"Lawrence","email":"","affiliations":[{"id":12703,"text":"San Francisco Estuary Institute","active":true,"usgs":false}],"preferred":false,"id":897803,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70252241,"text":"70252241 - 2024 - Multiple stressors mediate the effects of warming on leaf decomposition in a large regulated river","interactions":[],"lastModifiedDate":"2024-03-21T11:57:22.485186","indexId":"70252241","displayToPublicDate":"2024-03-18T06:53:45","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Multiple stressors mediate the effects of warming on leaf decomposition in a large regulated river","docAbstract":"Predicting how increasing temperatures interact with other global change drivers to influence the structure and dynamics of Earth's ecosystems is a primary challenge in ecology. Our study made use of multiple simultaneous “natural experiments” to examine how rapid warming, declining nutrients, invasive consumers, and riparian invasive species management interact to influence leaf decomposition in a large and regulated river. Specifically, we compared the breakdown of cottonwood (Populus fremontii), willow (Salix exigua), and saltcedar (Tamarix sp.) leaf litter in 2022 to a previous experiment from 1998 that occurred under much cooler water temperatures, and had higher water phosphorus concentrations, low numbers of invasive New Zealand mudsnails (Potamopyrgus antipodarum), and unaltered litter chemistry from the herbivory of saltcedar leaf beetles (Diorhabda carinulata). We found that the effects of up to 10°C warmer temperatures on leaf decomposition were mediated by the establishment and management of invasive species and declining water nutrient concentrations arising from upstream reservoir lowering. Such interactions led to accelerated breakdown of saltcedar, but relatively minor effects of warming on the rate of cottonwood and willow decomposition. Additionally, our results demonstrate the potential for favorable invasive species management outcomes in the terrestrial environment to produce unintended responses in adjacent freshwater ecosystems. As temperatures continue to rise, it is critical that future studies consider how warming interacts with multiple stressors and environmental factors to influence processes such as decomposition in freshwater ecosystems.
The United States Geological Survey (USGS) has continuously monitored the Mohawk River between Lock 7 and Lock 9 of the New York State Barge Canal since 2011. There was a brief period, from 1914 to 1919, when a streamgage was operated at Vischer Ferry Dam (Lock 7), however, frequent damage to the gage from ice-jam related flooding in 1914 (figure 1) and 1916 resulted in establishing the Mohawk River streamgage at Cohoes, NY (USGS station ID 01357500) in 1917 and discontinuing the Vischer Ferry streamgage in 1919. The current monitoring network includes measurements of gage height (water level) and water temperature at various points within the reach, streamflow at Freeman’s Bridge, and realtime imagery from multiple pan-tilt-zoom web cameras, all of which provide situational awareness to the public, emergency managers, and other stakeholders during periods of ice-jam flooding. The USGS operates and maintains these stations in cooperation with the New York Power Authority, New York State Department of Environmental Conservation, Union College, and Brookfield Renewable Power.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 2024 Mohawk Watershed Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Union College","usgsCitation":"Gazoorian, C.L., 2024, United States Geological Survey ice jam monitoring network on the Mohawk River in Schenectady, NY, in Proceedings of the 2024 Mohawk Watershed Symposium, v. 14, p. 27-28.","productDescription":"2 p.","startPage":"27","endPage":"28","ipdsId":"IP-162726","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":501500,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501499,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://minerva.union.edu/garverj/mws/2024/symposium.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","city":"Schenectady","otherGeospatial":"Mohawk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.92518238681888,\n 42.83580687077557\n ],\n [\n -73.97515733707353,\n 42.83580687077557\n ],\n [\n -73.97515733707353,\n 42.81032004405722\n ],\n [\n -73.92518238681888,\n 42.81032004405722\n ],\n [\n -73.92518238681888,\n 42.83580687077557\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gazoorian, Christopher L. 0000-0002-5408-6212 cgazoori@usgs.gov","orcid":"https://orcid.org/0000-0002-5408-6212","contributorId":2929,"corporation":false,"usgs":true,"family":"Gazoorian","given":"Christopher","email":"cgazoori@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":894589,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70252191,"text":"70252191 - 2024 - Flood of October 31 to November 3, 2019, East Canada Creek, West Canada Creek, and Sacandaga River Basins","interactions":[],"lastModifiedDate":"2024-03-19T13:23:25.892201","indexId":"70252191","displayToPublicDate":"2024-03-15T08:21:38","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"title":"Flood of October 31 to November 3, 2019, East Canada Creek, West Canada Creek, and Sacandaga River Basins","docAbstract":"Between October 31 and November 3, 2019, historic flooding in parts of the Mohawk Valley and southern Adirondack region resulted in one fatality, an estimated $33 million in damages, and the declaration of a state of emergency for 13 New York counties. Flooding resulted from high-intensity rainfall within a 24-hour period between October 31 and November 1, 2019, at the end of an October that had much higher rainfall than normal. In that 24-hour period, rainfall amounts in the most heavily affected parts of the region largely ranged from about 2 to 5 inches, but a maximum rainfall amount of 7.00 inches was recorded in Speculator, NY in Hamilton County. In this location, a rainfall of 7.00 inches in a 24-hour period is estimated to have between a 200- and 500-year recurrence interval or between a 0.5- and 0.2-percent chance of happening or being exceeded in any given year.\n\nThe most severe flooding from October 31 to November 3, 2019, was mainly in the East and West Canada Creek basins, which are within the Mohawk River basin, and the Sacandaga River basin, which is within the upper Hudson River basin. Flooding resulted in new peak streamflow records at eight of nine selected U.S. Geological Survey streamgages from the region, including at three streamgages that have been in operation for about 100 years. At East Canada Creek at East Creek, NY (01348000), flooding resulted in the second highest peak streamflow in its 71-year period of record. National Weather Service major flood stages were exceeded at the three streamgages in the region where National Weather Service flood stages have been established and were exceeded at Hinckley Reservoir at Hinckley, NY (01343600). Hinckley Reservoir has a drainage area of 372 square miles and regulates West Canada Creek streamflow about 31 miles upstream of West Canada Creek at Kast Bridge (01346000) and 3 miles downstream of West Canada Creek near Wilmurt, NY (01343060). \n\nIn West Canada Creek, downstream of Hinckley Reservoir, a distinct double peak of streamflow happened during the 2019 flood and was recorded at West Canada Creek at Kast Bridge, NY (01346000). A similar, but less distinct, double peak was recorded at Mohawk River near Little Falls, NY (01347000), which is located 14.8 miles downstream of West Canada Creek at Kast Bridge, NY (01346000). The first peak of the double peak was likely caused by inflows to West Canada Creek from unregulated tributaries downstream of Hinckley Reservoir, such as Cincinnati Creek, which drains a relatively large area of 48.5 square miles in the northwest corner of the West Canada Creek watershed. Cincinnati Creek was ungaged at the time, but in response to the flood, the U.S. Geological Survey, in cooperation with the New York State Canal Corporation, installed a gage at Cincinnati Creek at Barneveld, NY (01344795) that has been in operation since November 2022. The second peak of the double