Mobility is an important element of landslide hazard and risk assessments yet has been seldom studied for shallow landslides and debris flows in tropical environments. In September 2017, Hurricane Maria triggered > 70,000 landslides across Puerto Rico. Using aerial imagery and a lidar digital elevation model (DEM), we mapped and characterized the mobility of debris slides and flows in four different geologic materials: (1) mudstone, siltstone, and sandstone; (2) submarine basalt and chert; (3) marine volcaniclastics; and (4) granodiorite. We used the ratio of landslide-fall height (H) to travel length (L), H/L, to assess the mobility of landslides in each material. Additionally, we differentiated between landslides with single and multiple source areas and landslides that either did or did not enter drainages. Overall, extreme rainfall contributed to the mobility of landslides during Hurricane Maria, and our results showed that the mobility of debris slides and flows in Puerto Rico increased linearly as a function of the number of source areas that coalesced. Additionally, landslides that entered drainages were more mobile than those that did not. We found that landslides in soils developed on marine volcaniclastics were the most mobile and landslides in soils on submarine basalt and chert were the least mobile. While landslides were generally small (< 100 m2) and displayed a wide range of H/L values (0.1–2), coalescence increased the mobility of landslides that transitioned to debris flows. The high but variable mobility of landslides that occurred during Hurricane Maria and the associated hazards highlight the importance of characterizing and understanding the factors influencing landslide mobility in Puerto Rico and other tropical environments.
The study was conducted in the Northern Atlantic Coastal Plain aquifer system, in the eastern USA.
Groundwater pH and redox conditions are fundamental chemical characteristics controlling the distribution of many contaminants of concern for drinking water or the ecological health of receiving waters. In this study, pH and redox conditions were modeled and mapped in a complex, layered aquifer system. Machine-learning methods (boosted regression trees) were applied to data from 3000 to 5000 wells. Predicted pH and the probability of anoxic conditions, defined by three thresholds of dissolved oxygen (0.5, 1, and 2 mg/L), were mapped at the 1-km2 scale for each of 10 regional aquifer layers.
Maps depict the extent of acidic groundwater and oxic conditions in the shallow, unconfined surficial aquifer and in unconfined, recharge-proximal areas of underlying aquifers, in contrast to alkaline and anoxic groundwater elsewhere. Geographic patterns and influential predictors–including elevation, overlying confining-units thickness, and simulated groundwater age and flux–are consistent with prior understanding of the processes controlling pH and redox in the aquifer system. The model-based maps support robust estimates of aquifer proportions, either areal or volumetric, likely to contain groundwater of a specified quality or be vulnerable to specific pH- or redox-sensitive contaminants. The machine-learning methods were an effective tool to map groundwater quality at the regional scale.
Mars has several different types of slope feature that resemble aqueous flows. However, the current cold, dry conditions are inimical to liquid water, resulting in uncertainty about its role in modern surface processes. Dark slope streaks were among the first distinctive young slope features to be identified on Mars and the first with activity seen in orbital images. They form markings on steep slopes that can persist for decades, and the role of water in their formation remains a matter of debate. Here I analyse the geomorphic features of new slope streaks using high-resolution orbital images. Comparison of images before and after streak formation reveal how this process affects the surface and provides information about the cause. These observations demonstrate that slope streaks erode and deposit material in some instances. They also reveal that streaks can jump slopes and may be erosive very near their termini. These observations support a formation model where dark slope streaks form as ground-hugging, low-density avalanches of dry surface dust. Such streaks need not be treated as Special Regions for planetary protection.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/s41561-020-0598-x","usgsCitation":"Dundas, C.M., 2020, Geomorphological evidence for a dry dust avalanche origin of slope streaks on Mars: Nature Geoscience, v. 13, p. 473-476, https://doi.org/10.1038/s41561-020-0598-x.","productDescription":"4 p.","startPage":"473","endPage":"476","ipdsId":"IP-110925","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":456222,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8243413","text":"External Repository"},{"id":385891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"13","noUsgsAuthors":false,"publicationDate":"2020-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Dundas, Colin M. 0000-0003-2343-7224 cdundas@usgs.gov","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":2937,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin","email":"cdundas@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":816346,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70210866,"text":"70210866 - 2020 - Using saline or brackish aquifers as reservoirs for thermal energy storage, with example calculations for direct-use heating in the Portland Basin, Oregon, USA","interactions":[],"lastModifiedDate":"2020-06-30T12:38:45.776529","indexId":"70210866","displayToPublicDate":"2020-06-28T07:32:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"title":"Using saline or brackish aquifers as reservoirs for thermal energy storage, with example calculations for direct-use heating in the Portland Basin, Oregon, USA","docAbstract":"Tools to evaluate reservoir thermal energy storage (RTES; heat storage in slow-moving or stagnant geochemically evolved permeable zones in strata that underlie well-connected regional aquifers) are developed and applied to the Columbia River Basalt Group (CRBG) beneath the Portland Basin, Oregon, USA. The performance of RTES for heat storage and recovery in the Portland Basin is strongly dependent on the operational schedule of heat injection and extraction. We examined the effects of the operational schedule, based on an annual solar hot water supply pattern and a building heating demand model, using heat and fluid flow simulations with SUTRA. We show RTES to be feasible for supply of heating energy for a large combined research/teaching building on the Oregon Health and Science University South Waterfront expansion, an area of planned future development. Initially, heat is consumed to increase the reservoir temperature, and conductive heat loss is high due to high temperature gradients between the reservoir and surrounding rock. Conductive heat loss continues into the future, but the rate of heat loss decreases, and heat recovery efficiency of the RTES system increases over time. Simulations demonstrate the effects of varying heat-delivery rate and temperature on the heat production history of the reservoir. If 100% of building heating needs are to be supplied by combined solar/RTES, then the solar system must be sized to meet building needs plus long-term thermal losses (i.e., conductive losses once the system is heated to pseudo-steady state) from the RTES system. If the solar heating system barely meets these criteria, then during early years, less than 100% of the building demand will be supplied until the reservoir is fully-heated. The duration of supplying less than 100% of building demand can be greatly shortened by pre-heating the reservoir before building heating operations or by adding extra heat from external sources during early years. Analytic solutions are developed to evaluate efficacy and to help design RTES systems (e.g., well-spacing, thermal source sizing, etc.). A map of thermal energy storage capacity is produced for the CRBG beneath the Portland Basin. The simulated building has an annual heat load of ∼1.9 GWh, and the total annual storage capacity of the Portland Basin is estimated to be 43,400 GWh assuming seasonal storage of heat yields water from which 10 °C can be extracted via heat exchange, indicating a tremendous heating capacity of the CRBG.
