Long duration tree-ring records with annual precision allow for the reconstruction of past growing conditions. Investigations limited to the most common tree-ring proxy of ring width can be difficult to interpret, however, because radial growth is affected by multiple environmental processes. Furthermore, studies of living trees may miss important effects of drought on tree survival and forest changes. Stable carbon isotopes can help distinguish drought from other environmental factors that influence tree-ring width and forest stand condition. We quantified tree-ring radial expansion and stable carbon isotope ratios (δ13C) in riparian cottonwoods (Populus angustifolia and P. angustifolia x P. trichocarpa) along Snake Creek in Nevada, USA. We investigated how hydrological drought affected tree growth and death at annual to half-century scales in a partially dewatered reach (DW) compared to reference reaches immediately upstream and downstream. A gradual decline in tree-ring basal area increment (BAI) began at DW concurrent to streamflow diversion in 1961. BAI at DW diverged from one reference reach immediately but not from the other until nearly 50 years later. In contrast, tree-ring δ13C had a rapid and sustained increase following diversion at DW only, providing the stronger and clearer drought signal. BAI and δ13C were not significantly correlated prior to diversion; after diversion they both reflected drought and were correlated for DW trees only. Cluster analyses distinguished all trees in DW from those in reference reaches based on δ13C, but BAI patterns left trees intermixed across reaches. Branch and tree mortality were also highest and canopy vigor was lowest in DW. Results indicate that water scarcity strongly limited cottonwood photosynthesis following flow diversion, thus reducing carbon assimilation, basal growth and survival. The dieback was not sudden, but occurred over decades as carbon deficits mounted and depleted streamflow left trees increasingly vulnerable to local meteorological drought.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.139523","usgsCitation":"Schook, D.M., Friedman, J.M., Stricker, C.A., Csank, A.Z., and Cooper, D.J., 2020, Short- and long-term responses of riparian cottonwoods (Populus spp.) to flow diversion: Analysis of tree-ring radial growth and stable carbon isotopes: Science of the Total Environment, v. 735, 139523, 11 p., https://doi.org/10.1016/j.scitotenv.2020.139523.","productDescription":"139523, 11 p.","ipdsId":"IP-117602","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":456676,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2020.139523","text":"Publisher Index Page"},{"id":377443,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Great Basin Nation Park, Snake Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.41436767578124,\n 38.791556581282244\n ],\n [\n -114.09576416015624,\n 38.791556581282244\n ],\n [\n -114.09576416015624,\n 39.07784203269269\n ],\n [\n -114.41436767578124,\n 39.07784203269269\n ],\n [\n -114.41436767578124,\n 38.791556581282244\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"735","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schook, Derek M.","contributorId":178325,"corporation":false,"usgs":false,"family":"Schook","given":"Derek","email":"","middleInitial":"M.","affiliations":[{"id":13539,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":795997,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":795998,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":795999,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Csank, Adam Z.","contributorId":238091,"corporation":false,"usgs":false,"family":"Csank","given":"Adam","email":"","middleInitial":"Z.","affiliations":[{"id":37455,"text":"University of Nevada","active":true,"usgs":false}],"preferred":false,"id":796000,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cooper, David J.","contributorId":196510,"corporation":false,"usgs":false,"family":"Cooper","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":13017,"text":"Department of Forest and Rangeland Stewardship, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":796001,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215496,"text":"70215496 - 2020 - Effects of climate and land-use changes on fish catches across lakes at a global scale","interactions":[],"lastModifiedDate":"2021-01-22T21:47:26.382439","indexId":"70215496","displayToPublicDate":"2020-05-20T15:40:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Effects of climate and land-use changes on fish catches across lakes at a global scale","docAbstract":"Globally, our knowledge on lake fisheries is still limited despite their importance to food security and livelihoods. Here we show that fish catches can respond either positively or negatively to climate and land-use changes, by analyzing time-series data (1970–2014) for 31 lakes across five continents. We find that effects of a climate or land-use driver (e.g., air temperature) on lake environment could be relatively consistent in directions, but consequential changes in a lake-environmental factor (e.g., water temperature) could result in either increases or decreases in fish catch in a given lake. A subsequent correlation analysis indicates that reductions in fish catch was less likely to occur in response to potential climate and land-use changes if a lake is located in a region with greater access to clean water. This finding suggests that adequate investments for water-quality protection and water-use efficiency can provide additional benefits to lake fisheries and food security.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41467-020-14624-2","usgsCitation":"Kao, Y., Rogers, M.W., Bunnell, D., Cowx, I.G., Qian, S.S., Anneville, O., Beard, Brinker, A., Britton, J., Chura-Crusz, R., Gownaris, N.J., Jackson, J., Kangur, K., Kolding, J., Lukin, A., Lynch, A., Mercado-Silva, N., Moncayo-Estrada, R., Njaya, F.J., Ostrovsky, I., Rudstam, L., Sandstrom, A.L., Sato, Y., Siguayro-Mamani, H., Thorpe, A., van Zwieten, P.A., Volta, P., Wang, Y.Q., Weiperth, A., Weyl, O., and Young, J.D., 2020, Effects of climate and land-use changes on fish catches across lakes at a global scale: Nature Communications, v. 11, 2526, 14 p., https://doi.org/10.1038/s41467-020-14624-2.","productDescription":"2526, 14 p.","ipdsId":"IP-108774","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456678,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-020-14624-2","text":"Publisher Index Page"},{"id":382523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2020-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kao, Y.","contributorId":243522,"corporation":false,"usgs":false,"family":"Kao","given":"Y.