We present a new consequence-driven framework for earthquake scenario selection. For emergency managers, utility operators, policy makers, and other stakeholders, a scenario-based seismic risk assessment is often necessary for the purpose of emergency management and planning. In developing a scientifically defensible scenario, stakeholders can simulate a realistic event in order to pre-identify vulnerabilities in the system and support action to address these vulnerabilities. Selecting scenarios is particularly challenging for important population centers and critical infrastructure in stable tectonic environments, such as in the central and eastern United States, where uncertain long-term seismicity and unknown faults offer inadequate constraints. Notably, significant events in these so-called stable regions do occur (e.g., Nahanni, Canada, 1985, M6.9; Tennant Creek, Australia, 1998, M6.7). In regions of low seismicity, even moderate events can be consequential due to the higher vulnerability of buildings typical of such regions when compared to regions of higher seismicity. Furthermore, communicating seismic risk to stakeholders and the general public in these regions can be especially challenging due to the complexities of characterizing the hazard level. This framework has been developed to address these challenges for scenario selection in low seismic hazard regions. In this new approach, the analysis begins instead with the explicit definition of a consequence of concern to the specific stakeholder. This can range from a definition of loss (in lives, dollars, or another metric of interest), or a performance metric for critical infrastructure. The framework leverages United States Geological Survey software to run the hazard and consequence analysis. Driven by this stakeholder-defined consequence, an inversion analysis generates a complete event set of candidate scenarios that could breach this consequence. The final selection of a scenario, or family of scenarios, is then scientifically informed, but not limited by our lack of constraints in characterizing the hazard.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Lifelines","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Lifelines 2022","conferenceDate":"January 31 - February 11, 2022","conferenceLocation":"Online","language":"English","publisher":"American Society of Civil Engineers","usgsCitation":"Lin, Y.C., Wald, D.J., Thompson, E.M., and Lallemant, D., 2022, Applying consequence-driven scenario selection to lifelines, in Lifelines, Online, January 31 - February 11, 2022, p. 411-422.","productDescription":"12 p.","startPage":"411","endPage":"422","ipdsId":"IP-130792","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":421131,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421117,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://ascelibrary.org/doi/abs/10.1061/9780784484449.036","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lin, Yolanda C 0000-0002-0423-4248","orcid":"https://orcid.org/0000-0002-0423-4248","contributorId":317878,"corporation":false,"usgs":false,"family":"Lin","given":"Yolanda","email":"","middleInitial":"C","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":884130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":884131,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Eric M. 0000-0002-6943-4806 emthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-6943-4806","contributorId":150897,"corporation":false,"usgs":true,"family":"Thompson","given":"Eric","email":"emthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":884132,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lallemant, David 0000-0001-5759-9972","orcid":"https://orcid.org/0000-0001-5759-9972","contributorId":290680,"corporation":false,"usgs":false,"family":"Lallemant","given":"David","email":"","affiliations":[{"id":16631,"text":"Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":884133,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70247864,"text":"70247864 - 2022 - Conduit processes in crystal-rich dacitic magma and implications for eruptive cycles at Guagua Pichincha volcano, Ecuador","interactions":[],"lastModifiedDate":"2023-08-22T12:13:20.9477","indexId":"70247864","displayToPublicDate":"2022-11-16T07:09:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Conduit processes in crystal-rich dacitic magma and implications for eruptive cycles at Guagua Pichincha volcano, Ecuador","docAbstract":"Stratovolcanoes are commonly characterised by cyclic eruptive activity marked by transitions between dome-forming, Vulcanian, Subplinian and Plinian eruptions. Guagua Pichincha volcano (Ecuador) has been a location of such cyclicity for the past ~ 2000 years, with Plinian eruptions in the first and tenth centuries AD (Anno Domini/after Christ), and CE (Common Era) 1660, which were separated by dome-forming to Subplinian eruptions, such as the recent 1999–2001 eruption. These cycles are therefore a prominent example of effusive-explosive transitions at varying timescales. Here, we investigate the reasons for such shifts in activity by focusing on degassing and outgassing processes within the conduit. We have coupled a petrophysical and textural analysis of dacites from the CE 1660 Plinian eruption and the 1999–2001 dome-forming/Vulcanian eruption, with different percolation models in order to better understand the role of degassing on eruptive style. We demonstrate that the transition from dome-forming to Plinian activity is correlated with differences in phenocryst content and consequently in bulk viscosity. A lower initial phenocryst content and viscosity is inferred for the Plinian case, which promotes faster ascent, closed-system degassing, fragmentation and explosive activity. In contrast, dome-forming phases are promoted by a higher magma viscosity due to higher phenocryst content, with slower ascent enhancing gas escape and microlite crystallization, decreasing explosivity and yielding effusive activity.
","language":"English","publisher":"Springer","doi":"10.1007/s00445-022-01612-1","usgsCitation":"Colombier, M., Bernard, B., Wright, H.M., Le Pennec, J., Caceres, F., Cimarelli, C., Heap, M.J., Samaniego, P., Vasseur, J., and Dingwell, D.B., 2022, Conduit processes in crystal-rich dacitic magma and implications for eruptive cycles at Guagua Pichincha volcano, Ecuador: Bulletin of Volcanology, v. 84, 105, 23 p., https://doi.org/10.1007/s00445-022-01612-1.","productDescription":"105, 23 p.","ipdsId":"IP-143030","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":445864,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-022-01612-1","text":"Publisher Index Page"},{"id":420006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ecuador","otherGeospatial":"Guagua Pichincha volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.96144509158114,\n 0.9806815363857169\n ],\n [\n -77.96144509158114,\n 0.45944639801014375\n ],\n [\n -77.4628312552756,\n 0.45944639801014375\n ],\n [\n -77.4628312552756,\n 0.9806815363857169\n ],\n [\n -77.96144509158114,\n 0.9806815363857169\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"84","noUsgsAuthors":false,"publicationDate":"2022-11-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Colombier, Mathieu","contributorId":328620,"corporation":false,"usgs":false,"family":"Colombier","given":"Mathieu","email":"","affiliations":[{"id":78422,"text":"LMU Munich","active":true,"usgs":false}],"preferred":false,"id":880777,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bernard, Benjamin","contributorId":178529,"corporation":false,"usgs":false,"family":"Bernard","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":880778,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wright, Heather M. 0000-0001-9013-507X hwright@usgs.gov","orcid":"https://orcid.org/0000-0001-9013-507X","contributorId":3949,"corporation":false,"usgs":true,"family":"Wright","given":"Heather","email":"hwright@usgs.gov","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":880779,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Le Pennec, Jean-Luc","contributorId":315394,"corporation":false,"usgs":false,"family":"Le Pennec","given":"Jean-Luc","affiliations":[{"id":68303,"text":"CNRS, France","active":true,"usgs":false}],"preferred":false,"id":880780,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caceres, Francisco","contributorId":328621,"corporation":false,"usgs":false,"family":"Caceres","given":"Francisco","email":"","affiliations":[{"id":78422,"text":"LMU Munich","active":true,"usgs":false}],"preferred":false,"id":880781,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cimarelli, Corrado","contributorId":257017,"corporation":false,"usgs":false,"family":"Cimarelli","given":"Corrado","affiliations":[{"id":47800,"text":"Ludwig Maximilian University of Munich","active":true,"usgs":false}],"preferred":false,"id":880782,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heap, Michael J. 0000-0002-4748-735X","orcid":"https://orcid.org/0000-0002-4748-735X","contributorId":297882,"corporation":false,"usgs":false,"family":"Heap","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":64429,"text":"Université de Strasbourg","active":true,"usgs":false}],"preferred":false,"id":880783,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Samaniego, Pablo","contributorId":205724,"corporation":false,"usgs":false,"family":"Samaniego","given":"Pablo","email":"","affiliations":[{"id":37157,"text":"Université Clermont Auvergne, CNRS, IRD, OPGC, Laboratoire Magmas et Volcans, F-63000 Clermont-Ferrand, France","active":true,"usgs":false}],"preferred":false,"id":880784,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Vasseur, Jeremie","contributorId":315405,"corporation":false,"usgs":false,"family":"Vasseur","given":"Jeremie","email":"","affiliations":[{"id":36958,"text":"LMU Munich, Germany","active":true,"usgs":false}],"preferred":false,"id":880785,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dingwell, Donald B.","contributorId":201841,"corporation":false,"usgs":false,"family":"Dingwell","given":"Donald","email":"","middleInitial":"B.","affiliations":[{"id":36273,"text":"Ludwig-Maximilians-Universität (LMU) München","active":true,"usgs":false}],"preferred":false,"id":880786,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70256623,"text":"70256623 - 2022 - Lithology and disturbance drive cavefish and cave crayfish occurrence in the Ozark Highlands ecoregion","interactions":[],"lastModifiedDate":"2024-08-27T15:10:10.350182","indexId":"70256623","displayToPublicDate":"2022-11-15T09:58:19","publicationYear":"2022","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":"Lithology and disturbance drive cavefish and cave crayfish occurrence in the Ozark Highlands ecoregion","docAbstract":"Diverse communities of groundwater-dwelling organisms (i.e., stygobionts) are important for human wellbeing; however, we lack an understanding of the factors driving their distributions, making it difficult to protect many at-risk species. Therefore, our study objective was to determine the landscape factors related to the occurrence of cavefishes and cave crayfishes in the Ozark Highlands ecoregion, USA. We sampled cavefishes and cave crayfishes at 61 sampling units using both visual and environmental DNA surveys. We then modeled occurrence probability in relation to lithology and human disturbance while accounting for imperfect detection. Our results indicated that occurrence probability of cave crayfishes was negatively associated with human disturbance, whereas there was a weak positive relationship between cavefish occurrence and disturbance. Both cavefishes and cave crayfishes were more likely to occur in limestone rather than dolostone lithology. Our results indicate structuring factors are related to the distribution of these taxa, but with human disturbance as a prevalent modifier of distributions for cave crayfishes. Limiting human alteration near karst features may be warranted to promote the persistence of some stygobionts. Moreover, our results indicate current sampling efforts are inadequate to detect cryptic species; therefore, expanding sampling may be needed to develop effective conservation actions.