1) Movement has been studied extensively in stream salmonids, and most data suggest that population-level behavior is best described by a leptokurtic distribution. This distribution emphasizes the large proportion of sedentary individuals in a population, which can implicitly lead to assumptions of low population connectivity and overlook the ecological significance of rare individuals with more mobile phenotypes. 2) We report findings of a multi-season radio telemetry study conducted on four adjacent populations of wild brook trout (Salvelinus fontinalis) connected by Loyalsock Creek in northcentral Pennsylvania. We used these data to investigate temporal and spatial patterns in movement and fitness tradeoffs associated with behavioral phenotype. 3) Similar to previous studies, we found that 59 of the 120 radio-tagged individuals (49%) were sedentary and moved less than 200 m. Only 18% of individuals dispersed more than 1 km, but the maximum distanced moved exceeded 13 km. We also found that mobile individuals had significantly higher summer and fall survival than did sedentary fish, which could indicate that there are fitness benefits associated with vagility. 4) Most long-distance movements were the result of fish migrating from small tributaries into a larger mainstem river in the days after spawning. Therefore, even though mobility was only expressed for a short duration and by relatively few individuals in the population, the behavior appears to maintain metapopulation connectivity throughout the watershed. 5) Our study highlights the ecological significance of rare phenotypes for population demography across large spatial scales and the need to understand movement across multiple temporal and spatial scales to ensure adequate conservation of critical forms of cryptic life history diversity.
","language":"English","publisher":"Wiley","doi":"10.1111/fwb.13637","usgsCitation":"Wagner, T., and White, S., 2020, Behavior at short temporal scales drives dispersal dynamics and survival in a metapopulation of brook trout (Salvelinus fontinalis): Freshwater Biology, v. 66, no. 2, p. 278-285, https://doi.org/10.1111/fwb.13637.","productDescription":"8 p.","startPage":"278","endPage":"285","ipdsId":"IP-118703","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":454727,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/fwb.13637","text":"Publisher Index Page"},{"id":395892,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Double Creek, East Branch Creek, Loyalsock Creek, Pole Bridge Creek, Shanerburg Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.93528175354004,\n 41.254193933121606\n ],\n [\n -76.92240715026855,\n 41.254193933121606\n ],\n [\n -76.92240715026855,\n 41.266646415620784\n ],\n [\n -76.93528175354004,\n 41.266646415620784\n ],\n [\n -76.93528175354004,\n 41.254193933121606\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"66","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834735,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Shannon","contributorId":276311,"corporation":false,"usgs":false,"family":"White","given":"Shannon","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":834736,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70219169,"text":"70219169 - 2020 - Decreases in aluminum toxicity and mortality of caged brook trout in Adirondack Mountain Streams","interactions":[],"lastModifiedDate":"2021-03-29T14:38:42.342371","indexId":"70219169","displayToPublicDate":"2020-11-30T09:33:20","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5792,"text":"Summary Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"20-35","title":"Decreases in aluminum toxicity and mortality of caged brook trout in Adirondack Mountain Streams","docAbstract":"Mortality of juvenile brook trout and water chemistry were characterized in six western Adirondack streams in northern New York State during spring 2015, 2016, and 2017 and compared with results from comparable tests done between 1980 and 2003 in many of the same streams to assess temporal changes in inorganic monomeric aluminum (Ali) concentrations, Ali-toxicity, and the role of Ali-exposure duration on mortality. Ali concentrations of 2 and 4 micromoles per liter (µmol L-1) corresponded to chronic- and acute-mortality thresholds for brook trout, but prolonged exposure to ≥ 1 µmol Ali L-1 also produced low-to-moderate mortality levels. The variability, mean, and highest Ali concentrations in Buck Creek (BUC) year-round, and in several other streams during spring, decreased significantly over the past 30 years. Predictive models indicate that Ali surpassed highly toxic concentrations at BUC for three to four months annually during 2001–2003 but for only two to three weeks annually during 2015–2017. The current lack of extremely high Ali concentrations indicate toxicity has declined markedly between the 1989–1990, 2001–2003, and 2015–2017 test periods, yet acid- Ali episodes can still cause moderate-to-high levels of brook trout mortality during high springtime flows. Assembled models show how mortality of brook trout in several Adirondack streams likely declined in response to the 1990 Clean Air Act Amendments and offer a means to predict how changes in United States regulations that limit the atmospheric emissions of nitrogen (N) and sulfur (S) oxides, and the deposition of N and S, could affect brook trout survival and impaired stream ecosystems in the western Adirondack region.","language":"English","publisher":"New York Energy Research and Development Authority","usgsCitation":"Baldigo, B.P., and George, S.D., 2020, Decreases in aluminum toxicity and mortality of caged brook trout in Adirondack Mountain Streams: Summary Report 20-35, vi, 31 p.","productDescription":"vi, 31 p.","ipdsId":"IP-107974","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":384719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":384707,"type":{"id":15,"text":"Index Page"},"url":"https://www.nyserda.ny.gov/-/media/Files/Publications/Research/Transportation/20-35-Decreases-in-Aluminium-toxicity-and-mortality-of-caged-brook-trout.pdf"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.0146484375,\n 43.56447158721811\n ],\n [\n -74.44335937499999,\n 43.56447158721811\n ],\n [\n -74.44335937499999,\n 43.830564195198264\n ],\n [\n -75.0146484375,\n 43.830564195198264\n ],\n [\n -75.0146484375,\n 43.56447158721811\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baldigo, Barry P. 0000-0002-9862-9119 bbaldigo@usgs.gov","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":1234,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry","email":"bbaldigo@usgs.gov","middleInitial":"P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813104,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216731,"text":"70216731 - 2020 - An analysis of streamflow trends in the southern and southeastern US from 1950-2015","interactions":[],"lastModifiedDate":"2020-12-03T13:55:04.548935","indexId":"70216731","displayToPublicDate":"2020-11-29T07:46:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"An analysis of streamflow trends in the southern and southeastern US from 1950-2015","docAbstract":"| The R package colorspace provides a flexible toolbox for selecting individual colors or color palettes, manipulating these colors, and employing them in statistical graphics and data visualizations. In particular, the package provides a broad range of color palettes based on the HCL (hue-chroma-luminance) color space. The three HCL dimensions have been shown to match those of the human visual system very well, thus facilitating intuitive selection of color palettes through trajectories in this space. Using the HCL color model, general strategies for three types of palettes are implemented: (1) Qualitative for coding categorical information, i.e., where no particular ordering of categories is available. (2) Sequential for coding ordered/numeric information, i.e., going from high to low (or vice versa). (3) Diverging for coding ordered/numeric information around a central neutral value, i.e., where colors diverge from neutral to two extremes. To aid selection and application of these palettes, the package also contains scales for use with ggplot2, shiny and tcltk apps for interactive exploration, visualizations of palette properties, accompanying manipulation utilities (like desaturation and lighten/darken), and emulation of color vision deficiencies. The shiny apps are also hosted online at http://hclwizard.org/. |
Degradation of peatlands via drainage is increasing globally and destabilizing peat carbon (C) stores. The effects of drainage on the timing and magnitude of lateral C losses from degraded peatlands remains understudied. We measured spatial and temporal variability in lateral C exports from three drained peat islands in the Sacramento‐San Joaquin Delta in California across the 2017 and 2018 water years using measurements of dissolved inorganic C (DIC), dissolved organic C (DOC), and suspended particulate organic C (POC) concentration combined with discharge. These measurements were supplemented with stable isotope data (δ13C‐DIC, δ13C‐POC, δ15N‐PON, and δ2H‐H2O values) to provide insight into hydrological and biogeochemical controls on lateral C exports from drained peatlands. Drainage DOC and DIC concentrations were seasonally variable with the highest values in the winter rainy season, when discharge was also elevated. Seasonal differences in the mobilization of dissolved C appeared to result from changing water sources and water table levels. Peat island drainage C contributions to surrounding waterways were also greatest during the winter. Although temporal variability in C cycling processes and trends were generally similar across islands, baseline drainage DIC, DOC, and POC concentrations were spatially variable, likely a result of sub‐island‐scale differences in soil organic matter content and hydrology. This spatial variability complicates system‐wide assessments of C budgets. Net lateral C exports were water year dependent and comparable to previously published vertical C emission rates for this system. This work highlights the importance of including lateral C exports from drained peatlands in local and regional C budgets.