Most plant species are spatially aggregated. Local demographic and ecological processes (e.g. vegetative growth and limited seed dispersal) result in a clustered spatial pattern within an environmentally homogenous area. Spatial aggregation should be considered when modelling plant abundance data.
Commonly, plant abundance is quantified by measuring cover within multiple areal plots or along multiple lines randomly placed within a study area. A common practice for analyzing plant cover is to use statistical methods that rely on the normal distribution for quantifying uncertainty. This is problematic because plant cover data tend to be left‐skewed (J‐shaped), right skewed (L‐shaped) or U‐shaped and, therefore, commonly violate classic statistical assumptions, such as normality.
We outline statistical analyses that explicitly account for spatial aggregation by assuming that plant cover is beta‐distributed. The beta distribution is a flexible choice because within the open unit interval it can take on a wide range of shapes (L, J, U, or a bell‐shaped). We discuss and introduce extensions to the beta distribution that address common analysis issues encountered in plant cover datasets, such as i) the treatment of zero and one cover values, ii) hierarchical data structures, and iii) observations errors. For heuristic purposes, we focus on single species analyses, but we demonstrate how the outlined methods can be generalized to more species.
The assumption that plant cover is beta‐distributed allows us to estimate the degree of spatial aggregation, and the ecological significance of this new knowledge is discussed. We provide a summary of available software for analyses (emphasizing standard R packages) and include worked examples and a simulation study comparing analysis options as supplemental information.
Synthesis. Previously, the state of the statistical software made it practically difficult for empirical plant ecologists to analyze their cover data correctly, but new theory and R‐packages have been developed, and this difficulty no longer exists. We recommend that empirical plant ecologists embrace the new statistical possibilities for exploring the exciting ecological features in spatial variation of plant cover.
","language":"English","publisher":"Wiley","doi":"10.1111/1365-2745.13200","usgsCitation":"Damgaard, C., and Irvine, K., 2019, Using the beta distribution to analyze plant cover data: Journal of Ecology, v. 107, no. 6, p. 2747-2759, https://doi.org/10.1111/1365-2745.13200.","productDescription":"13 p.","startPage":"2747","endPage":"2759","onlineOnly":"Y","ipdsId":"IP-097332","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467640,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2745.13200","text":"Publisher Index Page"},{"id":363781,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"107","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Damgaard, Christian","contributorId":215533,"corporation":false,"usgs":false,"family":"Damgaard","given":"Christian","email":"","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":762605,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":214591,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":762604,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202241,"text":"fs20193001 - 2019 - The use of national datasets to produce an average annual water budget for the Mississippi Alluvial Plain, 2000–13","interactions":[],"lastModifiedDate":"2019-05-07T10:09:35","indexId":"fs20193001","displayToPublicDate":"2019-05-06T07:01:22","publicationYear":"2019","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":"2019-3001","displayTitle":"The Use of National Datasets to Produce an Average Annual Water Budget for the Mississippi Alluvial Plain, 2000–13","title":"The use of national datasets to produce an average annual water budget for the Mississippi Alluvial Plain, 2000–13","docAbstract":"Water is a critically important resource for the Mississippi Alluvial Plain (MAP) region, supporting a multibillion-dollar agricultural industry. There are concerns that continued withdrawals of groundwater for irrigation may decrease future water supplies. The U.S. Geological Survey has a history of conducting research in the MAP region and recently began an effort to integrate multiple monitoring analyses and modeling to characterize and project water availability for the region. Here, we utilize the data and results from existing national-scale datasets and refine them to create long-term steady state annual water budgets at a regional scale (the MAP) from 2000 to 2013. The water budget is described and mapped as the distribution of available water into three components: (1) evapotranspiration (65 percent); (2) quickflow runoff to streams (27 percent); and (3) groundwater recharge (8 percent). We also present a comparison of long-term recharge rates with groundwater extraction rates. These results will be useful as a starting point for the water budget and evaluations of future water availability in the MAP.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193001","usgsCitation":"Reitz, M., and Kress, W.H., The use of national datasets to produce an average annual water budget for the Mississippi Alluvial Plain, 2000–13: U.S. Geological Survey Fact Sheet 2019–3001, 4 p., https://doi.org/10.3133/fs20193001.","productDescription":"Report: 4 p.; Data Release","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-095792","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":363443,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3001/coverthb.jpg"},{"id":363444,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3001/fs20193001.pdf","text":"Report","size":"2.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019–3001"},{"id":363447,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PN93P0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Annual estimates of recharge, quick-flow runoff, and ET for the contiguous US using empirical regression equations, 2000–2013"}],"country":"United States","state":"Arkansas, Kentucky, Louisiana, Mississippi, Missouri, Texas","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 -95.3173828125,\n 29.075375179558346\n ],\n [\n -87.5830078125,\n 29.075375179558346\n ],\n [\n -87.5830078125,\n 37.38761749978395\n ],\n [\n -95.3173828125,\n 37.38761749978395\n ],\n [\n -95.3173828125,\n 29.075375179558346\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park Drive
Nashville, TN 37211
The combination of rainfall and snowmelt in northern New England and rainfall in southern New England resulted in minor to major flooding from April 15 to 24, 2019, according to stage and streamflow data collected at 63 selected U.S. Geological Survey (USGS) streamgages. A typical USGS streamgage measures and records stream stage and estimates streamflow based on a relation (rating curve) of discrete measurements of streamflow and the recorded stage. USGS hydrographers were deployed during and after these storms to measure the streamflow of the flooded rivers and confirm streamgage rating curves.
Preliminary Data Indicate...
Director,
New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
A basic understanding of how the landscape impedes, or creates resistance to, the dispersal of organisms and hence gene flow is paramount for successful conservation science and management. Spatially structured ecological networks are often used to represent spatial landscape‐genetic relationships, where nodes represent individuals or populations and resistance to movement is represented using non‐binary edge weights. Weights are typically assigned or estimated by the user, rather than observed, and validating such weights is challenging. We provide a synthesis of current methods used to estimate edge weights and an overview of common model types, stressing the advantages and disadvantages of each approach and their ability to model landscape‐genetic data. We further explore a set of spatial‐statistical methods that provide ecologists with alternative approaches for modeling spatially explicit processes that may affect genetic structure. This includes an overview of spatial autoregressive models, with a particular focus on how correlation and partial correlation are used to represent neighborhood structure with the inverse of the covariance matrix (i.e., precision matrix). We then demonstrate how to model resistance by specifying an appropriate statistical model on the nodes, conditioned on the edge weights, through the precision matrix. This integration of network ecology and spatial statistics provides a practical analytical framework for landscape‐genetic studies. The results can be used to make statistical inferences about the relative importance of individual landscape characteristics, such as the vegetative cover, hillslope, or the presence of roads or rivers, on gene flow. In addition, the R code we include allows readers to explore landscape‐genetic structure in their own datasets, which will potentially provide new insights into the evolutionary processes that generated ecological networks, as well as valuable information about the optimal characteristics of conservation corridors.
Ozark Bass (Ambloplites constellatus) is an understudied, endemic fish species in the Upper White River Basin of northern Arkansas. This study was part of an effort by fisheries managers to gather baseline data about the Ozark Bass to aid in understanding population dynamics and contribute to the limited data available for use in determining the efficacy of harvest regulations. Select population characteristics of Ozark Bass in two northern Arkansas streams were determined, population characteristics of Ozark Bass were compared to Shadow Bass (Ambloplites ariommus) and Rock Bass (Ambloplites rupestris) data collected from previous studies in southern Missouri, and relative condition, length-at-age, and annual survival of Ozark Bass were compared between sample streams. Sampling occurred in Crooked Creek and the Buffalo River during summer 2013 via boat electroshocking. Length and weight data were recorded for all Ozark Bass collected, and fish ages were determined through selective otolith retrieval and age-length keys. Ozark Bass in Crooked Creek had greater relative condition than Ozark Bass in Buffalo River (P < 0.001). Neither Ozark bass lengths nor log-transformed weights differed (P > 0.05) between sexes for fish collected from only the Buffalo River. Ozark Bass mean annual survival was similar between Crooked Creek (55% ± 5% as 95% confidence interval (CI)) and the Buffalo River (50% ± 7% CI) for fish age 2 to 9. Calculated Ozark Bass lengths-at-age for fish from both streams were comparable to the Von Bertalanffy growth estimates, except the Buffalo River age 7 category where there was only one observation. The relationship between Ozark Bass age and length differed between sampled streams, and variability in growth rates and length-at-age were observed among Ambloplites species. Results of this study contribute to the understanding of the population dynamics of the Ozark Bass that will lead to improved fisheries management.
