{"pageNumber":"115","pageRowStart":"2850","pageSize":"25","recordCount":41032,"records":[{"id":70241162,"text":"70241162 - 2023 - Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA","interactions":[],"lastModifiedDate":"2023-11-08T11:48:36.011532","indexId":"70241162","displayToPublicDate":"2023-10-18T11:44:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2824,"text":"Natural Hazards and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA","docAbstract":"

Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

","language":"English","publisher":"European Geosciences Union","doi":"10.5194/nhess-23-3261-2023","usgsCitation":"Patton, A., Luna, L., Roering, J.J., Jacobs, A., Korup, O., and Mirus, B., 2023, Landslide initiation thresholds in data-sparse regions: Application to landslide early warning criteria in Sitka, Alaska, USA: Natural Hazards and Earth System Sciences, v. 23, no. 10, p. 3261-3284, https://doi.org/10.5194/nhess-23-3261-2023.","productDescription":"24 p.","startPage":"3261","endPage":"3284","ipdsId":"IP-148647","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":441845,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-23-3261-2023","text":"Publisher Index Page"},{"id":422429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Sitka","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -135.5083890264902,\n 57.18995904083906\n ],\n [\n -135.5083890264902,\n 56.972958920434166\n ],\n [\n -135.177168114468,\n 56.972958920434166\n ],\n [\n -135.177168114468,\n 57.18995904083906\n ],\n [\n -135.5083890264902,\n 57.18995904083906\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"23","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Patton, Annette","contributorId":303028,"corporation":false,"usgs":false,"family":"Patton","given":"Annette","email":"","affiliations":[{"id":65615,"text":"Sitka Sound Science Center","active":true,"usgs":false}],"preferred":false,"id":866314,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luna, Lisa","contributorId":303029,"corporation":false,"usgs":false,"family":"Luna","given":"Lisa","email":"","affiliations":[{"id":52955,"text":"University of Potsdam","active":true,"usgs":false}],"preferred":false,"id":866315,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roering, Josh J.","contributorId":303030,"corporation":false,"usgs":false,"family":"Roering","given":"Josh","email":"","middleInitial":"J.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":866316,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacobs, Aaron","contributorId":204855,"corporation":false,"usgs":false,"family":"Jacobs","given":"Aaron","email":"","affiliations":[{"id":36995,"text":"NWS","active":true,"usgs":false}],"preferred":false,"id":866317,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Korup, Oliver","contributorId":218071,"corporation":false,"usgs":false,"family":"Korup","given":"Oliver","email":"","affiliations":[{"id":39735,"text":"Institute of Earth and Environmental Science, University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":866318,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":866319,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249600,"text":"70249600 - 2023 - Inter-comparison of measurements of inorganic chemical components in precipitation from NADP and CAPMoN at collocated sites in the USA and Canada during 1986–2019","interactions":[],"lastModifiedDate":"2023-10-20T13:19:06.53518","indexId":"70249600","displayToPublicDate":"2023-10-18T09:23:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"Inter-comparison of measurements of inorganic chemical components in precipitation from NADP and CAPMoN at collocated sites in the USA and Canada during 1986–2019","docAbstract":"

Wet deposition monitoring is a critical part of the long-term monitoring of acid deposition, which aims to assess the ecological impact of anthropogenic emissions of SO2 and NOx. In North America, long-term wet deposition has been monitored through two national networks: the Canadian Air and Precipitation Monitoring Network (CAPMoN) and the US National Atmospheric Deposition Program (NADP), for Canada and the USA, respectively. In order to assess the comparability of measurements from the two networks, collocated measurements have been made at two sites, one in each country, since 1986 (Sirois et al., in Environmental Monitoring and Assessment, 62, 273–303, 2000; Wetherbee et al., in Environmental Monitoring and Assessment, 1995–2004, 2010). In this study, we compared the measurements from NADP and CAPMoN instrumentation at the collocated sites at the Pennsylvania State University (Penn State), USA, from 1989 to 2016, and Frelighsburg, Quebec, Canada, from 2002 to 2019. We also included in the study the collocated daily-vs-weekly measurements by the CAPMoN network during 1999–2001 and 2016–2017 in order to evaluate the differences in wet concentration of ions due to sampling frequency alone. The study serves as an extension to two previous CAPMoN-NADP inter-comparisons by Sirois et al. (Environmental Monitoring and Assessment, 62, 273–303, 2000) and Wetherbee et al., in (Environmental Monitoring and Assessment, 1995–2004, 2010). At the Penn State University site, for 1986–2019, CAPMoN was higher than NADP for all ions, in terms of weekly concentration, precipitation-weighted annual mean concentration, and annual wet deposition. The precipitation-weighted annual mean concentrations were higher for SO42− (2%), NO3− (12%), NH4+ (16%), H+ (6%), and base cations and Cl− (11–15%). For annual wet deposition, CAPMoN was higher for SO4−2, NO3−, NH4+ and H+ (5–17%), and base cations and Cl− (12–17%) during 1986–2019. At the Frelighsburg site, NADP changed the sample collector in October 2011. For 2002–2011, the relative differences at the Frelighsburg site were positive and similar in magnitude to those at the Penn State site. For 2012–2019, the precipitation-weighted annual mean concentrations were 5–27% lower than NADP, except for H+, which was 23% higher. The change in sample collector by NADP had the largest effect on between-network biases. The comparisons of daily-vs-weekly measurements conducted by the CAPMoN network during 1999–2001 and 2016–2017 show that the weekly measurements were higher than the daily measurements by 1–3% for SO42−, NO3−, and NH4+; 3–9% for Ca2+, Mg2+, Na+, and Cl−; 10–24% for K+; and lower for H+ by 8–30% in terms of precipitation-weighted mean concentration. Thus, differences in sampling frequencies did not contribute to the systematically higher CAPMoN measurements. Understanding the biases in the data for these networks is important for interpretation of continental scale deposition models and transboundary comparison of wet deposition trends.