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JG005883","usgsCitation":"Richardson, C.M., Fackrell, J.K., Kraus, T.E., Young, M.B., and Paytan, A., 2020, Lateral carbon exports from drained peatlands: An understudied carbon pathway in the Sacramento-San Joaquin Delta, California: Journal of Geophysical Research: Biogeosciences, v. 125, no. 12, e2020JG005883, 21 p., https://doi.org/10.1029/2020JG005883.","productDescription":"e2020JG005883, 21 p.","ipdsId":"IP-119742","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":467269,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://repository.library.noaa.gov/view/noaa/37323","text":"External Repository"},{"id":381943,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.200927734375,\n 37.98100996893789\n ],\n [\n -121.68731689453125,\n 37.98100996893789\n ],\n [\n -121.68731689453125,\n 38.225235239076824\n ],\n [\n -122.200927734375,\n 38.225235239076824\n ],\n [\n -122.200927734375,\n 37.98100996893789\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Richardson, Christina M. 0000-0003-0597-8836","orcid":"https://orcid.org/0000-0003-0597-8836","contributorId":147438,"corporation":false,"usgs":false,"family":"Richardson","given":"Christina","email":"","middleInitial":"M.","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":807604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fackrell, Joseph K. 0000-0001-8148-3734","orcid":"https://orcid.org/0000-0001-8148-3734","contributorId":225515,"corporation":false,"usgs":true,"family":"Fackrell","given":"Joseph","email":"","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807605,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kraus, Tamara E. C. 0000-0002-5187-8644 tkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-5187-8644","contributorId":147560,"corporation":false,"usgs":true,"family":"Kraus","given":"Tamara","email":"tkraus@usgs.gov","middleInitial":"E. C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":807606,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Young, Megan B. 0000-0002-0229-4108 mbyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-0229-4108","contributorId":3315,"corporation":false,"usgs":true,"family":"Young","given":"Megan","email":"mbyoung@usgs.gov","middleInitial":"B.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":807607,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Paytan, Adina 0000-0001-8360-4712","orcid":"https://orcid.org/0000-0001-8360-4712","contributorId":193046,"corporation":false,"usgs":false,"family":"Paytan","given":"Adina","email":"","affiliations":[],"preferred":false,"id":807608,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216842,"text":"70216842 - 2020 - Evaluating the impacts of foreshore sand and birds on microbiological contamination at a freshwater beach","interactions":[],"lastModifiedDate":"2020-12-10T12:47:42.117676","indexId":"70216842","displayToPublicDate":"2020-11-26T08:00:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the impacts of foreshore sand and birds on microbiological contamination at a freshwater beach","docAbstract":"Beaches along the Great Lakes shorelines are important recreational and economic resources. However, contamination at the beaches can threaten their usage during the swimming season, potentially resulting in beach closures and/or advisories. Thus, understanding the dynamics that control nearshore water quality is integral to effective beach management. There have been significant improvements in this effort, including incorporating modeling (empirical, mechanistic) in recent years. Mechanistic modeling frameworks can contribute to this understanding of dynamics by determining sources and interactions that substantially impact fecal indicator bacteria concentrations, an index routinely used in water quality monitoring programs. To simulate E. coli concentrations at Jeorse Park beaches in southwest Lake Michigan, a coupled hydrodynamic and wave–current interaction model was developed that progressively added contaminant sources from river inputs, avian presence, bacteria–sediment interactions, and bacteria–sand–sediment interactions. Results indicated that riverine inputs affected E. coli concentrations at Jeorse Park beaches only marginally, while avian, shoreline sand, and sediment sources were much more substantial drivers of E. coli contamination at the beach. By including avian and riverine inputs, as well as bacteria–sand–sediment interactions at the beach, models can reasonably capture the variability in observed E. coli concentrations in nearshore water and bed sediments at Jeorse Park beaches. Consequently, it will be crucial to consider avian contamination sources and water-sand-sediment interactions in effective management of the beach for public health and as a recreational resource and to extend these findings to similar beaches affected by shoreline embayment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2020.116671","usgsCitation":"Saffaie, A., Weiskerger, C.J., Nevers, M., Byappanahalli, M., and Phanikumar, M.S., 2020, Evaluating the impacts of foreshore sand and birds on microbiological contamination at a freshwater beach: Water Research, v. 190, 116671, 13 p., https://doi.org/10.1016/j.watres.2020.116671.","productDescription":"116671, 13 p.","ipdsId":"IP-120391","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":381163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illlinois","city":"Chicago","otherGeospatial":"Jeorse Park Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.440185546875,\n 41.64803176818231\n ],\n [\n -87.43228912353514,\n 41.64803176818231\n ],\n [\n -87.43228912353514,\n 41.656625449889276\n ],\n [\n -87.440185546875,\n 41.656625449889276\n ],\n [\n -87.440185546875,\n 41.64803176818231\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"190","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Saffaie, Ammar","contributorId":245601,"corporation":false,"usgs":false,"family":"Saffaie","given":"Ammar","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":806590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weiskerger, Chelsea J.","contributorId":150865,"corporation":false,"usgs":false,"family":"Weiskerger","given":"Chelsea","email":"","middleInitial":"J.","affiliations":[{"id":18126,"text":"National Park Service, Indiana Dunes National Lakeshore","active":true,"usgs":false}],"preferred":false,"id":806591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":806592,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X byappan@usgs.gov","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":147923,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","email":"byappan@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":806593,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Phanikumar, Mantha S.","contributorId":208872,"corporation":false,"usgs":false,"family":"Phanikumar","given":"Mantha","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":806594,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224312,"text":"70224312 - 2020 - Post-fire vegetation response in a repeatedly burned low-elevation sagebrush steppe protected area provides insights about resilience and invasion resistance","interactions":[],"lastModifiedDate":"2021-09-21T12:37:06.443373","indexId":"70224312","displayToPublicDate":"2020-11-26T07:33:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Post-fire vegetation response in a repeatedly burned low-elevation sagebrush steppe protected area provides insights about resilience and invasion resistance","docAbstract":"Sagebrush steppe ecosystems are threatened by human land-use legacies, biological invasions, and altered fire and climate dynamics. Steppe protected areas are therefore of heightened conservation importance but are few and vulnerable to the same impacts broadly affecting sagebrush steppe. To address this problem, sagebrush steppe conservation science is increasingly emphasizing a focus on resilience to fire and resistance to non-native annual grass invasion as a decision framework. It is well-established that the positive feedback loop between fire and annual grass invasion is the driving process of most contemporary steppe degradation. We use a newly developed ordinal zero-augmented beta regression model fit to large-sample vegetation monitoring data from John Day Fossil Beds National Monument, USA, spanning 7 years to evaluate fire responses of two native perennial foundation bunchgrasses and two non-native invasive annual grasses in a repeatedly burned, historically grazed, and inherently low-resilient protected area. We structured our model hierarchically to support inferences about variation among ecological site types and over time after also accounting for growing-season water deficit, fine-scale topographic variation, and burn severity. We use a state-and-transition conceptual diagram and abundances of plants listed in ecological site reference conditions to formalize our hypothesis of fire-accelerated transition to ecologically novel annual grassland. Notably, big sagebrush (Artemisia tridentata) and other woody species were entirely removed by fire. The two perennial grasses, bluebunch wheatgrass (Pseudoroegneria spicata) and Thurber's needlegrass (Achnatherum thurberianum) exhibited fire resiliency, with no apparent trend after fire. The two annual grasses, cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), increased in response to burn severity, most notably medusahead. Surprisingly, we found no variation in grass cover among ecological sites, suggesting fire-driven homogenization as shrubs were removed and annual grasses became dominant. We found contrasting responses among all four grass species along gradients of topography and water deficit, informative to protected-area conservation strategies. The fine-grained influence of topography was particularly important to variation in cover among species and provides a foothold for conservation in low-resilient, aridic steppe. Broadly, our study demonstrates how to operationalize resilience and resistance concepts for protected areas by integrating empirical data with conceptual and statistical models.
Earthquakes are known to affect groundwater properties, yet the mechanisms causing chemical and physical aquifer changes are still unclear. The Apennines mountain belt in Italy presents a rich literature of case studies documenting hydrogeochemical response to seismicity, due to the high frequency of seismic events and the presence of different regional aquifers in the area. In this study, we synthesize published data from the last 30 years in the Apennine region in order to shed light on the main mechanisms causing earthquake induced water changes. The results suggest the geologic and hydrologic setting specific to a given spring play an important role in spring response, as well as the timing of the observed response. In contrast to setting, the main focal mechanisms of earthquake and the distance between epicenter and the analyzed springs seems to present a minor role in defining the response. The analysis of different response variables, moreover, indicates that an important driver of change is the degassing of CO2, especially in thermal springs, whereas a rapid increase in solute concentration due to permeability enhancement is observable in different cold and shallow springs. These findings also leave open the debate regarding whether earthquake precursors can be recognized beyond site-specific responses. Such responses can be understood more comprehensively through the establishment of a regional long-term monitoring system and continuous harmonization of data and sampling strategies, achievable in the Apennine region through the set-up of a monitoring network.