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":802469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":802470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bunnell, David 0000-0003-3521-7747","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":217344,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":802471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cowx, I. G.","contributorId":243523,"corporation":false,"usgs":false,"family":"Cowx","given":"I.","email":"","middleInitial":"G.","affiliations":[{"id":40174,"text":"University of Hull","active":true,"usgs":false}],"preferred":false,"id":802472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Qian, S. S.","contributorId":243524,"corporation":false,"usgs":false,"family":"Qian","given":"S.","email":"","middleInitial":"S.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":802473,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Anneville, O.","contributorId":243525,"corporation":false,"usgs":false,"family":"Anneville","given":"O.","affiliations":[{"id":48714,"text":"Université Savoie","active":true,"usgs":false}],"preferred":false,"id":802474,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Beard, Jr. 0000-0003-2632-2350 dbeard@usgs.gov","orcid":"https://orcid.org/0000-0003-2632-2350","contributorId":169459,"corporation":false,"usgs":true,"family":"Beard","suffix":"Jr.","email":"dbeard@usgs.gov","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":802475,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brinker, A.","contributorId":243526,"corporation":false,"usgs":false,"family":"Brinker","given":"A.","email":"","affiliations":[{"id":48715,"text":"Fisheries Research Station of Baden-Württemberg","active":true,"usgs":false}],"preferred":false,"id":802476,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Britton, J. 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E.","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":802490,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Sato, Y.","contributorId":243540,"corporation":false,"usgs":false,"family":"Sato","given":"Y.","email":"","affiliations":[{"id":48722,"text":"Lake Biwa Environmental Research Institute","active":true,"usgs":false}],"preferred":false,"id":802491,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Siguayro-Mamani, Humberto","contributorId":243541,"corporation":false,"usgs":false,"family":"Siguayro-Mamani","given":"Humberto","email":"","affiliations":[{"id":48717,"text":"Laboratorio Continental de Puno, Instituto del Mar del Perú","active":true,"usgs":false}],"preferred":false,"id":802492,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Thorpe, A.","contributorId":243542,"corporation":false,"usgs":false,"family":"Thorpe","given":"A.","affiliations":[{"id":38839,"text":"University of Portsmouth","active":true,"usgs":false}],"preferred":false,"id":802493,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"van Zwieten, P. A. M.","contributorId":243543,"corporation":false,"usgs":false,"family":"van Zwieten","given":"P.","email":"","middleInitial":"A. M.","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":802494,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Volta, P.","contributorId":243544,"corporation":false,"usgs":false,"family":"Volta","given":"P.","email":"","affiliations":[{"id":48723,"text":"CNR Water Research Institute","active":true,"usgs":false}],"preferred":false,"id":802495,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Wang, Y. Q.","contributorId":221210,"corporation":false,"usgs":false,"family":"Wang","given":"Y.","email":"","middleInitial":"Q.","affiliations":[],"preferred":false,"id":802496,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Weiperth, A.","contributorId":243545,"corporation":false,"usgs":false,"family":"Weiperth","given":"A.","email":"","affiliations":[{"id":48724,"text":"Szent István University","active":true,"usgs":false}],"preferred":false,"id":802497,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Weyl, O. L. F.","contributorId":243546,"corporation":false,"usgs":false,"family":"Weyl","given":"O. L. F.","affiliations":[{"id":48725,"text":"South African Institute for Aquatic Biodiversity","active":true,"usgs":false}],"preferred":false,"id":802498,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Young, Joelle D.","contributorId":248310,"corporation":false,"usgs":false,"family":"Young","given":"Joelle","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":808854,"contributorType":{"id":1,"text":"Authors"},"rank":31}]}} ,{"id":70203888,"text":"sir20195030 - 2020 - Precipitation runoff modeling system (PRMS) as part of an integrated hydrologic model for the Osage Nation, northeastern Oklahoma, 1915–2014","interactions":[],"lastModifiedDate":"2020-05-21T12:01:12.54464","indexId":"sir20195030","displayToPublicDate":"2020-05-20T13:08:35","publicationYear":"2020","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":"2019-5030","displayTitle":"Precipitation Runoff Modeling System (PRMS) as Part of an Integrated Hydrologic Model for the Osage Nation, Northeastern Oklahoma, 1915–2014","title":"Precipitation runoff modeling system (PRMS) as part of an integrated hydrologic model for the Osage Nation, northeastern Oklahoma, 1915–2014","docAbstract":"The Osage Nation lacks a comprehensive tribal water plan to describe the quality and quantity of water resources in the Osage Nation, a 2,304-square-mile (mi2) area of rolling pastures, tallgrass prairie, and mixed woodlands in northeastern Oklahoma. A tribal water plan can be used to help manage the sustainable development of surface and groundwater resources, thereby helping to provide a better future for the Osage Nation and their neighbors, while preserving water resources for the benefit of the surrounding environment and future generations. To help meet these goals and contribute to increased knowledge of the quantity and quality of water resources and the hydrologic processes and factors affecting those resources, the U.S. Geological Survey (USGS) in cooperation with the Osage Nation began studies to evaluate the surface-water and groundwater resources of the Osage Nation. An important component of these studies is the development and application of numerical models to improve quantification and understanding of the hydrologic system. These models are needed to estimate and quantify the effects of historical and potential future water resource development for the Osage Nation.
