The U.S. Geological Survey, in cooperation with the Bernalillo County Public Works Division, conducted a 1-year study in 2015 to assess the spatial and temporal distribution of evapotranspiration (ET) and available water within the East Mountain area in Bernalillo County, New Mexico. ET and available water vary spatiotemporally because of complex interactions among environmental factors, including vegetation characteristics, soil characteristics, topography, and climate.
Precipitation data from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) (P) were used in conjunction with actual ET (ETa) data from the Operational Simplified Surface Energy Balance (SSEBop) model to estimate available water (P – ETa) at 100-meter (m) resolution in the study area. Maps, descriptive statistics, boxplots, regression analyses (continuous data), and multiple comparison tests (categorical data) were used to characterize P, ETa, and available water and their relations to topographic, soil, and vegetation datasets in the East Mountain area. Five categories of the natural land-cover type (evergreen forest, shrub, herbaceous, deciduous forest, and mixed forest) and four categories of developed land-cover type specific to residential intensity (developed open, developed low, developed medium, and developed high) were analyzed individually and in interaction with multiple elevation, tree canopy, and soil texture classes.
Annual mean P in 2015 in the East Mountain area was 608 millimeters (mm), and annual mean ETa was 543 mm (89 percent of annual P in 2015), indicating that in 2015, a spatial mean of about 65 mm of water was available for runoff, soil moisture replenishment, or groundwater recharge. Monthly ETa was greatest in July and smallest in January. The intervening months did not show smooth temporal or consistent spatial changes from month to month. Months with lower ETa (January to March, October to December) also tended to have greater available water, indicating that soil moisture (water supply) and potential ET (water demand) may have been out of phase.
Regression analyses showed that monthly ETa data had the highest correlation with annual ETa among the atmospheric, topographic, soil, or vegetation datasets, particularly during the early and late growing season (March, April, May, and September). In contrast, monthly P was highly variable and not as highly correlated with annual ETa. Among landscape variables, correlations with annual ETa were highest for tree canopy cover (coefficient of determination [R2] = 0.46). Correlations between ETa and other landscape variables were lower (R2 = 0.06–0.19): available soil water in the top 100 centimeters, soil bulk density of layer 1, slope, sand content of soil layer 1, soil depth, available soil water in the top 25 centimeters, leaf area index, aspect eastness, and elevation. Evergreen forest areas had the highest annual median ETa, followed by mixed forest, open residential areas, and deciduous forest. Available water typically was higher in landcover types with lower ETa: herbaceous cover, followed by deciduous forest, high-intensity developed areas, and shrub. Deciduous forest had the second highest median available water, despite having the fourth highest ETa, because deciduous forest had greater P than most other areas. Annual median ETa typically was greatest in the second highest elevation band (2,401–2,800 m above the North American Vertical Datum of 1988 [NAVD 88]), and lower in the highest elevation band (2,801–3,254 m above NAVD 88), despite having greater P, likely because of decreased tree canopy cover or a shift from evergreen to deciduous trees at the highest elevations.
Annual median ETa increased with tree canopy cover, regardless of landcover type. ETa correlation was higher with tree canopy than with leaf area index or normalized difference vegetation index. This result indicates that it is important to include the thermal band (from satellite multispectral data) in vegetation indices used to describe ETa, perhaps to account for the influence of energy limitation or water limitation on ET. Of all natural landcover types, finer soils had the most available water, whereas coarser soils had the least available water. Relations of soil type with P – ETa were different than with ETa, indicating ET and available water have a complex response to differences in soil type. Further modeling would be useful in determining soils’ infiltration, storage, conductivity, and plant-water availability relations to individual storms for each position in the landscape, as well as the corresponding effects of these processes on ET and available water.
The best multivariate linear model for annual ETa had an R2 value of 0.62. Monthly ETa models had R2 values between 0.16 and 0.65. Models usually, but not always, performed best during the growing season. These results indicate that even the best multivariate linear models cannot explain a notable amount of the variability in ET. The monthly ETa models with the highest correlations (August and September) followed a July having almost twice the mean precipitation for July (1981–2010), which indicates that a soil-moisture variable is needed to more accurately model monthly ETa. Further study is needed to better characterize this system, the variables that affect ET and available water, and the partitioning of available water into runoff, soil moisture storage, and groundwater recharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205095","collaboration":"Prepared in cooperation with the Bernalillo County Public Works Division","usgsCitation":"Douglas-Mankin, K.R., McCutcheon, R.J., Mitchell, A.C., and Senay, G.B., 2020, Landscape and climatic influences on actual evapotranspiration and available water using the Operational Simplified Surface Energy Balance (SSEBop) Model in eastern Bernalillo County, New Mexico, 2015: U.S. Geological Survey Scientific Investigations Report 2020–5095, 40 p., https://doi.org/10.3133/sir20205095.","productDescription":"x, 40 p.","numberOfPages":"53","onlineOnly":"Y","ipdsId":"IP-101269","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":380594,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5095/sir20205095.pdf","text":"Report","size":"3.90 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5095"},{"id":380593,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5095/coverthb.jpg"}],"country":"United States","state":"New Mexico","county":"Bernalillo County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.65252685546875,\n 34.879171662167664\n ],\n [\n -105.88623046874999,\n 34.879171662167664\n ],\n [\n -105.88623046874999,\n 35.35545618392078\n ],\n [\n -106.65252685546875,\n 35.35545618392078\n ],\n [\n -106.65252685546875,\n 34.879171662167664\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
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6700 Edith Blvd. NE
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Biologists and policy-makers have the difficult task of allocating limited resources to habitat conservation and management for endangered species in the face of changing environmental conditions. Satellite remote sensing can inform conservation because it is an efficient means to obtain environmental data over broad spatial and temporal extents. Yet, the challenges of accessing, processing, and analyzing remote sensing data hinder wider application of these techniques in conservation planning. We used Landsat data and hierarchical statistical models to link satellite-derived habitat measurements with abundance of endangered Yuma Ridgway's rails (Rallus obsoletus yumanensis) within the Lower Colorado River Basin and Salton Sink, USA. We addressed many of the challenges facing the application of remote sensing techniques by using the web-based, freely-available Google Earth Engine to process Landsat datasets, apply habitat models, and generate maps to predict habitat suitability at a fine spatial grain (30 m) across the range of the species. These maps are shareable, interactive, and easy to update annually as habitat conditions change using a Google Earth Engine App we developed. Thus, we provide a framework for building habitat suitability models and maps to help target adaptive habitat management over broad extents for sensitive species, enabling biologists to improve conservation and restoration efforts regularly as conditions change in highly variable ecosystems. We demonstrate this approach for Yuma Ridgway's rails, but our methods for merging hierarchical statistical models with open-source mapping software to describe spatial-temporal heterogeneity in habitat quality are applicable to any species, and are especially helpful to species inhabiting highly variable ecosystems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2020.108734","usgsCitation":"Harrity, E.J., Stevens, B., and Conway, C.J., 2020, Keeping up with the times: Mapping range-wide habitat suitability for endangered species in a changing environment: Biological Conservation, v. 250, 108734,10 p., https://doi.org/10.1016/j.biocon.2020.108734.","productDescription":"108734,10 p.","ipdsId":"IP-116214","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":395854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, California, Nevada","otherGeospatial":"Colorado River Basin, Salton Sink","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.840087890625,\n 32.52828936482526\n ],\n [\n -114.796142578125,\n 32.44024912337551\n ],\n [\n -114.08752441406249,\n 32.697177359290635\n ],\n [\n -114.246826171875,\n 33.55055114384406\n ],\n [\n -114.29077148437499,\n 33.8247936182649\n ],\n [\n -113.873291015625,\n 34.31621838080741\n ],\n [\n -114.356689453125,\n 34.88593094075317\n ],\n [\n -114.63134765625001,\n 34.858890491257796\n ],\n [\n -115.916748046875,\n 33.128351191631566\n ],\n [\n -114.840087890625,\n 32.52828936482526\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"250","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Harrity, Eamon J.","contributorId":275852,"corporation":false,"usgs":false,"family":"Harrity","given":"Eamon","email":"","middleInitial":"J.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":834366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Bryan S.","contributorId":275853,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan S.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":834367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834365,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215547,"text":"70215547 - 2020 - Application of the RSPARROW modeling tool to estimate total nitrogen sources to streams and evaluate source reduction management scenarios in the Grande River Basin, Brazil","interactions":[],"lastModifiedDate":"2020-10-22T14:32:56.742491","indexId":"70215547","displayToPublicDate":"2020-10-18T09:24:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Application of the RSPARROW modeling tool to estimate total nitrogen sources to streams and evaluate source reduction management scenarios in the Grande River Basin, Brazil","docAbstract":"Large-domain hydrological models are increasingly needed to support water-resource assessment and management in large river basins. Here, we describe results for the first Brazilian application of the SPAtially Referenced Regression On Watershed attributes (SPARROW) model using a new open-source modeling and interactive decision support system tool (RSPARROW) to quantify the origin, flux, and fate of total nitrogen (TN) in two sub-basins of the Grande River Basin (GRB; 43,000 km2). Land under cultivation for sugar cane, urban land, and point source inputs from wastewater treatment plants was estimated to each contribute approximately 30% of the TN load at the outlet, with pasture land contributing about 10% of the load. Hypothetical assessments of wastewater treatment plant upgrades and the building of new facilities that could treat currently untreated urban runoff suggest that these management actions could potentially reduce loading at the outlet by as much as 20–25%. This study highlights the ability of SPARROW and the RSPARROW mapping tool to assist with the development and evaluation of management actions aimed at reducing nutrient pollution and eutrophication. The freely available RSPARROW modeling tool provides new opportunities to improve understanding of the sources, delivery, and transport of water-quality contaminants in watersheds throughout the world.