","language":"English","publisher":"Scientific Research","doi":"10.4236/nr.2019.105009","usgsCitation":"Rodman, A.W., Brye, K., Magoulick, D.D., and Todd, S., 2019, Population characteristics of Ozark Bass (Ambloplites constellatus) in the upper White River basin of northern Arkansas: Natural Resources, v. 10, no. 5, p. 121-138, https://doi.org/10.4236/nr.2019.105009.","productDescription":"18 p.","startPage":"121","endPage":"138","ipdsId":"IP-100824","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":467651,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.4236/nr.2019.105009","text":"Publisher Index Page"},{"id":395350,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"upper White River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.96218872070312,\n 35.90684930677121\n ],\n [\n -92.28515625,\n 35.90684930677121\n ],\n [\n -92.28515625,\n 36.25645544252056\n ],\n [\n -92.96218872070312,\n 36.25645544252056\n ],\n [\n -92.96218872070312,\n 35.90684930677121\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rodman, A. W.","contributorId":201402,"corporation":false,"usgs":false,"family":"Rodman","given":"A.","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":832988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brye, K. R.","contributorId":274426,"corporation":false,"usgs":false,"family":"Brye","given":"K. R.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":832989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":832990,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Todd, S.","contributorId":274429,"corporation":false,"usgs":false,"family":"Todd","given":"S.","email":"","affiliations":[{"id":37007,"text":"Arkansas Game and Fish Commission","active":true,"usgs":false}],"preferred":false,"id":832991,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208521,"text":"70208521 - 2019 - A federal-state partnership for mapping Florida's coast and seafloor ","interactions":[],"lastModifiedDate":"2020-02-14T06:54:01","indexId":"70208521","displayToPublicDate":"2019-05-01T06:52:53","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3891,"text":"Coastal Sediments","active":true,"publicationSubtype":{"id":10}},"title":"A federal-state partnership for mapping Florida's coast and seafloor ","docAbstract":"The Florida Coastal Mapping Program, a partnership of state and federal agencies, has a goal of having modern, consistent, high- resolution sea-floor data for all of Florida’s coastal zone in the next decade to support a myriad of coastal zone science and management applications. One of the early steps in the implementation process is to prioritize and justify mapping needs. This is accomplished by holding six regional workshops across the state during which stakeholders and partners are introduced to a newly developed geospatial prioritization tool. Users independently utilize the tool to populate grid cells for a given region, and the result is a cumulative perspective on sea floor mapping priorities to inform future mapping initiatives. The tool also allows participants to indicate their primary use of sea floor mapping data and types of additional data required for their science or management need.","language":"English","publisher":"World Scientific","doi":"10.1142/9789811204487_0184","usgsCitation":"Hapke, C.J., Druyor, R., Baumstark, R.D., Kramer, P., Fitos, E., Fredericks, X., and Fetherston-Resch, E.H., 2019, A federal-state partnership for mapping Florida's coast and seafloor : Coastal Sediments, p. 2150-2158, https://doi.org/10.1142/9789811204487_0184.","productDescription":"9 p.","startPage":"2150","endPage":"2158","ipdsId":"IP-105639","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":372337,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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D.","contributorId":213975,"corporation":false,"usgs":false,"family":"Baumstark","given":"Rene","email":"","middleInitial":"D.","affiliations":[{"id":38947,"text":"FWRI","active":true,"usgs":false}],"preferred":false,"id":782274,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kramer, Philip","contributorId":35911,"corporation":false,"usgs":false,"family":"Kramer","given":"Philip","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":782275,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitos, Ekaterina","contributorId":213977,"corporation":false,"usgs":false,"family":"Fitos","given":"Ekaterina","email":"","affiliations":[{"id":38948,"text":"FDEP","active":true,"usgs":false}],"preferred":false,"id":782277,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fredericks, Xan 0000-0001-7186-6555 afredericks@usgs.gov","orcid":"https://orcid.org/0000-0001-7186-6555","contributorId":2972,"corporation":false,"usgs":true,"family":"Fredericks","given":"Xan","email":"afredericks@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":782276,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fetherston-Resch, Elizabeth H.","contributorId":213974,"corporation":false,"usgs":false,"family":"Fetherston-Resch","given":"Elizabeth","email":"","middleInitial":"H.","affiliations":[{"id":38946,"text":"FIO","active":true,"usgs":false}],"preferred":false,"id":782278,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70212474,"text":"70212474 - 2019 - Distributed fault slip in the eastern California shear zone: Adding pieces to the puzzle near Barstow, California","interactions":[],"lastModifiedDate":"2020-08-18T17:45:58.824039","indexId":"70212474","displayToPublicDate":"2019-04-30T12:38:05","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Distributed fault slip in the eastern California shear zone: Adding pieces to the puzzle near Barstow, California","docAbstract":"We investigate the dextral Lockhart and Mt. General faults, which are among four active structures in the northwestern portion of the eastern California shear zone (ECSZ). Early mapping depicts the Lockhart and Mt. General faults as discontinuous fault traces that continue northwest of the Lenwood Fault. Recent work indicates that the Lenwood Fault slips at ~0.2-1.0 mm/yr over the past ~8 ka and 0.8 ± 0.2 mm/yr since ~37 ± 7 ka. We reconstruct the record of fault slip for the Lockhart and Mt. General faults using high-resolution Structure-from-Motion built topography, field observations, geochronology, and gravity data. Geomorphic offsets along a Holocene-active trace of the Lockhart Fault indicate dextral displacement between ~4 and 6 m. A feldspar infrared stimulated luminescence (IRSL) age implies surface abandonment and at least one earthquake after 3540 ± 880 ka (2σ). The implied Holocene fault slip rate on the Lockhart Fault is between ~0.9 and 2.3 mm/yr. Holocene-active traces of the 19-km-long Mt. General Fault are marked by southwest-facing scarps and dextral offsets of ~4–5 m on alluvial fans, with down-to-the-southwest vertical offset of ~0.3 m. Summing dextral displacements across subparallel fault strands yields a maximum of ~7–8 m. A feldspar IRSL age indicates deposition of the alluvial fans since 11,380 ± 1700 ka (2σ). This results in a Holocene slip ~0.3–0.6 mm/yr, possibly ranging up to 1.0 mm/yr. Taken together, these observations imply a net Holocene dextral slip rate for active faults in Hinkley Valley at 1.2–3.3 mm/yr―higher than expected given published fault slip rates along-strike to the southeast.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Exploring the ends of eras in the eastern Mojave Desert: 2019 Desert symposium field guide and proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2019 Desert Symposium","conferenceDate":"Apr 2019","conferenceLocation":"Zzyzx, CA","language":"English","publisher":"Desert Symposium","usgsCitation":"Haddon, E., Miller, D., Langenheim, V., and Mahan, S.A., 2019, Distributed fault slip in the eastern California shear zone: Adding pieces to the puzzle near Barstow, California, in Exploring the ends of eras in the eastern Mojave Desert: 2019 Desert symposium field guide and proceedings, Zzyzx, CA, Apr 2019, p. 134-140.","productDescription":"7 p.","startPage":"134","endPage":"140","ipdsId":"IP-106799","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":377627,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377626,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.desertsymposium.org/History.html"}],"country":"United States","state":"California","city":"Barstow","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.68505859375,\n 34.62868797377059\n ],\n [\n -116.83959960937499,\n 34.62868797377059\n ],\n [\n -116.83959960937499,\n 35.73090666520053\n ],\n [\n -119.68505859375,\n 35.73090666520053\n ],\n [\n -119.68505859375,\n 34.62868797377059\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Haddon, Elizabeth K. 0000-0001-7601-7755","orcid":"https://orcid.org/0000-0001-7601-7755","contributorId":238720,"corporation":false,"usgs":true,"family":"Haddon","given":"Elizabeth K.