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-022-21791-3","usgsCitation":"Mouser, J., Brewer, S.K., Niemiller, M., Mollenhauer, R., and Van Den Bussche, R.A., 2022, Lithology and disturbance drive cavefish and cave crayfish occurrence in the Ozark Highlands ecoregion: Scientific Reports, v. 12, 19559, 10 p., https://doi.org/10.1038/s41598-022-21791-3.","productDescription":"19559, 10 p.","ipdsId":"IP-136389","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":445870,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-022-21791-3","text":"Publisher Index Page"},{"id":433200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Missouri, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.19813639078185,\n 36.971706993010315\n ],\n [\n -94.76416998727156,\n 36.88121743833784\n ],\n [\n -94.9821977429566,\n 36.432177796608784\n ],\n [\n -94.29247532353017,\n 36.292055756716266\n ],\n [\n -93.663549105208,\n 36.467590353199\n ],\n [\n -93.75579161722848,\n 36.98846283086834\n ],\n [\n -94.19813639078185,\n 36.971706993010315\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2022-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Mouser, Joshua B.","contributorId":341406,"corporation":false,"usgs":false,"family":"Mouser","given":"Joshua B.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908362,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niemiller, Matthew L.","contributorId":341407,"corporation":false,"usgs":false,"family":"Niemiller","given":"Matthew L.","affiliations":[{"id":81735,"text":"The University of Alabama in Huntsville","active":true,"usgs":false}],"preferred":false,"id":908363,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mollenhauer, Robert","contributorId":341408,"corporation":false,"usgs":false,"family":"Mollenhauer","given":"Robert","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908364,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van Den Bussche, Ronald A.","contributorId":341409,"corporation":false,"usgs":false,"family":"Van Den Bussche","given":"Ronald","email":"","middleInitial":"A.","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908365,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238326,"text":"70238326 - 2022 - Towards real-time probabilistic ash deposition forecasting for New Zealand","interactions":[],"lastModifiedDate":"2022-11-16T13:09:21.164087","indexId":"70238326","displayToPublicDate":"2022-11-14T07:07:43","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3841,"text":"Journal of Applied Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Towards real-time probabilistic ash deposition forecasting for New Zealand","docAbstract":"Volcanic ashfall forecasts are highly dependent on eruption source parameters (ESPs) and synoptic weather conditions at the time and location of the eruption. In New Zealand, MetService and GNS Science have been jointly developing an ashfall forecast system that incorporates four-dimensional high-resolution numerical weather prediction (NWP) and ESPs into the HYSPLIT model, a state-of-the art hybrid Eulerian and Lagrangian dispersion model widely used for volcanic ash. However, these forecasts are based on discrete ESPs combined with a deterministic weather forecast and thus provide no information on output uncertainty. This shortcoming hinders stakeholder decision making, particularly near the geographical margin of forecasted ashfall and in areas with large gradients in forecasted ash deposition. Our study presents a new approach that incorporates uncertainty from both eruptive and meteorological inputs to deliver uncertainty in the model output. To this end, we developed probability density functions (PDFs) for three key ESPs (plume height, mass eruption rate, eruption duration) tailored to New Zealand’s volcanoes and combine them with NWP ensemble datasets to generate probabilistic ashfall forecasts using the HYSPLIT model. We show that the Latin Hypercube Sampling (LHS) technique can be used to representatively span this four-dimensional parameter space and allow us to add uncertainty quantification to rapid response forecast systems. For a case study of a hypothetical eruption at Tongariro, New Zealand we suggest that large parts of New Zealand’s North Island would not receive adequate warning for potential ashfall if uncertainties were not included in the forecasts. We also propose new probabilistic summary products to support public information and emergency responders decision making.
Oceanic crust formed at mid-ocean ridges may be later modified by off-ridge magmatism forming seamounts, guyots, and islands. We investigate processes associated with seamount formation in the Gulf of Alaska Seamount Province using two coincident seismic reflection/wide-angle profiles. A north-south profile crosses the Kodiak-Bowie Seamount Chain and Aja fracture zone (FZ), and an orthogonal east-west profile is located about 90 km south of the seamount chain over Pacific plate oceanic crust. Structure along the profile away from the seamount chain is consistent with typical oceanic crust. Crust in our study region is thinnest (about 5.6 km) at the Aja FZ. Unlike observations from active transform faults, no low-velocity anomaly is observed at the Aja FZ suggesting that the crustal velocities have recovered to normal values through crack closure and crack healing. Higher lower crustal velocities (∼7.3 and > 7.5 km/s) and thicker crust (∼8.5 and ∼7.0 km) are observed near the Pratt and Durgin Seamounts and at the intersection of the Kodiak-Bowie Seamount Chain linear trend, respectively. These observations are attributed to magmatic underplating associated with seamount province magmatism. Lithospheric thickness variations across the Aja FZ may form a barrier or impediment to magmatic flow. The thickest crust (8.5 km) along our two profiles is located on the younger side of the FZ, and we suggest that the majority of magmatism jumped south of the Aja FZ when thinner lithosphere was encountered by the Bowie hot spot. The crustal structure near the Kodiak-Bowie Seamount Chain is most similar to that of other seamounts and guyots that formed on similarly young lithosphere (8–12 Ma). Our results suggest that lithospheric thickness at the time of hot spot interaction has a large control on magmatic underplating at seamounts and seamount provinces.
Subduction zone accretionary prisms are commonly modeled as elastic structures where permanent deformation is accommodated by faulting and folding of otherwise elastic materials, yet accretionary prisms may exhibit other deformation styles over relatively short time scales. In this study, we use 6.5-year (2014–2021) Sentinel-1 interferometric synthetic aperture radar (InSAR) time-series of post-seismic deformation in the Makran accretionary prism of southeast Pakistan to characterize non-linear viscoelastic deformation within an active accretionary prism on short timescales (months to years). We constructed a series of 3-D finite-element models of the Makran subduction zone, including an accretionary prism, and constrained the elastic thickness of the upper wedge and the flow-law parameters (power-law exponent, activation enthalpy, and pre-exponential constant) of the lower wedge through forward model fits to the InSAR time-series. Our results show that the prism is elastically thin (8–12 km) and the non-linear viscoelastic relaxation of the deep portions of the prism alone can sufficiently explain the post-seismic surface deformation. Our best fitting flow-law parameters (n = 3.76 ± 0.39, Q = 82.2 ± 37.73 kJ mol−1, and A = 10−3.36±4.69) are consistent with triggering of low temperature dislocation creep within fluid-saturated siliciclastic rocks. We believe that the fluids necessary for this weakening originate from sedimentary underplating and/or the presence the hydrocarbons. The presence of power-law rheology within the lower wedge impacts the estimated plate coupling and the stress state in the subduction system, with respect to the conventional elastic wedge model, and hence should to be considered in future earthquake cycle models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JB024493","usgsCitation":"Cheng, G., Barnhart, W.D., and Li, S., 2022, Power-law viscoelastic flow of the lower accretionary prism in the Makran subduction zone following the 2013 Baluchistan Earthquake: JGR Solid Earth, v. 127, no. 11, e2022JB024493, 17 p., https://doi.org/10.1029/2022JB024493.","productDescription":"e2022JB024493, 17 p.","ipdsId":"IP-139827","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"links":[{"id":502088,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jb024493","text":"Publisher Index Page"},{"id":502012,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Iran. Pakistan","otherGeospatial":"Makran accretionary prism","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 57.41648936499405,\n 30.69926527865796\n ],\n [\n 57.41648936499405,\n 24.018536636005464\n ],\n [\n 67.65925595369796,\n 24.018536636005464\n ],\n [\n 67.65925595369796,\n 30.69926527865796\n ],\n [\n 57.41648936499405,\n 30.69926527865796\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"127","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Cheng, Guo","contributorId":369215,"corporation":false,"usgs":false,"family":"Cheng","given":"Guo","affiliations":[],"preferred":false,"id":958498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnhart, William D. 0000-0003-0498-1697 wbarnhart@usgs.gov","orcid":"https://orcid.org/0000-0003-0498-1697","contributorId":294678,"corporation":false,"usgs":true,"family":"Barnhart","given":"William","email":"wbarnhart@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":958499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Shaoyang","contributorId":207597,"corporation":false,"usgs":false,"family":"Li","given":"Shaoyang","email":"","affiliations":[],"preferred":false,"id":958500,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238160,"text":"70238160 - 2022 - GCPs free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-Mounted GNSS RTK","interactions":[],"lastModifiedDate":"2022-11-15T12:55:04.571611","indexId":"70238160","displayToPublicDate":"2022-11-12T06:53:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"GCPs free photogrammetry for estimating tree height and crown diameter in Arizona cypress plantation using UAV-Mounted GNSS RTK","docAbstract":"Sea otters were extirpated throughout much of their range by the maritime fur trade in the 18th and 19th centuries, including the coast of Katmai National Park and Preserve in southcentral Alaska. Brown bears are an important component of the Katmai ecosystem where they are the focus of a thriving ecotourism bear-viewing industry as they forage in sedge meadows and dig clams in the extensive tidal flats that exist there. Sea otters began reoccupying Katmai in the 1970s where their use of intertidal clam resources overlapped that of brown bears. By 2008, the Katmai sea otter population had grown to an estimated 7,000 animals and was likely near carrying capacity; however, in 2006–2015, the age-at-death distribution (AADD) of sea otter carcasses collected at Katmai included a higher-than-expected proportion of prime-age animals compared to most other sea otter populations in Alaska. The unusual AADD warranted scientific investigation, particularly because the Katmai population is part of the Threatened southwest sea otter stock. Brown bears in Katmai are known to prey on marine mammals and sea otters, but depredation rates are unknown; thus, we investigated carnivore predation, especially by brown bears, as a potential explanation for abnormally high prime-age otter mortality. We installed camera traps at two island-based marine mammal haulout sites within Katmai to gather direct evidence that brown bears prey on seals and sea otters. Over a period of two summers, we gathered photo evidence of brown bears making 22 attempts to prey on sea otters of which nine (41%) were successful and 12 attempts to prey on harbor seals of which one (8%) was successful. We also developed a population model based on the AADD to determine if the living population is declining, as suggested by the high proportion of prime-age animals in the AADD. We found that the population trend predicted by the modeled AADDs was contradictory to aerial population surveys that indicated the population was not in steep decline but was consistent with otter predation. Future work should focus on the direct and indirect effects these top-level predators have on each other and the coastal community that connects them.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyac095","usgsCitation":"Monson, D., Taylor, R.L., Hilderbrand, G., Erlenbach, J., Coletti, H., and Bodkin, J.L., 2022, Brown bear–sea otter interactions along the Katmai coast: Terrestrial and nearshore communities linked by predation: Journal of Mammalogy, gyac095, 13 p., https://doi.org/10.1093/jmammal/gyac095.","productDescription":"gyac095, 13 p.","ipdsId":"IP-109601","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":445905,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyac095","text":"Publisher Index Page"},{"id":409348,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Katmai National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.9563357076709,\n 59.34074602972433\n ],\n [\n -156.9563357076709,\n 57.706193986474744\n ],\n [\n -153.1440798482959,\n 57.706193986474744\n ],\n [\n -153.1440798482959,\n 59.34074602972433\n ],\n [\n -156.9563357076709,\n 59.34074602972433\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-11-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":857010,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Rebecca L. 0000-0001-8459-7614 rebeccataylor@usgs.gov","orcid":"https://orcid.org/0000-0001-8459-7614","contributorId":5112,"corporation":false,"usgs":true,"family":"Taylor","given":"Rebecca","email":"rebeccataylor@usgs.gov","middleInitial":"L.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":857011,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hilderbrand, Grant 0000-0002-0051-8315 ghilderbrand@usgs.gov","orcid":"https://orcid.org/0000-0002-0051-8315","contributorId":297939,"corporation":false,"usgs":false,"family":"Hilderbrand","given":"Grant","email":"ghilderbrand@usgs.gov","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":857012,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erlenbach, Joy","contributorId":200750,"corporation":false,"usgs":false,"family":"Erlenbach","given":"Joy","affiliations":[],"preferred":false,"id":857013,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coletti, Heather","contributorId":258849,"corporation":false,"usgs":false,"family":"Coletti","given":"Heather","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":857014,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bodkin, James L. 0000-0003-1641-4438 jbodkin@usgs.gov","orcid":"https://orcid.org/0000-0003-1641-4438","contributorId":748,"corporation":false,"usgs":true,"family":"Bodkin","given":"James","email":"jbodkin@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":857015,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256723,"text":"70256723 - 2022 - Reproductive success of Red-Billed Tropicbirds (Phaethon aethereus) on St. Eustatius, Caribbean Netherlands","interactions":[],"lastModifiedDate":"2024-08-15T11:12:13.575616","indexId":"70256723","displayToPublicDate":"2022-11-11T06:09:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"Reproductive success of Red-Billed Tropicbirds (Phaethon aethereus) on St. Eustatius, Caribbean Netherlands","docAbstract":"The daily nest-survival rates of Red-billed Tropicbirds (Phaethon aethereus) were estimated over six breeding seasons on St. Eustatius in the Caribbean. We analyzed 338 nesting attempts between 2013 and 2020. The daily survival rate (DSR) of tropicbird nests was modeled as a function of nest initiation date, sea surface temperature (SST), elevation, vegetation in front of the nest, and year. Yearly nest survival rates (± SE) of the best fitting models ranged from 0.21 ± 0.06–0.74 ± 0.13 (n = 338 nests). DSR of the most parsimonious models averaged 0.39 ± 0.04 during the incubation period, 0.83 ± 0.05 during the chick-rearing period, and 0.30 ± 0.04 during the nesting period (incubation through fledging) when data were pooled across all years. Models with linear and quadratic trends of nest initiation date combined with SST and elevation received strong support in the incubation and nesting periods. Nests initiated in peak nesting season, when SSTs were lower, had higher DSR estimates than nests initiated early or late in the season. Compared to studies of the same species from Saba and the Gulf of California, survival probability on St. Eustatius was lower during the incubation stage but higher during the chick-rearing period. Similar to populations in the Gulf of California, tropicbird reproduction differed and laying date varied among years, and survival was influenced by SST. Our results are consistent with a study on White-tailed Tropicbirds (Phaethon lepturus) in Bermuda which found that survival was affected by temporal factors rather than physical site characteristics. Our study contributes to a better understanding of the factors that influence Red-billed Tropicbird survival on a small Caribbean island.
The research described in this report was conducted as part of the Natural Resource Damage Assessment and Restoration process in the Big River. Our purpose was to compare habitat features and landscape factors that may be important for the establishment and persistence of mussel concentrations between the Big River and the adjacent Bourbeuse and Meramec rivers, thereby testing their appropriateness as reference systems for establishing baseline expectations of mussel populations in the absence of mining impacts for the Big River. Based on these comparisons and a published model dileneating suitable habitat for freshwater mussels, we establish expected baseline conditions related to suitable freshwater mussel habitat in the Big River to assist injury determination for mining-related impacts in the Southeast Missouri Lead Mining District Natural Resource Damage Assessment case.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Rosenberger, A.E., and Lindner, G.A., 2022, Use of a riverscape-scale model of fundamental physical habitat requirements for freshwater mussels to quantify mussel declines in a mining-contaminated stream: The Big River, Old Lead Belt, Southeast Missouri: Cooperator Science Series FWS/CSS-147-2022, ii, 32 p.","productDescription":"ii, 32 p.","ipdsId":"IP-132994","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":431949,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/media/use-riverscape-scale-model-fundamental-physical-habitat-requirements-freshwater-mussels"},{"id":433629,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Big River watershed, Bourbeuse River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.34550598548627,\n 38.669645646671654\n ],\n [\n -91.90534506180033,\n 38.65386249841083\n ],\n [\n -91.86155171390972,\n 37.44368900102869\n ],\n [\n -90.22760081808802,\n 37.470421694633586\n ],\n [\n -90.34550598548627,\n 38.669645646671654\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":908322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lindner, Garth A.","contributorId":201828,"corporation":false,"usgs":false,"family":"Lindner","given":"Garth","email":"","middleInitial":"A.","affiliations":[{"id":36266,"text":"University of Missouri Cooperative Research Unit","active":true,"usgs":false}],"preferred":false,"id":908323,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70238513,"text":"70238513 - 2022 - Framework for assessing and mitigating the impacts of offshore wind energy development on marine birds","interactions":[],"lastModifiedDate":"2022-11-28T14:05:54.906225","indexId":"70238513","displayToPublicDate":"2022-11-10T07:56:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Framework for assessing and mitigating the impacts of offshore wind energy development on marine birds","docAbstract":"Offshore wind energy development (OWED) is rapidly expanding globally and has the potential to contribute significantly to renewable energy portfolios. However, development of infrastructure in the marine environment presents risks to wildlife. Marine birds in particular have life history traits that amplify population impacts from displacement and collision with offshore wind infrastructure. Here, we present a broadly applicable framework to assess and mitigate the impacts of OWED on marine birds. We outline existing techniques to quantify impact via monitoring and modeling (e.g., collision risk models, population viability analysis), and present a robust mitigation framework to avoid, minimize, or compensate for OWED impacts. Our framework addresses impacts within the context of multiple stressors across multiple wind energy developments. We also present technological and methodological approaches that can improve impact estimation and mitigation. We highlight compensatory mitigation as a tool that can be incorporated into regulatory frameworks to mitigate impacts that cannot be avoided or minimized via siting decisions or alterations to OWED infrastructure or operation. Our framework is intended as a globally-relevant approach for assessing and mitigating OWED impacts on marine birds that may be adapted to existing regulatory frameworks in regions with existing or planned OWED.