Crested Auklets (Aethia cristatella) and Least Auklets (A. pusilla) are crevice-nesting birds that breed in large mixed colonies at relatively few sites in the Aleutian Island archipelago, Bering Sea, Gulf of Alaska, and Sea of Okhotsk. Many of these colonies are located on active volcanic islands. The eruption of Kasatochi volcano, in the central Aleutians, on August 7, 2008, completely buried all crevice-nesting seabird habitat on the island. This provided an opportunity to examine the response of a large, mixed auklet colony to a major geological disturbance. Time-lapse imagery of nesting habitat indicated that both species returned to the largest pre-eruption colony site for several years, but subsequently abandoned it within 5 yr after the eruption. In 2010, a rockfall site in a cove north of the old colony site began to accumulate talus, and groups of auklets were observed using the site in 2011. Use of the new colony appeared to coincide with the abandonment of the old colony site by both species, though surface counts suggested that Least Auklets shifted to the new colony sooner than Crested Auklets. At-sea surveys of seabirds before and after the eruption indicated that both Crested and Least auklets shifted their at-sea distributions from the waters around Kasatochi Island to nearby Koniuji Island. In combination, at-sea counts and colony time-lapse imagery indicated that Crested and Least auklets using Kasatochi responded to the volcanic disturbance and complete loss of nesting habitat at the main colony on Kasatochi with dispersal either to newly created habitat on Kasatochi or to an alternate colony on a nearby island.
","language":"English","publisher":"American Ornithological Society","doi":"10.1642/AUK-17-180.1","usgsCitation":"Drew, G.S., Piatt, J.F., and Williams, J.C., 2018, Biological responses of Crested and Least auklets to volcanic destruction of nesting habitat in the Aleutian Islands, Alaska: The Auk, v. 135, no. 3, p. 477-485, https://doi.org/10.1642/AUK-17-180.1.","productDescription":"9 p.","startPage":"477","endPage":"485","ipdsId":"IP-084086","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":460921,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1642/auk-17-180.1","text":"Publisher Index Page"},{"id":354195,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Aleutian Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -175.56015014648438,\n 52.14360239845529\n ],\n [\n -175.09323120117188,\n 52.14360239845529\n ],\n [\n -175.09323120117188,\n 52.247562587932386\n ],\n [\n -175.56015014648438,\n 52.247562587932386\n ],\n [\n -175.56015014648438,\n 52.14360239845529\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"135","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6bce4b0da30c1bfbd86","contributors":{"authors":[{"text":"Drew, Gary S. 0000-0002-6789-0891 gdrew@usgs.gov","orcid":"https://orcid.org/0000-0002-6789-0891","contributorId":3311,"corporation":false,"usgs":true,"family":"Drew","given":"Gary","email":"gdrew@usgs.gov","middleInitial":"S.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":735300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Piatt, John F. 0000-0002-4417-5748 jpiatt@usgs.gov","orcid":"https://orcid.org/0000-0002-4417-5748","contributorId":3025,"corporation":false,"usgs":true,"family":"Piatt","given":"John","email":"jpiatt@usgs.gov","middleInitial":"F.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":735301,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Jeffrey C.","contributorId":126882,"corporation":false,"usgs":false,"family":"Williams","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":735302,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190581,"text":"sir20175100 - 2018 - Preliminary synthesis and assessment of environmental flows in the middle Verde River watershed, Arizona","interactions":[],"lastModifiedDate":"2019-05-15T09:24:27","indexId":"sir20175100","displayToPublicDate":"2018-05-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5100","title":"Preliminary synthesis and assessment of environmental flows in the middle Verde River watershed, Arizona","docAbstract":"A 3-year study was undertaken to evaluate the suitability of the available modeling tools for characterizing environmental flows in the middle Verde River watershed of central Arizona, describe riparian vegetation throughout the watershed, and estimate sediment mobilization in the river. Existing data on fish and macroinvertebrates were analyzed in relation to basin characteristics, flow regimes, and microhabitat, and a pilot study was conducted that sampled fish and macroinvertebrates and the microhabitats in which they were found. The sampling for the pilot study took place at five different locations in the middle Verde River watershed. This report presents the results of this 3-year study.
The Northern Arizona Groundwater Flow Model (NARGFM) was found to be capable of predicting long-term changes caused by alteration of regional recharge (such as may result from climate variability) and groundwater pumping in gaining, losing, and dry reaches of the major streams in the middle Verde River watershed. Over the period 1910 to 2006, the model simulated an increase in dry reaches, a small increase in reaches losing discharge to the groundwater aquifer, and a concurrent decrease in reaches gaining discharge from groundwater. Although evaluations of the suitability of using the NARGFM and Basin Characteristic Model to characterize various streamflow intervals showed that smallerscale basin monthly runoff could be estimated adequately at locations of interest, monthly stream-flow estimates were found unsatisfactory for determining environmental flows.
Orthoimagery and Moderate Resolution Imaging Spectroradiometer data were used to quantify stream and riparian vegetation properties related to biotic habitat. The relative abundance of riparian vegetation varied along the main channel of the Verde River. As would be expected, more upland plant species and fewer lowland species were found in the upper-middle section compared to the lower-middle section, and vice-versa. Vegetation changes within the upper-middle and lower-middle reaches are related to differences in climate and hydrology. In general, the riparian vegetation of the middle Verde River watershed is that of a healthy ecosystem’s mixed age, mixed patch structure, likely a result of the mostly unaltered disturbance regime.
The frequency of in-river hydrogeomorphic features (pool, riffle, run) varied along the middle Verde River channel. There was a greater abundance of riffle habitat in the upper-middle reach; the lower-middle reach included more pool habitat. The Oak Creek tributary was more homogenous in geomorphic stream habitat composition than West Clear Creek, where runs dominated the upper reaches and pools dominated many of the lower reaches.
On the basis of the period of record and discharges recorded at 15-minute intervals, five flows were found to reach the gravel-transport threshold. Sediment mobilization computed with flows averaged over daily time steps yielded just three flows that reached the gravel-transport threshold, and monthly averaged flows yielded none. In the middle Verde River watershed, 15-minute data should be used when possible to evaluate sediment transport in the river system.
Data from more than 300 fish surveys conducted from 1992 to 2011 were analyzed using two schemes, one that divided the river into five reaches based on basin characteristics, and a second that divided the river into five reaches based on degree of flow alteration (specifically, diversions). Fish community metrics and assemblage data were used to analyze patterns of species composition and abundance in the two approaches. Overall, native and non-native species were regularly interacting and probably competing for similar resources. Fish abundances were also analyzed in response to floods and other flow metrics. Although the data are limited, native fish abundances increased more rapidly than non-native fish abundances in response to large floods. The basin-characteristic reach analysis showed native fish in greater abundance in the upper-middle reaches of the Verde River watershed and generally decreasing with downstream distance. The median relative abundance of native fish decreased by 50 percent from reach 1 to reach 5. Using the reach scheme based on degree of flow alteration, nondiverted reaches were found to have a greater abundance of native fish than diverted reaches. In heavily diverted reaches, non-native species outnumbered native species.
Fish metrics and stream-flow metrics for the 30, 90, and 365-day periods before collection were computed and the results analyzed statistically. Only abundance of all fish species was associated with the 30-day flow metrics. The 90-day flow metrics were generally positively associated with fish metrics, whereas the 365-day flow metrics had more negative correlations. In particular, significant relations were found between fish metrics and the magnitude and frequency of high flows, including maximum monthly flow, median annual number of high-flow events, and median annual maximum streamflow. Native sucker (Catostomidae) populations tended to decrease in periods of extended base flow, and fish in the non-native sunfish family (Centrarchidae) decreased in periods of flashy, high magnitude flows.
A pilot study surveyed fish at five locations in the upper part of the middle Verde River watershed as a means to measure microhabitat availability and quantify native and non-native fish use of that available microhabitat. Results indicated that native and non-native species exhibit some clear differences in microhabitat use. Although at least some native and non-native fish were found in each velocity, depth, and substrate category, preferential microhabitat use was common. On a percentage basis, non-native species had a strong preference for slow-moving and deeper water with silt and sand substrate, with a secondary preference for faster moving and very shallow water and a coarse gravel substrate. Native species showed a general preference for somewhat faster, moderate depth water over coarse gravel and had no clear secondary preference.
Macroinvertebrate-variables index period, high-flow year, and collection location (upper-middle Verde River, lowermiddle Verde River, or Verde River tributaries) were found to be important explanatory variables in differentiating among community metrics. Overall richness (number of unique taxa), Shannon’s diversity index, and the percent of the most dominant taxa were all highly correlated, but their response to each macroinvertebrate variable was different. The percentage of mayfly (order Ephemeroptera) taxa was significantly higher in Oak Creek and the upper-middle and lower-middle Verde River reaches, locations which have higher flows and more urbanization than other reaches. When community metrics were related to hydrologic metrics, caddisfly (order Trichoptera) populations appeared to increase and mayfly populations to decrease in response to less flashy and more stable streamflows. Conversely, caddisfly populations appeared to decrease and mayfly populations to increase in response to greater flow variability.