In recent decades, many bumble bee species have declined due to changes in habitat, climate, and pressures from pathogens, pesticides, and introduced species. The western bumble bee (Bombus occidentalis ), once common throughout western North America, is a species of concern and will be considered for listing by the U.S. Fish and Wildlife Service (USFWS) under the Endangered Species Act (ESA). We attempt to improve alignment of data collection and research with USFWS needs to consider redundancy, resiliency, and representation in the upcoming species status assessment. We reviewed existing data and literature on B. occidentalis , highlighting information gaps and priority topics for research. Priorities include increased knowledge of trends, basic information on several life‐history stages, and improved understanding of the relative and interacting effects of stressors on population trends, especially the effects of pathogens, pesticides, climate change, and habitat loss. An understanding of how and where geographic range extent has changed for the two subspecies of B. occidentalis is also needed. We outline data that could be easily collected in other research projects that would increase their utility for understanding range‐wide trends of bumble bees. We modeled the overall trend in occupancy from 1998 to 2018 of Bombus occidentalis within the continental United States using existing data. The probability of local occupancy declined by 93% over 21 yr from 0.81 (95% CRI = 0.43, 0.98) in 1998 to 0.06 (95% CRI = 0.02, 0.16) in 2018. The decline in occupancy varied spatially by landcover and other environmental factors. Detection rates vary in both space and time, but peak detection across the continental United States occurs in mid‐July. We found considerable spatial gaps in recent sampling, with limited sampling in many regions, including most of Alaska, northwestern Canada, and the southwestern United States. We therefore propose a sampling design to address these gaps to best inform the ESA species status assessment through improved assessment of how the spatial distribution of stressors influences occupancy changes. Finally, we request involvement via data sharing, participation in occupancy sampling with repeated visits to distributed survey sites, and complementary research to address priorities outlined in this paper.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3141","usgsCitation":"Graves, T., Janousek, W.M., Gaulke, S., Nicholas, A., Keinath, D., Bell, C.M., Cannings, S., Hatfield, R.G., Heron, J.M., Koch, J.B., Loffland, H.L., Richardson, L., Rohde, A., Rykken, J., Strange, J.P., Tronstead, L., and Sheffield, C., 2020, Western bumble bee: Declines in United States and range-wide information gaps: Ecosphere, v. 11, no. 6, e03141, 13 p., https://doi.org/10.1002/ecs2.3141.","productDescription":"e03141, 13 p.","ipdsId":"IP-113225","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":456241,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3141","text":"Publisher Index Page"},{"id":436910,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QY8ZA0","text":"USGS data release","linkHelpText":"Western bumble bee 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0000-0003-4939-3047","orcid":"https://orcid.org/0000-0003-4939-3047","contributorId":204143,"corporation":false,"usgs":false,"family":"Rohde","given":"Ashley T.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":795750,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Rykken, Jessica","contributorId":150931,"corporation":false,"usgs":false,"family":"Rykken","given":"Jessica","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":795751,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Strange, James P.","contributorId":224183,"corporation":false,"usgs":false,"family":"Strange","given":"James","email":"","middleInitial":"P.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":795752,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Tronstead, Lusha","contributorId":237991,"corporation":false,"usgs":false,"family":"Tronstead","given":"Lusha","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":795753,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Sheffield, Cory","contributorId":237992,"corporation":false,"usgs":false,"family":"Sheffield","given":"Cory","email":"","affiliations":[{"id":47672,"text":"Royal Saskatchewan Museum","active":true,"usgs":false}],"preferred":false,"id":795754,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70210574,"text":"ofr20201044 - 2020 - Supporting natural resource-management decisions — The role of economics at the U.S. Department of the Interior (DOI) — 2018 DOI Economics Training Workshop","interactions":[],"lastModifiedDate":"2022-01-19T14:34:44.24721","indexId":"ofr20201044","displayToPublicDate":"2020-06-26T10:15:00","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":"2020-1044","displayTitle":"Supporting Natural Resource-Management Decisions—The Role of Economics at the U.S. Department of the Interior (DOI)—2018 DOI Economics Training Workshop","title":"Supporting natural resource-management decisions — The role of economics at the U.S. Department of the Interior (DOI) — 2018 DOI Economics Training Workshop","docAbstract":"The second U.S. Department of the Interior (DOI) Economics Training Workshop (hereafter “Workshop”) was held during September 25–27, 2018, in Washington, D.C., to identify, highlight, and better understand needs and opportunities for economic analysis to support DOI’s mission. Building on the first workshop in 2017, the second Workshop, jointly convened by the DOI Office of Policy Analysis and the U.S. Geological Survey (USGS) Science and Decisions Center, provided an opportunity for DOI economists to share expertise and experiences and to build collaboration and communication channels across DOI. In addition, the second Workshop provided training sessions on a variety of relevant economic and modeling topics. More than 40 DOI economists gathered at the Workshop to share their work, discuss shared challenges, and identify approaches to advance the use and contribution of economics at the DOI.