","language":"English","publisher":"Springer Link","doi":"10.1007/s10661-023-11771-z","usgsCitation":"Feng, J., Cole, A., Wetherbee, G.A., and Banwait, K., 2023, Inter-comparison of measurements of inorganic chemical components in precipitation from NADP and CAPMoN at collocated sites in the USA and Canada during 1986–2019: Environmental Monitoring and Assessment, v. 195, 1333, 34 p., https://doi.org/10.1007/s10661-023-11771-z.","productDescription":"1333, 34 p.","ipdsId":"IP-153496","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":441851,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10661-023-11771-z","text":"Publisher Index Page"},{"id":422000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"195","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Feng, Jian","contributorId":330980,"corporation":false,"usgs":false,"family":"Feng","given":"Jian","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":886400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cole, Amanda","contributorId":330981,"corporation":false,"usgs":false,"family":"Cole","given":"Amanda","email":"","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":886401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wetherbee, Gregory A. 0000-0002-6720-2294","orcid":"https://orcid.org/0000-0002-6720-2294","contributorId":215100,"corporation":false,"usgs":true,"family":"Wetherbee","given":"Gregory","email":"","middleInitial":"A.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":886402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Banwait, Kulbir","contributorId":330982,"corporation":false,"usgs":false,"family":"Banwait","given":"Kulbir","email":"","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":886403,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252214,"text":"70252214 - 2023 - Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast","interactions":[],"lastModifiedDate":"2024-03-20T11:53:06.811606","indexId":"70252214","displayToPublicDate":"2023-10-18T06:50:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast","docAbstract":"

Arctic coastal environments are eroding and rapidly changing. A lack of pan-Arctic observations limits our ability to understand controls on coastal erosion rates across the entire Arctic region. Here, we capitalize on an abundance of geospatial and remotely sensed data, in addition to model output, from the North Slope of Alaska to identify relationships between historical erosion rates and landscape characteristics to guide future modeling and observational efforts across the Arctic. Using existing datasets from the Alaska Beaufort Sea coast and a hierarchical clustering algorithm, we developed a set of 16 coastal typologies that captures the defining characteristics of environments susceptible to coastal erosion. Relationships between landscape characteristics and historical erosion rates show that no single variable alone is a good predictor of erosion rates. Variability in erosion rate decreases with increasing coastal elevation, but erosion rate magnitudes are highest for intermediate elevations. Areas along the Alaskan Beaufort Sea coast (ABSC) protected by barrier islands showed a three times lower erosion rate on average, suggesting that barrier islands are critical to maintaining mainland shore position. Finally, typologies with the highest erosion rates are not broadly representative of the ABSC and are generally associated with low elevation, north- to northeast-facing shorelines, a peaty pebbly silty lithology, and glaciomarine deposits with high ice content. All else being equal, warmer permafrost is also associated with higher erosion rates, suggesting that warming permafrost temperatures may contribute to higher future erosion rates on permafrost coasts. The suite of typologies can be used to guide future modeling and observational efforts by quantifying the distribution of coastlines with specific landscape characteristics and erosion rates.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ad04b8","usgsCitation":"Piliouras, A., Jones, B.M., Clevenger, T., Gibbs, A.E., and Rowland, J.C., 2023, Variability in terrestrial characteristics and erosion rates on the Alaskan Beaufort Sea coast: Environmental Research Letters, v. 18, 114050, 10 p., https://doi.org/10.1088/1748-9326/ad04b8.","productDescription":"114050, 10 p.","ipdsId":"IP-141537","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":441857,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ad04b8","text":"Publisher Index Page"},{"id":426794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -160.82972953158225,\n 72.33792024202972\n ],\n [\n -160.82972953158225,\n 68.87395946820305\n ],\n [\n -140.35121390658202,\n 68.87395946820305\n ],\n [\n -140.35121390658202,\n 72.33792024202972\n ],\n [\n -160.82972953158225,\n 72.33792024202972\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2023-10-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Piliouras, Anastasia","contributorId":334927,"corporation":false,"usgs":false,"family":"Piliouras","given":"Anastasia","email":"","affiliations":[{"id":80287,"text":"Department of Geosciences, Pennsylvania State University, University Park, PA","active":true,"usgs":false}],"preferred":false,"id":896945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Benjamin M.","contributorId":305542,"corporation":false,"usgs":false,"family":"Jones","given":"Benjamin","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":896946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Clevenger, Tabatha","contributorId":334928,"corporation":false,"usgs":false,"family":"Clevenger","given":"Tabatha","email":"","affiliations":[{"id":80288,"text":"Department of Earth Science and Geography, Vassar College, Poughkeepsie, NY","active":true,"usgs":false}],"preferred":false,"id":896947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gibbs, Ann E. 0000-0002-0883-3774 agibbs@usgs.gov","orcid":"https://orcid.org/0000-0002-0883-3774","contributorId":2644,"corporation":false,"usgs":true,"family":"Gibbs","given":"Ann","email":"agibbs@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":896948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rowland, Joel C.","contributorId":169046,"corporation":false,"usgs":false,"family":"Rowland","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":13447,"text":"Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":896949,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257232,"text":"70257232 - 2023 - Advances in wildlife abundance estimation using pedigree reconstruction","interactions":[],"lastModifiedDate":"2024-08-14T11:42:08.512608","indexId":"70257232","displayToPublicDate":"2023-10-18T06:38:48","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Advances in wildlife abundance estimation using pedigree reconstruction","docAbstract":"