","language":"English","publisher":"MDPI","doi":"10.3390/w12102911","usgsCitation":"Miller, M., de Souza, M.L., Alexander, R.B., Gorman Sanisaca, L.E., de Amorim Teixeira, A., and Appling, A.P., 2020, Application of the RSPARROW modeling tool to estimate total nitrogen sources to streams and evaluate source reduction management scenarios in the Grande River Basin, Brazil: Water, v. 12, no. 10, 2911, 20 p., https://doi.org/10.3390/w12102911.","productDescription":"2911, 20 p.","ipdsId":"IP-122604","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":455023,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12102911","text":"Publisher Index Page"},{"id":436752,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FZV0Z0","text":"USGS data release","linkHelpText":"RSPARROW Model Archive Files for the Grande River Basin TN SPARROW Model"},{"id":379649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Grande River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -50.95458984374999,\n -20.324023603422507\n ],\n [\n -49.32861328125,\n -21.46329344189928\n ],\n [\n -48.284912109375,\n -22.451648819126202\n ],\n [\n -46.73583984375,\n -23.29181053244191\n ],\n [\n -45.37353515625,\n -22.61401087437028\n ],\n [\n -44.05517578124999,\n -21.881889807629257\n ],\n [\n -43.5498046875,\n -21.125497636606266\n ],\n [\n -45.736083984375,\n -20.33432561683554\n ],\n [\n -46.35131835937499,\n -20.478481600090554\n ],\n [\n -46.966552734375,\n -20.014645445341355\n ],\n [\n -47.647705078125,\n -19.797717490704724\n ],\n [\n -48.944091796875,\n -19.9526963975442\n ],\n [\n -49.32861328125,\n -19.652934210612436\n ],\n [\n -50.28442382812499,\n -19.425153718960143\n ],\n [\n -50.86669921875,\n -19.756364230752375\n ],\n [\n -50.95458984374999,\n -20.324023603422507\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Souza, Marcelo L","contributorId":243598,"corporation":false,"usgs":false,"family":"de Souza","given":"Marcelo","email":"","middleInitial":"L","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":802666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Richard B 0000-0001-9166-0626","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":243599,"corporation":false,"usgs":false,"family":"Alexander","given":"Richard","email":"","middleInitial":"B","affiliations":[{"id":38108,"text":"NA","active":true,"usgs":false}],"preferred":false,"id":802667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gorman Sanisaca, Lillian E. 0000-0003-1711-3864","orcid":"https://orcid.org/0000-0003-1711-3864","contributorId":210381,"corporation":false,"usgs":true,"family":"Gorman Sanisaca","given":"Lillian","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"de Amorim Teixeira, Alexandre","contributorId":243600,"corporation":false,"usgs":false,"family":"de Amorim Teixeira","given":"Alexandre","email":"","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":802669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":802670,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70213232,"text":"70213232 - 2020 - How and why is the timing and occurrence of seasonal migrants in the Gulf of Maine changing due to climate?","interactions":[],"lastModifiedDate":"2020-12-14T17:38:21.907848","indexId":"70213232","displayToPublicDate":"2020-09-16T11:33:53","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":7468,"text":"Final Report","active":true,"publicationSubtype":{"id":9}},"title":"How and why is the timing and occurrence of seasonal migrants in the Gulf of Maine changing due to climate?","docAbstract":"Plants and animals undergo certain recurring life-cycle events, such as migrations between summer and winter habitats or the annual blooming of plants. Known as phenology, the timing of these events is very sensitive to changes in climate (and changes in one species’ phenology can impact entire food webs and ecosystems). Shifts in phenology have been described as a “fingerprint” of the temporal and spatial responses of wildlife to climate change impacts. Thus, phenology provides one of the strongest indicators of the adaptive capacity of organisms (or the ability of organisms to cope with future environmental conditions).
In this study, researchers are exploring how the timing and occurrence of a number of highly migratory marine animals is changing due to a series of climatic and ecological shifts. First, using existing long-term historical data series, they will determine the direction and magnitude of how migration, abundance, or other phenological factors have changed for marine mammals, sea turtles, and fishes that migrate into the Gulf of Maine on a seasonal basis. Because marine animals are inherently difficult to detect, the team will apply dynamic occupancy models to evaluate seasonal migration patterns and habitat use across multiple habitats in the Gulf of Maine region. The project team will also synthesize regional information on a key, ecologically-important prey fish, sandlance, whose timing and abundance is a strong predictor of the occurrence and behavior of predator species targeted in this study as well as a range of other regional fish and wildlife of conservation and management concern. Results from this component of the project will identify coastal fish and wildlife species that are relatively more or less able to adapt and thus potentially vulnerable to climate change; determine the likely primary drivers of those changes; and identify data gaps and future monitoring needs. Ultimately, this information will be available and useful for regional coastal management and adaptation decisions that will allow managers to effectively plan for the future.
In a second component of the project, researchers will focus specifically on changes in migration patterns of the endangered North Atlantic right whale. While shifts in the distribution and time of recurring life events are adaptive responses that may help species cope with climate impacts, they can also lead to changes in how species interact with humans. The North Atlantic right whale is one of the most endangered whale species on the planet. In the North Atlantic Ocean, ship strikes and entanglements with commercial fishing gear represent fatal threats to right whales. Recent reports suggest that North Atlantic right whale migration patterns have changed. Many researchers posit that shifts in migration are responsible for recent increases in the overlap between right whales and human activities, especially fishing. To help understand how changes in right whale movements and behaviors may overlap with ship traffic, and thus put the animals at risk of encountering vessels, we will combine right whale habitat models with ship traffic maps. The end result will be a set of maps identifying risk levels.
Drone use in wildlife biology has greatly increased as they become cheaper and easier to deploy in the field. In this paper we describe a less invasive method of using drones and exploring their limitations for studying colonial nesting waterbirds. Western Grebes, like most colonial nesting waterbirds, can be very sensitive to human interaction. Using a 3DR Solo quad copter equipped with a high-resolution digital camera we were able to effectively map and monitor a Western Grebe breeding colony throughout the nesting period with a series of 6 flights. We were able to use drone collected aerial imagery to model nest survival while minimizing disturbance to the birds. However, we were not able to deploy the drone at all of our study sites. Our ability to effectively deploy the drone was hindered by the environmental and vegetation characteristics of a site. Drone technology can be a useful tool, especially when studying a species sensitive to human interaction. However, there researchers should carefully consider their species and study site to evaluate if a drone is the proper tool to meet their objectives.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-020-09743-y","usgsCitation":"Lachman, D., Conway, C.J., Vierling, K., and Matthews, T., 2020, Drones provide a better method to find nests and estimate nest survival for colonial waterbirds: A demonstration with Western Grebes: Wetlands Ecology and Management, v. 28, p. 837-845, https://doi.org/10.1007/s11273-020-09743-y.","productDescription":"9 p.","startPage":"837","endPage":"845","ipdsId":"IP-119243","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396449,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","county":"Valley County","otherGeospatial":"Cascade Reservoir, Deer Flat National Wildlife Refuge, Lake 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Deo","contributorId":280030,"corporation":false,"usgs":false,"family":"Lachman","given":"Deo","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":835914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":835913,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vierling, Kerri","contributorId":280031,"corporation":false,"usgs":false,"family":"Vierling","given":"Kerri","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":835915,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Matthews, Ty","contributorId":280032,"corporation":false,"usgs":false,"family":"Matthews","given":"Ty","affiliations":[{"id":37461,"text":"fws","active":true,"usgs":false}],"preferred":false,"id":835916,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70217208,"text":"70217208 - 2020 - Hydrothermal alteration on composite volcanoes: Mineralogy, hyperspectral imaging and aeromagnetic study of Mt Ruapehu, New Zealand","interactions":[],"lastModifiedDate":"2021-01-12T12:51:59.427134","indexId":"70217208","displayToPublicDate":"2020-08-24T06:45:24","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Hydrothermal alteration on composite volcanoes: Mineralogy, hyperspectral imaging and aeromagnetic study of Mt Ruapehu, New Zealand","docAbstract":"Prolonged volcanic activity can induce surface weathering and hydrothermal alteration that is a primary control on edifice instability, posing a complex hazard with its challenges to accurately forecast and mitigate. This study uses a frequently active composite volcano, Mt Ruapehu, New Zealand, to develop a conceptual model of surface weathering and hydrothermal alteration applicable to long‐lived composite volcanoes. The alteration on Mt Ruapehu was classified using ground samples as non‐altered, supergene argillic, intermediate argillic, and advanced argillic. The first two classes have a paragenesis that is consistent with surficial infiltration and circulation of low‐temperature (<40°C) neutral to mildly acidic fluids, inducing chemical weathering and formation of weathering rims on rock surfaces. The intermediate and advanced argillic alteration formed from hotter (≥100°C) hydrothermal fluids with lower pH, interacting with the andesitic to dacitic host rocks. The distribution of weathering and hydrothermal alteration has been mapped with airborne hyperspectral imaging through image classification, while aeromagnetic data inversion was used to map alteration to up to 500‐m depth. The joint use of hyperspectral imaging complements the geophysical methods since it can spectrally identify hydrothermal alteration mineralogy. This study established a conceptual model of hydrothermal alteration history of Mt Ruapehu, exemplifying a long‐lived and nested active and ancient hydrothermal system. This study's combination approach can be used to indicate the most likely sources of future debris avalanches, which are a significant hazard on Ruapehu.