Ceanothus verrucosus (CEVE) is a globally rare, long-lived, chaparral shrub endemic to coastal southern California (CA) and northern Mexico. There is concern for CEVE persistence because of habitat loss, fire, and climate change, yet little is known about basic features of the plant, including whether it contains annual rings, plant age, and climate–growth response. Growth-ring analysis was challenging because of semi-ring-porous structure, false, and missing rings. We successfully crossdated CEVE annual rings, primarily from Cabrillo National Monument, CA, using a nearby Pinus torreyana chronology. The oldest living individual had 116 rings; the oldest inner-ring date was 1873; and most of the plants established between 1894 and 1905, all older than previous estimates. CEVE mortality occurred during a dry period from the late 1940s through the early 1960s. Correlations between age and stem measurements were weak to moderate (r = 0.10 to 0.56) posing challenges for field-based estimates of plant ages, which are important for population modeling. Variability in CEVE ring width had a strong positive correlation with prior cool-season (October–April) precipitation, yet 2- to 7-day warm-season precipitation events were recorded as rare false rings in multiple years, indicating extreme plasticity in cambial phenology and growth response to moisture.
The 196 parties to the Convention on Biological Diversity (CBD) will soon agree to a post-2020 global framework for conserving the three elements of biodiversity (genetic, species, and ecosystem diversity) while ensuring sustainable development and benefit sharing. As the most significant global conservation policy mechanism, the new CBD framework has far-reaching consequences- it will guide conservation actions and reporting for each member country until 2050. In previous CBD strategies, as well as other major conservation policy mechanisms, targets and indicators for genetic diversity (variation at the DNA level within species, which facilitates species adaptation and ecosystem function) were undeveloped and focused on species of agricultural relevance. We assert that, to meet global conservation goals, genetic diversity within all species, not just domesticated species and their wild relatives, must be conserved and monitored using appropriate metrics. Building on suggestions in a recent Letter in Science (Laikre et al., 2020) we expand argumentation for three new, pragmatic genetic indicators and modifications to two current indicators for maintaining genetic diversity and adaptive capacity of all species, and provide guidance on their practical use. The indicators are: 1) the number of populations with effective population size above versus below 500, 2) the proportion of populations maintained within species, 3) the number of species and populations in which genetic diversity is monitored using DNA-based methods. We also present and discuss Goals and Action Targets for post-2020 biodiversity conservation which are connected to these indicators and underlying data. These pragmatic indicators and goals have utility beyond the CBD; they should benefit conservation and monitoring of genetic diversity via national and global policy for decades to come.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2020.108654","usgsCitation":"Hoban, S.M., Bruford, M.W., D’Urban Jackson, J., Lopes-Fernandes, M., Heuertz, M., Hohenlohe, P.A., Sjogren-Gulve, P., Segelbacher, G., Vernesi, C., Aitken, S., Bertola, L.D., Bloomer, P., Breed, M., Rodriguez-Correa, H., Funk, W., Grueber, C.E., Hunter, M., Jaffe, R., Liggins, L., Mergeay, J., Moharrek, F., O'Brien, D., Ogden, R., Palma-Silva, C., Paz-Vinas, I., Pierson, J., Ramakrishnan, U., Simo-Droissart, M., Tani, N., Waits, L., and Laikre, L., 2020, Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved: Biological Conservation, v. 248, 108654, 11 p., https://doi.org/10.1016/j.biocon.2020.108654.","productDescription":"108654, 11 p.","ipdsId":"IP-117703","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":455484,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2020.108654","text":"Publisher Index Page"},{"id":378255,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"248","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hoban, Sean M. 0000-0002-0348-8449","orcid":"https://orcid.org/0000-0002-0348-8449","contributorId":206582,"corporation":false,"usgs":false,"family":"Hoban","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":37343,"text":"The Morton Arboretum","active":true,"usgs":false}],"preferred":false,"id":798136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bruford, Michael W.","contributorId":190769,"corporation":false,"usgs":false,"family":"Bruford","given":"Michael","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":798137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"D’Urban Jackson, Josephine","contributorId":239918,"corporation":false,"usgs":false,"family":"D’Urban Jackson","given":"Josephine","email":"","affiliations":[{"id":48047,"text":"School of Biosciences, Cardiff University, Cardiff","active":true,"usgs":false}],"preferred":false,"id":798138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lopes-Fernandes, Margarida","contributorId":239919,"corporation":false,"usgs":false,"family":"Lopes-Fernandes","given":"Margarida","email":"","affiliations":[{"id":48048,"text":"Instituto da Conservação da Natureza e das Florestas, IP, Lisbon, Portugal","active":true,"usgs":false}],"preferred":false,"id":798257,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heuertz, Myriam","contributorId":239920,"corporation":false,"usgs":false,"family":"Heuertz","given":"Myriam","email":"","affiliations":[{"id":48049,"text":"INRAE, Univ. Bordeaux","active":true,"usgs":false}],"preferred":false,"id":798140,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hohenlohe, Paul A.","contributorId":46399,"corporation":false,"usgs":false,"family":"Hohenlohe","given":"Paul","email":"","middleInitial":"A.","affiliations":[{"id":12708,"text":"Institute for Bioinformatics and Evolutionary Studies, Department of Biological Sciences, University of Idaho, Moscow, ID 83844","active":true,"usgs":false}],"preferred":false,"id":798141,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sjogren-Gulve, Per","contributorId":239921,"corporation":false,"usgs":false,"family":"Sjogren-Gulve","given":"Per","email":"","affiliations":[{"id":48050,"text":"The Wildlife Analysis Unit, The Swedish Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":798142,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Segelbacher, Gernot","contributorId":206584,"corporation":false,"usgs":false,"family":"Segelbacher","given":"Gernot","email":"","affiliations":[{"id":37345,"text":"University of Freiburg, Germany","active":true,"usgs":false}],"preferred":false,"id":798143,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vernesi, Cristiano","contributorId":239922,"corporation":false,"usgs":false,"family":"Vernesi","given":"Cristiano","email":"","affiliations":[{"id":48051,"text":"Dept. of Sustainable Agroecosystems and Bioresources, Research and Innovation Centre - Fondazione Edmund Mach","active":true,"usgs":false}],"preferred":false,"id":798144,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Aitken, Sally","contributorId":239923,"corporation":false,"usgs":false,"family":"Aitken","given":"Sally","email":"","affiliations":[{"id":48052,"text":"Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":798145,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bertola, Laura D.","contributorId":239924,"corporation":false,"usgs":false,"family":"Bertola","given":"Laura","email":"","middleInitial":"D.","affiliations":[{"id":38178,"text":"City College of New York","active":true,"usgs":false}],"preferred":false,"id":798146,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Bloomer, Paulette","contributorId":239925,"corporation":false,"usgs":false,"family":"Bloomer","given":"Paulette","email":"","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":798147,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Breed, Martin","contributorId":239609,"corporation":false,"usgs":false,"family":"Breed","given":"Martin","affiliations":[{"id":47928,"text":"College of Science and Engineering, Flinders University","active":true,"usgs":false}],"preferred":false,"id":798148,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Rodriguez-Correa, Hernando","contributorId":239926,"corporation":false,"usgs":false,"family":"Rodriguez-Correa","given":"Hernando","email":"","affiliations":[{"id":48054,"text":"Escuela Nacional de Estudios Superiores Unidad Morelia, Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":798149,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Funk, W. Chris 0000-0002-9254-6718","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":189580,"corporation":false,"usgs":false,"family":"Funk","given":"W. Chris","affiliations":[],"preferred":false,"id":798150,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Grueber, Catherine E.","contributorId":239927,"corporation":false,"usgs":false,"family":"Grueber","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":48055,"text":"School of Life and Environmental Sciences, Faculty of Science, The University of Sydney","active":true,"usgs":false}],"preferred":false,"id":798151,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":214742,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":798152,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Jaffe, Rodolfo","contributorId":239612,"corporation":false,"usgs":false,"family":"Jaffe","given":"Rodolfo","email":"","affiliations":[{"id":47932,"text":"Instituto Tecnológico Vale; Department of Ecology, University of São Paulo","active":true,"usgs":false}],"preferred":false,"id":798153,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Liggins, Libby","contributorId":239928,"corporation":false,"usgs":false,"family":"Liggins","given":"Libby","email":"","affiliations":[{"id":48056,"text":"School of Natural and Computational Sciences, Massey University","active":true,"usgs":false}],"preferred":false,"id":798154,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Mergeay, Joachim","contributorId":239929,"corporation":false,"usgs":false,"family":"Mergeay","given":"Joachim","email":"","affiliations":[{"id":48057,"text":"Research Institute for Nature and Forest, Aquatic Ecology, Evolution and Conservation, KULeuven","active":true,"usgs":false}],"preferred":false,"id":798155,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Moharrek, Farideh","contributorId":239930,"corporation":false,"usgs":false,"family":"Moharrek","given":"Farideh","email":"","affiliations":[{"id":48060,"text":"Department of Life Sciences, Natural History Museum, Tarbiat Modares