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These changes in habitat are often attributed to a diverse suite of factors including prolonged and increasingly severe droughts, conifer expansion, anthropogenic development, domestic and feral livestock grazing, and invasion of exotic annual grasses, which promotes increased wildfire frequency and severity. Greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) are considered an indicator of sagebrush ecosystem health and have experienced widespread population decline associated with habitat loss and degradation, as well as changes in predator communities. Our objectives were to model and map sage-grouse habitat selection and survival during the important brood-rearing life stage in relation to landscape-scale environmental predictors. Furthermore, we sought to understand impacts of wildfire and annual grass invasion on brood habitat, as these accelerated disturbance regimes are a primary cause of habitat loss within the Great Basin region of the USA. We used a hierarchical Bayesian modeling framework to estimate resource selection functions and survival for early and late brood-rearing stages of sage-grouse in relation to a broad suite of habitat characteristics evaluated at multiple spatial scales within the Great Basin from 2009 to 2019. Sage-grouse selected for greater perennial grass cover, higher relative elevations, and areas closer to springs and wet meadows during both early and late brood-rearing. Terrain characteristics, including heat load and aspect, were important in survival models, as was variation in shrub height. We also found strong evidence for higher survival for both early and late broods within previously burned areas, but survival within burned areas decreased as annual grass cover (i.e. cheatgrass, Bromus tectorum) increased. This interaction effect demonstrates how invasion of annual grasses into burned areas, which has become prevalent in Great Basin sagebrush ecosystems, can lead to maladaptive habitat selection by brood-rearing greater sage-grouse. Understanding these complex relationships aids wildlife conservation and habitat management as wildfire and annual grass cycles continue to accelerate across western ecosystems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02147","usgsCitation":"Brussee, B.E., Coates, P.S., O’Neil, S.T., Casazza, M.L., Espinosa, S.P., Boone, J., Ammon, E., Gardner, S.C., and Delehanty, D.J., 2022, Invasion of annual grasses following wildfire corresponds to maladaptive habitat selection by a sagebrush ecosystem indicator species: Global Ecology and Conservation, v. 37, e02147, 19 p., https://doi.org/10.1016/j.gecco.2022.e02147.","productDescription":"e02147, 19 p.","ipdsId":"IP-133908","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447908,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02147","text":"Publisher Index Page"},{"id":435856,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B593DZ","text":"USGS data release","linkHelpText":"Spatially-Explicit Predictive Maps of Greater Sage-Grouse Brood Selection Integrated with Brood Survival in Nevada and Northeastern California, USA"},{"id":412740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.11100469577016,\n 37.65679718030911\n ],\n [\n -114.01367957532398,\n 37.81079149850166\n ],\n [\n -114.06841886178916,\n 41.950546575009156\n ],\n [\n -120.51366289783158,\n 41.94877001660362\n ],\n [\n -120.95831043718897,\n 40.25557232247007\n ],\n [\n -120.37729928858255,\n 39.092072629058066\n ],\n [\n -119.0529053921718,\n 39.74527180686712\n ],\n [\n -117.0729066045891,\n 38.04894793599695\n ],\n [\n -115.11005550665487,\n 37.598613857563834\n ],\n [\n -115.11100469577016,\n 37.65679718030911\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863535,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863536,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Espinosa, Shawn P.","contributorId":195583,"corporation":false,"usgs":false,"family":"Espinosa","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":863537,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boone, John D.","contributorId":300334,"corporation":false,"usgs":false,"family":"Boone","given":"John D.","affiliations":[{"id":65087,"text":"Great Basin Bird Observatory, Reno, Nevada, USA","active":true,"usgs":false}],"preferred":false,"id":863538,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ammon, Elisabeth M.","contributorId":302116,"corporation":false,"usgs":false,"family":"Ammon","given":"Elisabeth M.","affiliations":[{"id":65418,"text":"Great Basin Bird Observatory, 1755 E. Plumb Ln Ste 256 A, Reno, NV 89502, USA","active":true,"usgs":false}],"preferred":false,"id":863539,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gardner, Scott C.","contributorId":192081,"corporation":false,"usgs":false,"family":"Gardner","given":"Scott","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":863540,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":863541,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70231318,"text":"70231318 - 2022 - Hydroclimate response of spring ecosystems to a two-stage Younger Dryas event in western North America","interactions":[],"lastModifiedDate":"2022-05-06T14:29:19.84111","indexId":"70231318","displayToPublicDate":"2022-05-05T09:26:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Hydroclimate response of spring ecosystems to a two-stage Younger Dryas event in western North America","docAbstract":"The Younger Dryas (YD) climate event is the preeminent example of abrupt climate change in the recent geologic past. Climate conditions during the YD were spatially complex, and high-resolution sediment cores in the North Atlantic, western Europe, and East Asia have revealed it unfolded in two distinct stages, including an initial stable climatic period between ~ 12.9 and 12.2 ka associated with a weakened Atlantic Meridional Overturning Circulation (AMOC) and a second phase characterized by variable conditions until 11.7 ka as the AMOC recovered. Decades of investigations into the climate of western North America during the YD have failed to identify this stepped phenomenon. Here we present hydroclimate data from paleospring deposits in Death Valley National Park (California, USA) that demonstrate unequivocal evidence of two-stage partitioning within the YD event. High groundwater levels supported persistent and long-lived spring ecosystems between ~ 13.0 and 12.2 ka, which were immediately replaced by alternating wet and dry environments until ~ 11.8 ka. These results establish the mid-YD climate transition extended into western North America at approximately the same time it was recorded by hydrologic systems elsewhere in the Northern Hemisphere and show that even short-lived changes in the AMOC can have profound consequences for ecosystems worldwide.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41598-022-11377-4","usgsCitation":"Pigati, J.S., and Springer, K.B., 2022, Hydroclimate response of spring ecosystems to a two-stage Younger Dryas event in western North America: Scientific Reports, v. 12, 7373, 7 p., https://doi.org/10.1038/s41598-022-11377-4.","productDescription":"7373, 7 p.","ipdsId":"IP-122733","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":447910,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-022-11377-4","text":"Publisher Index Page"},{"id":435857,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U5XSRY","text":"USGS data release","linkHelpText":"Data release for Hydroclimate response of spring ecosystems to a two-stage Younger Dryas event in western North America"},{"id":400284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Death Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.070068359375,\n 36.217687122250574\n ],\n [\n -116.66931152343749,\n 36.217687122250574\n ],\n [\n -116.66931152343749,\n 37.4356124041315\n ],\n [\n -118.070068359375,\n 37.4356124041315\n ],\n [\n -118.070068359375,\n 36.217687122250574\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":842301,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Springer, Kathleen B. 0000-0002-2404-0264 kspringer@usgs.gov","orcid":"https://orcid.org/0000-0002-2404-0264","contributorId":149826,"corporation":false,"usgs":true,"family":"Springer","given":"Kathleen","email":"kspringer@usgs.gov","middleInitial":"B.