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GC009270","usgsCitation":"Kereszturi, G., Schaefer, L.N., Miller, C.A., and Mead, S., 2020, Hydrothermal alteration on composite volcanoes: Mineralogy, hyperspectral imaging and aeromagnetic study of Mt Ruapehu, New Zealand: Geochemistry, Geophysics, Geosystems, v. 21, no. 9, e2020GC009270, 28 p., https://doi.org/10.1029/2020GC009270.","productDescription":"e2020GC009270, 28 p.","ipdsId":"IP-121751","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":455555,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/7f0a71d89f2449cb949ef5b223d16534","text":"External Repository"},{"id":382079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","otherGeospatial":"Mt Ruapehu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 175.166015625,\n -40.71395582628604\n ],\n [\n 176.57226562500003,\n -40.71395582628604\n ],\n [\n 176.57226562500003,\n -36.738884124394296\n ],\n [\n 175.166015625,\n -36.738884124394296\n ],\n [\n 175.166015625,\n -40.71395582628604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-09-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Kereszturi, Gabor 0000-0003-4336-2012","orcid":"https://orcid.org/0000-0003-4336-2012","contributorId":247601,"corporation":false,"usgs":false,"family":"Kereszturi","given":"Gabor","email":"","affiliations":[{"id":49587,"text":"Volcanic Risk Solutions, Massey University, Palmerston North, 4474, New Zealand","active":true,"usgs":false}],"preferred":false,"id":808007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaefer, Lauren N. 0000-0003-3216-7983","orcid":"https://orcid.org/0000-0003-3216-7983","contributorId":241997,"corporation":false,"usgs":true,"family":"Schaefer","given":"Lauren","email":"","middleInitial":"N.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":808008,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Craig A. 0000-0001-8499-0352","orcid":"https://orcid.org/0000-0001-8499-0352","contributorId":219638,"corporation":false,"usgs":false,"family":"Miller","given":"Craig","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":808009,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mead, Stuart","contributorId":247602,"corporation":false,"usgs":false,"family":"Mead","given":"Stuart","email":"","affiliations":[{"id":49587,"text":"Volcanic Risk Solutions, Massey University, Palmerston North, 4474, New Zealand","active":true,"usgs":false}],"preferred":false,"id":808010,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211561,"text":"ofr20201063 - 2020 - Fate and behavior tools related to inland spill response—Workshop on the U.S. Geological Survey’s role in Federal science support","interactions":[],"lastModifiedDate":"2020-08-04T20:27:40.323412","indexId":"ofr20201063","displayToPublicDate":"2020-08-04T09:16:40","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1063","displayTitle":"Fate and Behavior Tools Related to Inland Spill Response—Workshop on the U.S. Geological Survey’s Role in Federal Science Support","title":"Fate and behavior tools related to inland spill response—Workshop on the U.S. Geological Survey’s role in Federal science support","docAbstract":"There is a growing body of tools available for science support for determining the fate and behavior of industrial and agricultural chemicals that are rapidly injected (“spilled”) into aquatic environments. A 2-day roundtable-style workshop was held by the U.S. Geological Survey (USGS) in Middleton, Wisconsin, in December 2017 to describe and explore existing Federal science support for spill fate and behavior tools used for inland spills, ongoing and new fate and behavior studies, and science gaps in planning and response tools as part of the USGS Midcontinent Region’s efforts to include spill response as part of its strategic plans. A total of 28 attendees representing a variety of Federal, State, and regional entities presented on programs and tools used in various aspects of spill response. Most programs and tools discussed were for spills in riverine environments but tools and applications for spills in lakes, on land surfaces, in urban storm sewer networks, and groundwater also were discussed. A primary workshop focus was to facilitate communication and increase potential for future collaboration among agencies for inland spill science support. The role and need for more USGS science support within the inland spill community was discussed. Enhanced communication is needed within the USGS and the U.S. Department of the Interior science programs, as well as within and among other agencies that do emergency planning and response. A main conclusion of the workshop was that there are untapped resources of the USGS outlined in the agency’s science strategy that could strengthen science support for fate and behavior tools in inland areas, especially in the Upper Mississippi River, Ohio River, and Great Lakes Basins where large freshwater resources overlap with dense corridors of oil and hazardous substances, with transportation networks, and with large populations centers.
Fate and behavior tools are being developed quickly for inland spill response by multiple Federal agencies in partnership with local and regional entities. Applicability of these tools ranges from planning and preparedness, to the early stages of spill response for protection of human life and property, and to the application of monitoring and models to assess the long-term consequences of spills. Key findings from the workshop, with an emphasis on potential further development of USGS science support, include the following:
•The national and regional response to spills occurs within an established system that must be respected by all parties involved in spill response. The USGS’s role is to support spill responders who are physically working at a spill scene, deploying booms and using other efforts to contain and recover spilled materials.
•The USGS has tools that have been used throughout spill response operations, from early response to recovery and restoration. Developing a more formal role for the USGS to participate in science support for inland spills on a consistent basis is a desired outcome. This will require the USGS to improve internal and external communication and would be best accomplished by assigning one or more coordinator positions within the agency to plan and oversee USGS spill-response efforts. More involvement of the USGS on National and Regional Response Teams, especially in the realm of the Science and Technology Subcommittees, will gofar in increasing external communication and integration of fate and behavior tools.
•Rapid response to spills requires modeling and mapping of plumes and associated time-of-travel estimation for a range of stream sizes across the United States. Many existing models use USGS streamgage data and the USGS National Hydrography Dataset. Nearly all existing models would benefit from updated linkages to USGS StreamStats and its soon-to-be released time-of-travel estimates,real-time velocity, stream morphology, and slope data. Integrating USGS tools with those from other agencies could be done to better serve the larger spill response community.
• A problem is that existing models to rapidly predict plume extent, as well as more followup/longer-term fate and transport models, can be unknown or unavailable to spill responders. Thus, creating and strengthening linkages among USGS scientists skilled at using these tools is needed to support spill response with the on-scene responders.
• Research for inland spill fate and behavior done outside of an immediate spill response can assist with spill planning and preparedness by (1) revealing sites likely to experience spills in the future (high-risk sites) and (2) understanding how a spilled substance might behave under a range of environmental conditions. However, USGS research on this topic has been scarce and subject to funding availability. Examples include the 2010 Line 6B Spill release into the Kalamazoo River in Michigan, where the USGS provided science support for a variety of fate and behavior tools for stream and impoundment environments. A long-term research site in Bemidji, Minnesota, provides important insights into transformations and longevity of spilled oil in groundwater and groundwater-surface water interactions.
• Linking stream models to other components of this inland environment, including groundwater, overland flow, and karst, is needed. Stream network data can be linked to underground conduits such as storm sewers and karst groundwater systems. Stream models can also be linked with geospatial data such as that contained in U.S. Environmental Protection Agency’s
interactive mapping tools.
• The USGS is uniquely qualified to collect water-quality data during spills in the United States because of its many geographically dispersed water science centers, its knowledge and preparedness for flood measurement and documentation, and its cadre of skilled water-quality employees. Rapid-deployment gages, used for floods, could also be used for spills if they included spill-specific sensors. Coordinated expertise at USGS water and environmental science centers can be used for monitoring spill effects and for assessing risk to water quality and ecological communities.
• Scientists at the USGS have proven capable of providing science coordination and technical assistance within the Incident Command Structure at the request of the lead on-scene coordinator. This external coordination, as well as internal communication within USGS Water, Hazards, and Ecosystems Mission Areas, could be improved by establishing and naming a USGS spills coordinator. Scott Morlock, Jo Ellen Hinck, and Faith Fitzpatrick are currently (2017) serving in informal coordination roles in addition to their traditional duties.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201063","usgsCitation":"Sullivan, D.J., and Fitzpatrick, F.A., 2020, Fate and behavior tools related to inland spill response—Workshop on the U.S. Geological Survey’s role in Federal science support: U.S. Geological Survey Open-File Report 2020–1063, 22 p., https://doi.org/10.3133/ofr20201063.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-111089","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1063/ofr20201063.pdf","text":"Report","size":"8.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1063"},{"id":376919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1063/coverthb.jpg"}],"contact":"Director, Upper Midwest Water Science Center
U.S Geological Survey
8505 Research Way
Middleton, WI 53562
A disease can be a source of disturbance, causing population declines or extirpations, altering species interactions, and affecting habitat structure. This is particularly relevant for diseases that affect keystone species or ecosystem engineers, leading to potentially cascading effects on ecosystems.
We investigated the invasion of a non-native disease, plague, to a keystone species, prairie dogs, and documented the resulting extent of fragmentation and habitat loss in western grasslands. Specifically, we assessed how the arrival of plague in the Conata Basin, South Dakota, United States, affected the size, shape, and aggregation of prairie dog colonies, an animal species known to be highly susceptible to plague.
Colonies in the prairie dog complex were mapped every 1 to 3 years from 1993 to 2015. Plague was first confirmed in 2008 and we compared prairie dog complex and colony characteristics before and after the arrival of plague.
As expected the colony complex and the patches in colonies became smaller and more fragmented after the arrival of plague; the total area of each colony and the mean area per patch within a colony decreased, the number of patches per colony increased, and mean contiguity of each patch decreased, leading to habitat fragmentation.
We demonstrate how an emerging infectious disease can act as a source of disturbance to natural systems and lead to potentially permanent alteration of habitat characteristics. While perhaps not traditionally thought of as a source of ecosystem disturbances, in recent years emerging infectious diseases have shown to be able to have large effects on ecosystems if they affect keystone species.