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2022.109795","usgsCitation":"Croll, D.A., Ellis, A.A., Adams, J., Cook, A.S., Garthe, S., Wing Goodale, M., Hall, C.S., Hazen, E.L., Keitt, B.S., Kelsey, E.C., Leirness, J.B., Lyons, D.E., McKown, M., Potiek, A., Searle, K.R., Soudjin, F.H., Rockwood, R.C., Tershy, B.R., Tinker, M., Vanderwerf, E.A., Williams, K.A., Young, L.C., and Zilliacus, K., 2022, Framework for assessing and mitigating the impacts of offshore wind energy development on marine birds: Biological Conservation, v. 276, 109795, 15 p., https://doi.org/10.1016/j.biocon.2022.109795.","productDescription":"109795, 15 p.","ipdsId":"IP-142666","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":445913,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2022.109795","text":"Publisher Index Page"},{"id":409690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"276","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Croll, Donald A","contributorId":299391,"corporation":false,"usgs":false,"family":"Croll","given":"Donald","email":"","middleInitial":"A","affiliations":[{"id":64830,"text":"UC Santa Cruz, Ocean Health Building, 115 McAllister Way, Santa Cruz, CA 95060, United States","active":true,"usgs":false}],"preferred":false,"id":857691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellis, Aspen A","contributorId":299392,"corporation":false,"usgs":false,"family":"Ellis","given":"Aspen","email":"","middleInitial":"A","affiliations":[{"id":64830,"text":"UC Santa Cruz, Ocean Health Building, 115 McAllister Way, Santa Cruz, CA 95060, United States","active":true,"usgs":false}],"preferred":false,"id":857692,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adams, Josh 0000-0003-3056-925X","orcid":"https://orcid.org/0000-0003-3056-925X","contributorId":213442,"corporation":false,"usgs":true,"family":"Adams","given":"Josh","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857693,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cook, Aonghais S. C. P.","contributorId":299393,"corporation":false,"usgs":false,"family":"Cook","given":"Aonghais","email":"","middleInitial":"S. C. P.","affiliations":[{"id":64832,"text":"British Trust for Ornithology, The Nunnery, Thetford, Norfolk IP26 4BG, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":857694,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Garthe, Stefan","contributorId":243464,"corporation":false,"usgs":false,"family":"Garthe","given":"Stefan","affiliations":[],"preferred":false,"id":857695,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wing Goodale, Morgan","contributorId":299394,"corporation":false,"usgs":false,"family":"Wing Goodale","given":"Morgan","email":"","affiliations":[{"id":64834,"text":"Biodiversity Research Institute, 276 Canco Road, Portland, ME 04103, United States","active":true,"usgs":false}],"preferred":false,"id":857696,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hall, C. Scott","contributorId":299395,"corporation":false,"usgs":false,"family":"Hall","given":"C.","email":"","middleInitial":"Scott","affiliations":[{"id":64835,"text":"National Fish and Wildlife Foundation, 1133 15thSt. N.W., Suite 1000, Washington, DC 20005, United States","active":true,"usgs":false}],"preferred":false,"id":857697,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hazen, Elliott L.","contributorId":217590,"corporation":false,"usgs":false,"family":"Hazen","given":"Elliott","email":"","middleInitial":"L.","affiliations":[{"id":39677,"text":"National Marine Fisheries Service, National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":857698,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Keitt, Bradford S.","contributorId":299396,"corporation":false,"usgs":false,"family":"Keitt","given":"Bradford","email":"","middleInitial":"S.","affiliations":[{"id":64836,"text":"American Bird Conservancy, Santa Cruz, CA, United States","active":true,"usgs":false}],"preferred":false,"id":857699,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kelsey, Emily C. 0000-0002-0107-3530 ekelsey@usgs.gov","orcid":"https://orcid.org/0000-0002-0107-3530","contributorId":206505,"corporation":false,"usgs":true,"family":"Kelsey","given":"Emily","email":"ekelsey@usgs.gov","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857700,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Leirness, Jeffery B","contributorId":299397,"corporation":false,"usgs":false,"family":"Leirness","given":"Jeffery","email":"","middleInitial":"B","affiliations":[{"id":64837,"text":"CSS Inc., 2750 Prosperity Avenue, Suite 220, Fairfax, VA 22031, United States","active":true,"usgs":false}],"preferred":false,"id":857701,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lyons, Don E","contributorId":299398,"corporation":false,"usgs":false,"family":"Lyons","given":"Don","email":"","middleInitial":"E","affiliations":[{"id":64838,"text":"Audubon Seabird Institute, National Audubon Society, 12 Audubon Road, Bremen, ME 04551, United States","active":true,"usgs":false}],"preferred":false,"id":857702,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"McKown, Matthew W.","contributorId":298878,"corporation":false,"usgs":false,"family":"McKown","given":"Matthew W.","affiliations":[{"id":64717,"text":"Conservation Metrics, Inc., Santa Cruz, CA, USA","active":true,"usgs":false}],"preferred":false,"id":857703,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Potiek, Astrid","contributorId":299399,"corporation":false,"usgs":false,"family":"Potiek","given":"Astrid","email":"","affiliations":[{"id":64839,"text":"Waardenburg Ecology, Varkensmarkt 9, 4101 CK Culemborg, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":857704,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Searle, Kate R","contributorId":299400,"corporation":false,"usgs":false,"family":"Searle","given":"Kate","email":"","middleInitial":"R","affiliations":[{"id":64840,"text":"UK Centre for Ecology & Hydrology, Bush Estate, Penicuik EH26 0QB, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":857705,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Soudjin, Floor H.","contributorId":299401,"corporation":false,"usgs":false,"family":"Soudjin","given":"Floor","email":"","middleInitial":"H.","affiliations":[{"id":64841,"text":"Ecological Dynamics Group, Wageningen Marine Research, 1976 CP Ijmuiden, the Netherlands","active":true,"usgs":false}],"preferred":false,"id":857706,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Rockwood, R. Cotton","contributorId":299402,"corporation":false,"usgs":false,"family":"Rockwood","given":"R.","email":"","middleInitial":"Cotton","affiliations":[{"id":64842,"text":"Point Blue Conservation Science, 3820 Cypress Drive, Suite 11, Petaluma, CA 94954, United States","active":true,"usgs":false}],"preferred":false,"id":857707,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Tershy, Bernie R.","contributorId":299403,"corporation":false,"usgs":false,"family":"Tershy","given":"Bernie","email":"","middleInitial":"R.","affiliations":[{"id":64830,"text":"UC Santa Cruz, Ocean Health Building, 115 McAllister Way, Santa Cruz, CA 95060, United States","active":true,"usgs":false}],"preferred":false,"id":857708,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Tinker, Martin","contributorId":299404,"corporation":false,"usgs":false,"family":"Tinker","given":"Martin","email":"","affiliations":[{"id":64843,"text":"Nhydra Ecological, Head of St Margarets Bay, Nova Scotia, Canada","active":true,"usgs":false}],"preferred":false,"id":857709,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Vanderwerf, Eric A.","contributorId":104689,"corporation":false,"usgs":false,"family":"Vanderwerf","given":"Eric","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":857710,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Williams, Kathryn A","contributorId":299405,"corporation":false,"usgs":false,"family":"Williams","given":"Kathryn","email":"","middleInitial":"A","affiliations":[{"id":64834,"text":"Biodiversity Research Institute, 276 Canco Road, Portland, ME 04103, United States","active":true,"usgs":false}],"preferred":false,"id":857711,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Young, Lindsay C.","contributorId":149044,"corporation":false,"usgs":false,"family":"Young","given":"Lindsay","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":857712,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Zilliacus, Kelly","contributorId":299406,"corporation":false,"usgs":false,"family":"Zilliacus","given":"Kelly","email":"","affiliations":[{"id":64830,"text":"UC Santa Cruz, Ocean Health Building, 115 McAllister Way, Santa Cruz, CA 95060, United States","active":true,"usgs":false}],"preferred":false,"id":857713,"contributorType":{"id":1,"text":"Authors"},"rank":23}]}} ,{"id":70238049,"text":"sir20225102 - 2022 - Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana","interactions":[],"lastModifiedDate":"2022-11-11T17:47:08.905958","indexId":"sir20225102","displayToPublicDate":"2022-11-10T07:15:06","publicationYear":"2022","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":"2022-5102","displayTitle":"Effect of Uncertainty of Discharge Data on Uncertainty of Discharge Simulation for the Lake Michigan Diversion, Northeastern Illinois and Northwestern Indiana","title":"Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana","docAbstract":"Simulation models of watershed hydrology (also referred to as “rainfall-runoff models”) are calibrated to the best available streamflow data, which are typically published discharge time series at the outlet of the watershed. Even after calibration, the model generally cannot replicate the published discharges because of simplifications of the physical system embedded in the model structure and uncertainties of the input data and of the estimated model parameters, which, although optimized for the given calibration data, remain uncertain. The input data errors are caused by uncertainties in the forcing data, such as precipitation and other climatological data, and in the published discharges used for calibration. In the numerical algorithms used for calibration, the published discharges are often assumed to be without error, but they are themselves uncertain, typically having been computed using ratings, which are models fitted to uncertain discharge measurements.
