Debris flows represent one of the most dangerous types of mass movements, because of their high velocities, large impact forces and long runout distances. This review describes the available debris-flow monitoring techniques and proposes recommendations to inform the design of future monitoring and warning/alarm systems. The selection and application of these techniques is highly dependent on site and hazard characterization, which is illustrated through detailed descriptions of nine monitoring sites: five in Europe, three in Asia and one in the USA. Most of these monitored catchments cover less than ∼10 km2 and are topographically rugged with Melton Indices greater than 0.5. Hourly rainfall intensities between 5 and 15 mm/h are sufficient to trigger debris flows at many of the sites, and observed debris-flow volumes range from a few hundred up to almost one million cubic meters. The sensors found in these monitoring systems can be separated into two classes: a class measuring the initiation mechanisms, and another class measuring the flow dynamics. The first class principally includes rain gauges, but also contains of soil moisture and pore-water pressure sensors. The second class involves a large variety of sensors focusing on flow stage or ground vibrations and commonly includes video cameras to validate and aid in the data interpretation. Given the sporadic nature of debris flows, an essential characteristic of the monitoring systems is the differentiation between a continuous mode that samples at low frequency (“non-event mode”) and another mode that records the measurements at high frequency (“event mode”). The event detection algorithm, used to switch into the “event mode” depends on a threshold that is typically based on rainfall or ground vibration. Identifying the correct definition of these thresholds is a fundamental task not only for monitoring purposes, but also for the implementation of warning and alarm systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.earscirev.2019.102981","usgsCitation":"Hurlimann, M., Coviello, V., Bel, C., Guo, X., Berti, M., Graf, C., Hubl, J., Miyata, S., Smith, J.B., and Yin, H., 2019, Debris-flow monitoring and warning: Review and examples: Earth-Science Reviews, v. 199, 102981, 26 p., https://doi.org/10.1016/j.earscirev.2019.102981.","productDescription":"102981, 26 p.","ipdsId":"IP-112575","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":459437,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/2117/177770","text":"External Repository"},{"id":378905,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"199","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hurlimann, Marcel","contributorId":241626,"corporation":false,"usgs":false,"family":"Hurlimann","given":"Marcel","email":"","affiliations":[{"id":48365,"text":"Department Division of Geotechnical Engineering and Geosciences, Department of Civil and Environmental Engineering UPC BarcelonaTECH, Barcelona, Spain","active":true,"usgs":false}],"preferred":false,"id":799791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coviello, Velio","contributorId":241627,"corporation":false,"usgs":false,"family":"Coviello","given":"Velio","email":"","affiliations":[{"id":48366,"text":"Faculty of Science and Technology, Free University of Bozen-Bolzano, Italy","active":true,"usgs":false}],"preferred":false,"id":799792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bel, Coraline","contributorId":241628,"corporation":false,"usgs":false,"family":"Bel","given":"Coraline","email":"","affiliations":[{"id":48367,"text":"Université Grenoble Alpes, Irstea, UR ETNA, St-Martin-d’Hères, France","active":true,"usgs":false}],"preferred":false,"id":799793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guo, Xiaojun","contributorId":241629,"corporation":false,"usgs":false,"family":"Guo","given":"Xiaojun","email":"","affiliations":[{"id":48368,"text":"Key Laboratory of Mountain Surface Process and Hazards/Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu, China","active":true,"usgs":false}],"preferred":false,"id":799794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Berti, Matteo","contributorId":241630,"corporation":false,"usgs":false,"family":"Berti","given":"Matteo","affiliations":[{"id":48369,"text":"Dipartimento di Scienze Biologiche, Geologiche e Ambientali, Università di Bologna, Bologna, Italy","active":true,"usgs":false}],"preferred":false,"id":799795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Graf, Christoph","contributorId":241631,"corporation":false,"usgs":false,"family":"Graf","given":"Christoph","email":"","affiliations":[{"id":34058,"text":"Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland","active":true,"usgs":false}],"preferred":false,"id":799796,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hubl, Johannes","contributorId":241632,"corporation":false,"usgs":false,"family":"Hubl","given":"Johannes","email":"","affiliations":[{"id":48370,"text":"Institute of Mountain Risk engineering, Department of Natural Hazards and Civil Engineering, University of Natural Resources and Life Sciences (BOKU), Vienna, Austria","active":true,"usgs":false}],"preferred":false,"id":799797,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miyata, Shusuke","contributorId":241633,"corporation":false,"usgs":false,"family":"Miyata","given":"Shusuke","email":"","affiliations":[{"id":48371,"text":"Disaster Prevention Research Institute, Kyoto University, Takayama, Japan","active":true,"usgs":false}],"preferred":false,"id":799798,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":799799,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Yin, Hsiao-Yuan","contributorId":241634,"corporation":false,"usgs":false,"family":"Yin","given":"Hsiao-Yuan","email":"","affiliations":[{"id":48373,"text":"Soil and Water Conservation Bureau, Council of Agriculture, Nantou, Taiwan","active":true,"usgs":false}],"preferred":false,"id":799800,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70215421,"text":"70215421 - 2019 - Ecosystem size predicts social dynamics in recreational fisheries","interactions":[],"lastModifiedDate":"2020-10-19T20:16:26.631262","indexId":"70215421","displayToPublicDate":"2019-10-19T15:11:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Ecosystem size predicts social dynamics in recreational fisheries","docAbstract":"Recreational fisheries are complex adaptive systems that are inherently difficult to manage due to a heterogeneous user group (consumptive vs. non-consumptive) that utilize patchily distributed resources on the landscape (lakes, rivers, coastlines). There is a need to identify which system components can effectively predict and be used to manage nonlinear and cross-scale dynamics within these systems. We examine how ecosystem size or waterbody size can be used to explain complicated and elusive angler-resource dynamics in recreational fisheries. Waterbody size determined angler behavior among 48 Nebraska, U.S.A. waterbodies during an 11-year study period. Angler behavior was often unique and nonlinear across waterbody sizes. For example, anglers spent more time fishing and harvested more fish at larger waterbodies compared to smaller waterbodies. Time fished increased across smaller waterbodies but reached a threshold at larger