Due to advances in EDS technology, electron microscopy techniques have become an important tool to determine the relative abundance of mineral phases. However, few studies have directly compared EDS X‐ray mineralogy with traditional techniques for assessing bulk mineralogy and elemental composition. We show that analysing a limited area (~ 0.5–3.2 mm2) of fine‐grained metal extraction samples using EDS X‐ray principal component analysis phase mapping yields results that agree within 10% with more traditional techniques for mineral phases present at greater than 5% m/m. Electron beam sensitive minerals, such as the carbonates, have poor correlations between EDS and X‐ray Diffraction (XRD) and/or WD‐XRF. Likewise, poor correlations between methods can be expected for particles that are smaller than the interaction volume of the electron beam (~ 1.5 µm); this strongly affected the phyllosilicates. One strength of EDS phase mapping is that it can identify phases present below the detection limit of powder XRD (< 1%). Our results demonstrate that EDS phase mapping is sufficient to estimate bulk sample mineralogy. If polished thin sections have been prepared, this approach may save time and/or money relative to the more traditional approaches of preparing separate subsamples for XRD and/or WD‐XRF.
Bogoslof volcano is a shallow submarine/subaerial volcano in the southern Bering Sea about 100 km west of the community of Dutch Harbor, Alaska. The subaerial parts of the volcano consist of two small islands, Bogoslof Island and Fire Island, that together have a total area of about 1.6 km2. Bogoslof was first depicted on a Russian map in 1772 and since then has been observed and visited occasionally. The volcano has had at least nine periods of eruptive activity since 1796 and all of its historical eruptions have been similar in style. Historical Bogoslof eruptions involved the effusion of basalt, trachybasalt, basaltic trachyandesite, and trachyandesite lava domes with above sea level relief of 100–200 m. Many of the eruptions are accompanied by the formation of tuff rings and ejection of ballistic particles. Historical observations suggest that eruption clouds are relatively ash-poor. Minor ash fallout has typically occurred within about 100 km of the volcano. Many of the historical eruptions began at vents that were below sea level, and thus, seawater has played an important role in the style of eruptive activity exhibited by the volcano. At times, eruptive activity has been characterized by Surtseyan style eruptions and magma interaction with wet vent-fill deposits. At other times, the eruptive style has been more magmatically driven and has resulted in the formation of pyroclastic flows and small ash clouds. Preliminary studies of the deposits produced during the 2016–2017 eruption indicate vertical sequences of coarse-grained, horizontally bedded pyroclastic flow and fall deposits with numerous blocks, bombs, and lapilli of dense juvenile and accidental lithic material. These deposits were emplaced by near-vent pyroclastic flows, surges, and explosions some of which originated from shallow, highly crystalline cryptodomes.
Karst is a landscape formed from the dissolution of soluble rock or rock containing minerals that are easily dissolved from within the rock. The landscape is characterized by sinkholes, caves, losing streams, springs, and underground drainage systems, which rapidly move water through the karst. The two forms of karst in New York State include carbonate karst, which forms in carbonate rock (limestone, marble, and dolostone), and evaporite karst, which forms in rock that contains the evaporite minerals gypsum and halite.
Past and recent studies of karst across the State have shown that areas of focused recharge in karstic carbonate rock allow contaminants to enter aquifer systems with little attenuation. Focused areas of recharge need to be identified to help prevent such contamination from sources on or adjacent to the karst. The New York State Departments of Environmental Conservation and Health are collaborating with the agricultural community to make farmers and farm-planning advisors more aware of karst and how to manage daily farming activities to reduce their impact on surface water and groundwater resources, especially in karst areas. There is also a need to make regulators, planners, and the general public aware of New York’s karst resources and to properly protect and manage these resources to protect the quality of groundwater and surface water that can flow into, through, and from karst bedrock.
Using publicly available geospatial data, karst bedrock and closed depressions over or near karst rock were identified across New York. Carbonate, evaporite, and marble geologic units were selected from a statewide 1:250,000-scale bedrock geology dataset. The selected geologic units were intersected with 7.5-minute quadrangle maps to define the study area.
The U.S. Geological Survey has compiled an inventory of closed depressions from statewide digital contour data, scanned 7.5-minute topographic maps known as a digital raster graphics, and light detection and ranging (lidar) digital elevation models. Analysis of the data resulted in the identification of 5,023 closed depressions statewide. The inventory was conducted to eliminate duplication of results from analysis of the three data sources. A series of overlay analyses was conducted using the closed depressions and thematic data known to be key factors in determining the probability of a closed depression contributing to focused groundwater recharge; the thematic data include bedrock geology, soil type, soil infiltration rate, and land cover.
Though the extent of karst development is important in understanding the interaction between surface water and groundwater in karst terrains, some of the worst cases of groundwater contamination in karst can occur where only minor karst features might be present. The presence of karst—be it a short section of a solutioned fracture or an extensive cave system—requires careful consideration, forward-looking environmental planning, and consistent water-quality protection to preserve New York State’s water resources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205030","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Kappel, W.M., Reddy, J.E., and Root, J.C., 2020, Statewide assessment of karst aquifers in New York with an inventory of closed-depression and focused-recharge features: U.S. Geological Survey Scientific Investigations Report 2020–5030, 74 p., https://doi.org/10.3133/sir20205030.","productDescription":"Report: viii, 74 p.","numberOfPages":"74","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-090019","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":375401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5030/coverthb.jpg"},{"id":375404,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HGN5IJ","text":"USGS data release","linkHelpText":"Data for statewide assessment of New York’s karst aquifers with an inventory of closed-depression and focused-recharge features"},{"id":375534,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5030/sir20205030.pdf","text":"Report","size":"19.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5030"},{"id":375482,"rank":2,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5030/sir20205030_table1.pdf","text":"Table 1","size":"140 KB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Stratigraphic column of New York State bedrock indicating those units in which karst features might be present"}],"country":"United States","state":"New 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York\",\"nation\":\"USA \"}}]}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Temperature has a fundamental influence on physical, chemical and biological processing in aquatic ecosystems. River temperatures respond to a diverse array of drivers including air temperature, streamflow, and thermal inputs, but the physical template has been shown to play a significant role in structuring spatial and temporal variation in water temperature. How these factors interact to affect water temperature in complex floodplain river habitats such as those present in the Upper Mississippi River System (UMRS) is not well-studied. We used a combination of airborne thermal imagery and continuous temperature loggers deployed across aquatic area types to evaluate spatial and temporal patterns in water temperature in Navigation Pool 8 during the summer and fall of 2017. The mid-wave infrared thermal camera available for this study is not commonly used for thermal imagery acquisition over water, so we discuss accommodations that were made to account for potential interferences and describe considerations for future users interested in using the technology. We quantified thermal metrics from imagery and continuous loggers (e.g., mean, coefficient of variation, range) and compared those to hydrogeomorphic variability across aquatic areas using a Geographic Information System (GIS) dataset. Our findings showed that both temporal and spatial temperature patterns were linked to variation in depth and connectivity of aquatic areas across the pool. Despite some of the technical challenges associated with acquiring this imagery, the method shows promise for characterizing spatial variation in surface temperatures in the UMRS associated with complex physical features such as habitat rehabilitation and enhancement projects.