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":796411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, David M. 0000-0003-3711-0441","orcid":"https://orcid.org/0000-0003-3711-0441","contributorId":238721,"corporation":false,"usgs":true,"family":"Miller","given":"David M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":796412,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langenheim, Victoria 0000-0003-2170-5213","orcid":"https://orcid.org/0000-0003-2170-5213","contributorId":216217,"corporation":false,"usgs":true,"family":"Langenheim","given":"Victoria","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":796413,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mahan, Shannon A. 0000-0001-5214-7774 smahan@usgs.gov","orcid":"https://orcid.org/0000-0001-5214-7774","contributorId":147159,"corporation":false,"usgs":true,"family":"Mahan","given":"Shannon","email":"smahan@usgs.gov","middleInitial":"A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":796414,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70202643,"text":"ofr20191027 - 2019 - Rigorously valuing the role of U.S. coral reefs in coastal hazard risk reduction","interactions":[],"lastModifiedDate":"2019-04-30T16:00:30","indexId":"ofr20191027","displayToPublicDate":"2019-04-30T10:55:00","publicationYear":"2019","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":"2019-1027","displayTitle":"Rigorously Valuing the Role of U.S. Coral Reefs in Coastal Hazard Risk Reduction","title":"Rigorously valuing the role of U.S. coral reefs in coastal hazard risk reduction","docAbstract":"The degradation of coastal habitats, particularly coral reefs, raises risks by increasing the exposure of coastal communities to flooding hazards. The protective services of these natural defenses are not assessed in the same rigorous economic terms as artificial defenses, such as seawalls, and therefore often are not considered in decision making. Here we combine engineering, ecologic, geospatial, social, and economic tools to provide a rigorous valuation of the coastal protection benefits of all U.S. coral reefs in the States of Hawaiʻi and Florida, the territories of Guam, American Samoa, Puerto Rico, and Virgin Islands, and the Commonwealth of the Northern Mariana Islands. We follow risk-based valuation approaches to map flood zones at 10-square-meter resolution along all 3,100+ kilometers of U.S. reef-lined shorelines for different storm probabilities to account for the effect of coral reefs in reducing coastal flooding. We quantify the coastal flood risk reduction benefits provided by coral reefs across storm return intervals using the latest information from the U.S. Census Bureau, Federal Emergency Management Agency, and Bureau of Economic Analysis to identify their annual expected benefits, a measure of the annual protection provided by coral reefs. Based on these results, the annual protection provided by U.S. coral reefs is estimated in:
Thus, the annual value of flood risk reduction provided by U.S. coral reefs is more than 18,000 lives and \\$1.805 billion in 2010 U.S. dollars. These data provide stakeholders and decision makers with spatially explicit, rigorous valuation of how, where, and when U.S. coral reefs provide critical coastal storm flood reduction benefits. The overall goal is to ultimately reduce the risk to, and increase the resiliency of, U.S. coastal communities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191027","collaboration":"Prepared in cooperation with the University of California Santa Cruz and The Nature Conservancy","usgsCitation":"Storlazzi, C.D., Reguero, B.G., Cole, A.D., Lowe, E., Shope, J.B., Gibbs, A.E., Nickel, B.A., McCall, R.T., van Dongeren, A.R., and Beck, M.W., 2019, Rigorously valuing the role of U.S. coral reefs in coastal hazard risk reduction: U.S. Geological Survey Open-File Report 2019–1027, 42 p., https://doi.org/10.3133/ofr20191027.","productDescription":"Report: vi, 42 p.","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103961","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":363119,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KMH2VX","linkHelpText":"Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the States of Hawaii and Florida, the Territories of Guam, American Samoa, Puerto Rico, and the U.S. Virgin Islands, and the Commonwealth of the Northern Mariana Islands"},{"id":363114,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1027/ofr20191027.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Open-File Report 2019-1027"},{"id":363113,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1027/coverthb.jpg"}],"country":"United States","state":"American Samoa, Commonwealth of the Northern Mariana Islands, Florida, Guam, Hawaii, Puerto Rico, U.S. Virgin Islands","contact":"Contact Information,
Pacific Coastal and Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
Hydrologic influences on water levels were investigated at Three Oaks Recreation Area (TORA), a former sand-and-gravel quarry converted into recreational lakes in Crystal Lake, Illinois. From 2009 to 2015, average water levels in the lakes declined nearly 4 feet. It was not clear if these declines were related to variations in weather (precipitation or evaporation) or other hydrologic influences such as municipal supply pumping or nearby quarry operations. Data were collected using three approaches to determine the possibility of such hydrologic influences. First, water levels were collected at 15 minute intervals at three wells equipped with pressure transducers from April 14 through September 27, 2016. The continuous data allowed assessment of lake and well water level responses to precipitation, pumping influences, and quarry operations. Second, a single-day synoptic water-level survey was completed to create a water table map to determine groundwater flow directions. Third, single-well aquifer tests (slug tests) were completed on the three data-collection wells to estimate the aquifer’s horizontal hydraulic conductivity. Collectively, these data were used to determine the velocity and volume of water entering and exiting TORA.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20185105","collaboration":"Prepared in cooperation with the City of Crystal Lake, Illinois","usgsCitation":"Gahala, A.M., 2019, Hydrologic influences on water levels at Three Oaks Recreation Area, Crystal Lake, Illinois, April 14 through September 27, 2016: U.S. Geological Survey Scientific Investigations Report 2019–5105, 22 p., https://doi.org/10.3133/sir20185105.","productDescription":"Report: vii, 22 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-082111","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":437479,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SA4LZZ","text":"USGS data release","linkHelpText":"Water level test data for groundwater monitoring wells near Three Oaks Recreational Area, Crystal Lake, Illinois"},{"id":362987,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5b801758e4b05f6e32194c4b","text":"USGS data release","description":"USGS data release","linkHelpText":"Water Level Test Data for Groundwater Monitoring Wells Near Three Oaks Recreational Area, Crystal Lake, Illinois"},{"id":362986,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5105/sir20185105.pdf","text":"Report","size":"3.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5105"},{"id":362985,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5105/coverthb.jpg"}],"country":"United States","state":"Illinois","city":"Crystal Lake","otherGeospatial":"Three Oaks Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.32527160644531,\n 42.165184775416826\n ],\n [\n -88.23446273803711,\n 42.165184775416826\n ],\n [\n -88.23446273803711,\n 42.226610675467626\n ],\n [\n -88.32527160644531,\n 42.226610675467626\n ],\n [\n -88.32527160644531,\n 42.165184775416826\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, Wisconsin 53562
A case study of groundwater contamination is a detailed study of a single site contaminated with a chemical or mixture that is known to be a problem at many sites. The goal of case studies is to provide insights into the physical, chemical, and biological processes controlling migration, natural attenuation, or remediation of common groundwater contaminants. Ideally, processes occurring at a case study site are representative of other sites so that knowledge gained from these intensive studies can be applied at thousands of sites where fewer data are available. Several characteristics of case studies contribute to their value. First, they may have tens to hundreds of monitoring wells, compared to fewer than ten wells at some contaminated sites. Second, some case studies continue for many years or even decades, providing insights into temporal progression of slow processes. Third, analytical methods prohibitively expensive for routine use or under development may be tested at case study sites. Finally, the ongoing characterization typical of case study sites builds a foundation of knowledge that facilitates sophisticated experimental design and testing of new methods. This article is divided into sections based on the contaminant type because the chemical and biological processes required for remediation vary for each contaminant. Most importantly, some contaminants can be biodegraded whereas metals and radionuclides cannot be destroyed but can be immobilized or rendered less toxic. The emphasis is on case studies of natural processes that control the fate and transport of contaminants in groundwater rather than on active remediation methods. The principles learned from these studies may form the basis for design of remedial strategies. The organic contaminants are divided into: petroleum hydrocarbons, fuel oxygenates, coal tar and wastes from manufactured gas plants, and chlorinated solvents. The inorganic contaminants covered are metals and radionuclides, arsenic, and nitrate. Case studies of mixed waste plumes from landfills are also described. Experimental sites where contaminants have been introduced into an aquifer as an emplaced source or a controlled release may not meet the above definition of case studies, but some are included because the overall goal is to impart lessons learned from detailed field studies. It is impossible to cover all case studies in this short format. Conversely, focusing on one or two does not convey the breadth of research results in entire range of case studies. Instead, the strategy is to describe the evolution of knowledge for each contaminant class while providing citations of relevant case studies. Much of the progress in understanding of the fate of contaminants in groundwater is based on laboratory studies; thus whenever possible, papers that included both field and laboratory results have been included among the citations. Two topics of growing importance have not been covered. These are the fate of pharmaceuticals in groundwater and discharge of contaminant plumes to surface water. These topics merit coverage in the future as knowledge grows and case studies increase in number.