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":842302,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70232248,"text":"70232248 - 2022 - Supporting the development and use of native plant materials for restoration on the Colorado Plateau (Fiscal Year 2021 Report)","interactions":[],"lastModifiedDate":"2022-06-17T14:21:51.237888","indexId":"70232248","displayToPublicDate":"2022-05-05T09:21:04","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":10936,"text":"Fiscal Year Report","active":true,"publicationSubtype":{"id":1}},"title":"Supporting the development and use of native plant materials for restoration on the Colorado Plateau (Fiscal Year 2021 Report)","docAbstract":"A primary focus of the Colorado Plateau Native Plant Program (CPNPP) is to identify and develop appropriate native plant materials (NPMs) for current and future restoration projects. Multiple efforts have characterized the myriad challenges inherent in providing appropriate seed resources to enable effective, widespread restoration and have identified a broad suite of research activities to provide the information necessary to overcome those challenges (e.g., Plant Conservation Alliance 2015; Breed et al. 2018; Winkler et al. 2018; McCormick et al. 2021). Many of the most complex information needs relate to identifying the appropriate sources of plant species that can successfully establish in dryland environments, like the Colorado Plateau, where low and highly variable precipitation is standard. Providing this information requires synergistic research efforts in which results from earlier investigations inform the design of subsequent investigations. The U.S. Geological Survey (USGS) Southwest Biological Science Center’s (SBSC’s) research activities to support CPNPP in FY21 followed the FY21 Statement of Work to support a research framework that is continually adapting based on the needs of the restoration community and results from previous investigations; the long-term research framework is outlined in the 2019-2023 5-Year Research Strategy (hereafter referred to as the 5-year plan). This research framework provides support for the National Seed Strategy for Rehabilitation and Restoration (Plant Conservation Alliance 2015), Biden-Harris Administration Executive Order 14008 (Tackling the Climate Crisis at Home and Abroad), and Department of Interior Priority #4 (Working to conserve at least 30% each of our lands and waters by the year 2030).
Research activities in FY21 centered on landscape genomics, implementing and monitoring a common garden experiment near Vernal, UT, conducting experimental treatments using the GRID (Germination for Restoration Information and Decision-making) framework, and initiating new genetics projects to investigate the impact of production techniques on plant materials and restoration treatments on native plant communities. These activities were supported by four biological science technicians. The SARS-CoV-2 pandemic delayed some aspects of the FY21 workplan, especially for outside laboratory services. However, goals were largely met, and the overall progress of research remains on track with respect to the 5-year plan. While Dr. Rob Massatti was the only scientist supported by the SBSC-CPNPP agreement in FY21, other scientists, including Drs. John Bradford, Seth Munson, Mike Duniway, Sasha Reed, and Daniel Winkler, spent a considerable amount of time providing expertise and support for individual projects. Work activities performed in support of each 5-year plan goal are discussed in turn. Products resulting from FY21 research activities are reported in Appendix 1.
","language":"English","publisher":"Bureau of Land Management","collaboration":"Bureau of Land Management","usgsCitation":"Massatti, R., Winkler, D.E., Reed, S., Duniway, M.C., Munson, S.M., and Bradford, J., 2022, Supporting the development and use of native plant materials for restoration on the Colorado Plateau (Fiscal Year 2021 Report): Fiscal Year Report, 19 p.","productDescription":"19 p.","ipdsId":"IP-139434","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":402326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":402322,"type":{"id":15,"text":"Index Page"},"url":"https://www.blm.gov/sites/blm.gov/files/docs/2022-06/CPNPP_USGS_FY21_AnnualReport.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.02734374999999,\n 31.87755764334002\n ],\n [\n -104.765625,\n 31.87755764334002\n ],\n [\n -104.765625,\n 40.17887331434696\n ],\n [\n -113.02734374999999,\n 40.17887331434696\n ],\n [\n -113.02734374999999,\n 31.87755764334002\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Massatti, Robert 0000-0001-5854-5597","orcid":"https://orcid.org/0000-0001-5854-5597","contributorId":207294,"corporation":false,"usgs":true,"family":"Massatti","given":"Robert","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844795,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844796,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844797,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844798,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844799,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":844800,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236087,"text":"70236087 - 2022 - High‐frequency rupture processes of the 2014 Mw 8.2 Iquique and 2015 Mw 8.3 Illapel, Chile, earthquakes determined from strong‐motion recordings","interactions":[],"lastModifiedDate":"2022-08-29T12:07:32.607079","indexId":"70236087","displayToPublicDate":"2022-05-05T07:04:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"High‐frequency rupture processes of the 2014 Mw 8.2 Iquique and 2015 Mw 8.3 Illapel, Chile, earthquakes determined from strong‐motion recordings","docAbstract":"Strong‐motion recordings of the 2014
Feral burros (Equus asinus) and horses (E. ferus caballus) inhabiting public land in the western United States are intended to be managed at population levels established to promote a thriving, natural ecological balance. Double-observer sightability (MDS) models, which use detection records from multiple observers and sighting covariates, perform well for estimating feral horse abundances, but their effectiveness for use in burro populations is less understood. These MDS models help minimize detection bias, yet bias can be further reduced with models that account for unmodeled variation, or residual heterogeneity, in detection probability. In populations containing radio-marked individuals, residual heterogeneity can be estimated with MDS models by including a covariate that corresponds to the marked status of a group (MH models). Another approach is to use information from detections missed by both observers to account for the characteristics that make groups more or less likely to be detected, or recaptured, by the second observer (MR models). We used aerial survey data from 3 burro populations (Sinbad Herd Management Area, UT [2016–2018], Lake Pleasant Herd Management Area, AZ [2017], and Fort Irwin National Training Center, CA [2016–2017]) to develop MDS models applicable for feral burros in the southwestern United States. Our objectives were to quantify precision and bias of standard MDS surveys for feral burros and to examine which model type for incorporating residual heterogeneity (MH or MR) would result in the least-biased estimates of burro populations relative to the minimum number known alive (MNKA) within the Sinbad Herd Management Area. Standard MDS model estimates achieved a mean coefficient of variation of 0.08, while underestimating MNKA by an average of 27.1%. Accounting for residual heterogeneity through recapture probability in MR models resulted in estimates closer to MNKA than MH models (9.5% vs. 16.5% less than MNKA). Our results indicate that MDS models can achieve precise enough estimates to monitor feral burro populations, but they routinely produce negatively biased estimates. We encourage the use of radio-collars to reduce bias in future burro surveys by accounting for residual heterogeneity through MR models.