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Ultimately, these insights can lead to groups of local stakeholders taking action to build their resilience to a changing climate. Using CE, decision-makers can visualize decade-by-decade changes in climate conditions in their county and the magnitude of changes projected for the end of this century under two plausible emissions pathways. They can also check how projected changes relate to user-defined thresholds that represent points at which valued assets may become stressed, damaged, or destroyed. By providing easy access to authoritative information in an elegant interface, the Climate Explorer can help communities recognize—and prepare to avoid or respond to—emerging climate hazards. Another important step in the evolution of CE builds on the purposeful alignment of the CRT with the U.S. Global Change Research Program’s (USGCRP) National Climate Assessment (NCA). By closely linking these two authoritative resources, we envision that users can easily transition from static maps and graphs within NCA reports to dynamic, interactive versions of the same data within CE and other resources within the CRT, which they can explore at higher spatial scales or customize for their own purposes. The provision of consistent climate data and information—a result of collaboration among USGCRP’s federal agencies—will assist decision-making by other governmental entities, nongovernmental organizations, businesses, and individuals.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-18-0298.1","collaboration":"","usgsCitation":"Lipschultz, F., Herring, D., Ray, A.J., Alder, J.R., Dahlman, L., DeGaetano, A., Fox, J.F., Gardiner, E., Herring, J., Hicks, J., Melton, F., Morefield, P.E., and Sweet, W., 2020, Climate explorer: Improved access to local climate projections, v. 101, no. 3, p. e265-e273, https://doi.org/10.1175/BAMS-D-18-0298.1.","productDescription":"9 p.","startPage":"e265","endPage":"e273","ipdsId":"IP-091613","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457311,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Cornell University, Ithaca, NY","active":true,"usgs":false}],"preferred":false,"id":786617,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fox, James F.","contributorId":223922,"corporation":false,"usgs":false,"family":"Fox","given":"James","email":"","middleInitial":"F.","affiliations":[{"id":40794,"text":"National Environmental Modeling and Analysis Center, University of North Carolina","active":true,"usgs":false}],"preferred":false,"id":786624,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gardiner, Edward","contributorId":223916,"corporation":false,"usgs":false,"family":"Gardiner","given":"Edward","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":786618,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Herring, Jamie","contributorId":223917,"corporation":false,"usgs":false,"family":"Herring","given":"Jamie","affiliations":[{"id":40792,"text":"Habitat Seven","active":true,"usgs":false}],"preferred":false,"id":786619,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hicks, Jeff","contributorId":223918,"corporation":false,"usgs":false,"family":"Hicks","given":"Jeff","email":"","affiliations":[{"id":40793,"text":"Fernleaf Interactive","active":true,"usgs":false}],"preferred":false,"id":786620,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Melton, Forrest","contributorId":223919,"corporation":false,"usgs":false,"family":"Melton","given":"Forrest","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":786621,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Morefield, Philip E.","contributorId":223920,"corporation":false,"usgs":false,"family":"Morefield","given":"Philip","email":"","middleInitial":"E.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":786622,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sweet, William ","contributorId":223921,"corporation":false,"usgs":false,"family":"Sweet","given":"William ","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":786623,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70209093,"text":"70209093 - 2020 - Mapping metabolic activity at single cell resolution in intact volcanic fumarole soil","interactions":[],"lastModifiedDate":"2020-03-16T06:49:17","indexId":"70209093","displayToPublicDate":"2020-02-14T06:46:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1620,"text":"FEMS Microbiology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Mapping metabolic activity at single cell resolution in intact volcanic fumarole soil","docAbstract":"Interactions among microorganisms and their mineralogical substrates govern the structure, function, and emergent properties of microbial communities. These interactions are predicated on spatial relationships, which dictate metabolite exchange and access to key substrates. To quantitatively assess links between spatial relationships and metabolic activity, this study presents a novel approach to map all organisms, the metabolically active subset, and associated mineral grains, all while maintaining spatial integrity of an environmental microbiome. We applied this method at an outgassing fumarole of Vanuatu’s Marum Crater, one of the largest point sources of several environmentally relevant gaseous compounds, including H2O, CO2, and SO2. With increasing distance from the soil-air surface and from mineral grain outer boundaries, organism abundance decreased but the proportion of metabolically active organisms often increased. These protected niches may provide more stable conditions that promote consistent metabolic activity of a streamlined community. Conversely, mineral exteriors accumulate more organisms that may cover a wider range of preferred conditions, implying that only a subset of the community will be active under any particular environmental regime. More broadly, the approach presented here allows investigators to see microbial communities “as they really are” and explore determinants of metabolic activity across a range of microbiomes.","language":"English","publisher":"Oxford Academic","doi":"10.1093/femsle/fnaa031","usgsCitation":"Marlow, J., Colocci, I., Jungbluth, S., Weber, N.M., Gartman, A., and Kallmeyer, J., 2020, Mapping metabolic activity at single cell resolution in intact volcanic fumarole soil: FEMS Microbiology Letters, v. 367, no. 1, fnaa031, https://doi.org/10.1093/femsle/fnaa031.","productDescription":"fnaa031","ipdsId":"IP-114025","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":457717,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://gfzpublic.gfz-potsdam.de/pubman/item/item_5001483","text":"External Repository"},{"id":373282,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"367","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-02-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Marlow, Jeffrey J. ","contributorId":223380,"corporation":false,"usgs":false,"family":"Marlow","given":"Jeffrey J. ","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":784908,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Colocci, Isabella","contributorId":223381,"corporation":false,"usgs":false,"family":"Colocci","given":"Isabella","email":"","affiliations":[{"id":16811,"text":"Harvard University","active":true,"usgs":false}],"preferred":false,"id":784909,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jungbluth, Sean ","contributorId":223382,"corporation":false,"usgs":false,"family":"Jungbluth","given":"Sean ","affiliations":[{"id":40704,"text":"Department of Energy, Joint Genome Institute","active":true,"usgs":false}],"preferred":false,"id":784910,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weber, Nils Moritz","contributorId":223383,"corporation":false,"usgs":false,"family":"Weber","given":"Nils","email":"","middleInitial":"Moritz","affiliations":[{"id":39797,"text":"GFZ German Research Centre for Geosciences","active":true,"usgs":false}],"preferred":false,"id":784911,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gartman, Amy 0000-0001-9307-3062 agartman@usgs.gov","orcid":"https://orcid.org/0000-0001-9307-3062","contributorId":177057,"corporation":false,"usgs":true,"family":"Gartman","given":"Amy","email":"agartman@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":784907,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kallmeyer, Jens","contributorId":223384,"corporation":false,"usgs":false,"family":"Kallmeyer","given":"Jens","email":"","affiliations":[{"id":39797,"text":"GFZ German Research Centre for Geosciences","active":true,"usgs":false}],"preferred":false,"id":784912,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208489,"text":"ofr20191136 - 2020 - The surface trace tool — Modeling complex planar interactions using ArcGIS","interactions":[],"lastModifiedDate":"2022-04-21T19:38:18.369387","indexId":"ofr20191136","displayToPublicDate":"2020-02-12T15:40:53","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1136","displayTitle":"The Surface Trace Tool — Modeling Complex Planar Interactions Using ArcGIS","title":"The surface trace tool — Modeling complex planar interactions using ArcGIS","docAbstract":"The surface trace tool comprises a Python script written for ArcGIS that will determine the line of intersection between a planar feature and a surface. Specifically, this tool was designed for geologic applications where geologic planar-feature orientations are reported as strike and dip, and the intersecting surface is the ground. The tool output will show how planar geologic layers intersect with topography.
Determining where geologic features crop out on the surface can be used to guide new geologic mapping as well as reviewing existing geologic mapping. This tool was developed to aid in more efficient mapping of an unknown area. These unknown areas may be missing data, either owing to a lack of suitable outcrops or being difficult to traverse, and data about the areas may be extrapolated using this tool and surrounding data to determine where planar features might appear on the ground.
Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Effective conservation requires prioritizing areas that are vulnerable to large, irreversible changes. Unfortunately, rigorously documenting these changes with experiments and long-term monitoring is not only costly, but may provide evidence that is too late to facilitate proactive decisions.
We use a simple model to illustrate that commonly available short-term spatial, “snapshot”, data from a given ecosystem along an environmental gradient can be used to identify environmental conditions under which different ecosystem states (e.g. different species compositions) co-occur in space. These environmental conditions are those under which future perturbations have the potential for discontinuous large, sometimes irreversible, effects; and can be mapped in space to predict potential spatial hotspots of ecosystem fragility.
We apply these insights to ecologically important high-elevation subalpine meadows of the Sierra Nevada (California). Our analysis reveals specific areas within meadows that may be more vulnerable than others because their plant communities have the potential to shift to a different state. These shifts can be mechanistically explained by interactions between the vegetation and the local water regimes and/or the upper soil conditions.
Our study provides a simple workflow using commonly available data to help prioritize conservation areas based on their potential sensitivity to upcoming perturbations. Such an approach could be very valuable to make most efficient use of conservation and management resources in the context of ongoing global changes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2019.108388","usgsCitation":"Genin, A., Lee, S.R., Berlow, E.L., Ostoja, S., and Kefi, S., 2020, Mapping hotspots of potential ecosystem fragility using commonly available spatial data: Biological Conservation, v. 241, 108388, 11 p., https://doi.org/10.1016/j.biocon.2019.108388.","productDescription":"108388, 11 p.","ipdsId":"IP-084595","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":457858,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2019.108388","text":"Publisher Index Page"},{"id":385279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sequoia National Park, Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.90527343750001,\n 36.05798104702501\n ],\n [\n -118.3447265625,\n 37.29153547292737\n ],\n [\n -119.36645507812499,\n 38.30718056188316\n ],\n [\n -119.81689453125,\n 38.324420427006544\n ],\n [\n -120.047607421875,\n 37.83148014503288\n ],\n [\n -119.937744140625,\n 37.32648861334206\n ],\n [\n -119.278564453125,\n 36.77409249464195\n ],\n [\n -118.94897460937499,\n 36.20882309283712\n ],\n [\n -118.24584960937499,\n 35.47856499535729\n ],\n [\n -117.87231445312499,\n 35.43381992014202\n ],\n [\n -117.90527343750001,\n 36.05798104702501\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"241","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Genin, Alexandre","contributorId":192956,"corporation":false,"usgs":false,"family":"Genin","given":"Alexandre","email":"","affiliations":[],"preferred":false,"id":814635,"contributorType":{"id":1,"text":"Authors"},"rank":0},{"text":"Lee, Steven R. 0000-0002-4581-3684 srlee@usgs.gov","orcid":"https://orcid.org/0000-0002-4581-3684","contributorId":5630,"corporation":false,"usgs":true,"family":"Lee","given":"Steven","email":"srlee@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":814636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berlow, Eric L.","contributorId":91416,"corporation":false,"usgs":false,"family":"Berlow","given":"Eric","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":814637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ostoja, Steven M.","contributorId":225183,"corporation":false,"usgs":false,"family":"Ostoja","given":"Steven M.","affiliations":[{"id":32922,"text":"USDA California Climate Hub","active":true,"usgs":false}],"preferred":false,"id":814638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kefi, Sonia","contributorId":257566,"corporation":false,"usgs":false,"family":"Kefi","given":"Sonia","affiliations":[{"id":37581,"text":"Université de Montpellier, France","active":true,"usgs":false}],"preferred":false,"id":814639,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70260131,"text":"70260131 - 2020 - Evolution of the submarine–subaerial edifice of Bogoslof volcano, Alaska, during its 2016–2017 eruption based on analysis of satellite imagery","interactions":[],"lastModifiedDate":"2024-10-30T11:19:12.419484","indexId":"70260131","displayToPublicDate":"2020-02-03T06:15:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Evolution of the submarine–subaerial edifice of Bogoslof volcano, Alaska, during its 2016–2017 eruption based on analysis of satellite imagery","docAbstract":"The 2016–2017 eruption of Bogoslof volcano involved at least 70 detected eruptive events between mid-December 2016 and August 30, 2017. Acquisition of high-resolution satellite imagery throughout the duration of the eruptive period allowed us to document and map the various morphologic changes that occurred on the subaerial part of Bogoslof Island. The emplacement of pyroclastic-flow and surge deposits caused the island to increase in area by about 1.5 km2. The dominant volcanic landforms of the eruption were a series of tuff rings emplaced around various submarine vents. Many of the tuff rings were mantled with surface dunes and impressive amounts of ballistic ejecta, likely derived from erupting magma bodies or previously emplaced submarine lava domes. Debris-flow deposits and surface channels extending over tuff ring surfaces apparent in multiple satellite images are evidence for explosive ejection of seawater. In most cases, erupting vents were initially submarine or began at subaerial lava domes and were largely flooded by seawater suggesting that water-magma ratios were likely high. Under such conditions where water is abundant, eruptive products typically reflect a high degree of water involvement and are dominated by the formation of wet tephra jets and flows and associated deposits typically consist of fine ash and lapilli, contain accretionary lapilli and ash aggregates, and usually form tuff cones and mounds. We observed none of these features in our analysis of satellite data or during our examination of eruptive deposits on Bogoslof Island in 2018. On the contrary, the dominant landform associated with the Bogoslof eruption was tuff rings. The development of tuff rings and surface dunes are commonly associated with the formation of pyroclastic base surges that are by comparison emplaced relatively dry. Dry base surge deposits can be generated from phreatomagmatic explosions involving superheated steam. It is possible that shallow submarine, magma–wet sediment interactions were a characteristic and possibly a dominant eruptive process of the 2016–2017 Bogoslof eruption.