University","active":true,"usgs":false}],"preferred":false,"id":798156,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"O'Brien, David","contributorId":192192,"corporation":false,"usgs":false,"family":"O'Brien","given":"David","affiliations":[],"preferred":false,"id":798157,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Ogden, Rob","contributorId":239611,"corporation":false,"usgs":false,"family":"Ogden","given":"Rob","email":"","affiliations":[{"id":47931,"text":"Royal (Dick) School of Veterinary Studies & the Roslin Institute, University of Edinburgh","active":true,"usgs":false}],"preferred":false,"id":798158,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Palma-Silva, Clarisse","contributorId":239931,"corporation":false,"usgs":false,"family":"Palma-Silva","given":"Clarisse","email":"","affiliations":[{"id":48061,"text":"Department of Plant Science, Institute of Biology, University of Campinas","active":true,"usgs":false}],"preferred":false,"id":798159,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Paz-Vinas, Ivan","contributorId":239614,"corporation":false,"usgs":false,"family":"Paz-Vinas","given":"Ivan","email":"","affiliations":[{"id":47934,"text":"Laboratoire Ecologie Fonctionnelle et Environnement, Université de Toulouse","active":true,"usgs":false}],"preferred":false,"id":798160,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Pierson, Jennifer","contributorId":239932,"corporation":false,"usgs":false,"family":"Pierson","given":"Jennifer","email":"","affiliations":[],"preferred":false,"id":798161,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Ramakrishnan, Uma","contributorId":197653,"corporation":false,"usgs":false,"family":"Ramakrishnan","given":"Uma","email":"","affiliations":[],"preferred":false,"id":798162,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Simo-Droissart, Murielle","contributorId":239933,"corporation":false,"usgs":false,"family":"Simo-Droissart","given":"Murielle","email":"","affiliations":[{"id":48062,"text":"Plant Systematics and Ecology Laboratory, Higher Teachers’ Training College, University of Yaoundé","active":true,"usgs":false}],"preferred":false,"id":798163,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Tani, Naoki","contributorId":239934,"corporation":false,"usgs":false,"family":"Tani","given":"Naoki","email":"","affiliations":[{"id":48063,"text":"Forestry Division, Japan International Research Center for Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":798164,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Waits, Lisette","contributorId":189210,"corporation":false,"usgs":false,"family":"Waits","given":"Lisette","affiliations":[],"preferred":false,"id":798165,"contributorType":{"id":1,"text":"Authors"},"rank":30},{"text":"Laikre, Linda","contributorId":198139,"corporation":false,"usgs":false,"family":"Laikre","given":"Linda","email":"","affiliations":[],"preferred":false,"id":798166,"contributorType":{"id":1,"text":"Authors"},"rank":31}]}} ,{"id":70222534,"text":"70222534 - 2020 - Combined seismic and geodetic analysis before, during and after the 2018 Mt. Etna eruption","interactions":[],"lastModifiedDate":"2021-08-03T12:39:33.277092","indexId":"70222534","displayToPublicDate":"2020-08-31T07:36:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Combined seismic and geodetic analysis before, during and after the 2018 Mt. Etna eruption","docAbstract":"In December 2018, Etna volcano experienced one of the largest episodes of unrest since the installation of geophysical monitoring networks in 1970. The unrest culminated in a short eruption with a small volume of lava erupted, a significant seismic crisis and deformation of the entire volcanic edifice of magnitude never recorded before at Mount Etna. Here we describe the evolution of the 2018 eruptive cycle from the analysis of seismic and geodetic data collected in the months preceding, during, and following the intrusion. We model the space-time evolution of high-rate deformation data starting from the active source previously identified from deformation data and the propagation of seismicity in a 3-D velocity model. The intrusion model suggests emplacement of two dikes: a smaller dike located beneath the eruptive fissure and a second, deeper dike between 1 and 5 km below sea level that opened ~2 m. The rise and eruption of magma from the shallower dike did not interrupt the pressurization of a long-lasting deeper reservoir (~6 km) that induced continuous inflation and intense deformation of the eastern flank. Shortly after the intrusion, on 26 December 2018, a ML4.8 earthquake occurred near Pisano, destroying buildings and roads in two villages. We propose a time-dependent intrusion model that supports the hypothesis of the inflation inducing flank deformation and that this process has been active since September 2018.
The gravel-bedded White River drains a 1279 km2 basin in Washington State, with lowlands sculpted by continental glaciation and headwaters on an actively glaciated stratovolcano. Chronic aggradation along an alluvial fan near the river’s mouth has progressively reduced flood conveyance. In order to better understand how forecasted climate change may influence coarse sediment delivery and aggradation rates in this lowland depositional setting, we assessed the contemporary delivery and routing of coarse sediment through the watershed; this assessment was based on a rich set of topographic, sedimentologic, and hydrologic data from the past century, with a focus on repeat high-resolution topographic surveys from the past decade.
We found that most of the lower river’s contemporary bed-load flux originates from persistent erosion of alluvial deposits in the lower watershed. This erosion is a response to a drop in local base level caused by a major avulsion across the fan in 1906 and then augmented by subsequent dredging. The 1906 avulsion and modern disequilibrium valley profiles reflect landscape conditioning by continental glaciation and a massive mid-Holocene lahar. In the proglacial headwaters, infrequent large sediment pulses have accomplished most of the observed coarse sediment export, with exported material blanketing downstream valley floors; during typical floods, transported bed material is largely sourced from erosion of these valley floor deposits. Throughout the watershed, we observe decadal-scale coarse sediment dynamics strongly related to the filling or emptying of valley-scale sediment storage over 102−104 yr time scales, often in response to major disturbances that either emplace large deposits or influence their redistribution. Paraglacial responses in large watersheds are suggested to be inherently complicated and punctuated as a result of internal landform interactions and stochastic/threshold-dependent events. We argue, in combination, that Holocene disturbance, storage dynamics, and human flow modification make coarse sediment fluxes in the lower White River relatively insensitive to decadal climate variability. Results highlight the degree to which river sensitivity to contemporary disturbance, climatic or otherwise, may be contingent on local and idiosyncratic watershed histories, underscoring the need to unpack those histories while demonstrating the utility of watershed-scale high-resolution topography toward that end.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35530.1","usgsCitation":"Anderson, S.W., and Jaeger, K.L., 2020, Coarse sediment dynamics in a large glaciated river system: Holocene history and storage dynamics dictate contemporary climate sensitivity: Geological Society of America Bulletin, 24 p., https://doi.org/10.1130/B35530.1.","productDescription":"24 p.","ipdsId":"IP-106664","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":436809,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HT46KB","text":"USGS data release","linkHelpText":"Supporting Data for Sediment Studies in the White River Watershed"},{"id":383708,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"White River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.68981933593749,\n 46.72856582519053\n ],\n [\n -121.58843994140625,\n 46.72856582519053\n ],\n [\n -121.58843994140625,\n 47.31648293428332\n ],\n [\n -122.68981933593749,\n 47.31648293428332\n ],\n [\n -122.68981933593749,\n 46.72856582519053\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Anderson, Scott W. 0000-0003-1678-5204 swanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-1678-5204","contributorId":196687,"corporation":false,"usgs":true,"family":"Anderson","given":"Scott","email":"swanderson@usgs.gov","middleInitial":"W.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811209,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811210,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70212830,"text":"70212830 - 2020 - Investigating apparent misalignment of predator-prey dynamics: Great Lakes lake trout and sea lampreys","interactions":[],"lastModifiedDate":"2020-08-31T12:59:48.097407","indexId":"70212830","displayToPublicDate":"2020-08-29T07:56:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1661,"text":"Fisheries Research","active":true,"publicationSubtype":{"id":10}},"title":"Investigating apparent misalignment of predator-prey dynamics: Great Lakes lake trout and sea lampreys","docAbstract":"Interpreting ecological dynamics is challenging when observed patterns are not aligned with presumed models. Investigating possible sources of uncertainty is critical to understand the underlying system and ultimately inform management decisions. In this study, we used simulation to investigate the hypothesis that observed inconsistencies in Great Lakes lake trout (Salvelinus namaycush) and sea lamprey (Petromyzon marinus) predator-prey dynamics were caused by measurement error in the abundance and predation metrics. When lake trout abundances increase and sea lamprey abundances decrease, predation rates are expected to decline (and vice versa). Occasionally predation rates do not change as expected, leading to an inconsistency in expected predator-prey dynamics. We used a Type II functional response model to align lake trout relative abundance, adult sea lamprey abundance, and sea lamprey marking rates of lake trout in each Great Lake. Then we added measurement error to each of the simulated metrics to see how it contributed to observed inconsistencies in the marking rates. The simulated inconsistency rate was far less than the observed inconsistency rate in Lakes Superior and Erie, indicating that measurement error was not primarily responsible for the misalignment of metrics, contrary to our hypothesis. Rather than ignoring these inconsistencies as unfortunate consequences of imperfect assessments, we recommend that future inconsistencies be scrutinized for possible mechanistic explanations. We suspect that predator-prey dynamics are being influenced by spatially structured within-lake components and the presence of alternative hosts, neither of which were accounted for in the functional response model we used.