Six locations along the Verde River were sampled for macroinvertebrates as part of a pilot study associated with this report—(1) below Granite Creek, (2) near Campbell Ranch, (3) at the U.S. Geological Survey Paulden gage, (4) at the Perkinsville Bridge, (5) at the USGS Clarkdale gage, and (6) near the Reitz Ranch property. A nonmetric multidimensional scaling ordination of macroinvertebrate assemblages showed that the Verde River below Granite Creek site was different from the five other sites and that the Perkinsville Bridge and near Reitz Ranch samples had similar community structure. The near Campbell Ranch and Paulden gage locations had similar microhabitat characteristics, with the exception of riparian cover, yet the assemblage structure was very different. The different community composition at Verde River below Granite Creek was likely due to it having the smallest substrate sizes, lowest velocities, shallowest depths, and most riparian cover of the six sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175100","collaboration":"Prepared in cooperation with The Nature Conservancy and Salt River Project","usgsCitation":"Paretti, N.V., Brasher, A.M.D., Pearlstein, S.L., Skow, D.M., Gungle, Bruce, and Garner, B.D., 2018, Preliminary synthesis and assessment of environmental flows in the middle Verde River watershed, Arizona: U.S. Geological Survey Scientific Investigations Report 2017–5100, 104 p., https://doi.org/10.3133/sir20175100.","productDescription":"Report: xii; 104 p.; 3 Tables","numberOfPages":"120","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084364","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":354141,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100_table14.csv","text":"Table 14","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"Scientific Investigation Report 2017-5100 Table 12"},{"id":354142,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100_tables12_14.xlsx","text":"Table 12 and 14","size":"25 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Scientific Investigation Report 2017-5100 Table 12 and 14 Excel file"},{"id":354139,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100.pdf","text":"Report","size":"17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigation Report 2017-5100"},{"id":354138,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5100/coverthb.jpg"},{"id":354140,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5100/sir20175100_table12.csv","text":"Table 12","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"Scientific Investigation Report 2017-5100 Table 12"}],"country":"United States","state":"Arizona","otherGeospatial":"Verde River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5,\n 34.5\n ],\n [\n -112.5,\n 34.5\n ],\n [\n -112.5,\n 35.5\n ],\n [\n -111.5,\n 35.5\n ],\n [\n -111.5,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Predictions of the projected changes in species distributions and potential adaptation action benefits can help guide conservation actions. There is substantial uncertainty in projecting species distributions into an unknown future, however, which can undermine confidence in predictions or misdirect conservation actions if not properly considered. Recent studies have shown that the selection of alternative climate metrics describing very different climatic aspects (e.g., mean air temperature vs. mean precipitation) can be a substantial source of projection uncertainty. It is unclear, however, how much projection uncertainty might stem from selecting among highly correlated, ecologically similar climate metrics (e.g., maximum temperature in July, maximum 30‐day temperature) describing the same climatic aspect (e.g., maximum temperatures) known to limit a species’ distribution. It is also unclear how projection uncertainty might propagate into predictions of the potential benefits of adaptation actions that might lessen climate change effects. We provide probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty stemming from the selection of four maximum temperature metrics for brook trout (Salvelinus fontinalis), a cold‐water salmonid of conservation concern in the eastern United States. Projected losses in suitable stream length varied by as much as 20% among alternative maximum temperature metrics for mid‐century climate projections, which was similar to variation among three climate models. Similarly, the regional average predicted increase in brook trout occurrence probability under an adaptation action scenario of full riparian forest restoration varied by as much as .2 among metrics. Our use of Bayesian inference provides probabilistic measures of vulnerability and adaptation action benefits for individual stream reaches that properly address statistical uncertainty and can help guide conservation actions. Our study demonstrates that even relatively small differences in the definitions of climate metrics can result in very different projections and reveal high uncertainty in predicted climate change effects.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14101","usgsCitation":"DeWeber, J.T., and Wagner, T., 2018, Probabilistic measures of climate change vulnerability, adaptation action benefits, and related uncertainty from maximum temperature metric selection: Global Change Biology, v. 24, no. 6, p. 2735-2748, https://doi.org/10.1111/gcb.14101.","productDescription":"14 p.","startPage":"2735","endPage":"2748","ipdsId":"IP-090617","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":468761,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.14101","text":"Publisher Index Page"},{"id":354199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-27","publicationStatus":"PW","scienceBaseUri":"5afee6bce4b0da30c1bfbd88","contributors":{"authors":[{"text":"DeWeber, Jefferson T.","contributorId":199675,"corporation":false,"usgs":false,"family":"DeWeber","given":"Jefferson","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":735454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":735167,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196889,"text":"70196889 - 2018 - Hydrologic characteristics of freshwater mussel habitat: novel insights from modeled flows","interactions":[],"lastModifiedDate":"2018-05-21T13:03:19","indexId":"70196889","displayToPublicDate":"2018-05-14T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1699,"text":"Freshwater Science","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic characteristics of freshwater mussel habitat: novel insights from modeled flows","docAbstract":"The ability to model freshwater stream habitat and species distributions is limited by the spatially sparse flow data available from long-term gauging stations. Flow data beyond the immediate vicinity of gauging stations would enhance our ability to explore and characterize hydrologic habitat suitability. The southeastern USA supports high aquatic biodiversity, but threats, such as landuse alteration, climate change, conflicting water-resource demands, and pollution, have led to the imperilment and legal protection of many species. The ability to distinguish suitable from unsuitable habitat conditions, including hydrologic suitability, is a key criterion for successful conservation and restoration of aquatic species. We used the example of the critically endangered Tar River Spinymussel (Parvaspina steinstansana) and associated species to demonstrate the value of modeled flow data (WaterFALL™) to generate novel insights into population structure and testable hypotheses regarding hydrologic suitability. With ordination models, we: 1) identified all catchments with potentially suitable hydrology, 2) identified 2 distinct hydrologic environments occupied by the Tar River Spinymussel, and 3) estimated greater hydrological habitat niche breadth of assumed surrogate species associates at the catchment scale. Our findings provide the first demonstrated application of complete, continuous, regional modeled hydrologic data to freshwater mussel distribution and management. This research highlights the utility of modeling and data-mining methods to facilitate further exploration and application of such modeled environmental conditions to inform aquatic species management. We conclude that such an approach can support landscape-scale management decisions that require spatial information at fine resolution (e.g., enhanced National Hydrology Dataset catchments) and broad extent (e.g., multiple river basins).
","language":"English","publisher":"The University of Chicago Press","doi":"10.1086/697947","usgsCitation":"Drew, C.A., Eddy, M., Kwak, T.J., Cope, W., and Augspurger, T., 2018, Hydrologic characteristics of freshwater mussel habitat: novel insights from modeled flows: Freshwater Science, v. 37, no. 2, p. 343-356, https://doi.org/10.1086/697947.","productDescription":"14 p.","startPage":"343","endPage":"356","ipdsId":"IP-095471","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","volume":"37","issue":"2","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6bee4b0da30c1bfbd94","contributors":{"authors":[{"text":"Drew, C. Ashton","contributorId":140953,"corporation":false,"usgs":false,"family":"Drew","given":"C.","email":"","middleInitial":"Ashton","affiliations":[],"preferred":false,"id":735242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eddy, Michele","contributorId":198941,"corporation":false,"usgs":false,"family":"Eddy","given":"Michele","email":"","affiliations":[],"preferred":false,"id":735243,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cope, W. Gregory","contributorId":70353,"corporation":false,"usgs":true,"family":"Cope","given":"W. Gregory","affiliations":[],"preferred":false,"id":735244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Augspurger, Tom","contributorId":189894,"corporation":false,"usgs":false,"family":"Augspurger","given":"Tom","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":735245,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70196886,"text":"70196886 - 2018 - Spatial extent of analysis influences observed patterns of population genetic structure in a widespread darter species (Percidae)","interactions":[],"lastModifiedDate":"2018-09-20T16:32:08","indexId":"70196886","displayToPublicDate":"2018-05-14T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Spatial extent of analysis influences observed patterns of population genetic structure in a widespread darter species (Percidae)","docAbstract":"This study tested a focused strategy for reducing phosphorus (P) and sediment loads in agricultural streams. The strategy involved selecting small watersheds identified as likely to respond relatively quickly, and then focusing conservation practices on high-contributing fields within those watersheds. Two 5,000 ha (12,360 ac) watersheds in the Driftless Area of south central Wisconsin, previously ranked in the top 6% of similarly sized Wisconsin watersheds for expected responsiveness to conservation efforts to reduce high P and sediment loads, were chosen for the study. The stream outlets from both watersheds were monitored from October of 2006 through September of 2016 for streamflow and concentrations of sediment, total P, and, beginning in October of 2009, total dissolved P. Fields and pastures having the highest potential P delivery to the streams in each watershed were identified using the Wisconsin P Index (Good et al. 2012). After three years of baseline monitoring (2006 to 2009), farmers implemented both field- and farm-based conservation practices in one watershed (treatment) as a means to reduce sediment and P inputs to the stream from the highest contributing areas, whereas there were no out-of-the-ordinary conservation efforts in the second watershed (control). Implementation occurred primarily in 2011 and 2012. In the four years following implementation of conservation practices (2013 through 2016), there was a statistically significant reduction in storm-event suspended sediment loads in the treatment watershed compared to the control watershed when the ground was not frozen (p = 0.047). While there was an apparent reduction in year-round suspended sediment event loads, it was not statistically significant at the 95% confidence level (p = 0.15). Total P loads were significantly reduced for runoff events (p < 0.01) with a median reduction of 50%. Total P and total dissolved P concentrations for low-flow conditions were also significantly reduced (p < 0.01) compared to the control watershed. This study demonstrated that a strategy that first identifies watersheds likely to respond to conservation efforts and then focuses implementation on relatively high-contributing fields within those watersheds can be successful in reducing stream P concentrations and loads.