The US Geological Survey (USGS) “Did You Feel It?”® (DYFI) system is an automated system for rapidly collecting macroseismic intensity data from Internet users’ shaking and damage reports and generating intensity maps immediately following earthquakes.
Although the collection and assignment of DYFI-based Macroseismic Intensity (MI) data depart from traditional assignments, they are made more quickly, provide more complete coverage at higher spatial resolution, offer citizen input and interaction, and allow data collection at rates and quantities that were not previously possible. These aspects of Internet-based data collection, in turn, allow for data analyses, graphics, and ways to communicate with the public, opportunities that were not feasible with traditional data-collection approaches.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of Solid Earth Geophysics","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-10475-7_254-1","usgsCitation":"Wald, D.J., Quitoriano, V., and Dewey, J.W., 2020, Earthquakes, did you feel it?, chap. of Encyclopedia of Solid Earth Geophysics, https://doi.org/10.1007/978-3-030-10475-7_254-1.","ipdsId":"IP-109501","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":372488,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":782737,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quitoriano, Vince 0000-0003-4157-1101 vinceq@usgs.gov","orcid":"https://orcid.org/0000-0003-4157-1101","contributorId":2582,"corporation":false,"usgs":true,"family":"Quitoriano","given":"Vince","email":"vinceq@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":782735,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dewey, James W. 0000-0001-8838-2450 jdewey@usgs.gov","orcid":"https://orcid.org/0000-0001-8838-2450","contributorId":5819,"corporation":false,"usgs":true,"family":"Dewey","given":"James","email":"jdewey@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":782736,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243269,"text":"70243269 - 2020 - Looking forward, looking back: Building resilience today community report: St. Michael, AK","interactions":[],"lastModifiedDate":"2023-05-05T15:35:05.945942","indexId":"70243269","displayToPublicDate":"2020-01-01T10:30:15","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Looking forward, looking back: Building resilience today community report: St. Michael, AK","docAbstract":"The Alaska Climate Adaptation Science Center (AK CASC), in partnership with the Aleutian Pribilof Islands Association (APIA), designed the Looking Forward, Looking Back: Building Resilience Today (hereafter ‘BRT’) project as a series of trainings and workshops with tribal community leadership and members. The overarching goal of the project was\nto collaboratively develop the Indigenous knowledge and western science knowledge for adaptation planning. We worked with five community teams consisting of up to four leaders from communities that chose to participate in\nthe project: Iliamna, Kotlik, Kwigillingok, Quinhagak, and St. Michael. Community teams were developed through the application process and the project duration. Community teams were encouraged to have involvement from multiple governing bodies within the community that could include the Tribal Council, city government, and village corpora- tion. The project title, with its references to the future (Looking Forward), past (Looking Back), and present (Building Resilience Today), refers to the idea that adaptation planning relies on all three perspectives. Equally important, how- ever, is the dialogue to exchange past and present information, context, and what we expect in the future. According- ly, two training sessions held at the International Arctic Research Center in Fairbanks, Alaska at the beginning and near the end of the project were developed to provide community team interaction with each other and with university and federal science partners. The project team also traveled to the partner communities and held a series of onsite events with community members to document locally-relevant information and share climate science tailored to the needs and conditions of each community. This report represents the community information shared during those onsite events. The Meeting Announcement (page 5) shows the date and description of the outreach events.\nThe purpose of these events was to: 1) facilitate mapping of a Traditional Use Area to refine an area for climate pro- jections; 2) construct current and past seasonal Subsistence Calendars to identify important species and times of\nthe year; 3) document Indigenous and local knowledge from current community members about environmental changes they have observed over their lifetimes; and 4) assist with documenting what the community perceived to\nbe climate-related issues through photos and interviews. The agenda of the visits was co-produced with the commu- nity team. In each community, the community team and the project team co-hosted an open-to-the-public meeting and met with various groups. The community team advertised the meetings by posting community fliers, making announcements on the community radio, and reaching out to individuals that would contribute to the engagement discussions. Each community meeting focused on activities to develop seasonal Subsistence Calendars, map Tradition- al Use Areas, and document observed environmental changes. Community members spent time at stations dedicated to each of these activities working with project team members. The project team also met with various groups of indi- viduals that included village corporation, tribal council, and city representatives where additional information about observed environmental changes was gathered. This community report presents some of the information developed in these activities.","language":"English","publisher":"Aleutian Pribilof Islands Association","usgsCitation":"St. Michael, C., Littell, J., Fresco, N., Toohey, R.C., and Chase, M., 2020, Looking forward, looking back: Building resilience today community report: St. Michael, AK, 46 p.","productDescription":"46 p.","ipdsId":"IP-120591","costCenters":[{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":416768,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416766,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://akcasc.org/wp-content/uploads/2021/03/StMichael_Community-Report_1_15_21-1.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Alaska","city":"St. Michael","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -160.43301856305254,\n 64.93703305551423\n ],\n [\n -164.34632306624601,\n 64.93703305551423\n ],\n [\n -164.34632306624601,\n 62.5458092981215\n ],\n [\n -160.43301856305254,\n 62.5458092981215\n ],\n [\n -160.43301856305254,\n 64.93703305551423\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"St. Michael, Community of","contributorId":304854,"corporation":false,"usgs":false,"family":"St. Michael","given":"Community of","email":"","affiliations":[{"id":66169,"text":"Native Village of St. Michael and St. Michael Village, City of St. Michael Council,","active":true,"usgs":false}],"preferred":false,"id":871744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":871745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fresco, Nancy","contributorId":304853,"corporation":false,"usgs":false,"family":"Fresco","given":"Nancy","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":871746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toohey, Ryan C. 0000-0001-8248-5045 rtoohey@usgs.gov","orcid":"https://orcid.org/0000-0001-8248-5045","contributorId":5674,"corporation":false,"usgs":true,"family":"Toohey","given":"Ryan","email":"rtoohey@usgs.gov","middleInitial":"C.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":871747,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chase, Malinda","contributorId":304848,"corporation":false,"usgs":false,"family":"Chase","given":"Malinda","email":"","affiliations":[{"id":66165,"text":"Aleutian Pribilof Islands Association","active":true,"usgs":false}],"preferred":false,"id":871748,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243270,"text":"70243270 - 2020 - Looking forward, looking back: Building resilience today community report: Kwigillingok, AK","interactions":[],"lastModifiedDate":"2023-05-05T15:09:33.599026","indexId":"70243270","displayToPublicDate":"2020-01-01T10:04:38","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Looking forward, looking back: Building resilience today community report: Kwigillingok, AK","docAbstract":"The Alaska Climate Adaptation Science Center (AK CASC), in partnership with the Aleutian Pribilof Islands Association (APIA), designed the Looking Forward, Looking Back: Building Resilience Today (hereafter ‘BRT’) project as a series of trainings and workshops with tribal community leadership and members. The overarching goal of the project was\nto collaboratively develop the Indigenous knowledge and western science knowledge for adaptation planning. We worked with five community teams consisting of up to four leaders from communities that chose to participate in\nthe project: Iliamna, Kotlik, Kwigillingok, Quinhagak, and St. Michael. Community teams were developed through the application process and the project duration. Community teams were encouraged to have involvement from multiple governing bodies within the community that could include the Tribal Council, the city government, and the village corporation. The project title, with its references to the future (Looking Forward), past (Looking Back), and present (Building Resilience Today), refers to the idea that adaptation planning relies on all three perspectives. Equally import- ant, however, is the dialogue to exchange past and present information, context, and what we expect in the future. Ac- cordingly, two training sessions held at the International Arctic Research Center in Fairbanks, Alaska at the beginning and near the end of the project were developed to provide community team interaction with each other and with university and federal science partners. The project team also traveled to the partner communities and held a series of onsite events with community members to document locally-relevant information and share climate science tailored to the needs and conditions of each community. This report represents the community information shared during those onsite events. The Meeting Announcement (page 5) shows the date and description of the outreach events.\nThe purpose of these events was to: 1) facilitate mapping of a Traditional Use Area to refine an area for climate pro- jections; 2) construct current and past seasonal Subsistence Calendars to identify important species and times of\nthe year; 3) document Indigenous and local knowledge from current community members about environmental changes they have observed over their lifetimes; and 4) assist with documenting what the community perceived to\nbe climate-related issues through photos and interviews. The agenda of the visits was co-produced with the commu- nity team. In each community, the community team and the project team co-hosted an open-to-the-public meeting and met with various groups. The community team advertised the meetings by posting community fliers, making announcements on the community radio, and reaching out to individuals that would contribute to the engagement discussions. Each community meeting focused on activities to develop seasonal Subsistence Calendars, map Tradition- al Use Areas, and document observed environmental changes. Community members spent time at stations dedicated to each of these activities working with project team members. The project team also met with various groups of indi- viduals that included village corporation, tribal council, and city representatives where additional information about observed environmental changes was gathered. This community report presents some of the information developed in these activities.","language":"English","publisher":"Aleutian Pribilof Islands Association","usgsCitation":"Kwigillingok, C., Littell, J., Fresco, N., Toohey, R.C., and Chase, M., 2020, Looking forward, looking back: Building resilience today community report: Kwigillingok, AK, 49 p.","productDescription":"49 p.","ipdsId":"IP-120596","costCenters":[{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":416764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416763,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://akcasc.org/wp-content/uploads/2021/03/Kwigillingok_Community-Report_1_15_21-1.