Tree loss is increasing rapidly due to drought- and heat-related mortality and intensifying fire activity. Consequently, the fate of many forests depends on the ability of juvenile trees to withstand heightened climate and disturbance anomalies. Extreme climatic events, such as droughts and heatwaves, are increasing in frequency and severity, and trees in mountainous regions must contend with these landscape-level climate episodes. Recent research focuses on how mortality of individual tree species may be driven by drought and heatwaves, but how juvenile mortality under these conditions would vary among species spanning an elevational gradient—given concurrent variation in climate, ecohydrology, and physiology–remains unclear. We address this knowledge gap by implementing a growth chamber study, imposing extreme drought with and without a compounding heatwave, for juveniles of five species that span a forested life zones in the Southwestern United States. Overall, the length of a progressive drought required to trigger mortality differed by up to 20 weeks among species. Inclusion of a heatwave hastened mean time to mortality for all species by about 1 week. Lower-elevation species that grow in warmer ambient conditions died earlier (Pinus ponderosa in 10 weeks, Pinus edulis in 14 weeks) than did higher-elevation species from cooler ambient conditions (Picea engelmannii and Pseudotsuga menziesii in 19 weeks, and Pinus flexilis in 30 weeks). When exposed to a heatwave in conjunction with drought, mortality advanced significantly only for species from cooler ambient conditions (Pinus flexilis: 2.7 weeks earlier; Pseudotsuga menziesii: 2.0 weeks earlier). Cooler ambient temperatures may have buffered against moisture loss during drought, resulting in longer survival of higher-elevation species despite expected drought tolerance of lower-elevation species due to tree physiology. Our study suggests that droughts will play a leading role in juvenile tree mortality and will most directly impact species at warmer climate thresholds, with heatwaves in tandem with drought potentially exacerbating mortality especially of high elevation species. These responses are relevant for assessing the potential success of both natural and managed reforestation, as differential juvenile survival following episodic extreme events will determine future landscape-scale vegetation trajectories under changing climate.
This study presents a simplified method and empirical relationships for determining organic matter thermal maturity using a portable Raman system equipped with a 785 nm laser, for analysis of crushed, whole-rock samples. Suites of rocks represented by shale and coal samples with various mineralogical composition, thermal maturity, and total organic carbon (TOC) were used to test the method and build correlations between Raman band separation (RBS) values and traditional thermal maturity indicators, organic matter reflectance (Ro), and programmed temperature pyrolysis (Tmax) values. A set of disparate shale samples, where both vitrinite and solid bitumen reflectance values were reported, have Ro values that range from 0.40 to 4.62%. Above 3.35% Ro, the corresponding RBS values plateau at ∼290 cm−1, thus correlations were evaluated with a linear regression (R2 = 0.96) between 0.40 and 3.35% Ro. Shale samples with Ro < 2% and Tmax < 551 were also used to correlate Tmax and RBS, yielding a linear correlation with an R2 of 0.94. For the coal data set, Ro values range from 1.21 to 4.08% and correlated RBS values plateau at ∼250 cm−1 above Ro = 3.0%, suggesting its correlative application below this maturity level. Several sample preparation methods were tested on cuttings material and standard deviation values for RBS were minimized by washing, drying, and hand crushing the material to pass through a 40-mesh sieve, although less preparation can still yield reliable results. The high degrees of correlation between whole-rock RBS data and two thermal maturity indicators demonstrate the utility of this approach for generating source rock thermal maturity data from minimally processed, whole-rock samples which could easily be applied in field or laboratory settings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2023.104374","usgsCitation":"Stokes, M., Jubb, A., Hackley, P.C., Birdwell, J.E., Barnhart, E.P., Scott, C., Shelton, J., Sanders, M.M., and Hatcherian, J.J., 2023, Evaluation of portable Raman spectroscopic analysis for source-rock thermal maturity assessments on bulk crushed rock: International Journal of Coal Geology, v. 279, 104374, https://doi.org/10.1016/j.coal.2023.104374.","productDescription":"104374","ipdsId":"IP-157149","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":467086,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coal.2023.104374","text":"Publisher Index Page"},{"id":425713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"279","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stokes, Martha 0000-0002-2838-8380","orcid":"https://orcid.org/0000-0002-2838-8380","contributorId":269608,"corporation":false,"usgs":true,"family":"Stokes","given":"Martha","email":"","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":894935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":894936,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":894937,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":894938,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":894939,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scott, Clint 0000-0003-2778-2711 clintonscott@usgs.gov","orcid":"https://orcid.org/0000-0003-2778-2711","contributorId":5332,"corporation":false,"usgs":true,"family":"Scott","given":"Clint","email":"clintonscott@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":894940,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shelton, Jenna L. 0000-0002-1377-0675 jlshelton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":5025,"corporation":false,"usgs":true,"family":"Shelton","given":"Jenna L.","email":"jlshelton@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":894941,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sanders, Margaret M. 0000-0003-3505-874X","orcid":"https://orcid.org/0000-0003-3505-874X","contributorId":248709,"corporation":false,"usgs":true,"family":"Sanders","given":"Margaret","email":"","middleInitial":"M.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":894942,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":894943,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70249500,"text":"ofr20231002 - 2023 - The enigmatic Rattlesnake Knoll, Spring Valley, east-central Nevada—A geophysical perspective","interactions":[],"lastModifiedDate":"2026-02-10T21:24:41.808706","indexId":"ofr20231002","displayToPublicDate":"2023-10-11T11:03:42","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1002","displayTitle":"The Enigmatic Rattlesnake Knoll, Spring Valley, East-Central Nevada—A Geophysical Perspective","title":"The enigmatic Rattlesnake Knoll, Spring Valley, east-central Nevada—A geophysical perspective","docAbstract":"Rattlesnake Knoll is a small, 30-meter-high mound of igneous breccia in the center of Spring Valley, east-central Nevada. In the past, researchers have disagreed as to whether the unusual-looking outcrop is intrusive or volcanic. The breccia possesses a normal magnetic polarity, but this is not apparent in aeromagnetic survey data. These data instead show that the knoll lies within a small aeromagnetic low that partially overlaps the extent of a small gravity high. The small gravity anomaly associated with the knoll, combined with an initial, limited ground magnetic survey taken at the knoll, indicates that the knoll rocks extend northward in the subsurface. A second, more extensive ground magnetic traverse was also done north of the knoll. Taking into consideration these new survey data and preexisting data, a two and one-half dimensional modeling program based on Webring (1985) was used to produce a geophysical model that accounts for gravity and magnetic properties, satisfies available geologic information, and conforms to current estimates of basin thickness. This model and the field observations support the interpretation that the knoll consists of gently west-dipping beds of Tertiary volcanic flow breccia, mudflow breccia, and conglomerate.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231002","usgsCitation":"Mankinen, E.A., Rowley, P.D., and McKee, E.H., 2023, The enigmatic Rattlesnake Knoll, Spring Valley, east-central Nevada—A geophysical perspective: U.S. Geological Survey Open-File Report 2023–1002, 13 p., https://doi.org/10.3133/ofr20231002.","productDescription":"Report: vi, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-133281","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":435149,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WL97XY","text":"USGS data release","linkHelpText":"Ground magnetic data, Spring Valley, White Pine County, Nevada"},{"id":421859,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1002/covrthb_.jpg"},{"id":421860,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1002/ofr20231002.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":499729,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115506.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Nevada","otherGeospatial":"Spring Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.36,\n 39.06\n ],\n [\n -114.36,\n 39.00\n ],\n [\n -114.24,\n 39.00\n ],\n [\n -114.24,\n 39.06\n ],\n [\n -114.36,\n 39.06\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