In this study, uncertainty of published daily discharge data and how the discharge uncertainty is transmitted to the parameter values of the Hydrological Simulation Program–FORTRAN (HSPF) rainfall-runoff model and to the simulated discharge at both calibration and prediction locations were investigated for the Lake Michigan diversion in northeastern Illinois and northwestern Indiana. The HSPF model used in this study is used by the U.S. Army Corps of Engineers as part of quantifying the diversion of water from Lake Michigan by the State of Illinois. In this study, the model is calibrated jointly at two watersheds in the study area; the resulting model is considered the base model in this study. Seven other gaged watersheds in the study area are used for testing predictive simulations. A Bayesian rating curve estimation (BaRatin) approach, the BaRatin stage-period-discharge (SPD) method, was used to estimate the uncertainty of the published discharge from the calibration watersheds. To characterize the effect of the discharge uncertainty on parameter values, the HSPF model parameters were recalibrated to 17 nonrandomly selected pairs of discharge series from the BaRatin SPD analysis. To provide an indicator of the effect of parameter uncertainty to compare to the effect of discharge uncertainty, 1,000 parameter sets also were randomly generated from the estimated parameter covariance matrix of the base model. The recalibrated and random parameter sets were then used in HSPF simulations of discharge at the two calibration watersheds and at the seven prediction watersheds. Selected discharge summary statistics—the period-of-study (POS, water years 1997 to 2015) mean discharge, selected flow-duration curve (FDC) quantiles, and water year mean discharges—are used to characterize the variability between simulated and published discharge.
A normalized variability index (VN) is used as a measure of the uncertainty of flow statistics arising from the uncertainty of the sources considered in this study. When this index is at least 1, the variability of the simulations is large enough to explain the median error between simulated and published values, although offsetting errors from other sources are also likely. When the index is appreciably less than 1, the variability of the simulations is clearly insufficient to explain the median error between simulated and published values. At the two calibration watersheds and for results of the two simulation sets considered together, the VN values ranged from 0.2 to 0.8 for POS mean discharge, from 0.3 to 0.6 in the median for a set of FDC quantiles, and from 0.1 to 0.2 in the median for water year mean discharges. These values indicate that substantial uncertainty remains unexplained. Even though two watersheds were used in calibration, that calibration was highly constrained because it was applied to the watersheds simultaneously and was subject to parameter regularization that constrained the adjustment of the parameters from their initial values. These constraints were applied to avoid overfitting to the calibration watersheds and thus to increase the likelihood that the resulting parameters would give accurate results at watersheds not used in the calibration, but they created a parameter transfer error in the calibration watershed results shown by the balancing of errors between the two watersheds. Additional remaining error sources include model structural error and meteorological forcing error to the degree that the calibration was unable to adjust the parameters to account for these errors. At the prediction watersheds, the corresponding VN values were almost always substantially lower than those values at the calibration watersheds. This result is expected because the prediction watersheds have additional uncertainty, including parameter transfer error.
The work described in this report provides preliminary estimates of a limited range of sources of error in predicted discharge uncertainty. Future work would be beneficial to obtain a better statistical characterization of the effect of the uncertainty of calibration discharge series and to address additional sources of uncertainty, such as from precipitation input data used in calibration and prediction and from structural (model) errors.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225102","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers, Chicago District","usgsCitation":"Soong, D.T., and Over, T.M., 2022, Effect of uncertainty of discharge data on uncertainty of discharge simulation for the Lake Michigan Diversion, northeastern Illinois and northwestern Indiana: U.S. Geological Survey Scientific Investigations Report 2022–5102, 54 p., https://doi.org/10.3133/sir20225102.","productDescription":"Report: ix, 54 p.; 2 Data releases; Dataset","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-120412","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":409202,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97S2IID","text":"USGS data release","linkHelpText":"National Land Cover Database (NLCD) 2011 Land Cover Conterminous United States"},{"id":409201,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UC21B0","text":"USGS data release","linkHelpText":"Models, inputs, and outputs for estimating the uncertainty of discharge simulations for the Lake Michigan Diversion using the Hydrological Simulation Program – FORTRAN model"},{"id":409196,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5102/coverthb.jpg"},{"id":409197,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5102/sir20225102.pdf","text":"Report","size":"8.21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5102"},{"id":409198,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5102/sir20225102.XML"},{"id":409199,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5102/images"},{"id":409200,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Illinois, Indiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.34881814497747,\n 42.492126793048925\n ],\n [\n -88.34881814497747,\n 41.20266079763215\n ],\n [\n -87.22772634687415,\n 41.20266079763215\n ],\n [\n -87.22772634687415,\n 42.492126793048925\n ],\n [\n -88.34881814497747,\n 42.492126793048925\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
Swarms are bursts of earthquakes without an obvious mainshock. Some have been observed to be associated with transient aseismic fault slip, while others are thought to be related to fluids. However, the association is rarely quantitative due to insufficient data quality. We use high-quality GPS/GNSS, InSAR, and relocated seismicity to study a swarm of >2,000 earthquakes which occurred between 30 September and 6 October 2020, near Westmorland, California. Using 5 min sampled Global Positioning System (GPS) supplemented with InSAR, we document a spontaneous shallow Mw 5.2 slow slip event that preceded the swarm by 2–15 hr. The earthquakes in the early phase were predominantly non-interacting and driven primarily by the slow slip event resulting in a nonlinear expansion. A stress-driven model based on the rate-and-state friction successfully explains the overall spatial and temporal evolution of earthquakes, including the time lag between the onset of the slow slip event and the swarm. Later, a distinct back front and a square root of time expansion of clustered seismicity on en-echelon fault structures suggest that fluids helped sustain the swarm. Static stress triggering analysis using Coulomb stress and statistics of interevent times suggest that 45%–65% of seismicity was driven by the slow slip event, 10%–35% by inter-earthquake interactions, and 10%–30% by fluids. Our model also provides constraints on the friction parameter and the pore pressure and suggests that this swarm behaved like an aftershock sequence but with the mainshock replaced by the slow slip event.
Billions of people living in poverty are at risk of environmentally mediated infectious diseases—that is, pathogens with environmental reservoirs that affect disease persistence and control and where environmental control of pathogens can reduce human risk. The complex ecology of these diseases creates a global health problem not easily solved with medical treatment alone.
We quantified the current global disease burden caused by environmentally mediated infectious diseases and used a structural equation model to explore environmental and socioeconomic factors associated with the human burden of environmentally mediated pathogens across all countries.
We found that around 80% (455 of 560) of WHO-tracked pathogen species known to infect humans are environmentally mediated, causing about 40% (129 488 of 359 341 disability-adjusted life years) of contemporary infectious disease burden (global loss of 130 million years of healthy life annually). The majority of this environmentally mediated disease burden occurs in tropical countries, and the poorest countries carry the highest burdens across all latitudes. We found weak associations between disease burden and biodiversity or agricultural land use at the global scale. In contrast, the proportion of people with rural poor livelihoods in a country was a strong proximate indicator of environmentally mediated infectious disease burden. Political stability and wealth were associated with improved sanitation, better health care, and lower proportions of rural poverty, indirectly resulting in lower burdens of environmentally mediated infections. Rarely, environmentally mediated pathogens can evolve into global pandemics (eg, HIV, COVID-19) affecting even the wealthiest communities.
The high and uneven burden of environmentally mediated infections highlights the need for innovative social and ecological interventions to complement biomedical advances in the pursuit of global health and sustainability goals.
Bill & Melinda Gates Foundation, National Institutes of Health, National Science Foundation, Alfred P. Sloan Foundation, National Institute for Mathematical and Biological Synthesis, Stanford University, and the US Defense Advanced Research Projects Agency.
The importance of thermal refuges in a rapidly warming world is particularly evident for migratory species, where individuals encounter a wide range of conditions throughout their lives. In this study, we used a spatially explicit, individual-based simulation model to evaluate the buffering potential of cold-water thermal refuges for anadromous salmon and trout (Oncorhynchus spp.) migrating upstream through a warm river corridor that can expose individuals to physiologically stressful temperatures. We considered upstream migration in relation to migratory phenotypes that were defined in terms of migration timing, spawn timing, swim speed, and use of cold-water thermal refuges. Individuals with different migratory phenotypes migrated upstream through riverine corridors with variable availability of cold-water thermal refuges and mainstem temperatures. Use of cold-water refuges (CWRs) decreased accumulated sublethal exposures to physiologically stressful temperatures when measured in degree-days above 20, 21, and 22°C. The availability of CWRs was an order of magnitude more effective in lowering accumulated sublethal exposures under current and future mainstem temperatures for summer steelhead than fall Chinook Salmon. We considered two emergent model outcomes, survival and percent of available energy used, in relation to thermal heterogeneity and migratory phenotype. Mean percent energy loss attributed to future warmer mainstem temperatures was at least two times larger than the difference in energy used in simulations without CWRs for steelhead and salmon. We also found that loss of CWRs reduced the diversity of energy-conserving migratory phenotypes when we examined the variability in entry timing and travel time outside of CWRs in relation to energy loss. Energy-conserving phenotypic space contracted by 7%–23% when CWRs were unavailable under the current thermal regime. Our simulations suggest that, while CWRs do not entirely mitigate for stressful thermal exposures in mainstem rivers, these features are important for maintaining a diversity of migration phenotypes. Our study suggests that the maintenance of diverse portfolios of migratory phenotypes and cool- and cold-water refuges might be added to the suite of policies and management actions presently being deployed to improve the likelihood of Pacific salmonid persistence into a future characterized by climate change.