waterbodies. The number of fish released increased as a function of waterbody size across smaller waterbodies but then plateaued. Subtle changes in waterbody size caused abrupt changes in angler behavior—that is, waterbody size structures angler-resource dynamics in recreational fisheries. We believe that including waterbody size, a simple and easily measured metric, in fisheries management will increase effectiveness of cross-scale actions and minimize unintended consequences for recreational fisheries. Applying uniform management actions (e.g., harvest regulations) across small and large waterbodies may elicit contrasting angler-resource responses. Waterbody size may also be useful for understanding angler typologies. Based on our findings, we expect that ecosystem size is a prominent and valuable system component that will determine and explain coupled user-resource dynamics in other complex adaptive systems.","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ES-10961-240217","usgsCitation":"Kaemingk, M., Chizinski, C.J., Allen, C.R., and Pope, K.L., 2019, Ecosystem size predicts social dynamics in recreational fisheries: Ecology and Society, v. 24, no. 2, 17, 12 p., https://doi.org/10.5751/ES-10961-240217.","productDescription":"17, 12 p.","ipdsId":"IP-097509","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":459443,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/es-10961-240217","text":"Publisher Index Page"},{"id":379532,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kaemingk, M. A.","contributorId":243357,"corporation":false,"usgs":false,"family":"Kaemingk","given":"M. A.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":802131,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chizinski, C. J.","contributorId":243358,"corporation":false,"usgs":false,"family":"Chizinski","given":"C.","email":"","middleInitial":"J.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":802132,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":802133,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pope, Kevin L. 0000-0003-1876-1687 kpope@usgs.gov","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":1574,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"kpope@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":802134,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215416,"text":"70215416 - 2019 - Comparing and improving methods for reconstructing peatland water-table depth from testate amoebae","interactions":[],"lastModifiedDate":"2020-10-19T19:40:15.940198","indexId":"70215416","displayToPublicDate":"2019-10-19T14:19:23","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1905,"text":"Holocene","active":true,"publicationSubtype":{"id":10}},"title":"Comparing and improving methods for reconstructing peatland water-table depth from testate amoebae","docAbstract":"Proxies that use changes in the composition of ecological communities to reconstruct temporal changes in an environmental covariate are commonly used in paleoclimatology and paleolimnology. Existing methods, such as weighted averaging and modern analog technique,\nrelate compositional data to the covariate in very simple ways, and different methods are seldom compared systematically. We present a new Bayesian model that better represents the underlying data and the complexity in the relationships between species’ abundances and a paleoenvironmental covariate. Using testate amoeba-based reconstructions of water-table depth as a test case, we systematically compare new and existing models in a cross-validation experiment on a large training dataset from North America. We then apply the different\nmodels to a new 7500-year record of testate amoeba assemblages from Caribou Bog in Maine and compare the resulting water-table depth reconstructions. We find that Bayesian models represent an improvement over existing methods in three key ways: more complete use of the underlying compositional data, full and meaningful treatment of uncertainty, and clear paths toward methodological improvements. Furthermore, we highlight how developing and systematically comparing methods leads to an improved understanding of the proxy system.\nThis paper focuses on testate amoebae and water-table depth, but the framework and ideas are widely applicable to other proxies based on compositional data.","language":"English","publisher":"SAGE Publications","doi":"10.1177/0959683619846969","usgsCitation":"Nolan, C., Tipton, J., Booth, R., Hooten, M., and Jackson, S., 2019, Comparing and improving methods for reconstructing peatland water-table depth from testate amoebae: Holocene, v. 29, no. 8, p. 1350-1361, https://doi.org/10.1177/0959683619846969.","productDescription":"12 p.","startPage":"1350","endPage":"1361","ipdsId":"IP-098724","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":459448,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/0959683619846969","text":"Publisher Index 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John","contributorId":166999,"corporation":false,"usgs":false,"family":"Tipton","given":"John","affiliations":[],"preferred":false,"id":802105,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Booth, Robert K.","contributorId":243345,"corporation":false,"usgs":false,"family":"Booth","given":"Robert K.","affiliations":[{"id":16160,"text":"Lehigh University","active":true,"usgs":false}],"preferred":false,"id":802106,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":802107,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackson, Stephen 0000-0002-1487-4652","orcid":"https://orcid.org/0000-0002-1487-4652","contributorId":219995,"corporation":false,"usgs":true,"family":"Jackson","given":"Stephen","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":802108,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215413,"text":"70215413 - 2019 - A Generalized Additive Model approach to evaluating water quality: Chesapeake Bay Case Study","interactions":[],"lastModifiedDate":"2020-10-20T13:24:52.488251","indexId":"70215413","displayToPublicDate":"2019-10-19T14:01:59","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"A Generalized Additive Model approach to evaluating water quality: Chesapeake Bay Case Study","docAbstract":"Nutrient-reduction efforts have been undertaken in recent decades to mitigate the impacts of eutrophication in coastal and estuarine systems worldwide. To track progress in response to one of these efforts we use Generalized Additive Models (GAMs) to evaluate a diverse suite of water quality constituents over a 32-year period in the Chesapeake Bay, an estuary on the east coast of the United States. Model development included selecting a GAM structure to describe nonlinear seasonally-varying changes over time, incorporating hydrologic variability via either river flow or salinity, and using interventions to model method or laboratory changes suspected to impact data. This approach, transferable to other systems, allows for evaluation of water quality data in a statistically rigorous way, while being suitable for application to many sites and variables. This enables consistent generation of annual updates, while providing a tool for developing insights to a range of management- and research-focused