This report describes the development and application of a precipitation-runoff model, the Osage Nation watershed model (ONWM). The ONWM is needed as a component of the Osage Nation integrated hydrologic model (ONIHM). At the time of this study, the ONIHM was being developed using the USGS computer software MODFLOW-One Water Hydrologic Flow Model (MODFLOW-OWHM). The intended use of the ONIHM is to simulate all surface-water and groundwater components of the hydrologic system for a 2,905-mi2 study area centered on the Osage Nation. The ONWM was developed using the USGS Precipitation Runoff Modeling System, version 4 (PRMS-IV) computer software, also referred to as PRMS in this report, for an 8,343-mi2 study area in northeastern Oklahoma and southeastern Kansas, centered on and including the areas of the Osage Nation and the ONIHM. The ONWM is to be used as part of the ONIHM to provide a direct coupling with spatially and temporally varying daily climate conditions affecting the ONIHM study area. As an integral part of the ONIHM, the ONWM (1) simulates the inflow boundary conditions from tributary basins in the region outside and surrounding the ONIHM area; (2) provides estimates of spatially and temporally distributed precipitation, air temperature, potential evapotranspiration (PET), actual evapotranspiration (ET), soil moisture, recharge, and streamflow in the ONIHM area; and (3) provides a preliminary water budget for the ONIHM area and the surrounding region, including tributary drainage basins outside of and next to the ONIHM.
The specific objectives of this study were to use the ONWM to (1) provide a systematic inventory of the historical distribution of water inflows from precipitation (rain or snow) falling on the land surface and flowing through the surface-water network, (2) provide a historical context of the variability and spatial and temporal distribution of these waters, and (3) provide estimates of water inflows and potential observations to the ONIHM. The application of the ONWM as a component of the ONIHM is needed for planned simulations using the ONIHM to improve the understanding of the hydrologic system and to develop a fully comprehensive water budget, including the use and movement of water across the landscape, in the surface-water network, and in groundwater aquifers under historical and potential future conditions.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Groundwater makes up a primary portion of the water supply in many parts of Utah, with annual withdrawals estimated at more than 1,000,000 acre-feet per year. Increases to groundwater withdrawal and land use may negatively impact water availability. Ensuring availability of clean water requires understanding how water quality has changed over time and how natural and human activities and processes influence water quality. Changes in arsenic, nitrate, and dissolved-solids concentrations in the groundwater in basins with high groundwater withdrawals were evaluated between 1975 and 2015 as indicators of basinwide water quality and the suitability of water for drinking. Data were used from the U.S. Geological Survey’s National Water Information System (NWIS) database and the Safe Drinking Water Information System (SDWIS) maintained by the Utah Department of Environmental Quality, Division of Drinking Water. Mann-Kendall trend tests were used to assess temporal trends in decadal and 5-year (sub-decadal) median analyte concentrations in basins. Trends also were assessed in smaller parts of larger basins to focus on changes occurring at a smaller spatial scale. To evaluate the relationship between land-use change and water-quality changes, trends also were evaluated for wells where land use has changed. Trends in decadal and sub-decadal median arsenic, nitrate, and dissolved-solids concentrations over time were identified throughout the basins and sub-basins in this study. For combined NWIS and SDWIS data, rates of median arsenic concentration change in basins and sub-basins ranged between decreases of –0.24 microgram per liter (μg/L) per year and increases of 0.48 μg/L per year. Rates of median nitrate-concentration change ranged between decreases of –0.08 milligram per liter (mg/L) per year and increases of 0.02 mg/L per year. Rates of median dissolved solids concentration change ranged between decreases of –5 mg/L per year and increases of 7 mg/L per year. The rates of change for nitrate and dissolved solids were similar to or less than rates of change observed in other parts of the country. Trends were not directly related to land-use change approximal to a well, although more data from wells where land use has changed would improve this evaluation. These findings highlight that water quality at a well is related to a range of factors including land, demographics, and water use over a larger area surrounding and up-gradient from the well; rates and direction of groundwater movement; and geologic and hydrologic conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205047","collaboration":"Prepared in Cooperation with the Utah Department of Environmental Quality","usgsCitation":"Miller, O.L., 2020, Quantifying trends in arsenic, nitrate, and dissolved solids from selected wells in Utah: U.S. Geological Survey Scientific Investigations Report 2020–5047, 80 p., https://doi.org/10.3133/sir20205047.","productDescription":"viii, 80 p.","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-108685","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":374936,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5047/sir20205047.pdf","text":"Report","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374937,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5047/coverthb.jpg"}],"country":"United 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\"}}]}","contact":"Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, Utah 84119-2047
801-908-5000
Many coral reef-lined coasts are low-lying with elevations <4 m above mean sea level. Climate-change-driven sea-level rise, coral reef degradation, and changes in storm wave climate will lead to greater occurrence and impacts of wave-driven flooding. This poses a significant threat to their coastal communities. While greatly at risk, the complex hydrodynamics and bathymetry of reef-lined coasts make flood risk assessment and prediction costly and difficult. Here we use a large (>30,000) dataset of measured coral reef topobathymetric cross-shore profiles, statistics, machine learning, and numerical modeling to develop a set of representative cluster profiles (RCPs) that can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the large dataset is reduced by clustering cross-shore profiles based on morphology and hydrodynamic response to typical wind and swell wave conditions. By representing a large variety of coral reef morphologies with a reduced number of RCPs, a computationally feasible number of numerical model simulations can be done to obtain wave runup estimates, including setup at the shoreline and swash separated into infragravity and sea-swell components, of the entire dataset. The predictive capability of the RCPs is tested against 5,000 profiles from the dataset. The wave runup is predicted with a mean error of 9.7–13.1%, depending on the number of cluster profiles used, ranging from 312 to 50. The RCPs identified here can be combined with probabilistic tools that can provide an enhanced prediction given a multivariate wave and water level climate and reef ecology state. Such a tool can be used for climate change impact assessments and studying the effectiveness of reef restoration projects, as well as for the provision of coastal flood predictions in a simplified (global) early warning system.