The segment of the Upper Missouri River between Fort Peck Dam and the headwaters of Lake Sakakawea is home to a population of the endangered Scaphirhynchus albus (pallid sturgeon). Lack of population growth (recruitment failure) has been attributed to inadequate dispersal distance of larvae between spawning locations and the headwaters of Lake Sakakawea, where conventional wisdom holds that anoxic water-quality conditions are lethal to larval sturgeon. River-management objectives to recover pallid sturgeon in this segment therefore focus on increasing available drift distance, decreasing drift rate, increasing larval development rate, or a combination of these objectives. Understanding of channel morphological conditions along this about 400-kilometer segment may provide insight into upstream spawning habitat potential (where sturgeon likely spawn) and into geomorphic factors that may contribute to flow complexity, hence drift rate. This report documents a continuous geomorphic classification of the Fort Peck segment of the Upper Missouri River using remotely sensed datasets to provide contextual information about spatial variations potentially affecting pallid sturgeon reproductive ecology.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235109","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Missouri River Recovery Program","usgsCitation":"Jacobson, R.B., Elliott, C.M., and Bulliner, E., 2023, Geomorphic classification framework for assessing reproductive ecology of Scaphirhynchus albus (pallid sturgeon), Fort Peck segment, Upper Missouri River, Montana and North Dakota: U.S. Geological Survey Scientific Investigations Report 2023–5109, 15 p., https://doi.org/10.3133/sir20235109.","productDescription":"Report: vi, 15 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-155746","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":421828,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92HVKT3","text":"USGS data release","linkHelpText":"Geomorphic variables for classification of the Upper Missouri River, Montana and North Dakota"},{"id":421829,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235109/full","linkFileType":{"id":5,"text":"html"}},{"id":421827,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5109/images/"},{"id":421824,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5109/coverthb.jpg"},{"id":421825,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5109/sir20235109.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5109"},{"id":421826,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5109/sir20235109.XML","linkFileType":{"id":8,"text":"xml"}}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Fort Peck segment, Upper Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.31605153925373,\n 48.4037537878354\n ],\n [\n -106.52859788507773,\n 48.4037537878354\n ],\n [\n -106.60549394122786,\n 47.047516337061694\n ],\n [\n -103.30750753301488,\n 47.08228719733623\n ],\n [\n -103.31605153925373,\n 48.4037537878354\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
U.S. Geological Survey
4200 New Haven Road
Columbia, MO 65201
Anadromous fish returning to the lower American River are restricted to 36 kilometers of free-flowing river between Nimbus Dam and American River’s confluence with the Sacramento River, California. Salmon in the American River provide an important freshwater recreational fishery. However, annual salmon production in the American River in recent years has been low relative to the mid-1990s (Surface Water Resources, Inc., 2001). To investigate the low production of fall-run Chinook salmon (Oncorhynchus tshawytscha), the Bureau of Reclamation requested that the U.S. Geological Survey apply the Stream Salmonid Simulator (S3) model to the population of fall-run Chinook salmon on the American River.
The American River was chosen among seven candidate Sacramento Basin rivers for S3 application. The American River was selected because of its management and public interest, recently low anadromous fish production, and rich time series of key demographic data needed for S3 application. Data that were not available, however, were empirical estimates on juvenile salmon habitat suitability in the American River. Therefore, a large component of applying S3 to the American River was devoted to the estimation of juvenile salmon habitat suitability and capacity. This entailed snorkeling the lower American River for 3 weeks in March 2021 during the early out-migration period for juvenile Chinook salmon. These efforts were fruitful and showed that the typically small fish (<55 millimeters) in the American River preferred much shallower depths than predicted by habitat suitability criteria derived from the literature for this population. Having empirical estimates on juvenile salmon in the American River provided a solid foundation from which to simulate the population using the S3 model.
The S3 model is a spatially explicit population model that runs on a daily time step to simulate redd superimposition, egg maturation, fry emergence and the subsequent growth, survival, and emigration of juvenile Chinook salmon from the river. The key features of this model relevant to this report include (1) a temperature-dependent bioenergetics model driving daily growth rates; (2) density-dependent dynamics that are influenced by the effect of flow on suitable habitat area; and (3) within-year habitat, river flow, and water temperature effects specific to spawning, egg incubation, and fry, parr, and smolt life stages. We used estimates of spawning escapement and geo-referenced redd locations to quantify the spatial and temporal distribution of female spawners for brood years 2014–19. These estimates of female spawners initiate the simulation of each year’s juvenile salmon emergence and emigration over a spatial domain extending from Nimbus Dam to the river’s confluence with the Sacramento River.
Using weekly estimates of juvenile salmon abundance and size (fork length) that passed the Watt Avenue fish trap (river kilometer 14.7), we calibrated the S3 model by estimating three key demographic parameters for each year, y: (1) Sy, the average daily survival probability, (2) M0y, the intercept for density-dependence in movement, representing the average daily probability of remaining in a habitat at zero abundance, and (3) Cy, the average daily proportion of maximum consumption. These parameters were obtained by minimizing the Mallow’s distance (Lupu and others, 2017) between distributions of weekly abundances and sizes of fish at the traps and weekly simulated abundances and sizes (by S3). Investigation of model fit showed excellent agreement between simulated annual abundances and the abundance of fish passing the fish trap. However, when we compared weekly abundances at the fish trap, S3 under-predicted peaks and over-predicted troughs in the time series of weekly abundances at the fish trap. Thus, some unknown within-year effects have yet to be identified and incorporated in the S3 model. Identifying these important effects and incorporating them in the S3 model would help explain the lack of fit between estimated and simulated weekly abundances.
We estimated parameters for 6 years that included a wide range of female spawner abundances (3,057–10,753) and water year types (Critical–Wet). We contrast our estimated parameters to the corresponding number of female spawners and the water year type for the Sacramento Valley. By happenstance, years having higher annual spawner abundances concurred with Critical to Dry water year types. Estimates of survival trended lower with higher spawner abundances and Critical to Dry conditions. In contrast, the extremely wet water year of 2017 had the lowest M0y, suggesting less density-dependence in fish movement, and the lowest Cy, suggesting lower average consumption in this year. When this high-flow year was excluded, a trend towards higher probabilities of fish remaining in a habitat at low abundance and lower proportions of maximum consumption was apparent from Critical to Wet conditions, but only 5 years of data were included. Except for 2017, daily proportions of maximum consumption were relatively high (Cy > 0.83), suggesting that fish were feeding at reasonably high proportions relative to the expected maximum consumption as defined by the “Wisconsin” bioenergetics model (Stewart and Ibarra, 1991).
Survival estimates from fry emergence to outmigration at the Sacramento River confluence were generally low when integrated over time. The highest daily survival probability was Sy = 0.93 in 2019, or 50 percent total mortality after 10 days. In contrast, our lowest daily survival probability was Sy = 0.74 in 2015, or 95 percent total mortality after 10 days. Consequently, even our highest estimated daily survival probability might be considered low. This is especially true given that Sy was estimated over a relatively short distance (<14.7 kilometers) from emergence to the Watt Avenue fish trap. Several factors, including our assumed and relatively high daily egg survival rate of 0.9975, could influence juvenile survival estimates. For example, an egg survival rate of 0.9975 results in 3-percent total mortality after 10 days. Egg mortality estimates used in S3 calibration were approximated from egg survivorship studies in the Yakima River, Washington (Johnson and others, 2012), and remains one of the greater uncertainties in S3 when estimating survival across life stages. By including bona fide estimates of egg survival in S3 simulations, the validity of the S3’s current daily egg survival rate could be assessed specifically for the American River. Tagging studies also could provide S3 with direct estimates of juvenile survival and movement; survival during egg incubation then could be estimated indirectly via model fitting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231060","collaboration":"Prepared in cooperation with U.S. Bureau of Reclamation","usgsCitation":"Plumb, J.M., Perry, R.W., Hatton, T.W., Smith, C.D., and Hannon, J.M., 2023, Application of the Stream Salmonid Simulator (S3) model to assess fall Chinook salmon (Oncorhynchus tshawytscha) production in the American River, California: U.S. Geological Survey Open-File Report 2023–1060, 35 p., https://doi.org/10.3133/ofr20231060.","productDescription":"ix, 35 p.","onlineOnly":"Y","ipdsId":"IP-141661","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":421858,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1060/ofr20231060.XML"},{"id":421857,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1060/images"},{"id":421856,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2023-1060"},{"id":421855,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1060/ofr20231060.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1060"},{"id":421854,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1060/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"American River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.5064051133351,\n 38.727216763718815\n ],\n [\n -121.5064051133351,\n 38.523370433079805\n ],\n [\n -121.11639046489739,\n 38.523370433079805\n ],\n [\n -121.11639046489739,\n 38.727216763718815\n ],\n [\n -121.5064051133351,\n 38.727216763718815\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
Invasive freshwater mussels, such as the zebra (Dreissena polymorpha), quagga (Dreissena rostriformis bugensis), and golden (Limnoperna fortunei) mussel have spread outside their native ranges throughout many regions of the North American, South American, and European continents in recent decades, damaging infrastructure and the environment. This review describes ongoing efforts by multiple groups to develop genetic biocontrol methods for invasive mussels. First, we provide an overview of genetic biocontrol strategies that have been applied in other invasive or pest species. Next, we summarize physical and chemical methods that are currently in use for invasive mussel control. We then describe the multidisciplinary approaches our groups are employing to develop genetic biocontrol tools for invasive mussels. Finally, we discuss the challenges and limitations of applying genetic biocontrol tools to invasive mussels. Collectively, we aim to openly share information and combine expertise to develop practical tools to enable the management of invasive freshwater mussels.