The United Nations Sustainable Development Goals (SDGs) are a unifying call for change - guiding global actions at multiple levels of governance for a better planet and better lives. Consequently, achieving the “future we want” may be hindered by overlooking valuable natural resources and services that are not explicitly included in the SDGs. Not recognizing the direct, intrinsic value of some natural resources may threaten the sustainability of the services they provide and their contributions to the SDGs. Here, we use inland aquatic ecosystems, and the fish and fisheries therein, as an example to explore opportunities for recognition and inclusion of other natural resources that are missing from the SDGs. Key resources absent from the SDGs are less likely to be incorporated in global, national, and regional objectives, dialogues, and policies. We outline multiple potential pathways for better inclusion and capitalization of contributions from these overlooked natural resources during the operationalization of the SDGs and other global instruments.
The Hydrologic Toolbox is a Windows-based desktop software program that provides a graphical and mapping interface for analysis of hydrologic time-series data with a set of widely used and standardized computational methods. The software combines the analytical and statistical functionality provided in the U.S. Geological Survey Groundwater and Surface-Water Toolboxes and provides several enhancements to these programs. The main analytical methods are the computation of hydrologic-frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10); the computation of design flows, including biologically based flows; the computation of flow-duration curves and duration hydrographs; eight computer-programming methods for hydrograph separation of a streamflow time series, including the Base-Flow Index (BFI), HYSEP, PART, and SWAT Bflow methods and Eckhardt’s two-parameter digital-filtering method; and the RORA recession-curve displacement method and associated RECESS program to estimate groundwater-recharge values from streamflow data. Several of the statistical methods provided in the Hydrologic Toolbox are used primarily for computation of critical low-flow statistics. The Hydrologic Toolbox also facilitates retrieval of streamflow and groundwater-level time-series data from the U.S. Geological Survey National Water Information System and outputs text reports that describe their analyses.
The Hydrologic Toolbox was developed by use of the DotSpatial geographic information system (GIS) programming library, which is part of the MapWindow project. DotSpatial is a nonproprietary, open-source program written for the .NET framework that includes a spatial data viewer and GIS capabilities. Advantages of the DotSpatial system include its pure .NET implementation for both the user interface and the GIS mapping engine, and thus the DotSpatial system simplifies software deployment and installation. In addition to combining the functionality of the separate Groundwater and Surface-Water Toolboxes, the Hydrologic Toolbox also organizes the functionality by theme (Groundwater Tools, Surface-Water Tools, and general Time-Series Tools).
This report provides a description of how to build a Hydrologic Toolbox project and to download and manage hydrologic time-series data. It includes an overview of the analytical and statistical capabilities of the Hydrologic Toolbox and highlights the primary differences between the Hydrologic Toolbox and the Groundwater and Surface-Water Toolboxes. The report supplements information available in an extensive online Help manual and is intended to provide a set of instructions that will allow users to quickly develop skills to use the mapping, data-retrieval, and computational tools of the program.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Book 4, Hydrologic Analysis and Interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4D3","programNote":"Water Availability and Use Science Program","usgsCitation":"Barlow, P.M., McHugh, A.R., Kiang, J.E., Zhai, T., Hummel, P., Duda, P., and Hinz, S., 2022, U.S. Geological Survey Hydrologic Toolbox — A graphical and mapping interface for analysis of hydrologic data: U.S. Geological Survey Techniques and Methods, book 4, chap. D3, 23 p., https://doi.org/10.3133/tm4D3.","productDescription":"Report: vi, 23 p.; Software release","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130481","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":400024,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/d03/tm4d3.pdf","text":"Report","size":"4.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-D3"},{"id":400023,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/d03/coverthb.jpg"},{"id":400027,"rank":3,"type":{"id":35,"text":"Software Release"},"url":"https://doi.org/10.5066/P9DBLL43","text":"USGS software release","linkHelpText":"- U.S. Geological Survey Hydrologic Toolbox software archive"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Hawaii stands apart from the rest of the United States, literally and figuratively. The nearest of the eight islands that make up the Hawaiian archipelago is 2,000 miles from the U.S. mainland. Like every bit of land mass within the State, it emerged from the Pacific Ocean after thousands of years of undersea volcanic activity. Kona International Airport, on the “Big Island” of Hawai‘i, was built atop 220-year-old lava flows from the Hualālai volcano.
The volcanic soils and tropical vegetation that stretch across Hawaii’s postcard-perfect peaks and valleys, as well as the trade winds that blow precipitation northeast across the islands and the warm Kona winds that move in from the south, are among the factors that make the study of the State’s landscapes distinct from the rest of the Nation.
Six active volcanoes continue to alter the land surface. Some of them, like the constantly erupting Kīlauea or Mauna Loa, the world’s largest active volcano, can threaten human life and property. Man-made changes to the islands, such as the long-term agricultural production of sugar cane or pineapple, along with the introduction of non-native species, have also affected the health of the State’s ecosystems.