","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2020.00361","usgsCitation":"Scott, F., Antolinez, J.A., McCall, R.T., Storlazzi, C.D., Reiners, A., and Pearson, S., 2020, Hydro-morphological characterization of coral reefs for wave runup prediction: Frontiers in Marine Science, v. 7, 361, 20 p., https://doi.org/10.3389/fmars.2020.00361.","productDescription":"361, 20 p.","ipdsId":"IP-116940","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456686,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2020.00361","text":"Publisher Index Page"},{"id":436960,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C39WNE","text":"USGS data release","linkHelpText":"Coral reef profiles for wave-runup prediction"},{"id":376661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","noUsgsAuthors":false,"publicationDate":"2020-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Fred","contributorId":229615,"corporation":false,"usgs":false,"family":"Scott","given":"Fred","email":"","affiliations":[{"id":27619,"text":"TU Delft","active":true,"usgs":false}],"preferred":false,"id":793676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Antolinez, Jose A.A.","contributorId":177510,"corporation":false,"usgs":false,"family":"Antolinez","given":"Jose","email":"","middleInitial":"A.A.","affiliations":[],"preferred":false,"id":793677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCall, Robert T.","contributorId":148986,"corporation":false,"usgs":false,"family":"McCall","given":"Robert","email":"","middleInitial":"T.","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":793678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":793679,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reiners, Ad","contributorId":229616,"corporation":false,"usgs":false,"family":"Reiners","given":"Ad","email":"","affiliations":[{"id":27619,"text":"TU Delft","active":true,"usgs":false}],"preferred":false,"id":793680,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearson, Stuart","contributorId":193835,"corporation":false,"usgs":false,"family":"Pearson","given":"Stuart","affiliations":[],"preferred":false,"id":793681,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217908,"text":"70217908 - 2020 - The water-year water balance of the Colorado River Basin","interactions":[],"lastModifiedDate":"2021-02-11T17:38:35.270899","indexId":"70217908","displayToPublicDate":"2020-05-20T08:11:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"The water-year water balance of the Colorado River Basin","docAbstract":"Model‐estimated monthly water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff (R)) for 146 United States (U.S.) Geological Survey 8‐digit hydrologic units located in the Colorado River Basin (CRB) are used to examine the temporal and spatial variability of the CRB water balance for water years 1901 through 2014 (a water year is the period from October 1 of one year through September 30 of the following year). Results indicate that the CRB can be divided into six subregions with similar temporal variability in monthly R. The water balance analyses indicated that approximately 75% of total water‐year R is generated by just one CRB subregion and that most of the R in the basin is derived from surplus (S) water generated during the months of October through April. Furthermore, the analyses show that temporal variability in S is largely controlled by the occurrence of negative atmospheric pressure anomalies over the northwestern conterminous U.S. (CONUS) and positive atmospheric pressure anomalies over the southeastern CONUS. This combination of atmospheric pressure anomalies results in an anomalous flow of moist air from the North Pacific Ocean into the CRB, particularly the Upper CRB. Additionally, the occurrence of extreme dry and wet periods in the CRB appears to be related to variability of the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation.
Changes in future precipitation are of great importance to climate data users in South Florida. A recent U.S. Geological Survey workshop, “Increasing Confidence in Precipitation Projections for Everglades Restoration,” highlighted a gap between standard climate model outputs and the climate information needs of some key Florida natural resource managers. These natural resource managers (hereafter broadly defined as “climate data users”) need more tailored output than is commonly provided by the climate modeling community. This study responds to these user needs by outlining and testing an adaptable methodology to select output from ensemble climate‐model simulations based on user‐defined precipitation drivers, using statistical methods common across scientific disciplines. This methodology is developed to provide a “decision matrix” that guides climate data users to specify the subset of models most important to their work based on each user's season (winter, summer, and annual) and the condition (dry, wet, neutral, and no threshold events) of interest. The decision matrix is intended to better communicate the subset of models best representing precipitation drivers. This information could increase users' confidence in climate models as a resource for natural resource planning and can be used to direct future dynamical downscaling efforts. This methodology is based in dynamical processes controlling precipitation via remote and local teleconnections. We also suggest that future climate studies in South Florida include high‐resolution climate model runs (i.e., ocean eddy resolving) in conjunction with dynamical downscaling to adequately capture precipitation variability.
Barrier islands, headlands, and coastal shorelines provide numerous valuable ecosystem goods and services, including storm protection and erosion control for the mainland, habitat for fish and wildlife, salinity regulation in estuaries, carbon sequestration in marshes, and areas for recreation and tourism. These coastal features are dynamic environments because of their position at the land-sea interface. Storms, wave energy, tides, currents, and relative sea-level rise are powerful forces that shape local geomorphology and habitat distribution. In order to make more informed decisions, coastal resource managers require insights into how these dynamic systems are changing through time.
