According to classic stomatal optimization theory, plant stomata are regulated to maximize carbon assimilation for a given water loss. A key component of stomatal optimization models is marginal water-use efficiency (mWUE), the ratio of the change of transpiration to the change in carbon assimilation. Although the mWUE is often assumed to be constant, variability of mWUE under changing hydrologic conditions has been reported. However, there has yet to be a consensus on the patterns of mWUE variabilities and their relations with atmospheric aridity. We investigate the dynamics of mWUE in response to vapor pressure deficit (VPD) and aridity index using carbon and water fluxes from 115 eddy covariance towers available from the global database FLUXNET. We demonstrate a non-linear mWUE-VPD relationship at a sub-daily scale in general; mWUE varies substantially at both low and high VPD levels. However, mWUE remains relatively constant within the mid-range of VPD. Despite the highly non-linear relationship between mWUE and VPD, the relationship can be informed by the strong linear relationship between ecosystem-level inherent water-use efficiency (IWUE) and mWUE using the slope, m*. We further identify site-specific m* and its variability with changing site-level aridity across six vegetation types. We suggest accurately representing the relationship between IWUE and VPD using Michaelis–Menten or quadratic functions to ensure precise estimation of mWUE variability for individual sites.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007875","usgsCitation":"Yi, K., Novick, K.A., Zhang, Q., Wang, L., Hwang, T., Yang, X., Mallick, K., Beland, M., Senay, G.B., and Baldocchi, D., 2024, Responses of marginal and intrinsic water-use efficiency to changing aridity using FLUXNET observations: Journal of Geophysical Research Biogeosciences, v. 129, no. 6, e2023JG007875, 19 p., https://doi.org/10.1029/2023JG007875.","productDescription":"e2023JG007875, 19 p.","ipdsId":"IP-163083","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":466997,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023jg007875","text":"Publisher Index Page"},{"id":463530,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"129","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Yi, Koong","contributorId":345841,"corporation":false,"usgs":false,"family":"Yi","given":"Koong","email":"","affiliations":[{"id":82725,"text":"Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, CA, U.S.A","active":true,"usgs":false}],"preferred":false,"id":917685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Novick, Kimberly A.","contributorId":196379,"corporation":false,"usgs":false,"family":"Novick","given":"Kimberly","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":917686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Quan","contributorId":345842,"corporation":false,"usgs":false,"family":"Zhang","given":"Quan","email":"","affiliations":[{"id":82726,"text":"State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China.","active":true,"usgs":false}],"preferred":false,"id":917687,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Lixin","contributorId":300466,"corporation":false,"usgs":false,"family":"Wang","given":"Lixin","affiliations":[{"id":65165,"text":"Department of Earth Sciences, Indiana University–Purdue University Indianapolis (IUPUI), Indianapolis, IN, USA.","active":true,"usgs":false}],"preferred":false,"id":917688,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hwang, Taehee","contributorId":345843,"corporation":false,"usgs":false,"family":"Hwang","given":"Taehee","email":"","affiliations":[{"id":82727,"text":"Department of Geography, Indiana University Bloomington, Bloomington, IN, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917689,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Xi","contributorId":245237,"corporation":false,"usgs":false,"family":"Yang","given":"Xi","email":"","affiliations":[],"preferred":false,"id":917690,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mallick, Kanishka","contributorId":345844,"corporation":false,"usgs":false,"family":"Mallick","given":"Kanishka","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917691,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Beland, Martin","contributorId":345845,"corporation":false,"usgs":false,"family":"Beland","given":"Martin","email":"","affiliations":[{"id":82729,"text":"Department of Environmental Science, Policy, and Management, University of California, Berkeley, CA, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":917692,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":917693,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Baldocchi, Dennis 0000-0003-3496-4919","orcid":"https://orcid.org/0000-0003-3496-4919","contributorId":167495,"corporation":false,"usgs":false,"family":"Baldocchi","given":"Dennis","affiliations":[{"id":24725,"text":"Ecosystem Science Division, Department of Environmental Science","active":true,"usgs":false}],"preferred":false,"id":917694,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70254921,"text":"sir20245034 - 2024 - Distribution of ancient carbon in groundwater and soil gas from degradation of petroleum near the Red Hill Bulk Fuel Storage Facility, O‘ahu, Hawai‘i","interactions":[],"lastModifiedDate":"2025-12-23T20:35:02.723363","indexId":"sir20245034","displayToPublicDate":"2024-06-11T12:30:56","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5034","displayTitle":"Distribution of Ancient Carbon in Groundwater and Soil Gas from Degradation of Petroleum near the Red Hill Bulk Fuel Storage Facility, Oʻahu, Hawaiʻi","title":"Distribution of ancient carbon in groundwater and soil gas from degradation of petroleum near the Red Hill Bulk Fuel Storage Facility, O‘ahu, Hawai‘i","docAbstract":"The groundwater below the Red Hill Bulk Fuel Storage Facility (the facility) in Oʻahu, Hawaiʻi, contains fuel compounds from past spills. This study used carbon-14 analyses to distinguish fuel-derived carbon from background carbon, along with other biodegradation indicators, to address two goals: (1) determine the extent and migration direction of groundwater affected by residual fuel below the facility and (2) determine if residual fuel locations in the subsurface could be identified by analyzing soil gas at the surface above the facility.
Groundwater from 19 wells was sampled between September 2022 and April 2023. Nonvolatile dissolved organic carbon (NVDOC) from a well presumed to be unaffected by past spills contained 38 percent ancient carbon indicating a natural source of ancient carbon in the subsurface. The NVDOC concentrations and ancient carbon percentages indicate fuel biodegradation products are likely present on the north and south of Red Hill with the greatest effects at well RHMW02 near the 2014 spill site. The NVDOC concentrations are almost three times higher than diesel range organic (DRO) concentrations in groundwater from the same sites. Major ion data indicate that iron reduction is an important biodegradation process.