Efforts to incorporate bioavailability adjustments into regulatory water quality criteria in the United States have included four major procedures: hardness-based single-linear regression equations, water-effect ratios (WERs), biotic ligand models (BLMs), and multiple-linear regression models (MLRs) that use dissolved organic carbon, hardness, and pH. The performance of each with copper (Cu) is evaluated, emphasizing the relative performance of hardness-based versus MLR-based criteria equations. The WER approach was shown to be inherently highly biased. The hardness-based model is in widest use, and the MLR approach is the US Environmental Protection Agency's (USEPA's) present recommended approach for developing aquatic life criteria for metals. The performance of criteria versions was evaluated with numerous toxicity datasets that were independent of those used to develop the MLR models, including olfactory and behavioral toxicity, and field and ecosystem studies. Within the range of water conditions used to develop the Cu MLR criteria equations, the MLR performed well in terms of predicting toxicity and protecting sensitive species and ecosystems. In soft waters, the MLR outperformed both the BLM and hardness models. In atypical waters with pH <5.5 or >9, neither the MLR nor BLM predictions were reliable, suggesting that site-specific testing would be needed to determine reliable Cu criteria for such settings. The hardness-based criteria performed poorly with all toxicity datasets, showing no or weak ability to predict observed toxicity. In natural waters, MLR and BLM criteria versions were strongly correlated. In contrast, the hardness-criteria version was often out of phase with the MLR and, depending on waterbody and season, could be either strongly overprotective or underprotective. The MLR-based USEPA-style chronic criterion appears to be more generally protective of ecosystems than other models.
LANDFIRE Help Desk
Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Due to Idaho’s inland location approximately 350 miles from the Pacific Ocean and its 80 recognized mountain ranges, the State’s climate varies widely, with maritime influence in the northern and western parts of Idaho and continental influence on the eastern side. The weather in the abundant mountains is unpredictable and often associated with natural hazards such as severe thunder and lightning storms leading to flooding, landslides, and wildfires. Issues important to Idaho’s economy include river, stream, and forest resource management, and infrastructure and construction management. Idaho participated in the U.S. Geological Survey 3D Elevation Program (3DEP) in 2016, the State’s first 3DEP project. The success of this project led to development of the Idaho Statewide Lidar Plan. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.
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\"}}]}","contact":"Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 511
Reston, VA 20192
Email: 3DEP@usgs.gov
","tableOfContents":"Non-invasive methods are important to the field of conservation physiology to reduce negative effects on organisms being studied. Glucocorticoid (GC) hormones are often used to assess health of individuals, but collection methods can be invasive. Many amphibians are imperiled worldwide, and saliva is a non- or semi-invasive matrix to measure GCs that has been partially validated for only four amphibian species. Validation ensures that assays are reliable and can detect changes in saliva corticosterone (sCORT) after exposure to stressors, but it is also necessary to ensure sCORT concentrations are correlated with plasma concentrations. To help validate the use of saliva in assessing CORT responses in amphibians, we captured uniquely marked Columbia spotted frogs (Rana luteiventris) on sequential days and collected baseline and stress-induced (after handling) samples. For a subset of individuals, we collected and quantified CORT in both saliva and blood samples, which have not been compared for amphibians. We tested several aspects of CORT responses and, by collecting across separate days, measured repeatability of CORT responses across days. We also evaluated whether methods common to amphibian conservation, such as handling alone or handling, clipping a toe and tagging elevated sCORT. Similar to previous studies, we show that sCORT is reliable concerning parallelism, recovery, precision and sensitivity. sCORT was weakly correlated with plasma CORT (R2 = 0.21), and we detected elevations in sCORT after handling, demonstrating biological validation. Toe clipping and tagging did not increase sCORT over handling alone, but repeated handling elevated sCORT for ~72 hours. However, sCORT responses were highly variable and repeatability was low within individuals and among capture sessions, contrary to previous studies with urinary and waterborne CORT. sCORT is a semi-invasive and rapid technique that could be useful to assess effects of anthropogenic change and conservation efforts, but will require careful study design and future validation.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/conphys/coad078","usgsCitation":"Tornabene, B.J., Hossack, B., and Breuner, C.W., 2023, Assay validation of saliva glucocorticoids in Columbia spotted frogs and effects of handling and marking: Conservation Physiology Toolbox, v. 11, no. 1, coad078, 10 p., https://doi.org/10.1093/conphys/coad078.","productDescription":"coad078, 10 p.","ipdsId":"IP-152516","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":441908,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coad078","text":"Publisher Index Page"},{"id":422229,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-10-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Tornabene, Brian J. 0000-0002-2348-3119","orcid":"https://orcid.org/0000-0002-2348-3119","contributorId":303977,"corporation":false,"usgs":true,"family":"Tornabene","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":887102,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":887103,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Breuner, Creagh W.","contributorId":331253,"corporation":false,"usgs":false,"family":"Breuner","given":"Creagh","email":"","middleInitial":"W.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":887104,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249786,"text":"70249786 - 2023 - Combining resilience and resistance with threat-based approaches for prioritizing management actions in sagebrush ecosystems","interactions":[],"lastModifiedDate":"2023-11-20T17:40:10.691627","indexId":"70249786","displayToPublicDate":"2023-10-10T07:06:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Combining resilience and resistance with threat-based approaches for prioritizing management actions in sagebrush ecosystems","docAbstract":"The sagebrush biome is a dryland region in the western United States experiencing rapid transformations to novel ecological states. Threat-based approaches for managing anthropogenic and ecosystem threats have recently become prominent, but successfully mitigating threats depends on the ecological resilience of ecosystems. We used a spatially explicit approach for prioritizing management actions that combined a threat-based model with models of resilience to disturbance and resistance to annual grass invasion. The threat-based model assessed geographic patterns in sagebrush ecological integrity (SEI) to identify core sagebrush, growth opportunity, and other rangeland areas. The resilience and resistance model identified ecologically relevant climate and soil water availability indicators from process-based ecohydrological models. The SEI areas and resilience and resistance indicators were consistent—the resilience and resistance indicators showed generally positive relationships with the SEI areas. They also were complementary—SEI areas provided information on intact sagebrush areas and threats, while resilience and resistance provided information on responses to disturbances and management actions. The SEI index and resilience and resistance indicators provide the basis for prioritizing conservation and restoration actions and determining appropriate strategies. The difficulty and time required to conserve or restore SEI areas increase as threats increases and resilience and resistance decrease.