In 2005, Louisiana’s Coastal Protection and Restoration Authority, in partnership with the University of New Orleans and the U.S. Geological Survey, developed the Barrier Island Comprehensive Monitoring (BICM) Program. The goal of the BICM Program is to develop long-term datasets for habitat coverage, shoreline assessments, shoreline position, topobathymetric changes, and sediment characterization to assist with planning, designing, evaluating, and maintaining current and future barrier shorelines. The overall objectives of the study described in this report were to (1) map habitats for 2008 and 2015–16 for BICM coastal reaches and (2) map habitat change between these two time periods.
This report highlights the second phase of habitat analyses for the BICM Program. This work builds on a previous habitat analysis conducted by the University of New Orleans, which included the development of habitat maps for 1996/1998, 2001, 2004, and 2005, along with habitat change maps. For this current effort, a new 15-class habitat scheme was developed from the original BICM scheme to further delineate various dune habitats, including meadow habitat found along the backslopes of dunes, to distinguish between marsh and mangrove, and to distinguish between beach and unvegetated barrier flat habitats. Additionally, a geographic object-based image analysis-based mapping framework was used to incorporate relative topography and address elevation uncertainty in light detection and ranging data to assist with mapping dune and intertidal habitats.
For the entire BICM region, the area experiencing a change in a land/water category (that is, land gain or land loss) was 3.4 percent, of which, 59.2 percent was land gain and 40.8 percent was land loss. Areal coverages of meadow, mangrove, scrub/shrub, and vegetated dune increased from 2008 to 2015–16, whereas areal coverages of beach, grassland, and intertidal decreased. The decrease in intertidal, however, was largely due to differing water levels in the orthophotography between the two time periods. Regional analyses of habitat coverage and habitat change captured the dynamic nature of these systems and the effects of restoration efforts, most notably in the Late Lafourche Delta, Modern Delta, and Chandeleur Islands regions. For instance, in the Modern Delta region there was a marked increase in unvegetated flat, meadow, mangrove, scrub/shrub, beach, unvegetated dune, and vegetated dune. As a result, this region experienced the highest percent change for land/water classes (6.6 percent) with land gain accounting for much of this change (70.8 percent). In contrast, the Acadiana Bays region had the highest relative percent loss of all regions. The region had a percent change for land/water classes of 2.8 percent, of which, 79.7 percent was land loss.
The results of this study provide information about the areal coverage and distribution of habitats for two recent time periods and change over about an 8-year period. These data can be used to evaluate changes along the Louisiana Gulf of Mexico shoreline, including gradual changes caused by coastal processes, restoration actions, and (or) episodic events, such as hurricanes and extreme storms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201030","collaboration":"Prepared in cooperation with the Louisiana Coastal Protection and Restoration Authority","usgsCitation":"Enwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program—Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16: U.S. Geological Survey Open-File Report 2020–1030, 57 p., https://doi.org/10.3133/ofr20201030.","productDescription":"Report: ix, 57 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-114268","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":436983,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRT54Z","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 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2008 to 2016 habitat change, Early Lafourche Delta Region"},{"id":436969,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P960YCTY","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2016 habitat map, Late Lafourche Delta Region"},{"id":436968,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98VJ9R6","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2016 habitat map, Modern Delta Region"},{"id":436967,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T1B66S","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2016 Habitat Map, Early Lafourche Delta Region"},{"id":436966,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LKFIJ5","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 habitat map, Modern Delta Region"},{"id":436965,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DV1J56","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program 2008 habitat map, Late Lafourche Delta Region"},{"id":436964,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BR8RDT","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program 2008 habitat map, Early Lafourche Delta Region"},{"id":374819,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1030/coverthb.jpg"},{"id":374820,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1030/ofr20201030.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1030"},{"id":374821,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XP7440","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program—Habitat mapping"}],"country":"United States","state":"Louisiana","otherGeospatial":"Gulf of Mexico shoreline","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.75732421875,\n 30.183121842195515\n ],\n [\n -93.93310546875,\n 29.57345707301757\n ],\n [\n -93.1201171875,\n 29.38217507514529\n ],\n [\n -91.91162109375,\n 28.97931203672246\n ],\n [\n -90.46142578125,\n 28.94086176940557\n ],\n [\n -88.92333984375,\n 28.97931203672246\n ],\n [\n -89.09912109375,\n 29.878755346037977\n ],\n [\n -89.8681640625,\n 30.600093873550072\n ],\n [\n -90.63720703125,\n 30.35391637229704\n ],\n [\n -90.54931640625,\n 30.050076521698735\n ],\n [\n -91.56005859375,\n 29.99300228455108\n ],\n [\n -93.75732421875,\n 30.183121842195515\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506–3152
Radon (Rnair) was monitored in open air in publicly accessible areas surrounding the Pinenut uranium (U) mine during mining and reclamation activities in 2015–16 to address concerns about mining related effects to areas surrounding Grand Canyon National Park (GCNP) in Arizona, USA. During July 2015, Rnair concentrations associated with the ore storage pile monitoring site were larger than those at the mine vent monitoring site and likely resulted from the relatively large amount of ore stored on site during this period. Higher wind velocities at the ore pile monitoring site generally resulted in lower Rnair concentrations; however, wind velocity did not appear to be an important factor in controlling Rnair concentrations at the mine vent monitoring site. Physical disturbances of the ore pile by heavy equipment did not coincide with elevated Rnair concentrations at the ore storage pile or mine vent monitoring sites. The relative size of the ore storage pile showed a positive trend with the daily mean Rnair concentration measured at the ore pile monitoring site. Principal component analysis (PCA) was applied to the ore pile and mine vent multivariate data sets for simultaneous comparison of all measured variables during 230 days of the study period. A significant positive coefficient for Rnair was associated with a significant negative coefficient for wind speed for principal component (PC) 2ore pile. Significant, positive PC2mine vent coefficients included Rnair, wind direction, and relative ore pile size indicating that Rnair variations at the mine vent monitoring site may be affected by Rn sourced from the ore pile. The ore pile is located about 200 m south of the mine vent Rn monitor with the prevalent wind direction coming from the south. All data generated during the field study and laboratory verification tests were published by Naftz et al. (2018) and are available online at:
The sediment–water interfaces (SWI) of streams serve as important biogeochemical hotspots in watersheds and contribute to whole-catchment reactive nitrogen budgets and water-quality conditions. Recently, the SWI has been identified as an important source of nitrous oxide (N2O) produced in streams, with SWI residence time among the principal controls on its production. Here, we conducted a series of controlled manipulations of SWI exchange in an urban stream that has high dissolved N2O concentrations and where we concurrently evaluated less-mobile porosity dynamics. Our experiments took place within isolated portions of two sediment types: a coarse sandy stream bed resulting from excess road-sand application in the watershed, and a coarse sand mixed with clay and organic particles. In these manipulation experiments we systematically varied SWI vertical-flux rates and residence times to evaluate their effect on the fate of nitrate and production rates of N2O. Our experiments demonstrate that the fate and transport of nitrate and N2O production are influenced by hydrologic flux rates through SWI sediments and associated residence times. Specifically, we show that manipulations of hydrologic flux systematically shifted the depth of the bulk oxic–anoxic interface in the sediments, and that nitrate removal increased with residence time. Our results also support the emerging hypothesis of a ‘Goldilocks’ timescale for the production of nitrous oxide, when transport and reaction timescales favor incomplete denitrification. Areal N2O production rates were up to threefold higher during an intermediate residence-time experiment, compared to shorter or longer residence times. In our companion study we documented that the studied sediments were dominated by a long-residence-time less-mobile porosity domain, which could explain why we observed N2O production even in bulk-oxic sediments. Overall, we have experimentally demonstrated that changes to SWI hydrologic residence times and SWI substrate associated with urbanization can change the biogeochemical function of the river corridor.
","language":"English","publisher":"Wiley","doi":"10.1007/s10533-020-00674-7","usgsCitation":"Hampton, T., Zarnetske, J., Briggs, M., Dehkordy, F.M., Singha, K., Day-Lewis, F., Harvey, J., Chowdhury, S.R., and Lane, J.W., 2020, Experimental shifts of hydrologic residence time in a sandy urban stream sediment-water interface alter nitrate removal and nitrous oxide fluxes: Biogeochemistry, v. 149, p. 195-219, https://doi.org/10.1007/s10533-020-00674-7.","productDescription":"25 p.","startPage":"195","endPage":"219","ipdsId":"IP-116946","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":385331,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Sawmill Brook","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.18739366531372,\n 42.52077860418496\n ],\n [\n -71.18534445762633,\n 42.52077860418496\n ],\n [\n -71.18534445762633,\n 42.52389408092782\n ],\n [\n -71.18739366531372,\n 42.52389408092782\n ],\n [\n -71.18739366531372,\n 42.52077860418496\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","noUsgsAuthors":false,"publicationDate":"2020-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Hampton, T.","contributorId":257642,"corporation":false,"usgs":false,"family":"Hampton","given":"T.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":814793,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zarnetske, J.","contributorId":222749,"corporation":false,"usgs":false,"family":"Zarnetske","given":"J.","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":814794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":814795,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dehkordy, F. M. P.","contributorId":257643,"corporation":false,"usgs":false,"family":"Dehkordy","given":"F.","email":"","middleInitial":"M. P.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":814796,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Singha, K.","contributorId":201025,"corporation":false,"usgs":false,"family":"Singha","given":"K.","email":"","affiliations":[],"preferred":false,"id":814797,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":814798,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":814799,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chowdhury, S. R.","contributorId":222748,"corporation":false,"usgs":false,"family":"Chowdhury","given":"S.","email":"","middleInitial":"R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":814800,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lane, John W. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":219742,"corporation":false,"usgs":true,"family":"Lane","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":814801,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70212799,"text":"70212799 - 2020 - The future of sediment transport and streamflow under a changing climate and the implications for long-term resilience of the San Francisco Bay-Delta","interactions":[],"lastModifiedDate":"2020-08-28T13:39:37.495182","indexId":"70212799","displayToPublicDate":"2020-05-16T08:35:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"The future of sediment transport and streamflow under a changing climate and the implications for long-term resilience of the San Francisco Bay-Delta","docAbstract":"Sedimentation and turbidity have effects on habitat suitability in the San Francisco Bay‐Delta (Bay‐Delta), concerning key species in the bay as well as the ability of the delta marshes to keep pace with sea level rise. A daily rainfall runoff and transport model of the Sacramento River Basin of northern California was developed to simulate streamflow and suspended sediment transport to the Bay‐Delta for the next century (water years, WY2010–2099). The model was calibrated to historical streamflow and sediment data and applied using 10 Global Climate Models with two representative concentration pathways (RCP) each for WY1980–2099 from the IPCC 5th Assessment Report. Results indicate average increases in peak streamflow of +58% and +66% for the RCP 4.5 and 8.5 ensembles, respectively, by mid‐century and +62 and +96% by end‐of‐century. Sediment loads increased by +39% and +69% by end‐of‐century. Suspended sediment concentrations (SSC) increased on average by +4.6% and +6.7% for RCP 4.5 and 8.5, respectively, by end‐of‐century. Individual scenario results varied, and statistically significant increasing trends of sediment loads to the Bay‐Delta were found for the RCP 4.5 and 8.5 ensembles and five individual scenarios. Increased suspended sediment loads may have negative effects such as contaminant transport but also have positive effects that help protect against sea level rise, increase turbidity and fish habitat, and sustain wetland habitats in the Bay‐Delta.