","language":"English","publisher":"MDPI","doi":"10.3390/min10121058","usgsCitation":"Binda, G., Pozzi, A., Michetti, A., Noble, P., and Rosen, M.R., 2020, Towards the understanding of hydrogeochemical seismic responses in karst aquifers: A retrospective meta-analysis focused on the Apennines (Italy): Minerals, v. 10, no. 12, 1058, 28 p., https://doi.org/10.3390/min10121058.","productDescription":"1058, 28 p.","ipdsId":"IP-120991","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":454745,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min10121058","text":"Publisher Index Page"},{"id":380832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Apennines mountain belt","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 7.6025390625,\n 43.67581809328341\n ],\n [\n 8.8330078125,\n 44.38669150215206\n ],\n [\n 10.48095703125,\n 42.94033923363181\n ],\n [\n 13.0078125,\n 41.27780646738183\n ],\n [\n 14.919433593750002,\n 40.17887331434696\n ],\n [\n 15.8203125,\n 39.85915479295669\n ],\n [\n 16.8310546875,\n 40.6306300839918\n ],\n [\n 16.06201171875,\n 41.86956082699455\n ],\n [\n 13.73291015625,\n 43.30919109985686\n ],\n [\n 12.568359375,\n 44.88701247981298\n ],\n [\n 13.7109375,\n 45.767522962149876\n ],\n [\n 13.798828125,\n 46.543749602738565\n ],\n [\n 12.0849609375,\n 47.15984001304432\n ],\n [\n 10.634765625,\n 47.010225655683485\n ],\n [\n 9.667968749999998,\n 46.543749602738565\n ],\n [\n 8.3056640625,\n 46.51351558059737\n ],\n [\n 6.74560546875,\n 45.84410779560204\n ],\n [\n 6.459960937499999,\n 44.793530904744074\n ],\n [\n 7.6025390625,\n 43.67581809328341\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Binda, Gilberto 0000-0001-5530-3939","orcid":"https://orcid.org/0000-0001-5530-3939","contributorId":206790,"corporation":false,"usgs":false,"family":"Binda","given":"Gilberto","email":"","affiliations":[{"id":37402,"text":"Università degli Studi dell’Insubria","active":true,"usgs":false}],"preferred":false,"id":805789,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pozzi, Andrea","contributorId":206791,"corporation":false,"usgs":false,"family":"Pozzi","given":"Andrea","email":"","affiliations":[{"id":37402,"text":"Università degli Studi dell’Insubria","active":true,"usgs":false}],"preferred":false,"id":805790,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michetti, Alessandro 0000-0002-1775-1340","orcid":"https://orcid.org/0000-0002-1775-1340","contributorId":206792,"corporation":false,"usgs":false,"family":"Michetti","given":"Alessandro","email":"","affiliations":[{"id":37402,"text":"Università degli Studi dell’Insubria","active":true,"usgs":false}],"preferred":false,"id":805791,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noble, Paula","contributorId":198953,"corporation":false,"usgs":false,"family":"Noble","given":"Paula","affiliations":[{"id":33648,"text":"Department of Geological Sciences and Engineering, University of Nevada","active":true,"usgs":false}],"preferred":false,"id":805792,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rosen, Michael R. 0000-0003-3991-0522 mrosen@usgs.gov","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":495,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael","email":"mrosen@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":805793,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216589,"text":"sir20205108 - 2020 - Use of real-time sensors to temporally characterize water quality in groundwater and surface water in Mason County, Illinois, 2017–19","interactions":[],"lastModifiedDate":"2020-12-08T21:22:21.624144","indexId":"sir20205108","displayToPublicDate":"2020-11-25T14:35:48","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":"2020-5108","displayTitle":"Use of Real-Time Sensors to Temporally Characterize Water Quality in Groundwater and Surface Water in Mason County, Illinois, 2017–19","title":"Use of real-time sensors to temporally characterize water quality in groundwater and surface water in Mason County, Illinois, 2017–19","docAbstract":"The persistence of high nitrate concentrations in shallow groundwater has been well documented in the shallow glacial aquifer of Mason County, Illinois. Nitrates in groundwater can be a concern when concentrations exceed 10 milligrams per liter in drinking water. Additionally, nitrate in groundwater can contribute to surface water nitrogen loads that can cause increased algal growth. Algal growth increases oxygen consumption causing anoxic conditions as observed in the Gulf of Mexico Hypoxic Zone.
From March 8, 2017, to March 31, 2019, groundwater level, continuous nitrate, dissolved oxygen, specific conductance, water temperature, and pH data were collected in a monitoring well to temporally assess changes in water quality using high frequency data. During this same period, instantaneous field measurements of water quality and groundwater levels were made in surface water and groundwater in and near Quiver Creek in the presumed groundwater flow path about 0.6 mile from the continuous monitoring well. Groundwater nitrate concentrations continuously measured in the aquifer during this time ranged from 14.7 to 23.2 milligrams per liter, whereas instantaneously measured nitrate concentrations in Quiver Creek ranged from 0.9 to 6.4 milligrams per liter. Nitrate concentrations measured by piezometer varied laterally and vertically in the Quiver Creek floodplain and beneath the stream. Irrigation and fertigation for agriculture is widely practiced in Mason County. This may seasonally affect the groundwater flow and movement as well as the persistence of nitrate in this area. Continuously and instantaneously measured nitrate concentrations and groundwater levels indicate that during the irrigation season, discharge to Quiver Creek from the shallow groundwater system may be limited. During and following periods when estimated irrigation use is highest, the low-nitrate deeper groundwater may be the dominant contributor to the Quiver Creek surface water, whereas during recharge events and when the system is not under the stress of irrigation, there is more contribution from the local and higher-nitrate shallow groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205108","collaboration":"Prepared in cooperation with the Illinois Environmental Protection Agency","usgsCitation":"Gruhn, L.R., and Morrow, W.S., 2020, Use of real-time sensors to temporally characterize water quality in groundwater and surface water in Mason County, Illinois, 2017–19: U.S. Geological Survey Scientific Investigations Report 2020–5108, 26 p., https://doi.org/10.3133/sir20205108.","productDescription":"viii, 26 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-108958","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380811,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5108/sir20205108.pdf","text":"Report","size":"19.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Thirty water wells were sampled in 2018 to understand the geochemistry and age of groundwater in the Williston Basin and assess potential effects of shale-oil production from the Three Forks-Bakken petroleum system (TBPS) on groundwater quality. Two geochemical groups are identified using hierarchical cluster analysis. Group 1 represents the younger (median 4He = 21.49 × 10−8 cm3 STP/g), less chemically evolved water. Group 2 represents the older (median 4He = 1389 × 10−8 cm3 STP/g), more chemically evolved water. At least two samples from each group contain elevated Cl concentrations (>70 mg/L). Br/Cl, B/Cl, and Li/Cl ratios indicate multiple sources account for the elevated Cl concentrations: septic-system leachate/road deicing salt, lignite beds in the aquifers, Pierre Shale beneath the aquifers, and water associated with the TBPS (one sample). 3H and 14C data indicate that 10.8, 21.6, and 67.6% of the samples are modern (post-1952), mixed age, and premodern (pre-1953), respectively. Lumped-parameter modeling of 3H, SF6, 3He, and 14C concentrations indicates mean ages of the modern and premodern fractions range from ~1 to 30 years and 1300 to >30,000 years, respectively. Group 2 contains the highest CH4 concentrations (0.0018–32 mg/L). δ13C–CH4 and C1/C2+C3 data in groundwater (−91.7 to −70.0‰ and 1280 to 13,600) indicate groundwater CH4 is biogenic in origin and not from thermogenic shale gas. Four volatile organic compounds (VOCs) were detected in two samples. One mixed-age sample contains chloroform (0.25 μg/L) and dichloromethane (0.05 μg/L), which are probably associated with septic leachate. One premodern sample contains butane (0.082 μg/L) and n-pentane (0.032 μg/L), which are probably associated with thermogenic gas from a nearby oil well. The data indicate hydrocarbon production activities do not currently (2018) widely affect Cl, CH4, and VOC concentrations in groundwater. The predominance of premodern recharge in the aquifers indicates the groundwater moves relatively slowly, which could inhibit widespread chemical movement in groundwater overlying the TBPS. Comparison of groundwater-age data from five major unconventional hydrocarbon-production areas indicates aquifer zones used for water supply in the TBPS area have a lower risk of widespread chemical movement in groundwater than similar aquifer zones in the Fayetteville (Arkansas) and Marcellus (Pennsylvania) Shale production areas, but have a higher risk than similar aquifer zones in the Eagle Ford (Texas) and Haynesville (Texas, Louisiana) Shale production areas.
Bathymetric and velocimetric data were collected by the U.S. Geological Survey, in cooperation with the Missouri Department of Transportation, near 7 bridges at 6 highway crossings of the Missouri and Mississippi Rivers on the periphery of the State of Missouri from July 16 to August 13, 2018. A multibeam echosounder mapping system was used to obtain channel-bed elevations for river reaches about 1,640 feet longitudinally and generally extending laterally across the active channel from bank to bank during moderate flood-flow conditions. These surveys indicate the channel conditions at the time of the surveys and provide characteristics of scour holes that may be useful in the development of predictive guidelines or equations for scour holes. These data also may be useful to the Missouri Department of Transportation as a low to moderate flood-flow comparison to help assess the bridges for stability and integrity issues with respect to bridge scour during floods.
Bathymetric data were collected around every pier that was in water, except those at the edge of water, and scour holes were present at most piers for which bathymetry could be obtained, except those on banks, on bedrock, or surrounded by riprap. Occasionally, the scour hole near a pier was difficult to discern from nearby bed features. The observed scour holes at the surveyed bridges were generally examined with respect to shape and depth.
Although partial exposure of substructural support elements was observed at several piers, at most sites the exposure likely can be considered minimal compared to the overall substructure that remains buried in bed material at these piers. The notable exceptions are piers 12 and 13 at structure L0135 on State Highway 51 at Chester, Illinois, at which the bedrock material was fully exposed around the piers.