Landsat satellites, with 50 years of repeat Earth observations and decades of data from infrared and thermal instruments capable of tracking lava flows, can offer unique opportunities for the study of Hawaii’s ever-changing landscapes. Here are a few examples of how Landsat benefits the State.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The widespread availability of high-fidelity topography combined with advances in geospatial analysis offer the opportunity to reimagine approaches to the difficult problem of predicting sediment delivery from watersheds. Here we present a model that uses high-resolution topography to filter sediment sources to quantify sediment delivery to the watershed outlet. It is a reduced-complexity, top-down model that defines transfer functions—topographic filters—between spatially distributed sediment sources and spatially integrated sediment delivery. The goal of the model is to forecast changes in watershed suspended sediment delivery in response to spatially distributed changes in sediment source magnitude or delivery, whether a result of watershed drivers or intentional management actions. Such an application requires the context of a watershed model that accounts for all sediment sources, enforces sediment mass balance throughout the spatial domain, and accommodates sediment storage and delivery over time. The model is developed for a HUC-8 watershed with a flat upland dominated by corn-soybean agriculture and deeply incised valleys near the watershed outlet with large sediment contributions from near-channel sources. Topofilter computes delivery and storage of field-derived sediment according to its spatial and structural connectivity to the stream channel network; subsequently, delivery of both field- and near-channel-derived sediment along with floodplain storage are computed in the stream channel network to the watershed outlet. The model outputs provide a spatially rich representation of sediment delivery and storage on field and along the stream that is consistent with available independent information on sediment accumulations and fluxes. Rather than a single best-calibrated solution, Topofilter uses the Generalized Likelihood Uncertainty Estimate (GLUE) approach to develop many possible solutions with sediment delivery rates expressed as probability distributions across the watershed. The ensemble of simulation outputs provides a useful basis for estimating uncertainty in sediment delivery and the effectiveness of different landscape management allocation across a watershed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.155701","usgsCitation":"Cho, J., Wilcock, P.R., and Gran, K.B., 2022, Implementing landscape connectivity with topographic filtering model: A simulation of suspended sediment delivery in an agricultural watershed: Science of the Total Environment, v. 836, 155701, 16 p., https://doi.org/10.1016/j.scitotenv.2022.155701.","productDescription":"155701, 16 p.","ipdsId":"IP-127448","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":447923,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.155701","text":"Publisher Index Page"},{"id":400202,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Le Sueur River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.46868896484375,\n 43.61619382369185\n ],\n [\n -93.087158203125,\n 43.61619382369185\n ],\n [\n -93.087158203125,\n 44.27273816279087\n ],\n [\n -94.46868896484375,\n 44.27273816279087\n ],\n [\n -94.46868896484375,\n 43.61619382369185\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"836","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cho, Jong 0000-0001-5514-6056","orcid":"https://orcid.org/0000-0001-5514-6056","contributorId":291384,"corporation":false,"usgs":true,"family":"Cho","given":"Jong","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":842241,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilcock, Peter R 0000-0001-5756-9829","orcid":"https://orcid.org/0000-0001-5756-9829","contributorId":291385,"corporation":false,"usgs":false,"family":"Wilcock","given":"Peter","email":"","middleInitial":"R","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":842242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gran, Karen B.","contributorId":288093,"corporation":false,"usgs":false,"family":"Gran","given":"Karen","email":"","middleInitial":"B.","affiliations":[{"id":6915,"text":"University of Minnesota - Duluth","active":true,"usgs":false}],"preferred":true,"id":842243,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70231237,"text":"fs20223025 - 2022 - Pennsylvania and Landsat","interactions":[],"lastModifiedDate":"2022-09-27T12:13:22.17303","indexId":"fs20223025","displayToPublicDate":"2022-05-04T08:45:52","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3025","displayTitle":"Pennsylvania and Landsat","title":"Pennsylvania and Landsat","docAbstract":"The Commonwealth of Pennsylvania straddles an array of landscapes. From east to west, its 46,055 square miles connect the sea-level lowlands of the Atlantic seaboard with the rolling hills of the Midwest. It also acts as a bridge between regions from north to south, with the Appalachian Mountains swooping through its center from its northern border with New York to its southern borders with Maryland and West Virginia.
The Declaration of Independence and U.S. Constitution were signed in the Commonwealth’s largest city, Philadelphia, and it was the second of the original 13 colonies to ratify the Constitution. The pivotal Civil War battle of Gettysburg took place in the southern part of Pennsylvania. Western Pennsylvania was an early hub for the coal, oil, and steel industries that fueled the Nation’s growth in the 20th century and still factor into Pennsylvania’s economy. The varied natural features of Pennsylvania have been an important proving ground for the value of satellite imagery from the USGS Landsat Program. Scientists began to lean on Landsat observations early in the program’s history to map the effect of invasive moths on Commonwealth forests. That activity continues to this day. Space-based imagery has also contributed to the study and management of land change from energy development, urban growth, and shifting land use patterns.
The Landsat Program’s unparalleled 50-year archive of repeat Earth observations remains a critical public resource for Pennsylvania as climate change and land use patterns present new challenges to land managers and urban planners in the Keystone State.
Here are just a few examples of how Landsat has benefited Pennsylvania.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The frog-killing chytrid fungus Batrachochytrium dendrobatidis (Bd) is decimating amphibian populations around the world. Bd has a biphasic life cycle, alternating between motile zoospores that disperse within aquatic environments and sessile sporangia that grow within the mucus-coated skin of amphibians. Zoospores lack cell walls and swim rapidly through aquatic environments using a posterior flagellum and crawl across solid surfaces using actin structures similar to those of human cells. Bd transitions from this motile dispersal form to its reproductive form by absorbing its flagellum, rearranging its actin cytoskeleton, and rapidly building a chitin-based cell wall—a process called “encystation.” The resulting sporangium increases in volume by two or three orders of magnitude while undergoing rounds of mitosis without cytokinesis to form a large ceonocyte. The sporangium then cellurizes by dividing its cytoplasm into dozens of new zoospores. After exiting the sporangium through a discharge tube onto the amphibian skin, daughter zoospores can then reinfect the same individual or find a new host. Although encystation is critical to Bd growth, whether and how this developmental transition is triggered by external signals was previously unknown. We discovered that exposure to amphibian mucus triggers rapid and reproducible encystation within minutes. This response can be recapitulated with purified mucin, the bulk component of mucus, but not by similarly viscous methylcellulose or simple sugars. Mucin-induced encystation does not require gene expression but does require surface adhesion, calcium signaling, and modulation of the actin cytoskeleton. Mucus-induced encystation may represent a key mechanism for synchronizing Bd development with the arrival at the host.
","language":"English","publisher":"Cell Press","doi":"10.1016/j.cub.2022.04.006","usgsCitation":"Robinson, K.A., Prostak, S.M., Campbell Grant, E.H., and Fritz-Laylin, L.K., 2022, Amphibian mucus triggers a developmental transition in the frog-killing chytrid fungus: Current Biology, v. 32, no. 12, p. 2765-2771.e4, https://doi.org/10.1016/j.cub.2022.04.006.","productDescription":"12 p.","startPage":"2765","endPage":"2771.e4","ipdsId":"IP-139126","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":447925,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cub.2022.04.006","text":"Publisher Index Page"},{"id":400128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Kristyn A.","contributorId":291342,"corporation":false,"usgs":false,"family":"Robinson","given":"Kristyn","email":"","middleInitial":"A.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":842115,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prostak, Sarah M.","contributorId":291343,"corporation":false,"usgs":false,"family":"Prostak","given":"Sarah","email":"","middleInitial":"M.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":842116,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":842117,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fritz-Laylin, Lillian K.","contributorId":291345,"corporation":false,"usgs":false,"family":"Fritz-Laylin","given":"Lillian","email":"","middleInitial":"K.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":842118,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236140,"text":"70236140 - 2022 - Standard atomic weights of the elements 2021 (IUPAC Technical Report)","interactions":[],"lastModifiedDate":"2022-08-31T11:01:43.760813","indexId":"70236140","displayToPublicDate":"2022-05-04T08:13:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3207,"text":"Pure and Applied Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Standard atomic weights of the elements 2021 (IUPAC Technical Report)","docAbstract":"Following the reviews of atomic-weight determinations and other cognate data in 2015, 2017, 2019 and 2021, the IUPAC (International Union of Pure and Applied Chemistry) Commission on Isotopic Abundances and Atomic Weights (CIAAW) reports changes of standard atomic weights. The symbol A r°(E) was selected for standard atomic weight of an element to distinguish it from the atomic weight of an element E in a specific substance P, designated A r(E, P). The CIAAW has changed the values of the standard atomic weights of five elements based on recent determinations of terrestrial isotopic abundances:
| Ar (argon): | from 39.948 ± 0.001 | to | [39.792, 39.963] |
| Hf (hafnium): | from 178.49 ± 0.02 | to | 178.486 ± 0.006 |
| Ir (iridium): | from 192.217 ± 0.003 | to | 192.217 ± 0.002 |
| Pb (lead): | from 207.2 ± 0.1 | to | [206.14, 207.94] |
| Yb (ytterbium): | from 173.054 ± 0.005 | to | 173.045 ± 0.010 |
The standard atomic weight of argon and lead have changed to an interval to reflect that the natural variation in isotopic composition exceeds the measurement uncertainty of A r(Ar) and A r(Pb) in a specific substance. The standard atomic weights and/or the uncertainties of fourteen elements have been changed based on the Atomic Mass Evaluations 2016 and 2020 accomplished under the auspices of the International Union of Pure and Applied Physics (IUPAP). A r° of Ho, Tb, Tm and Y were changed in 2017 and again updated in 2021:
| Al (aluminium), 2017: | from 26.981 5385 ± 0.000 0007 | to | 26.981 5384 ± 0.000 0003 |
| Au (gold), 2017: | from 196.966 569 ± 0.000 005 | to | 196.966 570 ± 0.000 004 |
| Co (cobalt), 2017: | from 58.933 194 ± 0.000 004 | to | 58.933 194 ± 0.000 003 |
| F (fluorine), 2021: | from 18.998 403 163 ± 0.000 000 006 | to | 18.998 403 162 ± 0.000 000 005 |
| (Ho (holmium), 2017: | from 164.930 33 ± 0.000 02 | to | 164.930 328 ± 0.000 007) |
| Ho (holmium), 2021: | from 164.930 328 ± 0.000 007 | to | 164.930 329 ± 0.000 005 |
| Mn (manganese), 2017: | from 54.938 044 ± 0.000 003 | to | 54.938 043 ± 0.000 002 |
| Nb (niobium), 2017: | from 92.906 37 ± 0.000 02 | to | 92.906 37 ± 0.000 01 |
| Pa (protactinium), 2017: | from 231.035 88 ± 0.000 02 | to | 231.035 88 ± 0.000 01 |
| Pr (praseodymium), 2017: | from 140.907 66 ± 0.000 02 | to | 140.907 66 ± 0.000 01 |
| Rh (rhodium), 2017: | from 102.905 50 ± 0.000 02 | to | 102.905 49 ± 0.000 02 |
| Sc (scandium), 2021: | from 44.955 908 ± 0.000 005 | to | 44.955 907 ± 0.000 004 |
| (Tb (terbium), 2017: | from 158.925 35 ± 0.000 02 | to | 158.925 354 ± 0.000 008) |
| Tb (terbium), 2021: | from 158.925 354 ± 0.000 008 | to | 158.925 354 ± 0.000 007 |
| (Tm (thulium), 2017: | from 168.934 22 ± 0.000 02 | to | 168.934 218 ± 0.000 006) |
| Tm (thulium), 2021: | from 168.934 218 ± 0.000 006 | to | 168.934 219 ± 0.000 005 |
| (Y (yttrium), 2017: | from 88.905 84 ± 0.000 02 | to | 88.905 84 ± 0.000 01) |
| Y (yttrium), 2021: | from 88.905 84 ± 0.000 01 | to | 88.905 838 ± 0.000 002 |
Bedrock property quantification is critical for predicting the hydrological response of watersheds to climate disturbances. Estimating bedrock hydraulic properties over watershed scales is inherently difficult, particularly in fracture-dominated regions. Our analysis tests the covariability of above- and belowground features on a watershed scale, by linking borehole geophysical data, near-surface geophysics, and remote sensing data. We use machine learning to quantify the relationships between bedrock geophysical/hydrological properties and geomorphological/vegetation indices and show that machine learning relationships can estimate most of their covariability. Although we can predict the electrical resistivity variation across the watershed, regions of lower variability in the input parameters are shown to provide better estimates, indicating a limitation of commonly applied geomorphological models. Our results emphasize that such an integrated approach can be used to derive detailed bedrock characteristics, allowing for identification of small-scale variations across an entire watershed that may be critical to assess the impact of disturbances on hydrological systems.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.abj2479","usgsCitation":"Uhlemann, S., Dafflon, B., Wainwright, H.M., Williams, K.H., Minsley, B.J., Zamudio, K.D., Carr, B., Falco, N., Ulrich, C., and Hubbard, S.S., 2022, Surface parameters and bedrock properties covary across a mountainous watershed: Insights from machine learning and geophysics: Science Advances, v. 8, no. 12, 15 p., https://doi.org/10.1126/sciadv.abj2479.","productDescription":"15 p.","ipdsId":"IP-134172","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":447933,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1126/sciadv.abj2479","text":"External Repository"},{"id":400125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"East River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.02726364135741,\n 38.86671143315032\n ],\n [\n -106.9134521484375,\n 38.86671143315032\n ],\n [\n -106.9134521484375,\n 38.97595868249733\n ],\n [\n -107.02726364135741,\n 38.97595868249733\n ],\n [\n -107.02726364135741,\n 38.86671143315032\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Uhlemann, Sebastian","contributorId":291360,"corporation":false,"usgs":false,"family":"Uhlemann","given":"Sebastian","email":"","affiliations":[{"id":36254,"text":"LBNL","active":true,"usgs":false}],"preferred":false,"id":842137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dafflon, Baptiste","contributorId":245441,"corporation":false,"usgs":false,"family":"Dafflon","given":"Baptiste","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":842138,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wainwright, Haruko Murakami","contributorId":291362,"corporation":false,"usgs":false,"family":"Wainwright","given":"Haruko","email":"","middleInitial":"Murakami","affiliations":[{"id":36254,"text":"LBNL","active":true,"usgs":false}],"preferred":false,"id":842139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Kenneth Hurst","contributorId":291364,"corporation":false,"usgs":false,"family":"Williams","given":"Kenneth","email":"","middleInitial":"Hurst","affiliations":[{"id":62696,"text":"LBNL, Rocky Mountain Biological Lab","active":true,"usgs":false}],"preferred":false,"id":842140,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Minsley, Burke J. 0000-0003-1689-1306","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":248573,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"","middleInitial":"J.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":842141,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zamudio, Katrina D. 0000-0003-0278-0154","orcid":"https://orcid.org/0000-0003-0278-0154","contributorId":203252,"corporation":false,"usgs":true,"family":"Zamudio","given":"Katrina","email":"","middleInitial":"D.","affiliations":[],"preferred":true,"id":842142,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carr, Bradley","contributorId":175482,"corporation":false,"usgs":false,"family":"Carr","given":"Bradley","email":"","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":842143,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Falco, Nicola","contributorId":245431,"corporation":false,"usgs":false,"family":"Falco","given":"Nicola","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":842144,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ulrich, Craig","contributorId":175248,"corporation":false,"usgs":false,"family":"Ulrich","given":"Craig","email":"","affiliations":[],"preferred":false,"id":842145,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hubbard, Susan S.","contributorId":175249,"corporation":false,"usgs":false,"family":"Hubbard","given":"Susan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":842146,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70231476,"text":"70231476 - 2022 - Determination of optimal set of spatio-temporal features for predicting burn probability in the state of California, USA","interactions":[],"lastModifiedDate":"2022-05-11T12:07:04.245455","indexId":"70231476","displayToPublicDate":"2022-05-04T07:03:53","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Determination of optimal set of spatio-temporal features for predicting burn probability in the state of California, USA","docAbstract":"Wildfires play a critical role in determining ecosystem structure and function and pose serious risks to human life, property and ecosystem services. Burn probability (BP) models the likelihood that a location could burn. Simulation models are typically used to predict BP but are computationally intensive. Machine learning (ML) pipelines can predict BP and reduce computational intensity. In this work, we tested approaches to reduce the set of input features used in an ML model to estimate BP for the state of California, USA, without loss of predictive performance. We used Principal Component Analysis (PCA) to determine the optimal set of features to use in our ML pipeline. Then, we mapped BP and compared model performance when using the reduced set and when using the whole set of features. Models using optimized input achieved similar prediction performance while using less than 50% of the input features.