Monitoring elusive, relatively low-density, large predators, such as the grey wolf (Canis lupus), has often been accomplished by live-capture and radiocollaring. Increasingly, non-invasive methods are considered best practice whenever it is possible to use them. Recently, environmental DNA (eDNA) deposited in snow tracks was demonstrated as useful for identifying lynx (Lynx canadensis), fisher (Pekania pennanti), wolverine (Gulo gulo), sika deer (Cervus nippon), red fox (Vulpes vulpes), and the Japanese marten (Martes melampus) to species level using mitochondrial DNA (mtDNA) markers. We tested whether eDNA from fresh wolf snow tracks collected in the Superior National Forest, Minnesota, USA during winter 2019 could be used to identify species, sex, and individual. Seven of the 8 snow track samples were successfully identified to “wolf-dog” species using mtDNA, with alleles amplifying in 5 of the samples at 1 or both of loci u250 and FH2096 in the allele range for wolves for this population. None yielded enough high-quality DNA to obtain genotypes to determine individual or sex. We recommend additional field trials to determine the minimum number of tracks required per individual to obtain sufficient, high-quality eDNA, as well as collecting associated urine or blood (from estrus) when possible. If individual wolves could be identified and sexed by the eDNA in their snow tracks, researchers should be able to determine population and family or group metrics with greater precision and less effort than typically required when conducting winter scat or hair-based genetic field studies of low density, elusive carnivores. Furthermore, this method would have applications to other areas of research and management, such as hunting quota determinations, validation of field methods and, in particular regards to wolves and other predators, livestock depredation issues. Overall, this technique holds significant future promise as field and laboratory methods are further refined for greater precision and optimized regarding the varying collection, filtration, and extraction protocols for different species in various environments.
Chemical gradients between fresh, brackish and saline waters shape biogeochemical reactions and organic matter transformation within subterranean estuaries. In the Yucatán Peninsula’s karst subterranean estuary (KSE), methane and dissolved organic matter generated during the anaerobic decomposition of tropical forest vegetation are transported into flooded cave networks where microbial consumption greatly reduces their concentrations in the groundwater. To test the hypothesis that chemoclines associated with salinity gradients of the KSE are sites of methane oxidation, we obtained methane concentration and δ13C profiles of unprecedented vertical resolution from within a fully-submerged cave system located 6.6 km inland from the coastline using the ‘OctoPiPi’ (OPP) water sampler. Along a 12–24 cm thick low-salinity-halocline at ∼4.5 m water depth, salinity increased from fresh to brackish (0.2–1.8 psu), methane concentrations decreased, and δ13C values increased, as expected for microbial methane oxidation. The underlying brackish water had elevated oxygen concentrations compared to the always anoxic freshwater, suggesting that aerobic methane oxidation is the dominant process facilitating methane consumption. By contrast, as salinity increased from 1.8 to 36 psu through a 24–36 cm thick high-salinity-halocline between the meteoric lens and the saline groundwater at ∼20 m water depth, methane concentrations and δ13C values were constant. Conservative mixing and kinetic isotope models incorporating the methane data confirm a hotspot for microbial methane oxidation at the low-salinity-halocline. At least 98% of methane originating in the anoxic freshwaters was removed before its transport via channelized flow towards the coastline. These findings provide novel insight into the spatial constraints of methane dynamics within a karst subterranean estuary.
Quantifying system-wide biogeochemical dynamics and ecosystem metabolism in estuaries is often attempted using a long-term continuous record at a single site or short-term records at multiple sites due to sampling limitations that preclude long-term monitoring. However, differences in the dominant primary producer at a given location (e.g., phytoplankton versus benthic producers) control diel variations in dissolved oxygen and associated ecosystem metabolism, and they may confound metabolic estimates that do not account for this variability. We hypothesize that even in shallow, well-mixed estuaries there is strong spatiotemporal variability in ecosystem metabolism due to benthic and water-column properties, as well as ensuing feedbacks to sediment resuspension, light attenuation, and primary production. We tested this hypothesis by measuring hydrodynamic properties, biogeochemical variables (fluorescent dissolved organic matter – fDOM, turbidity, chlorophyll a fluorescence, dissolved oxygen), and photosynthetically active radiation (PAR) over 1 year at 15 min intervals at paired channel (unvegetated) and shoal (vegetated by eelgrass) sites in Chincoteague Bay, Maryland–Virginia, USA, a shallow back-barrier estuary. Light attenuation (KdPAR) at all sites was dominated by turbidity from suspended sediment, with lower contributions from fDOM and chlorophyll a. However, there was significant seasonal variability in the resuspension–shear stress relationship on the vegetated shoals, but not in adjacent unvegetated channels. This indicated that KdPAR on the shoals was mediated by submerged aquatic vegetation (SAV) and possibly microphytobenthos presence in the summer, which reduced resuspension and therefore KdPAR. We also found that gross primary production (Pg) and KdPAR were significantly negatively correlated on the shoals and uncorrelated in the channels, indicating that Pg over the vegetated shoals is controlled by a feedback loop between benthic stabilization by SAV and/or microphytobenthos, sediment resuspension, and light availability. Metabolic estimates indicated substantial differences in net ecosystem metabolism between vegetated and unvegetated sites, with the former tending towards net autotrophy in the summer. Ongoing trends of SAV loss in this and other back-barrier estuaries suggest that these systems may also shift towards net heterotrophy, reducing their effectiveness as long-term carbon sinks. With regards to temporal variability, we found that varying sampling frequency between 15 min and 1 d resulted in comparable mean values of biogeochemical variables, but extreme values were missed by daily sampling. In fact, daily resampling minimized the variability between sites and falsely suggested spatial homogeneity in biogeochemistry, emphasizing the need for high-frequency sampling. This study confirms that properly quantifying ecosystem metabolism and associated biogeochemical variability requires characterization of the diverse estuarine environments, even in well-mixed systems, and demonstrates the deficiencies introduced by infrequent sampling to the interpretation of spatial variability.