The presence of riprap blankets, pier size and nose shape, and alignment to flow had a substantial effect on the size of the scour hole observed for a given pier. Piers that were surrounded by riprap blankets had scour holes that were substantially smaller (to nonexistent) compared to piers at which no rock or riprap were present. Narrow piers having round or sharp noses that were aligned with flow often had scour holes that were difficult to discern from nearby bed features, whereas piers having wide or blunt noses resulted in larger, deeper scour holes. Several of the structures had piers that were skewed to primary approach flow, and scour holes near these piers generally displayed deposition on the leeward side of the pier and greater depth on the side of the pier with impinging flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205088","collaboration":"Prepared in cooperation with the Missouri Department of Transportation","usgsCitation":"Huizinga, R.J., 2020, Bathymetric and velocimetric surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, July–August 2018: U.S. Geological Survey Scientific Investigations Report 2020–5088, 100 p., https://doi.org/10.3133/sir20205088.","productDescription":"Report: vii, 100 p.; Data Release","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-115831","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5088/coverthb.jpg"},{"id":380761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5088/sir20205088.pdf","text":"Report","size":"21.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5088"},{"id":380762,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WDI9YF","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Bathymetry and velocity data from surveys at highway bridges crossing the Missouri and Mississippi Rivers on the periphery of Missouri, December 2008 through August 2018"}],"country":"United States","state":"Missouri","otherGeospatial":"Mississippi River, Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.68017578125,\n 39.257778150283364\n ],\n [\n -94.72412109375,\n 39.07890809706475\n ],\n [\n -94.603271484375,\n 38.993572058209466\n ],\n [\n -94.28466796874999,\n 39.00211029922515\n ],\n [\n -93.88916015625,\n 39.00211029922515\n ],\n [\n -93.526611328125,\n 39.06184913429154\n ],\n [\n -93.09814453125,\n 39.155622393423215\n ],\n [\n -93.05419921875,\n 39.00211029922515\n ],\n [\n -92.92236328125,\n 38.87392853923629\n ],\n [\n -92.65869140625,\n 38.805470223177466\n ],\n [\n -92.59277343749999,\n 38.659777730712534\n ],\n [\n -92.30712890625,\n 38.53097889440024\n ],\n [\n -92.054443359375,\n 38.46219172306828\n ],\n [\n -91.42822265625,\n 38.51378825951165\n ],\n [\n -91.01074218749999,\n 38.436379603\n ],\n [\n -90.802001953125,\n 38.41916639395372\n ],\n [\n -90.3515625,\n 38.66835610151506\n ],\n [\n -90.164794921875,\n 38.77978137804918\n ],\n [\n -90.054931640625,\n 38.950865400919994\n ],\n [\n -90.19775390625,\n 38.95940879245423\n ],\n [\n -90.54931640625,\n 38.84826438869913\n ],\n [\n -90.90087890624999,\n 38.69408504756833\n ],\n [\n -91.16455078125,\n 38.831149809348744\n ],\n [\n -91.593017578125,\n 38.85682013474361\n ],\n [\n -92.10937499999999,\n 38.7283759182398\n ],\n [\n -92.373046875,\n 38.96795115401593\n ],\n [\n -92.7685546875,\n 39.27478966170308\n ],\n [\n -93.09814453125,\n 39.470125122358176\n ],\n [\n -93.955078125,\n 39.317300373271024\n ],\n [\n -94.22973632812499,\n 39.33429742980725\n ],\n [\n -94.68017578125,\n 39.257778150283364\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Regional aquifers in the Mississippi embayment are the principal sources of water used for public and domestic supply, irrigation, and industrial uses throughout the region. An understanding of how water quality varies spatially, temporally, and with depth are critical aspects to ensuring long-term sustainable use of these resources. A boosted regression tree (BRT) model was used by the U.S. Geological Survey (USGS) to map water quality in the three regional aquifers with the largest groundwater withdrawals in the embayment: the Mississippi River Valley alluvial (MRVA) aquifer, middle Claiborne aquifer (MCAQ), and lower Claiborne aquifer (LCAQ).
The BRT model was used to predict pH to 1-kilometer raster grid cells for seven aquifer layers (one MRVA, four MCAQ, two LCAQ) following the hydrogeologic framework of the Mississippi embayment aquifer system regional MODFLOW model. The methods and approach used for pH predictions are the same as those used recently by the USGS to predict specific conductance and chloride in the aquifers. Explanatory variables for the BRT models included variables describing well location and construction, surficial variables such as soil properties and land use, and variables extracted from the groundwater flow model, such as groundwater levels and ages. The primary source of pH data was the USGS National Water Information System database. Additional data from State ambient groundwater monitoring programs and the Safe Drinking Water Information System also were used. For wells sampled multiple times, the most recent sample was used. Because groundwater residence times are long (greater than 100 years) throughout much of the study area, the possible effects of changes in water quality over time were considered small compared to the improvement in overall model accuracy by using available historical data. Values of pH from 3,362 wells for samples collected between 1960 and 2018 were used as training data for the BRT model. An additional 839 samples were used as holdout data to evaluate model performance. The predictive performance of the pH model is lower than for the training dataset, as indicated by an r-squared value of 0.89 for the training data and an r-squared of 0.71 for the holdout data. The root mean squared errors for the training and holdout data are 0.32 and 0.50 standard pH units, respectively. Data generated during this study and the model output are available from the companion data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3465","usgsCitation":"Kingsbury, J.A., Knierim, K.J., and Haugh, C.J., 2020, Predicted pH of groundwater in the Mississippi River Valley alluvial and Claiborne aquifers, South-Central United States: U.S. Geological Survey Scientific Investigations Map 3465, 1 sheet, https://doi.org/10.3133/sim3465.","productDescription":"1 Sheet: 34.60 x 28.70 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-111848","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":380668,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CXX7LN","text":"USGS data release","linkHelpText":"Prediction grids of pH for the Mississippi River Valley alluvial and Claiborne aquifers"},{"id":380666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3465/coverthb2.jpg"},{"id":380667,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3465/sim3465.pdf","text":"Report","size":"3.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3465"}],"country":"United States","state":"Alabama, Arkansas, Louisiana, Mississippi, Missouri","otherGeospatial":"Mississippi River Valley alluvial, Claiborne aquifers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.296875,\n 37.020098201368114\n ],\n [\n -90.1318359375,\n 36.66841891894786\n ],\n [\n -91.93359375,\n 35.28150065789119\n ],\n [\n -93.33984375,\n 33.65120829920497\n ],\n [\n -94.04296874999999,\n 33.100745405144245\n ],\n [\n -93.91113281249999,\n 31.952162238024975\n ],\n [\n -93.1640625,\n 31.090574094954192\n ],\n [\n -91.7578125,\n 30.939924331023445\n ],\n [\n -91.0986328125,\n 31.952162238024975\n ],\n [\n -90.703125,\n 32.24997445586331\n ],\n [\n -89.3408203125,\n 32.175612478499325\n ],\n [\n -88.0224609375,\n 31.57853542647338\n ],\n [\n -87.4951171875,\n 31.80289258670676\n ],\n [\n -86.748046875,\n 32.99023555965106\n ],\n [\n -87.4072265625,\n 33.211116472416855\n ],\n [\n -88.9892578125,\n 33.94335994657882\n ],\n [\n -89.7802734375,\n 34.74161249883172\n ],\n [\n -90,\n 35.24561909420681\n ],\n [\n -89.56054687499999,\n 36.13787471840729\n ],\n [\n -89.3408203125,\n 36.421282443649496\n ],\n [\n -89.2529296875,\n 36.84446074079564\n ],\n [\n -89.296875,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The U.S. Geological Survey (USGS), in cooperation with the California State Water Resources Control Board, is evaluating several questions about oil and gas development and groundwater resources in California, including (1) the location of groundwater resources; (2) the proximity of oil and gas operations to groundwater and the geologic materials between them; (3) evidence (or no evidence) of fluids from oil and gas sources in groundwater; and (4) the pathways or processes responsible when fluids from oil and gas sources are present in groundwater (U.S. Geological Survey, 2017). As part of this evaluation, the USGS installed a multiple-well monitoring site in the southern San Joaquin Valley groundwater basin adjacent to the North and South Belridge oil fields, about 7 miles southwest of Lost Hills, California. Data collected at the Belridge multiple-well monitoring site (BWSD) provide information about the geology, hydrology, geophysical properties, and geochemistry of the aquifer system, thus enhancing understanding of relations between adjacent groundwater and the North and South Belridge oil fields in an area where there are few groundwater data. This report presents construction information for the BWSD and initial hydrogeologic data collected from the site. A similar site installed to the east of the Lost Hills oil field, 11.5 miles to the north of the BWSD site, was described by Everett and others (2020a).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201116","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Everett, R.R., Brown, A.A., Gillespie, J.M., Kjos, A., and Fenton, N.C., 2020, Multiple-well monitoring site adjacent to the North and South Belridge Oil Fields, Kern County, California: U.S. Geological Survey Open-File Report 2020-1116, 10 p., https://doi.org/10.3133/ofr20201116.","productDescription":"Report: 10 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-112077","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":380658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1116/ofr20201116.