","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.73.3.298","usgsCitation":"Carvin, R.B., Good, L.W., Fitzpatrick, F.A., Diehl, C., Songer, K., Meyer, K.J., Panuska, J.C., Richter, S., and Whalley, K., 2018, Testing a two-scale focused conservation strategy for reducing phosphorus and sediment loads from agricultural watersheds: Journal of Soil and Water Conservation, v. 73, no. 2, p. 298-309, https://doi.org/10.2489/jswc.73.3.298.","productDescription":"12 p.","startPage":"298","endPage":"309","ipdsId":"IP-077953","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":468766,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.73.3.298","text":"Publisher Index Page"},{"id":354107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","volume":"73","issue":"2","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-03","publicationStatus":"PW","scienceBaseUri":"5afee6bfe4b0da30c1bfbd9e","contributors":{"authors":[{"text":"Carvin, Rebecca B. 0000-0001-7778-4841 rbcarvin@usgs.gov","orcid":"https://orcid.org/0000-0001-7778-4841","contributorId":201609,"corporation":false,"usgs":true,"family":"Carvin","given":"Rebecca","email":"rbcarvin@usgs.gov","middleInitial":"B.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":734941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Good, Laura W.","contributorId":151057,"corporation":false,"usgs":false,"family":"Good","given":"Laura","email":"","middleInitial":"W.","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":734942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075 fafitzpa@usgs.gov","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":196543,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith","email":"fafitzpa@usgs.gov","middleInitial":"A.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":false,"id":734943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diehl, Curt","contributorId":204768,"corporation":false,"usgs":false,"family":"Diehl","given":"Curt","email":"","affiliations":[{"id":36979,"text":"Land Conservation Division, Land and Water Resources Department, Dane County, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":734944,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Songer, Katherine","contributorId":204769,"corporation":false,"usgs":false,"family":"Songer","given":"Katherine","email":"","affiliations":[{"id":36980,"text":"Land Conservation Division, Land and Water Resources Department, Dane County, Wisconsin; graduate student at the University of Wisconsin, Madison, Wisconsin.","active":true,"usgs":false}],"preferred":false,"id":734945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meyer, Kimberly J.","contributorId":204770,"corporation":false,"usgs":false,"family":"Meyer","given":"Kimberly","email":"","middleInitial":"J.","affiliations":[{"id":36981,"text":"Land Conservation Division, Land and Water Resources Department, Dane County, Wisconsin; graduate student at the University of Wisconsin, Madison, Wisconsin; Nutrient management specialist with Madison Metropolitan Sewerage District in Madison, Wisconsin.","active":true,"usgs":false}],"preferred":false,"id":734946,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Panuska, John C.","contributorId":204771,"corporation":false,"usgs":false,"family":"Panuska","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":36982,"text":"Department of Biological Systems Engineering, University of Wisconsin, Madison, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":734947,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Richter, Steve","contributorId":204772,"corporation":false,"usgs":false,"family":"Richter","given":"Steve","email":"","affiliations":[{"id":36983,"text":"The Nature Conservancy, Madison, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":734948,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Whalley, Kyle","contributorId":204773,"corporation":false,"usgs":false,"family":"Whalley","given":"Kyle","email":"","affiliations":[{"id":36979,"text":"Land Conservation Division, Land and Water Resources Department, Dane County, Wisconsin","active":true,"usgs":false}],"preferred":false,"id":734949,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70196894,"text":"70196894 - 2018 - Long-term changes in soil and stream chemistry across an acid deposition gradient in the northeastern United States","interactions":[],"lastModifiedDate":"2018-05-14T12:55:16","indexId":"70196894","displayToPublicDate":"2018-05-13T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2262,"text":"Journal of Environmental Quality","active":true,"publicationSubtype":{"id":10}},"title":"Long-term changes in soil and stream chemistry across an acid deposition gradient in the northeastern United States","docAbstract":"Declines in acidic deposition across Europe and North America have led to decreases in surface water acidity and signs of chemical recovery of soils from acidification. To better understand the link between recovery of soils and surface waters, chemical trends in precipitation, soils, and streamwater were investigated in three watersheds representing a depositional gradient from high to low across the northeastern United States. Significant declines in concentrations of H+ (ranging from −1.2 to −2.74 microequivalents [μeq] L−1 yr−1), NO3− (ranging from −0.6 to −0.84 μeq L−1 yr−1), and SO42− (ranging from −0.95 to −2.13 μeq L−1 yr−1) were detected in precipitation in the three watersheds during the period 1999 to 2013. Soil chemistry in the A horizon of the watershed with the greatest decrease in deposition showed significant decreases in exchangeable Al and increases in exchangeable bases. Soil chemistry did not significantly improve during the study in the other watersheds, and base saturation in the Oa and upper B horizons significantly declined in the watershed with the smallest decrease in deposition. Streamwater SO42−concentrations significantly declined in all three streams (ranging from −2.01 to −2.87 μeq L−1 yr−1) and acid neutralizing capacity increased (ranging from 1.38 to 1.60 μeq L−1 yr−1) in the two streams with the greatest decreases in deposition. Recovery of soils has likely been limited by decades of acid deposition that have leached base cations from soils with base-poor parent material.
Three permeable pavement surfaces - asphalt (PA), concrete (PC), and interlocking pavers (PIP) - were evaluated side-by-side to measure changes to the infiltrative capacity and water quality of stormwater runoff originating from a conventional asphalt parking lot in Madison, Wisconsin. During the 24-month monitoring period (2014-16), all three permeable pavements resulted in statistically significant reductions in the cumulative load of solids (total suspended solids and suspended sediment), total phosphorus, Escherichia coli (E. coli), and Enterococci. Most of the removal occurred through capture and retention in the void spaces of each permeable surface and aggregate base. The largest reduction in total suspended solids was for PC at 80 percent, followed by PIP and PA at 69 and 65 percent, respectively. Reductions (generally less than 50 percent) in total phosphorus also were observed, which might have been tempered by increases in the dissolved fraction observed in PIP and PA. Conversely, PC results indicated a slight reduction in dissolved phosphorus but failed to meet statistical significance. E. coli and Enterococci were reduced by about 80 percent for PC, almost twice the amount observed for PIP and PA.
Results for the PIP and PC surfaces initially indicated higher pollutant load reduction than results for the PA surface. The efficiency of PIP and PC surfaces capturing sediment, however, led to a decline in infiltration rates that resulted in more runoff flowing over, not through, the permeable surface. This result led to a decline in treatment until the permeable surface was partially restored through maintenance practices, to which PIP responded more dramatically than PC or PA. Conversely, the PA surface was capable of infiltrating most of the influent runoff volume during the monitoring period and, thus, continued to provide some level of treatment. The combined effect of underdrain and overflow drainage resulted in similar pollutant treatment for all three permeable surfaces.
