Long-term studies of stream ecosystems are essential for assessing restoration success because they allow researchers to quantify recovery trajectories, gauge the relative influence of episodic events, and determine the time required to achieve clean-up objectives. To quantify responses of benthic macroinvertebrate assemblages to stream remediation, we integrated results of 4 long-term (20–29 y) assessments of mining-impacted watersheds that were broadly distributed across the western US (California, Colorado, Idaho, Montana). Using a before–after control–impact (BACI) study design, we observed substantial reductions in metal concentrations and corresponding improvements of benthic assemblages following remediation. Recovery rates were relatively consistent, and streams typically recovered within 10 to 15 y after remediation was initiated (mean = 10.25 y), although episodic events changed trajectories at some sites. Differences in recovery among watersheds were likely determined by a number of factors, including the severity of contamination, effectiveness of remediation, proximity to upstream sources of colonization, and hydrologic variation. We also observed considerable variation in the rate and extent of recovery among assemblage metrics. For example, total abundance and richness recovered rapidly at most sites, but the composition of benthic macroinvertebrate assemblages remained substantially altered compared with reference sites. Using piecewise linear regression, we estimated a threshold response of Ephemeroptera, Plecoptera, and Trichoptera (EPT) species richness at ~1 cumulative criteria unit (CCU), which is the sum of the fractions of chronic water-quality criteria for metals measured, suggesting this value was protective of benthic assemblages. However, EPT richness was reduced by ~20% at 2× this CCU value, indicating that moderate exceedances of water-quality criteria could substantially affect stream biodiversity. Non-metric multidimensional scaling analyses identified common sets of species trait states across the 4 watersheds that were associated with either metal contamination or with recovering and intact reference stream assemblages. Our study illustrates the importance of long-term studies for quantifying responses to stream restoration and the usefulness of BACI designs for demonstrating cause-and-effect relationships between restoration treatments and community recovery. Because these 4 watersheds were among the most severely polluted sites in the western US, our study demonstrates the value of these investments in watershed restoration and the potential for success under the most extreme conditions.
Deadly and destructive debris flows often follow wildfire, but understanding of changes in the hazard potential with time since fire is poor. We develop a simulation‐based framework to quantify changes in the hydrologic triggering conditions for debris flows as postwildfire infiltration properties evolve through time. Our approach produces time‐varying rainfall intensity‐duration thresholds for runoff‐ and infiltration‐generated debris flows with physics‐based hydrologic simulations that are parameterized with widely available hydroclimatic, vegetation reflectance, and soil texture data. When we apply our thresholding protocol to a test case in the San Gabriel Mountains (California, USA), the results are consistent with existing regional empirical thresholds and rainstorms that caused runoff‐ and infiltration‐generated debris flows soon after and three years following a wildfire, respectively. We find that the hydrologic triggering mechanisms for the two observed debris flow types are coupled with the effects of fire on the soil saturated hydraulic conductivity. Specifically, the rainfall intensity needed to generate debris flows via runoff increases with time following wildfire while the rainfall duration needed to produce debris flows via subsurface pore‐water pressures decreases. We also find that variations in soil moisture, rainfall climatology, median grain size, and root reinforcement could impact the median annual probability of postwildfire debris flows. We conclude that a simulation‐based method for calculating rainfall thresholds is a tractable approach to improve situational awareness of debris flow hazard in the years following wildfire. Further development of our framework will be important to quantify postwildfire hazard levels in variable climates, vegetation types, and fire regimes.
","language":"English","publisher":"Wiley","doi":"10.1029/2021JF006091","usgsCitation":"Thomas, M.A., Rengers, F.K., Kean, J.W., McGuire, L.A., Staley, D.M., Barnhart, K.R., and Ebel, B., 2021, Postwildfire soil‐hydraulic recovery and the persistence of debris flow hazards: Journal of Geophysical Research: Earth Surface, v. 126, no. 6, e2021JF006091, 25 p., https://doi.org/10.1029/2021JF006091.","productDescription":"e2021JF006091, 25 p.","ipdsId":"IP-126218","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":452437,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021jf006091","text":"External Repository"},{"id":436384,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QLP6XG","text":"USGS data release","linkHelpText":"Soil moisture monitoring following the 2009 Station Fire, California, USA, 2016-2019"},{"id":385458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Thomas, Matthew A. 0000-0002-9828-5539 matthewthomas@usgs.gov","orcid":"https://orcid.org/0000-0002-9828-5539","contributorId":200616,"corporation":false,"usgs":true,"family":"Thomas","given":"Matthew","email":"matthewthomas@usgs.gov","middleInitial":"A.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGuire, Luke A. 0000-0001-8178-7922 lmcguire@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-7922","contributorId":203420,"corporation":false,"usgs":false,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":815192,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815193,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barnhart, Katherine R. 0000-0001-5682-455X","orcid":"https://orcid.org/0000-0001-5682-455X","contributorId":257870,"corporation":false,"usgs":true,"family":"Barnhart","given":"Katherine","email":"","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":815194,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":815195,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70220242,"text":"fs20213025 - 2021 - Storms and floods of July 30, 2016, and May 27, 2018, in Ellicott City, Howard County, Maryland","interactions":[],"lastModifiedDate":"2021-04-29T17:07:33.658702","indexId":"fs20213025","displayToPublicDate":"2021-04-29T10:30:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3025","displayTitle":"Storms and Floods of July 30, 2016, and May 27, 2018, in Ellicott City, Howard County, Maryland","title":"Storms and floods of July 30, 2016, and May 27, 2018, in Ellicott City, Howard County, Maryland","docAbstract":"On July 30, 2016, and May 27, 2018, the downtown area of Ellicott City, Maryland (fig. 1), was severely flooded by intense, short-duration rainfall that resulted in loss of life; significant damage to buildings, roads, infrastructure; and hundreds of vehicles washed away. Precipitation from the 2016 event totaled 6.60 inches in 3 hours (National Oceanic and Atmospheric Administration, 2016). Precipitation from the 2018 storm totaled 6.56 inches in 3 hours (National Oceanic and Atmospheric Administration, 2018).
In the aftermath of both storms, personnel from the U.S. Geological Survey (USGS) performed indirect discharge measurements to determine peak flow on the three streams that drain through the downtown area of Ellicott City and empty into the Patapsco River. High-water marks were flagged on selected reaches of three streams, Hudson Branch (station 01589017), Tiber Branch (station 01589019), and New Cut Branch (station 01589021) (fig. 2). Peak flows were computed using flow-through-culvert techniques with road overflow for Hudson Branch and slope-area techniques for Tiber Branch and New Cut Branch.
This fact sheet describes the basin characteristics, hydrologic characteristics, and flood history of the Ellicott City, Maryland, area. The storms and flood characteristics for July 30, 2016, and May 27, 2018, are described. Peak discharges computed from the indirect discharge measurements for Hudson Branch, Tiber Branch, and New Cut Branch are presented for the storms and floods of July 30, 2016, and May 27, 2018. To provide historical perspective on these floods in Ellicott City, results from the indirect discharge measurement computations were compared to peak flows from 75 USGS streamgages and 6 miscellaneous sites in Maryland and Delaware that resulted from intense storms in August and September 1971 (Carpenter, 1974). The findings indicate that although the Ellicott City storms and floods from July 30, 2016, and May 27, 2018, are considered very rare in terms of their probability of occurrence, other storms have occurred in the Maryland and Delaware regions in the past that have produced comparable runoff characteristics relative to drainage-area magnitude.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213025","usgsCitation":"Doheny, E.J., and Nealen, C.W., 2021, Storms and floods of July 30, 2016, and May 27, 2018, in Ellicott City, Howard County, Maryland: U.S. Geological Survey Fact Sheet 2021–3025, 6 p., https://doi.org/10.3133/fs20213025.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-114401","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":385345,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3025/coverthb.jpg"},{"id":385346,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3025/fs20213025.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021-3025"}],"country":"United States","state":"Maryland","county":"Howard County","city":"Ellicott 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Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
A cloudburst on 7 August 2018 in the coastal bluffs of the Atlantic Highlands, New Jersey, induced flooding, erosion and multiple shallow slope failures that adversely affected the surrounding hillside residential area. Historically, short-duration deluges are rare in the New York Bay region, with only eight cloudbursts of greater magnitude documented since 1948. The coastal bluffs consist of a variably thick, sandy surficial material overlying flat-lying, mostly non-indurated Cretaceous and Tertiary sediments, including some low-permeability glauconitic units. The bluffs have been affected by both historical deep-seated and shallow landslide movement, the latter typically related to heavy, relatively long-duration rainfall associated with tropical cyclones and nor'easters. The shallow hydrological response during the rare cloudburst was captured at two hydrological monitoring sites and yielded insights into rapidly changing moisture conditions resulting in slope failure. Additional information is provided on historical cloudbursts that have affected the region, antecedent moisture conditions, and documented landslide types and processes.