peak, which happened about a day after the first peak, likely resulted from regulated streamflow that passed through Hinckley Reservoir. At the other streamgages upstream of Hinckley Reservoir, single peak streamflows were recorded during the flood. More details on the nature of the flood of October 31 to November 3, 2019, including the historic context of the flood, and the results from flood-frequency analysis of six selected streamgages in the Mohawk Valley and southern Adirondack region, are discussed in Graziano and others (2024).","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 2024 Mohawk Watershed Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Mohawk Watershed Symposium","conferenceDate":"March 15, 2024","conferenceLocation":"Schenectady, NY","language":"English","publisher":"Union College","collaboration":"New York State Department of Environmental Conservation","usgsCitation":"Graziano, A.P., Smith, T.L., and Lilienthal, A.G., 2024, Flood of October 31 to November 3, 2019, East Canada Creek, West Canada Creek, and Sacandaga River Basins, in Proceedings of the 2024 Mohawk Watershed Symposium, v. 14, Schenectady, NY, March 15, 2024, p. 35-40.","productDescription":"6 p.","startPage":"35","endPage":"40","ipdsId":"IP-162519","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":426768,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":426765,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://minerva.union.edu/garverj/mws/2024/symposium.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New York","otherGeospatial":"East Canada Creek, West Canada Creek, and Sacandaga River Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.26955336733255,\n 43.83029414607452\n ],\n [\n -75.24342943210101,\n 43.48996180099857\n ],\n [\n -75.21373328157367,\n 43.29245297551128\n ],\n [\n -74.29992522793536,\n 43.507816029663445\n ],\n [\n -74.26955336733255,\n 43.83029414607452\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Graziano, Alexander P. 0000-0003-1978-0986","orcid":"https://orcid.org/0000-0003-1978-0986","contributorId":211607,"corporation":false,"usgs":true,"family":"Graziano","given":"Alexander","email":"","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Travis L. 0000-0002-3448-2787 tlsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-2787","contributorId":297400,"corporation":false,"usgs":true,"family":"Smith","given":"Travis","email":"tlsmith@usgs.gov","middleInitial":"L.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lilienthal, Arthur G. III 0000-0002-2906-6375","orcid":"https://orcid.org/0000-0002-2906-6375","contributorId":211366,"corporation":false,"usgs":true,"family":"Lilienthal","given":"Arthur","suffix":"III","email":"","middleInitial":"G.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896880,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252160,"text":"70252160 - 2024 - Temporal variability and sources of PFAS in the Rio Grande, New Mexico through an arid urban area using multiple tracers and high-frequency sampling","interactions":[],"lastModifiedDate":"2024-03-18T11:06:05.726685","indexId":"70252160","displayToPublicDate":"2024-03-15T06:02:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17177,"text":"Emerging Contaminants","active":true,"publicationSubtype":{"id":10}},"title":"Temporal variability and sources of PFAS in the Rio Grande, New Mexico through an arid urban area using multiple tracers and high-frequency sampling","docAbstract":"Per- and polyfluoroalkyl substances (PFAS) are ubiquitous in the environment but sources are not well defined for temporal and spatial aspects within an urban environment, and especially for an arid urban environment subject to seasonal short term high-intensity precipitation events. A focused diel sampling was conducted in the summer of 2021 to assess the temporal and spatial variability of PFAS in the Rio Grande near Albuquerque, New Mexico and showed an order of magnitude increase of PFAS as it flows through the Albuquerque urban area. Discrete samples were collected at two different locations on the Rio Grande in addition to wastewater treatment plant (WWTP) effluent that discharges directly to the Rio Grande between the sampling locations. Short-term high-intensity precipitation events occurred during the study period and mobilized PFAS from urban runoff. Dissolved organic matter composed of tryptophan-like organic substances and refined fuel and fuel byproducts, characteristic of an urban signature, were also related to the precipitation events. The PFAS in discharge from the WWTP was consistent over a 24-h period with slight differences in some compounds. Wastewater presence on the Rio Grande downstream of the WWTP was evidenced by a gadolinium anomaly as well as increases in several other trace elements, total dissolved nitrogen, and fluorescence indicators, in addition to PFAS. PFAS varied depending on source contribution, where urban runoff was associated with PFOA, PFOS, and PFBA, whereas PFHxA and PFPeA were associated with wastewater effluent. In addition, passive polar organic chemical integrative samplers (POCIS) using hydrophilic-lipid balance (HLB) sorption media were deployed for a month at two locations on the Rio Grande to assess longer term PFAS concentrations. The POCIS results show some compounds (PFPeA and PFHpA) were greater than the average concentration from discrete samples, whereas other compounds (PFHxA, PFOA, PFDA, and PFNA) were lower in the POCIS, and PFOS was very similar between the two. The POCIS did not detect PFBA, which may be related to the HLB media not performing well for short chain PFAS compounds. The results show promise for integrative samplers utilizing sorbent media. More detailed investigation of the spatial and temporal variability of water chemistry on the Rio Grande as it flows through Albuquerque could provide information applicable to urban areas worldwide.
The Rocky Mountain Population (RMP) of greater sandhill cranes uses a key stopover area, the San Luis Valley (SLV) in Colorado. Parameters of migration phenology can differ between autumn and spring and are affected by weather and environmental factors. We hypothesized that sandhill cranes in the SLV would have a longer stopover duration in autumn than in spring, and that wind assistance, crosswinds, temperature change, barometric air pressure, and surface water area would influence persistence probability. We used data from sandhill cranes fitted with transmitters that spanned autumn and spring, 2015-2022. We used an open robust design mark-recapture model to estimate stopover duration, arrival probability, and persistence probability. We examined the effects of weather and surface water on the persistence probability for 106 sandhill cranes in the SLV. Stopover duration was longer in autumn than in spring and had higher variability across years. Arrival probability to the SLV peaked on 13 October in autumn and 21 February in spring. Persistence probability declined around mid-December in autumn and mid-March in spring. We found that several weather covariates influenced persistence in both seasons. In autumn, sandhill cranes departed the SLV with higher tailwinds, lower crosswinds, and higher surface water availability. In spring, sandhill cranes departed the SLV with lower crosswinds and higher barometric air pressure at the surface and higher wind speeds at altitudes of about 3,000 m. The effect of wind speed was stronger later in the spring. Given the lower variability of arrival and persistence probability and shorter stopover duration in spring compared to autumn, we suspect that RMP sandhill cranes are using a time-minimization strategy during spring. However, given the use of supportive winds and weather conditions ideal for soaring, RMP sandhill cranes appear to be using strategies that save energy in both seasons. Our study identifies the optimal timing of water management and surveys for RMP sandhill cranes and confirms that weather influences their persistence. Understanding differences in migration patterns between seasons and the factors that influence persistence at stopover sites will also be important for anticipating phenological impacts from climate change and land use alterations.