Soil probe samples and soil carbon traps were used to determine the carbon-14 content of soil carbon dioxide. Ancient carbon from fuel biodegradation was not detected at any soil probe or carbon trap site in contrast to a 2017 study which reported ancient carbon detections. A reanalysis of the 2017 results using a range of local values for background carbon-14 indicates that ancient carbon from fuel biodegradation was probably only detected in lower tunnel exhaust system samples and not in any soil carbon trap samples. Measurements of carbon dioxide efflux with a dynamic closed chamber were highly variable. The soil gas results indicate that soil gas measurements at land surface were not useful for detecting residual fuel at the facility.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245034","collaboration":"Prepared in cooperation with the U.S. Navy and the Defense Logistics Agency","programNote":"Environmental Health Program","usgsCitation":"Trost, J.J., Bekins, B.A., Jaeschke, J.B., Delin, G.N., Sinclair, D.A., Stack, J.K., Nakama, R.K., Miyajima, U.M., Pagaduan, L.D., and Cozzarelli, I.M., 2024, Distribution of ancient carbon in groundwater and soil gas from degradation of petroleum near the Red Hill Bulk Fuel Storage Facility, Oʻahu, Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2024–5034, 54 p., https://doi.org/10.3133/sir20245034.","productDescription":"Report: xi, 54 p.; Data Release; Dataset","numberOfPages":"70","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-155367","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":429760,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5034/sir20245034.pdf","text":"Report","size":"42.6 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":429761,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5034/sir20245034.XML"},{"id":429767,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245034/full"},{"id":429766,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":429762,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5034/images/"},{"id":429759,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5034/coverthb.jpg"},{"id":429765,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TIDAA4","text":"USGS data release","linkHelpText":"Groundwater and soil gas data, methods, and quality assurance information for samples collected to determine ancient carbon distributions at Red Hill Bulk Fuel Storage Facility, Oʻahu, Hawaiʻi, 2022–2023"},{"id":497942,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117072.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Red Hill Bulk Fuel Storage Facility","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.91707912662315,\n 21.38710545210772\n ],\n [\n -157.91707912662315,\n 21.358702773157617\n ],\n [\n -157.87618345098332,\n 21.358702773157617\n ],\n [\n -157.87618345098332,\n 21.38710545210772\n ],\n [\n -157.91707912662315,\n 21.38710545210772\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
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Director, Pacific Islands Water Science Center
U.S. Geological Survey
1845 Wasp Blvd., B176
Honolulu, HI 96818
Large wood (LW) plays important geomorphic and ecological roles in rivers and is widely used as a restoration tool. Changes to floodplain land use and historical removal have altered wood dynamics in fluvial systems globally. We know little about the distribution and dynamics of LW in great rivers (approximately >105 km2) like the Upper Mississippi and Illinois Rivers despite its ecosystem importance and use in restoration projects. We assessed LW occurrence data collected by the fisheries component of the Upper Mississippi River Restoration Program's Long Term Resource Monitoring element. We analysed 25 years of data collected across six reaches of the Upper Mississippi and Illinois Rivers that represented contrasting physiographic settings, and across four aquatic area types comprising gradients of hydrology, connectivity and geomorphology. We tested hypotheses on drivers of LW occurrence using generalised linear mixed effects models, where occurrence was predicted by reach- and local-scale environmental variables. Occurrence varied significantly across reaches and aquatic area types. In general, wood occurred more frequently upriver and in side channels compared to other aquatic areas. Large wood was most strongly predicted systemically by reach identity but not local-scale variables, underscoring the importance of broad-scale physiographic gradients in defining hydrogeomorphic processes. Floodplain forests and shoreline revetment were consistently important predictors across reaches. Our findings show that the spatial variability of LW occurrence reflects the physical variability of the Upper Mississippi and Illinois Rivers. They also reveal the value in using geomorphic classifications as frameworks for understanding physical processes like LW dynamics because of their ability to contextualise site-scale conditions. The baseline understanding of LW abundance across different hydrogeomorphic gradients and scales presented here can give insight into how to more effectively target restoration efforts in great rivers and contribute to a broader understanding of LW dynamics where such studies have been lacking.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5911","usgsCitation":"Van Appledorn, M., Jankowski, K.J., Gahm, K., Budd, S., Baumann, D., Bennie, B., Erickson, R.A., Haro, R.J., and Rohweder, J.J., 2024, The where and why of large wood occurrence in the Upper Mississippi and Illinois Rivers: Earth Surface Processes and Landforms, v. 49, no. 11, p. 3383-3398, https://doi.org/10.1002/esp.5911.","productDescription":"16 p.","startPage":"3383","endPage":"3398","ipdsId":"IP-156995","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":430017,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Illinois River, Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.73578092131349,\n 45.12080357067936\n ],\n [\n -91.01148319916909,\n 42.06683000374717\n ],\n [\n -92.03223565670272,\n 40.356697147602766\n ],\n [\n -91.38931153946832,\n 38.69309735425202\n ],\n [\n -89.14510076231794,\n 36.650083721260515\n ],\n [\n -88.69332581972627,\n 40.70515855929407\n ],\n [\n -89.58402008394364,\n 41.68908264434286\n ],\n [\n -90.9223085282444,\n 44.553043048533425\n ],\n [\n -93.73578092131349,\n 45.12080357067936\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-06-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903500,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jankowski, Kathi Jo 0000-0002-3292-4182","orcid":"https://orcid.org/0000-0002-3292-4182","contributorId":207429,"corporation":false,"usgs":true,"family":"Jankowski","given":"Kathi","email":"","middleInitial":"Jo","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903501,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gahm, Kaija","contributorId":338731,"corporation":false,"usgs":false,"family":"Gahm","given":"Kaija","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":903502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Budd, Serenity","contributorId":338732,"corporation":false,"usgs":false,"family":"Budd","given":"Serenity","email":"","affiliations":[{"id":38728,"text":"Virginia Commonwealth University","active":true,"usgs":false}],"preferred":false,"id":903503,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baumann, Douglas","contributorId":328549,"corporation":false,"usgs":false,"family":"Baumann","given":"Douglas","affiliations":[{"id":68293,"text":"University of Wisconsin La Crosse","active":true,"usgs":false}],"preferred":false,"id":903504,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bennie, Barbara","contributorId":328550,"corporation":false,"usgs":false,"family":"Bennie","given":"Barbara","affiliations":[{"id":68293,"text":"University of Wisconsin La Crosse","active":true,"usgs":false}],"preferred":false,"id":903505,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903506,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Haro, Roger J.","contributorId":139538,"corporation":false,"usgs":false,"family":"Haro","given":"Roger","email":"","middleInitial":"J.","affiliations":[{"id":12793,"text":"University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":903507,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":903508,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70254846,"text":"70254846 - 2024 - Taking heat (downstream): Simulating groundwater and thermal equilibrium controls on annual paired air–water temperature signal transport in headwater streams","interactions":[],"lastModifiedDate":"2024-06-10T15:03:47.13455","indexId":"70254846","displayToPublicDate":"2024-06-07T09:58:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Taking heat (downstream): Simulating groundwater and thermal equilibrium controls on annual paired air–water temperature signal transport in headwater streams","docAbstract":"Headwater stream temperature often exhibits spatial variation at the kilometer-scale, but the relative importance of the underlying hydrogeological