Groundwater processes are often overlooked in permafrost environments, but subsurface storage and routing can strongly influence water and biogeochemical cycling in northern catchments. Groundwater flow in permafrost regions is controlled by the temporal and spatial distribution of frozen ground, causing the hydrogeologic framework to be temperature-dependent. Most flow occurs in geologic units above the permafrost table (supra-permafrost aquifers) or below the permafrost base (sub-permafrost aquifers). In the context of climate change, thawing permafrost is altering groundwater flowpaths and thereby inducing positive trends in river baseflow in many discontinuous permafrost basins. Activated groundwater systems can provide new conduits for flushing Arctic basins and transporting nutrients to basin outlets. The thermal and hydraulic physics that govern groundwater flow in permafrost regions are strongly coupled and more complex than those in non-permafrost settings. Recent research activity in permafrost hydrogeological modeling has resulted in several mainstream groundwater models (e.g., SUTRA, FEFLOW, HYDRUS) offering users advanced capabilities for simulating processes in aquifers that experience dynamic freeze-thaw. This chapter relies on field examples to review key processes and conditions that control groundwater dynamics in permafrost settings and presents an up-to-date synthesis of the mathematical representation of heat transfer and groundwater flow in northern landscapes.
Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Somatic growth exerts strong control on patterns in the abundance of animal populations via effects on maturation, fecundity, and survival rates of juveniles and adults. In this paper, we quantify abiotic and biotic drivers of rainbow trout growth in the Colorado River, AZ, and the resulting impact on spatial and temporal variation in abundance. Inferences are based on approximately 10,000 observations of individual growth grates obtained through an intensive mark‐recapture effort conducted over five years (2012‐2016) in a 130 km‐long study segment downstream of Glen Canyon Dam. Prey availability, turbidity‐driven feeding efficiency, and intra‐specific competition were the dominant drivers of rainbow trout growth. Discharge, water temperature, and solar insulation were also evaluated but had a smaller influence. Mixed‐effect models explained 79‐82% of the variability in observed growth rates, with fixed covariate effects explaining 79‐87% of the total variation in growth parameters across five reaches and 18 quarterly sampling intervals. Reductions in growth owing in part to a phosphorous‐driven decline in prey availability, led to substantive weight loss and poor fish condition. This in turn lowered survival rates and delayed maturation, which led to a rapid decline in abundance and later recruitments. Reductions in feeding efficiency, due to episodic inputs of fine sediment from tributaries, and warmer water temperatures, contributed to reduced growth in downstream reaches, which led to more severe declines in abundance. Somatic growth rates increased following the population collapse due to reduced competition, and in the absence of substantive increases in prey availability. Our study elucidates important linkages between abiotic and biotic factors, somatic growth, and vital rates, and demonstrates how variation in somatic growth influences temporal and spatial patterns in abundance.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1427","usgsCitation":"Korman, J., Yard, M.D., Dzul, M.C., Yackulic, C., Dodrill, M., Deemer, B., and Kennedy, T., 2020, Changes in prey, turbidity, and competition reduce somatic growth and cause the collapse of a fish population: Ecological Monographs, v. 91, no. 1, e01427, https://doi.org/10.1002/ecm.1427.","productDescription":"e01427","ipdsId":"IP-116364","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436811,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90ODKZ3","text":"USGS data release","linkHelpText":"Rainbow trout growth data and growth covariate data downstream of Glen Canyon Dam in the Colorado River, Arizona, 2012 - 2016"},{"id":378203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Glen Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.6925048828125,\n 36.76309161490538\n ],\n [\n -111.3519287109375,\n 36.76309161490538\n ],\n [\n -111.3519287109375,\n 37.00035919622158\n ],\n [\n -111.6925048828125,\n 37.00035919622158\n ],\n [\n -111.6925048828125,\n 36.76309161490538\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"91","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Korman, Josh","contributorId":139960,"corporation":false,"usgs":false,"family":"Korman","given":"Josh","email":"","affiliations":[{"id":13333,"text":"Ecometric Research Inc.","active":true,"usgs":false}],"preferred":false,"id":798084,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798068,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dzul, Maria C. 0000-0002-4798-5930 mdzul@usgs.gov","orcid":"https://orcid.org/0000-0002-4798-5930","contributorId":5469,"corporation":false,"usgs":true,"family":"Dzul","given":"Maria","email":"mdzul@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798069,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798070,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dodrill, Michael J. 0000-0002-7038-7170","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":206439,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798071,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Deemer, Bridget R. 0000-0002-5845-1002 bdeemer@usgs.gov","orcid":"https://orcid.org/0000-0002-5845-1002","contributorId":198160,"corporation":false,"usgs":true,"family":"Deemer","given":"Bridget","email":"bdeemer@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798072,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kennedy, Theodore 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":221741,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":798073,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70212845,"text":"70212845 - 2020 - Spatiotemporal modeling of dengue fever risk in Puerto Rico","interactions":[],"lastModifiedDate":"2020-08-31T14:07:32.839427","indexId":"70212845","displayToPublicDate":"2020-08-27T09:06:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6475,"text":"Spatial and Spatio-temporal Epidemiology","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal modeling of dengue fever risk in Puerto Rico","docAbstract":"Dengue Fever (DF) is a mosquito vector transmitted flavivirus and a reemerging global public health threat. Although several studies have addressed the relation between climatic and environmental factors and the epidemiology of DF, or looked at purely spatial or time series analysis, this article presents a joint spatio-temporal epidemiological analysis. Our approach accounts for both temporal and spatial autocorrelation in DF incidence and the effect of temperatures and precipitation by using a hierarchical Bayesian approach. We fitted several space-time areal models to predict relative risk at the municipality level and for each month from 1990 to 2014. Model selection was performed according to several criteria: the preferred models detected significant effects for temperature at time lags of up to four months and for precipitation up to three months. A boundary detection analysis is incorporated in the modeling approach, and it was successful in detecting municipalities with historically anomalous risk.