Temperatures below each permeable surface generally followed changes in air temperature with a more gradual response observed in deeper layers. Therefore, permeable pavement may do little to mitigate heated runoff during summer. During winter, deeper layers remained above freezing even when air temperature was below freezing. Although temperatures were not high enough to melt snow or ice accumulated on the surface, temperatures below each permeable pavement did allow void spaces to remain open, which promoted infiltration of melted ice and snow as air temperatures rose above freezing. These open void spaces could potentially reduce the need for application of deicing agents in winter because melted snow and ice would infiltrate, thereby preventing refreezing of pooled water in what is known as the “black ice” effect.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185037","usgsCitation":"Selbig, W.R., and Buer, Nicolas, 2018, Hydraulic, water-quality, and temperature performance of three types of permeable pavement under high sediment loading conditions: U.S. Geological Survey Scientific Investigations Report 2018–5037, 44 p., https://doi.org/10.3133/sir20185037.","productDescription":"Report: ix, 44 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087882","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":437894,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RX9B09","text":"USGS data release","linkHelpText":"Storm characteristics, concentrations, and loads measured at the Permeable Pavement Research Facility, Madison, Wisconsin (2014 - 2016)"},{"id":354071,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RX9B09 ","text":"USGS data release","description":"USGS data release","linkHelpText":"Storm characteristics, concentrations, and loads measured at the permeable pavement research facility, Madison, Wisconsin (2014 - 2016)"},{"id":354069,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5037/coverthb.jpg"},{"id":354070,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5037/sir20185037.pdf","text":"Report","size":"6.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5037"}],"country":"United States","state":"Wisconsin","city":"Madison","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
Streamflow and nutrient concentration data needed to compute nitrogen and phosphorus loads were compiled from Federal, State, Provincial, and local agency databases and also from selected university databases. The nitrogen and phosphorus loads are necessary inputs to Spatially Referenced Regressions on Watershed Attributes (SPARROW) models. SPARROW models are a way to estimate the distribution, sources, and transport of nutrients in streams throughout the Midcontinental region of Canada and the United States. After screening the data, approximately 1,500 sites sampled by 34 agencies were identified as having suitable data for calculating the long-term mean-annual nutrient loads required for SPARROW model calibration. These final sites represent a wide range in watershed sizes, types of nutrient sources, and land-use and watershed characteristics in the Midcontinental region of Canada and the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185051","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Saad, D.A., Benoy, G.A., and Robertson, D.M., 2018, Estimates of long-term mean-annual nutrient loads considered for use in SPARROW models of the Midcontinental region of Canada and the United States, 2002 base year: U.S. Geological Survey Scientific Investigations Report 2018–5051, 14 p., https://doi.org/10.3133/sir20185051.","productDescription":"Report: vi, 14 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-084092","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":353945,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VT1R1K","text":"USGS data release","description":"USGS data release","linkHelpText":"Water-quality and streamflow datasets used for estimating loads considered for use in the 2002 Midcontinent nutrient SPARROW models, United States and Canada, 1970-2012"},{"id":353943,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5051/sir20185051.pdf","text":"Report","size":"8.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5051"},{"id":353931,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5051/coverthb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.32617187499999,\n 49.26780455063753\n ],\n [\n -94.3505859375,\n 42.94033923363181\n ],\n [\n -91.7578125,\n 39.16414104768742\n ],\n [\n -88.9013671875,\n 36.63316209558658\n ],\n [\n -86.572265625,\n 35.17380831799959\n ],\n [\n -81.7822265625,\n 37.82280243352756\n ],\n [\n -79.5849609375,\n 40.17887331434696\n ],\n [\n -77.16796875,\n 42.293564192170095\n ],\n [\n -74.794921875,\n 43.739352079154706\n ],\n [\n -75.6298828125,\n 44.933696389694674\n ],\n [\n -78.44238281249999,\n 45.1510532655634\n ],\n [\n -80.2001953125,\n 46.58906908309182\n ],\n [\n -82.2216796875,\n 47.368594345213374\n ],\n [\n -84.287109375,\n 49.781264058178344\n ],\n [\n -87.2314453125,\n 50.233151832472245\n ],\n [\n -90.52734374999999,\n 50.708634400828224\n ],\n [\n -95.1416015625,\n 50.401515322782366\n ],\n [\n -99.66796875,\n 50.064191736659104\n ],\n [\n -100.986328125,\n 51.45400691005982\n ],\n [\n -103.4912109375,\n 51.536085601784755\n ],\n [\n -102.74414062499999,\n 49.97948776108648\n ],\n [\n -104.32617187499999,\n 49.26780455063753\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
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Middleton, WI 53562
The California Department of Water Resources and Bureau of Reclamation propose new water intake facilities on the Sacramento River in northern California that would convey some of the water for export to areas south of the Sacramento-San Joaquin River Delta (hereinafter referred to as the Delta) through tunnels rather than through the Delta. The collection of water intakes, tunnels, pumping facilities, associated structures, and proposed operations are collectively referred to as California WaterFix. The water intake facilities, hereinafter referred to as the North Delta Diversion (NDD), are proposed to be located on the Sacramento River downstream of the city of Sacramento and upstream of the first major river junction where Sutter Slough branches from the Sacramento River. The NDD can divert a maximum discharge of 9,000 cubic feet per second (ft3 /s) from the Sacramento River, which reduces the amount of Sacramento River inflow into the Delta.
In this report, we conduct four analyses to investigate the effect of the NDD and its proposed operation on survival of juvenile Chinook salmon (Oncorhynchus tshawytscha). All analyses used the results of a Bayesian survival model that allowed us to simulate travel time, migration routing, and survival of juvenile Chinook salmon migrating through the Delta in response to NDD operations, which affected both inflows to the Delta and operation of the Delta Cross Channel (DCC).
For the first analysis, we evaluated the effect of the NDD bypass rules on salmon survival. The NDD bypass rules are a set of operational rule curves designed to provide adaptive levels of fish protection by defining allowable diversion rates as a function of (1) Sacramento River discharge as measured at Freeport, and (2) time of year when endangered runs requiring the most protection are present. We determined that all bypass rule curves except constant low-level pumping (maximum diversion of 900 ft3 /s) could cause a sizeable decrease in survival by as much as 6–10 percentage points. The maximum decrease in survival occurred at an intermediate Sacramento River flow of about 20,000–30,000 ft3 /s. Diversion rates increased rapidly as Sacramento River flows increased from 20,000 ft3 /s to 30,000 ft3 /s, until a maximum diversion rate was reached at 9,000 ft3 /s. Because through-Delta survival increases sharply over this range of Sacramento River flow before beginning to level off with further flow increases, increasing diversion rates over this flow range causes a large decrease in survival relative to no diversion.
For the second analysis, we applied the survival model to 82 years of daily simulated flows under the Proposed Action (PA) and No Action Alternative (NAA). The PA includes operation of the Central Valley Project/State Water Project with implementation of the NDD and its operations prescribed by the NDD bypass rules, whereas the NAA assumes system operations without implementation of the NDD. We also evaluated a “Level 1” (L1) scenario, which was similar to the PA scenario but applied the most protective bypass rule known as Level 1 post-pulse operations. We noted a high probability that survival under the PA scenario was lower than under the NAA scenario, and that travel time was longer under PA relative to NAA in most simulation years. However, the largest survival differences between the PA and NAA scenarios occurred during October–November and May–June. Although bypass rules are less restrictive during these periods, we determined that more frequent use of the DCC under PA led to the largest differences in survival between the two scenarios. Additionally, we noted no difference in median survival decreases between the PA and L1 scenarios, although in some years the L1 scenario had a lower survival decrease than the PA scenario.
For the third analysis, we proposed a quantitative approach for developing NDD rule curves (that is, prescribed diversion flows for given inflows) by using the survival model to identify diversion rates that meet a criterion of a having a small probability of exceeding a given decrease in survival. We examined diversion rates that led to a 10% chance of exceeding a given decrease in survival for a range of absolute and relative decreases in survival. To maintain a given constant level of protection across the range of river flows, our analysis indicated that diversions had to increase at a much slower rate with respect to Sacramento River flow relative to the rule curves defined in the NDD bypass table. Additionally, we determined that diversion rates could be higher than under the bypass table rule curves at river flows less than 20,000 ft3 /s, but diversions had to be less than defined by NDD bypass rules at higher flows.
For the fourth analysis, we simulated the effect of “real-time operations” on salmon survival, where bypass flow rates were determined by the presence of juvenile salmon entering the Delta, as indicated by juvenile salmon catch in a rotary screw trap upstream of the Delta. For this analysis, we evaluated NDD operations as defined by the L1 scenario and an additional scenario (Unlimited Pulse Protection [UPP]) that provided protection to an unlimited number of fish pulses. This analysis indicated that the highest catches occurred during flow pulses when daily survival was high, which caused annual survival to be weighted towards periods of high daily survival, resulting in a high annual survival. We determined that the mean annual survival decreased by 1–4 percentage points, and annual survival decreases were more frequently smaller for the UPP scenario. Additionally, because the UPP scenario protected an unlimited number of fish pulses, decreases in daily survival under the UPP scenario were less than under the L1 scenario.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181078","collaboration":"Prepared in cooperation with National Oceanic and Atmospheric Administration, National Marine Fisheries Service","usgsCitation":"Perry, R.W., and Pope, A.C., 2018, Effects of the proposed California WaterFix North Delta Diversion on survival of juvenile Chinook salmon (Oncorhynchus tshawytscha) in the Sacramento-San Joaquin River Delta, northern California: U.S. Geological Survey Open-File Report 2018-1078, 94 p. plus appendixes,\nhttps://doi.org/10.3133/ofr20181078.","productDescription":"Report: x, 94 p.; 11 Appendixes","numberOfPages":"108","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-095992","costCenters":[{"id":654,"text":"Western Fisheries Research 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1922-2003"},{"id":354077,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1078/coverthb.jpg"},{"id":354078,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078.pdf","text":"Report","size":"18.