processes and riverine perturbations remains poorly understood. In this study, we investigated the relative importance of groundwater (GW) and other processes on downstream annual stream temperature signal characteristics using deterministic heat budget model (HFLUX) scenarios within an idealized stream reach representative of mountainous forested conditions. We summarized annual stream thermal regimes from the relationship of paired sinusoidal air and water temperature signals (amplitude ratio, phase lag, and mean ratio). Results showed that downstream changes in annual temperature depended on the thermal gradient between water and the hypothetical equilibrium temperature (where all heat fluxes sum to zero). GW inflow, riparian shading, and the boundary input signal were the most significant factors affecting downstream annual water temperature signals, while flow volume and channel dimensions impacted how quickly annual temperature signals changed. Effects of GW were dominated by advective rather than conductive heat exchange processes, but conduction played a larger role when GW input was more spatially diffuse. Our results indicated several mechanisms by which local processes may affect stream thermal resilience to disturbances and can help guide management of wildfire and climate change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.131391","usgsCitation":"Johnson, Z., Briggs, M., Snyder, C.D., Johnson, B.G., and Hitt, N.P., 2024, Taking heat (downstream): Simulating groundwater and thermal equilibrium controls on annual paired air–water temperature signal transport in headwater streams: Journal of Hydrology, v. 638, 131391, 18 p., https://doi.org/10.1016/j.jhydrol.2024.131391.","productDescription":"131391, 18 p.","ipdsId":"IP-117590","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":497980,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2024.131391","text":"Publisher Index Page"},{"id":429755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"638","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":902704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":222759,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":902705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Snyder, Craig D. 0000-0002-3448-597X csnyder@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-597X","contributorId":2568,"corporation":false,"usgs":true,"family":"Snyder","given":"Craig","email":"csnyder@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":902706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Brittany G. 0000-0002-8837-997X bdjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-8837-997X","contributorId":245863,"corporation":false,"usgs":false,"family":"Johnson","given":"Brittany","email":"bdjohnson@usgs.gov","middleInitial":"G.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":902707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hitt, Nathaniel P. 0000-0002-1046-4568","orcid":"https://orcid.org/0000-0002-1046-4568","contributorId":238185,"corporation":false,"usgs":true,"family":"Hitt","given":"Nathaniel","email":"","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":902708,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70254627,"text":"cir1519 - 2024 - The 3D National Topography Model Call for Action—Part 1. The 3D Hydrography Program","interactions":[],"lastModifiedDate":"2024-09-20T16:59:02.860799","indexId":"cir1519","displayToPublicDate":"2024-06-06T12:48:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1519","displayTitle":"The 3D National Topography Model Call for Action—Part 1. The 3D Hydrography Program","title":"The 3D National Topography Model Call for Action—Part 1. The 3D Hydrography Program","docAbstract":"The U.S. Geological Survey is initiating the 3D Hydrography Program (3DHP), the first systematic remapping of the Nation’s surface waters since the original 1:24,000-scale topographic mapping program was active from 1947 to 1992. Building on decades of experience maintaining the National Hydrography Dataset (NHD), the Watershed Boundary Dataset (WBD), and the NHDPlus High Resolution (NHDPlus HR), the 3DHP will completely refresh the Nation’s hydrography data and improve discovery and sharing of water-related data. The design of the 3DHP is based on the results of a study that estimated that the fully implemented program would have the potential to provide more than $1 billion in benefits to Federal, State, Tribal, Territorial, and local governments and to private and nonprofit organizations every year, in addition to myriad societal benefits. The 3DHP would directly support better decision making regarding water resources by providing more accurate, complete, and integrated information than is currently available.
The 3DHP datasets will include a three-dimensional (3D) hydrography network generated from and integrated with elevation data from the 3D Elevation Program (3DEP) to better represent stream gradients and channel conditions, along with waterbodies, hydrologic units, hydrologically enhanced elevation and other surfaces, and more consistent and accurate attributes. The 3DHP datasets will inherit key attributes of the NHD, WBD, and NHDPlus HR, and they also will include new attributes and links to other data such as the U.S. Fish and Wildlife Service National Wetlands Inventory, groundwater data, and engineered hydrologic systems such as stormwater networks. The 3DHP will be designed to provide a set of open and interoperable web-based tools, maps, and data catalogs, creating a robust system for users to reference their information about water; the system elements are collectively referred to as the “infostructure.” The 3DHP and the infostructure can provide a foundational geospatial underpinning for the Internet of Water, a community-based effort to modernize tools and technologies to share water data. As proposed, the 3DHP would begin providing products and services to the public in 2024.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1519","isbn":"978-1-4113-4579-9","programNote":"National Geospatial Program","usgsCitation":"Anderson, R., Lukas, V., and Aichele, S.S., 2024, The 3D National Topography Model Call for Action—Part 1. The 3D Hydrography Program (ver. 1.1, July 2024): U.S. Geological Survey Circular 1519, 12 p., https://doi.org/10.3133/cir1519.","productDescription":"iv, 12 p.","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-138071","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":429527,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1519/coverthb2.jpg"},{"id":429903,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1519/cir1519.XML"},{"id":429904,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1519/images/"},{"id":429528,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1519/cir1519.pdf","text":"Report","size":"6.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1519 PDF"},{"id":430036,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1519/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"CIR 1519 HTML"},{"id":430673,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/circ/1519/versionHist.txt","size":"1.42 KB","linkFileType":{"id":2,"text":"txt"}}],"edition":"Version 1.0: June 6, 2024; Version 1.1: July 1, 2024","contact":"Director, National Geospatial Program
U.S. Geological Survey
Mail Stop 511
12201 Sunrise Valley Drive
Reston, VA 20192
Fishes have spread into previously fishless wetlands, likely affecting other species. In the Prairie Pothole Region of North America, the invasion of fish into wetlands is facilitated by interactions of altered land use, climate, and hydrology. We aimed to understand the effects of fishes on amphipods, which are macroinvertebrates that vertebrates rely on as forage. We hypothesized the presence and abundance of fish, particularly benthivores, would have detrimental effects on amphipod abundance. Our study design targeted a large gradient of amphipod abundances among wetlands, including very high abundances of two amphipod species: Gammarus lacustris and Hyalella azteca. We found that fishless basins had twice as many amphipods as those with fish, on average. Gammarus lacustris were not detected in the presence of Black Bullhead Ameiurus melas. The abundance of both amphipod species had negative associations with the most common fishes, Fathead Minnow Pimephales promelas and Brook Stickleback Culaea inconstans. A multivariate community analysis showed the benthivore-fish functional feeding guild was negatively associated with the amphipod community, as hypothesized. However, our study design captured several wetlands with anomalies of high abundances of both fish and amphipods, obscuring their relationships. Our results aid resource managers by confirming several fish guilds and species are associated with lower abundances of amphipods. These findings can inform resource managers who make decisions about managing for fish and wildlife; for example, they may choose to manage existing fish populations or protect existing wetlands with high amphipod densities from new fish invasions.