Integrating recent scientific knowledge into management decisions supports effective natural resource management and can lead to better resource outcomes. However, finding and accessing scientific knowledge can be time consuming and costly. To assist in this process, the U.S. Geological Survey has created a series of annotated bibliographies on topics of management concern for lands in the western United States (U.S.). Oil and gas development on public lands is a long-standing and substantial component of local and regional economies and has expanded in recent decades, particularly on public lands in the western U.S. This development is associated with extensive networks of pipelines, roads, and processing facilities, across which reclamation is Federally mandated following initial well pad development (“interim” reclamation) and once resource extraction is complete (“final” reclamation). Reclamation is critical for recovering ecological services to energy-affected lands, including vegetation productivity, wildlife habitat, water and air quality, and soil stability (for example, resistance to wind and water erosion). However, reclamation of oil and gas affected lands in the western U.S. has proved challenging due to an array of regulatory and environmental factors, such as minimally developed soils, short growing seasons, herbivory, high winds, invasive species, rugged terrain, and in particular, arid climates associated with low total precipitation, high evapotranspiration rates, and highly variable precipitation patterns. We compiled and summarized journal articles, government reports, technical reports, proceedings, and theses and dissertations relevant to oil and gas reclamation. We constrained our search to products published on or before December 31, 2020 but did not limit our search by a starting date; the earliest product resulting from this effort was published in March 1969. Second, we manually scanned the last 15 years (2005-2020) of tables of contents in journals, bibliographies, and proceedings of which we were aware would contain articles highly relevant to this bibliography. We carried out the search for these products through multiple means: (1) performing a structured search of two reference databases, (2) examining articles published since 2005 in highly relevant scientific journals and conference proceedings, and (3) reviewing additional material suggested by authors of products identified in steps 1 and 2. Our search was intentionally broad in order to identify as much relevant work as possible, much of which is professionally applied and tested within the industry of oil and gas reclamation, but which remains unpublished in scientific journals. We refined the initial list of products by removing: (1) duplicates, (2) products not written in English, (3) products that were not relevant to the arid ecosystems of western North America, (4) products that were not released as research, data products, or review articles in journals or as formal scientific reports, and (5) products with data which were not relevant to reclamation of oil and gas-affected lands, or for which the study did not present new data, findings, or syntheses relevant to reclamation of oil and gas-affected lands.
We summarized each product using a consistent structure (background, objectives, methods, location, findings, and implications) and assigned standardized management topics to each. Management topics are intended to aid online searching within the bibliography and are described in more detail in the Methods Section of this report; they include what type of disturbance the product addresses (well pads, mining, pipelines), what aspect of oil and gas reclamation they pertain to (practices, standards, monitoring), what type of data are present in the product (for instance soil or vegetation recovery data), and an indication if the product were from a source other than a published, peer-reviewed outlet (such as dissertations or unpublished professional reports – these are identified as grey literature). The review process for this annotated bibliography included an initial internal colleague review of each summary, requesting input on each summary from an author of the original product, and a formal peer-review. Our initial searches resulted in 3,197 total products, of which 290 met our criteria for inclusion. “Reclamation Practices” is by far the management topic most addressed, followed by “Reclamation Monitoring,” for example, products assessing what and how monitoring methods are used to track and measure reclamation outcome. This document may be accessed at https://doi.org/10.3133/ofr20231068 or from the U.S. Geological Survey Publication Warehouse (https://pubs.usgs.gov/). The 1-page product summaries herein will also be used to create a bibliography at https://apps.usgs.gov/science-for-resource-managers that includes links to each original product, where available, and in which subject matter will be searchable by topic, location, and year. The studies compiled and summarized here may inform planning and management actions that seek to reclaim landscapes across the western U.S. which have been affected by oil and gas development.
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The GAMA-PBP is a comprehensive assessment of statewide groundwater quality in California. The first phase of the GAMA-PBP in 2004–15 assessed groundwater resources used for public drinking-water supplies. The second phase is assessing groundwater resources used for domestic drinking-water supplies. An estimated 2 million Californians rely on individual domestic wells or State small-system wells for drinking water, and far less information is available about these resources than about public-supply resources. The U.S. Geological Survey (USGS) began sampling wells for this second phase in 2012. Domestic wells typically are drilled to shallower depths in the groundwater system than public-supply wells. Shallow groundwater may respond more quickly and be more susceptible to contamination from human activities at the land surface than groundwater in deeper aquifers.