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078"},{"id":354079,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix01.pdf","text":"Appendix 1","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 1","linkHelpText":"Simulated daily survival by year, no action alternative compared to proposed action scenarios, 1922-2003"},{"id":354086,"rank":10,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix08.pdf","text":"Appendix 8","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 8","linkHelpText":"Simulated daily routing by year, no action alternative compared to level 1 scenarios, 1922-2003"},{"id":354080,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix02.pdf","text":"Appendix 2","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 2","linkHelpText":"Simulated daily travel time by year, no action alternative compared to proposed action scenarios, 1922-2003"},{"id":354081,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix03.pdf","text":"Appendix 3","size":"2.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 3","linkHelpText":"Simulated daily routing by year, no action alternative compared to proposed action scenarios, 1922-2003"},{"id":354082,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix04.pdf","text":"Appendix 4","size":"2.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 4","linkHelpText":"Simulated route-specific survival by year, no action alternative compared to PA scenarios, 1922-2003"},{"id":354083,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix05.pdf","text":"Appendix 5","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 5","linkHelpText":"Simulated route-specific travel time by year, no action alternative compared to PA scenarios, 1922-2003"},{"id":354084,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix06.pdf","text":"Appendix 6","size":"1.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 6","linkHelpText":"Simulated daily survival by year, no action alternative compared to level 1 scenarios, 1922-2003"},{"id":354088,"rank":12,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix10.pdf","text":"Appendix 10","size":"2.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 10","linkHelpText":"Simulated route-specific travel time by year, no action alternative compared to level 1 scenarios, 1922-2003"},{"id":354089,"rank":13,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2018/1078/ofr20181078_appendix11.pdf","text":"Appendix 11","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1078 Appendix 11","linkHelpText":"North Delta Diversion rule curve optimization"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin River Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
Groundwater-quality samples and water-level data were collected from 36 wells in the Jerome/Gooding County area of the eastern Snake River Plain aquifer during June 2017. The wells included 30 wells sampled for the U.S. Geological Survey’s National Water-Quality Assessment project, plus an additional 6 wells were selected to increase spatial distribution. The data provide water managers with the ability for an improved understanding of groundwater quality and flow directions in the area. Groundwater-quality samples were analyzed for nutrients, major ions, trace elements, and stable isotopes of water. Quality-assurance and quality-control measures consisted of multiple blank samples and a sequential replicate sample. All data are available online at the USGS National Water Information System.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1085","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources and Idaho Power Company","usgsCitation":"Skinner, K.D., 2018, Groundwater-quality data from the eastern Snake River Plain aquifer, Jerome and Gooding Counties, south-central Idaho, 2017: U.S. Geological Survey Data Series 1085, 20 p., https://doi.org/10.3133/ds1085.","productDescription":"iv, 20 p.","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-093930","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":354092,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1085/ds1085.pdf","text":"Report","size":"1.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1085"},{"id":354091,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1085/coverthb.jpg"}],"country":"United States","state":"Idaho","county":"Gooding County, Jerome County","otherGeospatial":"Snake River Plain Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.33,\n 42.56218435232186\n ],\n [\n -114.33,\n 42.917212160086194\n ],\n [\n -115,\n 42.917212160086194\n ],\n [\n -115,\n 42.56218435232186\n ],\n [\n -114.33,\n 42.56218435232186\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
This is the ninth annual report highlighting U.S. Geological Survey (USGS) science and decision-support activities conducted for the Wyoming Landscape Conservation Initiative (WLCI). The activities address specific management needs identified by WLCI partner agencies. In fiscal year (FY) 2016, there were 26 active USGS WLCI science-based projects. Of these 26 projects, one project was new for FY2016, and three were completed by the end of the fiscal year (though final products were still in preparation or review). USGS WLCI projects were grouped under five categories: (1) Baseline Synthesis, (2) Long-Term Monitoring, (3) Effectiveness Monitoring, (4) Mechanistic Studies of Wildlife, and (5) Data and Information Management. Each of these topic areas is designed to address WLCI management needs: identifying key drivers of change, identifying the condition and distribution of key wildlife species and habitats and of species’ habitat requirements, development of an integrated inventory and monitoring strategy, use of emerging technologies and development and testing of innovative methods for maximizing the efficiency and efficacy of monitoring efforts, evaluating the effectiveness of habitat treatment projects, evaluating the responses of wildlife to development, and developing a data clearinghouse and information management framework to support and provide access to results of most USGS WLCI projects.
In FY2016, we assisted with updating the WLCI Conservation Action Plan and associated databases as part of the Comprehensive Assessment, and we also assisted with the Bureau of Land Management 2015 WLCI annual report. By the end of FY2016, we completed or had nearly completed assessments of WLCI energy and mineral resources and had submitted a manuscript on modeled effects of oil and gas development on wildlife to a peer-reviewed journal. We also initiated a study on the effects of wind energy on wildlife in the WLCI region. A USGS circular on WLCI long-term monitoring was in review at the end of the fiscal year, and seven projects monitoring water and vegetation (including changes in sagebrush cover and patterns of sagebrush mortality) continued through the year. USGS scientists continued many projects in FY2016 that evaluate the effectiveness of habitat conservation actions (including sagebrush, cheatgrass, and aspen habitat treatments) and provide tools in support of mechanistic studies of wildlife. In FY2016, USGS scientists, along with university and State partners, continued work on five focal wildlife species/communities (pygmy rabbits [Brachylagus idahoensis], greater sage grouse , mule deer, sagebrush songbirds, and native fish). In FY2016, the USGS Information Management Team presented information to WLCI scientists on how USGS tools and resources can be used to fulfill the requirements of new USGS policies regarding data release, data management, and data visualization.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181048","usgsCitation":"Bowen, Z.H., Aikens, E., Aldridge, C.L., Anderson, P.J., Assal, T.J., Chalfoun, A.D., Chong, G.W., Eddy-Miller, C.A., Garman, S.L., Germaine, S.S., Homer, C.G., Johnston, A., Kauffman, M.J., Manier, D.J., Melcher, C.P., Miller, K.A., Walters, A.W., Wheeler, J.D., Wieferich, D., Wilson, A.B., Wyckoff, T.B., and Zeigenfuss, L.C., 2018, U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2016 annual report: U.S. Geological Survey Open-File Report 2018–1048, 49 p., https://doi.org/10.3133/ofr20181048.","productDescription":"vii, 49 p.","onlineOnly":"Y","ipdsId":"IP-091604","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science 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Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
Digital flood-inundation maps for an approximately 4.8-mile reach of the Wabash River at Lafayette, Indiana (Ind.) were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at USGS streamgage 03335500, Wabash River at Lafayette, Ind. Current streamflow conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the internet at https://waterdata.usgs.gov/in/nwis/uv?site_no=03335500. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood-warning system (https://water.weather.gov/ahps/). The NWS AHPS forecasts flood hydrographs at many places that are often colocated with USGS streamgages, including the Wabash River at Lafayette, Ind. NWS AHPS-forecast peak-stage information may be used with the maps developed in this study to show predicted areas of flood inundation.
For this study, flood profiles were computed for the Wabash River reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relations at USGS streamgage 03335500, Wabash River at Lafayette, Ind., and high-water marks from the flood of July 2003 (U.S. Army Corps of Engineers [USACE], 2007). The calibrated hydraulic model was then used to determine 23 water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The simulated water-surface profiles were then combined with a geographic information system digital elevation model derived from light detection and ranging to delineate the area flooded at each water level. The availability of these maps, along with internet information regarding current stage from the USGS streamgage 03335500, Wabash River at Lafayette, Ind., and forecasted high-flow stages from the NWS AHPS, will provide emergency management personnel and residents with information that is critical for flood-response activities such as evacuations and road closures, and for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185017","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Kim, M.H., 2018, Flood-inundation maps for the Wabash River at Lafayette, Indiana: U.S. Geological Survey Scientific Investigations Report 2018–5017,Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278
The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34°C in lake pixels >180 m from land, 4.89°C at the land-water boundary, and 1.11°C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/01431161.2018.1471545","usgsCitation":"Schaeffer, B.A., Iiames, J., Dwyer, J.L., Urquhart, E., Salls, W., Rover, J., and Seegers, B., 2018, An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries: International Journal of Remote Sensing, v. 39, no. 22, p. 7789-7805, https://doi.org/10.1080/01431161.2018.1471545.","productDescription":"17 p.","startPage":"7789","endPage":"7805","ipdsId":"IP-096965","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":468769,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/01431161.2018.1471545","text":"Publisher Index Page"},{"id":362002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"39","issue":"22","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Schaeffer, Blake A.","contributorId":201328,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":759166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iiames, John","contributorId":214110,"corporation":false,"usgs":false,"family":"Iiames","given":"John","email":"","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dwyer, John L. 0000-0002-8281-0896 dwyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-0896","contributorId":3481,"corporation":false,"usgs":true,"family":"Dwyer","given":"John","email":"dwyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":759164,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Urquhart, Erin","contributorId":214111,"corporation":false,"usgs":false,"family":"Urquhart","given":"Erin","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Salls, Wilson","contributorId":214112,"corporation":false,"usgs":false,"family":"Salls","given":"Wilson","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":759169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rover, Jennifer 