Integrated hydrological models (IHMs) help characterize the complexity of surface–groundwater interactions. The cascade routing and re-infiltration (CRR) concept, recently applied to a MODFLOW 6 IHM, improved conceptualization and simulation of overland flow processes. The CRR controls the transfer of rejected infiltration and groundwater exfiltration from upslope areas to adjacent downslope areas where that water can be evaporated, re-infiltrated back to subsurface, or discharged to streams as direct runoff. The partitioning between these three components is controlled by uncertain parameters that must be estimated. Thus, by quantifying and reducing those uncertainties, next to uncertainties of the other model parameters (e.g. hydraulic and storage parameters), the reliability of the CRR is improved and the IHM is better suited for decision support modelling, the two key objectives of this work. To this end, the remotely sensed MODIS-ET product was incorporated into the calibration process for complementing traditional hydraulic head and streamflow observations. A total of approximately 150,000 observations guided the calibration of a 13-year MODFLOW 6 IHM simulation of the Sardon catchment (Spain) with daily stress periods. The model input uncertainty was represented by grid-cell-scale parameterization, yielding approximately 500,000 unknown input parameters to be conditioned. The calibration was carried out through an iterative ensemble smoother. Incorporating the MODIS-ET data improved the CRR implementation, and reduced uncertainties associated with other model parameters. Additionally, it significantly reduced the uncertainty associated with net recharge, a critical flux for water management that cannot be directly measured and rather is commonly estimated by IHM simulations.
Streams draining karst areas with rapid groundwater transit times may respond relatively quickly to nitrogen reduction strategies, but the complex hydrologic network of interconnected sinkholes and springs is challenging for determining the placement and effectiveness of management practices. This study aims to inform nitrogen reduction strategies in a representative agricultural karst setting of the Chesapeake Bay watershed (Fishing Creek watershed, Pennsylvania) with known elevated nitrate contamination and a previous documented groundwater residence time of less than a decade. During baseflow conditions, streamflow did not increase with drainage area. Headwaters and the main stem lost substantial flow to sinkholes until eventually discharging along large springs downstream. Seasonal hydrologic conditions shift the flow and nitrogen load spatially among losing and gaining stream sections. A compilation of nitrogen source inputs with the geochemistry and the pattern of enrichment of δ15N and δ18O suggest that the nitrogen in streams and springs during baseflow represents a mixture of manure, fertilizer, and wastewater sources with low potential for denitrification. The pH and calcite saturation index increased along generalized flow paths from headwaters to springs and indicate shorter groundwater residence times in baseflow during the spring versus summer. Given the substantial investment in management practices, fixed monitoring sites could incorporate synoptic water sampling to properly monitor long-term progress and help inform management actions in karst watersheds. Although karst watersheds have the potential to respond to nitrogen reduction strategies due to shorter groundwater residence times, high nitrogen inputs, effectiveness of conservation practices, and release of legacy nutrients within the karst cavities could confound progress of water quality goals.
","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America","doi":"10.1002/jeq2.20578","usgsCitation":"Clune, J.W., Cravotta, C., Husic, A., Dozier, H.J., and Schmidt, K.E., 2024, Complex hydrology and variability of nitrogen sources in a karst watershed: Journal of Environmental Quality, v. 53, no. 4, p. 492-507, https://doi.org/10.1002/jeq2.20578.","productDescription":"16 p.","startPage":"492","endPage":"507","ipdsId":"IP-142123","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":439459,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jeq2.20578","text":"Publisher Index Page"},{"id":430890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Fishing Creek watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.83891540849118,\n 41.44436215062939\n ],\n [\n -76.8086844380523,\n 41.44436215062939\n ],\n [\n -76.8086844380523,\n 40.98993659464011\n ],\n [\n -75.83891540849118,\n 40.98993659464011\n ],\n [\n -75.83891540849118,\n 41.44436215062939\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"53","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Clune, John W. 0000-0002-3563-1975","orcid":"https://orcid.org/0000-0002-3563-1975","contributorId":209635,"corporation":false,"usgs":true,"family":"Clune","given":"John","middleInitial":"W.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":258816,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Husic, Admin 0000-0002-4225-2252","orcid":"https://orcid.org/0000-0002-4225-2252","contributorId":340064,"corporation":false,"usgs":false,"family":"Husic","given":"Admin","email":"","affiliations":[{"id":81445,"text":"Assistant Professor (Kansas University)","active":true,"usgs":false}],"preferred":false,"id":906049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dozier, Hilary J 0000-0001-6869-8466","orcid":"https://orcid.org/0000-0001-6869-8466","contributorId":335092,"corporation":false,"usgs":true,"family":"Dozier","given":"Hilary","email":"","middleInitial":"J","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906050,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmidt, Kurt Eric 0009-0002-5173-551X","orcid":"https://orcid.org/0009-0002-5173-551X","contributorId":340065,"corporation":false,"usgs":true,"family":"Schmidt","given":"Kurt","email":"","middleInitial":"Eric","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906051,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70254586,"text":"70254586 - 2024 - Towards entity-aware conditional variational inference for heterogeneous time-series prediction: An application to hydrology","interactions":[],"lastModifiedDate":"2024-06-04T11:50:56.137709","indexId":"70254586","displayToPublicDate":"2024-05-31T06:49:52","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Towards entity-aware conditional variational inference for heterogeneous time-series prediction: An application to hydrology","docAbstract":"Groundwater flow between 2014 through 2040 was simulated in the New Jersey Coastal Plain based on three withdrawal scenarios. Two of the scenarios were based on projected population trends and the assumption of water conservation; the nominal water-loss scenario projected a status quo in the efficiency of water loss in the delivery systems whereas the optimal water-loss scenario projected a better water-loss efficiency resulting in less withdrawals. The third scenario assumes that all wells will withdraw water at their full allocation level which is generally much more than reported withdrawals in 2013 or projected under the other two scenarios.
Maps and summaries of heads and drawdowns are presented for nine confined aquifers. All the aquifers have areas with heads below sea level by 2040. Of the three scenarios, the drawdowns are most extreme in the full allocation scenarios; there are large areas of head decline greater than 20 feet in 5 of the 9 confined aquifers. The exceptions are the Vincentown aquifer, despite some areas of large drawdown in the vicinity of wells, and the three Potomac-Raritan-Magothy (PRM) aquifers where withdrawals are regulated by Critical Area restrictions. The nominal and optimal water-loss scenarios have some areas of head declines; most are less than 15 feet. The simulation of these scenarios shows some extensive areas of head recovery as well—especially in the aquifers that are regulated by the Critical Area restrictions.