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\"}}]}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Simulation models are valuable tools for estimating ecosystem response to environmental conditions and are particularly relevant for investigating climate change impacts. However, because of high computational requirements, models are often applied over a coarse grid of points or for representative locations. Spatial interpolation of model output can be necessary to guide decision-making, yet interpolation is not straightforward because the interpolated values must maintain the covariance structure among variables. We present methods for two key steps for utilizing limited simulations to generate detailed maps of multivariate and time series output. First, we present a method to select an optimal set of simulation sites that maximize the area represented for a given number of sites. Then, we introduce a multivariate matching approach to interpolate simulation results to detailed maps for the represented area. This approach links simulation output to environmentally analogous matched sites according to user-defined criteria. We demonstrate the methods with case studies using output from (1) an individual-based plant simulation model to illustrate site selection, and (2) an ecosystem water balance simulation model to illustrate interpolation. For the site selection case study, we identified 200 simulation sites that represented 96% of a large study area (1.12 × 106 km2) at a ~1-km resolution. For the interpolation case study, we generated ~1-km resolution maps across 4.38 × 106 km2 of drylands in North America from a 10 × 10 km grid of simulated sites. Estimates of interpolation errors using cross validation were low (less than 10% of the range of each variable). Our point selection and interpolation methods, which are available as an easy-to-use R package, provide a means of cost-effectively generating detailed maps of expensive, complex simulation output (e.g., multivariate and time series) at scales relevant for local conservation planning. Our methods are flexible and allow the user to identify relevant matching criteria to balance interpolation uncertainty with areal coverage to enhance inference and decision-making at management-relevant scales across large areas.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4811","usgsCitation":"Renne, R.R., Schlaepfer, D.R., Palmquist, K.A., Lauenroth, W.K., and Bradford, J., 2024, Estimating multivariate ecological variables at high spatial resolution using a cost-effective matching algorithm: Ecosphere, v. 15, no. 3, e4811, 18 p., https://doi.org/10.1002/ecs2.4811.","productDescription":"e4811, 18 p.","ipdsId":"IP-133218","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":467024,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1002/ecs2.4811","text":"Publisher Index Page"},{"id":462276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Renne, Rachel R.","contributorId":213935,"corporation":false,"usgs":false,"family":"Renne","given":"Rachel","email":"","middleInitial":"R.","affiliations":[{"id":38934,"text":"School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA","active":true,"usgs":false}],"preferred":false,"id":914056,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schlaepfer, Daniel Rodolphe 0000-0001-9973-2065","orcid":"https://orcid.org/0000-0001-9973-2065","contributorId":225569,"corporation":false,"usgs":true,"family":"Schlaepfer","given":"Daniel","email":"","middleInitial":"Rodolphe","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":914057,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmquist, Kyle A.","contributorId":169517,"corporation":false,"usgs":false,"family":"Palmquist","given":"Kyle","email":"","middleInitial":"A.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":914058,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lauenroth, William K.","contributorId":80982,"corporation":false,"usgs":false,"family":"Lauenroth","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":914059,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":914060,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252498,"text":"70252498 - 2024 - Evaluating water-quality trends in agricultural watersheds prioritized for management-practice implementation","interactions":[],"lastModifiedDate":"2024-04-10T16:05:11.77161","indexId":"70252498","displayToPublicDate":"2024-03-14T06:51:53","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16692,"text":"Journal of the American Water Resources Assocation","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating water-quality trends in agricultural watersheds prioritized for management-practice implementation","docAbstract":"Many agricultural watersheds rely on the voluntary use of management practices (MPs) to reduce nonpoint source nutrient and sediment loads; however, the water-quality effects of MPs are uncertain. We interpreted water-quality responses from as early as 1985 through 2020 in three agricultural Chesapeake Bay watersheds that were prioritized for MP implementation, namely, the Smith Creek (Virginia), Upper Chester River (Maryland), and Conewago Creek (Pennsylvania) watersheds. We synthesized patterns in MPs, climate, land use, and nutrient inputs to better understand factors affecting nutrient and sediment loads. Relations between MPs and expected water-quality improvements were not consistently identifiable. The number of MPs increased in all watersheds since the early 2010s, but most monitored nutrient and sediment loads did not decrease. Nutrient and sediment loads increased or remained stable in Smith Creek and the Upper Chester River. Sediment loads and some nutrient loads decreased in Conewago Creek. In Smith Creek, a 36-year time-series model suggests that changes in manure affected flow-normalized total nitrogen loads. We hypothesize that increases in nutrient applications may overshadow some expected MP effects. MPs might have stemmed further water-quality degradation, but improvements in nutrient loads may rely on reducing manure and fertilizer applications. Our results highlight the importance of assessing MP performance with long-term monitoring-based studies.
Martian gullies resemble water-carved gullies on Earth, yet their present-day activity cannot be explained by water-driven processes. The sublimation of CO2 has been proposed as an alternative driver for sediment transport, but how this mechanism works remains unknown. Here we combine laboratory experiments of CO2-driven granular flows under Martian atmospheric pressure with 1D climate simulation modelling to unravel how, where, and when CO2 can drive present-day gully activity. Our work shows that sublimation of CO2 ice, under Martian atmospheric conditions can fluidize sediment and creates morphologies similar to those observed on Mars. Furthermore, the modelled climatic and topographic boundary conditions for this process, align with present-day gully activity. These results have implications for the influence of water versus CO2-driven processes in gully formation and for the interpretation of gully landforms on other planets, as their existence is no longer definitive proof for flowing liquids.