During 2015–17, the U.S. Geological Survey, in cooperation with the U.S. Department of Agriculture Forest Service (Forest Service), carried out a study to characterize the hydrology and water chemistry in two study areas within the Daniel Boone National Forest. One study area was within the Rock Creek drainage and the other study area included the Wildcat and Addison Branch drainages. Both study areas historically were mined for coal prior to the Surface Mining Control and Reclamation Act of 1977 and contain abandoned coal mine sites that have since been the focus of remediation efforts. Synoptic surveys of streamflow and water-quality properties (water temperature, pH, specific conductance, and dissolved oxygen) of Rock Creek were done during November 2015 and May 2016, and surveys of Wildcat and Addison Branches were done during June 2016 and May 2017. Streamflow measurements were used to quantify contributions from tributaries and to compute streamflow gain and loss in designated reaches. Discrete measurements of water temperature, pH, specific conductance, and dissolved oxygen were used to evaluate conditions during a short timeframe and for comparison between study areas. Study designs for the two study areas differed because there was an operating streamgage on Rock Creek near Yamacraw, Kentucky (station number 03410590) where streamflow and water-quality properties (water temperature, specific conductance, pH, dissolved oxygen, and turbidity) were monitored continuously, while Addison and Wildcat Branches were ungaged. Several hydrograph separation methods were used to estimate base flow and runoff at the Rock Creek gage. These data will be used by the Forest Service to evaluate the current (2018) conditions and plan remediation efforts.
The water quality at Rock Creek was less affected by acid mine drainage (AMD) than the Wildcat or Addison Branches. Appreciable losing reaches, where water flowed underground, were identified in both study areas. All losing reaches coincided with karst topography. Streamflow increased in areas with openings to underground mine tunnels, known as portals.
Six hydrograph separation methods (Base-flow index [BFI; standard and modified], HYSEP [fixed interval, sliding interval, and local minimum], and PART) were applied to daily mean streamflow collected from August 2015 to August 2017 at station number 03410590. The hydrograph separation methods partition total streamflow into base flow and streamflow that originated from surface runoff. Base flow typically reacts slowly to precipitation infiltration and is largely sustained by groundwater discharge. The estimated daily base flow and runoff made with the different separation methods are not highly different. On average, base flow accounted for more total streamflow than surface runoff during the study period, irrespective of method.
Water temperature, pH, dissolved oxygen, specific conductance, and turbidity values were measured from July 2016 through July 2017 with a continuous monitor installed at station number 03410590. Nearly neutral pH values that ranged from 6.8 to 7.9 standard units likely limited metal solubility in the surface water. The continuous specific conductance values ranged between 30 and 259 microsiemens per centimeter at 25 degrees Celsius. The previous remediation efforts are likely continuing to improve the effect of AMD in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195006","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture Forest Service ","usgsCitation":"Cherry, M.A., 2019, Streamflow gain and loss, hydrograph separation, and water quality of abandoned mine lands in the Daniel Boone National Forest, eastern Kentucky, 2015–17: U.S. Geological Survey Scientific Investigations Report 2019–5006, 36 p., https://doi.org/10.3133/sir20195006.","productDescription":"Report: viii, 36 p.;Data Release","numberOfPages":"49","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091989","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":363195,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5006/sir20195006.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5006"},{"id":363196,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX78D9","text":"USGS data release","description":"USGS data release","linkHelpText":"Streamflow and water-quality data for selected streams in the Daniel Boone National Forest, eastern Kentucky, 2015–17"},{"id":363194,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5006/coverthb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Daniel Boone National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.67437744140625,\n 36.63536611993544\n ],\n [\n -84.20333862304688,\n 36.63536611993544\n ],\n [\n -84.20333862304688,\n 37.004746084814784\n ],\n [\n -84.67437744140625,\n 37.004746084814784\n ],\n [\n -84.67437744140625,\n 36.63536611993544\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
9818 Bluegrass Parkway
Louisville, KY 40299
We assess monthly temperature and precipitation data produced by four statistically based techniques that were used to downscale general circulation models (GCMs) in the Climate Model Intercomparison Program Phase 5 (CMIP5) (Taylor et al., 2012). We drive a simple water-balance model with the downscaled data to demonstrate the effect of the methods on the cold season hydrology of three, snow dominated regions in the western U.S. Independent of substantial variation among the GCM simulations over the regions (maximum range of ~3.5 °C and 50% change in precipitation), the four methods produce disparate high resolution representations of the magnitude and spatial patterns of future temperature and precipitation simulated by the models that range for up to ~3 °C and 30% change in precipitation that propagate into the hydrologic simulations. Temperature-dependent snowfall, accumulation, and melt in the model are sensitive to how atmospheric lapse rates are applied in the gridded observations that are used to remove the bias in raw GCM temperatures. By the end of the century the same downscaling method (Bias Corrected Spatial Disaggregation) yields a loss of cold-season snowpack of 34% over the Greater Yellowstone Area under a constant lapse rate ( 6.5°C km-1), whereas spatially variable lapse rates nearly double the loss to 66%, highlighting the roll of both lapse rates and high elevation stations in the bias correction dataset. The two newest downscaling methods (Multivariate Adaptive Constructed Analogs and Localized Constructed Analogs) preserve the magnitude of change simulated GCMs better than the other methods and the produce comparable hydrologic projections. Because the downscaled data from the methods vary spatially and by GCM, the downscaled data should be evaluated carefully as part of the process of using downscaled climate products to drive hydrological models over the area of interest.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR023458","usgsCitation":"Alder, J.R., and Hostetler, S.W., 2019, The dependence of hydroclimate projections in snow‐dominated regions of the western United States on the choice of statistically downscaled climate data: Water Resources Research, v. 55, no. 3, p. 2279-2300, https://doi.org/10.1029/2018WR023458.","productDescription":"22 p.","startPage":"2279","endPage":"2300","ipdsId":"IP-097120","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":437482,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9EB1C","text":"USGS data release","linkHelpText":"Data Release for The dependence of hydroclimate projections in snow-dominated regions of the western U.S. on the choice of statistically downscaled climate data"},{"id":363240,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana,Nevada, New Mexico, Oregon, Washington, Wyoming","otherGeospatial":"Columbia River Basin, Greater Yellowstone Area, Sierra Nevada, Upper Colorado Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.33300781249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 34.66935854524543\n ],\n [\n -104.80957031249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 48.69096039092549\n ],\n [\n -121.33300781249999,\n 34.66935854524543\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"3","noUsgsAuthors":false,"publicationDate":"2019-03-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Alder, Jay R. 0000-0003-2378-2853 jalder@usgs.gov","orcid":"https://orcid.org/0000-0003-2378-2853","contributorId":5118,"corporation":false,"usgs":true,"family":"Alder","given":"Jay","email":"jalder@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":761576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetler, Steven W. 0000-0003-2272-8302 swhostet@usgs.gov","orcid":"https://orcid.org/0000-0003-2272-8302","contributorId":3249,"corporation":false,"usgs":true,"family":"Hostetler","given":"Steven","email":"swhostet@usgs.gov","middleInitial":"W.