Budgets of inflow and outflow components were calculated for 44 hydrologic budget areas (HBAs). The budget analysis shows that the water movement is complex and varies based on the aquifer geometry and location of pumping wells. Flow components between the unconfined and confined parts of the system were summarized by HUC11 (hydrologic unit code 11) basins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245028","collaboration":"Prepared in cooperation with New Jersey Department of Environmental Protection","usgsCitation":"Kauffman, L.J., 2024, Simulated effects of projected 2014–40 withdrawals on groundwater flow and water levels in the New Jersey Coastal Plain: U.S. Geological Survey Scientific Investigations Report 2024–5028, 149 p., https://doi.org/10.3133/sir20245028.","productDescription":"Report: x, 149 p.; Data Release","numberOfPages":"149","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-149036","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":429259,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5028/sir20245028.XML","description":"SIR 2024-5028 XML"},{"id":429258,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245028/full","description":"SIR 2024-5028 HTML"},{"id":429257,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5028/sir20245028.pdf","text":"Report","size":"45.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5028 PDF"},{"id":429256,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5028/coverthb.jpg"},{"id":499456,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117021.htm","linkFileType":{"id":5,"text":"html"}},{"id":429261,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JHGJA5","text":"USGS data release","linkHelpText":"MODFLOW-2005 model used to analyze water-use scenarios in the New Jersey Coastal Plain"},{"id":429260,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5028/images/"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.99277375585163,\n 38.68691666621089\n ],\n [\n -73.55380891210154,\n 38.68691666621089\n ],\n [\n -73.55380891210154,\n 40.54856979898429\n ],\n [\n -75.99277375585163,\n 40.54856979898429\n ],\n [\n -75.99277375585163,\n 38.68691666621089\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
3450 Princeton Pike
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Algal blooms in water, soils, dusts, and the environment have captured national attention because of concerns associated with exposure to algal toxins for humans and animals. Algal blooms naturally occur in all surface-water types and are important primary producers for aquatic ecosystems. However, excessive algae growth can be associated with many harmful effects ranging from aesthetic to toxicity concerns, so this excessive growth is commonly called a harmful algal bloom (HAB).
Ecological imbalances that can lead to excessive algal growth, such as increased nutrient availability to waterbodies from natural and anthropogenic sources, are well documented in scientific literature. On the other hand, fundamental scientific understandings of environmental causes and controls leading to algal toxin production, environmental exposures, and adverse health outcomes for humans and animals could benefit from more attention by U.S. Geological Survey (USGS) scientists. Understanding when, why, and how the toxin is produced by individual algal cells or communities and why the toxin is released to the surrounding waterbody requires fundamental research to determine a toxin’s role, whether it provides competitive advantage or if other potential reasons exist for toxin production and release, such as secretions from otherwise benign biological processes. This research will require groundbreaking scientific discovery about underlying biologic and abiotic (non-living) processes commonly complicated by local variation in land use, microbial species composition, and ecosystem structure of the surrounding watershed.
Although underlying processes by which HABs form may be similar from one waterbody to another, individual waterbodies may be controlled by local factors for HAB development and toxin production that are unique to the watershed. Consequently, many fundamental science gaps exist that prevent informed mitigation and prevention of toxic HAB events. There are also gaps in understanding local conditions that control algal growth unique to specific watersheds. Addressing these science gaps is needed to inform evidence-based decisions that protect human and animal health and that reduce recreational and socioeconomic losses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1520","usgsCitation":"Christensen, V.G., Crawford, C.J., Dusek, R.J., Focazio, M.J., Fogarty, L.R., Graham, J.L., Journey, C.A., Lee, M.E., Larson, J.H., Stackpoole, S.M., Mazzei, V., Pindilli, E.J., Rattner, B.A., Slonecker, T., McSwain, K.B., Reilly, T.J., and Lopez, A.E., 2024, Interdisciplinary science approach for harmful algal blooms (HABs) and algal toxins—A strategic science vision for the U.S. Geological Survey: U.S. Geological Survey Circular 1520, 39 p., https://doi.org/10.3133/cir1520.","productDescription":"Report: vi; 39 p.; Appendix","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-144573","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":38175,"text":"Toxics Substances Hydrology Program","active":true,"usgs":true}],"links":[{"id":429145,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/circ/1520/cir1520.XML","text":"XML","description":"CIR 1520"},{"id":429144,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1520/cir1520.pdf","text":"Report","size":"8.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1520","linkHelpText":"–Interdisciplinary Science Approach for Harmful Algal Blooms (HABs) and Algal Toxins—A Strategic Science Vision for the U.S. Geological Survey"},{"id":429143,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/circ/1520/images"},{"id":429142,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1520/coverthb.jpg"},{"id":429203,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/circ/1520/cir1520_app1.pdf","text":"Appendix 1","size":"332 KB","linkFileType":{"id":1,"text":"pdf"}},{"id":429146,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/cir1520/full"}],"contact":"Director, Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Groundwater interactions with mountain streams are often simplified in model projections, potentially leading to inaccurate estimates of streamflow response to climate change. Here, using a high-resolution, integrated hydrological model extending 400 m into the subsurface, we find groundwater an important and stable source of historical streamflow in a mountainous watershed of the Colorado River. In a warmer climate, increased forest water use is predicted to reduce groundwater recharge resulting in groundwater storage loss. Losses are expected to be most severe during dry years and cannot recover to historical levels even during simulated wet periods. Groundwater depletion substantially reduces annual streamflow with intermittent conditions predicted when precipitation is low. Expanding results across the region suggests groundwater declines will be highest in the Colorado Headwater and Gunnison basins. Our research highlights the tight coupling of vegetation and groundwater dynamics and that excluding explicit groundwater response to warming may underestimate future reductions in mountain streamflow.