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1116"},{"id":380659,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96WITX5","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Aquifer test data for the Belridge multiple-well monitoring site (BWSD), Kern County, California"},{"id":380657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1116/coverthb.jpg"}],"country":"United States","state":"California","county":"Kern County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-120.1945,35.788],[-120.1842,35.789],[-120.1655,35.7891],[-120.1474,35.7887],[-120.0816,35.7886],[-119.9688,35.7896],[-119.852,35.7891],[-119.7618,35.7906],[-119.6472,35.7895],[-119.5395,35.79],[-119.4301,35.7905],[-119.3308,35.7899],[-119.2169,35.7906],[-119.1182,35.7903],[-118.9027,35.789],[-118.6504,35.7897],[-118.6441,35.7896],[-118.5885,35.7897],[-118.5233,35.7892],[-118.4785,35.7915],[-118.4706,35.7919],[-118.4502,35.7908],[-118.2716,35.7896],[-118.2562,35.7894],[-118.2387,35.7897],[-118.2137,35.7894],[-118.1956,35.7896],[-118.1632,35.7893],[-118.0839,35.7865],[-118.0697,35.7859],[-118.009,35.7861],[-117.9234,35.7863],[-117.9249,35.7986],[-117.9005,35.7983],[-117.8738,35.7988],[-117.8523,35.7985],[-117.6362,35.7958],[-117.6355,35.7086],[-117.6537,35.7085],[-117.6527,35.6776],[-117.6176,35.6775],[-117.6166,35.6493],[-117.6353,35.6487],[-117.6354,35.6233],[-117.6352,35.5807],[-117.6356,35.5666],[-117.6351,35.5639],[-117.6346,35.4472],[-117.6352,35.3755],[-117.6353,35.3464],[-117.6351,35.3319],[-117.6343,35.3174],[-117.6341,35.3028],[-117.6345,35.2874],[-117.6343,35.2742],[-117.6341,35.2588],[-117.6339,35.2447],[-117.6342,35.2302],[-117.634,35.2157],[-117.6338,35.2011],[-117.6336,35.1861],[-117.6334,35.1707],[-117.6338,35.1562],[-117.6336,35.1417],[-117.6333,35.1271],[-117.6331,35.1126],[-117.6329,35.098],[-117.6352,35.0981],[-117.636,35.0872],[-117.6358,35.0727],[-117.6356,35.0581],[-117.6357,35.0295],[-117.6361,35.015],[-117.6357,34.985],[-117.6351,34.8233],[-117.6519,34.8227],[-117.6704,34.8221],[-117.7757,34.8229],[-118.1408,34.8195],[-118.1493,34.8195],[-118.5995,34.8175],[-118.8946,34.8181],[-118.8945,34.818],[-118.8825,34.791],[-118.9772,34.7902],[-118.9771,34.8126],[-119.2462,34.8147],[-119.2461,34.857],[-119.2797,34.858],[-119.2779,34.8793],[-119.3844,34.8794],[-119.385,34.884],[-119.3849,34.899],[-119.4382,34.8999],[-119.4438,34.8999],[-119.4544,34.8999],[-119.4571,34.9],[-119.4746,34.9004],[-119.4746,34.9005],[-119.4746,34.9136],[-119.474,34.9367],[-119.474,34.9499],[-119.474,34.9576],[-119.474,34.9721],[-119.4746,35.0184],[-119.4746,35.0325],[-119.4745,35.077],[-119.4908,35.077],[-119.4914,35.092],[-119.5004,35.0915],[-119.5088,35.0906],[-119.5628,35.0883],[-119.5583,35.1369],[-119.5566,35.1601],[-119.5549,35.1791],[-119.5769,35.1787],[-119.6095,35.1773],[-119.6675,35.1749],[-119.6675,35.1908],[-119.6675,35.2049],[-119.6688,35.2617],[-119.7397,35.2629],[-119.7572,35.2633],[-119.7746,35.2633],[-119.8113,35.2641],[-119.8122,35.3508],[-119.8815,35.3501],[-119.8824,35.41],[-119.8824,35.4246],[-119.8831,35.4377],[-119.9999,35.4396],[-120.0007,35.4695],[-120.0171,35.469],[-120.0194,35.4835],[-120.0358,35.4834],[-120.0359,35.497],[-120.0523,35.4974],[-120.053,35.5124],[-120.0699,35.5128],[-120.0711,35.5268],[-120.0875,35.5276],[-120.0876,35.6139],[-120.1951,35.6151],[-120.1947,35.7481],[-120.1942,35.7626],[-120.1945,35.788]]]},\"properties\":{\"name\":\"Kern\",\"state\":\"CA\"}}]}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Exceptionally low river flows are predicted to become more frequent and more severe across many global regions as a consequence of climate change. Investigations of trace metal transport dynamics across streamflows reveal stark changes in water chemistry, metal transformation processes, and remediation effectiveness under exceptionally low-flow conditions. High spatial resolution hydrological and water quality datasets indicate that metal-rich groundwater will exert a greater control on stream water chemistry and metal concentrations because of climate change. This is because the proportion of stream water sourced from mined areas and mineralized strata will increase under predicted future low-flow scenarios (from 25% under Q45 flow to 66% under Q99 flow in this study). However, mineral speciation modelling indicates that changes in stream pH and hydraulic conditions at low flow will decrease aqueous metal transport and increase sediment metal concentrations by enhancing metal sorption directly to streambed sediments. Solute transport modelling further demonstrates how increases in the importance of metal-rich diffuse groundwater sources at low flow could minimize the benefits of point source metal contamination treatment. Understanding metal transport dynamics under exceptionally low flows, as well as under high flows, is crucial to evaluate ecosystem service provision and remediation effectiveness in watersheds under future climate change scenarios.
This report presents abundance and occurrence data for three species of ambystomad salamanders (Ambystoma maculatum, A. jeffersonianum, and A. opacum) collected over a 3-year period (2000, 2001, and 2002) at 200 potentional breeding sies within the Delaware Water Gap National Recreation Area (DEWA). In addition, numerous measures of inpond, near-pond, and landscape attributes were measured and used to inform statistical models to determine species-habitat relationships in the DEWA.
The results of a 3-year study of ambystomatid salamander breeding habits and habitats in the (DEWA) that was conducted by the U.S. Geological Survey, in cooperation with the National Park Service, are described in the report. The objectives of the study were to document the population status and critical breeding habitats of the three species of ambystomatid salamanders known to be present in the DEWA—Ambystoma maculatum (spotted salamander), A. opacum (marbled salamander), and A. jeffersonianum (Jefferson salamander). DEWA managers are interested in ecological information on these species for several reasons. First, at the time the study began, there was little known regarding the status of pond-breeding amphibians and their habitats in the DEWA. Second, because they require undegraded habitats in both terrestrial and aquatic habitats to successfully complete their life cycles, the status of ambystomatid salamanders is widely viewed as indicative of overall ecosystem health. Third, because ambystomatid salamanders and other pond-breeding amphibians have been observed in numerous artificial impoundments with the DEWA, park managers would like to assess whether dismantling or discontinuing maintenance of artificial impoundments could affect pond-breeding amphibians and possibly other species that use pond or wetland habitats in the Park.
In 2001, 2002, and 2003, the size and location of 200 wetlands, ponds, and artificial impoundments, and related landscape positions (Ridge versus Valley; Pennsylvania side versus New Jersey side of the Delaware river) were mapped, and site habitat data relating to salamander occurrence and abundance patterns were collected. The data collected during this study provide important new baseline information on ambystomatid salamanders and wetland habitats in the DEWA that will enhance long-term inventory and monitoring efforts. In addition, breeding habitat assessments indicate that ambystomatid salamanders may be sensitive to a wide variety of stresses important in the DEWA and in the region. In particular, recent trends in development (for example, roads) in and near the DEWA, regional increases in the acidity of precipitation, and predicted long-term warming trends for the region could be detrimental to pond-breeding salamander populations because of their effects on breeding site quality and quantity, and on the integrity of migration corridors. In contrast, the results of the study indicate management plans to eliminate small impoundments are not likely to adversely affect salamanders in DEWA, at least in the short-term. However, it is possible that these small impoundments may offer stable habitats that provide a rescure effect during long-term droughts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205081","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Snyder, C.D., Young, J.A., Julian, J.T., King, T.L., and Julian, S.E., 2020, Assessment of Ambystomatid salamander populations and their breeding habitats in the Delaware Water Gap National Recreation Area: U.S. Geological Survey Scientific Investigations Report 2020–5081, 41 p., https://doi.org/10.3133/sir20205081.","productDescription":"Report: viii, 41 p.; Data Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113175","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":380510,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5081/coverthb.jpg"},{"id":380511,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5081/sir20205081.pdf","text":"Report","size":"3.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5081"},{"id":380512,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XCVHY3","text":"USGS data release","linkHelpText":"Ambystomatid salamander population and breeding pond habitat data for the Delaware Water Gap National Recreation Area (2001–2003)"}],"country":"United States","state":"New Jersey, Pennsylvania","otherGeospatial":"Delaware Water Gap National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.7564697265625,\n 41.380930388318\n ],\n [\n -74.8992919921875,\n 41.29844430929419\n ],\n [\n -74.9761962890625,\n 41.18278832811288\n ],\n [\n -75.1080322265625,\n 41.06692773019345\n ],\n [\n -75.179443359375,\n 40.992337919312305\n ],\n [\n -75.1629638671875,\n 40.93011520598305\n ],\n [\n -75.0970458984375,\n 40.93841495689795\n ],\n [\n -74.893798828125,\n 41.075210270566636\n ],\n [\n -74.6630859375,\n 41.253032440653186\n ],\n [\n -74.7564697265625,\n 41.380930388318\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
11649 Leetown Road
Kearneysville, WV 25430
Coastal cutthroat trout [Oncorhynchus clarkii clarkii (Richardson, 1836)] from the marine waters of Puget Sound, WA, was documented as a new host for the ectoparasite Argulus pugettensis (Dana, 1852). The prevalence of A. pugettensis was 66% (49 of 74) on cutthroat trout and 0% (0 of 55) on coho salmon [O. kisutch (Walbaum, 1792)] collected during the winter of 2017/2018. Infestations occurred most frequently on the dorsal surface, with intensities ranging from 1 to 26 argulids per fish (mean intensity 3.94 ± 4.93 S.D.). In contrast, the prevalence of the common salmon louse [Lepeophtheirus salmonis (Krøyer, 1837)] was 72% for cutthroat trout and 31% for coho salmon. Relative to other native salmonids, little is known regarding the status, ecology and threats for coastal cutthroat trout. New information reported here is a first step in understanding the relationship between this wild, native trout and infestations by parasitic sea lice and should be followed by future studies aimed to identify population level consequences.