The Big Lost River Basin, located in parts of Butte and Custer Counties in south-central Idaho, supports the communities surrounding the cities of Arco, Leslie, Mackay, and Moore and provides for agricultural resources that depend on a sustainable supply of surface water from the Big Lost River and its tributaries and groundwater from an unconfined aquifer. The aquifer, situated in a structurally controlled intermontane valley, is composed of unconsolidated alluvium, consolidated sedimentary and volcanic rocks, and younger interbedded volcanic rocks.
This report presents two separate groundwater budgets for the aquifer, one above and one below Mackay Dam, as well as a combined groundwater budget for the aquifer within the entire Big Lost River Basin. The budgets span a 20-year period (2000–19), characterizing average conditions, a dry year (2014), and a wet year (2017). The groundwater budgets will help address questions regarding the availability of groundwater supply in the Big Lost River Basin and inform future groundwater modeling. The Idaho Geological Survey has prepared the groundwater budgets as part of a larger hydrogeologic investigation completed by the U.S. Geological Survey and the Idaho Geological Survey in cooperation with the Idaho Department of Water Resources during 2018–21. Other reports describe the hydrogeologic framework and several streamflow-measurement events to evaluate gains and losses on the Big Lost River. Collectively, these reports provide an updated characterization of groundwater resources in the Big Lost River Basin which will help address water resources challenges.
A groundwater budget is a conceptual and numerical accounting of inflow (recharge) to groundwater and outflow (discharge) from groundwater. The predominant sources of recharge to the aquifer include losing river reaches (33 percent), areal recharge (as precipitation less evapotranspiration and surface runoff, comprising about 23 percent of the total inflow), tributary canyon underflow from higher altitudes (20 percent), canal seepage (13 percent), recharge through applied irrigation on fields below the root zone and other minor sources (11 percent), and Mackay Reservoir seepage (less than 1 percent). The primary sources of discharge from the aquifer are groundwater pumpage to meet irrigation demand, domestic supply, and municipal supply (76 percent) and gaining river reaches (24 percent).
The positive or negative difference between the sum of all inflows and outflows is regarded as the residual, representing the change in groundwater storage, groundwater outflow from the basin or subbasins, and uncertainty and errors in the budget. In the Big Lost River Basin, groundwater outflow is at the mouth of the basin below Arco into the eastern Snake River Plain aquifer.
The total mean annual estimated recharge to the Big Lost River Basin was 439,100 acre-feet per year (acre-ft/yr; 607 cubic feet per second [ft3/s]) for 2000–19, 373,900 acre-ft/yr (516 ft3/s) in 2014, and 762,100 acre-ft/yr (1,053 ft3/s) in 2017. The mean annual estimated groundwater discharge from the aquifer was about 112,300 acre-ft/yr (155 ft3/s) for 2000–19, 153,500 acre-ft/yr (212 ft3/s) in 2014, and 53,400 acre-ft/yr (74 ft3/s) in 2017. The estimated mean annual groundwater residual was 326,800 acre-ft/yr (451 ft3/s) for 2000–19, 220,400 acre-ft/yr (304 ft3/s) in 2014, and 708,700 acre-ft/yr (979 ft3/s) in 2017. The mean annual residual above Mackay Dam was 100,400 acre-ft/yr (2000-19), 96,700 acre-ft (2014), and 248,300 acre-ft (2017). The mean annual residual contribution below Mackay Dam, minus any groundwater-flow above Mackay Dam, was 226,400 acre-ft/yr (2000-19), 123,700 acre-ft (2014), and 460,400 acre-ft (2017).
These results are highly sensitive to assumptions about certain budget inflow parameters. In particular, the magnitude of the budget residuals during especially dry and wet periods is amplified by the groundwater-budget terms tributary streamflow and tributary underflow that contribute appreciable recharge but also have high uncertainty.
The results of the groundwater-budget evaluation describe an interconnected and complex hydrologic response throughout the basin to various climatic and water-use trends. The part of the basin above Mackay Dam typically has a positive groundwater residual derived from snowmelt recharge to tributary canyons and areal recharge in excess of groundwater pumpage for irrigation demand. This supply is used to meet irrigation demand above Mackay Dam and to provide for water supply below Mackay Dam. On average, groundwater inflow from above Mackay Dam to below Mackay Dam, assuming negligible reservoir storage effects, accounts for about 25 percent of the total groundwater recharge below Mackay Dam. Considerable recharge to groundwater below Mackay Dam occurs through seepage from the Big Lost River and canals and ditches. Most groundwater discharge from the aquifer is through irrigation pumping. The water supply below Mackay Dam is highly dependent on available upstream surface-water flows, the magnitude of the groundwater residual from above Mackay Dam, and annual variability in local groundwater conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215078C","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Clark, A., 2022, Groundwater budgets for the Big Lost River Basin, south-central Idaho, 2000–19, chap. C of Zinsser, L.M., ed., Characterization of water resources in the Big Lost River Basin, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2021–5078–C, 111 p., https://doi.org/10.3133/sir20215078C.","productDescription":"xi, 111 p.","ipdsId":"IP-125226","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":409232,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5078/c/coverthb.jpg"},{"id":409233,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5078/c/sir20215078C.pdf","text":"Reports","size":"6.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5078-C"},{"id":409235,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5078/c/images"},{"id":409236,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5078/c/sir20215078C.XML"},{"id":502105,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113824.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho","otherGeospatial":"Big Lost River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.1863738631967,\n 43.10571945845362\n ],\n [\n -113.42308735779262,\n 43.54649028685452\n ],\n [\n -112.13258233834704,\n 44.22138739870667\n ],\n [\n -112.23487846793722,\n 44.737914300373745\n ],\n [\n -114.26506595862107,\n 46.10751185031063\n ],\n [\n -115.75229430420214,\n 46.493497990156555\n ],\n [\n -117.884775159506,\n 45.476547804668826\n ],\n [\n -117.57788677073549,\n 45.01671717637413\n ],\n [\n -116.38967788087962,\n 44.5307302025393\n ],\n [\n -115.2014689910242,\n 43.60919623765622\n ],\n [\n -114.1863738631967,\n 43.10571945845362\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director , Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
Construction and operation of dams provide sources of clean drinking water, support large-scale irrigation, generate hydroelectricity, control floods, and improve river navigation. Yet these benefits are not without cost. Dams affect the natural flow regime, downstream sediment fluxes, and riverine and riparian ecosystems. The Jemez Canyon Dam in New Mexico was constructed in 1953 by the U.S. Army Corps of Engineers with authorizations for flood control and sediment retention. Water managers of the dam use various operational techniques to restore peak streamflow, improve sediment management, and restore altered ecosystem processes, while maintaining the authorized purposes of the dam. This study focuses on four distinct reservoir management operation periods implemented at the Jemez Canyon Dam: (1) predam (pre-1953), (2) a seasonal 24-hour hold pool (1953–79), (3) a permanent pool (1979–2001), and (4) dry reservoir (2001–18).
Results of this study indicate successful flood control and reduction in peak instantaneous streamflow events following construction of the dam, specifically documented in 1958 and 2013. During the second water management operation period, moderate sediment retention (also defined as trap efficiency, which is the percentage of incoming sediments trapped within a reservoir during a given time) occurred (between 41.0 and 67.0 percent of sediments were retained). During the third period (1979–2001), between 61.2 and 99.8 percent of sediments were retained. During the fourth period (2001–18), at least 1,909 acre-feet of accumulated sediment were remobilized. The estimated dam trap efficiency during the fourth water management operation period was −37.2 percent, indicating that more sediments were being removed from the Jemez Canyon Reservoir than were being deposited. These remobilized sediments supplemented the natural sediment delivery in the Jemez River to the middle Rio Grande. The current (2022) dry reservoir operation allows sediment delivery during periods when flooding is not a concern while still providing flood control when needed.