This report presents an updated grade and tonnage model for tungsten skarn deposits. As a critical component of the U.S. Geological Survey’s three-part form of quantitative mineral resource assessment, robust grade and tonnage models are essential to transforming mineral resource assessments into effective tools for decision makers. Using the best data available at the time of publication, this represents the first attempt in nearly 30 years to capture current mineral inventory and cumulative production data for worldwide tungsten skarn deposits. The accuracy of modern assessments of undiscovered tungsten skarn resources is highly influenced by the use of current data on the distribution of the grades and tonnages of well-explored tungsten skarn deposits. Primary factors affecting the changes to these distributions in the model presented here compared with those of previous models are the inclusion of important deposits, especially those in China that had been omitted in previous models; expanded mineral inventories resulting from increased exploration; and changes to international reporting standards. These factors have resulted in dramatic increases in average ore tonnage and slight decreases in the average grade of tungsten skarn deposits compared with previous models. Large increases in contained metal are observed among many of the individual deposits incorporated within this model that were also included in previous tungsten skarn grade and tonnage models. This report also provides recommendations for input parameters related to grade and tonnage models to use with software tools designed to facilitate the three-part form of quantitative mineral resource assessments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205085","usgsCitation":"Green, C.J., Lederer, G.W., Parks, H.L., and Zientek, M.L., 2020, Grade and tonnage model for tungsten skarn deposits—2020 update: U.S. Geological Survey Scientific Investigations Report 2020–5085, 23 p., https://doi.org/10.3133/sir20205085.","productDescription":"vi, 23 p.","numberOfPages":"23","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-117570","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":377895,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5085/sir20205085.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5085"},{"id":377894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5085/coverthb.jpg"}],"contact":"Eastern Mineral and Environmental Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
954 National Center
Reston, VA 20192
Drone use in wildlife biology has greatly increased as they become cheaper and easier to deploy in the field. In this paper we describe a less invasive method of using drones and exploring their limitations for studying colonial nesting waterbirds. Western Grebes, like most colonial nesting waterbirds, can be very sensitive to human interaction. Using a 3DR Solo quad copter equipped with a high-resolution digital camera we were able to effectively map and monitor a Western Grebe breeding colony throughout the nesting period with a series of 6 flights. We were able to use drone collected aerial imagery to model nest survival while minimizing disturbance to the birds. However, we were not able to deploy the drone at all of our study sites. Our ability to effectively deploy the drone was hindered by the environmental and vegetation characteristics of a site. Drone technology can be a useful tool, especially when studying a species sensitive to human interaction. However, there researchers should carefully consider their species and study site to evaluate if a drone is the proper tool to meet their objectives.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-020-09743-y","usgsCitation":"Lachman, D., Conway, C.J., Vierling, K., and Matthews, T., 2020, Drones provide a better method to find nests and estimate nest survival for colonial waterbirds: A demonstration with Western Grebes: Wetlands Ecology and Management, v. 28, p. 837-845, https://doi.org/10.1007/s11273-020-09743-y.","productDescription":"9 p.","startPage":"837","endPage":"845","ipdsId":"IP-119243","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","county":"Valley County","otherGeospatial":"Cascade Reservoir, Deer Flat National Wildlife Refuge, Lake 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Deo","contributorId":280030,"corporation":false,"usgs":false,"family":"Lachman","given":"Deo","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":835914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":835913,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vierling, Kerri","contributorId":280031,"corporation":false,"usgs":false,"family":"Vierling","given":"Kerri","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":835915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matthews, Ty","contributorId":280032,"corporation":false,"usgs":false,"family":"Matthews","given":"Ty","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":835916,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212678,"text":"ofr20201078 - 2020 - Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17","interactions":[],"lastModifiedDate":"2020-08-26T15:51:06.049297","indexId":"ofr20201078","displayToPublicDate":"2020-08-26T10:30:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1078","displayTitle":"Assessment of Dissolved-Selenium Concentrations and Loads in the Lower Gunnison River Basin, Colorado, as Part of the Selenium Management Program, 2011–17","title":"Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17","docAbstract":"The Gunnison Basin Selenium Management Program implemented a water-quality monitoring network in 2011 to measure concentrations of selenium in the lower Gunnison River Basin in Colorado. Selenium is a trace element that bioaccumulates in aquatic food chains. Selenium is essential for life, but elevated amounts can cause reproductive failure, deformities, and other harmful effects. The primary goal of the Selenium Management Program is to meet the State of Colorado water-quality standard of 4.6 micrograms per liter (µg/L) for dissolved selenium at the U.S. Geological Survey (USGS) streamflow-gaging station number 09152500—Gunnison River near Grand Junction, Colorado—herein referred to as “Whitewater.” The U.S. Geological Survey, in cooperation with the Bureau of Reclamation, has completed a review of dissolved-selenium data collected from the Selenium Management Program network during Water Year (WY) 2017 (October 1, 2016 through September 30, 2017) to further the understanding of the status and trends of selenium in the basin. This report presents the percentile values for selenium because regulatory agencies in Colorado make decisions based on the U.S. Environmental Protection Agency’s Clean Water Act section 303(d), which uses percentile values for concentrations. Also presented are dissolved-selenium loads at 14 sites in the lower Gunnison River Basin for WYs 2011–17. Annual dissolved-selenium loads were calculated for six sites with continuous U.S. Geological Survey streamflow-gaging stations. These six sites are referred to as “core” sites in this report. The remaining sites, which do not have streamflow-gaging stations, are referred to as “ancillary” sites in this report. During WY 2017, the loads calculated at the six core sites ranged from 306 pounds (lb) at Uncompahgre River at Colona to 12,600 lb at Whitewater, respectively.
By using discrete water-quality samples and the associated discharge measurements, instantaneous loads were calculated for 14 sites in WYs 2011–17 where discrete water-quality sampling took place. Median instantaneous loads ranged from 0.52 pounds per day (lb/d) at Uncompahgre River at Colona to 35.7 lb/d at Whitewater. Mean instantaneous loads ranged from 0.63 lb/d at Cummings Gulch at mouth to 35.5 lb/d at Whitewater. Most tributary sites in the basin had a median instantaneous dissolved-selenium load of less than 20.0 lb/d. In general, dissolved-selenium loads at Gunnison River main-stem sites showed an increase from upstream to downstream.
The State of Colorado’s water-quality standard for dissolved selenium of 4.6 µg/L was compared to the 85th percentiles for dissolved selenium at selected sites. Annual 85th percentiles for dissolved selenium were calculated by using estimated dissolved-selenium concentrations from linear regression models for the six core sites with U.S. Geological Survey streamflow-gaging stations. The 85th-percentile concentrations for WY 2017 based on this method ranged from 0.68 µg/L at Uncompahgre River at Colona to 140 µg/L at Loutzenhizer Arroyo at North River Road. The 85th percentiles for concentrations of dissolved selenium also were calculated from water-quality samples collected during WY 2017 from sites with sufficient data. The annual 85th-percentile concentrations based on the discrete samples ranged from 0.75 µg/L at Uncompahgre River at Colona to 106 µg/L at Loutzenhizer Arroyo at North River Road.
An analysis was completed for Whitewater to determine if an upward or downward trend exists for dissolved-selenium loads during two time periods. The first time period included all data at Whitewater, whereas the second time period focused on more recent data. The trend analysis indicates a decrease from 22,200 to 12,600 lb, which is a 43.1 percent (9,600 lb) reduction during the time period WY 1986 through WY 2017. The trend analysis for the annual dissolved-selenium load for WY 1995 through WY 2017 indicates a decrease of 6,600 lb per year, or 35.5 percent. An evaluation of laboratory bias was completed for selenium data which was used in the trend analysis. Findings indicated a potential positive bias of approximately 12 percent may exist in the data from October 2005 through August 2015.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201078","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Henneberg, M.F., 2020, Assessment of dissolved-selenium concentrations and loads in the Lower Gunnison River Basin, Colorado, as part of the Selenium Management Program, 2011–17: U.S. Geological Survey Open-File Report 2020–1078, 21 p., https://doi.org/10.3133/ofr20201078","productDescription":"v, 21 p.","onlineOnly":"Y","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":377861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1078/ofr20201078.pdf","text":"Report","size":"1.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1078"},{"id":377860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1078/coverthb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.80584716796875,\n 39.01064750994083\n ],\n [\n -109.11895751953125,\n 38.8782049970615\n ],\n [\n -108.6328125,\n 38.10214399750345\n ],\n [\n -108.69598388671875,\n 37.77288579232439\n ],\n [\n -107.87750244140625,\n 37.309014074275915\n ],\n [\n -107.4462890625,\n 37.31338308990806\n ],\n [\n -107.1441650390625,\n 37.727280276860036\n ],\n [\n -107.18536376953125,\n 38.07620357665235\n ],\n [\n -107.26776123046875,\n 38.50304202775689\n ],\n [\n -107.50671386718749,\n 38.9380483825641\n ],\n [\n -107.6495361328125,\n 39.115144700901475\n ],\n [\n -108.80584716796875,\n 39.01064750994083\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
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The immune system of ectotherms, particularly non-avian reptiles, remains poorly characterized regarding the genes involved in immune function, and their function in wild populations. We used RNA-Seq to explore the systemic response of Mojave desert tortoise (Gopherus agassizii) gene expression to three levels of Mycoplasma infection to better understand the host response to this bacterial pathogen. We found over an order of magnitude more genes differentially expressed between male and female tortoises (1,037 genes) than differentially expressed among immune groups (40 genes). There were 8 genes differentially expressed among both variables that can be considered sex-biased immune genes in this tortoise. Among experimental immune groups we find enriched GO biological processes for cysteine catabolism, regulation of type 1 interferon production, and regulation of cytokine production involved in immune response. Sex-biased transcription involves iron ion transport, iron ion homeostasis, and regulation of interferon-beta production to be enriched. More detailed work is needed to assess the seasonal response of the candidate genes found here. How seasonal fluctuation of testosterone and corticosterone modulate the immunosuppression of males and their susceptibility to Mycoplasma infection also warrants further investigation, as well as the importance of iron in the immune function and sex-biased differences of this species. Finally, future transcriptional studies should avoid drawing blood from tortoises via subcarapacial venipuncture as the variable aspiration of lymphatic fluid will confound the differential expression of genes.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0238202","usgsCitation":"Xu, C., Dolby, G.A., Drake, K.K., Esque, T., and Kusumi, K., 2020, Immune and sex-biased gene expression in the threatened Mojave desert tortoise, Gopherus agassizii: PLoS ONE, v. 15, no. 8, e0238202, 26 p., https://doi.org/10.1371/journal.pone.0238202.","productDescription":"e0238202, 26 p.","ipdsId":"IP-120652","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455519,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0238202","text":"Publisher Index Page"},{"id":414542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Cindy","contributorId":303295,"corporation":false,"usgs":false,"family":"Xu","given":"Cindy","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":867047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dolby, Greer A. 0000-0002-5923-0690","orcid":"https://orcid.org/0000-0002-5923-0690","contributorId":222726,"corporation":false,"usgs":false,"family":"Dolby","given":"Greer","email":"","middleInitial":"A.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":867048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drake, K. Kristina 0000-0003-0711-7634 kdrake@usgs.gov","orcid":"https://orcid.org/0000-0003-0711-7634","contributorId":3799,"corporation":false,"usgs":true,"family":"Drake","given":"K.","email":"kdrake@usgs.gov","middleInitial":"Kristina","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867050,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kusumi, Kenro","contributorId":167536,"corporation":false,"usgs":false,"family":"Kusumi","given":"Kenro","email":"","affiliations":[],"preferred":false,"id":867051,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212472,"text":"sir20205065 - 2020 - Flood-frequency estimation for very low annual exceedance probabilities using historical, paleoflood, and regional information with consideration of nonstationarity","interactions":[],"lastModifiedDate":"2020-08-26T12:58:26.704616","indexId":"sir20205065","displayToPublicDate":"2020-08-25T14:37:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5065","displayTitle":"Flood-Frequency Estimation for Very Low Annual Exceedance Probabilities Using Historical, Paleoflood, and Regional Information with Consideration of Nonstationarity","title":"Flood-frequency estimation for very low annual exceedance probabilities using historical, paleoflood, and regional information with consideration of nonstationarity","docAbstract":"Streamflow estimates for floods with an annual exceedance probability of 0.001 or lower are needed to accurately portray risks to critical infrastructure, such as nuclear powerplants and large dams. However, extrapolating flood-frequency curves developed from at-site systematic streamflow records to very low annual exceedance probabilities (less than 0.001) results in large uncertainties in the streamflow estimates. Traditionally, methods for statistically estimating flood frequency have relied on the systematic streamflow record, which provides a time series of annual maximum flood peaks, often including some historical peaks. However, most peak-flow records are less than 100 years, and uncertainties are large when trying to extrapolate magnitudes of very low annual exceedance probability events.