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":761577,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203422,"text":"70203422 - 2019 - Improving estimates of coral reef construction and erosion with in-situ measurements","interactions":[],"lastModifiedDate":"2019-09-16T12:15:11","indexId":"70203422","displayToPublicDate":"2019-04-25T12:37:50","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Improving estimates of coral reef construction and erosion with in-situ measurements","docAbstract":"The decline in living coral since the 1970s has conspicuously slowed reef construction on a global scale, but the related process of reef erosion is less visible and not often quantified. Here we present new data on the constructional and deconstructional side of the carbonate-budget equation in the Florida Keys, U.S.A. We documented Orbicella spp. calcification rates at four offshore reefs and quantified decadal-scale rates of Orbicella-reef erosion at a mid-shore patch reef. Using Orbicella coral heads fitted with permanent markers in 1998, we measured reef-elevation loss at 28 stations over 17.3 years to estimate a mean erosion rate of -5.5 (± 3.2, SD) mm yr-1. This loss equates to an erosion rate of -8.2 (± 4.8, SD) kg m-2 yr-1 on dead Orbicella colonies, or -6.6 kg m-2 yr-1 when adjusted reef-wide. Calculating net carbonate production using a census-based approach on the same patch reef in 2017, we estimated a reef-wide bioerosion rate of -1.9 (± 2.0, SD) kg m-2 yr-1, and a net carbonate production rate of 0.5 (± 0.3, SD) kg m-2 yr-1. Substituting the erosion rate we estimated with the markers would suggest that net carbonate production at this patch reef was lower and negative, -4.2 kg m-2 yr-1. This divergence could be a function of high erosion rates measured on the tops of Orbicella colonies, which may be preferentially targeted by parrotfish. Nonetheless, our study suggests the need for new field data to improve estimates of reef-structure persistence as coral reefs continue to degrade.","language":"English","publisher":"ASLO","doi":"10.1002/lno.11184","usgsCitation":"Kuffner, I.B., Toth, L., Hudson, J.H., Goodwin, W.B., Stathakopoulos, A., Bartlett, L., and Whitcher, E.M., 2019, Improving estimates of coral reef construction and erosion with in-situ measurements: Limnology and Oceanography, v. 64, no. 5, p. 2283-2294, https://doi.org/10.1002/lno.11184.","productDescription":"12 p.","startPage":"2283","endPage":"2294","ipdsId":"IP-101455","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":467672,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.11184","text":"Publisher Index Page"},{"id":437483,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92NVINW","text":"USGS data release","linkHelpText":"Experimental Data on Construction and Erosion of Orbicella Coral Reefs in the Florida Keys, U.S.A."},{"id":363776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.72430419921875,\n 24.084081797317943\n ],\n [\n -80.01068115234375,\n 24.084081797317943\n ],\n [\n -80.01068115234375,\n 26.165298896316042\n ],\n [\n -82.72430419921875,\n 26.165298896316042\n ],\n [\n -82.72430419921875,\n 24.084081797317943\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"64","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Kuffner, Ilsa B. 0000-0001-8804-7847 ikuffner@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7847","contributorId":3105,"corporation":false,"usgs":true,"family":"Kuffner","given":"Ilsa","email":"ikuffner@usgs.gov","middleInitial":"B.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762632,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Toth, Lauren T. 0000-0002-2568-802X ltoth@usgs.gov","orcid":"https://orcid.org/0000-0002-2568-802X","contributorId":181748,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren","email":"ltoth@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762633,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hudson, J. Harold","contributorId":214860,"corporation":false,"usgs":false,"family":"Hudson","given":"J.","email":"","middleInitial":"Harold","affiliations":[{"id":39127,"text":"Reef Tech, Inc.","active":true,"usgs":false}],"preferred":false,"id":762634,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goodwin, William B.","contributorId":214861,"corporation":false,"usgs":false,"family":"Goodwin","given":"William","email":"","middleInitial":"B.","affiliations":[{"id":39128,"text":"NOAA Florida Keys National Marine Sanctuary,","active":true,"usgs":false}],"preferred":false,"id":762635,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stathakopoulos, Anastasios 0000-0002-4404-035X astathakopoulos@usgs.gov","orcid":"https://orcid.org/0000-0002-4404-035X","contributorId":147744,"corporation":false,"usgs":true,"family":"Stathakopoulos","given":"Anastasios","email":"astathakopoulos@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762636,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bartlett, Lucy 0000-0001-6603-7090","orcid":"https://orcid.org/0000-0001-6603-7090","contributorId":214863,"corporation":false,"usgs":true,"family":"Bartlett","given":"Lucy","email":"","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762637,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Whitcher, Elizabeth M.","contributorId":214862,"corporation":false,"usgs":false,"family":"Whitcher","given":"Elizabeth","email":"","middleInitial":"M.","affiliations":[{"id":17748,"text":"Florida Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":762638,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70204235,"text":"70204235 - 2019 - Factors affecting prey availability and habitat usage of nonbreeding piping plovers (Charadrius melodus) in coastal Louisiana","interactions":[],"lastModifiedDate":"2019-07-16T10:17:07","indexId":"70204235","displayToPublicDate":"2019-04-25T10:09:19","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2220,"text":"Journal of Coastal Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Factors affecting prey availability and habitat usage of nonbreeding piping plovers (Charadrius melodus) in coastal Louisiana","title":"Factors affecting prey availability and habitat usage of nonbreeding piping plovers (Charadrius melodus) in coastal Louisiana","docAbstract":"The Gulf of Mexico is home to a large proportion of the wintering population of the threatened piping plover (Charadrius melodus), but little is known about the bird's ecology in this region. In Louisiana, the majority of nonbreeding piping plovers are found on the state's rapidly eroding barrier islands. Between August 2013 and May 2014, surveys were conducted to assess the abundance and habitat use of piping plovers, as well as to characterize their invertebrate prey base, on Whiskey and Trinity islands. Seventy-eight percent of piping plovers observed were foraging, 18% roosting, and 4% engaged in other ambulatory activities. Intertidal habitat, such as foreshore beach and tidal flats, was used by 87% of foraging and 96% of roosting piping plovers. Though available, backshore beach, interior sand flats, and dunes were rarely used. The invertebrate community was dominated by haustoriid amphipods (87.5% of individuals collected), followed by bivalves (9.3%) and polychaetes (2.7%). Seasonal patterns and between-island differences were observed in all three invertebrate taxa, but these effects differed between beach habitat and the gulfside and bayside of prominent sand spits. Moisture had a positive effect on amphipod abundance and polychaete presence. There was no association between invertebrate and plover abundance, and prey abundance did not differ between sample sites where piping plovers were observed foraging and random sites. The low abundances of birds and prey, coupled with high variation among samples, are challenges for establishing baseline datasets to evaluate the consequences of coastal restoration activities.