Management objectives for long-lived species are difficult to define because many taxa have delayed maturity and variable recruitment. White Sturgeon Acipenser transmontanus is an example of a species with a complex life history that complicates long-term status monitoring and establishment of management objectives. Historically, White Sturgeon in the Sacramento–San Joaquin River basin have been monitored by tracking the abundance of age-15 individuals as outlined by the Central Valley Project Improvement Act. However, infrequent recruitment complicates progress toward Central Valley Project Improvement Act management objectives because abundance of a single cohort fails to represent overall population trends. By using a Leslie population matrix, we demonstrate that the probability of reaching the Central Valley Project Improvement Act objective of 11,000 age-15 White Sturgeon is highly unlikely. We propose an alternative metric of 155,000 adults, which better represents overall population trends of White Sturgeon in the Sacramento–San Joaquin River basin, can be efficiently monitored, and can support both the goal of the Central Valley Project Improvement Act and management objectives.
Older urban landscapes present unique and complex stressors to urban streams and their habitats through the introduction of legacy and emerging toxic contaminants. Contaminant sources are often associated with various developed land uses such as older residential areas, active and former industrial sites, contaminated sites, and effluents from municipal wastewater treatment plant discharges. These landscapes have a history of legacy contaminant use such as polychlorinated biphenyls (PCBs) resulting in impacts to sediment and water in these complex environments. Despite the ban of PCBs in new commercial use in 1979, PCB contamination is still widespread in the environment, with many fish consumption advisories throughout the Chesapeake Bay region based on elevated PCBs. Several watersheds in the Baltimore region have mandated reductions in PCBs per total maximum daily loads in tidal waters of the watersheds in order to promote compliance with water quality standards. Some of these mandated reductions (for example, regulated watershed runoff) specified in the total maximum daily loads are the responsibility of the local jurisdictions as part of their phase 1 National Pollutant Discharge Elimination System municipal separate storm sewer system permit. In cooperation with the Baltimore City Department of Public Works and Maryland Department of the Environment, the U.S. Geological Survey and University of Maryland, Baltimore County conducted a study from 2018 to 2020 to refine the sources of PCBs from the City of Baltimore into Back River and to use the results to improve the conceptual site model of PCBs in the Back River watershed.
PCB concentrations in the water column of the nontidal streams in Back River watershed are relatively consistent throughout both tributaries, with greater concentrations detected in samples collected from Moores Run but greater loads estimated in samples collected from Herring Run. PCB concentrations measured in the bed sediments and analysis of the flux between sediment porewater (hereafter porewater) and surface water within the tributaries suggest that there are no stationary legacy sources within the stream channels.
The bulk of PCB mass entering the system from these nontidal tributaries appears to be introduced primarily during storm events. While only one storm event was sampled and concentrations were quantified only in Herring Run, solids captured during the storm were characterized by increases in PCB mass and overall suspended solids concentrations. Although the bioavailability of the PCB-associated sediment is unknown, this mechanism appears to warrant additional attention to better understand how concentrations vary under different storm conditions and temporally. The importance of contaminated stormwater in loading to Herring Run is further supported by the PCB concentrations in storm drain sediments collected near the tributary, which were present in higher concentrations and were characterized by different homolog signatures compared to that in bed sediments.
The observations in the tributaries differed from PCB concentrations and sediment characteristics downstream from the City of Baltimore boundary, in the upper tidal area of the main stem of Back River, particularly at the passive sampler locations BRT–1 and BRT–3. This depositional environment is characterized by higher organic content in sediments and higher concentrations of PCBs in porewater, which result in the possible flux of contaminants from sediment to the water column. This flux is generally opposite of that observed in the nontidal tributaries and the farthest upstream tidal site (BRT–2) and may be a result of the possible settling of sediment particles introduced via suspended solids in stormwater.
Despite an observed considerable reduction in overall PCB mass loading to and from the Back River Wastewater Treatment Plant (BRWWTP) (and similar reductions observed in biosolids) compared to the estimates previously reported from 2015, effluent from the BRWWTP continues to be a primary source of PCBs to Back River. The current study confirmed the likeliness of fat, oil, and grease deposits within the miles of sewer pipe as a source of PCBs to the BRWWTP influent. The differences between PCB concentrations in fat, oil, and grease deposits found in pipes (during replacement) compared to that of the BRWWTP suggest that legacy deposits may contain higher PCB concentrations and may act as a source of PCBs to passing sewage, eventually entering the BRWWTP. Variation in freely dissolved concentrations in the sewer system was apparent through the analysis of PCBs in the primary pump stations using passive sampling, with the largest contribution to the influent attributed to a single pump station and associated piping.
The contribution of PCBs to Herring Run and Moores Run via sanitary sewer overflows compared to the BRWWTP effluent is negligible, similar to reports from another large urban wastewater treatment plant. Therefore, decreased occurrence of sanitary sewer overflows is not expected to largely decrease PCB loads.
Results of this study suggest that targeted, sediment-capture best management practices in Back River watershed could be an effective way to reduce PCB mass loading assuming that deposited contaminated sediments are effectively isolated. Recent studies of some common urban best management practices such as bioretention have shown removal of PCBs within the stormwater control structures. In addition, appropriately timed street sweeping practices with appropriate collection equipment may be an effective way to reduce contaminants such as PCBs from road runoff sources. Reductions in concentrations and mass loading within the sewer system measured in this study compared to that estimated 5 years prior reflect the possible success of ongoing gray infrastructure management actions. Reductions may be attributable to enhanced nutrient reduction upgrades to the BRWWTP and extensive capital improvements and maintenance to the sewer system.