","language":"English","publisher":"Geological Society of America","doi":"10.1144/qjegh2020-127","usgsCitation":"Ashland, F., Reilly, P.A., and Fiore, A.R., 2021, Capturing the transient hydrological response in sandy soils during a rare cloudburst associated with shallow slope failures; A case study in the Atlantic Highlands, New Jersey, USA: Quarterly Journal of Engineering Geology and Hydrogeology, v. 54, no. 4, qjegh2020-127, 10 p., https://doi.org/10.1144/qjegh2020-127.","productDescription":"qjegh2020-127, 10 p.","ipdsId":"IP-113986","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":436389,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A601HC","text":"USGS data release","linkHelpText":"Hydrologic, slope movement, and soil property data from the coastal bluffs of the Atlantic Highlands, New Jersey, 2016-2018"},{"id":387111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Atlantic Highlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.1851806640625,\n 40.250184183819854\n ],\n [\n -73.8226318359375,\n 40.250184183819854\n ],\n [\n -73.8226318359375,\n 40.48873742102282\n ],\n [\n -74.1851806640625,\n 40.48873742102282\n ],\n [\n -74.1851806640625,\n 40.250184183819854\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Ashland, Francis 0000-0001-9948-0195 fashland@usgs.gov","orcid":"https://orcid.org/0000-0001-9948-0195","contributorId":198587,"corporation":false,"usgs":true,"family":"Ashland","given":"Francis","email":"fashland@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":819186,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reilly, Pamela A. 0000-0002-2937-4490 jankowsk@usgs.gov","orcid":"https://orcid.org/0000-0002-2937-4490","contributorId":653,"corporation":false,"usgs":true,"family":"Reilly","given":"Pamela","email":"jankowsk@usgs.gov","middleInitial":"A.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819187,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fiore, Alex R. 0000-0002-0986-5225 afiore@usgs.gov","orcid":"https://orcid.org/0000-0002-0986-5225","contributorId":4977,"corporation":false,"usgs":true,"family":"Fiore","given":"Alex","email":"afiore@usgs.gov","middleInitial":"R.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819188,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220275,"text":"70220275 - 2021 - Performance of bedload sediment transport formulas applied to the Lower Minnesota River","interactions":[],"lastModifiedDate":"2021-05-04T11:42:09.813275","indexId":"70220275","displayToPublicDate":"2021-04-29T07:24:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Performance of bedload sediment transport formulas applied to the Lower Minnesota River","docAbstract":"Despite limitations in reproducing complex bedload sediment transport processes in rivers, formulas have been preferred over collection and analysis of field data due to the high cost and time-consuming nature of bedload discharge measurements. However, the performance of such formulas depends on the hydraulic and sedimentological conditions they attempt to describe. The availability of field measurements provides a unique opportunity to test bedload transport formulas to better guide formula selection. Hydraulic parameters and bedload discharge data from the Lower Minnesota River and two of its tributaries were used to evaluate nine bedload transport formulas using three different indices. The bedload data for the different sites were collected by the United States Geological Survey (USGS) from 2011 through 2014, with bed material varying from very coarse to medium sand. The formulas calculated higher bedload rates than were measured due to a combination of site-specific physical characteristics, including the presence of bed forms (dunes), and sampling uncertainties. Because of the lack of reproducibility of the tested formulas, five power functions, based on the relation between the specific unit power (independent hydraulic variable) and the USGS measured data (dependent variable), were derived as provisional equations to estimate the bedload discharge on the Lower Minnesota River and tributaries.
We assessed the effects of hydrology on growth and hatching success of age‐0 Channel Catfish Ictalurus punctatus in regulated and unregulated reaches of the Tallapoosa River basin, Alabama. Age‐0 Channel Catfish (N = 91) were collected from sites in both the Coastal Plain and Piedmont regions in fall 2003 and fall 2005. Lapillus otoliths were used to estimate the daily ages of age‐0 Channel Catfish, for which hatch dates were back‐calculated. We performed growth analysis to determine growth histories of each fish at 20‐d increments from hatch. Across the 2 years of sampling, Channel Catfish hatches were documented from June 7 to September 15. Ages and growth rates of age‐0 Channel Catfish ranged from 20 to 126 d and 0.60 to 1.5 mm/d, respectively. In general, growth was highest among age‐0 Channel Catfish from unregulated sites in the lower Coastal Plain, lowest among fish from unregulated sites in the Piedmont, and intermediate from regulated sites in the Piedmont. Successful hatching typically occurred during periods when mean discharges were in the upper two quartiles of flows for each site but not during exceptionally high peaks in flow. Physiographic province, the frequency of high pulses, and the number of flow reversals were the most important factors influencing the growth of recently hatched Channel Catfish. Results suggest that a low to moderate frequency of high pulses (25–150 pulses per 20‐d increment) and a moderate number of flow reversals (~100–175 reversals per 20‐d increment) enhances early growth of Channel Catfish in the Tallapoosa River system. Managing flow, when possible, to minimize large releases of water that result in exceptionally high pulses and providing minimal hydropeaking may improve hatching success during the Channel Catfish spawning season.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/nafm.10600","usgsCitation":"Erickson, K., Sakaris, P., Conner, H., and Irwin, E.R., 2021, Hydrologic effects on growth and hatching success of age-0 Channel Catfish in the Tallapoosa River basin: Implications for management in regulated systems: North American Journal of Fisheries Management, v. 41, no. S1, p. S118-S132, https://doi.org/10.1002/nafm.10600.","productDescription":"15 p.","startPage":"S118","endPage":"S132","ipdsId":"IP-117156","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":484988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Tallapoosa River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.13103826076762,\n 34.346950443170655\n ],\n [\n -86.13103826076762,\n 33.770251015559566\n ],\n [\n -85.39701150663335,\n 33.770251015559566\n ],\n [\n -85.39701150663335,\n 34.346950443170655\n ],\n [\n -86.13103826076762,\n 34.346950443170655\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"S1","noUsgsAuthors":false,"publicationDate":"2021-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Keith A.","contributorId":353730,"corporation":false,"usgs":false,"family":"Erickson","given":"Keith A.","affiliations":[{"id":84494,"text":"Georgia Gwinnett College","active":true,"usgs":false}],"preferred":false,"id":934427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sakaris, Peter C.","contributorId":353731,"corporation":false,"usgs":false,"family":"Sakaris","given":"Peter C.","affiliations":[{"id":84494,"text":"Georgia Gwinnett College","active":true,"usgs":false}],"preferred":false,"id":934428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conner, Hannah","contributorId":353732,"corporation":false,"usgs":false,"family":"Conner","given":"Hannah","affiliations":[{"id":84494,"text":"Georgia Gwinnett College","active":true,"usgs":false}],"preferred":false,"id":934429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irwin, Elise R. 0000-0002-6866-4976 eirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-6866-4976","contributorId":2588,"corporation":false,"usgs":true,"family":"Irwin","given":"Elise","email":"eirwin@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":934430,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220099,"text":"fs20213005 - 2021 - EverForecast—A near-term forecasting application for ecological decision support","interactions":[],"lastModifiedDate":"2021-04-21T11:50:04.150156","indexId":"fs20213005","displayToPublicDate":"2021-04-20T14:48:29","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3005","displayTitle":"EverForecast—A Near-Term Forecasting Application for Ecological Decision Support","title":"EverForecast—A near-term forecasting application for ecological decision support","docAbstract":"The Everglades Forecasting application (EverForecast) provides decision makers with a support tool to examine optimal allocations of water across the managed landscape while explicitly quantifying the conflicting needs of multiple species. Covering the Greater Everglades (a vast, subtropical wetland ecosystem in South Florida), EverForecast provides 6-month forecasts of daily projected water stage across the region. It then runs these forecasts through a suite of species models and illustrates potential tradeoffs. All output is summarized by subregion and hydrologic category. Decision makers can use these near-term forecasts to manage the transition from current conditions to future alternatives according to their management priorities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213005","collaboration":"U.S. Geological Survey Greater Everglades Priority Ecosystems Program","usgsCitation":"Haider, S.M., Romañach, S.S., McKelvy, M., Suir, K., and Pearlstine, L., EverForecast—A near-term forecasting application for ecological decision support: U.S. Geological Survey Fact Sheet 2021–3005, 2 p., https://doi.org/10.3133/fs20213005.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-123566","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":385203,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3005/fs20213005.pdf","text":"Report","size":"3.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3005"},{"id":385202,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3005/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.80969238281249,\n 24.991036982463722\n ],\n [\n -80.19470214843749,\n 24.991036982463722\n ],\n [\n -80.19470214843749,\n 26.74070480712781\n ],\n [\n -81.80969238281249,\n 26.74070480712781\n ],\n [\n -81.80969238281249,\n 24.991036982463722\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
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7920 NW 71st St.