","language":"English","publisher":"Nature","doi":"10.1038/s43247-024-01298-7","usgsCitation":"Roelofs, L., Conway, S.J., de Haas, T., Dundas, C., Lewis, S.R., McElwaine, J., Pasquon, K., Raack, J., Sylvest, M., and Patel, M., 2024, How, when and where current mass flows in Martian gullies are driven by CO2 sublimation: Communications Earth and Environment, v. 5, 125, 9 p., https://doi.org/10.1038/s43247-024-01298-7.","productDescription":"125, 9 p.","ipdsId":"IP-143281","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":440137,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s43247-024-01298-7","text":"Publisher Index Page"},{"id":433000,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Roelofs, Lonneke","contributorId":343523,"corporation":false,"usgs":false,"family":"Roelofs","given":"Lonneke","email":"","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":911294,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Susan J.","contributorId":203697,"corporation":false,"usgs":false,"family":"Conway","given":"Susan","email":"","middleInitial":"J.","affiliations":[{"id":36693,"text":"University of Nantes","active":true,"usgs":false}],"preferred":false,"id":911295,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"de Haas, Tjalling","contributorId":336830,"corporation":false,"usgs":false,"family":"de Haas","given":"Tjalling","affiliations":[],"preferred":false,"id":911296,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dundas, Colin M. 0000-0003-2343-7224","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":237028,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":911297,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lewis, Stephen R.","contributorId":64081,"corporation":false,"usgs":true,"family":"Lewis","given":"Stephen","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":911298,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McElwaine, Jim","contributorId":201623,"corporation":false,"usgs":false,"family":"McElwaine","given":"Jim","affiliations":[{"id":25252,"text":"Durham University","active":true,"usgs":false}],"preferred":false,"id":911299,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pasquon, Kelly","contributorId":343526,"corporation":false,"usgs":false,"family":"Pasquon","given":"Kelly","email":"","affiliations":[{"id":82106,"text":"Nantes Universite","active":true,"usgs":false}],"preferred":false,"id":911300,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Raack, Jan","contributorId":343527,"corporation":false,"usgs":false,"family":"Raack","given":"Jan","email":"","affiliations":[{"id":82107,"text":"Westfalische Wilhelms-Universitat, Innomago GmbH","active":true,"usgs":false}],"preferred":false,"id":911301,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sylvest, Matt","contributorId":343528,"corporation":false,"usgs":false,"family":"Sylvest","given":"Matt","email":"","affiliations":[{"id":47593,"text":"The Open University","active":true,"usgs":false}],"preferred":false,"id":911302,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Patel, Manish","contributorId":343529,"corporation":false,"usgs":false,"family":"Patel","given":"Manish","email":"","affiliations":[{"id":47593,"text":"The Open University","active":true,"usgs":false}],"preferred":false,"id":911303,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70259800,"text":"70259800 - 2024 - Arsenic and other geogenic contaminants in global groundwater","interactions":[],"lastModifiedDate":"2024-10-25T15:56:51.495002","indexId":"70259800","displayToPublicDate":"2024-03-12T10:50:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7460,"text":"Nature Reviews Earth & Environment","active":true,"publicationSubtype":{"id":10}},"title":"Arsenic and other geogenic contaminants in global groundwater","docAbstract":"Geogenic groundwater contaminants (GGCs) affect drinking-water availability and safety, with up to 60% of groundwater sources in some regions contaminated by more than recommended concentrations. As a result, an estimated 300–500 million people are at risk of severe health impacts and premature mortality. In this Review, we discuss the sources, occurrences and cycling of arsenic, fluoride, selenium and uranium, which are GGCs with widespread distribution and/or high toxicity. The global distribution of GGCs is controlled by basin geology and tectonics, with GGC enrichment in both orogenic systems and cratonic basement rocks. This regional distribution is broadly influenced by climate, geomorphology and hydrogeochemical evolution along groundwater flow paths. GGC distribution is locally heterogeneous and affected by in situ lithology, groundwater flow and water–rock interactions. Local biogeochemical cycling also determines GGC concentrations, as arsenic, selenium and uranium mobilizations are strongly redox-dependent. Increasing groundwater extraction and land-use changes are likely to modify GGC distribution and extent, potentially exacerbating human exposure to GGCs, but the net impact of these activities is unknown. Integration of science, policy, community involvement programmes and technological interventions is needed to manage GGC-enriched groundwater and ensure equitable access to clean water.
","language":"English","publisher":"Nature","doi":"10.1038/s43017-024-00519-z","usgsCitation":"Mukherjee, A., Coomar, P., Sarkar, S., Johannesson, K., Fryar, A., Schreiber, M., Ahmed, K.M., Alam, M.A., Bhattacharya, P., Bundschuh, J., Burgess, W., Chakraborty, M., Coyte, R., Farooqi, A., Guo, H., Ijumulana, J., Jeelani, G., Mondal, D., Nordstrom, D.K., Podgorski, J., Polya, D., Scanlon, B.R., Shamsudduha, M., Tapia, J., and Vengosh, A., 2024, Arsenic and other geogenic contaminants in global groundwater: Nature Reviews Earth & Environment, v. 5, p. 312-328, https://doi.org/10.1038/s43017-024-00519-z.","productDescription":"17 p.","startPage":"312","endPage":"328","ipdsId":"IP-162090","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467025,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.dora.lib4ri.ch/eawag/islandora/object/eawag%3A32679","text":"External Repository"},{"id":463197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","noUsgsAuthors":false,"publicationDate":"2024-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Mukherjee, Abhijit","contributorId":213833,"corporation":false,"usgs":false,"family":"Mukherjee","given":"Abhijit","email":"","affiliations":[],"preferred":false,"id":916735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coomar, Poulomee","contributorId":345478,"corporation":false,"usgs":false,"family":"Coomar","given":"Poulomee","email":"","affiliations":[{"id":82595,"text":"Indian Institute of Technology Kharagpur","active":true,"usgs":false}],"preferred":false,"id":916736,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sarkar, Soumyajit","contributorId":345479,"corporation":false,"usgs":false,"family":"Sarkar","given":"Soumyajit","email":"","affiliations":[{"id":82595,"text":"Indian Institute of Technology Kharagpur","active":true,"usgs":false}],"preferred":false,"id":916737,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johannesson, Karen H.","contributorId":150171,"corporation":false,"usgs":false,"family":"Johannesson","given":"Karen H.","affiliations":[{"id":13500,"text":"Tulane University","active":true,"usgs":false}],"preferred":false,"id":916749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fryar, Alan","contributorId":345484,"corporation":false,"usgs":false,"family":"Fryar","given":"Alan","email":"","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":916745,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schreiber, Madeline","contributorId":248255,"corporation":false,"usgs":false,"family":"Schreiber","given":"Madeline","affiliations":[{"id":49841,"text":"Virginia Tech, Department of Geosciences","active":true,"usgs":false}],"preferred":false,"id":916755,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ahmed, Kazi