","language":"English","publisher":"BioOne","doi":"10.2112/JCOASTRES-D-17-00147.1","usgsCitation":"Schulz, J.L., and Leberg, P., 2019, Factors affecting prey availability and habitat usage of nonbreeding piping plovers (Charadrius melodus) in coastal Louisiana: Journal of Coastal Research, v. 35, no. 4, p. 861-871, https://doi.org/10.2112/JCOASTRES-D-17-00147.1.","productDescription":"11 p.","startPage":"861","endPage":"871","ipdsId":"IP-089847","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":437485,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RJ4GP7","text":"USGS data release","linkHelpText":"Factors affecting prey availability and habitat use of nonbreeding piping plovers (Charadrius melodus) in coastal Louisiana"},{"id":365573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","county":"Terrebonne Parish","otherGeospatial":"Isles Dernières Barrier Island Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.021728515625,\n 28.954080659357132\n ],\n [\n -90.42022705078125,\n 28.954080659357132\n ],\n [\n -90.42022705078125,\n 29.28160772298835\n ],\n [\n -91.021728515625,\n 29.28160772298835\n ],\n [\n -91.021728515625,\n 28.954080659357132\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schulz, Jessica L. 0000-0002-8311-9423 jschulz@usgs.gov","orcid":"https://orcid.org/0000-0002-8311-9423","contributorId":200299,"corporation":false,"usgs":true,"family":"Schulz","given":"Jessica","email":"jschulz@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":766166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leberg, Paul","contributorId":216903,"corporation":false,"usgs":false,"family":"Leberg","given":"Paul","affiliations":[{"id":7155,"text":"University of Louisiana at Lafayette","active":true,"usgs":false}],"preferred":false,"id":766167,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203211,"text":"70203211 - 2019 - Cloud cover and delayed herbivory relative to timing of spring onset interact to dampen climate change impacts on net ecosystem exchange in a coastal Alaskan wetland","interactions":[],"lastModifiedDate":"2019-09-18T15:24:01","indexId":"70203211","displayToPublicDate":"2019-04-25T08:23:14","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Cloud cover and delayed herbivory relative to timing of spring onset interact to dampen climate change impacts on net ecosystem exchange in a coastal Alaskan wetland","docAbstract":"Rapid warming in northern ecosystems over the past four decades has resulted in earlier spring, increased precipitation, and altered timing of plant–animal interactions, such as herbivory. Advanced spring phenology can lead to longer growing seasons and increased carbon (C) uptake. Greater precipitation coincides with greater cloud cover possibly suppressing photosynthesis. Timing of herbivory relative to spring phenology influences plant biomass. None of these changes are mutually exclusive and their interactions could lead to unexpected consequences for Arctic ecosystem function. We examined the influence of advanced spring phenology, cloud cover, and timing of grazing on C exchange in the Yukon–Kuskokwim Delta of western Alaska for three years. We combined advancement of the growing season using passive-warming open-top chambers (OTC) with controlled timing of goose grazing (early, typical, and late season) and removal of grazing. We also monitored natural variation in incident sunlight to examine the C exchange consequences of these interacting forcings. We monitored net ecosystem exchange of C (NEE) hourly using an autochamber system. Data were used to construct daily light curves for each experimental plot and sunlight data coupled with a clear-sky model was used to quantify daily and seasonal NEE over a range of incident sunlight conditions. Cloudy days resulted in the largest suppression of NEE, reducing C uptake by approximately 2 g C m−2 d−1 regardless of the timing of the season or timing of grazing. Delaying grazing enhanced C uptake by approximately 3 g C m−2 d−1. Advancing spring phenology reduced C uptake by approximately 1.5 g C m−2 d−1, but only when plots were directly warmed by the OTCs; spring advancement did not have a long-term influence on NEE. Consequently, the two strongest drivers of NEE, cloud cover and grazing, can have opposing effects and thus future growing season NEE will depend on the magnitude of change in timing of grazing and incident sunlight.
","language":"English","publisher":"IOPscience","doi":"10.1088/1748-9326/ab1c91","usgsCitation":"Leffler, J., Beard, K.H., Kelsey, K.C., Choi, R.T., Schmutz, J.A., and Welker, J., 2019, Cloud cover and delayed herbivory relative to timing of spring onset interact to dampen climate change impacts on net ecosystem exchange in a coastal Alaskan wetland: Environmental Research Letters, v. 14, no. 8, 084030, 11 p., https://doi.org/10.1088/1748-9326/ab1c91.","productDescription":"084030, 11 p.","ipdsId":"IP-103167","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":467674,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab1c91","text":"Publisher Index Page"},{"id":363281,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -140.625,\n 69.83962194067463\n ],\n [\n -157.58789062499997,\n 72.28906720017675\n ],\n [\n -166.2890625,\n 69.80930869552193\n ],\n [\n -168.310546875,\n 66.23145747862573\n ],\n [\n -167.607421875,\n 61.270232790000634\n ],\n [\n -158.90625,\n 55.1286490684888\n ],\n [\n -151.171875,\n 56.218923189166624\n ],\n [\n -147.91992187499997,\n 59.5343180010956\n ],\n [\n -143.525390625,\n 59.44507509904714\n ],\n [\n -134.6484375,\n 53.12040528310657\n ],\n [\n -129.814453125,\n 50.84757295365389\n ],\n [\n -129.28710937499997,\n 53.38332836757156\n ],\n [\n -128.32031249999997,\n 55.62799595426723\n ],\n [\n -134.736328125,\n 59.93300042374631\n ],\n [\n -139.482421875,\n 60.673178565817715\n ],\n [\n -140.625,\n 69.83962194067463\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-08-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Leffler, Josh","contributorId":215123,"corporation":false,"usgs":false,"family":"Leffler","given":"Josh","email":"","affiliations":[{"id":39181,"text":"South Dakota State","active":true,"usgs":false}],"preferred":false,"id":761682,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beard, Karen H.","contributorId":205934,"corporation":false,"usgs":false,"family":"Beard","given":"Karen","email":"","middleInitial":"H.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":761683,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kelsey, Katharine C.","contributorId":195397,"corporation":false,"usgs":false,"family":"Kelsey","given":"Katharine","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":761684,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choi, Ryan T.","contributorId":205936,"corporation":false,"usgs":false,"family":"Choi","given":"Ryan","email":"","middleInitial":"T.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":761686,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmutz, Joel A. 0000-0002-6516-0836 jschmutz@usgs.gov","orcid":"https://orcid.org/0000-0002-6516-0836","contributorId":1805,"corporation":false,"usgs":true,"family":"Schmutz","given":"Joel","email":"jschmutz@usgs.gov","middleInitial":"A.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":761681,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Welker, Jeffrey","contributorId":214926,"corporation":false,"usgs":false,"family":"Welker","given":"Jeffrey","affiliations":[{"id":37194,"text":"University of Alaska Anchorage","active":true,"usgs":false}],"preferred":false,"id":761685,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236837,"text":"70236837 - 2019 - S2HM of buildings in USA","interactions":[],"lastModifiedDate":"2022-10-06T15:53:46.029855","indexId":"70236837","displayToPublicDate":"2019-04-25T08:10:19","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"displayTitle":"S2HM of buildings in USA","title":"S2HM of buildings in USA","docAbstract":"The evolution of seismic structural-health monitoring (S2HM) of buildings in the USA is described in this chapter, emphasizing real-time monitoring. Rapid and accurate assessment of post-earthquake building damage is of paramount importance to stakeholders (including owners, occupants, city officials, and rescue teams). Relying merely on rapid visual inspection could result in serious damage being missed because it is hidden by building finishes and fireproofing. Absent visible damage to a building’s frame, most steel or reinforced-concrete moment-frame buildings will be green-tagged based on limited visual indications of deformation, such as damage to partitions or glazing. Contrary, uncertainty in judging extent of structural damage may lead an inspector toward a relatively conservative tag, such as a red tag. In such cases, expensive, intrusive, and time-consuming inspections may be recommended to building owners (e.g., following the Mw 6.7 1994 Northridge, Calif., earthquake, approximately 300 buildings were subjected to costly inspection of connections (FEMA 352)). Using real-time data-driven computation of drift ratios as the parametric indicator of structural deformation and damage to a structure could be of great value to minimize potential judgmental errors in such assessments. Recorded sensor data are an indication of performance, and performance-based design standards stipulate that the amplitude of relative displacement of a building’s roof (with respect to its base) indicates performance. Establishing sound criteria for performance is the most important issue for S2HM process, and since 2000 (in the USA), using real-time computed drift ratios and acceptable threshold criteria form the basis for almost all applications in S2HM.