Other data may be available that extend the record beyond the systematic dataset. Historical data are defined as data from outside the period of systematic records but within the period of human records. Examples of historical information include flood estimates from other agencies and newspaper accounts that can be translated to flood magnitude point estimates, interval estimates, or perception thresholds (such as a statement that an 1880 flood was the largest since 1869). Paleoflood data, which may also extend the dataset, include a broad range of information about flood occurrence or magnitude from sources like sediment deposits or tree rings.
Several assumptions are made in flood-frequency analysis, and an understanding of whether the data conform to these assumptions is desired. A particularly difficult assumption to evaluate for flood-frequency analysis is the underlying assumption that the flood series is stationary—the assumption that a time series of peak flow varies around a constant mean within a particular range of values (constant variance). As the hydrologic community’s understanding of natural systems and anthropogenic effects on streamflows has evolved, the community has come to understand that many surface-water systems exhibit one or more forms of nonstationarity, and thus the stationarity assumption is often violated to some degree. However, there is currently (2020) no consensus among hydrologists regarding the most appropriate flood-frequency-analysis methods for nonstationary systems, and this topic remains an active area of research.
A literature review was completed to summarize the state of the science of flood frequency. The literature review highlights tools available to detect nonstationarities and identifies approaches that include external information to inform flood-frequency analysis. To demonstrate methods for initial data analysis and for incorporating historical and paleoflood information in flood-frequency analysis, five sites were selected: the Red River of the North at James Avenue Pumping Station, Winnipeg, Manitoba, Canada; lower reach, Rapid Creek, South Dakota; Spring Creek, South Dakota; Cherry Creek near Melvin, Colorado; and Escalante River near Escalante, Utah. The sites were chosen for the availability of published historical and paleoflood data and for their geographic diversity and unique characteristics, which highlighted issues such as autocorrelation, change points, trends, outlier peaks, or short periods of record.
An initial data analysis that involved examining records for autocorrelation, change points, and trends was completed for all sites. The flood-frequency analysis completed for this study used version 7.2 of the U.S. Geological Survey PeakFQ program. Multiple analyses were done on each site documenting the change in the flood-frequency curve when additional historical or paleoflood data were added. When other flood-frequency studies were available, their results were compared to the results here. The comparisons in some cases simply show the effect of additional years of data, whereas other comparisons show results from probability distributions or fitting methods other than those used in PeakFQ.
For the Red River of the North, flood-frequency analysis shows that paleoflood data appear necessary to reasonably estimate very low annual exceedance probabilities. For the analysis of the lower reach of Rapid Creek and Spring Creek, paleoflood information helped put a high outlier from the systematic period in context; however, very low annual exceedance probabilities at these sites still had extraordinarily large confidence bounds. These sites also showed that paleoflood information might be transferred from one site to another, with the caveat that this is a case where we had existing paleoflood data to test the transfer of paleoflood information—this is not the case at many sites, and transferring paleoflood information requires assumptions about the comparability of floods at the sites. The Cherry Creek analysis affirmed the result of an earlier study that showed that the generalized Pareto distribution was not a good distribution for estimating very low annual exceedance probabilities. The Escalante River analysis showed that adding paleoflood information might increase uncertainty for very low annual exceedance probabilities, compared to analysis with the systematic period of record information only, when the paleoflood peaks are of much larger magnitudes than the systematic record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205065","collaboration":"Prepared in cooperation with the U.S. Nuclear Regulatory Commission","usgsCitation":"Ryberg, K.R., Kolars, K.A., Kiang, J.E., and Carr, M.L., 2020, Flood-frequency estimation for very low annual exceedance probabilities using historical, paleoflood, and regional information with consideration of nonstationarity: U.S. Geological Survey Scientific Investigations Report 2020–5065, 89 p., https://doi.org/10.3133/sir20205065.","productDescription":"Report: xii, 89 p.; 5 Tables; Appendix; Dataset","numberOfPages":"105","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088812","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":377559,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_appendix.zip","text":"Appendix 1. Data, Settings, and Output for Each Site and Scenario","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5065 Appendix 1","linkHelpText":"— Each zipped file represents the analysis for a particular site and scenario"},{"id":377557,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_7.pdf","text":"Table 7","size":"114 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 7","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under two different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 06712500 Cherry Creek near Melvin, Colorado, with comparisons to other distributions and fitting methods."},{"id":377553,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065.pdf","text":"Report","size":"5.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065"},{"id":377554,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_4.pdf","text":"Table 4","size":"139 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 4","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under 10 different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 05OJ015 Red River of the North at James Avenue Pumping Station, Winnipeg, Manitoba, Canada, as well as results from flood-frequency studies by Burn and Goel (2001) and Harden (1999)."},{"id":377697,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"},{"id":377555,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_5.pdf","text":"Table 5","size":"122 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 5","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for the lower reach of Rapid Creek, South Dakota, with comparisons to Harden and others (2011)."},{"id":377556,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_6.pdf","text":"Table 6","size":"112 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 6","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for Spring Creek, South Dakota, with comparisons to Harden and others (2011)."},{"id":377552,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5065/coverthb.jpg"},{"id":377558,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5065/sir20205065_table_8.pdf","text":"Table 8","size":"116 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5065 Table 8","linkHelpText":"— Streamflow estimates (fit) for selected annual exceedance probabilities and associated confidence intervals (lower and upper) and variance estimates for flood-frequency analysis under three different scenarios using U.S. Geological Survey PeakFQ software (Veilleux and others, 2014) version 7.2 for streamgage station 09337500 Escalante River near Escalante, Utah, with comparisons to Webb and others (1988), Webb and Rathburn (1988), and Kenney and others (2008)."}],"contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
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1608 Mountain View Road
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Flood-frequency (hereinafter frequency) estimates provide information used to design, operate, and maintain hydraulic structures such as bridges and dams. Failures of these structures could cause catastrophic loss of property, life, or both. In addition to frequency estimates that use annual peak streamflow, frequency estimates of flood durations are required to safely and effectively operate the numerous dams in the Columbia River Basin of the northwestern United States, and British Columbia, Canada. Frequency studies rely on U.S. Geological Survey Guidelines for Determining Flood Flow Frequency (Bulletin 17C, published in 2018). A major consideration in estimating frequencies is the use of skew coefficients, which measure the asymmetry of flood flow distributions. Large uncertainties are associated with estimating the at-site skew coefficients directly from streamflow records, which are limited in length. Skew also is sensitive to extreme events for limited record lengths. Bulletin 17C recommends using regional skew coefficients to weight with the at-site skew estimate for more reliable frequency estimates. In this study, streamflow records from 313 unregulated U.S. Geological Survey streamgage sites and 97 regulated sites with naturalized streamflow records provided by the U.S. Army Corps of Engineers were used to develop regional skew models for the Columbia River Basin. The naturalized streamflow records were synthesized by removing regulatory components such as withdrawals and reservoir storage. Skew models were developed for 1-, 3-, 7-, 10-, 15-, 30-, and 60-day flood durations and used to estimate regional skew coefficients for the Columbia River Basin.