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Basic data included the identification of iron fouling from aerial imagery and field visits at 374 New Hampshire rock cut locations, and their associated rock-fill sites. Based on field water quality measurements from wet areas at 36 of the rock-fill sites, the occurrence of iron fouling was associated with higher values of specific conductance, lower concentrations of dissolved oxygen and lower pH compared to areas without iron fouling. A statistical model, using boosted regression trees, was developed to predict the occurrence of iron fouling in wet areas adjacent to roadways where rock-fill from nearby rock cuts was used in roadway construction. The model was used to develop a continuous iron fouling probability map for the state of New Hampshire that can be used to better understand the occurrence of iron fouling. Geochemical models illustrate how iron fouling of waters increases along roadways built with fill from sulfidic rock cuts as a result of acid generation from pyrite dissolution and ferrous iron (Fe2+) oxidation and increases in areas with greater specific conductance from deicing runoff caused by cation exchange. More iron is precipitated as goethite in simulations that include pyrite, and in simulations with deicing salts added, indicating that rock-fill sites with rocks that contain pyrite and water with greater salt content could have enhanced iron fouling.
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Melissa A. 0000-0001-5924-6556 mlombard@usgs.gov","orcid":"https://orcid.org/0000-0001-5924-6556","contributorId":198254,"corporation":false,"usgs":true,"family":"Lombard","given":"Melissa","email":"mlombard@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":794079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lombard, Pamela J. 0000-0002-0983-1906","orcid":"https://orcid.org/0000-0002-0983-1906","contributorId":203509,"corporation":false,"usgs":true,"family":"Lombard","given":"Pamela","email":"","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794080,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Craig J. 0000-0002-3858-3964","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":210450,"corporation":false,"usgs":true,"family":"Brown","given":"Craig J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794081,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Degnan, James R. 0000-0002-5665-9010","orcid":"https://orcid.org/0000-0002-5665-9010","contributorId":218796,"corporation":false,"usgs":true,"family":"Degnan","given":"James R.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794082,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228606,"text":"70228606 - 2020 - Tracking fisheries through time: The American Fisheries Society as an historical lens","interactions":[],"lastModifiedDate":"2024-09-18T15:54:07.350505","indexId":"70228606","displayToPublicDate":"2020-05-14T09:20:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5686,"text":"Fisheries Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Tracking fisheries through time: The American Fisheries Society as an historical lens","docAbstract":"The historical context of current environment conditions offers vital guidance to North American fisheries professionals as they strive to develop effective management plans and policies. This retrospective includes understanding the remarkable historic fisheries resource and causative reasons for establishing their public fisheries agencies and the American Fisheries Society (Society). Through a review of primary historical documents and literature for North America, this paper identifies: factors contributing to the of founding of the Society and public fish commissions; shows how selected resource issues evolved; and documents how and why selected fisheries and aquatic habitat policies changed in response to those issues. Overexploitation, landscape-scale habitat alterations, mining, and dams were the causative agents for emergence of fisheries agencies and the Society, and these environmental factors remain relevant today. Beginning in the 1960s, after 100 years of inaction, North Americans grew tired of degraded waters and fisheries and forced policy changes that have directly and indirectly affected the fisheries we manage today. The historical events and resulting corrective legislation are taken for granted by those unaware that our Society has actively participated in developing policies to address these environmental harms. Further, the Society continues to play a vital role in identifying key issues, providing conduits for information to cope with impairments, and advocating for policies to conserve intact habitats and improve degraded systems. The struggles documented in this paper offer crucial lessons as we continue to be challenged by legacy resource issues and face emerging environmental stressors such as climate change as well as regression in long-standing environmental protection policies.","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.10457","usgsCitation":"Whelan, G., Day, D., Casselman, J., Gephart, L., Hall, C., Lichatowich, J., Matylewich, M., Miranda, L.E., Roulson, L., Shirey, P., Mercado-Silva, N., Waldman, J., and Winters, D., 2020, Tracking fisheries through time: The American Fisheries Society as an historical lens: Fisheries Magazine, v. 45, no. 8, p. 392-426, https://doi.org/10.1002/fsh.10457.","productDescription":"35 p.","startPage":"392","endPage":"426","ipdsId":"IP-117673","costCenters":[{"id":198,"text":"Coop Res Unit 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