M.","contributorId":345480,"corporation":false,"usgs":false,"family":"Ahmed","given":"Kazi","email":"","middleInitial":"M.","affiliations":[{"id":65425,"text":"University of Dhaka","active":true,"usgs":false}],"preferred":false,"id":916738,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Alam, Mohd. A.","contributorId":345481,"corporation":false,"usgs":false,"family":"Alam","given":"Mohd.","email":"","middleInitial":"A.","affiliations":[{"id":82597,"text":"University de Santiago de Chile","active":true,"usgs":false}],"preferred":false,"id":916739,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bhattacharya, Prosun","contributorId":184213,"corporation":false,"usgs":false,"family":"Bhattacharya","given":"Prosun","email":"","affiliations":[],"preferred":false,"id":916740,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bundschuh, Jochen","contributorId":184215,"corporation":false,"usgs":false,"family":"Bundschuh","given":"Jochen","email":"","affiliations":[],"preferred":false,"id":916741,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Burgess, William","contributorId":345482,"corporation":false,"usgs":false,"family":"Burgess","given":"William","email":"","affiliations":[{"id":6957,"text":"University College London","active":true,"usgs":false}],"preferred":false,"id":916742,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Chakraborty, Madhumita","contributorId":345510,"corporation":false,"usgs":false,"family":"Chakraborty","given":"Madhumita","email":"","affiliations":[],"preferred":false,"id":916817,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Coyte, Rachel","contributorId":340050,"corporation":false,"usgs":false,"family":"Coyte","given":"Rachel","email":"","affiliations":[{"id":81437,"text":"New Mexico Institute of Mining and Technology, Earth and Environmental Science Department, Socorro, NM","active":true,"usgs":false}],"preferred":false,"id":916743,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Farooqi, Abida","contributorId":345483,"corporation":false,"usgs":false,"family":"Farooqi","given":"Abida","email":"","affiliations":[{"id":82598,"text":"Quaid-i-Azam University, Islamabad","active":true,"usgs":false}],"preferred":false,"id":916744,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Guo, Huaming","contributorId":138510,"corporation":false,"usgs":false,"family":"Guo","given":"Huaming","email":"","affiliations":[{"id":12433,"text":"China University of Geosciences","active":true,"usgs":false}],"preferred":false,"id":916746,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Ijumulana, Julian","contributorId":345485,"corporation":false,"usgs":false,"family":"Ijumulana","given":"Julian","email":"","affiliations":[{"id":82599,"text":"KTH Royal Institute of Technology, Stockholm","active":true,"usgs":false}],"preferred":false,"id":916747,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Jeelani, Gh","contributorId":345486,"corporation":false,"usgs":false,"family":"Jeelani","given":"Gh","email":"","affiliations":[{"id":82600,"text":"University of Kashmir","active":true,"usgs":false}],"preferred":false,"id":916748,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Mondal, Debapriya","contributorId":345487,"corporation":false,"usgs":false,"family":"Mondal","given":"Debapriya","email":"","affiliations":[{"id":82601,"text":"London School of Hygiene and Tropical Medicine","active":true,"usgs":false}],"preferred":false,"id":916750,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":916751,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Podgorski, Joel 0000-0003-2522-1021","orcid":"https://orcid.org/0000-0003-2522-1021","contributorId":336777,"corporation":false,"usgs":false,"family":"Podgorski","given":"Joel","email":"","affiliations":[{"id":80861,"text":"Department of Water Resources and Drinking Water, Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland","active":true,"usgs":false}],"preferred":false,"id":916752,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Polya, David","contributorId":197748,"corporation":false,"usgs":false,"family":"Polya","given":"David","email":"","affiliations":[],"preferred":false,"id":916753,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Scanlon, Bridget R. 0000-0002-1234-4199","orcid":"https://orcid.org/0000-0002-1234-4199","contributorId":328586,"corporation":false,"usgs":false,"family":"Scanlon","given":"Bridget","email":"","middleInitial":"R.","affiliations":[{"id":78414,"text":"Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin, J.J. Pickle Research Campus, Bldg. 130, 10100 Burnet Rd., Austin, TX 78758-4445","active":true,"usgs":false}],"preferred":false,"id":916754,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Shamsudduha, Mohd.","contributorId":345488,"corporation":false,"usgs":false,"family":"Shamsudduha","given":"Mohd.","affiliations":[{"id":6957,"text":"University College London","active":true,"usgs":false}],"preferred":false,"id":916756,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Tapia, Joseline","contributorId":345489,"corporation":false,"usgs":false,"family":"Tapia","given":"Joseline","email":"","affiliations":[{"id":82602,"text":"Universidad Católica Del Norte, Antofagasta, Chile","active":true,"usgs":false}],"preferred":false,"id":916757,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Vengosh, Avner","contributorId":208460,"corporation":false,"usgs":false,"family":"Vengosh","given":"Avner","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":916758,"contributorType":{"id":1,"text":"Authors"},"rank":25}]}} ,{"id":70253910,"text":"70253910 - 2024 - Influence of irrigation water and soil on annual mercury dynamics in Sacramento Valley rice fields","interactions":[],"lastModifiedDate":"2024-05-20T15:41:00.064408","indexId":"70253910","displayToPublicDate":"2024-03-12T09:06:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Influence of irrigation water and soil on annual mercury dynamics in Sacramento Valley rice fields","docAbstract":"Methylmercury (MeHg) is a human and environmental toxin produced in flooded soils. Little is known about MeHg in rice (Oryza Sativa L.) fields in Sacramento Valley, California. The objectives of this study were to quantify mercury fractions in irrigation water and within rice fields and to determine their mercury pools in surface water, soil, and grain. Soil, grain, and surface water (dissolved and particulate) MeHg and total mercury (THg) were monitored in six commercial rice fields throughout a winter fallow season and subsequent growing season. Both dissolved and particulate mercury fractions were higher in fallow season rice field water. Total suspended solids and particulate mercury concentrations were positively correlated (r = 0.99 and 0.98 for THg and MeHg, respectively), suggesting that soil MeHg was suspended in the water column and potentially exported. Dissolved THg and MeHg concentrations were positively correlated with absorbance at 254 nm (r = 0.47 and 0.58, respectively) in fallow season field water. In the growing season, fields with higher irrigation water MeHg concentrations (due to recycled water use) had elevated field-water MeHg (r = 0.86) and grain MeHg concentrations (r = 0.96). Based on a mass balance analysis, soil mercury pools were orders of magnitude larger than surface water or grain mercury pools; however, fallow season drainage and grain harvest were the primary pathways for MeHg export. Based on these findings, reducing (1) discharge when water is turbid, (2) straw inputs, and (3) use of recycled irrigation water could help reduce mercury exports in rice field drainage water.