","language":"English","publisher":"BioOne","doi":"10.3955/046.094.0202","usgsCitation":"Losee, J.P., Jones, S.R., McKinstry, C.A., Batts, W.N., and Hershberger, P., 2020, Anadromous coastal cutthroat trout Oncorhynchus clarkii clarkii as a host for Argulus pugettensis (Crustacea, Branchiura): Parasite prevalence, intensity and distribution: Northwest Science, v. 94, no. 2, p. 111-117, https://doi.org/10.3955/046.094.0202.","productDescription":"7 p.","startPage":"111","endPage":"117","ipdsId":"IP-102738","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":382044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Losee, James P","contributorId":239689,"corporation":false,"usgs":false,"family":"Losee","given":"James","email":"","middleInitial":"P","affiliations":[{"id":47976,"text":"Washington Department of Fish and Wildlife, Fish Program, Olympia, WA, 98501, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":797629,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Simon R M","contributorId":239690,"corporation":false,"usgs":false,"family":"Jones","given":"Simon","email":"","middleInitial":"R M","affiliations":[{"id":47977,"text":"Fisheries and Oceans Canada, Pacific Biological Station, Nanaimo, BC V9T 6N7, Canada, U.S.A.","active":true,"usgs":false}],"preferred":false,"id":797630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKinstry, Caitlin A E","contributorId":239691,"corporation":false,"usgs":false,"family":"McKinstry","given":"Caitlin","email":"","middleInitial":"A E","affiliations":[{"id":47978,"text":"Prince William Sound Science Center, Cordova, AK 99574","active":true,"usgs":false}],"preferred":false,"id":797631,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Batts, William N. 0000-0002-6469-9004 bbatts@usgs.gov","orcid":"https://orcid.org/0000-0002-6469-9004","contributorId":3815,"corporation":false,"usgs":true,"family":"Batts","given":"William","email":"bbatts@usgs.gov","middleInitial":"N.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":797632,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hershberger, Paul 0000-0002-2261-7760","orcid":"https://orcid.org/0000-0002-2261-7760","contributorId":203322,"corporation":false,"usgs":true,"family":"Hershberger","given":"Paul","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":797633,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216491,"text":"70216491 - 2020 - Development of a novel framework for modeling field-scale conservation effects of depressional wetlands in agricultural landscapes","interactions":[],"lastModifiedDate":"2020-11-23T13:47:37.113837","indexId":"70216491","displayToPublicDate":"2020-11-20T07:46:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Development of a novel framework for modeling field-scale conservation effects of depressional wetlands in agricultural landscapes","docAbstract":"The intermixed cropland, grassland, and wetland ecosystems of the upper mid-western United States combine to provide a suite of valuable ecological services. Grassland and wetland losses in the upper midwestern United States have been extensive, but government-funded conservation programs have protected and restored hundreds of thousands of acres of wetland and grassland habitat in the region. The value of restored wetlands in agricultural fields is complex, and the USDA Natural Resource Conservation Service, Conservation Effects Assessment Project (CEAP) has been lacking the methodology to include these conservation practices in their analyses. Our aim is to develop a reproducible methodology for simulating wetlands within the CEAP cropland modeling framework used to evaluate other agricultural conservation practices. Furthermore, we evaluate the effect of using upland conservation practices on the functioning of restored wetlands. By simulating the addition of a depressional wetland that effectively removes 6% of the field from crop production, we obtained a 15% reduction in annual runoff and a 29% and 28% reduction in mean annual nitrogen (N) and phosphorus (P) losses, respectively. The presence of the depressional wetland in the field is estimated to also reduce edge-of-field losses of sediments by 20% and sediment-bound N and P by 19% and 23%, respectively. Additionally, adding a grass filter strip around the wetland greatly decreased sediment inputs to the wetland, increasing the effective life of the wetland, in terms of its ability to perform valued services, by decades to centuries. Our method for modeling depressional wetlands embedded in cropped fields provides a means to quantify the effects of wetland conservation practices on field-level losses for regional assessments, such as the CEAP.
Geysers are rare geologic features that intermittently discharge liquid water and steam driven by heating and decompression boiling. The cause of variability in eruptive styles and the associated seismic signals are not well understood. Data collected from five broadband seismometers at Lone Star Geyser, Yellowstone National Park are used to determine the properties, location, and temporal patterns of hydrothermal tremor. The tremor is harmonic at some stages of the eruption cycle and is caused by near‐periodic repetition of discrete seismic events. Using the polarization of ground motion, we identify the location of tremor sources throughout several eruption cycles. During preplay episodes (smaller eruptions preceding the more vigorous major eruption), tremor occurs at depths of 7–10 m and is laterally offset from the geyser's cone by ~5 m. At the onset of the main eruption, tremor sources migrate laterally and become shallower. As the eruption progresses, tremor sources migrate along the same path but in the opposite direction, ending where preplay tremor originates. The upward and then downward migration of tremor sources during eruptions are consistent with warming of the conduit followed by evacuation of water during the main eruption. We identify systematic relations among the two types of preplays, discharge, and the main eruption. A point‐source moment tensor fit to low‐frequency waveforms of an individual tremor event using half‐space velocity models indicates average VS ≳ 0.8 km/s, source depths ~4–20 m, and moment tensors with primarily positive isotropic and compensated linear vector dipole moments.
Riparian forests play key roles in protecting biodiversity and water resources, making them priorities for conservation in human-dominated landscapes, but fragmentation associated with expanding tropical croplands threatens their ecological integrity. We compared the structure of tropical riparian forests within intact and cropland catchments in a region of intensive soybean production in the southeastern Brazilian Amazon. We studied forest plots (varying from 120 to 210 m long) that bisected riparian zone forests and headwater streams in ten catchments. Four plots were within large areas of intact primary forest and six were in bands of protected riparian forest along streams within croplands as required by the Brazilian Forest Code. We found that riparian forests in croplands harbored fewer species of trees and seedlings/saplings, and had higher proportions of opportunistic, pioneer tree species. We also found greater variation in tree species composition, and higher internal dissimilarity in croplands compared with forests. The observed patterns in tree species composition were driven mainly by differences between riparian forest-cropland edges and those bordering intact upland forests. Forests nearest to streams in cropland and forested catchments were more similar to one another. Results suggest that wider buffers are needed at the edges of croplands to maintain riparian forest structure. The minimum 30-m riparian buffers now required by the Brazilian Forest Code may thus be insufficient to prevent long-term shifts in riparian forest species composition and structure.
The U.S. Geological Survey, in cooperation with the Bernalillo County Public Works Division, conducted a 1-year study in 2015 to assess the spatial and temporal distribution of evapotranspiration (ET) and available water within the East Mountain area in Bernalillo County, New Mexico. ET and available water vary spatiotemporally because of complex interactions among environmental factors, including vegetation characteristics, soil characteristics, topography, and climate.
Precipitation data from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (P) were used in conjunction with actual ET (ETa) data from the Operational Simplified Surface Energy Balance (SSEBop) model to estimate available water (P – ETa) at 100-meter (m) resolution in the study area. Maps, descriptive statistics, boxplots, regression analyses (continuous data), and multiple comparison tests (categorical data) were used to characterize P, ETa, and available water and their relations to topographic, soil, and vegetation datasets in the East Mountain area. Five categories of the natural land-cover type (evergreen forest, shrub, herbaceous, deciduous forest, and mixed forest) and four categories of developed land-cover type specific to residential intensity (developed open, developed low, developed medium, and developed high) were analyzed individually and in interaction with multiple elevation, tree canopy, and soil texture classes.
Annual mean P in 2015 in the East Mountain area was 608 millimeters (mm), and annual mean ETa was 543 mm (89 percent of annual P in 2015), indicating that in 2015, a spatial mean of about 65 mm of water was available for runoff, soil moisture replenishment, or groundwater recharge. Monthly ETa was greatest in July and smallest in January. The intervening months did not show smooth temporal or consistent spatial changes from month to month. Months with lower ETa (January to March, October to December) also tended to have greater available water, indicating that soil moisture (water supply) and potential ET (water demand) may have been out of phase.