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Alaska","city":"Kwigillingok","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -158.3086310956916,\n 62.3676728718529\n ],\n [\n -165.6212160234818,\n 62.3676728718529\n ],\n [\n -165.6212160234818,\n 58.09749565016324\n ],\n [\n -158.3086310956916,\n 58.09749565016324\n ],\n [\n -158.3086310956916,\n 62.3676728718529\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kwigillingok, Community of","contributorId":304855,"corporation":false,"usgs":false,"family":"Kwigillingok","given":"Community of","email":"","affiliations":[{"id":66171,"text":"Native Village of Kwigillingok, Kwik Incorporated","active":true,"usgs":false}],"preferred":false,"id":871749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":871750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fresco, Nancy","contributorId":304853,"corporation":false,"usgs":false,"family":"Fresco","given":"Nancy","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":871751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toohey, Ryan C. 0000-0001-8248-5045 rtoohey@usgs.gov","orcid":"https://orcid.org/0000-0001-8248-5045","contributorId":5674,"corporation":false,"usgs":true,"family":"Toohey","given":"Ryan","email":"rtoohey@usgs.gov","middleInitial":"C.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":871752,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chase, Malinda","contributorId":304848,"corporation":false,"usgs":false,"family":"Chase","given":"Malinda","email":"","affiliations":[{"id":66165,"text":"Aleutian Pribilof Islands Association","active":true,"usgs":false}],"preferred":false,"id":871753,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243273,"text":"70243273 - 2020 - Looking forward, looking back: Building resilience today community report: Kotlik, AK","interactions":[],"lastModifiedDate":"2023-05-05T15:36:22.979159","indexId":"70243273","displayToPublicDate":"2020-01-01T09:57:28","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Looking forward, looking back: Building resilience today community report: Kotlik, AK","docAbstract":"The Alaska Climate Adaptation Science Center (AK CASC), in partnership with the Aleutian Pribilof Islands Association (APIA), designed the Looking Forward, Looking Back: Building Resilience Today (hereafter ‘BRT’) project as a series of trainings and workshops with tribal community leadership and members. The overarching goal of the project was to collaboratively develop the Indigenous knowledge and western science knowledge for adaptation planning. We worked with five community teams consisting of up to four leaders from communities that chose to participate in the project: Iliamna, Kotlik, Kwigillingok, Quinhagak, and St. Michael. Community teams were developed through the application process and the project duration. Community teams were encouraged to have involvement from multiple governing bodies within the community that could include the Tribal Council, the city governments, and the village corporations. The project title, with its references to the future (Looking Forward), past (Looking Back), and present (Building Resil- ience Today), refers to the idea that adaptation planning relies on all three perspectives. Equally important, however,\nis the dialogue to exchange past and present information, context, and what we expect in the future. Accordingly, two training sessions held at the International Arctic Research Center in Fairbanks, Alaska at the beginning and near the end of the project were developed to provide community team interaction with each other and with university and feder- al science partners. The project team also traveled to the partner communities and held a series of onsite events with community members to document locally-relevant information and share climate science tailored to the needs and conditions of each community. This report represents the community information shared during those onsite events. The Meeting Announcement (page 5) shows the date and description of the outreach events.\nThe purpose of these events was to: 1) facilitate mapping of a Traditional Use Area to refine an area for climate projec- tions; 2) construct current and past seasonal Subsistence Calendars to identify important species and times of the year; 3) document Indigenous and local knowledge from current community members about environmental changes they have observed over their lifetimes; and 4) assist with documenting what the community perceived to be climate-relat- ed issues through photos and interviews. The agenda of the visits was co-produced with the community team. In each community, the community team and the project team co-hosted an open-to-the-public meeting and met with various groups. The community team advertised the meetings by posting community fliers, making announcements on the community radio, and reaching out to individuals that would contribute to the engagement discussions. Each commu- nity meeting focused on activities to develop seasonal Subsistence Calendars, map Traditional Use Areas, and document observed environmental changes. Community members spent time at stations dedicated to each of these activities working with project team members. The project team also met with various groups of individuals that included village corporation, tribal council, and city representatives where additional information about observed environmental chang-\nes was gathered. This community report presents some of the information developed in these activities.","language":"English","publisher":"Aleutian Pribilof Islands Association","usgsCitation":"Kotlik, C., Littell, J., Fresco, N., Toohey, R.C., and Chase, M., 2020, Looking forward, looking back: Building resilience today community report: Kotlik, AK, 50 p.","productDescription":"50 p.","ipdsId":"IP-120593","costCenters":[{"id":49028,"text":"Alaska Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":416762,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://akcasc.org/wp-content/uploads/2021/03/Kotlik_Community-Report_1_19_21.pdf"}],"country":"United States","state":"Alaska","city":"Kotlik","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -160.50821051701863,\n 63.9801934006619\n ],\n [\n -165.20969449011616,\n 63.9801934006619\n ],\n [\n -165.20969449011616,\n 61.8923130348019\n ],\n [\n -160.50821051701863,\n 61.8923130348019\n ],\n [\n -160.50821051701863,\n 63.9801934006619\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kotlik, Community of","contributorId":304859,"corporation":false,"usgs":false,"family":"Kotlik","given":"Community of","email":"","affiliations":[{"id":66175,"text":"Village of Kotlik, Kotlik Yupik Corporation","active":true,"usgs":false}],"preferred":false,"id":871759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Littell, Jeremy S. 0000-0002-5302-8280","orcid":"https://orcid.org/0000-0002-5302-8280","contributorId":205907,"corporation":false,"usgs":true,"family":"Littell","given":"Jeremy","middleInitial":"S.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":871760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fresco, Nancy","contributorId":304853,"corporation":false,"usgs":false,"family":"Fresco","given":"Nancy","affiliations":[{"id":6752,"text":"University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":871761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Toohey, Ryan C. 0000-0001-8248-5045 rtoohey@usgs.gov","orcid":"https://orcid.org/0000-0001-8248-5045","contributorId":5674,"corporation":false,"usgs":true,"family":"Toohey","given":"Ryan","email":"rtoohey@usgs.gov","middleInitial":"C.","affiliations":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":871762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chase, Malinda","contributorId":304848,"corporation":false,"usgs":false,"family":"Chase","given":"Malinda","email":"","affiliations":[{"id":66165,"text":"Aleutian Pribilof Islands Association","active":true,"usgs":false}],"preferred":false,"id":871763,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210831,"text":"70210831 - 2020 - Connectivity in the Crown: Highway 2 wildlife crossings","interactions":[],"lastModifiedDate":"2020-06-30T11:59:57.206245","indexId":"70210831","displayToPublicDate":"2019-07-17T09:58:34","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Connectivity in the Crown: Highway 2 wildlife crossings","docAbstract":"This report summarizes data collected to inform decisions on how to best mitigate the effects on wildlife migration from increasing traffic, development, and recreation along US highway 2. The highway, railway, and river split the Crown of the Continent Ecosystem. This data addresses SO 3362 by providing information on major wildlife trails, observed wildilfe crossings and road kills, and identifying the elk, deer, and other animals that use the areas near 6 potential highway crossing structure locations. \n\nThis effort resulted in 621 wildlife observations of 26 species collected from hundreds of interactions with employees and the public, 31 businesses visited, and 11 events held or attended. We mapped 230 previously unrecorded wildlife trails between West Glacier and Columbia Falls and measured and photographed 390 culverts between East Glacier and Columbia Falls. We installed 12 trail cameras that captured 9248 wildlife images comprised of 12 species.","language":"English","publisher":"NPS","collaboration":"National Park Service (GNP), USFS, Montana DOT, Montana FWP, University of Montana","usgsCitation":"Waller, J.S., Graves, T., Anderson, B., Kittson, B., and Gaulke, S.M., 2020, Connectivity in the Crown: Highway 2 wildlife crossings, 37 p.","productDescription":"37 p.","startPage":"1","endPage":"37","ipdsId":"IP-117730","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":375972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375960,"type":{"id":15,"text":"Index Page"},"url":"https://npshistory.com/publications/glac/hwy-2-wildlife-crossings-2019.pdf"}],"country":"United States","state":"Montana","otherGeospatial":"Glacier National Park, Highway 2","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.0435791015625,\n 48.268569112964336\n ],\n [\n -113.09326171875,\n 48.268569112964336\n ],\n [\n -113.09326171875,\n 48.56388521347092\n ],\n [\n -114.0435791015625,\n 48.56388521347092\n ],\n [\n -114.0435791015625,\n 48.268569112964336\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Waller, John S.","contributorId":167055,"corporation":false,"usgs":false,"family":"Waller","given":"John","email":"","middleInitial":"S.","affiliations":[{"id":16272,"text":"National Park Service, Glacier National Park, West Glacier, MT","active":true,"usgs":false}],"preferred":false,"id":791629,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":791628,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Brad","contributorId":225562,"corporation":false,"usgs":false,"family":"Anderson","given":"Brad","email":"","affiliations":[{"id":41162,"text":"Glacier National Park Conservancy","active":true,"usgs":false}],"preferred":false,"id":791630,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kittson, Brandon","contributorId":225563,"corporation":false,"usgs":false,"family":"Kittson","given":"Brandon","email":"","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":791631,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gaulke, Sarah Mccrimmon 0000-0002-2657-5844","orcid":"https://orcid.org/0000-0002-2657-5844","contributorId":225564,"corporation":false,"usgs":true,"family":"Gaulke","given":"Sarah","email":"","middleInitial":"Mccrimmon","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":791632,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70206037,"text":"sir20195117 - 2019 - Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota","interactions":[],"lastModifiedDate":"2019-11-27T09:54:48","indexId":"sir20195117","displayToPublicDate":"2019-11-27T06:42:07","publicationYear":"2019","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":"2019-5117","displayTitle":"Groundwater-Flow Model and Analysis of Groundwater and Surface-Water Interactions for the Big Sioux Aquifer, Sioux Falls, South Dakota","title":"Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota","docAbstract":"The city of Sioux Falls, in southeastern South Dakota, is the largest city in South Dakota. The U.S. Geological Survey (USGS), in cooperation with the city of Sioux Falls, completed a groundwater-flow model to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer in the model area.