","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America","doi":"10.1002/jeq2.20557","usgsCitation":"Salvato, L.A., Marvin-DiPasquale, M.C., Fleck, J., McCord, S.A., and Linquist, B.A., 2024, Influence of irrigation water and soil on annual mercury dynamics in Sacramento Valley rice fields: Journal of Environmental Quality, v. 53, no. 3, p. 327-339, https://doi.org/10.1002/jeq2.20557.","productDescription":"13 p.","startPage":"327","endPage":"339","ipdsId":"IP-147436","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":428352,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.02785801251214,\n 38.60550432638692\n ],\n [\n -121.08415098853837,\n 38.6847651728167\n ],\n [\n -121.52032108154039,\n 40.714161240040625\n ],\n [\n -122.66695647928859,\n 40.90630588141738\n ],\n [\n -123.1538757539135,\n 40.4291144057602\n ],\n [\n -122.75820217083967,\n 39.47246952236267\n ],\n [\n -122.2916215456014,\n 38.77186923103136\n ],\n [\n -122.02785801251214,\n 38.60550432638692\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"53","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Salvato, Luke A. 0009-0002-7091-6586","orcid":"https://orcid.org/0009-0002-7091-6586","contributorId":315378,"corporation":false,"usgs":false,"family":"Salvato","given":"Luke","email":"","middleInitial":"A.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":900076,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marvin-DiPasquale, Mark C. 0000-0002-8186-9167 mmarvin@usgs.gov","orcid":"https://orcid.org/0000-0002-8186-9167","contributorId":1485,"corporation":false,"usgs":true,"family":"Marvin-DiPasquale","given":"Mark","email":"mmarvin@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":900077,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fleck, Jacob 0000-0002-3217-3972 jafleck@usgs.gov","orcid":"https://orcid.org/0000-0002-3217-3972","contributorId":168694,"corporation":false,"usgs":true,"family":"Fleck","given":"Jacob","email":"jafleck@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900078,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCord, Stephen A.","contributorId":179309,"corporation":false,"usgs":false,"family":"McCord","given":"Stephen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":900079,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Linquist, Bruce A.","contributorId":179310,"corporation":false,"usgs":false,"family":"Linquist","given":"Bruce","email":"","middleInitial":"A.","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":900080,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252861,"text":"70252861 - 2024 - Sulphide petrology and ore genesis of the stratabound Sheep Creek sediment-hosted Zn–Pb–Ag–Sn prospect, and U–Pb zircon constraints on the timing of magmatism in the northern Alaska Range","interactions":[],"lastModifiedDate":"2024-09-23T15:25:30.720271","indexId":"70252861","displayToPublicDate":"2024-03-12T07:06:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1168,"text":"Canadian Journal of Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sulphide petrology and ore genesis of the stratabound Sheep Creek sediment-hosted Zn–Pb–Ag–Sn prospect, and U–Pb zircon constraints on the timing of magmatism in the northern Alaska Range","docAbstract":"Less than a year after the 2018 Kīlauea caldera collapse and eruption, water appeared in newly deepened Halemaʻumaʻu crater. The lake—unprecedented in the written record—grew to a depth of ∼50 m before lava from the December 2020 eruption boiled it away. Surface water heightened concerns of potential phreatic or phreatomagmatic explosions but also offered a new means of possibly identifying eruption precursors. The U.S. Geological Survey Hawaiian Volcano Observatory (HVO) monitored the lake via direct visual observation, webcams, thermal imaging, colorimetry, and laser rangefinders. HVO also employed uncrewed aircraft systems to sample the water and measure near-lake gas composition. The lake's δD and δ18O indicate a groundwater source with substantial evaporation. The initial sample had a salinity (total dissolved solids concentration) of 71,000 mg/L and was rich in sulfate (∼53,000 mg/L), iron (∼500 mg/L), and magnesium (∼10,000 mg/L). Subsequent samples were slightly more dilute. The water's pH (∼4), δ34S (+4.3‰), and surface temperatures (up to 85°C) suggest, rather than significant scrubbing of magmatic volatiles, leaching of basalt and reactions with sulfate minerals resulted in high concentrations of sulfate and other solutes. Thermodynamic modeling and precipitate mineralogy indicate that water composition was controlled by iron oxidation and sulfate dissolution. Although the lake exhibited no detectable precursors before the next eruption, and phreatic or phreatomagmatic explosions did not materialize, our multi-parameter approach to monitoring yielded an enhanced understanding of the hydrologic, geologic, and magmatic conditions that led to the formation of the unique and short-lived lake.
Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.
The karstic Edwards and Trinity aquifers are classified as major sources of water in south-central Texas by the Texas Water Development Board. During 2019–23 the U.S. Geological Survey, in cooperation with the Edwards Aquifer Authority, mapped and described the geology and hydrostratigraphy of the rocks composing the Edwards and Trinity aquifers within parts of Bandera and Kendall Counties from field observations of the surficial expressions of the rocks. The thicknesses of the mapped lithostratigraphic and hydrostratigraphic units were also estimated from field observations in the study area.
The Cretaceous rocks in the study area are part of the Trinity Group and Edwards Group. The groups, formations, and members are composed primarily of layers of marls, shales, and limestones. The limestones are composed of mudstone through grainstone, framestone and boundstone, dolomite, and argillaceous and evaporitic rocks.
The principal structural feature in the study area is the Balcones fault zone. The Balcones fault zone is the result of late Oligocene and early Miocene extensional faulting and fracturing that was a result of the eastern Edwards Plateau uplift. In the Balcones fault zone, most of the faults in the study area are high-angle to vertical, en echelon, normal faults that are predominantly downthrown to the southeast.
Hydrostratigraphically, the rocks exposed in the study area are those that contain the Edwards aquifer, the upper zone of the Trinity aquifer, and the middle zone of the Trinity aquifer. Descriptions of the hydrostratigraphic units, thicknesses, hydrologic function, porosity types, and field identification and observations are provided, including those for the informal Bandera and Love Creek hydrostratigraphic units of the Edwards aquifer, which were identified through the mapping for this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3518","issn":"2329-132X","collaboration":"Prepared in cooperation with the Edwards Aquifer Authority","usgsCitation":"Clark, A.K., Morris, R.R., and Lamberts, A.P., 2024, Geologic framework and hydrostratigraphy of the Edwards and Trinity aquifers within parts of Bandera and Kendall Counties, Texas: U.S. Geological Survey Scientific Investigations Map 3518, 1 sheet, scale 1:24,000, 11-p. pamphlet, https://doi.org/10.3133/sim3518.","productDescription":"Pamphlet: vi, 11 p., 1 Sheet: 49.01 x 39.83 inches; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-149157","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":426433,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QKO5E1","text":"USGS data release","linkHelpText":"Geospatial dataset for the geologic framework and 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U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The work reported in this publication provides updated data and interpretation for sampling years 2015 and 2022 of the juvenile monitoring project. The study objectives, background, study area, species description, and methods remained the same or similar throughout the years, while the executive summary, results, and discussion were updated each year. Therefore much of this paper was originally presented in previous reports (Bart and others 2020a, b; Bart and others, 2021; Burdick and others, 2016; Burdick and others, 2018; Martin and others, 2022) and is repeated here for the reader’s convenience.
Populations of federally endangered Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir (hereinafter, Clear Lake), California, are experiencing long-term decreases in abundance. Upper Klamath Lake populations are decreasing not only because of adult mortality, which is relatively low, but also because they are not being balanced by recruitment of young adult suckers into adult spawning aggregations.
Long-term monitoring of juvenile sucker populations is conducted to (1) determine if there are annual and species-specific differences in production, survival, and growth; (2) better understand when juvenile sucker mortality is greatest; and (3) identify potential causes of high juvenile sucker mortality particularly in Upper Klamath Lake. The U.S. Geological Survey (USGS) monitoring program, begun in 2015, tracks cohorts through summer months and among years in Upper Klamath and Clear Lakes. Data on juvenile suckers captured in trap nets are used to provide information on annual variability in age-0 sucker production, juvenile sucker apparent survival, growth, species composition, and health.