The conservation and management of wildlife populations, particularly for threatened and endangered species are greatly aided with abundance, growth rate, and density measures. Traditional methods of estimating abundance and related metrics represent trade-offs in effort and precision of estimates. Pedigree reconstruction is an emerging, attractive alternate approach because its use of one-time, noninvasive sampling of individuals to infer the existence of unsampled individuals. However, advances in pedigree reconstruction could improve its utility, including forming a measure of precision for the method, establishing required spatial sampling effort for accurate estimates, ascertaining the spatial extent of abundance estimates derived from pedigree reconstruction, and assessing how population density affects the estimator's performance. Using established relationships for a stochastic, spatially explicit simulated moose (Alces americanus) population, pedigree reconstruction provided accurate estimates of the adult moose population size and trend. Novel bootstrapped confidence intervals performed as expected with intensive sampling but underperformed with moderate sampling efforts that could produce abundance estimates with low bias. Adult population estimates more closely reflected the total number of adults in the extant population, rather than number of adults inhabiting the area where sampling occurred. Increasing sampling effort, measured as the proportion of individuals sampled and as the proportion of a hypothetical study area, yielded similar asymptotic patterns over time. Simulations indicated a positive relationship between animal density and sampling effort required for unbiased estimates. These results indicate that pedigree reconstruction can produce accurate abundance estimates and may be particularly valuable for surveying smaller areas and low-density populations.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10650","usgsCitation":"Rosenblatt, E., Creel, S., Gieder, K., Murdoch, J., and Donovan, T.M., 2023, Advances in wildlife abundance estimation using pedigree reconstruction: Ecology and Evolution, v. 13, no. 10, e10650, 18 p., https://doi.org/10.1002/ece3.10650.","productDescription":"e10650, 18 p.","ipdsId":"IP-139722","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":441859,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10650","text":"Publisher Index Page"},{"id":432645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenblatt, Elias","contributorId":342124,"corporation":false,"usgs":false,"family":"Rosenblatt","given":"Elias","email":"","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":909736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Creel, Scott","contributorId":342128,"corporation":false,"usgs":false,"family":"Creel","given":"Scott","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":909738,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gieder, Katherina","contributorId":342131,"corporation":false,"usgs":false,"family":"Gieder","given":"Katherina","affiliations":[{"id":27622,"text":"Vermont Fish and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":909739,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Murdoch, James","contributorId":342134,"corporation":false,"usgs":false,"family":"Murdoch","given":"James","affiliations":[{"id":13253,"text":"University of Vermont","active":true,"usgs":false}],"preferred":false,"id":909740,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Donovan, Therese M. 0000-0001-8124-9251 tdonovan@usgs.gov","orcid":"https://orcid.org/0000-0001-8124-9251","contributorId":204296,"corporation":false,"usgs":true,"family":"Donovan","given":"Therese","email":"tdonovan@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":909741,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256170,"text":"70256170 - 2023 - The Mojave section of the San Andreas fault (California), 1: Shaping the terrace stratigraphy of Littlerock Creek through the competition between rapid strike-slip faulting and lateral stream erosion over the last 40ka.","interactions":[],"lastModifiedDate":"2024-07-26T00:13:41.320242","indexId":"70256170","displayToPublicDate":"2023-10-16T19:11:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7143,"text":"Geochemistry, Geophysics, and Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"The Mojave section of the San Andreas fault (California), 1: Shaping the terrace stratigraphy of Littlerock Creek through the competition between rapid strike-slip faulting and lateral stream erosion over the last 40ka.","docAbstract":"

To determine the post-40 ka slip-rate along the Mojave section of the San Andreas Fault (MSAF) we re-analyze the sedimentary record preserved where Little Rock (LR) Creek flows across the fault. At this location, interaction between the northeast-flowing stream and right-lateral fault has resulted in the abandonment and preservation of 11 strath terraces and one paleo-floodplain in the downstream trailing corner of the river, two of which are also preserved upstream to provide cross-fault matches. A new model of fault-induced river deflection, together with standard terrace riser restoration, yields strike-slip displacements of 1,140 ± 160 m for the older terrace and 360 ± 70 m for the younger one. When combined with new 10Be dating and reinterpretation of prior measurements the displaced terraces yield right-lateral slip-rates of 27.7+6.9/−3.5 and 26.8+3.4/−3.0 mm/yr over the last 23 k.y. and last 40 k.y., where uncertainties are at 95% credible intervals. These new rate determinations are consistent with independent late Holocene estimates, indicating that the long-term rate of strain accumulation along the MSAF is relatively fast and does not vary significantly when averaged over timescales of 15–20 k.y. Using our new model of stream deflection, we find that the fluvial sequence was emplaced in two distinct periods, each characterized by a temporally stable but markedly different deflected river geometry. Each period coincides with a distinct stage of erosive power along LR Creek determined from independent paleoclimate proxies. Importantly, application of the new river-deflection model allows strike-slip displacements to be determined in the absence of upstream piercing points.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GC010869","usgsCitation":"Moulin, A., Cowgill, E., Scharer, K., McPhillips, D., and Heimsath, A., 2023, The Mojave section of the San Andreas fault (California), 1: Shaping the terrace stratigraphy of Littlerock Creek through the competition between rapid strike-slip faulting and lateral stream erosion over the last 40ka.: Geochemistry, Geophysics, and Geosystems, v. 24, no. 10, e2023GC010869, 40 p., https://doi.org/10.1029/2023GC010869.","productDescription":"e2023GC010869, 40 p.","ipdsId":"IP-154087","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":441866,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gc010869","text":"Publisher Index Page"},{"id":431456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Andreas fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.8640320695366,\n 34.6946394144248\n ],\n [\n -117.8640320695366,\n 33.13569579634151\n ],\n [\n -115.44703988203676,\n 33.13569579634151\n ],\n [\n -115.44703988203676,\n 34.6946394144248\n ],\n [\n -117.8640320695366,\n 34.6946394144248\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Moulin, Adrien","contributorId":340360,"corporation":false,"usgs":false,"family":"Moulin","given":"Adrien","email":"","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":906968,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cowgill, Eric","contributorId":192850,"corporation":false,"usgs":false,"family":"Cowgill","given":"Eric","affiliations":[],"preferred":false,"id":906969,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scharer, Katherine M. 0000-0003-2811-2496","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":217361,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":906970,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McPhillips, Devin 0000-0003-1987-9249","orcid":"https://orcid.org/0000-0003-1987-9249","contributorId":217362,"corporation":false,"usgs":true,"family":"McPhillips","given":"Devin","email":"","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":906971,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heimsath, Arjun","contributorId":340361,"corporation":false,"usgs":false,"family":"Heimsath","given":"Arjun","email":"","affiliations":[{"id":12431,"text":"ASU","active":true,"usgs":false}],"preferred":false,"id":906972,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252802,"text":"70252802 - 2023 - The 1886 Charleston, South Carolina, Earthquake: Relic railroad offset reveals rupture","interactions":[],"lastModifiedDate":"2024-04-05T14:44:12.872874","indexId":"70252802","displayToPublicDate":"2023-10-16T09:42:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"The 1886 Charleston, South Carolina, Earthquake: Relic railroad offset reveals rupture","docAbstract":"