This study employed a combined sampling approach and a variety of sampling methods to include low-density polyethylene passive samplers, high-volume water samples, and grab samples of both water and sediment to characterize the PCB inputs to Herring Run, Moores Run, and Back River. Incorporating the passive samplers provided a time-weighted average of the freely dissolved concentration in the surface water, porewater, WWTP influent and effluent, and pump station influent over the deployment period with picogram per liter detection limits. A similar monitoring approach from this study could be implemented within other subwatersheds or municipal separate storm sewer system jurisdictions to assist in refining primary sources of PCBs in order to inform appropriate mitigation approaches.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225012","collaboration":"Prepared in cooperation with Baltimore City Department of Public Works and Maryland Department of the Environment","usgsCitation":"Majcher, E., Ghosh, U., Needham, T., Lombard, N., Foss, E., Bokare, M., Joshee, S., Cheung, L., Damond, J., and Lorah, M., 2022, Refining sources of polychlorinated biphenyls in the Back River watershed, Baltimore, Maryland, 2018–2020: U.S. Geological Survey Scientific Investigations Report 2022–5012, 58 p., https://doi.org/10.3133/sir20225012.","productDescription":"Report: x, 58 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-123587","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":399906,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WFDIBM","text":"USGS data release","linkHelpText":"Polychlorinated Biphenyl (PCB) Concentrations of Passive Samplers, Solids, Fat, Oil, and Greases (FOG), and Road Sediments; and Dissolved Organic Carbon (DOC), Total Suspended Solids (TSS), and Particulate Organic Carbon (POC) Concentrations in the Back River Watershed, Baltimore City, Maryland, 2018–2020"},{"id":399905,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5012/sir20225012.pdf","text":"Report","size":"79.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5012"},{"id":399904,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5012/coverthb.jpg"},{"id":502298,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112977.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Maryland","city":"Baltimore","otherGeospatial":"Back River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.84112548828125,\n 39.18969082109678\n ],\n [\n -76.32202148437499,\n 39.18969082109678\n ],\n [\n -76.32202148437499,\n 39.51675478434244\n ],\n [\n -76.84112548828125,\n 39.51675478434244\n ],\n [\n -76.84112548828125,\n 39.18969082109678\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Nevada’s geography is colorful—and contradictory. As one of the most mountainous States, Nevada shares the country’s second-deepest lake, Lake Tahoe, with neighboring California. It is also the driest State and largely covered by desert. Northern Nevada has long, cold winters, whereas the south has long, hot summers. It is the seventh-largest State, but it ranks in the bottom one-half of States for population. More than 72 percent of its 3.1 million residents live in the Las Vegas area.
In Nevada, the desert is not dull. An extraordinary variety of wildflowers bloom in the spring, and other plants include mesquite, cacti, creosote, and yucca such as Joshua trees (Yucca brevifolia). Sagebrush (Artemisia tridentata) is more than the State flower; it is a hardy, enduring shrub foundational to a vast ecosystem in the Great Basin that feeds and shelters hundreds of wildlife species. The Silver State has a strong mining tradition, and agriculture centers around livestock ranching and irrigated crops. Although mining and agriculture once formed the base of Nevada’s economy, tourism now leads the way, mostly from gambling and entertainment in Las Vegas, Reno, and other cities. Water is a critical resource for supporting residents, visitors, and industries, and Lake Mead behind Hoover Dam on the Colorado River supplies most of it for southern Nevada.
Nevada has significant natural resources, but they face threats—especially in a warming climate. Here are a few ways Landsat benefits Nevada.
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U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Wyoming has the smallest population of any State—fewer than 600,000 people—but an abundance of wildlife. The largest number of pronghorn (Antilocapra americana), often called antelope, and the biggest public bison (Bison bison) herd in the United States live in Wyoming, which also hosts elk (Cervus elaphus), moose (Alces americanus), bighorn sheep (Ovis canadensis), black bears (Ursus americanus), and grizzly bears (U. arctos). The terrain of the Nation’s 10th largest State varies from the Black Hills to the Rocky Mountains, and from Great Plains grassland to Wyoming Basin desert sagebrush.
Natural resources attract millions of visitors annually, especially to the country’s first national park, Yellowstone, and the ski slopes in Grand Teton National Park and elsewhere. Natural resources account for much of the employment in the Cowboy State, from tourism and ranching to coal, natural gas, and oil mining.
To help monitor these natural resources, researchers and land managers have relied on USGS Landsat imagery and data. The scale of images from the first Landsat satellites helped geologists detect previously unknown uranium deposits in Wyoming, which is the leading State for uranium production. Today, land managers and researchers take advantage of Landsat and its 50-year archive to explore landscape change over time—and continue to make new discoveries.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Collection of juvenile salmonids at Round Butte Dam is a critical part of the effort to enhance populations of anadromous fish species in the upper Deschutes River because fish that are not collected at the dam may either incur increased mortality during dam passage or remain landlocked and lost to the anadromous fish population. Adaptive resolution imaging sonar systems were used to assess the behavior, abundance, and timing of fish at the entrance to the Selective Water Withdrawal (SWW) intake and fish collection structure located in the forebay of Round Butte Dam during the spring of 2020. The purpose of the SWW is to direct surface currents in the forebay to attract and collect downriver migrating juvenile salmonid smolts (Chinook salmon [Oncorhynchus tshawytscha], sockeye salmon [O. nerka], and steelhead [O. mykiss]) from Lake Billy Chinook and to enable operators of the SWW to withdraw water from surface and benthic elevations in the reservoir to manage downriver water temperatures. The objective of this study was to assess the abundance and behaviors of smolt-size fish (95–300 millimeters) observed near the SWW and to determine if the presence of bull trout (Salvelinus confluentus; >350 millimeters), the predominant predator of juvenile salmonids, influenced the behavior of downriver migrants.