0000-0002-3437-4030","orcid":"https://orcid.org/0000-0002-3437-4030","contributorId":211850,"corporation":false,"usgs":true,"family":"Rover","given":"Jennifer","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":759165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Seegers, Bridget","contributorId":214113,"corporation":false,"usgs":false,"family":"Seegers","given":"Bridget","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":759170,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196929,"text":"70196929 - 2018 - Phosphorus speciation and solubility in aeolian dust deposited in the interior American West","interactions":[],"lastModifiedDate":"2018-05-10T13:32:59","indexId":"70196929","displayToPublicDate":"2018-05-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Phosphorus speciation and solubility in aeolian dust deposited in the interior American West","docAbstract":"Aeolian dust is a significant source of phosphorus (P) to alpine oligotrophic lakes, but P speciation in dust and source sediments and its release kinetics to lake water remain unknown. Phosphorus K-edge XANES spectroscopy shows that calcium-bound P (Ca−P) is dominant in 10 of 12 dust samples (41−74%) deposited on snow in the central Rocky Mountains and all 42 source sediment samples (the fine fraction) (68−80%), with a lower proportion in dust probably because acidic snowmelt dissolves some Ca−P in dust before collection. Iron-bound P (Fe−P, ∼54%) dominates in the remaining two dust samples. Chemical extractions (SEDEX) on these samples provide inaccurate results because of unselective extraction of targeted species and\nartifacts introduced by the extractions. Dust releases increasingly more P in synthetic lake water within 6−72 h thanks to dissolution of Ca−P, but dust release of P declines afterward due to back adsorption of P onto Fe oxides present in the dust. The back sorption is stronger for the dust with a lower degree of P saturation determined by oxalate extraction. This work suggests\nthat P speciation, poorly crystalline minerals in the dust, and lake acidification all affect the availability and fate of dust-borne P in lakes.","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.7b04729","usgsCitation":"Zhang, Z., Goldstein, H.L., Reynolds, R.L., Hu, Y., Wang, X., and Zhu, M., 2018, Phosphorus speciation and solubility in aeolian dust deposited in the interior American West: Environmental Science & Technology, v. 52, no. 5, p. 2658-2667, https://doi.org/10.1021/acs.est.7b04729.","productDescription":"10 p.","startPage":"2658","endPage":"2667","ipdsId":"IP-091229","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":437917,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RF5T8B","text":"USGS data release","linkHelpText":"Data Release for Phosphorous speciation and solubility in aeolian dust deposited in the interior American West"},{"id":354056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.7197265625,\n 32.602361666817515\n ],\n [\n -104.65576171875,\n 32.602361666817515\n ],\n [\n -104.65576171875,\n 40.01078714046552\n ],\n [\n -112.7197265625,\n 40.01078714046552\n ],\n [\n -112.7197265625,\n 32.602361666817515\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-08","publicationStatus":"PW","scienceBaseUri":"5afee6c1e4b0da30c1bfbdbc","contributors":{"authors":[{"text":"Zhang, Zhuojun","contributorId":204798,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhuojun","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":735025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goldstein, Harland L. 0000-0002-6092-8818 hgoldstein@usgs.gov","orcid":"https://orcid.org/0000-0002-6092-8818","contributorId":147881,"corporation":false,"usgs":true,"family":"Goldstein","given":"Harland","email":"hgoldstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":false,"id":735024,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reynolds, Richard L. 0000-0002-4572-2942 rreynolds@usgs.gov","orcid":"https://orcid.org/0000-0002-4572-2942","contributorId":147880,"corporation":false,"usgs":true,"family":"Reynolds","given":"Richard","email":"rreynolds@usgs.gov","middleInitial":"L.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":271,"text":"Federal Center","active":false,"usgs":true}],"preferred":true,"id":735026,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hu, Yongfeng","contributorId":204799,"corporation":false,"usgs":false,"family":"Hu","given":"Yongfeng","email":"","affiliations":[{"id":13248,"text":"University of Saskatchewan","active":true,"usgs":false}],"preferred":false,"id":735027,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wang, Xiaoming","contributorId":204800,"corporation":false,"usgs":false,"family":"Wang","given":"Xiaoming","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":735028,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Mengqiang","contributorId":204801,"corporation":false,"usgs":false,"family":"Zhu","given":"Mengqiang","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":735029,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196155,"text":"sir20185046 - 2018 - Methods for peak-flow frequency analysis and reporting for streamgages in or near Montana based on data through water year 2015","interactions":[],"lastModifiedDate":"2018-09-25T05:33:19","indexId":"sir20185046","displayToPublicDate":"2018-05-10T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5046","title":"Methods for peak-flow frequency analysis and reporting for streamgages in or near Montana based on data through water year 2015","docAbstract":"This report documents the methods for peak-flow frequency (hereinafter “frequency”) analysis and reporting for streamgages in and near Montana following implementation of the Bulletin 17C guidelines. The methods are used to provide estimates of peak-flow quantiles for 50-, 42.9-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities for selected streamgages operated by the U.S. Geological Survey Wyoming-Montana Water Science Center (WY–MT WSC). These annual exceedance probabilities correspond to 2-, 2.33-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year recurrence intervals, respectively.
Standard procedures specific to the WY–MT WSC for implementing the Bulletin 17C guidelines include (1) the use of the Expected Moments Algorithm analysis for fitting the log-Pearson Type III distribution, incorporating historical information where applicable; (2) the use of weighted skew coefficients (based on weighting at-site station skew coefficients with generalized skew coefficients from the Bulletin 17B national skew map); and (3) the use of the Multiple Grubbs-Beck Test for identifying potentially influential low flows. For some streamgages, the peak-flow records are not well represented by the standard procedures and require user-specified adjustments informed by hydrologic judgement. The specific characteristics of peak-flow records addressed by the informed-user adjustments include (1) regulated peak-flow records, (2) atypical upper-tail peak-flow records, and (3) atypical lower-tail peak-flow records. In all cases, the informed-user adjustments use the Expected Moments Algorithm fit of the log-Pearson Type III distribution using the at-site station skew coefficient, a manual potentially influential low flow threshold, or both.
Appropriate methods can be applied to at-site frequency estimates to provide improved representation of long-term hydroclimatic conditions. The methods for improving at-site frequency estimates by weighting with regional regression equations and by Maintenance of Variance Extension Type III record extension are described.
Frequency analyses were conducted for 99 example streamgages to indicate various aspects of the frequency-
analysis methods described in this report. The frequency analyses and results for the example streamgages are presented in a separate data release associated with this report consisting of tables and graphical plots that are structured to include information concerning the interpretive decisions involved in the frequency analyses. Further, the separate data release includes the input files to the PeakFQ program, version 7.1, including the peak-flow data file and the analysis specification file that were used in the peak-flow frequency analyses. Peak-flow frequencies are also reported in separate data releases for selected streamgages in the Beaverhead River and Clark Fork Basins and also for selected streamgages in the Ruby, Jefferson, and Madison River Basins.
Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Lahars large enough to reach populated areas are a hazard at Mount Adams, a massive volcano in the southern Cascade Range of Washington State (fig. 1). It is considered to be still active and has the potential to erupt again. By definition, lahars are gravity-driven flows of water-saturated mixtures of mud and rock (plus or minus ice, wood, and other debris), which originate from volcanoes and have a variety of potential triggering mechanisms (Vallance, 2000; Vallance and Iverson, 2015). Flowing mixtures can range in fluid consistency from something like a milkshake to something more like wet concrete, and they behave like flash floods, in that they can appear suddenly in river channels with little warning and commonly have boulder- or log-choked flow fronts. Lahars are hazardous because they can flow rapidly in confined valleys (commonly 20–35 miles per hour [mph] or 9–16 meters per second [m/s]), can travel more than 100 miles (mi) (161 kilometers [km]) from a source volcano, and can move with incredible destructive force, carrying multi-ton boulders and logs that can act as battering rams (Pierson, 1998). The biggest threats from lahars to downstream communities are present during eruptive activity, and impacts to communities can be dire. For example, a very large eruption-triggered lahar in Colombia in 1985 surprised and killed more than 20,000 people in a large town located about 45 mi (72 km) downstream and out of sight of the volcano that produced it (Pierson and others, 1990).
Mount Adams, one of the largest volcanoes in the Cascade Range, is a composite stratocone composed primarily of andesite lava flows. It has been the most continuously active volcano within the 480-mi2 Mount Adams volcanic field—a region covering parts of Klickitat, Skamania, Yakima, andLewis Counties and part of the Yakama Nation Reservation in Washington State (Hildreth and Fierstein,1995, 1997). About 500,000 years in age, Mount Adams reached its present size by about 15,000 years ago, primarily through the episodic effusion of lava flows; it has not had a history of major explosive eruptions like Mount St. Helens, its neighbor to the west. Timing of the most recent eruptive activity (recorded by four thin tephra layers) is on the order of 1,000 years ago; the tephras are bracketed by 2,500-year-old and 500-year-old ash layers from Mount St. Helens (Hildreth and Fierstein, 1995, 1997). Mount Adams currently shows no signs of renewed unrest.
Eruptive history does not tell us everything we need to know about hazards at Mount Adams, however, which are fully addressed in the volcano hazard assessment for Mount Adams (W.E. Scott and others, 1995). This volcano has had a long-active hydrothermal system that circulated acidic hydrothermal fluids, formed by the solution of volcanic gases in heated groundwater, through fractures and permeable zones into upper parts of the volcanic cone. Acid sulfate leaching of rocks in the summit area may still be occurring, but chemical and thermal evidence suggests that the main hydrothermal system is no longer active at Mount Adams (Nathenson and Mariner, 2013). However, these rock-weakening chemical reactions have operated long enough to change about 0.4 cubic miles (mi3) (1.7 cubic kilometers [km3]) of the hard lava rock in the volcano’s upper cone to a much weaker clay-rich rock, thus significantly reducing rock strength and thereby slope stability in parts of the cone (Finn and others, 2007). The two largest previous lahars from Mount Adams were triggered by landslides of hydrothermally altered rock from the upper southwestern flank of the cone, and any future large lahars are likely to be triggered by the same mechanism. Mount Rainier also has had extensive hydrothermal alteration of rock in its upper edifice, and it also has a history of large landslides that transform into lahars (K.M. Scott and others, 1995; Vallance and Scott, 1997; Reid and others, 2001).