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time”","interactions":[],"lastModifiedDate":"2019-04-25T06:29:42","indexId":"70203185","displayToPublicDate":"2019-04-25T06:23:25","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”","docAbstract":"Over the last decade, studies at the Shale Hills Critical Zone Observatory (Shale Hills) have greatly expanded knowledge of weathering in previously understudied, shale-mantled terrains, as well as Earth's Critical Zone as a whole. Among the many discoveries made was the importance of redistribution and losses of micron-sized particles during development of shale-derived soils. A geochemical fingerprint of this process for Al and Fe was illustrated quantitatively by Jin et al. (2010). Subsequent papers, too numerous to list in a Comment, built upon this new recognition by evaluating the spatial and temporal aspects element mobilization. Recently, Kim et al. (2018) examined the composition of suspended, generally micron-sized particles in the Shale Hills stream, along with the dissolved load, across seasons and ranges of discharge.
One prominent conclusion from Kim et al. (2018) is that Zr is essentially immobile at Shale Hills. Such a broad conclusion is in direct contradiction with one from Bern and Yesavage (2018) that Zr has been mobilized from soils at Shale Hills, and the losses relative to soil parent material are significant (median 41%). The point is important, because assuming Zr immobility is necessary to index gains and losses of other elements using the open-chemical-system transport function (τ). Both papers draw upon patterns and calculations using elemental concentration data from Shale Hills and attempt to construct conceptual frameworks to explain the results. Here, the argument is made that the understanding of substantial Zr mobility from soils at Shale Hills described by Bern and Yesavage (2018) is more accurate. Additionally, issues with adaptations of the standard τ equations used in Kim et al. (2018) and some previous papers are also addressed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2019.02.014","usgsCitation":"Bern, C.R., and Yesavage, T., 2019, Comment on “Particle fluxes in groundwater change subsurface rock chemistry over geologic time”: Earth and Planetary Science Letters, v. 514, p. 166-168, https://doi.org/10.1016/j.epsl.2019.02.014.","productDescription":"3 p.","startPage":"166","endPage":"168","ipdsId":"IP-102182","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":363221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Shale Hills Critical Zone Observatory ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.17596435546875,\n 40.4323142901375\n ],\n [\n -77.33001708984375,\n 40.4323142901375\n ],\n [\n -77.33001708984375,\n 40.967455873296714\n ],\n [\n -78.17596435546875,\n 40.967455873296714\n ],\n [\n -78.17596435546875,\n 40.4323142901375\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"514","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bern, Carleton R. 0000-0002-8980-1781 cbern@usgs.gov","orcid":"https://orcid.org/0000-0002-8980-1781","contributorId":201152,"corporation":false,"usgs":true,"family":"Bern","given":"Carleton","email":"cbern@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":761537,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yesavage, Tiffany 0000-0001-9433-763X","orcid":"https://orcid.org/0000-0001-9433-763X","contributorId":215057,"corporation":false,"usgs":false,"family":"Yesavage","given":"Tiffany","email":"","affiliations":[{"id":39167,"text":"USGS Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":false}],"preferred":false,"id":761538,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203191,"text":"70203191 - 2019 - Geomorphic change and biogeomorphic feedbacks in a dryland river: The Little Colorado River, Arizona, USA","interactions":[],"lastModifiedDate":"2019-04-26T17:20:45","indexId":"70203191","displayToPublicDate":"2019-04-24T17:11:18","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1723,"text":"GSA Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Geomorphic change and biogeomorphic feedbacks in a dryland river: The Little Colorado River, Arizona, USA","docAbstract":"The Little Colorado River in Arizona, U.S.A. has undergone substantial geomorphic change since the early 1900s. We analyzed hydrologic and geomorphic data at different spatial and temporal scales to determine the type, magnitude, and rate of geomorphic change that has occurred since the early 20th century. Since the 1920s, there have been 4 alternating periods of high and low total-annual flow. Peak-flow magnitude, however, has progressively declined. In some reaches, the channel has narrowed between 72 and 88% since the 1930s. Increases in sinuosity in wide alluvial valleys have resulted in reductions in channel slope by ~21 to 32%; channel bed aggradation up to 1.4 m has also occurred in some reaches. Newly developed floodplains have been colonized by dense stands of vegetation that appear to have stabilized these surfaces. Large, long duration floods may cause some channel widening, and meander migration, however, these floods are infrequent, and narrowing resumes shortly thereafter. Channel narrowing, increases in sinuosity, decreases in slope, and increases in vegetative roughness appear to have caused biogeomorphic feedbacks, thereby exacerbating sediment deposition, and disrupting flood conveyance. In recent decades, there has been an increase in the travel time of floods up to ~100% compared to floods of the 1940s and 1950s, and this has likely led to increased flood attenuation, contributing to decreases in peak-flow magnitude. The progressive increase in water development in parts of the basin has also likely played some role in the progressive declines in peak flow over the duration of the study.
","language":"English","publisher":"The Geological Society of America","doi":"10.1130/B35047.1","usgsCitation":"Dean, D.J., and Topping, D.J., 2019, Geomorphic change and biogeomorphic feedbacks in a dryland river: The Little Colorado River, Arizona, USA: GSA Bulletin, Repository Item: 2019158; 23 p., https://doi.org/10.1130/B35047.1.","productDescription":"Repository Item: 2019158; 23 p.","ipdsId":"IP-099021","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437486,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XPWIBM","text":"USGS data release","linkHelpText":"Geomorphic Change Data for the Little Colorado River, Arizona, USA"},{"id":363278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Little Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.412353515625,\n 35.54116627999815\n ],\n [\n -107.830810546875,\n 35.54116627999815\n ],\n [\n -107.830810546875,\n 37.13404537126446\n ],\n [\n -111.412353515625,\n 37.13404537126446\n ],\n [\n -111.412353515625,\n 35.54116627999815\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Dean, David J. 0000-0003-0203-088X djdean@usgs.gov","orcid":"https://orcid.org/0000-0003-0203-088X","contributorId":215067,"corporation":false,"usgs":true,"family":"Dean","given":"David","email":"djdean@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761569,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Topping, David J. 0000-0002-2104-4577","orcid":"https://orcid.org/0000-0002-2104-4577","contributorId":215068,"corporation":false,"usgs":true,"family":"Topping","given":"David","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":761570,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203195,"text":"70203195 - 2019 - Modeling barrier island habitats using landscape position information","interactions":[],"lastModifiedDate":"2019-08-19T16:53:07","indexId":"70203195","displayToPublicDate":"2019-04-24T16:23:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Modeling barrier island habitats using landscape position information","docAbstract":"Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.","language":"English","publisher":"MDPI","doi":"10.3390/rs11080976","usgsCitation":"Enwright, N., Lei Wang, Wang, H., Osland, M., Feher, L., Borchert, S., and Day, R., 2019, Modeling barrier island habitats using landscape position information: Remote Sensing, v. 11, no. 8, Article 976; 24 p., https://doi.org/10.3390/rs11080976.","productDescription":"Article 976; 24 p.","ipdsId":"IP-105601","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":460395,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11080976","text":"Publisher Index Page"},{"id":437488,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90MACYS","text":"USGS data release","linkHelpText":"Modeling barrier island habitats using landscape position information for Dauphin Island, Alabama"},{"id":363276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"8","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":215077,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lei Wang","contributorId":215078,"corporation":false,"usgs":false,"family":"Lei Wang","affiliations":[{"id":39170,"text":"Department of Geography and Anthropology, Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":761586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Hongqing 0000-0002-2977-7732 wangh@usgs.gov","orcid":"https://orcid.org/0000-0002-2977-7732","contributorId":215079,"corporation":false,"usgs":true,"family":"Wang","given":"Hongqing","email":"wangh@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Osland, Michael 0000-0001-9902-8692","orcid":"https://orcid.org/0000-0001-9902-8692","contributorId":215080,"corporation":false,"usgs":true,"family":"Osland","given":"Michael","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Feher, Laura 0000-0002-5983-6190","orcid":"https://orcid.org/0000-0002-5983-6190","contributorId":215081,"corporation":false,"usgs":true,"family":"Feher","given":"Laura","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Borchert, Sinéad M. 0000-0002-6665-7115","orcid":"https://orcid.org/0000-0002-6665-7115","contributorId":193278,"corporation":false,"usgs":false,"family":"Borchert","given":"Sinéad M.","affiliations":[],"preferred":false,"id":761590,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Day, Richard 0000-0002-5959-7054","orcid":"https://orcid.org/0000-0002-5959-7054","contributorId":215082,"corporation":false,"usgs":true,"family":"Day","given":"Richard","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":761591,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202839,"text":"sir20195022 - 2019 - Calibration of Precipitation-Runoff Modeling System (PRMS) to simulate prefire and postfire hydrologic response in the upper Rio Hondo Basin, New Mexico","interactions":[],"lastModifiedDate":"2019-04-26T14:47:08","indexId":"sir20195022","displayToPublicDate":"2019-04-24T13:17:01","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5022","displayTitle":"Calibration of Precipitation-Runoff Modeling System (PRMS) to Simulate Prefire and Postfire Hydrologic Response in the Upper Rio Hondo Basin, New Mexico","title":"Calibration of Precipitation-Runoff Modeling System (PRMS) to simulate prefire and postfire hydrologic response in the upper Rio Hondo Basin, New Mexico","docAbstract":"The Precipitation-Runoff Modeling System (PRMS) is widely used to simulate the effects of climate, topography, land cover, and soils on landscape-level hydrologic responses and streamflow. The U.S. Geological Survey (USGS), in cooperation with the New Mexico Department of Homeland Security and Emergency Management, developed procedures to apply the PRMS model to simulate the effects of fire on hydrologic responses.