To improve flood-frequency estimates for rural streams in the Coastal Plain region of Louisiana, generalized least-squares regression techniques were used to relate corresponding annual exceedance probability streamflows for 211 streamgages in the region to a suite of explanatory variables that include physical, climatic, pedologic, and land-use characteristics of the streamgage drainage area. The resulting generalized least-squares models can be used to estimate selected annual exceedance probability streamflows for rural ungaged locations in the Coastal Plain region of Louisiana. For the 211 streamgages in the Coastal Plain region of Louisiana and surrounding States, annual peak-streamflow data available through the 2016 water year were used in this study. Two unique flood regions, the Mississippi Alluvial Plain and Coastal Plain, were identified as separate hydrologic regions based on statistical evaluation and significance of categorical variables representing the regions regressed against the 1-percent annual exceedance probability streamflow (the 100-year flood). Regional regression equations for estimating annual exceedance probability streamflow for the Mississippi Alluvial Plain region have been previously published; therefore, the purpose of this study was to generate updated regional regression equations for the Coastal Plain region of Louisiana. The final regression models used drainage area and channel slope as explanatory variables based on performance metrics.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245031","issn":"2328-0328","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Ensminger, P.A., Wagner, D.M., and Whaling, A., 2024, Magnitude and frequency of floods in the Coastal Plain region of Louisiana, 2016: U.S. Geological Survey Scientific Investigations Report 2024–5031, 18 p., https://doi.org/10.3133/sir20245031.","productDescription":"Report: viii, 18 p.; 2 Data Releases","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-091992","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":428761,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5031/coverthb.jpg"},{"id":428762,"rank":2,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5031/images"},{"id":428766,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":499458,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117008.htm","linkFileType":{"id":5,"text":"html"}},{"id":428767,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B48P2W","text":"USGS data release","linkHelpText":"Flood-frequency of rural, non-tidal streams in Louisiana and part of Arkansas, Mississippi, and Texas, 1877–2016"},{"id":428765,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245031/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5031 HTML"},{"id":428764,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5031/sir20245031.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5031 XML"},{"id":428763,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5031/sir20245031.pdf","size":"2.41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5031"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.00967112876856,\n 29.444945586186265\n ],\n [\n -88.68056894510033,\n 29.126157783971323\n ],\n [\n -88.60834825216723,\n 33.49872733992268\n ],\n [\n -89.99512218551115,\n 34.151214622331636\n ],\n [\n -90.94796783046621,\n 34.514518506267706\n ],\n [\n -93.48068301952253,\n 34.58384293733218\n ],\n [\n -94.52066293605651,\n 33.519758483995176\n ],\n [\n -94.62647823892294,\n 29.923162538843826\n ],\n [\n -94.00967112876856,\n 29.444945586186265\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Climate change in the Arctic is altering watershed hydrologic processes and biogeochemistry. Here, we present an emergent threat to Arctic watersheds based on observations from 75 streams in Alaska’s Brooks Range that recently turned orange, reflecting increased loading of iron and toxic metals. Using remote sensing, we constrain the timing of stream discoloration to the last 10 years, a period of rapid warming and snowfall, suggesting impairment is likely due to permafrost thaw. Thawing permafrost can foster chemical weathering of minerals, microbial reduction of soil iron, and groundwater transport of metals to streams. Compared to clear reference streams, orange streams have lower pH, higher turbidity, and higher sulfate, iron, and trace metal concentrations, supporting sulfide mineral weathering as a primary mobilization process. Stream discoloration was associated with dramatic declines in macroinvertebrate diversity and fish abundance. These findings have considerable implications for drinking water supplies and subsistence fisheries in rural Alaska.
Debris-flow volumes can increase along their flow path by entraining sediment stored in the channel bed and banks, thus also increasing hazard potential. Theoretical considerations, laboratory experiments and field investigations all indicate that the saturation conditions of the sediment along the flow path can greatly influence the amount of sediment entrained. However, this process is usually not considered for practical applications. This study aims to close this gap by combining runout and hydrological models into a predictive framework that is calibrated and tested using unique observations of sediment erosion and debris-flow properties available at a Swiss debris-flow observation station (Illgraben). To this end, hourly water input to the erodible channel is predicted using a simple, process-based hydrological model, and the resulting water saturation level in the upper sediment layer of the channel is modelled based on a Hortonian infiltration concept. Debris-flow entrainment is then predicted using the RAMMS debris-flow runout model. We find a strong correlation between the modelled saturation level of the sediment on the flow path and the channel-bed erodibility for single-surge debris-flow events with distinct fronts, indicating that the modelled water content is a good predictor for erosion simulated in RAMMS. Debris-flow properties with more complex flow behaviour (e.g., multiple surges or roll waves) are not as well predicted using this procedure, indicating that more physically complete models are necessary. Finally, we demonstrate how this modelling framework can be used for climate change impact assessment and show that earlier snowmelt may shift the peak of the debris-flow season to earlier in the year. Our novel modelling framework provides a plausible approach to reproduce saturation-dependent entrainment and thus better constrain event volumes for current and future hazard assessment.
Groundwater-influenced ecosystems (GIEs) are increasingly vulnerable due to groundwater extraction, land-use practices, and climate change. These ecosystems receive groundwater inflow as a portion of their baseflow or water budget, which can maintain water levels, water temperature, and chemistry necessary to sustain the biodiversity that they support. In some systems (e.g., springs, seeps, fens), this connection with groundwater is central to the system’s integrity and persistence. Groundwater management decisions for human use often do not consider the ecological effects of those actions on GIEs. This disparity can be attributed, in part, to a lack of information regarding the physical relationships these systems have with the surrounding landscape and climate, which may influence the environmental conditions and associated biodiversity. We estimate the vulnerability of areas predicted to be highly suitable for the presence of GIEs based on watershed (U.S. Geological Survey Hydrologic Unit Code 12 watersheds: 24–100 km2) and pixel (30 m × 30 m pixels) resolution in the Atlantic Highlands and Mixed Wood Plains EPA Level II Ecoregions in the northeastern United States. We represent vulnerability with variables describing adaptive capacity (topographic wetness index, hydric soil, physiographic diversity), exposure (climatic niche), and sensitivity (aquatic barriers, proportion urbanized or agriculture). Vulnerability scores indicate that ~26% of GIEs were within 30 m of areas with moderate vulnerability. Within these GIEs, climate exposure is an important contributor to vulnerability of 40% of the areas, followed by land use (19%, agriculture or urbanized). There are few areas predicted to be suitable for GIEs that are also predicted to be highly vulnerable, and of those, climate exposure is the most important contributor to their vulnerability. Persistence of GIEs in the northeastern United States may be challenged as changes in the amount and timing of precipitation and increasing air temperatures attributed to climate change affect the groundwater that sustains these systems.