The model area includes the Big Sioux aquifer and the underlying hydrogeologic units from Dell Rapids, South Dakota, to the confluence of the Big Sioux River and the outlet of the Sioux Falls Diversion Channel in eastern Sioux Falls, S. Dak. The Big Sioux aquifer is the primary aquifer in the model area and the focus of the groundwater-flow model. The Big Sioux River is the largest stream in the model area and is in hydraulic connection with the Big Sioux aquifer.
A conceptual model for the area was constructed and includes a characterization of the hydrogeologic framework, analysis and construction of potentiometric surfaces, and summary of estimated water budget components in the model area. The primary hydrogeologic units in the model area consist of (1) the Big Sioux aquifer, (2) a glacial till confining unit, and (3) bedrock aquifers (Split Rock Creek and Sioux Quartzite aquifers). Sources of groundwater recharge included infiltration of precipitation, stream seepage, and groundwater exchanges among the hydraulically connected Big Sioux aquifer, glacial till confining unit, and bedrock aquifers. Groundwater losses included evapotranspiration, groundwater discharge to streams, and groundwater withdrawal to supply water-use needs.
A numerical groundwater-flow model (numerical model) was constructed and was used to simulate all aspects of the conceptual model for predevelopment (steady-state) and time-varying (transient) monthly conditions for 1950–2017. The numerical model was constructed using the USGS modular hydrologic simulation program, MODFLOW–6, and was calibrated using the Parameter ESTimation software, PEST++.
The transient numerical model was calibrated for steady-state and transient monthly conditions for 1950–2017. Calibration targets were observations of hydraulic head, changes in hydraulic head, monthly mean streamflow (as a rate), and cumulative monthly stream discharge (as a volume). Parameters adjusted during model calibration were horizontal and vertical hydraulic conductivity, specific storage, specific yield, recharge and evapotranspiration multipliers, and streambed hydraulic conductivity. Horizontal and vertical hydraulic conductivity were estimated at pilot points distributed within the model area; specific storage and specific yield were assigned to uniform values in each layer in the model area; recharge and evapotranspiration multipliers were assigned uniformly for every stress period in the numerical model; and streambed hydraulic conductivity values were assigned uniformly between stream confluences.
The final calibrated parameter values of horizontal and vertical hydraulic conductivity, specific yield, specific storage, streambed hydraulic conductivity, recharge, and evapotranspiration were considered reasonable for the hydrogeologic materials and conditions in the model area for 1950–2017.
Overall, simulated hydraulic head altitudes had a linear regression coefficient of determination (R2) of 0.48. Hydraulic head altitude residuals for the glacial till confining unit and bedrock aquifers were typically greater in magnitude when compared to residuals in the Big Sioux aquifer, but simulated hydraulic head altitudes in the Big Sioux aquifer compared favorably with mean observed hydraulic head altitudes and had a linear regression R2 of 0.93.
Simulated streamflow hydrographs matched the general trends of observed increases and decreases in streamflow for USGS streamgages 06482000 (Big Sioux River at Sioux Falls, S. Dak.) and 06482020 (Big Sioux River at North Cliff Avenue at Sioux Falls, S. Dak.), but larger streamflows were overestimated at the first streamgage and underestimated at the second streamgage. The numerical model reasonably estimated cumulative monthly stream discharge for the first 10–15 years of available streamflow records at both USGS streamgages. After the first 10–15 years of available streamflow record, cumulative monthly stream discharge was closely estimated for USGS streamgage 06482000 and underestimated at USGS streamgage 06482020.
Composite sensitivities without regularization were calculated by PEST++ for the calibrated numerical model parameters and were averaged by parameter group. The parameter group with the highest mean composite sensitivity was the recharge multiplier parameter group.
Model simplifications, assumptions, and limitations were necessary for construction of the conceptual and numerical models and for calibration efficiency. Spatial simplification of hydraulic properties could cause the numerical model to misrepresent reactions to changes in localized stresses, such as additional demands for groundwater withdrawal. The numerical model was temporally discretized into monthly periods and required scaling daily rates into representative monthly rates for model input and calibration targets. Based on the comparison between the observed and simulated groundwater levels, monthly mean streamflow and cumulative monthly stream discharge, and general groundwater distribution and flow, the numerical model favorably simulated the flow in the Big Sioux aquifer.
Eventual capture was calculated in the model area using a steady-state numerical groundwater-flow model. The eventual capture map shows areas of higher streamflow capture adjacent to the Big Sioux River north of the city of Sioux Falls and along the lower part of the Sioux Falls Diversion Channel, and areas of lower streamflow capture along aquifer boundaries and near the southern Sioux Quartzite barrier.
The timing of capture was determined using a transient numerical groundwater-flow model to determine the likely captured water sources for 30 years of groundwater withdrawal at three hypothetical wells using three continuous withdrawal rates (112.5, 450.0, and 900.0 gallons per minute). Supply for all three hypothetical wells became capture-dominated after only a short period of continuous withdrawal. Capture stabilized after about 10–15 years for well A, and after 20–25 years for well B, and after about 10–15 years for well C.
The groundwater-flow model is a suitable tool to use for improving the understanding of groundwater-flow processes, estimating hydrogeologic properties, and analyzing groundwater and surface-water interactions for the Big Sioux aquifer near Sioux Falls, S. Dak. The numerical model can be used to simulate hydrologic scenarios, advance understanding of groundwater budgets, compute system response to stress, and determine likely sources of water supplied to wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195117","collaboration":"Prepared in cooperation with the city of Sioux Falls","usgsCitation":"Davis, K.W., Eldridge, W.G., Valder, J.F., and Valseth, K.J., 2019, Groundwater-flow model and analysis of groundwater and surface-water interactions for the Big Sioux aquifer, Sioux Falls, South Dakota: U.S. Geological Survey Scientific Investigations Report 2019–5117, 86 p., https://doi.org/10.3133/sir20195117.","productDescription":"Report: xi, 86 p.; Data Release","numberOfPages":"102","onlineOnly":"Y","ipdsId":"IP-105956","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":369602,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20195013","text":"SIR 2019–5013","linkHelpText":"– Hydraulic conductivity estimates from slug tests in the Big Sioux aquifer near Sioux Falls, South Dakota"},{"id":369600,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3393","text":"SIM 3393","linkHelpText":"– Delineation of the hydrogeologic framework of the Big Sioux aquifer near Sioux Falls, South Dakota, using airborne electromagnetic data"},{"id":369601,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/F79885XC","text":"USGS data release for SIM 3393","linkHelpText":"– Airborne electromagnetic and magnetic survey data, Big Sioux aquifer, October 2015, Sioux Falls, South Dakota"},{"id":369603,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9LUB44J","text":"USGS data release for SIR 2019–5013","linkHelpText":"– Water-level data and AQTESOLV Pro analysis results for slug tests in the Big Sioux Aquifer, Sioux Falls, South Dakota, 2017"},{"id":369535,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5117/coverthb.jpg"},{"id":369536,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5117/sir20195117.pdf","text":"Report","size":"13.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5117"},{"id":369537,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O59RO0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-6 model of the Big Sioux aquifer, Sioux Falls, South Dakota"}],"country":"United States","state":"South Dakota","city":"Sioux Falls","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.06146240234375,\n 43.29919735147067\n ],\n [\n -96.42425537109375,\n 43.29919735147067\n ],\n [\n -96.42425537109375,\n 43.757208878849376\n ],\n [\n -97.06146240234375,\n 43.757208878849376\n ],\n [\n -97.06146240234375,\n 43.29919735147067\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702