This report used Bayesian statistical regression methods to develop and analyze regional skew models based on hydrologically important basin characteristics. After examining a suite of available basin characteristics, mean annual precipitation had the strongest correlation to skew across the flood durations. Regional skew regression models were fit using mean annual precipitation for selected subbasins in the Columbia River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205073","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Lind, G.D., Lamontagne, J.R., and Stonewall, A.J., 2020, Development of regional skew coefficients for selected flood durations in the Columbia River Basin, northwestern United States and British Columbia, Canada (ver. 1.1, October 2020): U.S. Geological Survey Scientific Investigations Report 2020–5073, 48 p., https://doi.org/10.3133/sir20205073.","productDescription":"Report: viii, 48 p.; 8 Tables; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109443","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":377840,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5073/sir20205073_table1.csv","text":"Table 1","size":"26 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2130 SW 5th Avenue
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Nearly one-sixth of U.S. river basins are unable to consistently meet societal water demands while also providing sufficient water for the environment. Water scarcity is expected to intensify and spread as populations increase, new water demands emerge, and climate changes. Improving water productivity by meeting realistic benchmarks for all water users could allow U.S. communities to expand economic activity and improve environmental flows. Here we utilize a spatially detailed database of water productivity to set realistic benchmarks for over 400 industries and products. We assess unrealized water savings achievable by each industry in each river basin within the conterminous U.S. by bringing all water users up to industry- and region-specific water productivity benchmarks. Some of the most water stressed areas throughout the U.S. West and South have the greatest potential for water savings, with around half of these water savings obtained by improving water productivity in the production of corn, cotton, and alfalfa. By incorporating benchmark-meeting water savings within a national hydrological model (WaSSI), we demonstrate that depletion of river flows across Western U.S. regions can be reduced on average by 6.2–23.2%, without reducing economic production. Lastly, we employ an environmentally extended input-output model to identify the U.S. industries and locations that can make the biggest impact by working with their suppliers to reduce water use 'upstream' in their supply chain. The agriculture and manufacturing sectors have the largest indirect water footprint due to their reliance on water-intensive inputs but these sectors also show the greatest capacity to reduce water consumption throughout their supply chains.
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University","active":true,"usgs":false}],"preferred":false,"id":797428,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamsal, Gambhir","contributorId":239627,"corporation":false,"usgs":false,"family":"Lamsal","given":"Gambhir","email":"","affiliations":[{"id":47941,"text":"Department of Civil Engineering, Kansas State University","active":true,"usgs":false}],"preferred":false,"id":797429,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ancona, Zachary H. 0000-0001-5430-0218 zancona@usgs.gov","orcid":"https://orcid.org/0000-0001-5430-0218","contributorId":5578,"corporation":false,"usgs":true,"family":"Ancona","given":"Zachary","email":"zancona@usgs.gov","middleInitial":"H.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":797430,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Caldwell, Peter V","contributorId":145892,"corporation":false,"usgs":false,"family":"Caldwell","given":"Peter","email":"","middleInitial":"V","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":797431,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richter, Brian","contributorId":239628,"corporation":false,"usgs":false,"family":"Richter","given":"Brian","email":"","affiliations":[],"preferred":false,"id":797432,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ruddell, Benjamin 0000-0003-2967-9339","orcid":"https://orcid.org/0000-0003-2967-9339","contributorId":239629,"corporation":false,"usgs":false,"family":"Ruddell","given":"Benjamin","email":"","affiliations":[{"id":47944,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":797433,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rushforth, Richard","contributorId":239630,"corporation":false,"usgs":false,"family":"Rushforth","given":"Richard","email":"","affiliations":[],"preferred":false,"id":797434,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Davis, Kyle F. 0000-0003-4504-1407","orcid":"https://orcid.org/0000-0003-4504-1407","contributorId":239631,"corporation":false,"usgs":false,"family":"Davis","given":"Kyle","email":"","middleInitial":"F.","affiliations":[{"id":47945,"text":"Department of Geography and Spatial Sciences & Department of Plant and Soil Sciences, University of Delaware","active":true,"usgs":false}],"preferred":false,"id":797435,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70215537,"text":"70215537 - 2020 - Holocene paleoclimate change in the western US: The importance of chronology in discerning patterns and drivers","interactions":[],"lastModifiedDate":"2020-10-22T14:56:39.511724","indexId":"70215537","displayToPublicDate":"2020-08-25T09:52:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Holocene paleoclimate change in the western US: The importance of chronology in discerning patterns and drivers","docAbstract":"Sediment in lakes and meadows forms a powerful archive that can be used to reconstruct environmental change through time. Reconstructions of lake level, of chemical, biological, and hydrological conditions, and of surrounding vegetation provide detailed information about past climate conditions, both locally and regionally. Indeed, most of our current knowledge of centennial- to millennial-scale climate variability in the arid western United States, where information about past hydroclimate is particularly important, comes from such sediment-based reconstructions. The pressing need for robust, precise predictions of future conditions is a significant motivation for paleoclimate science, and current research questions frequently require Holocene reconstructions to be resolved at sub-centennial timescales. Increasingly, regional syntheses seek to identify synoptic-scale patterns similar to those defined from modern observations (seasonal, interannual, multi-decadal, etc.) or to compare with the output of climate model simulations. However, the age control on existing records, especially those more than about 20 years old, is often sufficient only for millennial-scale interpretation. Here we assess the age control for 84 published and unpublished records from lakes and meadows in the Great Basin, California, and desert southwest, and use Bayesian modeling to evaluate the 95% uncertainty ranges for the 42 best-dated records. In the Late Holocene, about half of the 42 records have <400-year mean uncertainty ranges; however, high-precision age control is especially critical for young records, used to develop an accurate understanding of a proxy’s response to known climate variations. In the Middle Holocene, records vary from 400 to >800-year mean uncertainty and records of the Early Holocene have 600- to >1400-year mean uncertainty ranges. We find that the largest control on modeled uncertainties is dating density, with at least 2 dates/kyr being optimal and suggest obtaining “range-finder” dates at the onset of a study to better predict the total number of dates needed for an adequate age model. Such a density avoids a commonly observed phenomenon of significant peaks in uncertainty arising in gaps between age control points. Analysis of the uncertainties associated with proxy shifts reveal that more than half are >400 years. Although such large uncertainties currently prevent sub-centennial interpretations in most cases, increased dating density, strategic use of limited funds (including budgeting for a 2 date/kyr minimum at the proposal stage), construction of age-depth models with Bayesian methods, and critical evaluation of chronological uncertainty will shed light on past climate variability at finer timescales, enhancing our understanding of global and regional drivers of western U.S. climate.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106487","usgsCitation":"Zimmerman, S., and Wahl, D., 2020, Holocene paleoclimate change in the western US: The importance of chronology in discerning patterns and drivers: Quaternary Science Reviews, v. 246, 106487, 26 p., https://doi.org/10.1016/j.quascirev.2020.106487.","productDescription":"106487, 26 p.","ipdsId":"IP-117485","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":455535,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1901504","text":"Publisher Index Page"},{"id":379657,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.78271484375,\n 32.69486597787505\n ],\n [\n -111.005859375,\n 32.69486597787505\n ],\n [\n -111.005859375,\n 43.97700467496408\n ],\n [\n -124.78271484375,\n 43.97700467496408\n ],\n [\n -124.78271484375,\n 32.69486597787505\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"246","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zimmerman, Susan 0000-0002-1320-1878","orcid":"https://orcid.org/0000-0002-1320-1878","contributorId":243580,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Susan","email":"","affiliations":[{"id":48737,"text":"CAMS, LLNL","active":true,"usgs":false}],"preferred":false,"id":802616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wahl, David 0000-0002-0451-3554","orcid":"https://orcid.org/0000-0002-0451-3554","contributorId":206113,"corporation":false,"usgs":true,"family":"Wahl","given":"David","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":802617,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70213158,"text":"70213158 - 2020 - Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw","interactions":[],"lastModifiedDate":"2020-09-10T13:48:58.413233","indexId":"70213158","displayToPublicDate":"2020-08-25T08:34:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw","docAbstract":"Over many millennia, northern peatlands have accumulated large amounts of carbon and nitrogen, thus cooling the global climate. Over shorter timescales, peatland disturbances can trigger losses of peat and release of greenhouses gases. Despite their importance to the global climate, peatlands remain poorly mapped, and the vulnerability of permafrost peatlands to warming is uncertain. This study compiles over 7,000 field observations to present a data-driven map of northern peatlands and their carbon and nitrogen stocks. We use these maps to model the impact of permafrost thaw on peatlands and find that warming will likely shift the greenhouse gas balance of northern peatlands. At present, peatlands cool the climate, but anthropogenic warming can shift them into a net source of warming.