In the absence of documented surface rupture during the 1 September 1886 Charleston earthquake, there has been considerable speculation about the location and mechanism of the causative fault. We use an inferred coseismic offset of the South Carolina Railroad and additional numerical constraints to develop an elastic deformation model—a west‐dipping fault following strands of two previously identified faults. The constraints are consistent with a blind rupture with 6.5 ± 0.3 m of dextral slip and 2 ± 0.5 m of reverse slip below 450 m depth. We propose that repeated slip on this fault has raised the Penholoway Marine Terrace >6 m since ∼770 ka. The inferred coseismic slip on the fault in an Mw 7.3 earthquake is consistent with the distribution of damage in 1886.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230022","usgsCitation":"Bilham, R., and Hough, S.E., 2023, The 1886 Charleston, South Carolina, Earthquake: Relic railroad offset reveals rupture: The Seismic Record, v. 3, no. 4, p. 278-288, https://doi.org/10.1785/0320230022.","productDescription":"11 p.","startPage":"278","endPage":"288","ipdsId":"IP-152926","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":441868,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230022","text":"Publisher Index Page"},{"id":427514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","city":"Charleston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.24634165513044,\n 33.088760020667124\n ],\n [\n -80.24634165513044,\n 32.62202807250516\n ],\n [\n -79.75159258126686,\n 32.62202807250516\n ],\n [\n -79.75159258126686,\n 33.088760020667124\n ],\n [\n -80.24634165513044,\n 33.088760020667124\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Bilham, Roger","contributorId":225117,"corporation":false,"usgs":false,"family":"Bilham","given":"Roger","affiliations":[{"id":13693,"text":"University of Colorado Boulder","active":true,"usgs":false}],"preferred":false,"id":898271,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":898272,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70256086,"text":"70256086 - 2023 - BatTool: Projecting bat populations facing multiple stressors using a demographic model","interactions":[],"lastModifiedDate":"2024-07-19T11:57:51.992521","indexId":"70256086","displayToPublicDate":"2023-10-16T06:55:58","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"BatTool: Projecting bat populations facing multiple stressors using a demographic model","docAbstract":"

Bats provide ecologically and agriculturally important ecosystem services but are currently experiencing population declines caused by multiple environmental stressors, including mortality from white-nose syndrome and wind energy development. Analyses of the current and future health and viability of these species may support conservation management decision making. Demographic modeling provides a quantitative tool for decision makers and conservation managers to make more informed decisions, but widespread adoption of these tools can be limited because of the complexity of the mathematical, statistical, and computational components involved in implementing these models. In this work, we provide an exposition of the BatTool R package, detailing the primary components of the matrix projection model, a publicly accessible graphical user interface (https://rconnect.usgs.gov/battool) facilitating user-defined scenario analyses, and its intended uses and limitations (Wiens et al., US Geol Surv Data Release 2022; Wiens et al., US Geol Surv Softw Release 2022). We present a case study involving wind energy permitting, weighing the effects of potential mortality caused by a hypothetical wind energy facility on the projected abundance of four imperiled bat species in the Midwestern United States.

","language":"English","publisher":"British Ecological Society","doi":"10.1186/s12862-023-02159-1","usgsCitation":"Wiens, A.M., Schorg, A., Szymanski, J., and Thogmartin, W.E., 2023, BatTool: Projecting bat populations facing multiple stressors using a demographic model: Methods in Ecology and Evolution, v. 23, 61, 16 p., https://doi.org/10.1186/s12862-023-02159-1.","productDescription":"61, 16 p.","ipdsId":"IP-132438","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":441872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s12862-023-02159-1","text":"Publisher Index Page"},{"id":431237,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationDate":"2023-10-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Wiens, Ashton M. 0000-0002-7030-0602","orcid":"https://orcid.org/0000-0002-7030-0602","contributorId":271176,"corporation":false,"usgs":true,"family":"Wiens","given":"Ashton","email":"","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":906644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schorg, Amber","contributorId":333055,"corporation":false,"usgs":false,"family":"Schorg","given":"Amber","email":"","affiliations":[{"id":68344,"text":"U.S. Fish and Wildlife Service (USFWS)","active":true,"usgs":false}],"preferred":false,"id":906645,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Szymanski, Jennifer","contributorId":15123,"corporation":false,"usgs":false,"family":"Szymanski","given":"Jennifer","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":906646,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906647,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249590,"text":"70249590 - 2023 - Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar","interactions":[],"lastModifiedDate":"2023-10-18T11:59:10.643012","indexId":"70249590","displayToPublicDate":"2023-10-16T06:55:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar","docAbstract":"