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Redox hot spots occurring as metal-rich anoxic groundwater discharges through oxic wetland and river sediments commonly result in the formation of iron (Fe) oxide precipitates. These redox-sensitive precipitates influence the release of nutrients and metals to surface water and can act as ‘contaminant sponges’ by absorbing toxic compounds. We explore the feasibility of a non-invasive, high-resolution magnetic susceptibility (MS) technique to efficiently map the spatial variations of magnetic Fe oxide precipitates in the shallow bed of three rivers impacted by anoxic groundwater discharge. Laboratory analyses on Mashpee River (MA, USA) sediments demonstrate the sensitivity of MS to sediment Fe concentrations. Field surveys in the Mashpee and Quashnet rivers (MA, USA) reveal several discrete high MS zones, which are associated with likely anoxic groundwater discharge as evaluated by riverbed temperature, vertical head gradient, and groundwater chemistry measurements. In the East River (CO, USA), widespread cobbles/rocks exhibit high background MS from geological ferrimagnetic minerals, thereby obscuring the relatively small enhancement of MS from groundwater induced Fe oxide precipitates. Our study suggests that, in settings with low geological sources of magnetic minerals such as lowland rivers and wetlands, MS may serve as a complementary tool to temperature methods for efficiently mapping Fe oxide accumulation zones due to anoxic groundwater discharges that may function as biogeochemical hot spots and water quality control points in gaining systems.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14184","usgsCitation":"Wang, C., Briggs, M., Day-Lewis, F., and Slater, L., 2021, Evaluation of riverbed magnetic susceptibility for mapping biogeochemical hot spots in groundwater-impacted rivers: Hydrological Processes, v. 35, no. 5, e14184, 14 p., https://doi.org/10.1002/hyp.14184.","productDescription":"e14184, 14 p.","ipdsId":"IP-127672","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":488589,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1784356","text":"External Repository"},{"id":387673,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Massachusetts","otherGeospatial":"East River, Quashnet River, Mashpee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.05764770507812,\n 38.67264490154078\n ],\n [\n -106.8255615234375,\n 38.67264490154078\n ],\n [\n -106.8255615234375,\n 38.904927027872844\n ],\n [\n -107.05764770507812,\n 38.904927027872844\n ],\n [\n -107.05764770507812,\n 38.67264490154078\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.48192977905273,\n 41.588742636696765\n ],\n [\n -70.45412063598633,\n 41.588742636696765\n ],\n [\n -70.45412063598633,\n 41.61826568409901\n ],\n [\n -70.48192977905273,\n 41.61826568409901\n ],\n [\n -70.48192977905273,\n 41.588742636696765\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.5208969116211,\n 41.57127917558171\n ],\n [\n -70.50682067871094,\n 41.57127917558171\n ],\n [\n -70.50682067871094,\n 41.59580372470895\n ],\n [\n -70.5208969116211,\n 41.59580372470895\n ],\n [\n -70.5208969116211,\n 41.57127917558171\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Cheng-Hui 0000-0001-9508-7425","orcid":"https://orcid.org/0000-0001-9508-7425","contributorId":194062,"corporation":false,"usgs":false,"family":"Wang","given":"Cheng-Hui","email":"","affiliations":[],"preferred":false,"id":820536,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":820537,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Day-Lewis, Frederick 0000-0003-3526-886X","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":216359,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":820538,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Slater, L. 0000-0003-0292-746X","orcid":"https://orcid.org/0000-0003-0292-746X","contributorId":247506,"corporation":false,"usgs":false,"family":"Slater","given":"L.","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":820539,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219983,"text":"ofr20211033 - 2021 - Connectivity of Mojave Desert tortoise populations—Management implications for maintaining a viable recovery network","interactions":[],"lastModifiedDate":"2021-04-19T11:44:39.479074","indexId":"ofr20211033","displayToPublicDate":"2021-04-16T12:10:46","publicationYear":"2021","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":"2021-1033","displayTitle":"Connectivity of Mojave Desert Tortoise Populations: Management Implications for Maintaining a Viable Recovery Network","title":"Connectivity of Mojave Desert tortoise populations—Management implications for maintaining a viable recovery network","docAbstract":"The historic distribution of Mojave desert tortoises (Gopherus agassizii) was relatively continuous across the range, and the importance of tortoise habitat outside of designated tortoise conservation areas (TCAs) to recovery has long been recognized for its contributions to supporting gene flow between TCAs and to minimizing impacts and edge effects within TCAs. However, connectivity of Mojave desert tortoise populations has become a concern because of recent and proposed development of large tracts of desert tortoise habitat that cross, fragment, and surround designated conservation areas. This paper summarizes the underlying concepts and importance of connectivity for Mojave desert tortoise populations by reviewing current information on connectivity and providing information to managers for maintaining or enhancing desert tortoise population connectivity as they consider future proposals for development and management actions.
Maintaining an ecological network for the Mojave desert tortoise, with a system of core habitats (TCAs) connected by linkages, is necessary to support demographically viable populations and long-term gene flow within and between TCAs. There are four points for wildlife and land-management agencies to consider when making decisions that could affect connectivity of Mojave desert tortoise populations (for example, in updating actions in resource management plans or amendments that could help maintain or restore functional connectivity in light of the latest information):
Each TCA has unique strengths and weaknesses regarding its ability to support minimum sustainable populations based on areal extent and its ability to support population increases based on landscape connection with adjacent populations. Considering how proposed projects (inside or outside of TCAs) affect connectivity and the ability of TCAs to support at least 5,000 adult tortoises (the numerical goal for each TCA) could help managers to maintain the resilience of TCAs to population declines. The same project, in an alternative location, could have very different impacts on local and regional populations. For example, within the habitat matrix surrounding TCAs, narrowly delineated corridors may not allow for natural population dynamics if they do not accommodate overlapping home ranges along most of their widths so that tortoises reside, grow, find mates, and produce offspring that can replace older tortoises. In addition, most habitat outside TCAs may receive more surface disturbance than habitat within TCAs. Therefore, managing the entire remaining matrix of desert tortoise habitat for permeability may be better than delineating fixed corridors. These concepts apply, especially given uncertainty about long-term condition of habitat, within and outside of TCAs under a changing climate.
Ultimately, questions such as “What are the critical linkages that need to be protected?” could be better framed as “How can we manage the remaining habitat matrix in ways that sustain ecological processes and habitat suitability for special status species?” Land-management decisions made in the context of the latter question may be more conducive to maintenance of a functional ecological network.
In California, the Bureau of Land Management established 0.1–1.0 percent caps on new surface-disturbance for TCAs and mapped linkages that address the issues described in number 1 of this list.
Nevada, Utah, and Arizona currently do not have surface-disturbance limits. Limits comparable to those in the Desert Renewable Energy Conservation Plan (DRECP) would be 0.5 percent within TCAs and 1 percent within the linkages modeled by Averill-Murray and others (2013). Limits in some areas of California within the Desert Renewable Energy Conservation Plan, such as Ivanpah Valley, are more restrictive, at 0.1 percent. Continuity across the state line in Nevada could be achieved with comparable limits in the adjacent portion of Ivanpah Valley, as well as the Greater Trout Canyon Translocation Area and the Stump Springs Regional Augmentation Site. These more restrictive limits would help protect remaining habitat in the major interstate connectivity pathway through Ivanpah Valley and focal areas of population augmentation that provide additional population connectivity along the western flank of the Spring Mountains.
In a recent study that analyzed 13 years of desert tortoise monitoring data, nearly all desert tortoise observations were at sites in which 5 percent or less of the surrounding landscape within 1 kilometer was disturbed (Carter and others, 2020a). To help maintain tortoise habitability and permeability across all other non-conservation-designated tortoise habitat, all surface disturbance could be limited to less than 5-percent development per square kilometer because the 5-percent threshold for development is the point at which tortoise occupation drops precipitously (Carter and others, 2020a). However, although individual desert tortoises were observed at development levels up to 5 percent, we do not know the fitness or reproductive characteristics of these individuals. This level of development also may not allow for long-term persistence of healthy populations that are of adequate size needed for demographic or functional connectivity; therefore, a conservative interpretation suggests that, ideally, development could be lower. Lower development levels would be particularly useful in areas within the upper 5th percentile of connectivity values modeled by Gray and others (2019).