Upper Klamath Lake indices of year-class strength suggest that the 2022 age-0 cohort is the lowest since standardized monitoring began. The 2021 cohort, like most cohorts, had moderately low catch rates their first year of life, with a steep drop off during the second year. Although the 2020 cohort persisted through the September 2022 sampling, this cohort was sparsely represented after the first year with no representatives from this cohort captured from July 2021 through July 2022. Despite apparently low fall through spring apparent survival, the relatively large 2019 cohort persisted in our 2020–21 samples, but has not been detected since June 2021. Klamath largescale (Catostomus snyderi) and shortnose suckers were only differentiated from each other starting in 2020. Shortnose suckers dominated the age-1 catch in 2020 and 2022, whereas age-1 Klamath largescale suckers were slightly more prevalent in 2021. Although there were occasionally age-2 and older suckers captured, none of these fish were Lost River suckers. Except for 2015, 2017, and 2021, there were more age-0 Lost River suckers than presumed shortnose suckers in Upper Klamath Lake. However, in all years sampled, there were more age-1 presumed shortnose suckers than Lost River suckers.
Age distribution of suckers captured in Clear Lake indicates greater juvenile survival than in Upper Klamath Lake. Most juvenile suckers captured throughout the years were from the 2016 and 2017 cohorts; however, by 2022 most of these fish were no longer susceptible to standard trap nets and were not as prevalent in 2022 juvenile catches, and these suckers presumedly recruited to the adult population. As the 2016 and 2017 cohorts catches declined, so did the catch in overall numbers of suckers. Excluding age-0 catches, the 2016 cohort catches peaked at age-3 and the 2017 catches peaked at age-2. In 2022, the majority of the catch was composed of age-3 to age-5 suckers. The majority of suckers captured in Clear Lake during this multiyear project were classified as the combination of Klamath largescale suckers and shortnose suckers from the Lost River Basin, from the 2016 and 2017 cohorts. The few suckers identified as Lost River or definitive shortnose suckers were from the 2016 and 2017 cohorts. A lack of age-0 suckers captured in Clear Lake during years with low spawning tributary inflow or lake levels suggested that low water prevented spawning and year class formation. However, recent data indicate that some cohorts with Klamath largescale and shortnose sucker genetics that were not captured as age-0 suckers were detected in later years at age-1 or age-2. This finding indicates that juvenile suckers in Clear Lake may spend one or more years in the tributaries and that these cohorts may primarily be represented by Klamath largescale suckers.
The first 7 years of this monitoring program indicated different patterns in recruitment and survival of juvenile suckers between Upper Klamath and Clear Lakes. Since the monitoring program began in 2015, age-0 sucker catch rates, interpreted as indices of year-class strength, were greatest in Upper Klamath Lake in 2016 and 2019. In those years, Lost River suckers made up the majority of age-0 sucker catches. However, in 2017 and 2020, the age-1 sucker catches from these cohorts were mainly composed of shortnose suckers or suckers with genetic markers of both Klamath largescale and shortnose suckers, indicating a low first year survival for Lost River suckers even when age-0 catches were high. Age-0 suckers do not fully recruit to our sampling gear in Upper Klamath Lake until August, experience high mortality by September, and are almost undetectable in subsequent years. In Clear Lake, suckers are often not captured until age-1 or age-2 and juvenile annual survival appears much greater; however, there does appear to be a drop-off in catch rates as the suckers age and become less susceptible to the fishing gear.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241013","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Martin, B.A., Caldwell, J.M., Krause, J.R., and Harris, A.C., 2024, Growth, survival, and cohort formation of juvenile Lost River (Deltistes luxatus) and shortnose suckers (Chasmistes brevirostris) in Upper Klamath Lake, Oregon, and Clear Lake Reservoir, California—2021–22 monitoring report: U.S. Geological Survey Open-File Report 2024–1013, 39 p., https://doi.org/10.3133/ofr20241013.","productDescription":"Report: vi, 39 p.; 1 Data Release","onlineOnly":"Y","ipdsId":"IP-159115","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":426337,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.XML"},{"id":426335,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93KYGEG","text":"USGS data release","description":"USGS data release","linkHelpText":"Upper Klamath Lake and Clear Lake sampling for suckers from 2015 through 2022. Reston, Virginia: U.S. Geological Survey, Klamath Falls Field Station, Klamath Falls, Oregon"},{"id":426334,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241013/full","linkFileType":{"id":5,"text":"html"},"description":"OFR 2024-1013"},{"id":426333,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.pdf","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024-1013"},{"id":426332,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1013/ofr20241013.jpg"},{"id":426336,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1013/images"}],"country":"United States","state":"California, Oregon","otherGeospatial":"Clear Lake Reservoir, Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.01266997658502,\n 41.93494622821743\n ],\n [\n -121.26100788006954,\n 41.93494622821743\n ],\n [\n -121.26100788006954,\n 41.786587280075025\n ],\n [\n -121.01266997658502,\n 41.786587280075025\n ],\n [\n -121.01266997658502,\n 41.93494622821743\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.78179025160858,\n 42.649405814487636\n ],\n [\n -122.11246478619483,\n 42.649405814487636\n ],\n [\n -122.11246478619483,\n 42.22404414347665\n ],\n [\n -121.78179025160858,\n 42.22404414347665\n ],\n [\n -121.78179025160858,\n 42.649405814487636\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
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Seattle, Washington 98115-5016
Discovered by Galileo Galilei more than 400 years ago and imaged in detail by the Voyager 2 Galileo spacecraft, Jupiter’s icy moon Europa has been a source of intrigue. A range of science investigations indicate that it contains the key ingredients for habitability, notably energy, chemistry, and liquid water. Europa’s surface is geologically complex and, based on the dearth of impact craters, interpreted to be as young as ~60 Ma. The array of geologic features that characterize the surface include extensive ridged plains, regions of broad disruption termed chaotic terrain, long, quasi-linear ridges that span thousands of kilometers, and bands as much as 60 kilometers wide and that extend 100s of kilometers. These features, along with other geophysical measurements, indicate the presence of a global briny liquid water ocean beneath the ice shell. It was not until the arrival of the Galileo spacecraft in 1995 that the true nature and level of complexity of the surface was revealed. Although image data returned by Galileo provided insight into the structure of a variety of regions, the entire satellite has yet to be observed at a regional scale (less than 250 meters per pixel) and the detailed geologic nature of much of its surface remains a mystery. Establishing the global context of the distribution and timing of Europan geologic units forms a basis to understand regional and local scale processes, serves as a tool for the planning of future missions, and most of all is essential to gaining insight into the potential habitability of this icy world.
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