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times (twt) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m−3). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14996","usgsCitation":"Bonnell, R., McGrath, D., Hedrick, A., Trujillo, E., Meehan, T., Williams, K., Marshall, H., Sexstone, G., Fulton, J.W., Ronayne, M., Fassnacht, S.R., Webb, R., and Hale, K., 2023, Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar: Hydrological Processes, v. 37, no. 10, e14996, 17 p., https://doi.org/10.1002/hyp.14996.","productDescription":"e14996, 17 p.","ipdsId":"IP-153984","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":441874,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14996","text":"Publisher Index Page"},{"id":421953,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Research Center, USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":886341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Trujillo, Ernesto","contributorId":330961,"corporation":false,"usgs":false,"family":"Trujillo","given":"Ernesto","email":"","affiliations":[{"id":33038,"text":"Department of Geosciences, Boise State University","active":true,"usgs":false}],"preferred":false,"id":886342,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meehan, Tate","contributorId":330962,"corporation":false,"usgs":false,"family":"Meehan","given":"Tate","email":"","affiliations":[{"id":79079,"text":"Cold Regions Research and Engineering Laboratory, U.S. Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":886343,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Williams, 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R.","contributorId":306269,"corporation":false,"usgs":false,"family":"Fassnacht","given":"Steven","email":"","middleInitial":"R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":886349,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Webb, Ryan","contributorId":330966,"corporation":false,"usgs":false,"family":"Webb","given":"Ryan","email":"","affiliations":[{"id":79081,"text":"Department of Civil and Architectural Engineering and Construction Management, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":886350,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hale, Katherine","contributorId":330967,"corporation":false,"usgs":false,"family":"Hale","given":"Katherine","email":"","affiliations":[{"id":79082,"text":"Department of Civil and Environmental Engineering, University of Vermont","active":true,"usgs":false}],"preferred":false,"id":886351,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70250400,"text":"70250400 - 2023 - Science to support conservation action in a large river system: The Willamette River, Oregon, USA","interactions":[],"lastModifiedDate":"2023-12-07T12:58:26.054885","indexId":"70250400","displayToPublicDate":"2023-10-14T06:52:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17103,"text":"Water Biology and Security","active":true,"publicationSubtype":{"id":10}},"title":"Science to support conservation action in a large river system: The Willamette River, Oregon, USA","docAbstract":"

Management and conservation efforts that support the recovery and protection of large rivers are daunting, reflecting the complexity of the challenge and extent of effort (in terms of policy, economic investment, and spatial extent) needed to afford measurable change. These large systems have generally experienced intensive development and regulation, compromising their capacity to respond to disturbances such as climate change or wildfire. Functionally, large river and basin management require insights gained from social, ecological, geophysical, and hydrological sciences. This multi-disciplinary perspective can unveil the integrated relationship between a river network's biotic community and seasonally variable environmental conditions that are often influenced by human activities. Large rivers and their basins are constantly changing due to anthropogenic influences and as climate modifies patterns of temperature and precipitation. Because of these factors, the state of knowledge must advance to address changing conditions. The Willamette River, in western Oregon, USA, is a prime example of a basin that has experienced significant degradation and investment in rehabilitation in recent decades. Innovative science has facilitated development of fine-scale, spatially extensive datasets and models that can generate targeted conservation and rehabilitation actions that are prioritized across the entire river network. This prioritization allows investment decisions to be driven by site-specific conditions while simultaneously considering potentials for ecological improvement. Here, we review hydrologic, geomorphic, ecologic, and social conditions in the Willamette River basin through time—including pre-settlement, river development, and contemporary periods—and offer a future vision for consideration. Currently, detailed information about fish populations and habitat, hydrologic conditions, geomorphology, water quality, and land use can be leveraged to make informed decisions about protection, rehabilitation, and development. The time is ripe for strategic management and goal development for the entire Willamette River, and these efforts can be informed by comprehensive science realized through established institutions (e.g., public agencies, non-profit watershed groups, Tribes, and universities) focused on conservation and management. The approaches to science and social-network creation that were pioneered in the Willamette River basin offer insights into the development of comprehensive conservation-based planning that could be implemented in other large river systems globally.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.watbs.2023.100203","usgsCitation":"Flitcroft, R.L., Whitman, L., White, J., Wallick, J., Stratton Garvin, L.E., Smith, C., Plotnikoff, R., Mulvey, M., Kock, T.J., Jones, K., Gruendike, P., Gombert, C., Giannico, G., Dutterer, A., Brown, D.G., Barrett, H., and Hughes, R.M., 2023, Science to support conservation action in a large river system: The Willamette River, Oregon, USA: Water Biology and Security, v. 2, no. 4, 100203, 16 p., https://doi.org/10.1016/j.watbs.2023.100203.","productDescription":"100203, 16 p.","ipdsId":"IP-148710","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":441883,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watbs.2023.100203","text":"Publisher Index Page"},{"id":423291,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.70885566657823,\n 46.467292298881915\n ],\n [\n -124.70885566657823,\n 43.55562581742163\n ],\n [\n -121.35802558845327,\n 43.55562581742163\n ],\n [\n -121.35802558845327,\n 46.467292298881915\n ],\n [\n -124.70885566657823,\n 46.467292298881915\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Flitcroft, Rebecca L. 0000-0003-3341-996X","orcid":"https://orcid.org/0000-0003-3341-996X","contributorId":172180,"corporation":false,"usgs":false,"family":"Flitcroft","given":"Rebecca","email":"","middleInitial":"L.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":889772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whitman, Luke","contributorId":290613,"corporation":false,"usgs":false,"family":"Whitman","given":"Luke","email":"","affiliations":[{"id":36223,"text":"Oregon Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":889773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, James 0000-0002-7255-3785 jameswhite@usgs.gov","orcid":"https://orcid.org/0000-0002-7255-3785","contributorId":193492,"corporation":false,"usgs":true,"family":"White","given":"James","email":"jameswhite@usgs.gov","affiliations":[],"preferred":true,"id":889774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wallick, J. Rose 0000-0002-9392-272X rosewall@usgs.gov","orcid":"https://orcid.org/0000-0002-9392-272X","contributorId":3583,"corporation":false,"usgs":true,"family":"Wallick","given":"J. 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2023 - Growth performance of Rainbow Trout in reservoir tributaries and implications for steelhead growth potential above Skagit River dams","interactions":[],"lastModifiedDate":"2023-11-22T16:06:40.787482","indexId":"70250123","displayToPublicDate":"2023-10-13T09:55:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Growth performance of Rainbow Trout in reservoir tributaries and implications for steelhead growth potential above Skagit River dams","docAbstract":"