The spatial depiction of modeled lahar inundation zones accompanying this report, shown in two different map perspectives, is intended to augment (not replace) the existing hazard maps for Mount Adams (W.E. Scott and others, 1995; Vallance, 1999). The maps in this report show potential areas of inundation by lahars of different initial volumes, which are determined by a computer model, LAHARZ (Iverson and others, 1998; Schilling, 1998). One map sheet presents LAHARZ-determined inundation areas on a normal plan-view shaded-relief map of the study area; the other gives an oblique perspective of the landscape with raised topography, as if one were viewing the landscape at an angle from an aircraft (Jenny and Patterson, 2007). LAHARZ was developed after the original hazard maps (based only on mapping of geologic deposits) were made. Predicted inundation zones on these maps provide an alternative approach to estimation of areas that could be inundated as lahars of different volumes pass through the valley. However, there is considerable uncertainty in the exact location of the hazard-zone boundaries shown on these maps, as well as on earlier maps.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181013","usgsCitation":"Griswold, J.P., Pierson, T.C., and Bard J.A., 2018, Modeled inundation limits of potential lahars from Mount Adams in the White Salmon River valley, Washington: U.S. Geological Survey Open-File Report 2018–1013, scale 1:75,000, 14 p., https://doi.org/10.3133/ofr20181013.","productDescription":"Sheet: 42.0 x 42.0 inches; Pamphlet: iii, 14 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078093","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":353953,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1013/ofr20181013_pamphlet.pdf","text":"Pamphlet","size":"18.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1013 Pamphlet"},{"id":353952,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2018/1013/ofr20181013_sheet_.pdf","size":"41 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1013"},{"id":353951,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1013/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Adams, While Salmon River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.8333,\n 45.682198608003404\n ],\n [\n -121.25,\n 45.682198608003404\n ],\n [\n -121.25,\n 46.25\n ],\n [\n -121.8333,\n 46.25\n ],\n [\n -121.8333,\n 45.682198608003404\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center
Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683
Bountiful fisheries, healthy and resilient wildlife, flourishing forests and vibrant grasslands are coveted resources that benefit all Americans. U.S. Geological Survey (USGS) science supports the conservation and management of the Nation’s fish and wildlife, and the landscapes they inhabit. Our biological resources—ecosystems and the wild things that live in them—are the foundation of our conservation heritage and an economic asset to current and future generations of Americans.
The USGS Ecosystems Mission Area, the biological research arm of the Department of the Interior (DOI), provides science to help America achieve sustainable management and conservation of its biological resources. This work is done within the broader mission of the USGS—to serve the Nation with science that advances understanding of our natural resources, informs land and water stewardship, and helps safeguard communities from natural and environmental hazards. The Ecosystems Mission Area provides research, technical assistance, and education conducted by Cooperative Research Units and Science Centers located in nearly every State.
The quality of life and economic strength in America hinges on healthy ecosystems that support living things and natural processes. Ecosystem science better enables society to understand how and why ecosystems change and to guide actions that can prevent damage to, and restore and sustain ecosystems. It is through this knowledge that informed decisions are made about natural resources that can enhance our Nation’s economic and environmental well-being.
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Groundwater is an important source of drinking and irrigation water throughout Idaho, and groundwater quality is monitored by various Federal, State, and local agencies. The historical, multi-agency records of groundwater quality include a valuable dataset that has yet to be compiled or analyzed on a statewide level. The purpose of this study is to combine groundwater-quality data from multiple sources into a single database, to summarize this dataset, and to perform bulk analyses to reveal spatial and temporal patterns of water quality throughout Idaho. Data were retrieved from the Water Quality Portal (https://www.waterqualitydata.us/), the Idaho Department of Environmental Quality, and the Idaho Department of Water Resources. Analyses included counting the number of times a sample location had concentrations above Maximum Contaminant Levels (MCL), performing trends tests, and calculating correlations between water-quality analytes. The water-quality database and the analysis results are available through USGS ScienceBase (https://doi.org/10.5066/F72V2FBG).
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\"}}]}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
Lahar, an Indonesian word for volcanic mudflow, is a mixture of water, mud, and volcanic rock flowing swiftly along a channel draining a volcano. Lahars can form during or after eruptions, or even during periods of inactivity. They are among the greatest threats volcanoes pose to people and property. Lahars can occur with little to no warning, and may travel great distances at high speeds, destroying or burying everything in their paths.
Lahars form in many ways. They commonly occur when eruptions melt snow and ice on snow-clad volcanoes; when rains fall on steep slopes covered with fresh volcanic ash; when crater lakes, volcano glaciers or lakes dammed by volcanic debris suddenly release water; and when volcanic landslides evolve into flowing debris. Lahars are especially likely to occur at erupting or recently active volcanoes.
Because lahars are so hazardous, U.S. Geological Survey scientists pay them close attention. They study lahar deposits and limits of inundation, model flow behavior, develop lahar-hazard maps, and work with community leaders and governmental authorities to help them understand and minimize the risks of devastating lahars.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183024","usgsCitation":"Major, J.J., Pierson, T.C., and Vallance, J.W., 2018, Lahar—River of volcanic mud and debris: U.S. Geological Survey Fact Sheet 2018–3024, 6 p., https://doi.org/10.3133/fs20183024.","productDescription":"Report: 6 p.; Video","numberOfPages":"6","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090719","costCenters":[{"id":157,"text":"Cascades Volcano Observatory","active":false,"usgs":true}],"links":[{"id":354035,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3024/fs20183024.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2018-3024"},{"id":354036,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/fs/2018/3024/fs20183024_laharvideo.mp4","text":"Video","size":"18 MB","description":"Fact Sheet 2018-3024 Video"},{"id":354034,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3024/coverthb_.jpg"}],"contact":"Volcano Science Center
Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA, 98683
Submarine groundwater fluxes across the seafloor facilitate important hydrological and biogeochemical exchanges between oceans and seabed sediment, yet few studies have investigated spatially distributed groundwater fluxes in deep‐ocean environments such as continental slopes. Heat has been previously applied as a submarine groundwater tracer using an analytical solution to a heat flow equation assuming steady state conditions and homogeneous thermal conductivity. These assumptions are often violated in shallow seabeds due to ocean bottom temperature changes or sediment property variations. Here heat tracing analysis techniques recently developed for terrestrial settings are applied in concert to examine the influences of groundwater flow, ocean temperature changes, and seabed thermal conductivity variations on deep‐ocean sediment temperature profiles. Temperature observations from the sediment and bottom ocean water on the Scotian Slope off eastern Canada are used to demonstrate how simple thermal methods for tracing groundwater can be employed if more comprehensive techniques indicate that the simplifying assumptions are valid. The spatial distribution of the inferred groundwater fluxes on the slope suggests a downward groundwater flow system with recharge occurring over the upper‐middle slope and discharge on the lower slope. We speculate that the downward groundwater flow inferred on the Scotian Slope is due to density‐driven processes arising from underlying salt domes, in contrast with upward slope systems driven by geothermal convection. Improvements in the design of future submarine hydrogeological studies are proposed for thermal data collection and groundwater flow analysis, including new equations that quantify the minimum detectable flux magnitude for a given sensor accuracy and profile length.
","language":"English","publisher":"Wiley","doi":"10.1029/2017WR022353","usgsCitation":"Kurylyk, B.L., Irvine, D.J., Mohammed, A., Bense, V.F., Briggs, M.A., Loder, J., and Geshelin, Y., 2018, Rethinking the use of seabed sediment temperature profiles to trace submarine groundwater flow: Water Resources Research, v. 54, no. 7, p. 4595-4614, https://doi.org/10.1029/2017WR022353.","productDescription":"20 p.","startPage":"4595","endPage":"4614","ipdsId":"IP-095613","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":468770,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017wr022353","text":"Publisher Index Page"},{"id":360328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Scotian Slope","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -63,\n 41\n ],\n [\n -60,\n 41\n ],\n [\n -60,\n 43\n ],\n [\n -63,\n 43\n ],\n [\n -63,\n 41\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"54","issue":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-07","publicationStatus":"PW","scienceBaseUri":"5c14cfb8e4b006c4f8545d3f","contributors":{"authors":[{"text":"Kurylyk, Barret L.","contributorId":176296,"corporation":false,"usgs":false,"family":"Kurylyk","given":"Barret","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":754277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Dylan J.","contributorId":190404,"corporation":false,"usgs":false,"family":"Irvine","given":"Dylan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":754278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mohammed, A.A.","contributorId":211492,"corporation":false,"usgs":false,"family":"Mohammed","given":"A.A.","email":"","affiliations":[{"id":16660,"text":"University of Calgary","active":true,"usgs":false}],"preferred":false,"id":754279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bense, V. F.","contributorId":211493,"corporation":false,"usgs":false,"family":"Bense","given":"V.","email":"","middleInitial":"F.","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":754280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Briggs, Martin A. 0000-0003-3206-4132 mbriggs@usgs.gov","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":4114,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin","email":"mbriggs@usgs.gov","middleInitial":"A.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":754276,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loder, J.W.","contributorId":211494,"corporation":false,"usgs":false,"family":"Loder","given":"J.W.","email":"","affiliations":[{"id":38259,"text":"Bedford Institute of Oceanography, Dartmouth, Canada","active":true,"usgs":false}],"preferred":false,"id":754281,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Geshelin, Y.","contributorId":211495,"corporation":false,"usgs":false,"family":"Geshelin","given":"Y.","email":"","affiliations":[{"id":38259,"text":"Bedford Institute of Oceanography, Dartmouth, Canada","active":true,"usgs":false}],"preferred":false,"id":754282,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196868,"text":"70196868 - 2018 - Downstream impacts of dams: shifts in benthic invertivorous fish assemblages","interactions":[],"lastModifiedDate":"2018-05-14T13:12:42","indexId":"70196868","displayToPublicDate":"2018-05-08T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":873,"text":"Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Downstream impacts of dams: shifts in benthic invertivorous fish assemblages","docAbstract":"Impoundments alter connectivity, sediment transport and water discharge in rivers and floodplains, affecting recruitment, habitat and resource availability for fish including benthic invertivorous fish, which represent an important link between primary producers and higher trophic levels in tropical aquatic ecosystems. We investigated long-term changes to water regime, water quality, and invertivorous fish assemblages pre and post impoundment in three rivers downstream of Porto Primavera Reservoir in south Brazil: Paraná, Baía and Ivinhema rivers. Impacts were distinct in the Paraná River, which is fully obstructed by the dam, less evident in the Baía River which is partially obstructed by the dam, but absent in the unimpounded Ivinhema River. Changes in water regime were reflected mainly as changes in water-level fluctuation with little effect on timing. Water transparency increased in the Paraná River post impoundment but did not change in the Baía and Ivinhema rivers. Changes in fish assemblages included a decrease in benthic invertivorous fish in the Paraná River and a shift in invertivorous fish assemblage structure in the Baía and Paraná rivers but not in the unimpounded Ivinhema River. Changes in water regime and water transparency, caused by impoundment, directly or indirectly impacted invertivorous fish assemblages. Alterations of fish assemblages following environmental changes have consequences over the entire ecosystem, including a potential decrease in the diversity of mechanisms for energy flow. We suggest that keeping existing unimpounded tributaries free of dams, engineering artificial floods, and intensive management of fish habitat within the floodplain may preserve native fish assemblages and help maintain functionality and ecosystem services in highly impounded rivers.