questions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2019.03.027","usgsCitation":"Murphy, R., Perry, E., Harcum, J., and Keisman, J.L., 2019, A Generalized Additive Model approach to evaluating water quality: Chesapeake Bay Case Study: Environmental Modelling & Software, v. 118, 13 p., https://doi.org/10.1016/j.envsoft.2019.03.027.","productDescription":"13 p.","ipdsId":"IP-105288","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":379527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.40966796875,\n 36.756490329505176\n ],\n [\n -75.5419921875,\n 36.756490329505176\n ],\n [\n -75.5419921875,\n 39.57182223734374\n ],\n [\n -77.40966796875,\n 39.57182223734374\n ],\n [\n -77.40966796875,\n 36.756490329505176\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"118","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Rebecca 0000-0003-3391-1823","orcid":"https://orcid.org/0000-0003-3391-1823","contributorId":199777,"corporation":false,"usgs":false,"family":"Murphy","given":"Rebecca","email":"","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":true,"id":802095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Elgin","contributorId":243340,"corporation":false,"usgs":false,"family":"Perry","given":"Elgin","affiliations":[{"id":48694,"text":"Statistics Consultant","active":true,"usgs":false}],"preferred":false,"id":802096,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harcum, Jon","contributorId":243341,"corporation":false,"usgs":false,"family":"Harcum","given":"Jon","email":"","affiliations":[{"id":48695,"text":"Tetra Tech, Inc.","active":true,"usgs":false}],"preferred":false,"id":802097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keisman, Jennifer L. 0000-0001-6808-9193 jkeisman@usgs.gov","orcid":"https://orcid.org/0000-0001-6808-9193","contributorId":198107,"corporation":false,"usgs":true,"family":"Keisman","given":"Jennifer","email":"jkeisman@usgs.gov","middleInitial":"L.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802098,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215409,"text":"70215409 - 2019 - Relevance of wind stress and wave-dependent ocean surface roughness on the generation of winter meteotsunamis in Northern Gulf of Mexico","interactions":[],"lastModifiedDate":"2020-10-19T18:01:53.210147","indexId":"70215409","displayToPublicDate":"2019-10-19T12:45:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5979,"text":"Ocean Modeling","active":true,"publicationSubtype":{"id":10}},"title":"Relevance of wind stress and wave-dependent ocean surface roughness on the generation of winter meteotsunamis in Northern Gulf of Mexico","docAbstract":"Meteotsunamis associated with passing squall lines are often observed ahead of cold fronts during winter seasons in Northern Gulf of Mexico. These types of meteotsunamis occur simultaneously with wind speed variations (~5-20 m/s) and sea-level atmospheric pressure oscillations (~1-6 hPa) with periods between 2 hours to several minutes. In order to enhance understanding of meteotsunami generation and propagation mechanisms, a Coupled-Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system is applied to one of the most intense winter meteotsunamis measured in Northern Gulf of Mexico in the last decade (2009-2018). The model verification with sea level and atmospheric observations show that the fully-coupled model is able to reproduce the timing and intensity of the 10-m wind and sea level atmospheric pressure fluctuations. The mean bias between observed and measured wind speeds and atmospheric pressure are 1.73 m/s and 0.63 hPa respectively. The maximum meteotsunami elevation and its timing are successfully captured by modeled (with a 7% underestimation of the maximum elevation). The relative effect of atmospheric pressure and wind stress divergence on meteotsunami generation is assessed with different numerical simulations. Results indicate that both wind stress and atmospheric pressure oscillations contributed to the generation of the meteotsunami. Wind stress was the dominant force in shallow waters (<15 m in this application), while the effects of atmospheric pressure disturbances dominated over areas with Froude number close to one (~40 m in this application). During the passage of the squall line, the sea surface became rougher in a sea state characterized by young and steep local ocean waves. Compared to a purely wind-speed-dependent roughness scheme, the application of a wave-dependent roughness parameterization improved in 37% modeled meteotsunami maximum elevation.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ocemod.2019.101408","usgsCitation":"Shi, L., Olabarrieta, M., Valle-Levinson, A., and Warner, J., 2019, Relevance of wind stress and wave-dependent ocean surface roughness on the generation of winter meteotsunamis in Northern Gulf of Mexico: Ocean Modeling, v. 140, 101408, 15 p., https://doi.org/10.1016/j.ocemod.2019.101408.","productDescription":"101408, 15 p.","ipdsId":"IP-099874","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":459460,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ocemod.2019.101408","text":"Publisher Index Page"},{"id":379522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northern Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.8671875,\n 27.352252938063845\n ],\n [\n -82.353515625,\n 27.352252938063845\n ],\n [\n -82.353515625,\n 30.92107637538488\n ],\n [\n -93.8671875,\n 30.92107637538488\n ],\n [\n -93.8671875,\n 27.352252938063845\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"140","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Lijing","contributorId":192873,"corporation":false,"usgs":false,"family":"Shi","given":"Lijing","email":"","affiliations":[],"preferred":false,"id":802081,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Olabarrieta, Maitane 0000-0002-7619-7992 molabarrieta@usgs.gov","orcid":"https://orcid.org/0000-0002-7619-7992","contributorId":211373,"corporation":false,"usgs":false,"family":"Olabarrieta","given":"Maitane","email":"molabarrieta@usgs.gov","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":802082,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valle-Levinson, Arnoldo","contributorId":243337,"corporation":false,"usgs":false,"family":"Valle-Levinson","given":"Arnoldo","email":"","affiliations":[{"id":48691,"text":"Civil and Coastal Engineering Department, ESSIE, University of Florida 365 Weil Hall, Gainesville, FL","active":true,"usgs":false}],"preferred":false,"id":802083,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warner, John C. 0000-0002-3734-8903 jcwarner@usgs.gov","orcid":"https://orcid.org/0000-0002-3734-8903","contributorId":2681,"corporation":false,"usgs":true,"family":"Warner","given":"John C.","email":"jcwarner@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":802084,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215406,"text":"70215406 - 2019 - Identifying salt marsh shorelines from remotely sensed elevation data and imagery","interactions":[],"lastModifiedDate":"2020-10-20T13:58:45.477548","indexId":"70215406","displayToPublicDate":"2019-10-19T11:04:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Identifying salt marsh shorelines from remotely sensed elevation data and imagery","docAbstract":"Salt marshes are valuable ecosystems that are vulnerable to lateral erosion, submergence, and