Regression analyses showed that monthly ETa data had the highest correlation with annual ETa among the atmospheric, topographic, soil, or vegetation datasets, particularly during the early and late growing season (March, April, May, and September). In contrast, monthly P was highly variable and not as highly correlated with annual ETa. Among landscape variables, correlations with annual ETa were highest for tree canopy cover (coefficient of determination [R2] = 0.46). Correlations between ETa and other landscape variables were lower (R2 = 0.06–0.19): available soil water in the top 100 centimeters, soil bulk density of layer 1, slope, sand content of soil layer 1, soil depth, available soil water in the top 25 centimeters, leaf area index, aspect eastness, and elevation. Evergreen forest areas had the highest annual median ETa, followed by mixed forest, open residential areas, and deciduous forest. Available water typically was higher in landcover types with lower ETa: herbaceous cover, followed by deciduous forest, high-intensity developed areas, and shrub. Deciduous forest had the second highest median available water, despite having the fourth highest ETa, because deciduous forest had greater P than most other areas. Annual median ETa typically was greatest in the second highest elevation band (2,401–2,800 m above the North American Vertical Datum of 1988 [NAVD 88]), and lower in the highest elevation band (2,801–3,254 m above NAVD 88), despite having greater P, likely because of decreased tree canopy cover or a shift from evergreen to deciduous trees at the highest elevations.
Annual median ETa increased with tree canopy cover, regardless of landcover type. ETa correlation was higher with tree canopy than with leaf area index or normalized difference vegetation index. This result indicates that it is important to include the thermal band (from satellite multispectral data) in vegetation indices used to describe ETa, perhaps to account for the influence of energy limitation or water limitation on ET. Of all natural landcover types, finer soils had the most available water, whereas coarser soils had the least available water. Relations of soil type with P – ETa were different than with ETa, indicating ET and available water have a complex response to differences in soil type. Further modeling would be useful in determining soils’ infiltration, storage, conductivity, and plant-water availability relations to individual storms for each position in the landscape, as well as the corresponding effects of these processes on ET and available water.
The best multivariate linear model for annual ETa had an R2 value of 0.62. Monthly ETa models had R2 values between 0.16 and 0.65. Models usually, but not always, performed best during the growing season. These results indicate that even the best multivariate linear models cannot explain a notable amount of the variability in ET. The monthly ETa models with the highest correlations (August and September) followed a July having almost twice the mean precipitation for July (1981–2010), which indicates that a soil-moisture variable is needed to more accurately model monthly ETa. Further study is needed to better characterize this system, the variables that affect ET and available water, and the partitioning of available water into runoff, soil moisture storage, and groundwater recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205095","collaboration":"Prepared in cooperation with the Bernalillo County Public Works Division","usgsCitation":"Douglas-Mankin, K.R., McCutcheon, R.J., Mitchell, A.C., and Senay, G.B., 2020, Landscape and climatic influences on actual evapotranspiration and available water using the Operational Simplified Surface Energy Balance (SSEBop) Model in eastern Bernalillo County, New Mexico, 2015: U.S. Geological Survey Scientific Investigations Report 2020–5095, 40 p., https://doi.org/10.3133/sir20205095.","productDescription":"x, 40 p.","numberOfPages":"53","onlineOnly":"Y","ipdsId":"IP-101269","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":380594,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5095/sir20205095.pdf","text":"Report","size":"3.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5095"},{"id":380593,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5095/coverthb.jpg"}],"country":"United States","state":"New Mexico","county":"Bernalillo County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.65252685546875,\n 34.879171662167664\n ],\n [\n -105.88623046874999,\n 34.879171662167664\n ],\n [\n -105.88623046874999,\n 35.35545618392078\n ],\n [\n -106.65252685546875,\n 35.35545618392078\n ],\n [\n -106.65252685546875,\n 34.879171662167664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
The effectiveness of submerged vanes for stabilizing streamside bluffs varied over a 10-year monitoring period in a tributary to Lake Superior, United States. Submerged vanes are a river training device used to divert river flows away from eroding banks along meander bends and ultimately hold constant or reverse the direction of lateral migration. At the study site, the relatively steep slope, large substrate size, and flashy flow regime pushed the upper end of the design limitations of submerged vanes. Changes in channel location and morphology due to the vanes were monitored using repeat channel cross-section surveys along a 110-m reach. The vanes experienced 15 floods over the monitoring period. The two most damaging floods happened in the summer and fall of 2005 with annual exceedance probabilities of 7% and 6% respectively. A new data analysis method for rivers, using centroids of cross sections, was useful to track channel migration rapidly and objectively and, along with calculations of changes in bankfull channel size, provide metrics to describe channel change.
Deeper flows through bedrock in mountain watersheds could be important, but lack of data to characterize bedrock properties limits understanding. To address data scarcity, we combine a previously published integrated hydrologic model of a snow‐dominated, headwater basin of the Colorado River with a new method for dating baseflow age using dissolved gas tracers SF6, CFC‐113, N2, and Ar. The original flow model predicts the majority of groundwater flow through shallow alluvium (<8 m) sitting on top of less permeable bedrock. The water moves too quickly and is unable to reproduce observed SF6 concentrations. To match gas data, bedrock permeability is increased to allow a larger fraction of deeper and older groundwater flow (median 112 m). The updated hydrologic model indicates interannual variability in baseflow age (3–12 years) is controlled by the volume of seasonal interflow and tightly coupled to snow accumulation and monsoon rain. Deeper groundwater flow remains stable (11.7 ± 0.7 years) as a function mean historical recharge to bedrock hydraulic conductivity (R/K). A sensitivity analysis suggests that increasing bedrock K effectively moves this alpine basin away from its original conceptualization of hyperlocalized groundwater flow (high R/K) with groundwater age insensitive to changes in water inputs. Instead, this basin is situated close to the precipitation threshold defining recharge controlled groundwater flow conditions (low R/K) in which groundwater age increases with small reductions in precipitation. Work stresses the need to explore alternative methods characterizing bedrock properties in mountain basins to better quantify deeper groundwater flow and predict their hydrologic response to change.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028161","usgsCitation":"Carroll, R.W., Manning, A.H., Niswonger, R.G., Marchetti, D.W., and Williams, K.H., 2020, Baseflow age distributions and depth of active groundwater flow in a snow‐dominated mountain headwater basin: Water Resources Research, v. 56, no. 12, e2020WR028161, 19 p., https://doi.org/10.1029/2020WR028161.","productDescription":"e2020WR028161, 19 p.","ipdsId":"IP-115011","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":454804,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr028161","text":"Publisher Index Page"},{"id":384624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.039306640625,\n 37.00255267215955\n ],\n [\n -106.138916015625,\n 37.00255267215955\n ],\n [\n -106.138916015625,\n 40.98819156349393\n ],\n [\n -109.039306640625,\n 40.98819156349393\n ],\n [\n -109.039306640625,\n 37.00255267215955\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Carroll, Rosemary W.H. 0000-0002-9302-8074","orcid":"https://orcid.org/0000-0002-9302-8074","contributorId":178784,"corporation":false,"usgs":false,"family":"Carroll","given":"Rosemary","email":"","middleInitial":"W.H.","affiliations":[],"preferred":false,"id":812816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812817,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":812818,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marchetti, David W 0000-0002-1246-0798","orcid":"https://orcid.org/0000-0002-1246-0798","contributorId":255716,"corporation":false,"usgs":false,"family":"Marchetti","given":"David","email":"","middleInitial":"W","affiliations":[{"id":38118,"text":"Western Colorado University","active":true,"usgs":false}],"preferred":false,"id":812819,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Kenneth H. 0000-0002-3568-1155","orcid":"https://orcid.org/0000-0002-3568-1155","contributorId":176791,"corporation":false,"usgs":false,"family":"Williams","given":"Kenneth","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":812820,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216349,"text":"ofr20201094 - 2020 - Measured and calculated nitrate and dissolved organic carbon concentrations and loads at the W.P. Franklin Lock and Dam, S-79, south Florida, 2014-17","interactions":[],"lastModifiedDate":"2020-11-17T23:20:23.252871","indexId":"ofr20201094","displayToPublicDate":"2020-11-17T08:05:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1094","displayTitle":"Measured and Calculated Nitrate and Dissolved Organic Carbon Concentrations and Loads at the W.P. Franklin Lock and Dam, S-79, South Florida, 2014–17","title":"Measured and calculated nitrate and dissolved organic carbon concentrations and loads at the W.P. Franklin Lock and Dam, S-79, south Florida, 2014-17","docAbstract":"The U.S. Geological Survey monitored dissolved nitrate plus nitrite as nitrogen (N) and dissolved organic carbon (DOC) concentrations and calculated loads of these constituents at the W.P. Franklin Lock and Dam (S-79) from April 2014 to December 2017. Flows from Lake Okeechobee controlled by S-77, S-78 and S-79 affect water quality in the downstream Caloosahatchee River Estuary, where increased nutrients and dissolved organic matter are of concern. Numerous algal blooms have occurred in the Caloosahatchee River and downstream estuaries in recent years (2005–18) and are often attributed to eutrophication. Dissolved nitrate plus nitrite as N (hereafter, referred to as nitrate) data were collected at 15-minute intervals using a submersible ultraviolet optical nitrate sensor. The instrument data were corrected for interferences, as determined by the relation between instrument measurements and 20 concurrent laboratory values. A surrogate model, based on 36 concurrent measurements of DOC, fluorescence of chromophoric dissolved organic matter, and specific conductance, was developed to calculate DOC at 15-minute intervals.