Given the earthquake risk on the West Coast of the United States, individuals and communities require a basic understanding of ShakeAlert earthquake early warning technology, which may provide crucial seconds of warning. Free choice learning environments (FCLEs), such as museums, public libraries, and national parks, are uniquely positioned to expand the reach of earthquake early warning through educational initiatives and resources. Earthquake education in these spaces creates awareness of earthquake hazards and risk in areas where people live or visit and may also increase engagement in preparedness behavior as they are trusted sources of information in their communities. However, population demographics within the ShakeAlert states has yet to be examined; audience segmentation theories require a better understanding of the demographics of the people the system seeks to serve. Here we build upon previous typology research that examined over 150 earthquake displays around the United States and found that most did not include information about earthquake preparedness or associated protective actions. This new research shifts to a hierarchical clustering analysis that identifies seven main population groups within the ShakeAlert states. We also find that the cost of admission and the geographic distance away from FCLEs, including the potential cost and time of transportation, may lead to an urban-rural divide in visitor access. Using audience segmentation theories as related to earthquakes, we can understand critical barriers to FCLEs and opportunities for ShakeAlert education. As earthquake early warning systems expand internationally, thoughtful and equitable education initiatives are beneficial to reach critical audiences.
High juvenile mortality prevents recruitment into the adult populations of endangered Shortnose Sucker Chasmistes brevirostris and Lost River Sucker Deltistes luxatus in Upper Klamath Lake, Oregon. To address the lack of recruitment, the U.S. Fish and Wildlife Service implemented the Sucker Assisted Rearing Program (SARP). Managers developing the rearing program lack information about how length at release relates to survival. To determine how initial length affects survival of captively reared juvenile suckers, we introduced juvenile suckers from the SARP into three net-pens in Upper Klamath Lake.
The juvenile suckers ranged from 102 to 284 mm standard length, and each fish was tagged with a passive integrated transponder (PIT) tag. Fish were monitored continuously by PIT antennas and mortality was inferred when movements ceased.
Estimated survival over 57 days was high in all net-pens (0.79–1.00) and remained high at two net-pens for 76 and 86 days. Adjusted survival curves resulting from a stratified Cox model with standard length as a covariate, indicated that length positively influenced predicted survival by as much as 41% at one site. During the study, pH and dissolved oxygen regularly exceeded no-effect thresholds at two sites and briefly reached lethal thresholds at the same two sites but did not coincide with the observed mortalities. Slower growth and the lowest survival were observed at the third site, where water quality never exceeded thresholds.
A larger release size and the location of the net-pen can improve the survivability of juvenile suckers in net-pens in Upper Klamath Lake.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10933","usgsCitation":"Caldwell, J.M., Burdick, S.M., Krause, J.R., and Harris, A., 2023, Does release size into net-pens affect survival of captively reared juvenile endangered suckers in Upper Klamath Lake?: North American Journal of Fisheries Management, v. 43, no. 5, p. 1322-1336, https://doi.org/10.1002/nafm.10933.","productDescription":"15 p.","startPage":"1322","endPage":"1336","ipdsId":"IP-144776","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":435151,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JE15XZ","text":"USGS data release","linkHelpText":"Detections, Physical Captures, Water Quality, and Fish Health associated with Endangered Suckers in Three Net Pens in Upper Klamath Lake, 2020"},{"id":421902,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.2589402064643,\n 42.721377871574475\n ],\n [\n -122.2589402064643,\n 42.11308416751328\n ],\n [\n -121.54757546037055,\n 42.11308416751328\n ],\n [\n -121.54757546037055,\n 42.721377871574475\n ],\n [\n -122.2589402064643,\n 42.721377871574475\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Caldwell, John Michael 0000-0002-3210-2226","orcid":"https://orcid.org/0000-0002-3210-2226","contributorId":328462,"corporation":false,"usgs":true,"family":"Caldwell","given":"John","email":"","middleInitial":"Michael","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":886092,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burdick, Summer M. 0000-0002-3480-5793 sburdick@usgs.gov","orcid":"https://orcid.org/0000-0002-3480-5793","contributorId":3448,"corporation":false,"usgs":true,"family":"Burdick","given":"Summer","email":"sburdick@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":886093,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krause, Jacob Richard 0000-0002-9804-2481","orcid":"https://orcid.org/0000-0002-9804-2481","contributorId":300701,"corporation":false,"usgs":true,"family":"Krause","given":"Jacob","email":"","middleInitial":"Richard","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":886094,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harris, Alta C. 0000-0002-2123-3028 aharris@usgs.gov","orcid":"https://orcid.org/0000-0002-2123-3028","contributorId":3490,"corporation":false,"usgs":true,"family":"Harris","given":"Alta C.","email":"aharris@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":886095,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256494,"text":"70256494 - 2023 - Assessment of lesser prairie-chicken translocation through survival and lek surveys","interactions":[],"lastModifiedDate":"2024-08-19T17:40:37.165449","indexId":"70256494","displayToPublicDate":"2023-10-10T06:13:28","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of lesser prairie-chicken translocation through survival and lek surveys","docAbstract":"Translocation is a management tool used to restore or augment wildlife populations, but outcomes of translocations are often poorly documented and can have varying levels of success for improving wildlife population declines. The lesser prairie-chicken (Tympanuchus pallidicinctus) is a prairie grouse endemic to the southern Great Plains. In response to declining abundance and distribution, in 2023 lesser prairie-chickens were listed as threatened or endangered under the Endangered Species Act in different states. Translocation is a potential management response to population declines when there is an availability of unoccupied habitats, but translocation efficacy has not been evaluated for lesser prairie-chickens. We translocated 411 lesser prairie-chickens seasonally from 2016-2019 and monitored the translocated lesser prairie-chicken population from 2017–2022. To assess translocation as a management tool for lesser prairie-chickens, we estimated survival for 2017–2020 and conducted lek surveys during 2017–2022. Over a fifth (22.8%, n = 94) of translocated birds either died or went missing within the first 2 weeks following release. Survival rates of translocated birds during the breeding (0.44 ± 0.02 [SE]) and nonbreeding (0.55 ± 0.03 [SE]) seasons were relatively low compared to nontranslocated lesser prairie-chickens in other studies (0.63–0.93 for breeding season; 0.43–0.87 for non-breeding season). Twenty-seven percent of translocated birds survived to the breeding season after release (i.e., >1 year). Translocated lesser prairie-chickens initiated 28 lekking sites over the study period. We estimated 77% of males available >2 weeks post release participated in lekking activity. The number of leks and male high counts on leks in the study area increased after translocation, peaking one year post-translocation (an overall increase of 250% and 340%, respectively). However, both the number of leks and male high counts decreased (48% and 39%, respectively) within 3 years after translocation cessation. Establishment of leks and increasing lek attendance directly following translocation initially suggested that translocation could be a viable management tool. However, survival rates after translocation and declining lek counts following translocation indicates that the increased population abundance and occupied range from this translocation effort may be unsustainable. Our results highlight the necessity of monitoring to determine outcomes of a large lesser prairie-chicken translocation. Other management strategies, such as targeted grassland restoration and management in areas of greatest lesser prairie-chicken density, could be more beneficial for conservation of lesser prairie-chicken populations.