","language":"English","publisher":"MDPI","doi":"10.3390/w16101366","usgsCitation":"Snyder, S.D., Loftin, C., and Reeve, A., 2024, Vulnerability assessment of groundwater influenced ecosystems in the Northeastern United States: Water, v. 16, no. 10, 1366, 23 p., https://doi.org/10.3390/w16101366.","productDescription":"1366, 23 p.","ipdsId":"IP-156998","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":439625,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w16101366","text":"Publisher Index Page"},{"id":433619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Delaware, District of Columbia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia","otherGeospatial":"Northeastern United 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\"}}]}","volume":"16","issue":"10","noUsgsAuthors":false,"publicationDate":"2024-05-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Snyder, Shawn D.","contributorId":343132,"corporation":false,"usgs":false,"family":"Snyder","given":"Shawn","email":"","middleInitial":"D.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loftin, Cyndy 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":146427,"corporation":false,"usgs":true,"family":"Loftin","given":"Cyndy","email":"cyndy_loftin@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910639,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reeve, Andrew S.","contributorId":343135,"corporation":false,"usgs":false,"family":"Reeve","given":"Andrew S.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":910640,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70254213,"text":"70254213 - 2024 - Land-use interactions, Oil-Field infrastructure, and natural processes control hydrocarbon and arsenic concentrations in groundwater, Poso Creek Oil Field, California, USA","interactions":[],"lastModifiedDate":"2024-05-14T12:15:18.058912","indexId":"70254213","displayToPublicDate":"2024-05-07T07:12:20","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Land-use interactions, Oil-Field infrastructure, and natural processes control hydrocarbon and arsenic concentrations in groundwater, Poso Creek Oil Field, California, USA","docAbstract":"Like many hydrocarbon production areas in the U.S., the Poso Creek Oil Field in California includes and is adjacent to other land uses (agricultural and other developed lands) that affect the hydrology and geochemistry of the aquifer overlying and adjacent to oil development. We hypothesize that the distributions of hydrocarbons and arsenic in groundwater in such areas will be controlled by complex interactions between mixed land uses, oil-field infrastructure, and natural processes. In 2020–2021, samples of groundwater and surface water were collected and analyzed for a large suite of inorganic and organic chemicals and isotope and gas tracers to test this hypothesis. Those data are supplemented with ancillary data on historical geochemistry, hydrology, geology, and oil-field infrastructure. Hydrocarbons in groundwater (e.g., methane through pentane gases and benzene) are associated with natural processes (e.g., fault offsets or transition in sediment depositional environment) and oil-field infrastructure (e.g., fluid-migration pathways associated with uncemented annulus in oil wells or unlined pits). Arsenic concentrations >10 μg per liter (μg/L; maximum concentration 12.9 μg/L) are associated with natural processes in old, high-pH groundwater, and more recent recharge of water from natural and/or engineered recharge processes. Along the southwest margin of the oil field, pumping for drinking-water and irrigation supplies in combination with engineered groundwater recharge produce a depression in groundwater elevations where groundwater with elevated sulfate concentrations from agricultural areas and groundwater with hydrocarbons from the oil field mix to produce a zone of sulfate reduction that removes hydrocarbons and arsenic from groundwater but produces elevated sulfide (S2-) concentrations (maximum concentration 29 mg per liter, mg/L). In this study, multiple approaches were required to resolve the overlapping effects of land uses, oil-field infrastructure, and natural processes on the distributions of hydrocarbons and arsenic in groundwater. The combined use of geographic, historical, physical, chemical, isotopic, and other information to constrain processes could be a useful approach for studies in other hydrocarbon-production areas. This is particularly important where land uses affect aquifer hydrology to an extent that causes mixing of groundwaters with different chemical compositions.
Reductions in streamflow caused by groundwater pumping, known as “streamflow depletion,” link the hydrologic process of stream-aquifer interactions to human modifications of the water cycle. Isolating the impacts of groundwater pumping on streamflow is challenging because other climate and human activities concurrently impact streamflow, making it difficult to separate individual drivers of hydrologic change. In addition, there can be lags between when pumping occurs and when streamflow is affected. However, accurate quantification of streamflow depletion is critical to integrated groundwater and surface water management decision making. Here, we highlight research priorities to help advance fundamental hydrologic science and better serve the decision-making process. Key priorities include (a) linking streamflow depletion to decision-relevant outcomes such as ecosystem function and water users to align with partner needs; (b) enhancing partner trust and applicability of streamflow depletion methods through benchmarking and coupled model development; and (c) improving links between streamflow depletion quantification and decision-making processes. Catalyzing research efforts around the common goal of enhancing our streamflow depletion decision-support capabilities will require disciplinary advances within the water science community and a commitment to transdisciplinary collaboration with diverse water-connected disciplines, professions, governments, organizations, and communities.
Livestock grazing, a widespread land use in semi-arid systems, is often placed in opposition to the perpetuation of biological soil crusts (“biocrusts”: lichens, mosses, and algal crusts including cyanobacteria) that live on the soil surface and provide ecosystem functions. The composition of biocrusts and vascular plants varies with climate, soils, and disturbance. In general, ruderal mosses and light algal crusts make up greater proportions of biocrusts in the presence of disturbance, although morphogroups of biocrusts respond differently to various disturbances. It is unknown if there are scenarios under which grazing can occur and ruderal components of biocrust could be maintained. We examine the hypothesis that soil surface texture-moisture interactions influence the ability of biocrusts to withstand trampling, reasoning that finer-textured soils are firmer (therefore serving as a better substrate for biocrusts) when dry and that coarser-textured are firmer when wet. We test these relationships within Birds of Prey, National Conservation Area (Boise, Idaho, USA). Results demonstrate two associations of biocrusts, dependent on season of grazing: one dominated by light algal crusts and lichens that frequently occurs with wet season grazing, and a second dominated by tall mosses and cup lichens that frequently occurs with dry season grazing. High cover of the invasive annual grass, Bromus tectorum (L.) was observed on sites with coarse-textured soils, and high sand content, that are grazed at relatively high intensities, creating unstable surfaces, and likely putting biocrusts at greater susceptibility to trampling. Results suggest that livestock management that accounts for soil texture and moisture could be used to maintain cover of ruderal biocrusts on fine-textured soils, that are grazed in the dry season, at low intensity. We discuss our findings in the context of managing for species of interest. Our findings are timely as varying the season of grazing is increasingly discussed as a means of favoring desirable native perennial grasses. Although ruderal morphogroups of biocrusts are not interpreted as having equivalent ecosystem functions compared to intact biocrusts, their contributions to soil stability, fertility, hydrology, and weed abatement could increase if they were more intentionally targeted by management.
In Missouri, distinct geophysical gradients influence statewide patterns in water quality. Here, we quantify the spatiotemporal variability of dissolved organic carbon (DOC) in reservoirs and streams and the edaphic, hydrologic, and land cover variables that account for cross-system variation. Datasets included statewide inventories collected over decades and studies with greater temporal resolution (n = >6350 DOC measurements). Among reservoirs, the smallest DOC concentration was measured in a spring-fed system within a forested watershed, and the largest was where agricultural biosolids were applied to the land (range 1.0–15.9 mg/L, overall mean 5.8 mg/L). Reservoir values increased from the southern forested Highlands (mean 4.7 mg/L) to the northern agricultural Plains (mean 7.0 mg/L). Stream DOC was similar to reservoir values (overall mean in streams 6.3 mg/L; Highlands mean 4.0 mg/L; Plains mean 6.6 mg/L), despite differences in study design and collection period. Reservoir DOC increased in spring, indicative of allochthonous loading, with small autochthonous additions during a broad summer peak. Temporal variability in DOC was low relative to macronutrients and chlorophyll in both reservoirs and streams, indicating DOC may be a sensitive and readily detected indicator of temporal change in these systems. In regression analyses, watershed features accounted for more than 60% of overall cross-system variability in DOC in both reservoirs and streams. Driver-response relations, however, differed between regions. This analysis extends our understanding of environmental influences on surface water chemistry in Missouri and indicates DOC is nearly as predictable as macronutrients using landscape-level features.