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201044","collaboration":"Prepared in cooperation with the U.S. Department of the Interior Office of Policy Analysis","usgsCitation":"Alhassan, M., Pindilli, E.J., Crowley, C.S.L., Shapiro, C.D., and Simon, B.M., 2020, Supporting natural resource-management decisions—The role of economics at the U.S. Department of the Interior (DOI)—2018 DOI Economics Training Workshop: U.S. Geological Survey Open-File Report 2020–1044, 26 p., https://doi.org/10.3133/ofr20201044.","productDescription":"iv, 26 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112653","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":375485,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181054","text":"Open-File Report 2018-1054","linkHelpText":"- Supporting natural resource management—The role of economics at the Department of the Interior—A workshop report"},{"id":375947,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1044/ofr20201044.pdf","text":"Report","size":"6.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":375483,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1044/coverthb.jpg"}],"contact":"Director, Science and Decisions Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Fisheries managers have a growing interest in the use of carbon dioxide (CO2) as a tool for controlling invasive fishes. However, limited published data exist on susceptibility of many commonly encountered species to elevated CO2 concentrations. Our objective was to estimate the 24-h 50% lethal concentration (LC50) and 95% lethal concentration (LC95) of CO2 for four fishes (Rainbow Trout Oncorhynchus mykiss, Common Carp Cyprinus carpio, Channel Catfish Ictalurus punctatus, and Westslope Cutthroat Trout Oncorhynchus clarkii lewisi). In the laboratory, we exposed juvenile fish to a range of CO2 concentrations for 24 h in unpressurized, flow-through tanks. We developed a Bayesian hierarchical model to estimate the dose-response relationship for each fish species with associated uncertainty, and estimated 24-h LC50 and LC95 values based on laboratory trials for each species. The minimum concentration inducing mortality differed among cold water–adapted species and warm water–adapted species groups: 150 mg CO2/L for Westslope Cutthroat Trout and Rainbow Trout and 225 mg CO2/L for Common Carp and Channel Catfish. We observed complete mortality at 275 mg CO2/L (38,672 microatmospheres [μatm]), 225 mg CO2/L (30,711 μatm), and 495 mg CO2/L (65,708 μatm [Common Carp]; 77,213 μatm [Channel Catfish]) for Westslope Cutthroat Trout, Rainbow Trout, and both Common Carp and Channel Catfish, respectively. There was evidence of a statistical difference between the 24-h LC95 values of Westslope Cutthroat Trout and Rainbow Trout (245.0 [222.2–272.2] and 190.6 [177.2–207.8] mg CO2/L, respectively). Additionally, these values were almost half the estimated 24-h LC95 values for Common Carp and Channel Catfish (422.5 [374.7–474.5] and 434.2 [377.2–492.2] mg CO2/L, respectively). Although the experimental findings show strong relationships between increased CO2 concentration and higher mortality, additional work is required to assess the efficacy and feasibility of a CO2 application in a field setting.
Polycyclic aromatic hydrocarbons (PAHs) are among the most widespread and potentially toxic contaminants in Great Lakes (USA/Canada) tributaries. The sources of PAHs are numerous and diverse, and identifying the primary source(s) can be difficult. The present study used multiple lines of evidence to determine the likely sources of PAHs to surficial streambed sediments at 71 locations across 26 Great Lakes Basin watersheds. Profile correlations, principal component analysis, positive matrix factorization source‐receptor modeling, and mass fractions analysis were used to identify potential PAH sources, and land‐use analysis was used to relate streambed sediment PAH concentrations to different land uses. Based on the common conclusion of these analyses, coal‐tar–sealed pavement was the most likely source of PAHs to the majority of the locations sampled. The potential PAH‐related toxicity of streambed sediments to aquatic organisms was assessed by comparison of concentrations with sediment quality guidelines. The sum concentration of 16 US Environmental Protection Agency priority pollutant PAHs was 7.4–196 000 µg/kg, and the median was 2600 µg/kg. The threshold effect concentration was exceeded at 62% of sampling locations, and the probable effect concentration or the equilibrium partitioning sediment benchmark was exceeded at 41% of sampling locations. These results have important implications for watershed managers tasked with protecting and remediating aquatic habitats in the Great Lakes Basin.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.4727","usgsCitation":"Baldwin, A.K., Corsi, S., Oliver, S.K., Lenaker, P.L., Nott, M.A., Mills, M.A., Norris, G.A., and Paatero, P., 2020, Primary sources of polycyclic aromatic hydrocarbons to streambed sediment in Great Lakes tributaries using multiple lines of evidence: Environmental Toxicology and Chemistry, v. 39, no. 7, p. 1392-1408, https://doi.org/10.1002/etc.4727.","productDescription":"17 p.","startPage":"1392","endPage":"1408","numberOfPages":"17","ipdsId":"IP-106377","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":456268,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.4727","text":"Publisher Index Page"},{"id":376159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana, Michigan, Minnesota, New York, Ohio, Wisconsin","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.74658203125,\n 40.48038142908172\n ],\n [\n -75.1025390625,\n 40.48038142908172\n ],\n [\n -75.1025390625,\n 47.989921667414194\n ],\n [\n -92.74658203125,\n 47.989921667414194\n ],\n [\n -92.74658203125,\n 40.48038142908172\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Baldwin, Austin K. 0000-0002-6027-3823 akbaldwi@usgs.gov","orcid":"https://orcid.org/0000-0002-6027-3823","contributorId":4515,"corporation":false,"usgs":true,"family":"Baldwin","given":"Austin","email":"akbaldwi@usgs.gov","middleInitial":"K.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792259,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lenaker, Peter L. 0000-0002-9469-6285 plenaker@usgs.gov","orcid":"https://orcid.org/0000-0002-9469-6285","contributorId":5572,"corporation":false,"usgs":true,"family":"Lenaker","given":"Peter","email":"plenaker@usgs.gov","middleInitial":"L.