internal disintegration due to sea-level rise, storms, and sediment deficits. Because many salt marshes are losing area in response to these factors, it is important to monitor their lateral extent at high resolution over multiple timescales. In this study we describe two methods to calculate the location of the salt marsh shoreline. The Marsh Edge from Elevation Data (MEED) method uses remotely sensed elevation data to calculate an objective proxy for the shoreline of a salt marsh. This proxy is the abrupt change in elevation that usually characterizes the seaward edge of a salt marsh, designated the “marsh scarp.” It is detected as the maximum slope along a cross-shore transect between Mean High Water and Mean Tide Level. The method was tested using lidar topobathymetric and photogrammetric elevation data from Massachusetts, USA. The other method to calculate the salt marsh shoreline is the Marsh Edge by Image Processing (MEIP) method which finds the unvegetated/vegetated line. This method applies image classification techniques to multispectral imagery and elevation datasets for edge detection. The method was tested using aerial imagery and coastal elevation data from the Plum Island Estuary in Massachusetts, USA. Both methods calculate a line that closely follows the edge of vegetation seen in imagery. The root-mean-square deviation between the two methods within the test area is 0.6 meter. The two methods were compared to each other using high resolution Unmanned Aircraft Systems (UAS) data and to a heads-up digitized shoreline. The root-mean-square deviation was 0.6 meters between the two methods and less than 0.43 meters from the digitized shoreline. MEIP method was also applied to a lower resolution dataset to investigate the effect of horizontal resolution on the results. Both methods provide an accurate, efficient, and objective way to track salt marsh shorelines with spatially intensive data over large spatial scales, which is necessary to evaluate geomorphic change and wetland vulnerability","language":"English","publisher":"MDPI AG","doi":"10.3390/rs11151795","usgsCitation":"Farris, A.S., Defne, Z., and Ganju, N., 2019, Identifying salt marsh shorelines from remotely sensed elevation data and imagery: Remote Sensing, v. 11, no. 15, 1795, 17 p., https://doi.org/10.3390/rs11151795.","productDescription":"1795, 17 p.","ipdsId":"IP-109869","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":459466,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11151795","text":"Publisher Index Page"},{"id":379518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","city":"Buzzards Bay, Orleans, Quincy","otherGeospatial":"Broad Meadows Marsh, Brant Island Cove, Pleasant Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.092529296875,\n 42.21224516288584\n ],\n [\n -70.81787109374999,\n 42.21224516288584\n ],\n [\n -70.81787109374999,\n 42.391008609205045\n ],\n [\n -71.092529296875,\n 42.391008609205045\n ],\n [\n -71.092529296875,\n 42.21224516288584\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -69.98703002929688,\n 41.66367910784373\n ],\n [\n -69.89639282226562,\n 41.66367910784373\n ],\n [\n -69.89639282226562,\n 41.84501267270689\n ],\n [\n -69.98703002929688,\n 41.84501267270689\n ],\n [\n -69.98703002929688,\n 41.66367910784373\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.95245361328125,\n 41.59285100004952\n ],\n [\n -70.78765869140625,\n 41.59285100004952\n ],\n [\n -70.78765869140625,\n 41.68111756290652\n ],\n [\n -70.95245361328125,\n 41.68111756290652\n ],\n [\n -70.95245361328125,\n 41.59285100004952\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"15","noUsgsAuthors":false,"publicationDate":"2019-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Farris, Amy S. 0000-0002-4668-7261 afarris@usgs.gov","orcid":"https://orcid.org/0000-0002-4668-7261","contributorId":196866,"corporation":false,"usgs":true,"family":"Farris","given":"Amy","email":"afarris@usgs.gov","middleInitial":"S.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":802065,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Defne, Zafer 0000-0003-4544-4310 zdefne@usgs.gov","orcid":"https://orcid.org/0000-0003-4544-4310","contributorId":5520,"corporation":false,"usgs":true,"family":"Defne","given":"Zafer","email":"zdefne@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":802066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":802067,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204610,"text":"ofr20191086 - 2019 - Estimated use of water in Georgia for 2015 and water-use trends, 1985–2015","interactions":[],"lastModifiedDate":"2019-10-17T14:32:56","indexId":"ofr20191086","displayToPublicDate":"2019-10-17T15:50:00","publicationYear":"2019","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":"2019-1086","displayTitle":"Estimated Use of Water in Georgia for 2015 and Water-Use Trends, 1985–2015","title":"Estimated use of water in Georgia for 2015 and water-use trends, 1985–2015","docAbstract":"Water-withdrawal, water-use, and water-return information have been collected and compiled for each county in Georgia every 5 years since 1980 using data obtained from various Federal, State, and private agencies, as well as additional online sources. For 2015, water use, water withdrawal, and water returns were estimated for each county, water-planning region, major river basin, and principal aquifer in Georgia. Offstream water use in 2015 is estimated for the categories of domestic, commercial, industrial processing, mining, irrigation (subdivided into crop and golf course irrigation), livestock, aquaculture, and thermoelectric power cooling.
According to the U.S. Census Bureau, approximately 10.2 million people in Georgia needed water resources to meet their personal, commercial, and recreational needs in 2015. Public water suppliers provided water to about 85 percent of the population of Georgia. Estimated total water withdrawals from both surface-water and groundwater sources were about 3,384 million gallons per day (Mgal/d) in 2015, which is a 27-percent reduction from 2010, a 48.1-percent reduction from 2000, and a 49.7-percent reduction from 1980. In 2015, surface-water withdrawals were greatest for thermoelectric power cooling (839.8 Mgal/d), and groundwater withdrawals were greatest for irrigating crops (547.9 Mgal/d). Water needs in northern Georgia are typically met by withdrawing a larger percentage of water from surface-water than groundwater sources; conversely, counties in southern Georgia withdraw more water from groundwater sources. About 1,571 Mgal/d of water were returned to Georgia streams and lakes in 2015, which represents about 46 percent of the total water withdrawn from all sources in 2015.
Water users in the Apalachicola River Basin, in 2015, withdrew the highest percentage of water (35 percent) and returned the highest percentage of water to surface-water bodies (almost 40 percent) compared to other major river basins in Georgia. Withdrawals in the Apalachicola River Basin are primarily extracted by public-supply systems (43 percent) and irrigation (34 percent). The aquifer from which 68 percent of statewide groundwater withdrawals were extracted was the Floridan aquifer system, and the majority of the water was used for irrigation (57 percent).