Objective

In the Pacific Northwest (USA), Pacific salmon Oncorhynchus spp. populations have been declining significantly for decades, prompting stakeholders to respond with a variety of conservation and restoration measures. One such measure being considered in the Skagit River basin (Washington, USA) is the introduction of steelhead Oncorhynchus mykiss (anadromous Rainbow Trout) above the impassable Gorge, Diablo, and Ross dams to bolster their populations. Because freshwater growth is key to survival at subsequent life stages, we evaluated current trends in size and growth of Rainbow Trout among key tributaries to Gorge, Diablo, and Ross reservoirs using empirical data collection and bioenergetics modeling.

Methods

For nine candidate streams, a bioenergetics model was used to assess how temperature and prey consumption affected growth performance of Rainbow Trout between annuli 1 and 2, and 2 and 3. Thermal scenarios were created to evaluate how fish growth responded to temperature variability while total annual consumption was constrained within empirical growth estimates. We then compared these results to back-calculated size thresholds established by size-at-age observed in wild steelhead adults that returned to the Skagit River below the dams.

Result

Of the streams proposed for introductions, there was one instance (McMillan Creek) in the nominal simulations where growth met or exceeded the size at annulus 2 or 3 of a returning adult steelhead (24.9 g at annulus 2 and 50.3 g at annulus 3). Modeled growth under different thermal scenarios showed that colder temperatures (0.1–10.7°C, Canyon Creek) produced higher growth than under the nominal or warm scenarios (2.0–15.3°C, Canyon Creek), as well as one additional tributary where size at annulus 2 or 3 (±2 SE) was comparable to the threshold established by adult steelhead below the dams (Big Beaver Creek, annulus 3).

Conclusion

These results suggest Rainbow Trout growth is most limited by prey availability in the examined upper Skagit tributaries.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10944","usgsCitation":"Jensen, B.L., Johnson, R.C., Duda, J.J., Ostberg, C.O., Code, T.J., Mclean, J.H., Stenberg, K.D., Larsen, K., Hoy, M.S., and Beauchamp, D., 2023, Growth performance of Rainbow Trout in reservoir tributaries and implications for steelhead growth potential above Skagit River dams: North American Journal of Fisheries Management, v. 43, no. 5, p. 1427-1446, https://doi.org/10.1002/nafm.10944.","productDescription":"20 p.","startPage":"1427","endPage":"1446","ipdsId":"IP-147915","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":422838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Skagit River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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Center","active":true,"usgs":true}],"preferred":true,"id":888482,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70249676,"text":"70249676 - 2023 - An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau","interactions":[],"lastModifiedDate":"2023-10-24T13:40:35.956101","indexId":"70249676","displayToPublicDate":"2023-10-12T08:27:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau","docAbstract":"

Intense grazing may lead to grassland degradation on the Qinghai-Tibetan Plateau, but it is difficult to predict where this will occur and to quantify it. Based on a process-based ecosystem model, we define a productivity-based stocking rate threshold that induces extreme grassland degradation to assess whether and where the current grazing activity in the region is sustainable. We find that the current stocking rate is below the threshold in ~80% of grassland areas, but in 55% of these grasslands the stocking rate exceeds half the threshold. According to our model projections, positive effects of climate change including elevated CO2 can partly offset negative effects of grazing across nearly 70% of grasslands on the Plateau, but only in areas below the stocking rate threshold. Our analysis suggests that stocking rate that does not exceed 60% (within 50% to 70%) of the threshold may balance human demands with grassland protection in the face of climate change.

","language":"English","publisher":"Nature Publications","doi":"10.1038/s41467-023-42099-4","usgsCitation":"Zhu, Q., Chen, H., Peng, C., Liu, J., Piao, S., He, J., Wang, S., Zhao, X., Zhang, J., Fang, X., Jin, J., Yang, Q., Ren, L., and Wang, Y., 2023, An early warning signal for grassland degradation on the Qinghai-Tibetan Plateau: Nature Communications, v. 14, 6406, 13 p., https://doi.org/10.1038/s41467-023-42099-4.","productDescription":"6406, 13 p.","ipdsId":"IP-144126","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":441890,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-023-42099-4","text":"Publisher Index Page"},{"id":422065,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Qinghai-Tibetan Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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Canada","active":true,"usgs":false}],"preferred":false,"id":886689,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jin, Jiaxin","contributorId":175219,"corporation":false,"usgs":false,"family":"Jin","given":"Jiaxin","email":"","affiliations":[{"id":27538,"text":"International Institute for Earth System Science, Nanjing University, Xianlin Avenue 163, Nanjing 210093","active":true,"usgs":false}],"preferred":false,"id":886690,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Yang, Qi-En","contributorId":331070,"corporation":false,"usgs":false,"family":"Yang","given":"Qi-En","email":"","affiliations":[{"id":79114,"text":"Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, Qinghai, 810001, China","active":true,"usgs":false}],"preferred":false,"id":886691,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ren, Liliang","contributorId":331073,"corporation":false,"usgs":false,"family":"Ren","given":"Liliang","email":"","affiliations":[],"preferred":false,"id":886701,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Wang, Yanfen","contributorId":265955,"corporation":false,"usgs":false,"family":"Wang","given":"Yanfen","email":"","affiliations":[{"id":54838,"text":"College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China","active":true,"usgs":false}],"preferred":false,"id":886692,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"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