","language":"English","publisher":"Springer","doi":"10.1007/s00027-018-0579-y","usgsCitation":"Granzotti, R.V., Miranda, L.E., Agostinho, A.A., and Gomes, L.C., 2018, Downstream impacts of dams: shifts in benthic invertivorous fish assemblages: Aquatic Sciences, v. 80, p. 1-14, https://doi.org/10.1007/s00027-018-0579-y.","productDescription":"Article 28; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-090186","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Baía River; Ivinhema River; Paraná River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -53,\n -23\n ],\n [\n -53.66,\n -23\n ],\n [\n -53.66,\n -22.33\n ],\n [\n -53,\n -22.33\n ],\n [\n -53,\n -23\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"80","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-23","publicationStatus":"PW","scienceBaseUri":"5afee6c2e4b0da30c1bfbdd4","contributors":{"authors":[{"text":"Granzotti, Rafaela Vendrametto","contributorId":204754,"corporation":false,"usgs":false,"family":"Granzotti","given":"Rafaela","email":"","middleInitial":"Vendrametto","affiliations":[],"preferred":false,"id":734880,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734817,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Agostinho, Angelo A.","contributorId":204501,"corporation":false,"usgs":false,"family":"Agostinho","given":"Angelo","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":734881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gomes, Luiz Carlos","contributorId":88227,"corporation":false,"usgs":true,"family":"Gomes","given":"Luiz","email":"","middleInitial":"Carlos","affiliations":[],"preferred":false,"id":734882,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196872,"text":"70196872 - 2018 - Leaf to landscape responses of giant sequoia to hotter drought: An introduction and synthesis for the special section","interactions":[],"lastModifiedDate":"2018-05-08T10:10:22","indexId":"70196872","displayToPublicDate":"2018-05-08T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Leaf to landscape responses of giant sequoia to hotter drought: An introduction and synthesis for the special section","docAbstract":"Hotter droughts are becoming more common as climate change progresses, and they may already have caused instances of forest dieback on all forested continents. Learning from hotter droughts, including where on the landscape forests are more or less vulnerable to these events, is critical to help resource managers proactively prepare for the future. As part of our Leaf to Landscape Project, we measured the response of giant sequoia, the world’s largest tree species, to the extreme 2012–2016 hotter drought in California. The project integrated leaf-level physiology measurements, crown-level foliage dieback surveys, and remotely sensed canopy water content (CWC) to shed light on mechanisms and spatial patterns in drought response. Here we summarize initial findings, present a conceptual model of drought response, and discuss management implications; details are presented in the other four articles of the special section on Giant Sequoias and Drought. Giant sequoias exhibited both leaf- and canopy-level responses that were effective in protecting whole-tree hydraulic integrity for the vast majority of individual sequoias. Very few giant sequoias died during the drought compared to other mixed conifer tree species; however, the magnitude of sequoia drought response varied across the landscape. This variability was partially explained by local site characteristics, including variables related to site water balance. We found that low CWC is an indicator of recent foliage dieback, which occurs when stress levels are high enough that leaf-level adjustments alone are insufficient for giant sequoias to maintain hydraulic integrity. CWC or change in CWC may be useful indicators of drought stress that reveal patterns of vulnerability to future hotter droughts. Future work will measure recovery from the drought and strengthen our ability to interpret CWC maps. Our ultimate goal is to produce giant sequoia vulnerability maps to help target management actions, such as reducing other stressors, increasing resistance to hotter drought through prescribed fire or mechanical thinning, and planting sequoias in projected future suitable habitat, which may occur outside current grove distributions. We suggest that managers compare different types of vulnerability assessments and combine vulnerability maps with other sources of information to inform decisions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2018.03.028","usgsCitation":"Nydick, K.R., Stephenson, N.L., Ambrose, A.R., Asner, G.P., Baxter, W.L., Das, A., Dawson, T.E., Martin, R.E., and Paz-Kagan, T., 2018, Leaf to landscape responses of giant sequoia to hotter drought: An introduction and synthesis for the special section: Forest Ecology and Management, v. 419-420, p. 249-256, https://doi.org/10.1016/j.foreco.2018.03.028.","productDescription":"8 p.","startPage":"249","endPage":"256","ipdsId":"IP-091082","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468772,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2018.03.028","text":"Publisher Index Page"},{"id":353982,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","volume":"419-420","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6c2e4b0da30c1bfbdd0","contributors":{"authors":[{"text":"Nydick, Koren R.","contributorId":196601,"corporation":false,"usgs":false,"family":"Nydick","given":"Koren","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":734830,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stephenson, Nathan L. 0000-0003-0208-7229 nstephenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0208-7229","contributorId":2836,"corporation":false,"usgs":true,"family":"Stephenson","given":"Nathan","email":"nstephenson@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":734829,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ambrose, Anthony R.","contributorId":204732,"corporation":false,"usgs":false,"family":"Ambrose","given":"Anthony","email":"","middleInitial":"R.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":734831,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Asner, Gregory P.","contributorId":25393,"corporation":false,"usgs":false,"family":"Asner","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":734832,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Baxter, Wendy L.","contributorId":204733,"corporation":false,"usgs":false,"family":"Baxter","given":"Wendy","email":"","middleInitial":"L.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":734833,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Das, Adrian J. 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":3842,"corporation":false,"usgs":true,"family":"Das","given":"Adrian J.","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":734834,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dawson, Todd E.","contributorId":176594,"corporation":false,"usgs":false,"family":"Dawson","given":"Todd","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":734835,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Martin, Roberta E.","contributorId":201234,"corporation":false,"usgs":false,"family":"Martin","given":"Roberta","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":734836,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Paz-Kagan, Tarin","contributorId":196597,"corporation":false,"usgs":false,"family":"Paz-Kagan","given":"Tarin","email":"","affiliations":[],"preferred":false,"id":734837,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70206538,"text":"70206538 - 2018 - A snow density dataset for improving surface boundary conditions in Greenland ice sheet firn modeling","interactions":[],"lastModifiedDate":"2020-06-19T16:12:17.863811","indexId":"70206538","displayToPublicDate":"2018-05-07T10:04:58","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"A snow density dataset for improving surface boundary conditions in Greenland ice sheet firn modeling","docAbstract":"The surface snow density of glaciers and ice sheets is of fundamental importance in converting volume to mass in both altimetry and surface mass balance studies, yet it is often poorly constrained. Site-specific surface snow densities are typically derived from empirical relations based on temperature and wind speed. These parameterizations commonly calculate the average density of the top meter of snow, thereby systematically overestimating snow density at the actual surface. Therefore, constraining surface snow density to the top 0.1 m can improve boundary conditions in high-resolution firn-evolution modeling. We have compiled an extensive dataset of 200 point measurements of surface snow density from firn cores and snow pits on the Greenland ice sheet. We find that surface snow density within 0.1 m of the surface has an average value of 315 kg m−3 with a standard deviation of 44 kg m−3, and has an insignificant annual air temperature dependency. We demonstrate that two widely-used surface snow density parameterizations dependent on temperature systematically overestimate surface snow density over the Greenland ice sheet by 17–19%, and that using a constant density of 315 kg m−3 may give superior results when applied in surface mass budget modeling.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2018.00051","usgsCitation":"Fausto, R., Box, J.E., Baptiste Vandecrux, van As, D., Steffen, K., MacFerrin, M.J., Machguth, H., Colgan, W., Mcgrath, D., Koenig, L.S., Charalampidis, C., and Braithwaite, R.J., 2018, A snow density dataset for improving surface boundary conditions in Greenland ice sheet firn modeling: Frontiers in Earth Science, v. 6, 51, 10 p., https://doi.org/10.3389/feart.2018.00051.","productDescription":"51, 10 p.","ipdsId":"IP-082355","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":468774,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2018.00051","text":"Publisher Index 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