A PRMS model was built of the upper Rio Hondo Basin from the headwaters to approximately 19 miles downstream from the USGS streamgage Rio Hondo above Chavez Canyon near Hondo, New Mexico, by using 24 hydrologic response units (HRUs), or hydrologically similar subareas, from the National Hydrologic Model. A quasi-graphical user interface was created to easily query and analyze published PRMS sensitivity-analysis data. Simulation of mean daily streamflow was most sensitive to parameters related to snowmelt or infiltration throughout the upper Rio Hondo Basin. In the basin’s eastern and northern HRUs, flashiness and timing of streamflow were most sensitive to interflow; in many western-basin HRUs (higher elevations), flashiness of streamflow was most sensitive to soil moisture parameters, and timing of streamflow was most sensitive to infiltration and evapotranspiration parameters.
The PRMS model was calibrated for the fire-affected North Fork Eagle Creek subwatershed by comparing modeled to observed daily streamflow for the nonfrozen (May through October) period for a prefire and postfire time period. The prefire model was calibrated for the period 2007–12 before the 2012 fire, and the postfire model was calibrated for a 2-year (2014–15) period after the fire. Model parameterization combined manual adjustment of 8 parameters on the basis of prior knowledge and automated adjustment of the most sensitive parameters by using the Let Us Calibrate interface. A gridded, daily precipitation dataset that captured the spatial heterogeneity across the study watershed was used as the precipitation input for calibration. Model performance was assessed as satisfactory by using standard statistical measures for prefire and postfire periods.
The calibrated model was run by using data from a single precipitation gage to better represent the effect of localized, extreme storms on postfire hydrologic response. The calibrated models for prefire and postfire conditions simulated streamflows with greater consistency than the uncalibrated model for the corresponding (prefire or postfire) period of hydrographic record. The effect of fire on streamflow was found to be primarily a shift from streamflow dominated by base flow prior to fire to streamflow dominated by surface runoff after fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195022","collaboration":"Prepared in cooperation with the New Mexico Department of Homeland Security and Emergency Management","usgsCitation":"Douglas-Mankin, K.R., and Moeser, C.D., 2019, Calibration of Precipitation-Runoff Modeling System (PRMS) to simulate prefire and postfire hydrologic response in the upper Rio Hondo Basin, New Mexico: U.S. Geological Survey Scientific Investigations Report 2019–5022, 25 p., https://doi.org/10.3133/sir20195022.","productDescription":"Report: vi, 25 p.; Data Release","numberOfPages":"36","ipdsId":"IP-094970","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":363146,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KD1X7Q","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model input and output for prefire and postfire hydrologic simulations in the Upper Rio Hondo Basin, New Mexico using the Precipitation-Runoff Modeling System (PRMS)"},{"id":363157,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5022/coverthb2.jpg"},{"id":363145,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5022/sir20195022.pdf","text":"Report","size":"2.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5022"}],"country":"United States","state":"New Mexico","county":"Lincoln County, Otero County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.83610534667969,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.33741240611175\n ],\n [\n -105.74203491210938,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.465816745730024\n ],\n [\n -105.83610534667969,\n 33.33741240611175\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, New Mexico 87113
This report provides a synthesis of geologic mapping and geochronologic research along the South Platte River between the town of Masters and the city of Fort Morgan, northeastern Colorado. This work was undertaken to better understand landscape development along this part of the river corridor. The focus is on times of rapid change within the fluvial system that had a marked effect on the landscape. The study area is susceptible to drought, which destabilizes vegetation and makes the landscape vulnerable to eolian activity. This is reflected in a landscape that is largely covered by eolian sand and lesser amounts of loess. Past glaciation of the river’s headwaters had a major influence on river discharge and sediment supply, as have major flood events particularly on unglaciated tributaries heading on the piedmont.
In the mapping area, fluvial deposits of the South Platte River system span the Pliocene and early Pleistocene(?) deposits of Nussbaum Alluvium to present-day deposits of the active channel and floodplain. Results of the study indicate that along this stretch of the South Platte River, the early Pleistocene and first half of the middle Pleistocene were times of net incision, periodically interrupted by episodes of aggradation that resulted in deposition of alluvium that has been correlated to Rocky Flats Alluvium, Verdos Alluvium, and Slocum Alluvium. Net incision between depositional events formed a series of poorly preserved terrace deposits along the valley sides that are now largely covered by eolian deposits. Sometime after about 380 thousand years, the river cut a deep paleovalley into Upper Cretaceous Pierre Shale that was then filled with a thick sequence of inferred Louviers Alluvium (coeval with Bull Lake glaciation). Net aggradation continued during the late Pleistocene, resulting in burial of the Louviers paleovalley with a thick sequence of mainstream and sidestream Broadway Alluvium (coeval with Pinedale glaciation). Subsequent incision during the late Pleistocene–Holocene transition formed the Kersey (Broadway) terrace, whose riser forms a prominent bluff on the south side of the river valley. This episode of incision spanned a very short period and was followed by renewed aggradation that deposited the next-lower terrace alluvium (Kuner terrace alluvium). The Kuner terrace level was probably abandoned sometime around the beginning of the middle Holocene. Low terraces on the valley floor indicate that the river has been primarily cutting and backfilling laterally rather than incising during the late Holocene.
Synthesis of geologic mapping and chronologic data generated in this study indicate that the South Platte River in northeastern Colorado likely was highly sensitive to rapidly changing environmental conditions or crossed threshold conditions that triggered rapid geomorphic response during major climate changes associated with the late Pleistocene–Holocene transition. Historical times have been another period marked by rapid incision, reflected by gully incision and headward erosion in tributary valleys draining the north side of the South Platte River. This historical erosion could be related at least in part to extensive construction of irrigation ditches and reservoirs in the late 1800s–early 1900s, which altered drainage paths and groundwater flow and could have amplified natural factors such as climate change or intrinsic geomorphic instabilities within the system.
Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS 980
Denver, CO 80225