Understanding the effects of predicted rising sea levels, combined with changes in precipitation and freshwater inflow on key estuarine ecosystem engineers such as the eastern oyster would provide critical information to inform restoration design and predictive models. Using oyster ladders with shell bags placed at three heights to capture a range of inundation levels, oyster growth of naturally recruited spat was monitored over the course of 6 months. Oyster numbers and shell heights were consistently highest in bottom and mid bags experiencing greater than 50% inundation (mid: 63 ± 7%; bottom: 95 ± 3%). Identifying thresholds for optimal oyster growth and survival to enhance restoration engineering would require finer scale evaluation of inundation levels.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/exp.2020.35","usgsCitation":"Marshall, D., and La Peyre, M., 2020, Effects of inundation duration on southeastern Louisiana oyster reefs: Experimental Results, v. 1, e30, 8 p., https://doi.org/10.1017/exp.2020.35.","productDescription":"e30, 8 p.","ipdsId":"IP-117860","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":455546,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/exp.2020.35","text":"Publisher Index Page"},{"id":395933,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.2691650390625,\n 29.563901551414418\n ],\n [\n -89.3353271484375,\n 29.563901551414418\n ],\n [\n -89.3353271484375,\n 30.259067203213018\n ],\n [\n -90.2691650390625,\n 30.259067203213018\n ],\n [\n -90.2691650390625,\n 29.563901551414418\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","noUsgsAuthors":false,"publicationDate":"2020-08-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Marshall, Danielle A.","contributorId":239867,"corporation":false,"usgs":false,"family":"Marshall","given":"Danielle A.","affiliations":[{"id":48014,"text":"School of Renewable Natural Resources, Louisiana State University Agricultural Center, Baton Rouge, LA","active":true,"usgs":false}],"preferred":false,"id":834532,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"La Peyre, Megan 0000-0001-9936-2252 mlapeyre@usgs.gov","orcid":"https://orcid.org/0000-0001-9936-2252","contributorId":79375,"corporation":false,"usgs":true,"family":"La Peyre","given":"Megan","email":"mlapeyre@usgs.gov","affiliations":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834533,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209320,"text":"sir20205033 - 2020 - Temporal and spatial variability of water quality in the San Antonio segment of the Edwards aquifer recharge zone, Texas, with an emphasis on periods of groundwater recharge, September 2017–July 2019","interactions":[],"lastModifiedDate":"2020-08-24T17:39:27.590296","indexId":"sir20205033","displayToPublicDate":"2020-08-24T09:57:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5033","displayTitle":"Temporal and Spatial Variability of Water Quality in the San Antonio Segment of the Edwards Aquifer Recharge Zone, Texas, With an Emphasis on Periods of Groundwater Recharge, September 2017–July 2019","title":"Temporal and spatial variability of water quality in the San Antonio segment of the Edwards aquifer recharge zone, Texas, with an emphasis on periods of groundwater recharge, September 2017–July 2019","docAbstract":"Ongoing urbanization on the Edwards aquifer recharge zone in the greater San Antonio area raises concern about the potential adverse effects on the public water supply from development. To address this concern, the U.S. Geological Survey, in cooperation with the City of San Antonio, studied patterns of temporal and spatial changes in water quality at selected surface-water and groundwater sites in the Edwards aquifer recharge zone, with an emphasis on changes during periods of groundwater recharge. Water-quality characteristics were continuously monitored and discrete water samples were collected at two sets of paired surface-water (stream) and groundwater (well) sites during a 2-year period (2017–19) that included relatively dry conditions and a large recharge event in September 2018 when as much as 16 inches of rain fell in parts of the study area.
Continuous monitoring of water-level altitude, specific conductance, and concentrations of nitrate in two wells completed in the Edwards aquifer provided high-resolution data showing detailed changes in water quality across a broad range of hydrologic conditions. Water levels in the wells responded rapidly (within hours to days) to recharge from both small and large rainfall and runoff events; changes in groundwater quality as a consequence of the influx of surface-derived recharge were indicated by changes in values of the monitored characteristics. A broad range in measured values of the stable isotopes of water expressed as delta deuterium and delta oxygen-18 in the water samples collected from two streams (Salado and West Elm Creeks), in comparison to the tight clustering of the values of these isotopes in groundwater samples, indicates that source waters (surface waters) of widely varying chemical characteristics become homogenized within the aquifer system.
Concentrations of major ions, trace ions, and nutrient concentrations in stormwater runoff indicate a combination of land-derived and rainfall-derived constituents. The distribution of concentrations of nitrogen species (nitrite, nitrate, and nitrogen in ammonia) among sampling sites transitions from a more variable distribution in stormwater runoff to a more uniform distribution in groundwater in which the dominant form is nitrate. Differences in nitrate isotopic composition and concentration in groundwater across the study area are likely controlled by the relative contributions of natural and anthropogenic nitrogen (with the anthropogenic nitrogen component including a wastewater source) and by the process of nitrification. Among all measured constituents, pesticides detected in discrete stormwater-runoff samples provided the clearest indication that urbanization was adversely affecting water quality; specifically, the more urbanized surface-water site had a greater number of detections and greater variety of detected pesticides. Though temporal variability in the numbers and types of pesticides was evident, the overall proportion of pesticides was dominated by triazine herbicides including atrazine, atrazine degradates, and simazine. The observed hydrologic responses to rainfall and corresponding changes in water quality in wells are thought to result from the direct hydrologic connectivity of surface water and unconfined groundwater; however, patterns of groundwater-quality change indicate mixing from multiple sources such as ambient groundwater, recent surface-derived recharge, and possibly inflow from other aquifers. Therefore, understanding the connection between urbanization and groundwater quality cannot be inferred from the input of stormwater runoff alone as changes related to local and regional hydrologic conditions also need to be considered. It should be noted that a single study comparing the results from two site pairs is not able to support definitive conclusions about the full effect of urbanization on surface water/groundwater quality; however, this study does provide useful insights about the spatial and temporal variability of both stormwater runoff and unconfined groundwater that are consistent with expectations based on the current conceptual model that depicts the Edwards aquifer surface-water/groundwater system as a single water resource.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205033","collaboration":"Prepared in cooperation with the City of San Antonio","usgsCitation":"Opsahl, S.P., Musgrove, M., and Mecum, K.E., 2020, Temporal and spatial variability of water quality in the San Antonio segment of the Edwards aquifer recharge zone, Texas, with an emphasis on periods of groundwater recharge, September 2017–July 2019: U.S. Geological Survey Scientific Investigations Report 2020–5033, 37 p., https://doi.org/10.3133/sir20205033.","productDescription":"Report: x, 37 p.; Companion Report","numberOfPages":"51","onlineOnly":"Y","ipdsId":"IP-112400","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":376131,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5033/sir20205033.pdf","text":"Report","size":"1.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5033"},{"id":376132,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/fs20203028","text":"FS 2020-3028","size":"852 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–5028","linkHelpText":"— Effects of urbanization on water quality in the Edwards aquifer, San Antonio and Bexar County"},{"id":376130,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5033/coverthb.jpg"}],"country":"United States","state":"Texas","city":"San Antonio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.909912109375,\n 28.613459424004414\n ],\n [\n -97.05322265625,\n 29.635545914466675\n ],\n [\n -98.02001953125,\n 30.472348632640834\n ],\n [\n -99.744873046875,\n 29.49698759653577\n ],\n [\n -98.909912109375,\n 28.613459424004414\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Droughts are expected to increase in frequency and severity with climate change. Population impacts of such harsh environmental events are theorized to vary with life history strategies among species. However, existing demographic models generally do not consider behavioural plasticity that may modify the impact of harsh events. Here we show that tropical songbirds in the New and Old Worlds reduced reproduction during drought, with greater reductions in species with higher average long-term survival. Large reductions in reproduction by longer-lived species were associated with higher survival during drought than predrought years in Malaysia, whereas shorter-lived species maintained reproduction and survival decreased. Behavioural strategies of longer-lived, but not shorter-lived, species mitigated the effect of increasing drought frequency on long-term population growth. Behavioural plasticity can buffer the impact of climate change on populations of some species and differences in plasticity among species related to their life histories are critical for predicting population trajectories.