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792260,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nott, Michelle A. 0000-0003-3968-7586","orcid":"https://orcid.org/0000-0003-3968-7586","contributorId":221766,"corporation":false,"usgs":true,"family":"Nott","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":792264,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mills, Marc A.","contributorId":141085,"corporation":false,"usgs":false,"family":"Mills","given":"Marc","email":"","middleInitial":"A.","affiliations":[{"id":12772,"text":"USEPA","active":true,"usgs":false}],"preferred":false,"id":792261,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Norris, Gary A.","contributorId":228850,"corporation":false,"usgs":false,"family":"Norris","given":"Gary","email":"","middleInitial":"A.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":792262,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Paatero, Pentti","contributorId":228851,"corporation":false,"usgs":false,"family":"Paatero","given":"Pentti","email":"","affiliations":[{"id":18162,"text":"University of Helsinki","active":true,"usgs":false}],"preferred":false,"id":792263,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70211892,"text":"70211892 - 2020 - A mixed length scale model for migrating fluvial bedforms","interactions":[],"lastModifiedDate":"2020-08-11T13:59:35.822256","indexId":"70211892","displayToPublicDate":"2020-06-24T08:54:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"A mixed length scale model for migrating fluvial bedforms","docAbstract":"With the expansion of hydropower, in‐stream converters, flood‐protection infrastructures, and growing concerns on deltas fragile ecosystems, there is a pressing need to evaluate and monitor bedform sediment mass flux. It is critical to estimate real‐time bedform size and migration velocity and provide a theoretical framework to convert easily accessible time histories of bed elevations into spatially evolving patterns. We collected spatiotemporally resolved bathymetries from laboratory flumes and the Colorado River in statistically steady, homogeneous, subcritical flow conditions. Wave number and frequency spectra of bed elevations show compelling evidence of scale‐dependent velocity for the hierarchy of migrating bedforms observed in the laboratory and field. New scaling laws were applied to describe the full range of migration velocities as function of two dimensionless groups based on the bed shear velocity, sediment diameter, and water depth. Further simplification resulted in a mixed length scale model estimating scale‐dependent migration velocities, without requiring bedform classification or identification.
On 14–15 April 2015, an intense intermountain cyclone in the western USA caused high winds and a dust storm that degraded air quality in the eastern Great Basin, and deposited dust-on-snow (DOS) in the Wasatch Range near Salt Lake City, Utah. We analyzed the storm and documented its “source-to-sink” development to relate the frontal passage with dust mobilization, air quality changes, and dust deposition on montane snowpack near Alta, Utah. This case study is first to track a dust storm and measure the elemental composition and radiative properties of the resulting DOS as a single specific event layer in Wasatch montane snowpack; prior studies have assessed seasonally aggregated DOS deposits. Dust plumes on MODIS imagery indicate mobilization from known regional “hotspots” for aeolian activity, including clay- and silt-rich alluvium, modern playas, and disturbed areas within the Pleistocene Paleolake Bonneville Basin. This 2015 single event dust layer was 1–3 cm thick with a median dust size of 10.81–12.55 µm; its measured radiative properties are similar to aggregated dusts previously assessed in Wasatch snowpack. Dust from the 2015 DOS event is enriched in the elements As, Cd, Cu, and Mo by a 10× factor relative to average elemental concentrations in the upper continental crust; its heavy metals (Cu, Pb, As, Cd, Mo, Zn) are probably derived from regional mine operations. Tracking elemental fluxes from source-to-sink is important for resolving environmental impacts, and informing future analysis of single storm dust loading, ecosystem impacts, and quantity and quality of meltwater-fed drinking water.
Science-based management strategies are needed to halt or reverse the global decline of amphibians. In many cases, sound management requires reliable models built using monitoring data. Historically, monitoring and statistical modeling efforts have focused on estimating occupancy using detection–nondetection data. Spatial occupancy models are useful for studying colonization–extinction dynamics, but richer insights can be gained from estimating abundance and density-dependent demographic rates. We developed an integrated abundance-based metapopulation model of the processes contributing to spatiotemporal variation in patch population density. We fit our model to a combination of detection–nondetection and count data from a 14-yr study of a reintroduced metapopulation of federally threatened Chiricahua Leopard Frogs (Lithobates chiricahuensis). Pond-specific population growth rate was influenced by pond hydroperiod and frog density, such that permanent and semipermanent ponds with low densities of adult frogs experienced the highest annual population growth rates. Immigration rate declined as the distance among ponds increased. After reintroduction in 2003, metapopulation-level abundance increased and appeared to stabilize around 1300 adult frogs (95% CI = 1192–1471) by year 2015. Further, changes in metapopulation abundance were driven mostly by changes in abundance at a few ponds. These high-density populations, which would not have been identifiable with traditional occupancy-based metapopulation models, are likely especially important for species recovery in the area. Abundance-based metapopulation models can be widely applied to inform conservation efforts, by providing higher quality information needed to prioritize habitat patches for management and can be used to make more accurate predictions of metapopulation extinction risk.