Two imaging sonar units were deployed during the spring of 2020 smolt out-migration period. One unit monitored fish movements near the entrances and one unit monitored in one of the collection flumes of the SWW. The imaging sonar technology was informative for assessing abundance and spatial and temporal behaviors of smolt and bull trout-size fish. Smolt and bull trout-size fish were regularly observed near the entrance to and in the collection flume. Increased abundances were observed during the night, with corresponding increased discharge through the SWW, compared to during the day when discharge was reduced. Behavioral differences also were observed at different discharge rates, with smolt-size fish exhibiting more directed movement toward the collector during periods of increased discharge. Additionally, the presence of bull trout-size fish may have affected the behavior of smolt-size fish because a greater percentage of smolt-size fish were observed traveling away from the SWW when bull trout-size fish were present than when bull trout-size fish were absent. Increased counts of bull trout-size fish coincided with the increased abundances of smolt-size fish. Overall, the results indicate that smolt-size fish are more abundant near the entrance and in the flume of the SWW during periods of increased discharge, and bull trout-size fish were present at the SWW and may have affected smolt collection.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221038","collaboration":"Prepared in cooperation with Portland General Electric","usgsCitation":"Smith, C.D., Hatton, T.W., and Adams, N.S., 2022, Monitoring fish abundance and behavior, using multi-beam acoustic imaging sonar, at a Selective Water Withdrawal structure in Lake Billy Chinook, Deschutes River, Oregon, 2020: U.S. Geological Survey Open-File Report 2022–1038, 31 p., https://doi.org/10.3133/ofr20221038.","productDescription":"viii, 31 p.","onlineOnly":"Y","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":400087,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1038/coverthb.jpg"},{"id":400088,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1038/ofr20221038.pdf","text":"Report","size":"12.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1038"},{"id":400089,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1038/images"},{"id":400090,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1038/ofr20221038.XML"}],"country":"United States","state":"Oregon","otherGeospatial":"Lake Billy Chinook, Round Butte Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.56990051269533,\n 44.43623132529392\n ],\n [\n -121.07414245605469,\n 44.43623132529392\n ],\n [\n -121.07414245605469,\n 44.73454012555642\n ],\n [\n -121.56990051269533,\n 44.73454012555642\n ],\n [\n -121.56990051269533,\n 44.43623132529392\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Kalispel Tribe of Indians (KT), U. S. Fish and Wildlife Service, and Washington Department of Fish and Wildlife are engaged in conservation of bull trout (Salvelinus confluentus) in the Lake Pend Oreille (LPO) Core Area. The LPO is a complex habitat core area which falls within three states (Montana, Idaho, and Washington) and a tribal entity. As part of the conservation process, KT worked in cooperation with the U. S. Geological Survey (USGS) to complete a risk assessment for introduction of bull trout into Sullivan Lake/Harvey Creek, northeastern, Washington. The risk assessment was designed to evaluate potential risks to resident fish species, to bull trout introduced into Sullivan Lake, and to bull trout donor source populations. This risk assessment describes the potential risks associated with pathogens (introduction of pathogens and increased pathogen burden), genetics (such as risk to donor sources, straying and breeding with native bull trout, and introduction of bull-brook hybrids), and ecological interactions (such as predation and competition). Potential donor source populations were identified and evaluated using a qualitative approach based on expert opinion and a decision framework.
Literature reviews were completed for fish species composition and abundance in Sullivan Lake basin to assess potential ecological interactions and risks to these populations and to the introduced bull trout. The USGS assessed pathogen risks through two major questions: (1) whether introduced bull trout might bring pathogens into the Sullivan Lake basin that were not previously present and (2) whether the health of introduced bull trout could be adversely affected by pathogens already present in the basin. Assessment of genetic risks included demographic risks to donor source populations, potential for hybridization with native bull trout, and the risk of introducing bull-brook hybrids. Literature reviews were used in conjunction with discussions among regional biologists to identify potential donor source populations and their population attributes. A decision framework was developed by USGS in collaboration with KT biologists that identified desirable population attributes (life history behavior, abundance, population viability, feasibility of collection, and environmental match) associated with donor source populations and established ranking criteria. The population attribute information was used with the (1) decision framework, (2) established ranking criteria, and (3) expert opinion of regional biologists, to assign scores for overall ranking of donor source populations.
The LPO source population was the highest ranked and is considered a robust and stable population. The risk of introducing pathogens from LPO into Sullivan Lake via a bull trout introduction program seems low, and indirect pathogen burden risks to resident species can be mitigated using established pathogen surveillance methods. The likelihood that bull trout, introduced into Sullivan Lake, stray and spawn with native bull trout is low. Nearest-neighbor donor source populations, such as LPO, could minimize negative fitness impacts that might occur from straying and interbreeding of individuals that become entrained and help maintain natural patterns of genetic diversity in native populations. The ecological risk that a bull trout introduction presents to resident species seems to be low but with some uncertainty. Pygmy whitefish, a Washington State Sensitive species, is likely most vulnerable to extirpation with increased predation pressure with introduction of an additional piscivore into the ecosystem. The status of the pygmy whitefish in Sullivan Lake is unknown. The ecological risks most likely to reduce the viability of introduced bull trout are predation by burbot and an adequate forage base in Sullivan Lake. Prior fish surveys provided data on resident species abundance, provided an established baseline for effective monitoring, and identifying ecosystem changes post-bull trout introduction to inform future adaptive management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221032","collaboration":"Prepared in cooperation with Kalispel Tribe of Indians","usgsCitation":"Hardiman, J.M., Breyta, R.B., and Ostberg, C.O., 2022, Risk assessment for bull trout introduction into Sullivan Lake and Harvey Creek, northeastern Washington: U.S. Geological Survey Open-File Report 2022–1032, 26 p., https://doi.org/10.3133/ofr20221032.","productDescription":"Report: vii, 26 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-128540","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":400079,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1032/images"},{"id":400080,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1032/ofr20221032.XML"},{"id":400074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1032/coverthb.jpg"},{"id":400075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1032/ofr20221032.pdf","text":"Report","size":"8.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1032"},{"id":400076,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCQEZ1","text":"USGS data release","description":"USGS data release","linkHelpText":"Information tables associated with a risk assessment for bull trout introduction into Sullivan Lake, northeastern, Washington including population donor sources and resident species, April 2021"}],"country":"United States","state":"Washington","otherGeospatial":"Harvey Creek, Sullivan Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.49191284179688,\n 48.67736049788919\n ],\n [\n -117.03598022460938,\n 48.67736049788919\n ],\n [\n -117.03598022460938,\n 48.89632393659644\n ],\n [\n -117.49191284179688,\n 48.89632393659644\n ],\n [\n -117.49191284179688,\n 48.67736049788919\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Informed analysis of policies related to food security, global climate change, wetland ecology, environmental nutrient flux, element cycling, groundwater weathering, continental denudation, human health, etc. depends to a large extent on quantitative estimates of solute mass fluxes into and out of all global element pools including the enigmatic global aquifer systems. Herein for the first time, we proffer the mean global solute concentration of all major and selected minor and trace solutes in the active groundwater that represents 99% of liquid fresh water on Earth. Concentrations in this significant element pool have yielded to a geospatial machine learning kNN-nearest neighbors’ algorithm with numerous geospatial predictors utilizing a large new lithology/climate/aquifer age/elevation based solute database. The predicted concentrations are consistent with traditional solute ratios, concentrations, and thermodynamic saturation indices.