Suspended-sediment particle size data indicate potential coarsening of suspended sediments during the fourth water management operation period, likely resulting from erosion of coarse bed sediments deposited in the reservoir. Suspended-sediment particle size data during the first and fourth water management operation periods indicate that finer sediment mobilized during monsoon season than during snowmelt. Also, suspended-sediment concentrations during the predam and post-hold pool periods indicate concentrations were higher during monsoon season than during snowmelt. Seasonal variations in suspended-sediment concentration and particle size may help dam managers make operational decisions by increasing the understanding of particle size, concentration, and variation of suspended sediment during a given year. The seasonality of suspended-sediment transport can also vary, depending not only on concentration and particle size, but on precipitation. The maximum annual suspended-sediment loads occurred during all three seasonal categories analyzed in this study: snowmelt, monsoon, and the remainder of the year. This indicates that, in addition to sediment particle size and concentration, understanding the variability of transport mechanisms of suspended-sediment load can also guide optimal water management operations at a dam.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225081","isbn":"978-1-4113-4481-5","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Brown, J.E., Matherne, A.M., Reale, J.K., and Miltenberger, K.E., 2022, Suspended-sediment transport and water management, Jemez Canyon Dam, New Mexico, 1948–2018: U.S. Geological Survey Scientific Investigations Report 2022–5081, 30 p., https://doi.org/10.3133/sir20225081.","productDescription":"Report: vii, 30 p.; 2 Datasets","numberOfPages":"42","onlineOnly":"N","ipdsId":"IP-107586","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":408900,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5081/coverthb.jpg"},{"id":408901,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5081/sir20225081.pdf","text":"Report","size":"2.01 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5081"},{"id":408902,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5081/sir20225081.XML"},{"id":409231,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225081/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408905,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":408904,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS Earth Resources Observation and Science Center database","linkHelpText":"—EarthExplorer"},{"id":408903,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5081/images"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.61655311950582,\n 35.438783063650746\n ],\n [\n -106.61655311950582,\n 35.32720598341298\n ],\n [\n -106.46641220000733,\n 35.32720598341298\n ],\n [\n -106.46641220000733,\n 35.438783063650746\n ],\n [\n -106.61655311950582,\n 35.438783063650746\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Drylands represent more than 41% of the global land surface and are at degradation risk due to land use and climate change. Developing strategies to mitigate degradation and restore drylands in the face of these threats requires an understanding of how drylands are shaped by not only soils and climate, but also geology and geomorphology. However, few studies have completed such a comprehensive analysis that relates spatial variation in plant communities to all aspects of the geologic–geomorphic–edaphic–plant–climate system. The focus of this study is the Colorado Plateau, a high-elevation dryland in the southwestern United States, which is particularly sensitive to future change due to climate vulnerability and increasing land-use pressure. Here, we examined 135 long-term vegetation-monitoring sites in three national parks and characterized connections between geology, geomorphology, soils, climate, and dryland plant communities. To first understand the geologic and geomorphic influences on soil formation and characteristics, we explore associations between soil pedons, bedrock geology, and geomorphology. Then, we characterize principal axes of variation in plant communities and ascertain controls and linkages between components of the edaphic–geomorphic system and plant community ordinations. Geologic and geomorphic substrate exerted controls on important properties of the soil profile, particularly depth, water-holding capacity, rockiness, salinity, and fine sands. Ordination identified five distinct plant communities and three primary axes of variation, representing gradients of woody- to herbaceous-dominated communities (Axis 1), saline scrublands to C3 grasslands (Axis 2), and annual to perennial communities (Axis 3). Geology, geomorphology, and soil explained a large proportion of variation in Axis 1 (74%), while climate variables largely explained Axis 2 (68%), and Axis 3 was not well explained by the random forest models. The variables identified as most influential to each axis were, respectively: (1) soil depth; (2) aridity, lithology, and soil salinity; and (3) temperature and precipitation. We posit that Axis 3 represents a land degradation gradient due to historic grazing, likely exacerbated by dry conditions. Results provide a novel framework that links the geologic and geomorphic evolution of landscapes, with the distribution of soils and plant communities that can guide ecosystem management, exemplifying an approach applicable to drylands globally.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4273","usgsCitation":"Duniway, M.C., Benson, C., Nauman, T.W., Knight, A.C., Bradford, J., Munson, S.M., Witwicki, D.L., Livensperger, C., Van Scoyoc, M.W., Fisk, T.T., Thoma, D., and Miller, M.E., 2022, Geologic, geomorphic, and edaphic underpinnings of dryland ecosystems: Colorado Plateau landscapes in a changing world: Ecosphere, v. 13, no. 11, e4273, 27 p., https://doi.org/10.1002/ecs2.4273.","productDescription":"e4273, 27 p.","ipdsId":"IP-135505","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":488760,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4273","text":"Publisher Index Page"},{"id":435625,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92Z8NDP","text":"USGS data release","linkHelpText":"Soil, geologic, geomorphic, climate, and vegetation data from long-term monitoring plots (2009 - 2018) in Arches, Canyonlands, and Capitol Reef National Parks, Utah, USA"},{"id":410697,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.63689444724997,\n 36.734805586098716\n ],\n [\n -108.00129122959586,\n 36.72595486884292\n ],\n [\n -107.41606847690218,\n 37.63008491281205\n ],\n [\n -107.57066880734789,\n 38.70708139014661\n ],\n [\n -107.76120496171856,\n 39.973850096503895\n ],\n [\n -108.78556565774403,\n 40.46292505503848\n ],\n [\n -110.23348660705287,\n 40.36792094423029\n ],\n [\n -111.02839675350845,\n 39.403592925523014\n ],\n [\n -111.43291910676305,\n 38.05141646696123\n ],\n [\n -112.77571792678364,\n 37.21069090791468\n ],\n [\n -112.28678230930984,\n 36.79511547655869\n ],\n [\n -111.63689444724997,\n 36.734805586098716\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":859388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benson, Christopher","contributorId":296064,"corporation":false,"usgs":false,"family":"Benson","given":"Christopher","email":"","affiliations":[{"id":63978,"text":"formerly) US Geological Survey, Southwest Biological Science Center, Moab, UT","active":true,"usgs":false}],"preferred":false,"id":859389,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":859390,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":859391,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":859392,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":859393,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Witwicki, Dana L.","contributorId":207763,"corporation":false,"usgs":false,"family":"Witwicki","given":"Dana","email":"","middleInitial":"L.","affiliations":[{"id":37628,"text":"National Park Service Inventory and Monitoring Program, P.O. Box 848, Moab, UT 84532, USA","active":true,"usgs":false}],"preferred":false,"id":859394,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Livensperger, Carolyn","contributorId":260927,"corporation":false,"usgs":false,"family":"Livensperger","given":"Carolyn","email":"","affiliations":[{"id":52723,"text":"National Park Service, Capitol Reef National Park, 52 Headquarters Dr., Torrey UT 84775","active":true,"usgs":false}],"preferred":false,"id":859395,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Van Scoyoc, Matthew W. 0000-0001-6821-4476","orcid":"https://orcid.org/0000-0001-6821-4476","contributorId":290213,"corporation":false,"usgs":false,"family":"Van Scoyoc","given":"Matthew","email":"","middleInitial":"W.","affiliations":[{"id":62383,"text":"Southeast Utah Group, National Park Service, Moab, UT","active":true,"usgs":false}],"preferred":false,"id":859396,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fisk, Terry T","contributorId":289096,"corporation":false,"usgs":false,"family":"Fisk","given":"Terry","email":"","middleInitial":"T","affiliations":[{"id":62042,"text":"Water Resources Division, National Park Service","active":true,"usgs":false}],"preferred":false,"id":859397,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Thoma, David","contributorId":265911,"corporation":false,"usgs":false,"family":"Thoma","given":"David","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":859398,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Miller, Mark E.","contributorId":91580,"corporation":false,"usgs":false,"family":"Miller","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":6959,"text":"National Park Service Southeast Utah Group","active":true,"usgs":false}],"preferred":false,"id":859399,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70238582,"text":"70238582 - 2022 - Mapping 2-D bedload rates throughout a sand-bed river reach from high-resolution acoustical surveys of migrating bedforms","interactions":[],"lastModifiedDate":"2022-11-30T12:53:57.31331","indexId":"70238582","displayToPublicDate":"2022-11-08T06:50:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Mapping 2-D bedload rates throughout a sand-bed river reach from high-resolution acoustical surveys of migrating bedforms","docAbstract":"This paper introduces a method for determining spatially-distributed, 2-D bedload rates using repeat, high-resolution surveys of the bed topography. As opposed to existing methods, bedform parameters and bedload rates are computed from bed elevation profiles interpolated along the local bedform velocities. The bedform velocity fields are computed applying Large-Scale Particle Image Velocimetry, initially developed for surface velocity measurements, to pairs of successive Digital Elevation Models (DEMs). The bathymetry data are interpolated along the direction of each bedform velocity and the mean height of the closest bedform is computed. The dune shape factor is also evaluated along each bedform direction of travel. The local bedload fluxes can be computed by multiplying the bedform velocity by its mean height averaged over the successive two DEMs, and they can be time-averaged over a series of DEM pairs. This method is applied to a high-resolution acoustical survey of an approximately 300 m long by 40 m wide reach of the Colorado River in Grand Canyon upstream from Diamond Creek, USA. The repeat period was about 6–10 min and bed elevation was interpolated every 0.25 m. The obtained results provide insight to the spatial and temporal variability of bedload rates, bedform parameters and bedload fluxes through cross-sections. The method can be applied to other repeated acoustical surveys of river reaches provided that the space and time resolutions are high enough to capture the local movement of bedforms.