Reducing ancillary threats in places where connectivity is restricted to narrow strips of habitat, for example, narrow mountain passes or vegetated strips between solar development, could enhance the functionality of these vulnerable linkages. In such areas, maintaining multiple, redundant linkages could further enhance overall connectivity.
Minimization of mortality from roads and maximization of passage under roads. Roads pose a significant threat to the long-term persistence of local tortoise populations, and roads of high traffic volume lead to severe population declines, which ultimately fragments populations farther away from the roads. Three points (a.–c.) pertain to reducing direct mortality of tortoises on the many paved roads that cross desert tortoise habitat and to maintaining a minimal level of permeability across these roads:
Tortoise-exclusion fencing tied into culverts, underpasses, overpasses, or other passages below roads in desert tortoise habitat, would limit vehicular mortality of tortoises and provide opportunities for movement across the roads. Installation of shade structures on the habitat side of fences installed in areas with narrow population-depletion zones would limit overheating of tortoises that may pace the fence.
Passages below highways could be maintained or retrofitted to ensure safe tortoise access, for example, by filling eroded drop-offs or modifying erosion-control features such as rip-rap to make them safer and more passable for tortoises. Wildlife management agencies could work with transportation departments to develop construction standards that are consistent with hydrologic/erosion management goals, while also incorporating a design and materials consistent with tortoise survival and passage and make the standards widely available. The process would be most effective if the status of passages was regularly monitored and built into management plans.
Healthy tortoise populations along fenced highways could be supported by ensuring that land inside tortoise-exclusion fences is not so degraded that it leads to degradation of tortoise habitat outside the exclusion areas. For example, severe invasive plant infestations inside a highway exclusion could cause an increase of invasive plants outside the exclusion area and degrade habitat; therefore, invasive plants inside road rights of way could be mown or treated with herbicide to limit their spread into adjacent tortoise habitat and minimize the risk of these plants carrying wildfires into adjacent habitat.
Adaptation of management based on new information. Future research will continue to build upon and refine models related to desert tortoise population connectivity and develop new ones. New models could consider landscape levels of development and be constructed such that they share common foundations to support future synthesis efforts. If model development was undertaken in partnership with entities that are responsible for management of desert tortoise habitat, it would facilitate incorporation of current and future modeling results into their land management decisions. There are specific topics that may be clarified with further evaluation:
The effects of climate change on desert tortoise habitat, distribution, and population connectivity;
The effects of large-scale fires, especially within repeatedly burned habitat, on desert tortoise distribution and population connectivity;
The ability of solar energy facilities or similar developments to support tortoise movement and presence by leaving washes intact; leaving native vegetation intact whenever possible, or if not possible, mowing the site, allowing vegetation to re-sprout, and managing weeds; and allowing tortoises to occupy the sites; and
The design and frequency of underpasses necessary to maintain functional demographic and genetic connectivity across linear features, like highways.
Wildlife Program
Prepared in cooperation with the U.S. Fish and Wildlife Service
","usgsCitation":"Averill-Murray, R.C., Esque, T.C., Allison, L.J., Bassett, S., Carter, S.K., Dutcher, K.E., Hromada, S.J., Nussear, K.E., and Shoemaker, K., 2021, Connectivity of Mojave Desert tortoise populations—Management implications for maintaining a viable recovery network: U.S. Geological Survey Open-File Report 2021–1033, 23 p., https://doi.org/10.3133/ofr20211033.","productDescription":"vi, 23 p.","numberOfPages":"23","onlineOnly":"Y","ipdsId":"IP-125269","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":385161,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1033/covrthb.jpg"},{"id":385162,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1033/ofr20211033.pdf","text":"Report","size":"11 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":385163,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1033/images"},{"id":385164,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1033/ofr20211033.xml"}],"country":"United States","state":"Arizona, California, Nevada","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.71923828124999,\n 33.669496972795535\n ],\n [\n -113.8623046875,\n 33.578014746143985\n ],\n [\n -112.69775390625,\n 33.50475906922609\n ],\n [\n -111.51123046875,\n 33.284619968887675\n ],\n [\n -111.73095703125,\n 34.10725639663118\n ],\n [\n -111.9287109375,\n 35.51434313431818\n ],\n [\n -113.00537109375,\n 36.24427318493909\n ],\n [\n -114.3896484375,\n 36.73888412439431\n ],\n [\n -115.86181640625001,\n 37.07271048132943\n ],\n [\n -117.42187500000001,\n 37.68382032669382\n ],\n [\n -118.27880859375001,\n 37.579412513438385\n ],\n [\n -117.7734375,\n 35.97800618085566\n ],\n [\n -117.72949218749999,\n 35.44277092585766\n ],\n [\n -118.76220703125001,\n 34.75966612466248\n ],\n [\n -117.99316406249999,\n 34.488447837809304\n ],\n [\n -116.74072265625,\n 34.288991865037524\n ],\n [\n -114.71923828124999,\n 33.669496972795535\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Interactions between climate and hydrogeologic settings contribute to the hydrologic and chemical variability among depressional wetlands, which influences their aquatic communities. These interactions and resulting variability have led to inconsistent results in terms of identifying reliable predictors of aquatic-macroinvertebrate community composition for depressional wetlands. This is especially true in the Prairie Pothole Region of North America where, in addition to pronounced climate variability, studies are often confounded by fish introductions. We used environmental monitoring data collected over a 24-year period from a complex of sixteen depressional wetlands and structural equation modeling techniques that incorporated theoretical and empirical relationships outlined in the Wetland Continuum to identify key environmental (climate and hydrogeologic setting) and biotic (competition and predation) drivers of aquatic-macroinvertebrate community composition for prairie-pothole wetlands. Uplands in the study area were primarily native prairie, thus, embedded wetlands were impacted minimally by agricultural influences. Additionally, study wetlands were predominately fishless. In the absence of the overwhelming influence of fishes, major drivers influencing aquatic-macroinvertebrate communities were revealed through the use of data spanning multidecadal-long climate cycles. We found variables related to the placement of wetlands along axes of the Wetland Continuum, e.g., hydrogeologic setting (relative wetland elevation) and hydroclimatic setting (proportion of wetland ponded), to be influential drivers of within-wetland habitat characteristics, such as the proportion of open-water area, which in turn was the strongest predictor of macroinvertebrate community composition. In contrast, predatory invertebrate and salamander abundance and non-predatory invertebrate biomass (i.e., predation and competition) were found to have minimal influence on community composition.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107678","usgsCitation":"McLean, K., Mushet, D.M., Newton, W.E., and Sweetman, J.N., 2021, Long-term multidecadal data from a prairie-pothole wetland complex reveal controls on aquatic-macroinvertebrate communities: Ecological Indicators, v. 126, 107678, 11 p., https://doi.org/10.1016/j.ecolind.2021.107678.","productDescription":"107678, 11 p.","ipdsId":"IP-094142","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":452658,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107678","text":"Publisher Index Page"},{"id":396116,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Cottonwood Lake Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.70600509643555,\n 47.85014598272475\n ],\n [\n -100.60781478881836,\n 47.85014598272475\n ],\n [\n -100.60781478881836,\n 47.9002325297653\n ],\n [\n -100.70600509643555,\n 47.9002325297653\n ],\n [\n -100.70600509643555,\n 47.85014598272475\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835106,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":248538,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835107,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newton, Wesley E. 0000-0002-1377-043X wnewton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-043X","contributorId":3661,"corporation":false,"usgs":true,"family":"Newton","given":"Wesley","email":"wnewton@usgs.gov","middleInitial":"E.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835108,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sweetman, Jon N.","contributorId":279537,"corporation":false,"usgs":false,"family":"Sweetman","given":"Jon","email":"","middleInitial":"N.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":835109,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70221877,"text":"70221877 - 2021 - Critical shallow and deep hydrologic conditions associated with widespread landslides during a series of storms between February and April 2018 in Pittsburgh and vicinity, western Pennsylvania, USA","interactions":[],"lastModifiedDate":"2021-07-12T14:40:36.670307","indexId":"70221877","displayToPublicDate":"2021-04-14T09:37:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"Critical shallow and deep hydrologic conditions associated with widespread landslides during a series of storms between February and April 2018 in Pittsburgh and vicinity, western Pennsylvania, USA","docAbstract":"The potential for widespread landslides is generally increased when extraordinary wet periods occur during times of elevated subsurface hydrologic conditions. A series of storms in early 2018 in Pittsburgh, Pennsylvania, overlapped with a period of increased shallow soil moisture and rising bedrock groundwater levels resulting from seasonally diminished evapotranspiration and induced widespread landslides in the region. Most of the landslides were shallow slope failures in colluvium, landslide deposits, and/or fill. However, deep-seated landslide activity also occurred and corresponded with record cumulative precipitation from late February to April and bedrock groundwater levels rising to an annual high. Landslides blocked or damaged roads, adversely affected multiple houses, disrupted electrical service, crushed vehicles, and resulted in considerable economic losses. The initial landslides occurred during or immediately after a rare period of three successive days of heavy rain that began on February 14. Subsequent landslides between late February and April were induced by multiday storms with smaller rainfall totals. As shallow soil moisture at a monitoring site rose above a volumetric water content of 32%, the mean rainfall intensities necessary to induce slope failure in colluvium and other surficial deposits decreased. Deep-seated landslide movement occurred in the region mostly when the groundwater level in a bedrock observation well was shallower than 1.7 m. The availability of hydrologic and landslide movement monitoring data during this extraordinary series of storms highlighted the evolution of the landslide hazard with changing moisture conditions and yielded insights into potential hydrologic criteria for anticipating future widespread landslides in the region.