","language":"English","publisher":"The Herpetologists' League","doi":"10.1655/0018-0831-76.2.240","usgsCitation":"Howell, P.E., Hossack, B., Muths, E., Sigafus, B.H., and Chandler, R., 2020, Informing amphibian conservation efforts with abundance-based metapopulation models: Herpetologica, v. 76, no. 2, p. 240-250, https://doi.org/10.1655/0018-0831-76.2.240.","productDescription":"11 p.","startPage":"240","endPage":"250","ipdsId":"IP-111558","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":383399,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Howell, Paige E","contributorId":251713,"corporation":false,"usgs":false,"family":"Howell","given":"Paige","email":"","middleInitial":"E","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":810414,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":810415,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":243368,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":810416,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sigafus, Brent H. 0000-0002-7422-8927 bsigafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7422-8927","contributorId":4534,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent","email":"bsigafus@usgs.gov","middleInitial":"H.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":810417,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chandler, Richard B.","contributorId":251714,"corporation":false,"usgs":false,"family":"Chandler","given":"Richard B.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":810418,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211174,"text":"70211174 - 2020 - Effects of snowpack, temperature, and disease on the demography of a wild population of amphibians","interactions":[],"lastModifiedDate":"2020-08-06T19:20:03.217264","indexId":"70211174","displayToPublicDate":"2020-06-23T10:51:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1892,"text":"Herpetologica","active":true,"publicationSubtype":{"id":10}},"title":"Effects of snowpack, temperature, and disease on the demography of a wild population of amphibians","docAbstract":"Understanding the demographic consequences of interactions among pathogens, hosts, and weather conditions is critical in determining how amphibian populations respond to disease and in identifying site-specific conservation actions that can be developed to bolster persistence of amphibian populations. We investigated population dynamics in Boreal Toads (Anaxyrus boreas) relative to abiotic (fall temperatures and snowpack) and biotic (the abundance of another anuran host and disease) characteristics of the local environment in Wyoming, USA. We used capture–recapture data and a multistate model where state was treated as a hidden Markov process to incorporate disease state uncertainty and assess our a priori hypotheses. Our results indicated that snowpack during the coldest week of winter is more influential to toad survival, disease transition probabilities, and the population-level prevalence of the amphibian chytrid fungus (Batrachochytrium dendrobatidis) in the spring, than temperatures in the fall or the presence of another host. As hypothesized, apparent survival at low (i.e., <25 cm) snowpack (0.22; confidence interval [CI] = 0.15–0.31) was lower than apparent survival at high snowpack (90.65; CI = 0.50–0.78). Our findings highlight the potential for local environmental factors, like snowpack, to influence disease and host persistence, and demonstrate the ecological complexity of disease effects on population demography in natural environments. This work further emphasizes the need for improved understanding of how climate change may influence the relationships among pathogens, hosts, and their environment for wild animal populations challenged by disease.
","language":"English","publisher":"BioOne","doi":"10.1655/0018-0831-76.2.132","usgsCitation":"Muths, E., Hossack, B., Grant, E.H., Pilliod, D., and Mosher, B.A., 2020, Effects of snowpack, temperature, and disease on the demography of a wild population of amphibians: Herpetologica, v. 76, no. 2, p. 132-143, https://doi.org/10.1655/0018-0831-76.2.132.","productDescription":"12 p.","startPage":"132","endPage":"143","ipdsId":"IP-111041","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":436920,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VACHX0","text":"USGS data release","linkHelpText":"Capture-recapture, disease and covariate data for boreal toads from Blackrock Wyoming 2019"},{"id":376433,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bridger-Teton National Forest, Togwetee Pass","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.37139892578125,\n 43.49676775343911\n ],\n [\n -109.86602783203125,\n 43.49676775343911\n ],\n [\n -109.86602783203125,\n 43.866218006556394\n ],\n [\n -110.37139892578125,\n 43.866218006556394\n ],\n [\n -110.37139892578125,\n 43.49676775343911\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"76","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":229346,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":792944,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":792945,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grant, Evan H. 0000-0003-4401-6496","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":229348,"corporation":false,"usgs":true,"family":"Grant","given":"Evan","email":"","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792946,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":792947,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mosher, Brittany A.","contributorId":189579,"corporation":false,"usgs":false,"family":"Mosher","given":"Brittany","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":792948,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211180,"text":"70211180 - 2020 - A synthesis of evidence of drivers of amphibian declines","interactions":[],"lastModifiedDate":"2020-07-16T15:46:40.508724","indexId":"70211180","displayToPublicDate":"2020-06-23T10:43:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1892,"text":"Herpetologica","active":true,"publicationSubtype":{"id":10}},"title":"A synthesis of evidence of drivers of amphibian declines","docAbstract":"Early calls for robust long-term time series of amphibian population data, stemming from discussion following the first World Congress of Herpetology, are now being realized after 25 yr of focused research. Inference from individual studies and locations have contributed to a basic consensus on drivers of amphibian declines. Until recently there were no large-scale syntheses of long-term time series data to test hypotheses about the generality of factors driving population dynamics at broad spatial scales. Through the U.S. Geological Survey's Powell Center for Analysis and Synthesis, we brought together a group of scientists to elucidate mechanisms underlying amphibian declines in North America and Europe. We used time series of field data collected across dozens of study areas to make inferences with these combined data using hierarchical and spatial models. We bring together results from four syntheses of these data to summarize our state of knowledge of amphibian declines, identify commonalities that suggest further avenues of study, and suggest a way forward in addressing amphibian declines—by looking beyond specific drivers to how to achieve stability in remaining populations. The common thread of the syntheses is that declines are real but not ubiquitous, and that multiple factors drive declines but the relative importance of each factor varies among species, populations, and regions. We also found that climate is an important driver of amphibian population dynamics. However, the direction and magnitude of sensitivity to change vary among species in ways unlikely to explain overall rates of decline. Thirty years after the initial identification of a major catastrophe for global biodiversity, the scientific community has empirically demonstrated the reality of the problem, identified putative causes, provided evidence of their impacts, invested in broader-scale actions, and attempted meta-analyses to search out global drivers. We suggest an approach that focuses on key demographic rates that may improve amphibian population trends at multiple sites across the landscape.