The Special Contributing Area Loading Program (SCALP) is a hydrologic routing program that simulates reservoir routing through a linear-reservoir-in-series method. The Java version of SCALP was developed to replicate and replace the functionality of an older version of the program written in Fortran. SCALP models flow through three reservoirs in series using an input runoff depth time series and information describing the hydrologic characteristics and sanitary flow for one or more land areas within a basin, supplied by the user. Each basin is herein referred to as a “Special Contributing Area” (SCA); the SCAs are a central concept in SCALP. Although flow through each SCA is routed separately, the user may simulate multiple SCAs in a batch simulation. The outputs of SCALP include information about flows through and overflows from the three reservoirs in the series.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241021","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Doyle, H.F., and Domanski, M.M., 2024, Special Contributing Area Loading Program user’s manual: U.S. Geological Survey Open-File Report 2024–1021, 15 p., https://doi.org/10.3133/ofr20241021.","productDescription":"Report: vi, 15 p.; Software Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-137188","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":427858,"rank":6,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9EE0614","text":"USGS software release","linkHelpText":"—SCALP (Special Contributing Area Loading Program, ver. 1.0.0)"},{"id":427857,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241021/full"},{"id":427856,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1021/images/"},{"id":427855,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1021/ofr20241021.XML"},{"id":427854,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1021/ofr20241021.pdf","text":"Report","size":"4.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2024–1021"},{"id":427853,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1021/coverthb.jpg"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The California Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP) investigated water quality of groundwater resources used for drinking-water supplies in the Madera-Chowchilla, Kings, Kaweah, Tule, and Tulare Lake groundwater subbasins of the southeastern San Joaquin Valley during 2013–15. The study focused primarily on groundwater resources used for domestic-supply wells in the southeastern San Joaquin Valley (SESJV-D), which correspond mostly to shallower parts of aquifer systems, compared to the groundwater resources used for public-supply wells in the southeastern San Joaquin Valley (SESJV-P). The investigation had three components: (1) characterization of the status of water quality in the SESJV-D, (2) comparison between water quality in the SESJV-D and SESJV-P, and (3) identification of natural and anthropogenic factors that potentially could affect water quality in these resources.
The characterization of water quality in the SESJV-D was based on data collected from 198 domestic wells sampled during 2013–15 by the U.S. Geological Survey (USGS); characterization of water quality in the SESJV-P was based on data collected from 124 wells sampled by the USGS during 2005–18 and an additional 1,577 wells with publicly available data reported to the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW). Measured concentrations were compared to regulatory and non-regulatory drinking-water quality benchmarks. A grid-based method was used to estimate the areal proportions of each study area and the whole southeastern San Joaquin Valley with high (greater than benchmark concentration), moderate (greater than half of the benchmark for inorganic and one-tenth of the benchmark for organic), and low concentrations relative to those benchmarks.
Natural and anthropogenic factors that could affect groundwater quality for the SESJV-D were identified in the context of the hydrogeologic setting of the southeastern San Joaquin Valley. The considered factors represented hydrologic conditions and position in the groundwater flow system (well depth, lateral position, presence of hydric soils, percentage of coarse-grained sediment, and aridity index), land-use characteristics (percentages of agricultural, urban, and natural land use, percentage of orchard or vineyard land use, and densities of septic tanks and underground storage tanks near the wells), and geochemical conditions (groundwater age class, oxidation-reduction class, pH, and dissolved oxygen and bicarbonate concentrations). Factors are compared between SESJV-D and SESJV-P at the scale of the five study areas.
One or more inorganic constituents with U.S. Environmental Protection Agency (EPA) or California maximum contaminant levels (MCLs) were detected at high concentrations in 47 percent of the SESJV-D and in 32 percent of the SESJV-P. The inorganic constituents most commonly present at high concentrations in the SESJV-D were nitrate, uranium, and arsenic. Within the SESJV-D, the proportion of the study area with high concentrations of inorganic constituents ranged from 19 percent in Madera-Chowchilla to 60 percent in Kings and Tulare Lake. One or more inorganic constituents with California State Water Resources Control Board Division of Drinking Water secondary maximum contaminant levels (SMCL-CAs) were detected at high concentrations in 14 percent of the SESJV-D and in 19 percent of the SESJV-P. The constituents most commonly present at high concentrations were manganese, iron, and total dissolved solids (TDS). Although the proportion of SESJV-D and SESJV-P with high concentrations of TDS greater than the upper SMCL were similar at 4 percent, the proportion of the SESJV-D with moderate concentrations (between the recommended and upper SMCL-CA), 30 percent, was greater than the proportion of the SESJV-P with moderate concentrations, 12 percent.
One or more organic constituents with MCLs were present at high concentrations in 19 percent of the SESJV-D and in 12 percent of the SESJV-P. All the constituents detected at high concentrations in the SESJV-D were fumigants, primarily 1,2,3-trichloropropane (1,2,3-TCP) and 1,2-dibromo-3-chloropropane (DBCP). Fumigants also were the constituents most commonly detected at high concentrations in the SESJV-P, although high concentrations of solvents also were detected. The SESJV-D dataset included analysis of many organic constituents without MCL benchmarks and with detection levels far below drinking water benchmark concentrations; detections at these low concentrations can be used as tracers of anthropogenic influence on groundwater. Pesticides and degradates of pesticides were detected in 60 percent of the SESJV-D; the most frequently detected pesticides were the herbicides simazine, didealkylatrazine (CAAT, a degradate of simazine and atrazine), diuron, and bromacil.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245009","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Burow, K.R., Shelton, J.L., and Fram, M.S., 2024, Status of water quality in groundwater resources used for drinking-water supply in the southeastern San Joaquin Valley, 2013–15—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2024–5009, 135 p., https://doi.org/10.3133/sir20245009.","productDescription":"Report: xiii, 135 p.; Data Release","numberOfPages":"136","onlineOnly":"Y","ipdsId":"IP-094434","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":428122,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245009/full"},{"id":493742,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116370.htm","linkFileType":{"id":5,"text":"html"}},{"id":428123,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DCTLXV","text":"USGS Data Release","description":"Balkan, M., Burow, K.R., and Shelton, J.L., and Fram, M.S., 2024, Data sets for: Status of water quality in groundwater resources used for drinking water supply in the southeast San Joaquin Valley, 2013–2015—California GAMA Priority Basin Project: U.S. Geological Survey data release, accessed January, 22, 2024, at https://doi.org/10.5066/P9DCTLXV","linkHelpText":"Data sets for: Status of water quality in groundwater resources used for drinking water supply in the southeast San Joaquin Valley, 2013–2015—California GAMA Priority Basin Project"},{"id":428120,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5009/sir20245009.xml"},{"id":428118,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5009/covrthb.jpg"},{"id":428121,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5009/images"},{"id":428119,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5009/sir20245009.pdf","text":"Report","size":"16 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.36728753741212,\n 37.719936264455484\n ],\n [\n -121.36728753741212,\n 35.78355104851377\n ],\n [\n -118.20322503741215,\n 35.78355104851377\n ],\n [\n -118.20322503741215,\n 37.719936264455484\n ],\n [\n -121.36728753741212,\n 37.719936264455484\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
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