","language":"English","publisher":"Springer","doi":"10.1007/s10346-021-01665-x","usgsCitation":"Ashland, F., 2021, Critical shallow and deep hydrologic conditions associated with widespread landslides during a series of storms between February and April 2018 in Pittsburgh and vicinity, western Pennsylvania, USA: Landslides, v. 18, no. 6, p. 2159-2174, https://doi.org/10.1007/s10346-021-01665-x.","productDescription":"16 p.","startPage":"2159","endPage":"2174","ipdsId":"IP-099724","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":436408,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BHFXFS","text":"USGS data release","linkHelpText":"Monitoring data from the Aleppo rockslide, Allegheny County, Pennsylvania, November 2013 - December 2018"},{"id":387112,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Allegheny County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.15625,\n 40.07807142745009\n ],\n [\n -79.2333984375,\n 40.07807142745009\n ],\n [\n -79.2333984375,\n 40.68063802521456\n ],\n [\n -80.15625,\n 40.68063802521456\n ],\n [\n -80.15625,\n 40.07807142745009\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"18","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-04-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Ashland, Francis 0000-0001-9948-0195 fashland@usgs.gov","orcid":"https://orcid.org/0000-0001-9948-0195","contributorId":198587,"corporation":false,"usgs":true,"family":"Ashland","given":"Francis","email":"fashland@usgs.gov","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":819177,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70220197,"text":"70220197 - 2021 - An ecohydrological typology for thermal refuges in streams and rivers","interactions":[],"lastModifiedDate":"2021-08-03T14:02:58.556147","indexId":"70220197","displayToPublicDate":"2021-04-13T06:58:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"An ecohydrological typology for thermal refuges in streams and rivers","docAbstract":"Thermal refuges are thermally distinct riverscape features used by aquatic organisms during unfavorable thermal events, facilitating resilience in marginal environments. However, the thermal refuge concept is nebulous, and the often interchangeable use of the term ‘thermal refugia’ creates additional ambiguity. We argue that lexical differences resulting from divergent scholarly trainings hinder holistic understanding of thermal refuges; thus, existing studies would benefit from a structured framework for thermal refuge conceptualization. Herein, we articulate an ecohydrological typology for defining and characterizing thermal refuges in streams and rivers by identifying key hydrological and thermal characteristics and variations in ecological function described in the literature. We use concepts that are easily definable, measurable, and transferable across disciplines, riverscapes, and species to discriminate among thermal refuge types. Future work can use our typology as a basis for more informed interdisciplinary discussion and interpretation of thermal refuges’ role in riverscapes through more hypothesis‐driven research and conservation‐focused management.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.2295","usgsCitation":"Sullivan, C., Vokoun, J., Helton, A.M., Briggs, M.A., and Kurylyk, B., 2021, An ecohydrological typology for thermal refuges in streams and rivers: Ecohydrology, v. 14, no. 5, e2295, 15 p., https://doi.org/10.1002/eco.2295.","productDescription":"e2295, 15 p.","ipdsId":"IP-128008","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":452699,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2295","text":"Publisher Index Page"},{"id":436410,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B3TLNL","text":"USGS data release","linkHelpText":"Visible-light orthomosaic images collected by drone for two cold-water tributary confluences within the Housatonic River, CT, USA"},{"id":385316,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Sullivan, C. 0000-0001-7214-3789","orcid":"https://orcid.org/0000-0001-7214-3789","contributorId":257609,"corporation":false,"usgs":false,"family":"Sullivan","given":"C.","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":814719,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vokoun, J.","contributorId":257610,"corporation":false,"usgs":false,"family":"Vokoun","given":"J.","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":814720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Helton, A. M.","contributorId":93289,"corporation":false,"usgs":false,"family":"Helton","given":"A.","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":814721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":814722,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kurylyk, B.","contributorId":222758,"corporation":false,"usgs":false,"family":"Kurylyk","given":"B.","affiliations":[{"id":24650,"text":"Dalhousie University","active":true,"usgs":false}],"preferred":false,"id":814723,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224339,"text":"70224339 - 2021 - A tribute to Edward Perry Glenn (1947–2017), who created a legacy of environmental assessment and applications within hydrological processes","interactions":[],"lastModifiedDate":"2021-09-23T12:11:09.772239","indexId":"70224339","displayToPublicDate":"2021-04-11T07:10:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"A tribute to Edward Perry Glenn (1947–2017), who created a legacy of environmental assessment and applications within hydrological processes","docAbstract":"This issue of Hydrological Processes is dedicated to Dr. Edward P. Glenn, a frequent contributor to the journal, who suddenly passed away in late 2017. The articles within this volume are by a number of his former co-authors and others who have been greatly influenced by his professional work on hydrological processes.
Acidic deposition contributes to a range of environmental impacts across forested landscapes, including acidification of soil and drainage water, toxic aluminum mobilization, depletion of available soil nutrient cations, and impacts to forest and aquatic species health and biodiversity. In response to decreasing levels of acidic deposition, soils and drainage waters in some regions of North America have become gradually less acidic. Thresholds of atmospheric deposition at which adverse ecological effects are manifested are called critical loads (CLs) and/or target loads (TLs). Target loads are developed based on approaches that account for spatial and temporal aspects of acidification and recovery. Exceedance represents the extent to which current or projected future levels of acidic deposition exceed the level expected to cause ecological harm. We report TLs of sulfur (S) and nitrogen (N) deposition and the potential for ecosystem recovery of watershed soils and streams in the Adirondack region of New York State, resources that have been less thoroughly investigated than lakes. Regional TLs were calculated by statistical extrapolation of hindcast and forecast simulations of 25 watersheds using the process-based model PnET-BGC coupled with empirical observations of stream hydrology and established sensitivity of sugar maple (Acer saccharum) to soil base saturation and brook trout (Salvelinus fontinalis) to stream acid neutralizing capacity (ANC). Historical impacts and the expected recovery timeline of regional soil and stream chemistry and fish community condition within the Adirondack Park were evaluated. Analysis suggests that many low-order Adirondack streams and associated watershed soils have low TLs (<40 meq/m2/yr of N+S deposition) to achieve specified benchmarks for recovery of soil base saturation or stream ANC. Acid-sensitive headwater and low-order streams and watershed soils in the region are expected to experience continued adverse effects from N and S deposition well into the future even under aggressive emissions reductions. Watershed soils and streams in the western Adirondack Park are particularly vulnerable to acidic deposition and currently in exceedance of TLs. The methods used for linking statistical and process-based models to consider chemical and biological response under varying flow conditions at the regional scale in this study can be applied to other areas of concern.