","language":"English","publisher":"BioOne","doi":"10.1655/0018-0831-76.2.101","usgsCitation":"Grant, E.H., Miller, D., and Muths, E., 2020, A synthesis of evidence of drivers of amphibian declines: Herpetologica, v. 76, no. 2, p. 101-107, https://doi.org/10.1655/0018-0831-76.2.101.","productDescription":"7 p.","startPage":"101","endPage":"107","ipdsId":"IP-111040","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":376430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Grant, Evan H. 0000-0003-4401-6496","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":229348,"corporation":false,"usgs":true,"family":"Grant","given":"Evan","email":"","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, D. A. W.","contributorId":216930,"corporation":false,"usgs":false,"family":"Miller","given":"D. A. W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":792979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":224061,"corporation":false,"usgs":true,"family":"Muths","given":"Erin L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":792980,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210791,"text":"70210791 - 2020 - The predictive skills of elastic Coulomb rate-and-state aftershock forecasts during the 2019 Ridgecrest, California, earthquake sequence","interactions":[],"lastModifiedDate":"2020-08-26T19:12:50.615604","indexId":"70210791","displayToPublicDate":"2020-06-23T10:23:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"The predictive skills of elastic Coulomb rate-and-state aftershock forecasts during the 2019 Ridgecrest, California, earthquake sequence","docAbstract":"Operational earthquake forecasting protocols commonly use statistical models for their recognized ease of implementation and robustness in describing the short-term spatiotemporal patterns of triggered seismicity. However, recent advances on physics-based aftershock forecasting reveal comparable performance to the standard statistical counterparts with significantly improved predictive skills when fault and stress field heterogeneities are considered. Here, we perform a pseudo-prospective forecasting experiment during the first month of the 2019 Ridgecrest (California) earthquake sequence. We develop seven Coulomb rate-and-state models that couple static stress change estimates with continuum mechanics expressed by the rate-and-state friction laws. Our model parametrization supports a gradually increasing complexity; we start from a preliminary model implementation with simplified slip distributions and spatially homogeneous receiver faults to reach an enhanced one featuring optimized fault constitutive parameters, finite-fault slip models, secondary triggering effects, and spatially heterogenous planes informed by pre-existing ruptures. The data-rich environment of Southern California allows us to test whether incorporating data collected in near real-time during an unfolding earthquake sequence boosts our predictive power. We assess the absolute and relative performance of the forecasts by means of statistical tests used within the Collaboratory for the Study of Earthquake Predictability (CSEP) and compare their skills against a standard benchmark ETAS model for the short (24 hours after the two Ridgecrest mainshocks) and intermediate-term (one month). Stress-based forecasts expect heightened rates along the whole near-fault region and increased expected seismicity rates in Central Garlock Fault. Our comparative model evaluation supports that faulting heterogeneities coupled with secondary triggering effects are the most critical success components behind physics-based forecasts, but also underlines the importance of model updates incorporating near real-time available aftershock data reaching better performance than ETAS models. We explore the physical basis behind our results by investigating the localized shut down of pre-existing normal faults in the Ridgecrest near-source area.","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200028","usgsCitation":"Mancini, S., Segou, M., Werner, M., and Parsons, T.E., 2020, The predictive skills of elastic Coulomb rate-and-state aftershock forecasts during the 2019 Ridgecrest, California, earthquake sequence: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1736-1751, https://doi.org/10.1785/0120200028.","productDescription":"16 p.","startPage":"1736","endPage":"1751","ipdsId":"IP-117717","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456304,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research-information.bris.ac.uk/en/publications/b86ef22d-e493-45b3-b98c-b20b940530be","text":"External Repository"},{"id":375920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.33923339843749,\n 35.14237113713991\n ],\n [\n -116.83959960937499,\n 35.14237113713991\n ],\n [\n -116.83959960937499,\n 36.37706783983682\n ],\n [\n -118.33923339843749,\n 36.37706783983682\n ],\n [\n -118.33923339843749,\n 35.14237113713991\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Mancini, Simone 0000-0003-3415-2080","orcid":"https://orcid.org/0000-0003-3415-2080","contributorId":225525,"corporation":false,"usgs":false,"family":"Mancini","given":"Simone","email":"","affiliations":[{"id":37322,"text":"University of Bristol","active":true,"usgs":false}],"preferred":false,"id":791436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Segou, Margarita","contributorId":199044,"corporation":false,"usgs":false,"family":"Segou","given":"Margarita","affiliations":[],"preferred":false,"id":791437,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Werner, Maximillian J","contributorId":195950,"corporation":false,"usgs":false,"family":"Werner","given":"Maximillian J","affiliations":[],"preferred":false,"id":791438,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":791439,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210729,"text":"fs20203033 - 2020 - NHDPlus High Resolution (NHDPlus HR)---A hydrography framework for the Nation","interactions":[],"lastModifiedDate":"2020-06-23T14:32:17.573219","indexId":"fs20203033","displayToPublicDate":"2020-06-23T09:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3033","displayTitle":"NHDPlus High Resolution (NHDPlus HR)—A Hydrography Framework for the Nation","title":"NHDPlus High Resolution (NHDPlus HR)---A hydrography framework for the Nation","docAbstract":"Reliable and accurate high-resolution mapping of the Nation’s waters are critical inputs to models and decision support systems used to predict risk and enable response to impacts on water resources. 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Core Science Systems
U.S. Geological Survey
12201 Sunrise Valley Dr., MS 511
Reston, VA 20192
Email National Hydrography at nhd@usgs.gov
","tableOfContents":"An overreliance on groundwater resources in the Houston (Texas) metropolitan area led to aquifer drawdowns and land subsidence, so regional water suppliers have been turning to surface water resources to meet water demand. Lake Houston, an important water supply reservoir 24 kilometers (15 miles) northeast of downtown Houston, requires new water supply sources to continue to meet water supply demands for the next several decades. The upcoming Luce Bayou Interbasin Transfer Project will divert up to 500 million gallons per day of Trinity River water into Lake Houston. Trinity River water has significantly different water quality than the Lake Houston tributaries. To evaluate the project’s potential effect on water quality, the U.S. Geological Survey used an enhanced version of a previously released Lake Houston hydrodynamic model. With a focus on salinity and water-surface elevations, the model combined data from 2009 to 2017 with simulated flow from the Luce Bayou Interbasin Transfer to evaluate potential outcomes from three hypothetical flow scenarios. Overall, these scenarios found that the Luce Bayou Interbasin Transfer would cause salinities to moderately rise over most of the modeled time (2009–2017), although salinities were buffered under 2011 drought conditions. Large inflow events equalized salinities under baseline conditions as well as the enhanced flow scenarios.