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2023-10-11","noUsgsAuthors":false,"publicationDate":"2023-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Mankinen, Edward A. 0000-0001-7496-2681 emank@usgs.gov","orcid":"https://orcid.org/0000-0001-7496-2681","contributorId":1054,"corporation":false,"usgs":true,"family":"Mankinen","given":"Edward","email":"emank@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":885962,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rowley, Peter D.","contributorId":27435,"corporation":false,"usgs":true,"family":"Rowley","given":"Peter","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":885963,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKee, Edwin H. mckee@usgs.gov","contributorId":3728,"corporation":false,"usgs":true,"family":"McKee","given":"Edwin","email":"mckee@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":885964,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249499,"text":"ofr20231060 - 2023 - Application of the Stream Salmonid Simulator (S3) model to assess fall Chinook salmon (Oncorhynchus tshawytscha) production in the American River, California","interactions":[],"lastModifiedDate":"2023-10-12T10:55:46.983978","indexId":"ofr20231060","displayToPublicDate":"2023-10-11T10:11:03","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":"2023-1060","displayTitle":"Application of the Stream Salmonid Simulator (S3) Model to Assess Fall Chinook Salmon (Oncorhynchus tshawytscha) Production in the American River, California","title":"Application of the Stream Salmonid Simulator (S3) model to assess fall Chinook salmon (Oncorhynchus tshawytscha) production in the American River, California","docAbstract":"

Executive Summary

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

","tableOfContents":"","publishedDate":"2023-10-11","noUsgsAuthors":false,"publicationDate":"2023-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Plumb, John M. 0000-0003-4255-1612 jplumb@usgs.gov","orcid":"https://orcid.org/0000-0003-4255-1612","contributorId":3569,"corporation":false,"usgs":true,"family":"Plumb","given":"John","email":"jplumb@usgs.gov","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":885957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":885958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hatton, Tyson W. 0000-0002-2874-0719","orcid":"https://orcid.org/0000-0002-2874-0719","contributorId":9112,"corporation":false,"usgs":true,"family":"Hatton","given":"Tyson W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":885959,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Collin D. 0000-0003-4184-5686 cdsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-4184-5686","contributorId":7915,"corporation":false,"usgs":true,"family":"Smith","given":"Collin D.","email":"cdsmith@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":false,"id":885960,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hannon, John M.","contributorId":330804,"corporation":false,"usgs":false,"family":"Hannon","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":885961,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70249935,"text":"70249935 - 2023 - Bioavailability and toxicity models of copper to freshwater life: The state of regulatory science","interactions":[],"lastModifiedDate":"2023-12-04T17:25:25.241762","indexId":"70249935","displayToPublicDate":"2023-10-11T06:43:46","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Bioavailability and toxicity models of copper to freshwater life: The state of regulatory science","docAbstract":"

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.

","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5736","usgsCitation":"Mebane, C.A., 2023, Bioavailability and toxicity models of copper to freshwater life: The state of regulatory science: Environmental Toxicology and Chemistry, v. 42, no. 12, p. 2529-2563, https://doi.org/10.1002/etc.5736.","productDescription":"35 p.","startPage":"2529","endPage":"2563","ipdsId":"IP-139187","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":441904,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5736","text":"Publisher Index Page"},{"id":422417,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":887754,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70249403,"text":"fs20233044 - 2023 - LANDFIRE","interactions":[],"lastModifiedDate":"2023-10-10T21:18:58.443597","indexId":"fs20233044","displayToPublicDate":"2023-10-10T15:02:29","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3044","displayTitle":"LANDFIRE","title":"LANDFIRE","docAbstract":"Landscape Fire and Resource Management Planning Tools (LANDFIRE) is a key national geospatial data source for strategic fire and resource management planning and analysis. LANDFIRE is the first complete, nationally consistent collection of more than 25 geospatial layers, databases, and ecological models at a 30-meter resolution that describe disturbance, vegetation, fire, and fuel characteristics. Because fires do not stop at ownership borders, LANDFIRE products by design support cross-boundary planning, management, and operations across all lands of the conterminous United States (CONUS), Alaska, Hawaii, and insular areas.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233044","usgsCitation":"Long, J.L., and Hatten, T.D., 2023, LANDFIRE: U.S. Geological Survey Fact Sheet 2023–3044, 4 p., https://doi.org/10.3133/fs20233044.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-146927","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":501266,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P1XVKXRL","text":"USGS data release","linkHelpText":"LANDFIRE 2024 Update (ver. 1.1, March 2026)"},{"id":421688,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3044/fs20233044.XML","linkFileType":{"id":8,"text":"xml"}},{"id":421689,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20233044/full","linkFileType":{"id":5,"text":"html"}},{"id":421687,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3044/fs20233044.pdf","text":"Report","size":"5.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3044"},{"id":421694,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3044/images/"},{"id":421686,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3044/coverthb.jpg"}],"contact":"

LANDFIRE Help Desk
Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198

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Introduction

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":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-10-10","noUsgsAuthors":false,"publicationDate":"2023-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Carlson, Tom 0000-0002-5682-8988","orcid":"https://orcid.org/0000-0002-5682-8988","contributorId":304658,"corporation":false,"usgs":true,"family":"Carlson","given":"Tom","email":"","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":885034,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"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.