Predicting rainfall-induced landslide motion is challenging because shallow groundwater flow is extremely sensitive to the preexisting moisture content in the ground. Here, we use groundwater hydrology theory and numerical modeling combined with five years of field monitoring to illustrate how unsaturated groundwater flow processes modulate the seasonal pore water pressure rise and therefore the onset of motion for slow-moving landslides. The onset of landslide motion at Oak Ridge earthflow in California’s Diablo Range occurs after an abrupt water table rise to near the landslide surface 52–129 days after seasonal rainfall commences. Model results and theory suggest that this abrupt rise occurs from the advection of a nearly saturated wetting front, which marks the leading edge of the integrated downward flux of seasonal rainfall, to the water table. Prior to this abrupt rise, we observe little measured pore water pressure response within the landslide due to rainfall. However, once the wetting front reaches the water table, we observe nearly instantaneous pore water pressure transmission within the landslide body that is accompanied by landslide acceleration. We cast the timescale to reach a critical pore water pressure threshold using a simple mass balance model that considers variable moisture storage with depth and explains the onset of seasonal landslide motion with a rainfall intensity-duration threshold. Our model shows that the seasonal response time of slow-moving landslides is controlled by the dry season vadose zone depth rather than the total landslide thickness.
Globally, over 200 million people are chronically exposed to arsenic (As) and/or manganese (Mn) from drinking water. We used machine-learning (ML) boosted regression tree (BRT) models to predict high As (>10 μg/L) and Mn (>300 μg/L) in groundwater from the glacial aquifer system (GLAC), which spans 25 states in the northern United States and provides drinking water to 30 million people. Our BRT models’ predictor variables (PVs) included recently developed three-dimensional estimates of a suite of groundwater age metrics, redox condition, and pH. We also demonstrated a successful approach to significantly improve ML prediction sensitivity for imbalanced data sets (small percentage of high values). We present predictions of the probability of high As and high Mn concentrations in groundwater, and uncertainty, at two nonuniform depth surfaces that represent moving median depths of GLAC domestic and public supply wells within the three-dimensional model domain. Predicted high likelihood of anoxic condition (high iron or low dissolved oxygen), predicted pH, relative well depth, several modeled groundwater age metrics, and hydrologic position were all PVs retained in both models; however, PV importance and influence differed between the models. High-As and high-Mn groundwater was predicted with high likelihood over large portions of the central part of the GLAC.
Water use is a critical and often uncertain component of quantifying any water budget and securing reliable and sustainable water supplies. Recent water-level declines in the Mississippi Alluvial Plain (MAP), especially in the central part of the Mississippi Delta, pose a threat to water sustainability. Aquaculture and Irrigation Water-Use Model (AIWUM) 1.0, one of the first national agricultural water-use models that provides water use at the scale of most groundwater models, was developed and compared to other reported and estimated aquaculture and irrigation water-use values within the MAP study area for 1999 through 2017 to improve water-use estimates needed as input to a hydrologic decision-support system in the MAP. Results indicate annual total water-use estimates from 1999 through 2017 ranged from about 5 to 13 billion gallons per day and, on average, a majority of the water use was applied to rice (about 51 percent), followed by soybeans (about 26 percent), and less than (<) 10 percent each was applied to aquaculture, corn, cotton, and other crops. Comparisons indicated that annual total water-use estimates from AIWUM 1.0 were smaller than or comparable to all other sources of water-use data. Although there is disagreement at the monthly timescale in estimates in the Mississippi Delta within each part of the growing season, the annual total water use is comparable between AIWUM 1.0 and the Mississippi Embayment Regional Aquifer Study groundwater model 2.1. Estimates from AIWUM 1.0 could be used in models at all scales (for example, local, regional, national) and could provide a nationally consistent methodology in estimating water use driven by regional crop-specific withdrawal rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215011","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality, the Yazoo Mississippi Delta Joint Water Management District, and the Arkansas Natural Resources Commission","usgsCitation":"Wilson, J.L., 2021, Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0—An agricultural water-use model developed for the Mississippi Alluvial Plain, 1999–2017: U.S. Geological Survey Scientific Investigations Report 2021–5011, 36 p., https://doi.org/10.3133/sir20215011.","productDescription":"Report: viii, 36 p.; 3 Data releases; 2 Datasets; 1 Software release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-098146","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436420,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YDGJ7L","text":"USGS data release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM)"},{"id":415513,"rank":8,"type":{"id":35,"text":"Software Release"},"url":"https://code.usgs.gov/map/wu/aiwum_1.1","text":"USGS software release","linkHelpText":"—Mississippi Alluvial Plain / wu / AIWUM 1.1"},{"id":415512,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RGZOBZ","text":"USGS data release","linkHelpText":"Aquaculture and irrigation water-Use model (AIWUM) version 1.1 estimates and related datasets for the Mississippi Alluvial Plain"},{"id":384819,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products","text":"USGS National Hydrography web page","linkHelpText":"— National Hydrography Dataset"},{"id":384818,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS Water Data for the Nation"},{"id":384817,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JMO9G4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Aquaculture and Irrigation Water-Use Model (AIWUM) version 1.0 estimates and related datasets for the Mississippi Alluvial Plain, 1999–2017"},{"id":384816,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70R9MHS","text":"USGS data release","description":"USGS Data Release","linkHelpText":"National 1-kilometer rasters of selected Census of Agriculture statistics allocated to land use for the time period 1950 to 2012"},{"id":384814,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5011/coverthb.jpg"},{"id":384815,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5011/sir20215011.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5011"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.9892578125,\n 37.16031654673677\n ],\n [\n -89.296875,\n 37.33522435930639\n ],\n [\n -89.9560546875,\n 37.71859032558816\n ],\n [\n -90.8349609375,\n 36.914764288955936\n ],\n [\n -91.5380859375,\n 35.96022296929667\n ],\n [\n -92.1533203125,\n 34.66935854524543\n ],\n [\n -92.373046875,\n 33.50475906922609\n ],\n [\n -91.0546875,\n 33.100745405144245\n ],\n [\n -89.7802734375,\n 33.50475906922609\n ],\n [\n -88.9453125,\n 35.639441068973944\n ],\n [\n -88.9892578125,\n 37.16031654673677\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Big Soda Lake is a 63 m deep, 1.6 km2 maar lake in the Great Basin of Nevada, USA. Water level in the lake is controlled by groundwater inputs from the surrounding aquifer and the only surface water input is rainfall, which is negligible. A core taken in 2010 records an 8.75 m depositional history of the lake. A radiocarbon date on fossil pollen from 8.4 m below the sediment water interface (BSWI) of 14,740 (+1120/−825) cal yr BP suggests that the core may cover the latest Pleistocene and Holocene depositional history of the lake. Stable isotope values of oxygen and carbon (δ18O and δ13C) on authigenic calcite, diatom assemblages, and sedimentary structures all show consistent hydrological change from initially saline water at the bottom of the core to fresh/brackish water at about 6 m BWSI, back to saline water at 4.3 m. At 4.3 m depth, the bedding and color of the core change abruptly, and the stable- isotope and diatom assemblages indicate a consistently hypersaline lake until near the top of the core, when fresh water entered the lake due to irrigation and canal building in the twentieth century. The stable isotopes of the calcite abruptly change from inversely varying isotopic compositions below 4.3 m depth to covarying above. This break between relatively fresh and saline conditions in the lake occurs during the middle Holocene, although the exact timing of the transition is unknown due to variability in the 14C age determinations. The cause for such an abrupt change is difficult to explain through climate shifts, as evidence suggests climate in the Great Basin was different from what the Big Soda Lake record indicates in the Early Holocene. It is hypothesized that the Walker River flowed to the Carson River basin before 5600 cal yr BP, with water either flowing directly into the lake or raising the groundwater table sufficiently to freshen Big Soda Lake. The initial increase in salinity likely was caused by decreased flow of the Walker River due to Middle Holocene aridity. The lake level lowered slowly, and more saline conditions prevailed until 4.3 m depth when water from the Walker River stopped flowing into the Carson River basin. Above 4.3 m depth, diatom and isotopic evidence indicates that the lake became consistently saline. The isotopic and diatom assemblage transitions observed in Big Soda Lake sediment are not consistent with climate reconstructions and demonstrate that hydrologic shifts in a basin can be an important driver of change regardless of climatic conditions. However, climate shifts may also play a role in the hydrologic changes by supplying more or less water to river courses that may induce river avulsion.