","language":"English","publisher":"Texas Water Resources Institute","doi":"10.21423/twj.v11i1.7094","usgsCitation":"Smith, E.A., and Shah, S.D., 2020, Hydrodynamic modeling results showing the effects of the Luce Bayou interbasin transfer on salinity in Lake Houston, TX: Texas Water Journal, v. 11, no. 1, p. 64-88, https://doi.org/10.21423/twj.v11i1.7094.","productDescription":"25 p.","startPage":"64","endPage":"88","ipdsId":"IP-107391","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":456306,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21423/twj.v11i1.7094","text":"Publisher Index Page"},{"id":436921,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AVUJ73","text":"USGS data release","linkHelpText":"Lake Houston (Texas) EFDC hydrodynamic model for water-surface elevation and specific conductance simulations, 2009-2017"},{"id":375850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Lake Houston, Luce Bayou","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.11533737182616,\n 30.044430015213965\n ],\n [\n -95.10314941406249,\n 30.045767374787093\n ],\n [\n -95.09679794311523,\n 30.052453901811464\n ],\n [\n -95.08563995361328,\n 30.081423634757307\n ],\n [\n -95.07431030273438,\n 30.09894991821156\n ],\n [\n -95.0592041015625,\n 30.10592986084689\n ],\n [\n -95.0533676147461,\n 30.11944281648474\n ],\n [\n -95.03860473632812,\n 30.128351446338055\n ],\n [\n -95.01869201660156,\n 30.140376821599734\n ],\n [\n -95.0126838684082,\n 30.148392924428762\n ],\n [\n -95.04392623901367,\n 30.133250850150496\n ],\n [\n -95.0621223449707,\n 30.121224606751863\n ],\n [\n -95.06658554077148,\n 30.107117887092357\n ],\n [\n -95.08289337158203,\n 30.10236569641242\n ],\n [\n -95.09920120239256,\n 30.08216633691056\n ],\n [\n -95.10074615478516,\n 30.07280788225917\n ],\n [\n -95.1060676574707,\n 30.066865546842724\n ],\n [\n -95.10932922363281,\n 30.053642570466106\n ],\n [\n -95.1222038269043,\n 30.052899654229005\n ],\n [\n -95.12392044067383,\n 30.043835627386027\n ],\n [\n -95.11533737182616,\n 30.044430015213965\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.13198852539062,\n 29.91090055463952\n ],\n [\n -95.11344909667969,\n 29.92696926999306\n ],\n [\n -95.1361083984375,\n 29.94660528850718\n ],\n [\n -95.11207580566406,\n 29.9793233715619\n ],\n [\n -95.11276245117188,\n 29.99478635165809\n ],\n [\n -95.10177612304688,\n 30.010841512528764\n ],\n [\n -95.10726928710938,\n 30.0405664305846\n ],\n [\n -95.14778137207031,\n 30.088107753367257\n ],\n [\n -95.16288757324219,\n 30.080978010788556\n ],\n [\n -95.15602111816406,\n 30.06255713055085\n ],\n [\n -95.13473510742186,\n 30.041160838027047\n ],\n [\n -95.15602111816406,\n 30.050670872077248\n ],\n [\n -95.19378662109375,\n 30.035811042667792\n ],\n [\n -95.24940490722656,\n 30.028083050581905\n ],\n [\n -95.2459716796875,\n 30.012030680358613\n ],\n [\n -95.21713256835938,\n 30.015003537560943\n ],\n [\n -95.19584655761719,\n 30.012625258927418\n ],\n [\n -95.16288757324219,\n 30.01738175916562\n ],\n [\n -95.15190124511719,\n 30.007273923504556\n ],\n [\n -95.14915466308594,\n 29.989434054144688\n ],\n [\n -95.17662048339844,\n 29.952554831950987\n ],\n [\n -95.16838073730469,\n 29.91923280484215\n ],\n [\n -95.13198852539062,\n 29.91090055463952\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-06-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Erik A. 0000-0001-8434-0798 easmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8434-0798","contributorId":1405,"corporation":false,"usgs":true,"family":"Smith","given":"Erik","email":"easmith@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791375,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shah, Sachin D. 0000-0002-5440-5535 sdshah@usgs.gov","orcid":"https://orcid.org/0000-0002-5440-5535","contributorId":194450,"corporation":false,"usgs":true,"family":"Shah","given":"Sachin","email":"sdshah@usgs.gov","middleInitial":"D.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791376,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211119,"text":"70211119 - 2020 - The Fire and Tree Mortality Database, for empirical modeling of individual tree mortality after fire","interactions":[],"lastModifiedDate":"2020-07-16T17:44:25.115906","indexId":"70211119","displayToPublicDate":"2020-06-22T11:14:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"The Fire and Tree Mortality Database, for empirical modeling of individual tree mortality after fire","docAbstract":"Wildland fires have a multitude of ecological effects in forests, woodlands, and savannas across the globe. A major focus of past research has been on tree mortality from fire, as trees provide a vast range of biological services. We assembled a database of individual-tree records from prescribed fires and wildfires in the United States. The Fire and Tree Mortality (FTM) database includes records from 164,293 individual trees with records of fire injury (crown scorch, bole char, etc.), tree diameter, and either mortality or top-kill up to ten years post-fire. Data span 142 species and 62 genera, from 409 fires occurring from 1981-2016. Additional variables such as insect attack are included when available. The FTM database can be used to evaluate individual fire-caused mortality models for pre-fire planning and post-fire decision support, to develop improved models, and to explore general patterns of individual fire-induced tree death. The database can also be used to identify knowledge gaps that could be addressed in future research.