Historically, statewide water use in Georgia was highest in 1980 (6,735 Mgal/d), decreased to 5,353 Mgal/d in 1990, peaked at 6,531 Mgal/d in 2000, and has been declining since that time. The reduction in water use between 2000 and 2015 came primarily from surface-water withdrawals (90 percent of total reduction) and thermoelectric power cooling use (78 percent of total reduction). Water use for livestock and aquaculture increased between 1985 and 2015, and this increase correlates with the growth of agriculture in Georgia during that period. The driving forces behind the observed water-use changes include (1) shifts in population numbers and locations, (2) five periods of major drought, (3) water conservation efforts and education programs initiated by State and local governments and water utilities, and (4) changing water needs for thermoelectric power cooling, industry, and agricultural activities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191086","collaboration":"Prepared in cooperation with the Georgia Department of Natural Resources, Environmental Protection Division","usgsCitation":"Painter, J.A., 2019, Estimated use of water in Georgia for 2015 and water-use trends, 1985–2015: U.S. Geological Survey Open-File Report 2019–1086, 216 p., https://doi.org/10.3133/ofr20191086.","productDescription":"vi, 216 p.","numberOfPages":"226","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-096369","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":437300,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V2F373","text":"USGS data release","linkHelpText":"Georgia Water Use 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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
1770 Corporate Drive
Suite 500
Norcross, GA 30093
Embedded within the North American Prairie Pothole Region (PPR) are millions of small, depressional wetlands that annually support 50–80% of the continent’s waterfowl production. We recently assembled evidence that demonstrates a change towards a wetter climate that is driving a shift in the state of the region’s wetland ecosystems. This ecological state-shift has been primarily the result of a sustained wet climate that has influenced timing and magnitude of surface-water inputs to wetlands, connections to groundwater, and inputs of dissolved salts. As climate influences continue to change in the PPR, it is important to understand the potential of these changes to impact wetland habitats important for waterfowl production. Previous model simulations of prairie-pothole wetlands under future climate scenarios projected decreases in the ability of wetlands to facilitate waterfowl production throughout the majority of what is currently the most productive portion of the region. Results from these modeling efforts also suggested that suitable waterfowl breeding-habitat would be limited mostly to the southeastern portion of the PPR, a portion of the region in which most depressional wetlands (> 90%) have been drained. Thus, if these modeled outcomes materialize, a significant restoration effort would be needed in the southeastern PPR to support waterfowl production. However, the models used in earlier efforts were developed from a small number of wetlands using data from a relatively dry period and did not allow for changing mechanisms influencing surface-water, groundwater and dissolved salt inputs to prairie-pothole wetlands.
The primary objective of our research is to improve our understanding of future climate change on impacts to wetland ecosystems and breeding waterfowl habitat in the PPR. We used a newly developed Pothole Hydrology Linked Systems Simulator (PHyLiSS) model to estimate wetland ecosystem responses to 32 distinct climate models under 2 different emissions scenarios. Unlike previous wetland hydrology models, the PHyLiSS model allows for shifting hydrological and geochemical mechanisms influencing wetland ecosystems. We modeled one average-sized seasonal wetland basin at 18 different geographic locations (hereafter “sites”) across the PPR with 3 sites represented for each of 6 ecoregions coincident to early research. We applied the PHyLiSS model using historical daily precipitation and temperature data from 1982–2015 and developed linear models relative to ponded water depth in the simulated wetlands and the observed regional WBPHS May Pond count number for 16 of the 18 sites. Based on the output of 32 climate models and 2 emission scenarios we found a projected change in May pond numbers from -23% to +.02% when comparing the most recent climate period (1989–2018) to the end of the 21st century (2070–2099). We also found no evidence that the distribution of May ponds will shift in the future. These results suggest that management and conservation strategies for wetlands in the PPR should continue to focus on areas where high densities of intact wetland basins support large numbers of breeding duck pairs.
","language":"English","publisher":"Climate Adaptation Science Centers","usgsCitation":"Mushet, D.M., and McKenna, O.P., 2019, Climate-driven state shifts in the Prairie Pothole Region: Assessing future impacts relevant to the management of wetland habitats critical to waterfowl: Final Report, 15 p.","productDescription":"15 p.","ipdsId":"IP-117473","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":396800,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381010,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f83509de4b0e84f60868124/5b33be6fe4b040769c172fad"}],"country":"Canada, United States","state":"Alberta, Iowa, Manitoba, Minnesota, Montana, North Dakota, Saskatchewan, South Dakota","otherGeospatial":"Prairie Potholes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.4248046875,\n 53.25206880589411\n ],\n [\n -110.5224609375,\n 54.265224078605684\n ],\n [\n -115.6640625,\n 55.65279803318956\n ],\n [\n -116.01562499999999,\n 51.645294049305406\n ],\n [\n -113.99414062499999,\n 50.680797145321655\n ],\n [\n -113.115234375,\n 49.296471602658066\n ],\n [\n -113.37890625,\n 48.3416461723746\n ],\n [\n -111.884765625,\n 47.42808726171425\n ],\n [\n -106.4794921875,\n 47.45780853075031\n ],\n [\n -105.908203125,\n 47.87214396888731\n ],\n [\n -104.94140625,\n 47.931066347509784\n ],\n [\n -103.0078125,\n 47.84265762816538\n ],\n [\n -101.689453125,\n 47.42808726171425\n ],\n [\n -100.546875,\n 46.558860303117164\n ],\n [\n -100.5908203125,\n 45.058001435398275\n ],\n [\n -100.94238281249999,\n 44.59046718130883\n ],\n [\n -99.7119140625,\n 43.99281450048989\n ],\n [\n -98.8330078125,\n 42.84375132629021\n ],\n [\n -98.3056640625,\n 41.0130657870063\n ],\n [\n -96.8115234375,\n 40.91351257612758\n ],\n [\n -96.6357421875,\n 42.48830197960227\n ],\n [\n -97.0751953125,\n 43.26120612479979\n ],\n [\n -96.6357421875,\n 43.99281450048989\n ],\n [\n -94.9658203125,\n 42.90816007196054\n ],\n [\n -94.1748046875,\n 41.64007838467894\n ],\n [\n -93.42773437499999,\n 40.91351257612758\n ],\n [\n -91.7138671875,\n 41.21172151054787\n ],\n [\n -91.8896484375,\n 42.391008609205045\n ],\n [\n -93.33984375,\n 44.49650533109348\n ],\n [\n -94.1748046875,\n 44.33956524809713\n ],\n [\n -94.306640625,\n 45.089035564831036\n ],\n [\n -95.00976562499999,\n 45.55252525134013\n ],\n [\n -95.8447265625,\n 46.164614496897094\n ],\n [\n -96.1083984375,\n 47.54687159892238\n ],\n [\n -95.09765625,\n 47.87214396888731\n ],\n [\n -95.09765625,\n 48.3416461723746\n ],\n [\n -95.5810546875,\n 