Waterfowl with more body mass and a greater body condition during the non-breeding season are thought to be more likely to survive and have increased productivity during the following breeding season. Body mass and body condition in waterfowl should reflect the resources available to them locally. We analyzed the relationship of landscape composition on mallard (Anas platyrhynchos) body mass and body condition (mass-wing length index) among age and sex groups. We calculated these variables from hunter-harvested mallards during the 2019–2020 and 2020–2021 duck hunting seasons in the Lower Mississippi Alluvial Valley of Arkansas, USA. We used linear mixed-effects models to analyze changes in body mass and body condition with changes in the percent landscape composition of water cover, woody wetlands, herbaceous wetlands, rice, soybeans, and disturbance. We found that body mass and condition of harvested mallards were positively associated with greater proportions of water cover and woody wetlands but negatively associated with greater proportions of herbaceous wetlands and human disturbance from human infrastructure. Management actions focused on providing flooded and woody wetland areas on the landscape that allow waterfowl to access food resources, while decreasing the disturbance around wetlands in the form of road density and human infrastructure, should increase body mass and body condition in mallards spending the non-breeding season in the Lower Mississippi Alluvial Valley.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22509","usgsCitation":"Veon, J., Krementz, D., Naylor, L., and DeGregorio, B.A., 2023, Influences of landscape composition on hunter-harvested mallard body mass and condition in eastern Arkansas: The Journal of Wildlife Management, v. 88, no. 1, e22509, 22 p., https://doi.org/10.1002/jwmg.22509.","productDescription":"e22509, 22 p.","ipdsId":"IP-139908","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":490090,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22509","text":"Publisher Index Page"},{"id":482266,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"eastern 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\"}}]}","volume":"88","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Veon, John T.","contributorId":351068,"corporation":false,"usgs":false,"family":"Veon","given":"John T.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":927830,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krementz, David G.","contributorId":351069,"corporation":false,"usgs":false,"family":"Krementz","given":"David G.","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":927831,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Naylor, Luke W.","contributorId":351070,"corporation":false,"usgs":false,"family":"Naylor","given":"Luke W.","affiliations":[{"id":37007,"text":"Arkansas Game and Fish Commission","active":true,"usgs":false}],"preferred":false,"id":927832,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":927833,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249494,"text":"70249494 - 2023 - Spatially explicit models of seed availability improve predictions of conifer regeneration following the 2018 Carr Fire in northern California","interactions":[],"lastModifiedDate":"2023-10-11T11:53:00.481682","indexId":"70249494","displayToPublicDate":"2023-10-09T06:49:50","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Spatially explicit models of seed availability improve predictions of conifer regeneration following the 2018 Carr Fire in northern California","docAbstract":"For many conifer species in dry conifer forests of North America, seeds must be present for postfire regeneration to occur, suggesting that seed dispersal from surviving trees plays a critical role in postfire forest recovery. However, the application of tree fecundity and spatial arrangement to postfire conifer recovery predictions have only recently become more common, and is often included at relatively coarse scales (i.e., 30 meters). In this study, we mapped surviving trees using lidar and created a spatially explicit estimate of seed density (seed shadows) with 10 m, 50 m, and 100 m median dispersal distances. We estimated the number of seeds produced by each tree using allometric relationships between tree size and fecundity. Along with the seed shadows, we used a suite of topographic variables as inputs to negative binomial hurdle models to predict conifer seedling abundance in 131 plots following the 2018 Carr Fire in northern California, USA. We compared models using each of the seed shadows to each other as well as to a model using the distance to the nearest surviving tree, which served as a baseline. All model formulations indicated that estimated seed availability was positively associated with conifer regeneration. Despite the importance of seed availability plays in regeneration and the substantial differences in seed availability represented by the different seed shadows in our analysis, we found surprisingly little difference in model performance regardless of which seed shadow was used. However, the models employing seed shadows outperformed the models with distance to the nearest live tree. Although we have demonstrated a modest improvement in predicting postfire conifer regeneration, the uncertainty in our results highlights the importance of tree detection and classification in future studies of this kind. Future studies may find it useful to consider other factors such as predation, site suitability, and seed mortality as potential drivers of discrepancies between total and realized dispersal kernels.
Avalanche forecasters and snow scientists use physically based snow stratigraphy models to fill spatial and temporal gaps in field-based snow profile observations. These models generate stratigraphy predictions using meteorological input from automated weather stations (AWS) or numerical weather prediction (NWP) models. The choice of input data is often determined by data availability or convenience instead of giving full consideration to the most appropriate source for a particular application. For example, while AWS may provide weather observations that better represent a particular site, they have large up-front costs and require specialized personnel to service and maintain. The goal of this study is to quantify the accuracy of snow stratigraphy produced by the SNOWPACK model driven by different input data, with a particular focus on cost-benefit analysis for operational avalanche forecasting. We generate modeled snow profiles at a field site in the Bridger Range of southwestern Montana, USA, using a) observations from an AWS at the field site and b) NWP output from the NOAA High-Resolution Rapid Refresh (HRRR) model. Validation data consist of a season-long time series of 10 manual snow profiles. We use dynamic time-warping (DTW) to quantify the overall and grain-type categorized similarities between modeled and in-situ observed profiles that are collocated in time and in space. Based on the similarity results, we present a cost-benefit analysis that considers the cost of installing and maintaining an AWS alongside the improved representation of snow depth, grain size, and weak layer types.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Palomaki, R.T., and Miller, Z., 2023, Assessing snowpack stratigraphy accuracy based on different input data: Insights for operations avalanche forecasting, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 287-294.","productDescription":"8 p.","startPage":"287","endPage":"294","ipdsId":"IP-157004","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421974,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item.php?id=2889","linkFileType":{"id":5,"text":"html"}},{"id":421975,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Bridger Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.94066722835859,\n 45.83656619518504\n ],\n [\n -110.94066722835859,\n 45.83190909512919\n ],\n [\n -110.92970520296628,\n 45.83190909512919\n ],\n [\n -110.92970520296628,\n 45.83656619518504\n ],\n [\n -110.94066722835859,\n 45.83656619518504\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Palomaki, Ross T. 0000-0002-3304-9914","orcid":"https://orcid.org/0000-0002-3304-9914","contributorId":299761,"corporation":false,"usgs":false,"family":"Palomaki","given":"Ross","email":"","middleInitial":"T.","affiliations":[{"id":64943,"text":"Montana State University Earth Sciences Department","active":true,"usgs":false}],"preferred":false,"id":881596,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":881597,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70248045,"text":"70248045 - 2023 - Comparing snowpack meteorological inputs to support regional wet snow avalanche forecasting","interactions":[],"lastModifiedDate":"2023-10-18T15:42:28.662716","indexId":"70248045","displayToPublicDate":"2023-10-08T10:31:48","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Comparing snowpack meteorological inputs to support regional wet snow avalanche forecasting","docAbstract":"Wet snow avalanches are predicted to increase in frequency with climate change and are often difficult to forecast. Improving our understanding of wet snow avalanche timing will help with current forecasting challenges. The onset of wet snow avalanching is closely tied to the temporal progression of liquid water flow through the seasonal snowpack. Measuring the flow of water through the snowpack in-situ is difficult due to the spatial variability of snow depth and structure. However, physical snowpack models can potentially simulate this process. The accuracy of snowpack models is heavily dependent upon the quality of the meteorological input data. A thorough investigation of model output differences using several different meteorological inputs for forecasting water movement and wet snow avalanches has not yet been thoroughly investigated. Here, we evaluate indicators of regional wet snow avalanches produced by the SNOWPACK model using different meteorological input. We compare the accuracy of SNOWPACK modeled outputs driven by two different numerical weather prediction (NWP) forecast models: the High-Resolution Deterministic Prediction System (HRDPS) and the North American Model (NAMnest). We leverage hourly automated weather station data, daily operational avalanche observations along the Going-to-the-Sun Road in Glacier National Park, Montana, United States, and in-situ snow stratigraphy and wetness profile observations to validate the SNOWPACK modeled outputs. This research is directly applicable to avalanche forecasting operations and future avalanche research as wet snow avalanche timing evolves due to climate change.