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Limnogeology: Progress, challenges and opportunities: A tribute to Elizabeth Gierlowski-Kordesch","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-66576-0_10","usgsCitation":"Rosen, M., Reidy, L.M., Starratt, S.W., and Zimmerman, S., 2021, Middle Holocene hydrologic changes catalyzed by river avulsion in Big Soda Lake, Nevada, USA, chap. of Limnogeology: Progress, challenges and opportunities: A tribute to Elizabeth Gierlowski-Kordesch, p. 295-328, https://doi.org/10.1007/978-3-030-66576-0_10.","productDescription":"34 p.","startPage":"295","endPage":"328","ipdsId":"IP-109894","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":436422,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9INH1ID","text":"USGS data release","linkHelpText":"Mineralogic, grain-size, biologic, and stable isotopic analyses of core TOPGUN-SODA10 2A-K from Big Soda Lake, Nevada, USA"},{"id":413426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Big Soda Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118.87892835971928,\n 39.51788809213036\n ],\n [\n -118.8770297198971,\n 39.51835411876252\n ],\n [\n -118.87495847645494,\n 39.520218194026086\n ],\n [\n -118.87098859319025,\n 39.521849218843585\n ],\n [\n -118.86969406603852,\n 39.52288107190569\n ],\n [\n -118.86891734974776,\n 39.52690848070142\n ],\n [\n -118.86960776422836,\n 39.529504537764296\n ],\n [\n -118.87465642011955,\n 39.532200340442046\n ],\n [\n -118.87780643618821,\n 39.532200340442046\n ],\n [\n -118.87996398144054,\n 39.53123518854974\n ],\n [\n -118.88039549049098,\n 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B.","affiliations":[],"preferred":false,"id":865098,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Park Boush, Lisa 0000-0002-8169-4600","orcid":"https://orcid.org/0000-0002-8169-4600","contributorId":302674,"corporation":false,"usgs":false,"family":"Park Boush","given":"Lisa","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":865099,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Pla-Pueyo, Sila 0000-0003-4884-4096","orcid":"https://orcid.org/0000-0003-4884-4096","contributorId":302677,"corporation":false,"usgs":false,"family":"Pla-Pueyo","given":"Sila","email":"","affiliations":[{"id":33422,"text":"University of Granada","active":true,"usgs":false}],"preferred":false,"id":865100,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Rosen, Michael R. 0000-0003-3991-0522","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":224435,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reidy, Liam M.","contributorId":302678,"corporation":false,"usgs":false,"family":"Reidy","given":"Liam","email":"","middleInitial":"M.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":865075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Starratt, Scott W. 0000-0001-9405-1746 sstarrat@usgs.gov","orcid":"https://orcid.org/0000-0001-9405-1746","contributorId":302679,"corporation":false,"usgs":true,"family":"Starratt","given":"Scott","email":"sstarrat@usgs.gov","middleInitial":"W.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science 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tribute to Elizabeth Gierlowski-Kordesch","docAbstract":"Elizabeth Gierlowski-Kordesch (1956–2016) was a leader and innovator in the specialty field of limnogeology since its beginnings in the late 1980s. Her excitement for field work and examining sediments was contagious, and she was always testing new research ideas. Beth would have been thrilled with the diversity of papers presented in the volume and the wide array of techniques used to determine the history, geochemistry, paleontology, and paleoclimate preserved in the sediments in basins that are located on every continent except Australia and Antarctica. She would also have been delighted that half the chapters were first authored by highly cited women scientists. Beth spent her career teaching, mentoring, conducting research with students and colleagues, and planning limnogeology conferences, books, and field trips. Her contributions span deep-time lakes from North and South America, Africa, Asia, and Europe, starting with her work on the Lower Jurassic East Berlin Formation where she conducted her Ph.D. research. Her work with Kerry Kelts at the University of Minnesota produced two books summarizing global lake research. These volumes are still used by many researchers, particularly as a starting point in their limnogeological studies. Her collaboration with Springer Nature® resulted in the series entitled Syntheses in Limnogeology, a publication that likely would not exist without her enthusiasm and perseverance. The papers in this second volume in the series describe a variety of Jurassic to modern lakes that range from fresh to hypersaline, shallow to deep, vary in size from <1 km2 to 100s of km2, and are found in a number of tectonic settings. Various proxies, including microfossils and trace fossils and analyses of lacustrine sedimentology, stratigraphy, and stable isotopes are used to evaluate the sediment cores and stratigraphic sections to evaluate human and climate influences on the environment, the effects of tectonic, seismic, and volcanic activity, and variations in hydrology. The contributions in this volume reflect the diverse research that Beth conducted herself and we hope is a fitting honor to one of the founding scientists of Limnogeology.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Limnogeology: Progress, challenges and opportunities: A tribute to Elizabeth Gierlowski-Kordesch","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-66576-0_1","usgsCitation":"Rosen, M., Park Boush, L., Finkelstein, D., and Pla-Pueyo, S., 2021, Introduction to limnogeology: Progress, challenges, and opportunities: A tribute to Elizabeth Gierlowski-Kordesch, chap. of Limnogeology: Progress, challenges and opportunities: A tribute to Elizabeth Gierlowski-Kordesch, p. 3-16, https://doi.org/10.1007/978-3-030-66576-0_1.","productDescription":"14 p.","startPage":"3","endPage":"16","ipdsId":"IP-122491","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":413425,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosen, Michael R. 0000-0003-3991-0522","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":224435,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865070,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Park Boush, Lisa 0000-0002-8169-4600","orcid":"https://orcid.org/0000-0002-8169-4600","contributorId":302674,"corporation":false,"usgs":false,"family":"Park Boush","given":"Lisa","email":"","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":865071,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finkelstein, David 0000-0002-9787-1675","orcid":"https://orcid.org/0000-0002-9787-1675","contributorId":302675,"corporation":false,"usgs":false,"family":"Finkelstein","given":"David","email":"","affiliations":[{"id":65529,"text":"Hobart and William Smith Colleges","active":true,"usgs":false}],"preferred":false,"id":865072,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pla-Pueyo, Sila 0000-0003-4884-4096","orcid":"https://orcid.org/0000-0003-4884-4096","contributorId":302677,"corporation":false,"usgs":false,"family":"Pla-Pueyo","given":"Sila","email":"","affiliations":[{"id":33422,"text":"University of Granada","active":true,"usgs":false}],"preferred":false,"id":865073,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220262,"text":"70220262 - 2021 - Habitat suitability index model improvements","interactions":[],"lastModifiedDate":"2021-04-29T13:18:04.649339","indexId":"70220262","displayToPublicDate":"2021-03-31T08:17:18","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Habitat suitability index model improvements","docAbstract":"Habitat suitability index (HSI) models were developed for the 2023 Coastal Master Plan to evaluate the potential effects of coastal restoration and protection projects on habitat for key coastal fish, shellfish, and wildlife species. These species included: eastern oyster, brown shrimp, white shrimp, blue crab, crayfish, gulf menhaden, spotted seatrout, largemouth bass, American alligator, gadwall, mottled duck, brown pelican, seaside sparrow, and bald eagle. Most of these species were included in the 2017 Coastal Master Plan analyses, and the HSI models from that effort were refined and improved following the recommendations described in the technical memorandum: 2023 Coastal Master Plan Habitat Suitability Index Model Improvement Recommendations (Sable et al., 2019). In addition to model improvements, HSI models were created for seaside sparrow and bald eagle, both of which are new species for the master plan analyses. \n\nFor the HSI models that are primarily literature-based, literature reviews were conducted for recent studies that could be used to improve the suitability index (SI) relationships that compose the models. As a result of this review, modifications were made to the salinity-related SIs of the oyster model including: expanding the time period used for salinity effects to spawning; adjusting the range of suitable annual average salinity to be more representative of Louisiana populations; and making oyster’s minimum salinity tolerance temperature dependent. In addition, a new SI was incorporated in the oyster HSI model that accounts for the effects of sediment deposition on oysters. The crayfish HSI model was improved by adjusting the time periods used for the SIs that describe the hydrology required for the crayfish life cycle, and the soil characteristics SI that was part of the 2017 crayfish model was removed because soil conditions do not appear to be limiting for crayfish burrow construction in coastal Louisiana. The other literature-based HSI models from the 2017 Coastal Master Plan, i.e., American alligator, gadwall, mottled duck, and brown pelican, were unchanged, with the exception of a small adjustment made to the suitability of forested wetlands for gadwall. Lastly, a literature-based HSI model was created for seaside sparrow that consists of SIs related to vegetated habitat type, marsh vegetation coverage, and marsh elevation. \n\nStatistical-based HSI models were developed for brown shrimp (both small and large juvenile stages), white shrimp (small and large juvenile stages), blue crab (juvenile stage), gulf menhaden (juvenile and adult stages), spotted seatrout (juvenile and adult stages), largemouth bass, and bald eagle. The bald eagle HSI model was developed from a bald eagle nest probability of occurrence model that related nest occurrence from survey data with land cover type. The resulting model showed that combinations of forested wetlands, flotant marsh, and open water habitats were most suitable for nesting bald eagles. The 2023 fish, shrimp, and blue crab HSI models were developed using new approaches for the formulation of the water quality and structural habitat SIs that compose the models. For the 2017 models, the water quality SI was derived using only generalized linear mixed models (GLMMs) to estimate the relationship between salinity, water temperature, and species’ catch. For the 2023 models, however, multiple GLMMs and generalized additive models (GAMMs) were created for each species or life stage. These alternative models were compared and a single model that performed well statistically and was ecologically reasonable was selected for the species’ water quality SI. The structural habitat SI was developed using a meta-analysis of published literature to estimate the relative importance of various estuarine habitats to the fish and shellfish species. The results of this analysis were then used to modify the 2017 structural habitat SI relationship to account for the added habitat value of submerged aquatic vegetation and oyster reefs, which are also important habitats for juvenile fish and shellfish. Similar to the 2017 fish, shrimp, and blue crab models, the water quality and structural habitat SIs were then combined to create the 2023 HSI models. \n\nThe 2023 Coastal Master Plan HSI models were integrated with the Integrated Compartment Model (and are referred to as ICM-HSIs) and tested using environmental output from the 2017 Coastal Master Plan Future Without Action scenario. The tests showed that, in general, the models produced reasonable representations of species’ habitat distribution. Furthermore, the improvements made to the oyster, crayfish, fish, shrimp, and blue crab HSI models generally yielded more realistic results compared to the 2017 HSI models.