Prolonged volcanic activity can induce surface weathering and hydrothermal alteration that is a primary control on edifice instability, posing a complex hazard with its challenges to accurately forecast and mitigate. This study uses a frequently active composite volcano, Mt Ruapehu, New Zealand, to develop a conceptual model of surface weathering and hydrothermal alteration applicable to long‐lived composite volcanoes. The alteration on Mt Ruapehu was classified using ground samples as non‐altered, supergene argillic, intermediate argillic, and advanced argillic. The first two classes have a paragenesis that is consistent with surficial infiltration and circulation of low‐temperature (<40°C) neutral to mildly acidic fluids, inducing chemical weathering and formation of weathering rims on rock surfaces. The intermediate and advanced argillic alteration formed from hotter (≥100°C) hydrothermal fluids with lower pH, interacting with the andesitic to dacitic host rocks. The distribution of weathering and hydrothermal alteration has been mapped with airborne hyperspectral imaging through image classification, while aeromagnetic data inversion was used to map alteration to up to 500‐m depth. The joint use of hyperspectral imaging complements the geophysical methods since it can spectrally identify hydrothermal alteration mineralogy. This study established a conceptual model of hydrothermal alteration history of Mt Ruapehu, exemplifying a long‐lived and nested active and ancient hydrothermal system. This study's combination approach can be used to indicate the most likely sources of future debris avalanches, which are a significant hazard on Ruapehu.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GC009270","usgsCitation":"Kereszturi, G., Schaefer, L.N., Miller, C.A., and Mead, S., 2020, Hydrothermal alteration on composite volcanoes: Mineralogy, hyperspectral imaging and aeromagnetic study of Mt Ruapehu, New Zealand: Geochemistry, Geophysics, Geosystems, v. 21, no. 9, e2020GC009270, 28 p., https://doi.org/10.1029/2020GC009270.","productDescription":"e2020GC009270, 28 p.","ipdsId":"IP-121751","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":455555,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/7f0a71d89f2449cb949ef5b223d16534","text":"External Repository"},{"id":382079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","otherGeospatial":"Mt Ruapehu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 175.166015625,\n -40.71395582628604\n ],\n [\n 176.57226562500003,\n -40.71395582628604\n ],\n [\n 176.57226562500003,\n -36.738884124394296\n ],\n [\n 175.166015625,\n -36.738884124394296\n ],\n [\n 175.166015625,\n -40.71395582628604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Kereszturi, Gabor 0000-0003-4336-2012","orcid":"https://orcid.org/0000-0003-4336-2012","contributorId":247601,"corporation":false,"usgs":false,"family":"Kereszturi","given":"Gabor","email":"","affiliations":[{"id":49587,"text":"Volcanic Risk Solutions, Massey University, Palmerston North, 4474, New Zealand","active":true,"usgs":false}],"preferred":false,"id":808007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaefer, Lauren N. 0000-0003-3216-7983","orcid":"https://orcid.org/0000-0003-3216-7983","contributorId":241997,"corporation":false,"usgs":true,"family":"Schaefer","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":808008,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Craig A. 0000-0001-8499-0352","orcid":"https://orcid.org/0000-0001-8499-0352","contributorId":219638,"corporation":false,"usgs":false,"family":"Miller","given":"Craig","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":808009,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mead, Stuart","contributorId":247602,"corporation":false,"usgs":false,"family":"Mead","given":"Stuart","email":"","affiliations":[{"id":49587,"text":"Volcanic Risk Solutions, Massey University, Palmerston North, 4474, New Zealand","active":true,"usgs":false}],"preferred":false,"id":808010,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215531,"text":"70215531 - 2020 - 2,200-Year tree-ring and lake-sediment based snowpack reconstruction for the northern Rocky Mountains highlights the historic magnitude of recent snow drought","interactions":[],"lastModifiedDate":"2020-10-22T15:05:12.329713","indexId":"70215531","displayToPublicDate":"2020-08-22T09:57:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7169,"text":"Quaternary Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"2,200-Year tree-ring and lake-sediment based snowpack reconstruction for the northern Rocky Mountains highlights the historic magnitude of recent snow drought","docAbstract":"In recent decades, Rocky Mountain accumulated snowpack levels have experienced rapid declines, yet long-term records of snowpack prior to the installation of snowpack observation stations in the early and mid 20th century are limited. To date, a small number of tree-ring based reconstructions of April 1 Snow Water Equivalent (SWE) in the northern Rocky Mountains have extended modern records of snowpack variability to ∼1200 C.E. Carbonate isotope lake sediment records, provide an opportunity to further extend tree-ring based reconstructions through the Holocene, providing a millennial-scale temporal record that allows for an evaluation of multi-scale drivers of snowpack variability, from internal climate dynamics to orbital-scale forcings. Here we present a ∼2200 year preliminary reconstruction of northern Rockies snowpack based on δ18O measurements of sediment carbonates collected from Foy Lake, Montana. We explore the statistical calibration of lake sediment δ18O to an annually resolved snowpack reconstruction from tree rings, and develop an approach to assess and quantify potential sources of error in this reconstruction approach. The sediment-based snowpack reconstruction shows strong low-frequency variability in snowpack over the last two millennia with few snow droughts approaching the magnitude of recent snowpack declines. Given the growing availability of high-resolution, carbonate-rich lake sediment records, such reconstructions could help improve our understanding of how snowpack conditions varied under previous climatic events (mid-Holocene climate optimum ca. 9−6 ka), providing critical insights for anticipating future snowpack conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.qsa.2020.100013","usgsCitation":"Schoenemann, S., Martin, J.T., Pederson, G.T., and McWethy, D.B., 2020, 2,200-Year tree-ring and lake-sediment based snowpack reconstruction for the northern Rocky Mountains highlights the historic magnitude of recent snow drought: Quaternary Science Advances, v. 2, 100013, 13 p., https://doi.org/10.1016/j.qsa.2020.100013.","productDescription":"100013, 13 p.","ipdsId":"IP-118382","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":455564,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.qsa.2020.100013","text":"Publisher Index Page"},{"id":379658,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, British Columbia, Idaho, Montana, Nevada, Oregon, Washington, Wyoming","otherGeospatial":"Nothern Rocky Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.1044921875,\n 49.26780455063753\n ],\n [\n -116.76269531249999,\n 53.067626642387374\n ],\n [\n -122.16796875,\n 53.38332836757156\n ],\n [\n -121.37695312499999,\n 49.89463439573421\n ],\n [\n -119.00390625,\n 46.46813299215554\n ],\n [\n -117.20214843749999,\n 43.03677585761058\n ],\n [\n -115.4443359375,\n 43.48481212891603\n ],\n [\n -112.939453125,\n 43.29320031385282\n ],\n [\n -115.400390625,\n 41.83682786072714\n ],\n [\n -114.345703125,\n 40.245991504199026\n ],\n [\n -110.5224609375,\n 42.84375132629021\n ],\n [\n -107.314453125,\n 42.16340342422401\n ],\n [\n -105.3369140625,\n 43.644025847699496\n ],\n [\n -108.45703125,\n 46.649436163350245\n ],\n [\n -112.1044921875,\n 49.26780455063753\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schoenemann, Spruce W.","contributorId":243573,"corporation":false,"usgs":false,"family":"Schoenemann","given":"Spruce W.","affiliations":[{"id":48731,"text":"University of Western Montana","active":true,"usgs":false}],"preferred":false,"id":802603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Justin T. 0000-0002-3523-6596","orcid":"https://orcid.org/0000-0002-3523-6596","contributorId":215418,"corporation":false,"usgs":true,"family":"Martin","given":"Justin","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":802604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pederson, Gregory T. 0000-0002-6014-1425 gpederson@usgs.gov","orcid":"https://orcid.org/0000-0002-6014-1425","contributorId":3106,"corporation":false,"usgs":true,"family":"Pederson","given":"Gregory","email":"gpederson@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":802605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McWethy, David B.","contributorId":207232,"corporation":false,"usgs":false,"family":"McWethy","given":"David","email":"","middleInitial":"B.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":802606,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70212659,"text":"70212659 - 2020 - Evaluating stereo DTM quality at Jezero Crater, Mars with HRSC, CTX, and HiRISE images","interactions":[],"lastModifiedDate":"2020-08-25T15:51:05.37368","indexId":"70212659","displayToPublicDate":"2020-08-21T10:50:52","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Evaluating stereo DTM quality at Jezero Crater, Mars with HRSC, CTX, and HiRISE images","docAbstract":"We have used a high-precision, high-resolution digital terrain model (DTM) of the NASA Mars 2020 rover Perseverance landing site in Jezero crater based on mosaicked images from the Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment (MRO HiRISE) camera as a reference dataset to evaluate DTMs based on Mars Express High Resolution Stereo Camera (MEX HRSC) and MRO Context camera (CTX) images. Results are consistent with our earlier HRSC-HiRISE comparisons at the Mars Science Laboratory (MSL) Curiosity landing site in Gale crater, confirming that those results were not compromised by the small area compared and potential problems with spatial registration. Specifically, height errors are on the order of half a pixel and correspond to an image matching error of 0.2–0.3 pixel but estimates of horizontal resolution are 10–20 pixels. Products from the HRSC team pipeline at DLR are smoother but more precise vertically than those produced by using the commercial stereo package SOCET SET®. The DLR products are also homogenous in quality, whereas the SOCET products are less smoothed and have higher errors in rougher terrain. Despite this weak variation, our results are consistent with a rule of thumb of 0.2–0.3 pixel matching precision based on many prior studies. Horizontal resolution is significantly coarser than the DTM ground sample distance (GSD), which is typically 3–5 pixels.
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