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Miller, Z., Horton, S., Mitterer, C., and Peitzsch, E.H., 2023, Comparing snowpack meteorological inputs to support regional wet snow avalanche forecasting, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 264-271.","productDescription":"8 p.","startPage":"264","endPage":"271","ipdsId":"IP-157003","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421971,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item.php?id=2886","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park, Going-to-the-Sun Road study area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.55234262299646,\n 48.73306362472809\n ],\n [\n -113.74164451916323,\n 48.73306362472809\n ],\n [\n -113.74164451916323,\n 48.642925680389254\n ],\n [\n -113.55234262299646,\n 48.642925680389254\n ],\n [\n -113.55234262299646,\n 48.73306362472809\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":881607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horton, Simon 0000-0003-2936-8688","orcid":"https://orcid.org/0000-0003-2936-8688","contributorId":328885,"corporation":false,"usgs":false,"family":"Horton","given":"Simon","email":"","affiliations":[{"id":78515,"text":"Avalanche Canada, Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":881608,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitterer, Christoph 0000-0002-2268-8016","orcid":"https://orcid.org/0000-0002-2268-8016","contributorId":328886,"corporation":false,"usgs":false,"family":"Mitterer","given":"Christoph","email":"","affiliations":[{"id":78516,"text":"Avalanche Forecasting Service Tyrol","active":true,"usgs":false}],"preferred":false,"id":881609,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":881610,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249586,"text":"70249586 - 2023 - The relationship between whumpf observations and avalanche activity in Colorado, USA","interactions":[],"lastModifiedDate":"2023-10-18T15:14:19.422926","indexId":"70249586","displayToPublicDate":"2023-10-08T10:10:47","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"The relationship between whumpf observations and avalanche activity in Colorado, USA","docAbstract":"Triggering whumpfs is a primary indicator of unstable snowpack conditions. Although backcountry travelers and avalanche forecasters rely on whumpfs as a warning sign of potential avalanches, there is little formal research to confirm this relationship. This study investigated the temporal correlation between whumpfs and avalanche activity in data from Colorado's Front Range and southern San Juan Mountains between the winters of 2010/11 and 2022/23. To assess changing conditions over a variety of seasons, we compared the timing of whumpfs and avalanches to the total snow depth at a representative site. We used a 13-inch (33 cm) rolling-window average snow depth versus the median for observed whumpfs, and small avalanches (D1 to D1.5), and large to very large avalanches (D2 and greater). Our results support informal observations that whumpfs are important indicators of avalanche activity, especially at shallower snow depths. Later in the season, when snow depths are deeper and basal weak layers become more difficult to trigger, whumpfs become less common even during periods of increasing avalanche activity. Some of our results may be due to the thin, weak, and wind-affected snow in the Colorado Front Range, where whumpfing typically occurs due to collapsing basal depth hoar. Our findings are important for backcountry travelers assessing stability and for backcountry avalanche forecasters communicating conditions to the public. Our data show that although whumpfs generally indicate unstable conditions and correlate with avalanche activity, the largest avalanches of the winter may not always be preceded by whumpfing.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Konigsberg, J., Simenhois, R., Birkeland, K.W., Peitzsch, E.H., Chabot, D., and Greene, E.M., 2023, The relationship between whumpf observations and avalanche activity in Colorado, USA, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 1032-1038.","productDescription":"7 p.","startPage":"1032","endPage":"1038","ipdsId":"IP-156518","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421969,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421968,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item.php?id=3008","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","otherGeospatial":"Front Range Mountains, San Juan Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.44815485949579,\n 40.97327281710409\n ],\n [\n -106.44815485949579,\n 39.064936136772985\n ],\n [\n -105.28491868786489,\n 39.064936136772985\n ],\n [\n -105.28491868786489,\n 40.97327281710409\n ],\n [\n -106.44815485949579,\n 40.97327281710409\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.51362840857615,\n 37.99145028406636\n ],\n [\n -108.51362840857615,\n 37.16200987030051\n ],\n [\n -106.20006250061266,\n 37.16200987030051\n ],\n [\n -106.20006250061266,\n 37.99145028406636\n ],\n [\n -108.51362840857615,\n 37.99145028406636\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Konigsberg, Jason","contributorId":330955,"corporation":false,"usgs":false,"family":"Konigsberg","given":"Jason","email":"","affiliations":[{"id":40054,"text":"Colorado Avalanche Information Center","active":true,"usgs":false}],"preferred":false,"id":886321,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simenhois, Ron","contributorId":330956,"corporation":false,"usgs":false,"family":"Simenhois","given":"Ron","email":"","affiliations":[{"id":40054,"text":"Colorado Avalanche Information Center","active":true,"usgs":false}],"preferred":false,"id":886322,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Birkeland, Karl W.","contributorId":173366,"corporation":false,"usgs":false,"family":"Birkeland","given":"Karl","middleInitial":"W.","affiliations":[{"id":27213,"text":"USDA Forest Service National Avalanche Center, Bozeman, MT, USA","active":true,"usgs":false}],"preferred":false,"id":886323,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chabot, Doug","contributorId":330957,"corporation":false,"usgs":false,"family":"Chabot","given":"Doug","email":"","affiliations":[{"id":79076,"text":"Gallatin National Forest Avalanche Center","active":true,"usgs":false}],"preferred":false,"id":886324,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Greene, Ethan M.","contributorId":330958,"corporation":false,"usgs":false,"family":"Greene","given":"Ethan","middleInitial":"M.","affiliations":[{"id":40054,"text":"Colorado Avalanche Information Center","active":true,"usgs":false}],"preferred":false,"id":886326,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249584,"text":"70249584 - 2023 - Mapping a glide avalanche with terrestrial lidar in Glacier National Park, USA","interactions":[],"lastModifiedDate":"2023-10-18T15:08:24.195224","indexId":"70249584","displayToPublicDate":"2023-10-08T10:03:57","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Mapping a glide avalanche with terrestrial lidar in Glacier National Park, USA","docAbstract":"Thorough documentation of large avalanche events is important for forecasting efforts, infrastructure planning, and investigating the processes involved in avalanche formation and release. However, due in part to the isolated and dangerous nature of avalanche terrain, collecting in-situ, spatially continuous, and quantitative information surrounding avalanches remains difficult. Advances in remote sensing continue to address this knowledge gap. For example, terrestrial laser scanners (TLSs) can produce snow depth measurements at fine spatial resolutions over large areas. Repeat data acquisitions between precipitation events also allow for depth quantification atop an interface, as well as precise estimations of release volume and runout area after avalanche failure. Here, we explore the benefits of TLS-derived documentation from a large avalanche event by examining the development and release of a glide avalanche that occurred in Glacier National Park, Montana, USA, during the spring of 2022. Three sets of lidar point cloud data were acquired in the Haystack Creek drainage, focused on a well-known glide avalanche site. Lidar scans were collected after glide cracks emerged but prior to glide failure, and shortly (~ 1.5 days) after avalanche occurrence, in addition to a snow-free scan later in the year. With this temporal dataset, we were able to account for and visualize the spatial variability of snow depth across the avalanche start zone, such that we could precisely calculate the release volume (18674 m3) and average start zone depth (3.3 m) of the avalanche. Furthermore, TLS data were used to map the extent of the runout area and entrainment zone.
","largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","conferenceTitle":"International Snow Science Workshop 2023","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Dillon, J.W., Miller, Z., Peitzsch, E.H., and Hammonds, K.D., 2023, Mapping a glide avalanche with terrestrial lidar in Glacier National Park, USA, in Proceedings, International Snow Science Workshop 2023, p. 1431-1436.","productDescription":"6 p.","startPage":"1431","endPage":"1436","ipdsId":"IP-156879","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421966,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item.php?id=3073","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.741667,\n 48.744444\n ],\n [\n -113.741667,\n 48.736111\n ],\n [\n -113.723611,\n 48.736111\n ],\n [\n -113.723611,\n 48.744444\n ],\n [\n -113.741667,\n 48.744444\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dillon, James W.","contributorId":330951,"corporation":false,"usgs":false,"family":"Dillon","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":886311,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886312,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886313,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammonds, Kevin D.","contributorId":330952,"corporation":false,"usgs":false,"family":"Hammonds","given":"Kevin","email":"","middleInitial":"D.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":886314,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249583,"text":"70249583 - 2023 - Temporal evolution of slab and weak layer properties during the transition from dry to wet snowpack conditions","interactions":[],"lastModifiedDate":"2023-10-18T14:58:26.08698","indexId":"70249583","displayToPublicDate":"2023-10-08T09:49:06","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Temporal evolution of slab and weak layer properties during the transition from dry to wet snowpack conditions","docAbstract":"Wet-snow slab avalanches are destructive and may become more prevalent in a warming climate. This type of avalanche remains challenging to forecast because the underlying processes leading to wet-snow slab avalanche release are poorly understood. In this study, we examine the temporal evolution of weak layer and slab liquid water content (LWC), critical cut length, and propagation saw test (PST) results during the season's first critical melt period at our study site in the Madison Mountains of southwest Montana. We used snowpack profiles and in-situ weather station data to initialize and force the 1-D physics-based snow cover model SNOWPACK throughout the winter and spring seasons. We then used a high-resolution numerical weather model to force SNOWPACK simulations to forecast the onset of the transition from dry to wet conditions. From April 10-12, 2023, we conducted 67 PSTs, 1053 LWC measurements, 20 hardness profiles, and a full snow profile each morning and early evening. During the first two days of sampling, we observed a transition from low to high propagation propensity with decreasing cut lengths and increasing LWC. On Day 3, we observed consistently low propagation propensity, even as LWC levels remained elevated and comparable to the preceding period of high propagation propensity. This indicates that there is a point where the relationship we observed through the first two days between increasing LWC, increasing propagation propensity, and decreasing cut length no longer holds. Our results further suggest PST propagation mode may help pinpoint the onset, peak, and decline of wet-snow fracture propagation propensity.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","usgsCitation":"Lipkowitz, J., Peitzsch, E.H., Dixon, J., Kalb, M., McCabe, D., Ditmar, G., and Mitterer, C., 2023, Temporal evolution of slab and weak layer properties during the transition from dry to wet snowpack conditions, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 1374-1381.","productDescription":"8 p.","startPage":"1374","endPage":"1381","ipdsId":"IP-156847","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421965,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421964,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item.php?id=3063","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana","otherGeospatial":"Madison Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.445,\n 45.233\n ],\n [\n -111.445,\n 45.23\n ],\n [\n -111.44,\n 45.23\n ],\n [\n -111.44,\n 45.233\n ],\n [\n -111.445,\n 45.233\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lipkowitz, Josh","contributorId":330946,"corporation":false,"usgs":false,"family":"Lipkowitz","given":"Josh","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":886304,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886305,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dixon, Jean","contributorId":330947,"corporation":false,"usgs":false,"family":"Dixon","given":"Jean","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":886306,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kalb, Marcus","contributorId":330948,"corporation":false,"usgs":false,"family":"Kalb","given":"Marcus","email":"","affiliations":[{"id":79073,"text":"Avalanche Warning Service Tyrol","active":true,"usgs":false}],"preferred":false,"id":886307,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCabe, Douglas","contributorId":330949,"corporation":false,"usgs":false,"family":"McCabe","given":"Douglas","email":"","affiliations":[{"id":79074,"text":"Yellowstone Club Ski Patrol","active":true,"usgs":false}],"preferred":false,"id":886308,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ditmar, Griffin","contributorId":330950,"corporation":false,"usgs":false,"family":"Ditmar","given":"Griffin","email":"","affiliations":[{"id":79074,"text":"Yellowstone Club Ski Patrol","active":true,"usgs":false}],"preferred":false,"id":886309,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mitterer, Christoph 0000-0002-2268-8016","orcid":"https://orcid.org/0000-0002-2268-8016","contributorId":328886,"corporation":false,"usgs":false,"family":"Mitterer","given":"Christoph","email":"","affiliations":[{"id":78516,"text":"Avalanche Forecasting Service Tyrol","active":true,"usgs":false}],"preferred":false,"id":886310,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249595,"text":"70249595 - 2023 - Under-forecasting wet avalanche cycles: Case studies and lessons learned from two wet avalanche cycles in northwest Montana and central Colorado","interactions":[],"lastModifiedDate":"2023-10-18T14:47:56.374251","indexId":"70249595","displayToPublicDate":"2023-10-08T09:42:18","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Under-forecasting wet avalanche cycles: Case studies and lessons learned from two wet avalanche cycles in northwest Montana and central Colorado","docAbstract":"Predicting the timing and location of natural wet avalanche events is challenging, especially the release of wet slabs. In this study, we describe the existing snowpack structure, weather, and observed avalanche activity for two separate wet avalanche cycles in different avalanche climate types: northwest Montana and central Colorado. In both cases, the regional avalanche forecast centers initially predicted an avalanche hazard rating lower than the observed avalanche hazard and did not initially predict the occurrence of wet slab avalanches. Large wet slab avalanche activity began during the early stages of the first, rapid warming event of the season in both regions, challenging the notion that a lack of overnight refreeze is important for large wet avalanche cycles. This highlights the need for improvement in forecasting wet avalanche events/cycles. Here, we discuss lessons learned and operational strategies for improving forecast accuracy during significant melting events. These include focused field assessments in shallow snowpack areas and low elevation terrain, proactive late-day monitoring beyond normal field hours, and additional meteorological considerations, such as cloud cover and energy balance assessments, prior to and during notable warm-ups.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Guy, Z., and Peitzsch, E.H., 2023, Under-forecasting wet avalanche cycles: Case studies and lessons learned from two wet avalanche cycles in northwest Montana and central Colorado, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 1082-1092.","productDescription":"11 p.","startPage":"1082","endPage":"1092","ipdsId":"IP-156902","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421963,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421962,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://arc.lib.montana.edu/snow-science/item.php?id=3016","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado, Montana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.21992327350442,\n 48.93647228370364\n ],\n [\n -113.23076217600384,\n 46.80597132373853\n ],\n [\n -111.92011617525155,\n 47.19378057200959\n ],\n [\n -113.29729149274793,\n 48.99851674638353\n ],\n [\n -115.21992327350442,\n 48.93647228370364\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.82895276721169,\n 39.22835042820549\n ],\n [\n -107.82895276721169,\n 38.6345251296778\n ],\n [\n -106.47248685269962,\n 38.6345251296778\n ],\n [\n -106.47248685269962,\n 39.22835042820549\n ],\n [\n -107.82895276721169,\n 39.22835042820549\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Guy, Zachary","contributorId":330973,"corporation":false,"usgs":false,"family":"Guy","given":"Zachary","email":"","affiliations":[{"id":79084,"text":"Crested Butte Avalanche Center","active":true,"usgs":false}],"preferred":false,"id":886385,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886386,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70249585,"text":"70249585 - 2023 - Spatial extent of forested avalanche terrain impacted by wildfire across the Sawtooth National Forest","interactions":[],"lastModifiedDate":"2023-10-18T15:22:06.30971","indexId":"70249585","displayToPublicDate":"2023-10-08T08:39:31","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Spatial extent of forested avalanche terrain impacted by wildfire across the Sawtooth National Forest","docAbstract":"Forest structure is a major driver of mountain snowpacks and avalanche occurrence. Healthy forests can reduce the incidence of dangerous slab avalanches, slow avalanches when in motion, shorten their runout distances, and act as a safety buffer for backcountry users, infrastructure, and transportation corridors. Since 1984, wildfire area in the seasonal snow zone of the western United States has increased by 70% throughout the seasonal snow zone, creating significant changes to avalanche prone mountains and their connected communities. A major unknown is the impact a reduction of forested area due to forest fires will have on avalanche occurrence. We hypothesize increased potential for avalanching in forested areas impacted by wildfire. Reduced tree cover may make previously heavily forested terrain more susceptible to avalanching. Increases in the size of avalanche start zones, paths, and runouts due to forest fires may increase the destructive size of avalanches and create cascading ecological effects within the adjacent forested terrain. Forest fires may therefore increase the likelihood of avalanche release, resulting in further loss of tree cover and increased avalanche area as well as decreased protection for human infrastructure. In this study, we quantify avalanche area changes before and after the Ross Fork wildfire (2022) in Sawtooth National Forest, Idaho, USA. We utilized satellite imagery, a digital elevation model and historical fire spatial data to quantify and characterize avalanche area changes within the fire perimeter using the Auto-ATES workflow (Sykes et al., 2022). We found decreases in forest coverage that contributed to widespread increases in potential avalanche release areas, avalanche tracks, and potential runout zones throughout the study area as well as the creation of new potential avalanche release areas and a substantial decrease in non-avalanche connected terrain within the fire perimeter. These preliminary findings help inform avalanche and snow safety professionals as well as land managers working in wildfire-prone forested areas about potential post-wildfire changes in avalanche terrain.