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2023 Coastal Master Plan","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Coastal Protection and Restoration Authority","usgsCitation":"Lindquist, D.C., Sable, S.E., D’Acunto, L., Hijuelos, A., Johnson, E.I., Langlois, S.R., Michel, N.L., Nakashima, L., O’Connell, A.M., Percy, K.L., and Robinson, E.M., 2021, Habitat suitability index model improvements, 189 p.","productDescription":"189 p.","ipdsId":"IP-124495","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":385387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385375,"type":{"id":15,"text":"Index Page"},"url":"https://coastal.la.gov/our-plan/2023-coastal-master-plan/technical-resources/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lindquist, David C.","contributorId":257729,"corporation":false,"usgs":false,"family":"Lindquist","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":814929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sable, Shaye E.","contributorId":257728,"corporation":false,"usgs":false,"family":"Sable","given":"Shaye","email":"","middleInitial":"E.","affiliations":[{"id":52096,"text":"Dynamic Solutions, LLC","active":true,"usgs":false}],"preferred":false,"id":814930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"D’Acunto, Laura 0000-0001-6227-0143","orcid":"https://orcid.org/0000-0001-6227-0143","contributorId":215343,"corporation":false,"usgs":true,"family":"D’Acunto","given":"Laura","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":814931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hijuelos, Ann 0000-0003-0922-6754","orcid":"https://orcid.org/0000-0003-0922-6754","contributorId":216667,"corporation":false,"usgs":true,"family":"Hijuelos","given":"Ann","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":814932,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Erik I.","contributorId":257732,"corporation":false,"usgs":false,"family":"Johnson","given":"Erik","email":"","middleInitial":"I.","affiliations":[{"id":52099,"text":"Audubon Louisiana","active":true,"usgs":false}],"preferred":false,"id":814933,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Langlois, Summer R.M","contributorId":257733,"corporation":false,"usgs":false,"family":"Langlois","given":"Summer","email":"","middleInitial":"R.M","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":814934,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Michel, Nicole L.","contributorId":257734,"corporation":false,"usgs":false,"family":"Michel","given":"Nicole","email":"","middleInitial":"L.","affiliations":[{"id":52101,"text":"Audubon Louisiana, National Audubon Society","active":true,"usgs":false}],"preferred":false,"id":814935,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nakashima, Lindsay","contributorId":257735,"corporation":false,"usgs":false,"family":"Nakashima","given":"Lindsay","affiliations":[{"id":52099,"text":"Audubon Louisiana","active":true,"usgs":false}],"preferred":false,"id":814936,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"O’Connell, Ann M.","contributorId":257736,"corporation":false,"usgs":false,"family":"O’Connell","given":"Ann","email":"","middleInitial":"M.","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":814937,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Percy, Katie L.","contributorId":191722,"corporation":false,"usgs":false,"family":"Percy","given":"Katie","email":"","middleInitial":"L.","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":814938,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Robinson, Elizabeth M.","contributorId":257731,"corporation":false,"usgs":false,"family":"Robinson","given":"Elizabeth","email":"","middleInitial":"M.","affiliations":[{"id":40763,"text":"Coastal Protection and Restoration Authority","active":true,"usgs":false}],"preferred":false,"id":814939,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70220217,"text":"70220217 - 2021 - Implications of model selection: A comparison of publicly available, conterminous US-extent hydrologic component estimates","interactions":[],"lastModifiedDate":"2021-04-29T11:57:20.595886","indexId":"70220217","displayToPublicDate":"2021-03-26T08:20:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Implications of model selection: A comparison of publicly available, conterminous US-extent hydrologic component estimates","docAbstract":"Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing (RS) methods and are commonly applied as a supplement to, or a substitute for, in situ measurements when observational data are sparse or unavailable. This study compares estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique data sets generated by 47 hydrologic models, reanalysis data sets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured as the coefficient of variation) ranging from 11 % to 21 % for P, 14 % to 26 % for ET, 28 % to 82 % for R, 76 % to 84 % for SWE, and 36 % to 96 % for SM. Uncertainty between estimates was lower in the eastern CONUS, with medians ranging from 5 % to 14 % for P, 13 % to 22 % for ET, 28 % to 82 % for R, 53 % to 63 % for SWE, and 42 % to 83 % for SM. Interannual trends in estimates from 1982 to 2010 show common disagreement in R, SWE, and SM. Correlating fluxes and stores against remote-sensing-derived products show poor overall correlation in the western CONUS for ET and SM estimates. Study results show that disagreement between estimates can be substantial, sometimes exceeding the magnitude of the measurements themselves. The authors conclude that multimodel ensembles are not only useful but are in fact a necessity for accurately representing uncertainty in research results. Spatial biases of model disagreement values in the western United States show that targeted research efforts in arid and semiarid water-limited regions are warranted, with the greatest emphasis on storage and runoff components, to better describe complexities of the terrestrial hydrologic system and reconcile model disagreement.
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0000-0003-1151-8908","orcid":"https://orcid.org/0000-0003-1151-8908","contributorId":215753,"corporation":false,"usgs":true,"family":"Saxe","given":"Samuel","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":814837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farmer, William H. 0000-0002-2865-2196","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223181,"corporation":false,"usgs":true,"family":"Farmer","given":"William H.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":814838,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":814839,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hogue, Terri S.","contributorId":205175,"corporation":false,"usgs":false,"family":"Hogue","given":"Terri","email":"","middleInitial":"S.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":814840,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220458,"text":"70220458 - 2021 - Rapid-response unsaturated zone hydrology: Small-scale data, small-scale theory, big problems","interactions":[],"lastModifiedDate":"2021-05-14T12:15:26.478434","indexId":"70220458","displayToPublicDate":"2021-03-26T07:08:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7753,"text":"Frontiers in Earth Science","active":true,"publicationSubtype":{"id":10}},"title":"Rapid-response unsaturated zone hydrology: Small-scale data, small-scale theory, big problems","docAbstract":"The unsaturated zone (UZ) extends across the Earth’s terrestrial surface and is central to many problems related to land and water resource management. Flow of water through the UZ is typically thought to be slow and diffusive, such that it could attenuate fluxes and dampen variability between atmospheric inputs and underlying aquifer systems. This would reduce water resource vulnerability to contaminants and water-related hazards. Reducing or negating that effect, however, spatially concentrated and rapid flow and transport through the unsaturated zone is surprisingly common and becoming more so with the increasing frequency and magnitude of extreme hydroclimatic events. Arising from the wide range in the rates and complex modes of nonlinear flow processes, these effects are among the most poorly characterized hydrologic phenomena. Issues of scale present additional difficulties. Equations representing unsaturated processes have been developed and tested on the basis of field and laboratory measurements typically made at scales from pore size to plot size. In contrast, related problems of significant interest to society, including floods, aquifer recharge, landslides, and groundwater contamination, range from watershed to regional scales. The disparity between the scale of our understanding and the scale of interest for societal problems has spurred application of these model equations at increasingly coarse resolutions over larger areas than can be justified by existing measurements or theory. This mismatch in scales requires an assumption that spatially averaging slow diffusive flow and rapid preferential flow can effectively represent the influence of both processes across vast areas. Given the currently inadequate recognition and quantitative characterization of focused and rapid processes in unsaturated flow, these phenomena are critically in need of expanded attention and effort.