51.26191485308451\n ],\n [\n -96.416015625,\n 51.31688050404585\n ],\n [\n -96.7236328125,\n 50.20503326494332\n ],\n [\n -96.9873046875,\n 50.3734961443035\n ],\n [\n -96.8115234375,\n 51.590722643120145\n ],\n [\n -98.5693359375,\n 52.669720383688166\n ],\n [\n -99.7119140625,\n 52.348763181988105\n ],\n [\n -101.29394531249999,\n 52.802761415419674\n ],\n [\n -101.6015625,\n 52.16045455774706\n ],\n [\n -100.5029296875,\n 51.6180165487737\n ],\n [\n -99.7119140625,\n 51.04139389812637\n ],\n [\n -99.6240234375,\n 50.62507306341435\n ],\n [\n -100.634765625,\n 50.54136296522161\n ],\n [\n -101.42578124999999,\n 50.98609893339354\n ],\n [\n -102.0849609375,\n 52.05249047600099\n ],\n [\n -103.7548828125,\n 52.72298552457069\n ],\n [\n -105.4248046875,\n 53.25206880589411\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":806183,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":806182,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70206015,"text":"70206015 - 2019 - Competitive interactions among H, CU, and Zn ion moderate aqueous uptake of Cu and Zn by an aquatic insect","interactions":[],"lastModifiedDate":"2019-10-17T07:34:48","indexId":"70206015","displayToPublicDate":"2019-10-17T07:32:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1555,"text":"Environmental Pollution","active":true,"publicationSubtype":{"id":10}},"title":"Competitive interactions among H, CU, and Zn ion moderate aqueous uptake of Cu and Zn by an aquatic insect","docAbstract":"The absorption of aqueous copper (Cu) and zinc (Zn) by aquatic insects, a group widely used to assess water quality, is unresolved. This study examined interactions among Cu, Zn, and protons that potentially moderate Cu and Zn uptake by the acid-tolerant stonefly Zapada sp. Saturation uptake kinetics was imposed to identify competitive mechanisms. Decreasing pH reduced the maximum transport capacity, Jmax, in both metals, had little effect on the Cu dissociation constant, KD, and increased the Zn KD. Partial noncompetitive (Cu) and partial mixed competitive (Zn) inhibitor models most closely tracked the observed Cu and Zn influx across pH treatments. The estimated values for acid dissociation constants for the binary (proton-receptor) and ternary (proton-metal-receptor) complexes indicated the strong inhibitory effect of protons on Cu and Zn. In neutral pH water, Cu inhibited Zn uptake, but Zn had little effect on Cu uptake. The mechanism of Cu-Zn interaction was not identified. Results from separate Zn experiments suggested that the insect’s developmental stage may affect the apparent Jmax. The study underscores some of the challenges of modeling metal bioaccumulation and informs future research directions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2019.113220","usgsCitation":"Cain, D.J., Croteau, M.N., and Fuller, C.C., 2019, Competitive interactions among H, CU, and Zn ion moderate aqueous uptake of Cu and Zn by an aquatic insect: Environmental Pollution, v. 255, no. Part 1, 113220, https://doi.org/10.1016/j.envpol.2019.113220.","productDescription":"113220","ipdsId":"IP-111255","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":368359,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"255","issue":"Part 1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cain, Daniel J. 0000-0002-3443-0493 djcain@usgs.gov","orcid":"https://orcid.org/0000-0002-3443-0493","contributorId":1784,"corporation":false,"usgs":true,"family":"Cain","given":"Daniel","email":"djcain@usgs.gov","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":773298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Croteau, Marie Noele 0000-0003-0346-3580 mcroteau@usgs.gov","orcid":"https://orcid.org/0000-0003-0346-3580","contributorId":895,"corporation":false,"usgs":true,"family":"Croteau","given":"Marie","email":"mcroteau@usgs.gov","middleInitial":"Noele","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":773299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":773300,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70206117,"text":"70206117 - 2019 - The 30 November 2018 Mw7.1 Anchorage Earthquake","interactions":[],"lastModifiedDate":"2020-01-05T14:07:00","indexId":"70206117","displayToPublicDate":"2019-10-16T12:57:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"The 30 November 2018 Mw7.1 Anchorage Earthquake","docAbstract":"The
Gas hydrates possess lower electrical conductivity (inverse of resistivity) than either seawater or ice, but higher than clastic silts and sands, such that electromagnetic methods can be employed to help identify their natural formation in marine and permafrost environments. Controlled laboratory studies offer a means to isolate and quantify the effects of changing individual components within gas‐hydrate‐bearing systems, in turn yielding insight into the behavior of natural systems. Here we investigate the electrical properties of polycrystalline methane hydrate with ≥25% gas‐filled porosity and in mixture with brine. Initially, pure methane hydrate was synthesized from H2O ice and CH4 gas while undergoing electrical impedance measurement, then partially dissociated to assess the effects of pure pore water accumulation on electrical conductivity. Methane hydrate + brine mixtures were then formed by either adding NaCl (0.25–2.5 wt %) to high‐purity ice or by using frozen seawater as a reactant. Conductivity was obtained from impedance measurements made in situ throughout synthesis while temperature cycled between +15 °C and −25 °C. Several possible conduction mechanisms were subsequently determined using equivalent circuit modeling. Samples with low NaCl concentration show a doping/impurity effect and a log linear conductivity response as a function of temperature. For higher salt content samples, conductivity increases exponentially with temperature and the log linear relationship no longer holds; instead, we observe phase changes within the samples that follow NaCl–H2O–CH4 phase equilibrium predictions. Final samples were quenched in liquid nitrogen and imaged by cryogenic scanning electron microscopy (cryo‐SEM) to assess grain‐scale characteristics.
The Black-necked Stilt (Himantopus mexicanus) is a migratory shorebird of temperate and tropical America. Declining wetland quality and associated declines in hydrological integrity may contribute to widespread habitat loss for stilts nesting on the upper Texas Gulf of Mexico coast of the USA, as both fresh and brackish marshes are converting to open water and saline marsh. Nests (n = 356) were monitored in three wetland types on the upper Texas coast from 21 April-30 June 2011-2012. Of these 356 nests, 151 were located in managed freshwater wetlands (16 in 2011 and 135 in 2012), 128 were located in managed intermediate wetlands (75 in 2011 and 53 in 2012), and 77 were located in rice fields (all in 2012). Collectively, nest success was 0.2% (0 in rice fields and as high as 4.3% in freshwater wetlands in 2012), among the lowest ever reported for the species. The most frequent cause of nest failure was predation by mammalian and avian predators (∼50%). Daily nest survival rate was positively related to mudflat nesting substrates and negatively related to colony size, rice field, and brackish coastal wetland habitats. Future efforts to minimize edge effects in managed wetlands may prove valuable to improve nest success of stilts and other species that nest in similar wetland types.