","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.13021","usgsCitation":"Chambers, J., Brown, J.L., Bradford, J., Doherty, K., Crist, M., Schlaepfer, D.R., Urza, A.K., and Short, K., 2023, Combining resilience and resistance with threat-based approaches for prioritizing management actions in sagebrush ecosystems: Conservation Science and Practice, v. 5, no. 11, e13021, 16 p., https://doi.org/10.1111/csp2.13021.","productDescription":"e13021, 16 p.","ipdsId":"IP-155040","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":441910,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.13021","text":"Publisher Index Page"},{"id":422185,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -118,\n 46\n ],\n [\n -118,\n 35\n ],\n [\n -100,\n 35\n ],\n [\n -100,\n 46\n ],\n [\n -118,\n 46\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","issue":"11","noUsgsAuthors":false,"publicationDate":"2023-10-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Chambers, Jeanne C.","contributorId":328379,"corporation":false,"usgs":false,"family":"Chambers","given":"Jeanne C.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":887028,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jessi L.","contributorId":44817,"corporation":false,"usgs":false,"family":"Brown","given":"Jessi","email":"","middleInitial":"L.","affiliations":[{"id":13184,"text":"Program in Ecology, Evolution and Conservation Biology, University of Nevada","active":true,"usgs":false}],"preferred":false,"id":887029,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":887030,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Doherty, Kevin 0000-0003-3635-7346","orcid":"https://orcid.org/0000-0003-3635-7346","contributorId":176149,"corporation":false,"usgs":false,"family":"Doherty","given":"Kevin","email":"","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":true,"id":887031,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crist, Michele R.","contributorId":178453,"corporation":false,"usgs":false,"family":"Crist","given":"Michele R.","affiliations":[],"preferred":false,"id":887032,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schlaepfer, Daniel Rodolphe 0000-0001-9973-2065","orcid":"https://orcid.org/0000-0001-9973-2065","contributorId":225569,"corporation":false,"usgs":true,"family":"Schlaepfer","given":"Daniel","email":"","middleInitial":"Rodolphe","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":887033,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Urza, Alexandra K. 0000-0001-9795-6735","orcid":"https://orcid.org/0000-0001-9795-6735","contributorId":261259,"corporation":false,"usgs":false,"family":"Urza","given":"Alexandra","email":"","middleInitial":"K.","affiliations":[{"id":16848,"text":"USDA Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":887034,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Short, Karen","contributorId":328378,"corporation":false,"usgs":false,"family":"Short","given":"Karen","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":887035,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70249530,"text":"70249530 - 2023 - Does release size into net-pens affect survival of captively reared juvenile endangered suckers in Upper Klamath Lake?","interactions":[],"lastModifiedDate":"2023-11-07T16:19:31.381331","indexId":"70249530","displayToPublicDate":"2023-10-10T06:56:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Does release size into net-pens affect survival of captively reared juvenile endangered suckers in Upper Klamath Lake?","docAbstract":"

Objective

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.

Methods

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.

Result

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.

Conclusion

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":70263689,"text":"70263689 - 2023 - Influences of landscape composition on hunter-harvested mallard body mass and condition in eastern Arkansas","interactions":[],"lastModifiedDate":"2025-02-20T15:27:07.492566","indexId":"70263689","displayToPublicDate":"2023-10-10T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":16872,"text":"The Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Influences of landscape composition on hunter-harvested mallard body mass and condition in eastern Arkansas","docAbstract":"

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.

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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.

","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2023.1229123","usgsCitation":"Wright, M., van Mantgem, P., Buffington, K., Thorne, K., Engber, E., and Smith, S., 2023, Spatially explicit models of seed availability improve predictions of conifer regeneration following the 2018 Carr Fire in northern California: Frontiers in Ecology and Evolution, v. 11, 1229123, 15 p., https://doi.org/10.3389/fevo.2023.1229123.","productDescription":"1229123, 15 p.","ipdsId":"IP-144707","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":441917,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1229123","text":"Publisher Index Page"},{"id":435152,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95G11FE","text":"USGS data release","linkHelpText":"Data Describing Site Characteristics Including Conifer Regeneration Following the 2018 Carr Fire in Whiskeytown National Recreation Area"},{"id":421845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -124.70403381568622,\n 42.02674004186227\n ],\n [\n -124.70403381568622,\n 39.42971050773687\n ],\n [\n -121.56194397193622,\n 39.42971050773687\n ],\n [\n -121.56194397193622,\n 42.02674004186227\n ],\n [\n -124.70403381568622,\n 42.02674004186227\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-10-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Wright, Micah C. 0000-0002-5324-1110","orcid":"https://orcid.org/0000-0002-5324-1110","contributorId":229071,"corporation":false,"usgs":true,"family":"Wright","given":"Micah","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":885938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":885939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":885940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":885941,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Engber, Eamon","contributorId":202777,"corporation":false,"usgs":false,"family":"Engber","given":"Eamon","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":885942,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smith, Sean","contributorId":276400,"corporation":false,"usgs":false,"family":"Smith","given":"Sean","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":885943,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70248039,"text":"70248039 - 2023 - Assessing snowpack stratigraphy accuracy based on different input data: Insights for operations avalanche forecasting","interactions":[],"lastModifiedDate":"2023-10-18T15:51:35.765009","indexId":"70248039","displayToPublicDate":"2023-10-08T10:42:58","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Assessing snowpack stratigraphy accuracy based on different input data: Insights for operations avalanche forecasting","docAbstract":"

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":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":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.

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