Using multi-species time series data has long been of interest for estimating inter-specific interactions with vector autoregressive models (VAR) and state space VAR models (VARSS); these methods are also described in the ecological literature as multivariate autoregressive models (MAR, MARSS). To date, most studies have used these approaches on relatively small food webs where the total number of interactions to be estimated is relatively small. However, as the number of species or functional groups increases, the length of the time series must also increase to provide enough degrees of freedom with which to estimate the pairwise interactions. To address this issue, we use Bayesian methods to explore the potential benefits of using regularized priors, such as Laplace and regularized horseshoe, on estimating interspecific interactions with VAR and VARSS models. We first perform a large-scale simulation study, examining the performance of alternative priors across various levels of observation error. Results from these simulations show that for sparse matrices, the regularized horseshoe prior minimizes the bias and variance across all inter-specific interactions. We then apply the Bayesian VAR model with regularized priors to a output from a large marine food web model (37 species) from the west coast of the USA. Results from this analysis indicate that regularization improves predictive performance of the VAR model, while still identifying important inter-specific interactions.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.14332","usgsCitation":"Ward, E.J., Marshall, K.N., Scheuerell, M.D., 2022, Regularizing priors for Bayesian VAR applications to large ecological datasets: PeerJ, v. 10, e14332, 18 p., https://doi.org/10.7717/peerj.14332.","productDescription":"e14332, 18 p.","ipdsId":"IP-144838","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":500605,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.14332","text":"Publisher Index Page"},{"id":500429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Ward, Eric J.","contributorId":366786,"corporation":false,"usgs":false,"family":"Ward","given":"Eric","middleInitial":"J.","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":956224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marshall, Kristin N.","contributorId":366787,"corporation":false,"usgs":false,"family":"Marshall","given":"Kristin","middleInitial":"N.","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":956225,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scheuerell, Mark David 0000-0002-8284-1254","orcid":"https://orcid.org/0000-0002-8284-1254","contributorId":288621,"corporation":false,"usgs":true,"family":"Scheuerell","given":"Mark","email":"","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":956226,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237990,"text":"tm17A1 - 2022 - Rapidly assessing social characteristics of drought preparedness and decision making: A guide for practitioners","interactions":[],"lastModifiedDate":"2022-11-07T17:14:33.667614","indexId":"tm17A1","displayToPublicDate":"2022-11-07T11:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"17-A1","displayTitle":"Rapidly Assessing Social Characteristics of Drought Preparedness and Decision Making: A Guide for Practitioners","title":"Rapidly assessing social characteristics of drought preparedness and decision making: A guide for practitioners","docAbstract":"This guide is intended to provide managers, decision makers, and other practitioners with advice on conducting a rapid assessment of the social dimensions of drought. Findings from a rapid assessment can provide key social context that may aid in decision making, such as when preparing a drought plan, allocating local drought resilience funding, or gathering the support of local agencies and organizations for collective action related to drought mitigation.
Part I—In the introduction to Part I, we describe the unique problems associated with drought—particularly its slow onset and long duration, which make it difficult to define drought—and highlight five major types of drought (see Box 1). We introduce a few social dimensions of drought (such as economic and institutional perspectives), demonstrate how these dimensions can be interrelated, and describe a few of the modern challenges (such as transformational change and cascading risks) that practitioners face.
We also provide background on the rapid assessment method, first describing it as a “snapshot” of the social landscape, then providing some key advantages of the method (it can be quicker and cheaper than more in-depth methods), and lastly describing how secondary data and other methods can help overcome some of the disadvantages of rapid assessments.
Then, after summarizing the process of developing this guide, we outline the process of using the guide. Importantly, we compare the guide to a travel guide, which provides many different types of information and is best approached with specific interests in mind. Ultimately, we hope for this guide to be malleable enough that it can be helpful to researchers and practitioners in many different contexts, using many different research methods. Related to how to use the guide, we characterize the type of person who might be motivated to use this guide. We also specify key qualifications for a researcher conducting a rapid assessment, drawing particular attention to training on ethical considerations.
We sketch out key considerations when choosing social dimensions of drought to focus on, and the type of data used for analysis. First, it is important to note that in this guide we provide nine important social dimensions of drought, but this is by no means a comprehensive list, and a researcher may find that other dimensions better fit their local context. Second, we provide some pros and cons to a narrow (focusing on just a few dimensions or at a smaller scale) versus broad research focus. Lastly, we describe the pros and cons of using primary versus secondary data (one strategy is to use both, sequentially) and qualitative versus quantitative data.
Ultimately, Part I of this guide functions as an exploration of the various decisions a researcher will make when designing a rapid assessment. These decisions will inform the type of findings and other outcomes that result from the rapid assessment.
Part II—Part II of this guide introduces nine key social dimensions of drought: defining the problem of drought, individual perceptions, social relationships, technology, economics and livelihoods, water governance, decision making, information, and social vulnerability. Each section provides background and key considerations related to a particular dimension, as well as ideas for how to explore the dimension via a rapid assessment.
Part III—Part III of this guide provides two hypothetical examples of how one might use this guide to aid the practitioner in implementing the lessons learned here. In the first example, a watershed group uses two dimensions, defining the problem of drought and social relationships, to inform a community meeting about protecting fisheries from drought. In the second example, a resource manager uses the economics and livelihoods and social vulnerability dimensions to inform the development of a livestock grazing drought management plan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm17A1","usgsCitation":"Clifford, K.R., Goolsby, J.B., Cravens, A.E., and Cooper, A.E., 2022, Rapidly assessing social characteristics of drought preparedness and decision making: A guide for practitioners: U.S. Geological Survey Techniques and Methods 17-A1, 41 p., https://doi.org/10.3133/tm17A1.","productDescription":"vii, 41 p.","onlineOnly":"Y","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":409066,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/17/a1/tm17a1.xml"},{"id":409065,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/17/a1/images"},{"id":409061,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/17/a1/coverthb.jpg"},{"id":409062,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/17/a1/tm17a1.pdf","text":"Report","size":"1.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 17-A1"}],"contact":"Director, Fort Collins Science Center
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Like many impaired Great Lakes tributaries, Apple Creek, Wisconsin (119 km2) has Total Maximum Daily Load (TMDL) targets for reducing suspended sediment and total phosphorus by 51.2 % and 64.2 %, respectively. From August 2017 - October 2018, a stream sediment budget and fingerprinting integrated study was conducted to quantify upland and stream corridor sources of suspended sediment and sediment-bound phosphorus. Phosphorus concentrations varied among source groups and fluvial sediments, with higher concentrations among suspended sediment and cropland soils. Eroding streambanks identified in the stream corridor sediment budget accounted for 100 % of the TMDL Soil and Water Assessment Tool (SWAT) suspended sediment load but only 20 % of the total phosphorus load. Fine-grained streambed sediment equated to approximately-three years of modeled suspended sediment load but only one third of total phosphorus load. The two primary sources of fine-grained streambed sediment were streambanks and cropland, with relative streambank contributions increasing with downstream direction and watershed area. The relative proportion of suspended sediment varied by season and streamflow; however, cropland and streambank erosion accounted for 54 % and 23 % of the suspended sediment when weighted by of the proportion for representative streamflow. Urban land was a source in the upper watershed, but the signature was sequestered by a mid-watershed detention basin. Contributions from construction sites were higher in the fall 2018, likely corresponding to increased activity following a wet spring. These integrated techniques helped describe sources, transport, and sinks of fluvial sediment and phosphorus throughout the watershed at a range of spatial and temporal scales.
Across the Pacific Northwest, there are many examples of artificial structures created to allow passage of upstream-migrating salmon over natural barriers. We studied upstream passage across three structures installed in 1989 to allow passage of salmon over Lake Creek Falls, a series of three natural waterfalls at the outlet of Triangle Lake on Lake Creek, in the central Oregon Coast Range (lat 123.57508°; long 44.15735°). To track upstream passage by adult coho salmon (Oncorhynchus kisutch), 87 fish were tagged using gastrically implanted radio tags. Tracking was accomplished with a series of stationary receivers installed to detect crossings at each of three structures—over Lake Creek Falls using two upstream Denil-type ladders and a bypass downstream constructed to mimic a natural side channel. Tracking spanned the upstream migration and spawn timing for adult coho salmon in the basin and extended from October 2019 to February 2020. A total of 15 coho salmon (17 percent) were tagged in October, 30 coho salmon (35 percent) were tagged in November, and 42 coho salmon (48 percent) were tagged in December. Later-than-normal precipitation and associated low discharge delayed upstream migrations. Accordingly, most fish arrived late in the season (late November and December) and in sudden flushes with the erratic rain events. Fish that were tagged earlier were more likely to cross all three ladders, with more than 93 percent of fish tagged in October compared to 46.7 and 19.0 percent of November and December fish passing, respectively. The decline in passage rate could be attributed to the overlapping influences of stream discharge and advanced stage of maturation (lower energy reserves) of fish later in the season. Near the end of the study, both fish that crossed and fish obstructed by barriers were observed in tributaries known to be used for spawning by coho salmon. Without a much longer-term study involving many more fish than the current study, more intensive tracking, and coverage of different flow years, firm conclusions are difficult to draw regarding the overall influences of the passage structures on the likelihood of upstream passage by adult coho salmon. However, substantial numbers of fish are capable of crossing during certain conditions. The population-level consequences of the barriers on spawning distribution and the production of coho salmon in the watershed are not clear. Additional empirical study or population modeling could be used to address this question in more detail.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221083","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Fischer, R.B., Dunham, J., Scheidt, N., Hansen, A.C., and Heaston, E.D., 2022, Passage of adult coho salmon (Oncorhynchus kisutch) over Lake Creek Falls, Oregon, 2019: U.S. Geological Survey Open-File Report 2022–1083, 19 p., https://doi.org/10.3133/ofr20221083.","productDescription":"vii, 19 p.","onlineOnly":"Y","ipdsId":"IP-130393","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":409177,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1083/coverthb.jpg"},{"id":409181,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1083/ofr20221083.XML"},{"id":409180,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1083/images"},{"id":409179,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221083/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1083"},{"id":409178,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1083/ofr20221083.pdf","text":"Report","size":"21 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1083"}],"country":"United States","state":"Oregon","otherGeospatial":"Lake Creek Falls","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.61844237310005,\n 44.17170307975459\n ],\n [\n -123.61844237310005,\n 44.131057340436286\n ],\n [\n -123.5593908594283,\n 44.131057340436286\n ],\n [\n -123.5593908594283,\n 44.17170307975459\n ],\n [\n -123.61844237310005,\n 44.17170307975459\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
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