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41597-020-0522-7","usgsCitation":"Cansler, C., Hood, S.M., Varner, J., van Mantgem, P., Agne, M.C., Andrus, R.A., Ayres, M.P., Ayres, B.D., Bakker, J., Battaglia, M.A., Bentz, B.J., Breece, C.R., Brown, J.K., Cluck, D.R., Coleman, T.W., Corace, R.G., Covington, W.W., Cram, D.S., Cronan, J.B., Crouse, J.E., Das, A., Davis, R.S., Dickinson, D.M., Fitzgerald, S.A., Fule, P., Ganio, L.M., Grayson, L.M., Halpern, C.B., Hanula, J.L., Harvey, B.J., Hiers, J.K., Huffman, D.W., Keifer, M., Keyser, T.L., Kobziar, L.N., Kolb, T.E., Kolden, C.A., Kopper, K.E., Kreitler, J.R., Kreye, J.K., Latimer, A.M., Lerch, A.P., Lombardero, M.J., McDaniel, V.L., McHugh, C.W., McMillin, J.D., Moghaddas, J.J., O’Brien, J.J., Perrakis, D.D., Peterson, D.W., Pritchard, S.J., Progar, R.A., Raffa, K.F., Reinhardt, E.D., Restaino, J.C., Roccaforte, J.P., Rogers, B.M., Ryan, K.C., Safford, H.D., Santoro, A.E., Shearman, T.M., Shumate, A.M., Sieg, C., 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Ground motions and rupture scenario","interactions":[],"lastModifiedDate":"2021-08-17T12:40:37.989468","indexId":"70223191","displayToPublicDate":"2020-06-22T07:39:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"The 1933 Long Beach Earthquake (California, USA): Ground motions and rupture scenario","docAbstract":"We present a synoptic analysis of the ground motions from the 11 March 1933 Mw 6.4 Long Beach, California, earthquake, the largest known earthquake within the central Los Angeles Basin region. Our inferred shaking intensity pattern supports the association of the earthquake with the Newport-Inglewood fault; it further illuminates the concentration of severe damage in the town of Compton, where accounts suggest vertical ground motions exceeding 1 g. We use a broadband simulation approach to develop a rupture scenario for this earthquake, informed by the damage distribution. The predicted shaking for a 25-km-long fault matches the intensity distribution, with an indication that non-linear site response on soft sediments in some near-field regions was stronger than predicted using a simple model to account for non-linearity. Our results suggest that the concentration of damage near Compton can be explained by a combination of local site amplification, source-controlled directivity, and three-dimensional basin effects whereby energy was channeled towards the deepest part of the Los Angeles Basin.
Dam management often involves tradeoffs among hydropower generation capacity, environmental impacts, and project costs. However, our understandings of such tradeoffs under a full range of dam management options remain limited, which hinders our ability to make sound and scientifically defensible dam management decisions. In order to assess the scope for theoretical tradeoffs, a dynamic model of hydropower production, important fish populations, and project costs was developed using the system dynamics modeling technique. Three dam management options investigated the likely outcomes from: dam removal, fishway installation (e.g., pool-and-weir, Denil, and fish lift), and no action. The model was applied to the Penobscot River located in Maine, United States as a proof of concept, where recent actions (i.e., dam removal and fishway construction) have been undertaken. We modeled theoretical influence of these actions on four significant sea-run fish (alewife Alosa pseudoharengus, American shad Alosa sapidissima, Atlantic salmon Salmo salar, and sea lamprey Petromyzon marinus) by developing an index of spawner population potential based on population models for each species. Optimal dam management solutions may maximize spawner population potential and energy production to 60-62% of maximum achievable values while limiting the project cost to US$17 million (44% of the maximum value). Our results demonstrate that basin-scale management strategies may increase the migratory fish restoration while preserving hydropower generation capacity. Diversification of management options (e.g., combination of fishway installations, dam removals, and generation capacity) may increase the efficacy of strategic fish-energy-cost tradeoffs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.resconrec.2020.104990","usgsCitation":"Song, C., O’Malley, A., Zydlewski, J.D., and Mo, W., 2020, Balancing fish-energy-cost tradeoffs through strategic basin-wide dam management: Resources, Conservation and Recycling, v. 161, 104990, 12 p., https://doi.org/10.1016/j.resconrec.2020.104990.","productDescription":"104990, 12 p.","ipdsId":"IP-117399","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":456334,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.resconrec.2020.104990","text":"Publisher Index Page"},{"id":395927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -69.8126220703125,\n 44.286502899553156\n ],\n [\n -67.63458251953125,\n 44.286502899553156\n ],\n [\n -67.63458251953125,\n 45.79625461321962\n ],\n [\n -69.8126220703125,\n 45.79625461321962\n ],\n [\n -69.8126220703125,\n 44.286502899553156\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"161","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Song, Cuihong","contributorId":265998,"corporation":false,"usgs":false,"family":"Song","given":"Cuihong","email":"","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":834541,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Malley, Andrew","contributorId":169716,"corporation":false,"usgs":false,"family":"O’Malley","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":834542,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":834540,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mo, Weiwei","contributorId":266002,"corporation":false,"usgs":false,"family":"Mo","given":"Weiwei","affiliations":[{"id":12667,"text":"University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":834543,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}