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Miller, Z., Sykes, J., Guinn, M., VandenBos, B., Savage, S., and Peitzsch, E.H., 2023, Spatial extent of forested avalanche terrain impacted by wildfire across the Sawtooth National Forest, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 272-279.","productDescription":"8 p.","startPage":"272","endPage":"279","ipdsId":"IP-156644","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421970,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421961,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"http://arc.lib.montana.edu/snow-science/item/2887","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Idaho","otherGeospatial":"Sawtooth National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.25979416843813,\n 44.25122593623942\n ],\n [\n -115.25979416843813,\n 43.47618673393339\n ],\n [\n -113.88619800565512,\n 43.47618673393339\n ],\n [\n -113.88619800565512,\n 44.25122593623942\n ],\n [\n -115.25979416843813,\n 44.25122593623942\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886315,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sykes, John","contributorId":330953,"corporation":false,"usgs":false,"family":"Sykes","given":"John","email":"","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":886316,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guinn, Megan","contributorId":330954,"corporation":false,"usgs":false,"family":"Guinn","given":"Megan","email":"","affiliations":[{"id":79075,"text":"USDA Forest Service Chugach National Forest Avalanche Center","active":true,"usgs":false}],"preferred":false,"id":886317,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"VandenBos, Benjamin","contributorId":209940,"corporation":false,"usgs":false,"family":"VandenBos","given":"Benjamin","email":"","affiliations":[{"id":38032,"text":"U.S.D.A. Forest Service Sawtooth National Forest Avalanche Center, Ketchum, Idaho, USA","active":true,"usgs":false}],"preferred":false,"id":886319,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Savage, Scott","contributorId":209938,"corporation":false,"usgs":false,"family":"Savage","given":"Scott","email":"","affiliations":[{"id":38032,"text":"U.S.D.A. Forest Service Sawtooth National Forest Avalanche Center, Ketchum, Idaho, USA","active":true,"usgs":false}],"preferred":false,"id":886318,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":886320,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249594,"text":"70249594 - 2023 - Big avalanches in a changing climate: Using tree-ring derived avalanche chronologies to examine avalanche frequency across multiple climate types","interactions":[],"lastModifiedDate":"2023-10-18T14:32:35.897212","indexId":"70249594","displayToPublicDate":"2023-10-08T08:28:37","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Big avalanches in a changing climate: Using tree-ring derived avalanche chronologies to examine avalanche frequency across multiple climate types","docAbstract":"Large-magnitude snow avalanches pose a hazard to humans and infrastructure worldwide. Analyzing the spatiotemporal behavior of avalanches and the contributory climate factors is important for understanding historical variability in climate-avalanche relationships as well as improving avalanche forecasting. This study uses established dendrochronological methods to develop long-term regional avalanche chronologies for three different climate types: high-latitude maritime climate of southeast Alaska, intermountain climate of the northern Rocky Mountains, and continental climate of Colorado. In the maritime study area, we collected 434 cross sections throughout six avalanche paths near Juneau, Alaska. This resulted in 2706 identified avalanche growth disturbances between year 1720 and 2018 Common Era (CE), which allowed us to reconstruct 82 years with large magnitude avalanche activity across three sub-regions. By combining this tree-ring derived avalanche dataset with a suite of climate and atmospheric variables and applying a generalized linear model to fit a binomial regression, we found February and March precipitation and the Oceanic Niño Index (ONI) were significant predictors of large magnitude avalanche activity in the southeast Alaska study area. In the intermountain climate study area, tree-rings from 647 trees exhibited 2134 avalanche-related growth disturbances in the northern Rocky Mountains of northwest Montana from 1867 to 2019. The data show that the amount of snowpack across the northern Rocky Mountain region is directly related to avalanche probability. Coincident with warming and regional snowpack reductions, a decline of ~ 14% (~ 2% per decade) in overall large magnitude avalanche probability is apparent through the period 1950–2017 CE. In the continental climate of Colorado, we sampled 24 avalanche paths throughout the state and collected 1188 total samples with 4135 identified growth disturbances from 1698 to 2019. Preliminary results suggest years with large magnitude avalanche activity across the sub-regions of this study area are generally characterized by stormy winters with above average snowpack development but that early and late winter temperature and precipitation also play an important role in large avalanche activity. Characterizing historical climate-avalanche relationships across different climate types provides a broad baseline for understanding potential future changes in avalanche activity. Overall, this work helps forecasters and planners better understand the influence of climate on large magnitude avalanche frequency, and how potential changes in avalanche character and occurrence will affect their operations in the context of a warming climate.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop 2023","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Peitzsch, E.H., Pederson, G.T., Martin, J.T., Hood, E., Greene, E.M., Birkeland, K.W., Elder, K., Wolken, G., Kichas, N.E., Stahle, D.K., and Harley, J., 2023, Big avalanches in a changing climate: Using tree-ring derived avalanche chronologies to examine avalanche frequency across multiple climate types, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 547-553.","productDescription":"7 p.","startPage":"547","endPage":"553","ipdsId":"IP-156827","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science 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Kelly","contributorId":174398,"corporation":false,"usgs":false,"family":"Elder","given":"Kelly","email":"","affiliations":[{"id":5121,"text":"U.S. Forest Service, Rocky Mountain Research Station, 1221 South Main Street, Moscow, ID 83843","active":true,"usgs":false}],"preferred":false,"id":886380,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wolken, Gabriel","contributorId":305685,"corporation":false,"usgs":false,"family":"Wolken","given":"Gabriel","affiliations":[{"id":16126,"text":"Alaska Division of Geological and Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":886381,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kichas, Nickolas E.","contributorId":221182,"corporation":false,"usgs":false,"family":"Kichas","given":"Nickolas","email":"","middleInitial":"E.","affiliations":[{"id":36555,"text":"Montana State 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cycles","interactions":[],"lastModifiedDate":"2023-10-18T14:39:11.096438","indexId":"70249592","displayToPublicDate":"2023-10-08T07:18:42","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using tree rings to compare Colorado’s 2019 avalanche cycle to previous large avalanche cycles","docAbstract":"Large magnitude avalanches (size ≥D3) impact settlements, transportation corridors, and public safety worldwide. In Colorado, United States, avalanches have killed more people than any other natural hazard since 1950. In March 2019, a historically large magnitude avalanche cycle occurred throughout the entire mountainous portion of Colorado resulting in more than 1000 reported avalanches during a 2-week period. Nearly 200 of these avalanches were size D4 or larger with at least three D5 avalanches. The extensive number of downed trees from this avalanche cycle allowed us to collect 1188 cross-sections and cores from 1023 unique trees within 24 avalanche paths across the state. We recorded 4135 growth disturbances in these samples. These data comprise the largest known avalanche tree-ring dataset in the world. We employed a strategic nested sampling design to account for scale by including several individual avalanche paths within a given drainage to create sub-regions and then sampled six major sub-regions (counties) throughout the greater region (state). We identified 76 avalanche years within 24 individual avalanche paths from 1698 to 2020. Large magnitude empirical avalanche event frequency varied across paths and sub-regions. Our results indicate the most widespread avalanche cycle in our study area prior to 2019 occurred in 1899, where 12 avalanche paths show evidence of large magnitude avalanche activity. Historical records also highlight 1899 as a year with widespread and large magnitude avalanche activity. These results indicate the avalanche cycle of March 2019 was of similar magnitude. Understanding the spatial extent and return frequency of large magnitude avalanche cycles across multiple spatial scales, from individual paths to an entire state, helps avalanche forecasters improve their products and mitigation strategies and assists infrastructure planners when designing and planning in avalanche terrain.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, International Snow Science Workshop 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"International Snow Science Workshop","conferenceDate":"October 8-13, 2023","conferenceLocation":"Bend, OR","language":"English","publisher":"International Snow Science Workshop","usgsCitation":"Peitzsch, E.H., Greene, E.M., Konigsberg, J., Pederson, G.T., Martin, J.T., Kichas, N., Stahle, D.K., Favillier, A., Eckert, N., Birkeland, K.W., and Elder, K., 2023, Using tree rings to compare Colorado’s 2019 avalanche cycle to previous large avalanche cycles, in Proceedings, International Snow Science Workshop 2023, Bend, OR, October 8-13, 2023, p. 577-584.","productDescription":"8 p.","startPage":"577","endPage":"584","ipdsId":"IP-157055","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":421956,"rank":2,"type":{"id":15,"text":"Index 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