Mean and median calculated nitrate concentrations for the study period (2014–17) were both 0.21 milligram per liter (mg/L). Monthly mean nitrate concentrations ranged from 0.04 mg/L in April 2017 to 0.48 mg/L in November 2015. Monthly mean nitrate concentrations and the proportion of water that was attributed to Lake Okeechobee discharge, released through S-79, were weakly correlated and indicate that the nitrate concentrations typically decreased as the percentage of water released from the lake increased. Annual nitrate loads were 278 metric tons in 2015, 782 metric tons in 2016, and 525 metric tons in 2017. Monthly mean nitrate loads ranged from 1.2 metric tons in April 2017 to 171.3 metric tons in February 2016. Nitrate loads increased linearly with an increase in flow and typically increased during the wet season, May to October. Monthly loads of nitrate were strongly correlated with flow at S-77 and S-79.
Mean and median calculated DOC concentrations for the study period were 18.3 mg/L and 18.9 mg/L, respectively. Monthly mean DOC concentrations ranged from 12.6 mg/L in May 2017 to 21.5 mg/L in September 2015. Generally, DOC concentrations were lower during the dry season months (November to April) and higher during the wet season months. Monthly mean DOC concentrations were moderately correlated with monthly mean flow volumes at S-79. There was a strong correlation between monthly mean DOC concentrations and the proportion of water released at S-79 that can be attributed directly to Lake Okeechobee, indicating that contributions between Moore Haven Lock and Dam (S-77) and S-79 have a higher DOC concentration than water released from Lake Okeechobee. Monthly mean nitrate concentrations and monthly mean DOC concentrations were strongly correlated. Annual loads of DOC were 23,960 metric tons in 2015 and 65,610 metric tons in 2016 (2014 and 2017 data were incomplete). Monthly loads of DOC ranged from 284 metric tons in May 2017 to 15,122 metric tons in September 2017, the latter corresponding to the effects from Hurricane Irma. Monthly loads of DOC were strongly correlated with flow at S-77 and S-79.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201094","collaboration":"USGS Greater Everglades Priority Ecosystem Science Program","usgsCitation":"Booth, A., 2020, Measured and calculated nitrate and dissolved organic carbon concentrations and loads at the W.P. Franklin Lock and Dam, S-79, south Florida, 2014-17: U.S. Geological Survey Open-File Report 2020-1094, 37 p., https://doi.org/10.3133/ofr20201094.","productDescription":"Report: vi, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091619","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":380478,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1094/coverthb.jpg"},{"id":380479,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1094/ofr20201094.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1094"},{"id":380480,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V4ZGWU","text":"USGS data release","linkHelpText":"Calculated carbon concentrations, Franklin Lock and Dam (S-79) southern Florida, 2014-2017"}],"country":"United States","state":"Florida","otherGeospatial":"W.P. Franklin Lock and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.7437744140625,\n 26.701452590314368\n ],\n [\n -81.47735595703125,\n 26.701452590314368\n ],\n [\n -81.47735595703125,\n 26.74683674289727\n ],\n [\n -81.7437744140625,\n 26.74683674289727\n ],\n [\n -81.7437744140625,\n 26.701452590314368\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Many canyon‐bound rivers have been dammed and downstream flow and water temperatures modified. Climate change is expected to cause lower storage in reservoirs and warmer release temperatures, which may further alter downstream flow and thermal regimes. To anticipate potential future changes, we first need to understand the dominant heat transfer mechanisms in canyon‐bound river systems. Towards this end, we adapt a dynamic process‐based river routing and temperature model to account for complex shading and radiation characteristics found in canyon‐bound rivers. We apply the model to a 362 km segment of the Colorado River in Grand Canyon National Park, USA to simulate temperature over an 18‐year period. Extensive temperature and flow datasets from within the canyon were used to assess model performance. At the most downstream gaging location, root mean square errors of hourly flow routing and temperature predictions were 11.5 m3/s and 0.93 °C, respectively. We found that heat fluxes controlling temperatures were highly variable over space and time, primarily due to shortwave radiation dynamics and hydropeaking flow conditions. Additionally, the large differences between air and water temperature during summer periods resulted in high sensible and latent heat fluxes. Sensitivity analyses indicate that reservoir release temperatures are most influential above the RM88 gage (141 kilometers below Glen Canyon Dam), while a combination of discharge, shortwave radiation, and air temperature become more important farther downstream. This study illustrates the importance of understanding the spatial and temporal variability of topographic shading when predicting water temperatures in canyon‐bound rivers.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027566","usgsCitation":"Mihalevich, B.A., Neilson, B., Buahin, C.A., Yackulic, C., and Schmidt, J.C., 2020, Water temperature controls for regulated canyon-bound rivers: Water Resources Research, v. 56, e2020WR027566, 24 p., https://doi.org/10.1029/2020WR027566.","productDescription":"e2020WR027566, 24 p.","ipdsId":"IP-117871","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":381103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.961181640625,\n 35.639441068973944\n ],\n [\n -111.29150390625,\n 35.639441068973944\n ],\n [\n -111.29150390625,\n 36.923547681089296\n ],\n [\n -113.961181640625,\n 36.923547681089296\n ],\n [\n -113.961181640625,\n 35.639441068973944\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","noUsgsAuthors":false,"publicationDate":"2020-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Mihalevich, Bryce A.","contributorId":245512,"corporation":false,"usgs":false,"family":"Mihalevich","given":"Bryce","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":806340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neilson, Bethany","contributorId":178798,"corporation":false,"usgs":false,"family":"Neilson","given":"Bethany","affiliations":[],"preferred":false,"id":806341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buahin, Caleb A.","contributorId":245514,"corporation":false,"usgs":false,"family":"Buahin","given":"Caleb","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":806342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":806343,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmidt, John C.","contributorId":207751,"corporation":false,"usgs":false,"family":"Schmidt","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":37627,"text":"Department of Watershed Sciences, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":806344,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70217194,"text":"70217194 - 2020 - Using tracer variance decay to quantify variability of salinity mixing in the Hudson River Estuary","interactions":[],"lastModifiedDate":"2021-01-12T13:27:48.796607","indexId":"70217194","displayToPublicDate":"2020-11-15T07:21:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7505,"text":"Journal of Geophysical Research, Oceans","active":true,"publicationSubtype":{"id":10}},"title":"Using tracer variance decay to quantify variability of salinity mixing in the Hudson River Estuary","docAbstract":"The salinity structure in an estuary is controlled by time‐dependent mixing processes. However, the locations and temporal variability of where significant mixing occurs is not well‐understood. Here we utilize a tracer variance approach to demonstrate the spatial and temporal structure of salinity mixing in the Hudson River Estuary. We run a 4‐month hydrodynamic simulation of the tides, currents, and salinity that captures the spring‐neap tidal variability as well as wind‐driven and freshwater flow events. On a spring‐neap time scale, salinity variance dissipation (mixing) occurs predominantly during the transition from neap to spring tides. On a tidal time scale, 60% of the salinity variance dissipation occurs during ebb tides and 40% during flood tides. Spatially, mixing during ebbs occurs primarily where lateral bottom salinity fronts intersect the bed at the transition from the main channel to adjacent shoals. During ebbs, these lateral fronts form seaward of constrictions located at multiple locations along the estuary. During floods, mixing is generated by a shear layer elevated in the water column at the top of the mixed bottom boundary layer, where variations in the along channel density gradients locally enhance the baroclinic pressure gradient leading to stronger vertical shear and more mixing. For both ebb and flood, the mixing occurs at the location of overlap of strong vertical stratification and eddy diffusivity, not at the maximum of either of those quantities. This understanding lends a new insight to the spatial and time dependence of the estuarine salinity structure.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JC016096","usgsCitation":"Warner, J., Geyer, W.R., Ralston, D.K., and Kalra, T., 2020, Using tracer variance decay to quantify variability of salinity mixing in the Hudson River Estuary: Journal of Geophysical Research, Oceans, v. 125, no. 12, e2020JC016096, 18 p., https://doi.org/10.1029/2020JC016096.","productDescription":"e2020JC016096, 18 p.","ipdsId":"IP-117991","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454821,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020jc016096","text":"Publisher Index Page"},{"id":382091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Hudson River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.03137207031247,\n 40.551374198715166\n ],\n [\n -73.74023437499999,\n 40.551374198715166\n ],\n [\n -73.74023437499999,\n 42.90413649491736\n ],\n [\n -74.03137207031247,\n 42.90413649491736\n ],\n [\n -74.03137207031247,\n 40.551374198715166\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":807929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Geyer, W Rockwill 0000-0001-9030-1744","orcid":"https://orcid.org/0000-0001-9030-1744","contributorId":247570,"corporation":false,"usgs":false,"family":"Geyer","given":"W","email":"","middleInitial":"Rockwill","affiliations":[{"id":49582,"text":"Woods Hole Oceanographic Institution, Applied Ocean Physics and Engineering Department, MS #11, Woods Hole, MA,","active":true,"usgs":false}],"preferred":false,"id":807930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ralston, David K. 0000-0002-0774-3101","orcid":"https://orcid.org/0000-0002-0774-3101","contributorId":195909,"corporation":false,"usgs":false,"family":"Ralston","given":"David","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":807931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":807932,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}