","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.042.0302","usgsCitation":"Riecke, T., Conway, W.C., Haukos, D.A., Moon, J.A., and Comer, C.E., 2019, Nest survival of Black-necked Stilts (Himantopus mexicanus) on the upper Texas coast, USA: Waterbirds, v. 42, no. 3, p. 261-271, https://doi.org/10.1675/063.042.0302.","productDescription":"11 p.","startPage":"261","endPage":"271","ipdsId":"IP-094016","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","county":"Chambers County","otherGeospatial":"Anahuac National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.59709167480467,\n 29.561512529746743\n ],\n [\n -94.5703125,\n 29.570471069643638\n ],\n [\n -94.559326171875,\n 29.574054263095935\n ],\n [\n -94.54765319824219,\n 29.57106827738255\n ],\n [\n -94.5428466796875,\n 29.562707047643396\n ],\n [\n -94.53254699707031,\n 29.554942428835226\n ],\n [\n -94.52774047851562,\n 29.547774558769333\n ],\n [\n -94.52430725097656,\n 29.538216607905866\n ],\n [\n -94.49821472167969,\n 29.54419043309506\n ],\n [\n -94.49272155761719,\n 29.55076123308153\n ],\n [\n -94.47555541992188,\n 29.554942428835226\n ],\n [\n -94.46731567382812,\n 29.552553195302863\n ],\n [\n -94.45289611816406,\n 29.563304301294636\n ],\n [\n -94.42405700683594,\n 29.566290516578164\n ],\n [\n -94.41444396972656,\n 29.575248632655892\n ],\n [\n -94.41444396972656,\n 29.59375955382993\n ],\n [\n -94.39933776855469,\n 29.596147812456916\n ],\n [\n -94.38972473144531,\n 29.59435662378732\n ],\n [\n -94.37255859375,\n 29.6361427369564\n ],\n [\n -94.37461853027344,\n 29.6773149449586\n ],\n [\n -94.46868896484375,\n 29.67850809103362\n ],\n [\n -94.54421997070312,\n 29.660012735895606\n ],\n [\n -94.59846496582031,\n 29.676121784724305\n ],\n [\n -94.59709167480467,\n 29.561512529746743\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Riecke, Thomas V.","contributorId":171482,"corporation":false,"usgs":false,"family":"Riecke","given":"Thomas V.","affiliations":[],"preferred":false,"id":837061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Warren C.","contributorId":51550,"corporation":false,"usgs":true,"family":"Conway","given":"Warren","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":837060,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837062,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moon, Jena A.","contributorId":171483,"corporation":false,"usgs":false,"family":"Moon","given":"Jena","email":"","middleInitial":"A.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":837059,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Comer, Christopher E.","contributorId":166690,"corporation":false,"usgs":false,"family":"Comer","given":"Christopher","email":"","middleInitial":"E.","affiliations":[{"id":32360,"text":"Stephen F. Austin State University, Nacogdoches, TX","active":true,"usgs":false}],"preferred":false,"id":837058,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227127,"text":"70227127 - 2019 - A mechanistic understanding of ecological responses to land-use change in headwater streams","interactions":[],"lastModifiedDate":"2021-12-30T14:08:03.519866","indexId":"70227127","displayToPublicDate":"2019-10-16T08:06:00","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"A mechanistic understanding of ecological responses to land-use change in headwater streams","docAbstract":"Anthropogenic activities, such as oil and natural gas development (ONGD), have significantly altered the landscape. It is often challenging to identify the mechanistic processes underlying ecological responses to land-use change (LUC). In aquatic ecosystems, alterations to habitat and food availability and water quality associated with increased LUC are key mechanistic pathways that deserve management consideration. We used structural equation modeling to evaluate how LUC associated with ONGD could influence macroinvertebrate and fish across 40 sites in six headwater streams in the Wyoming Range of the Upper Green River Basin, Wyoming. The most important mechanistic pathway varied, but responses were frequently driven by a direct effect of LUC or related to changes in food availability and water quality. Habitat complexity was the least important mechanistic pathway in our models. Our results also highlight that responses may reflect an organism's degree of habitat or resource specialization and/or sensitivity to changes in water quality. Habitat pathways were more important for habitat specialists (e.g., Mottled Sculpin, Cottus bairdii), food pathways were more important for food specialists (e.g., Colorado River Cutthroat Trout, Oncorhynchus clarki pleuriticus; Mountain Sucker, Catostomus platyrhynchus), and sensitivity to increased salinity was important for intolerant species (e.g., O. clarki, C. bairdii, and predatory macroinvertebrates). Continued identification of the specific mechanisms underlying species’ responses to increased LUC will aid in the conservation of ecologically and economically important species.
Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Tumacácori National Historical Park (TUMA) in southern Arizona protects the culturally important Mission San José de Tumacácori, while also managing a part of the ecologically diverse riparian corridor of the Santa Cruz River. The quality of the water flowing through depends solely on upstream watershed activities, and among the water-quality issues concerning TUMA is the microbiological pathogens in the river introduced by human and animal sources that pose a significant human health risk to employees and visitors. The U.S. Geological Survey (USGS) conducted a 3-year study to understand the sources, timing, and distribution of the fecal-indicator bacteria Escherichia coli (E. coli) within TUMA and the upstream watershed.
The information provided in this investigation is a result of a comprehensive approach to quantify the spatial and temporal variability of E. coli and suspended sediment in the Upper Santa Cruz River Watershed. Several types of flow were sampled from base flow to flood flow and at high frequency intervals (rise, peak, and recession) to determine daily variability, as well as seasonal variability. Hydrologic data collection and estimation techniques were used to establish a hydrologic relation with E. coli and suspended sediment. Furthermore, source tracking was used to describe the potential sources of E. coli. Models were developed that are expected to be useful for predicting E. coli concentrations to help TUMA managers understand instantaneous conditions to keep the public and staff informed about potentially harmful water-quality conditions. In addition, the concentration, flux, and source information will provide more accurate data for other surface-water modeling and can be useful in the development of total maximum daily load standards. This will help TUMA describe the water-quality conditions at the park and waters flowing through the park, as well as prioritize and help carry out future best-management actions to address these issues.
Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Floodplain systems in the Driftless Area have experienced widespread historical transformations in hydrologic and sediment characteristics as well as rates of hydrogeomorphic processes. These changes exceed natural variability experienced during the Holocene and are driven by nearly two centuries of major land-cover alterations coupled with shifting precipitation patterns. On the pre–Euro-American landscape, tributaries to the Upper Mississippi River had clear, constant base flow and low sedimentation rates due to a protective cover of prairie, oak savanna, and woodland. The Upper Mississippi River was sandy and braided, with geomorphologically diverse backwaters, side channels, and vegetated islands. Soil erosion and gullying caused by agriculture-related land clearance have had the largest historical effects on Upper Mississippi River tributary stream morphology and floodplain sedimentation. Floodplain sedimentation rates for tributaries and the Upper Mississippi River were 0.2 and 0.9 mm/yr, respectively, before Euro-American settlement, compared to 2–20 and 5–20 mm/yr after Euro-American settlement, respectively. The soil conservation movement had its birthplace in the Driftless Area in the 1920s because of the region’s widespread landscape degradation. As soil erosion decreased and gullies were stabilized in the middle to late twentieth century, land management efforts turned toward the lingering problem of fine-grained, phosphorus-rich sediment stored in tributary floodplains and channels. This trend has been complicated by a climatic shift in the late twentieth century toward increased annual precipitation, increased flood variability, and more floods in late fall and winter months, when bare fields are vulnerable to runoff. Floods are major contributors to channel erosion and deposition, and variability in magnitudes and frequency will likely continue in the early twenty-first century. Restoration efforts in tributaries have included reducing bank erosion, reconnecting floodplains, and adding trout habitat features. Lock and dam structures have altered sediment transport and erosion processes within the Upper Mississippi River, and restoration efforts there have focused on creation and rehabilitation of islands and protection of remnant off-channel backwater habitats.
Accurate quantification of riverine water‐quality concentration and flux is challenging because monitoring programs typically collect concentration data at lower frequencies than discharge data. Statistical methods are often used to estimate concentration and flux on days without observations. One recently developed approach is the Weighted Regressions on Time, Discharge, and Season (WRTDS), which has been shown to provide among the most accurate estimates compared to other common methods. The main objective of this work was to improve WRTDS estimation by accounting for the autocorrelation structure of model residuals using the first‐order autoregressive model (AR1). This modified approach, called WRTDS‐Kalman Filter (WRTDS‐K), was compared with WRTDS for six constituents including nitrate‐plus‐nitrite (NOx), total phosphorus, total Kjeldahl nitrogen, soluble reactive phosphorus, suspended sediment, and chloride. Near‐daily concentration records at nine sites were used to generate subsets through Monte Carlo sampling for five different sampling scenarios. Results show that WRTDS‐K provided generally better daily estimates of concentration and flux than WRTDS under these sampling scenarios for all constituents, especially NOx. The degree of improvement is strongly affected by the underlying sampling scenario, with WRTDS‐K gaining more advantage when more samples are available, and hence more residuals can be exploited. The performance of WRTDS‐K depends on the AR1 coefficient (ρ) and that relationship varies with constituents and sampling scenarios. These results provided recommendations on the optimal ρ for each constituent and sampling scenario. Overall, WRTDS‐K has the potential for broad applications to monitoring records elsewhere, as demonstrated by a pilot application to Chesapeake Bay tributaries.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019wr025338","usgsCitation":"Zhang, Q., and Hirsch, R.M., 2019, River water-quality concentration and flux estimation can be improved by accounting for serial correlation through an autoregressive model: Water Resources Research, v. 55, no. 11, p. 9705-9723, https://doi.org/10.1029/2019wr025338.","productDescription":"19 p.","startPage":"9705","endPage":"9723","ipdsId":"IP-110106","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":488944,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019wr025338","text":"Publisher Index Page"},{"id":379382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Lake Erie and Ohio River tributaries","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.7705078125,\n 40.58058466412761\n ],\n [\n -84.48486328124999,\n 40.01078714046552\n ],\n [\n -83.9794921875,\n 39.53793974517628\n ],\n [\n -83.3642578125,\n 39.90973623453719\n ],\n [\n -82.72705078125,\n 39.2832938689385\n ],\n [\n -82.37548828125,\n 41.04621681452063\n ],\n [\n -83.408203125,\n 40.896905775860006\n ],\n [\n -83.84765625,\n 41.178653972331674\n ],\n [\n -83.7158203125,\n 41.82045509614034\n ],\n [\n -84.3310546875,\n 41.918628865183045\n ],\n [\n -84.6826171875,\n 41.393294288784865\n ],\n [\n -85.1220703125,\n 41.16211393939692\n ],\n [\n -85.0341796875,\n 40.54720023441049\n ],\n [\n -84.7705078125,\n 40.58058466412761\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.9912109375,\n 41.72213058512578\n ],\n [\n -81.36474609375,\n 41.343824581185686\n ],\n [\n -81.82617187499999,\n 41.19518982948959\n ],\n [\n -81.5185546875,\n 40.93011520598305\n ],\n [\n -80.96923828125,\n 41.04621681452063\n ],\n [\n -80.6396484375,\n 41.45919537950706\n ],\n [\n -80.6396484375,\n 41.78769700539063\n ],\n [\n -80.9912109375,\n 41.72213058512578\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"11","noUsgsAuthors":false,"publicationDate":"2019-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Qian 0000-0003-0500-5655","orcid":"https://orcid.org/0000-0003-0500-5655","contributorId":174393,"corporation":false,"usgs":false,"family":"Zhang","given":"Qian","email":"","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":801435,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":801436,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218246,"text":"70218246 - 2019 - Infrasound from giant bubbles during explosive submarine eruptions","interactions":[],"lastModifiedDate":"2021-02-19T20:15:54.51462","indexId":"70218246","displayToPublicDate":"2019-10-14T14:11:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Infrasound from giant bubbles during explosive submarine eruptions","docAbstract":"Shallow submarine volcanoes pose unique scientific and monitoring challenges. The interaction between water and magma can create violent explosions just below the surface, but the inaccessibility of submerged volcanoes means they are typically not instrumented. This both increases the risk to marine and aviation traffic and leaves the underlying eruption physics poorly understood. Here we use low-frequency sound in the atmosphere (infrasound) to examine the source mechanics of shallow submarine explosions from Bogoslof volcano, Alaska. We show that the infrasound originates from the oscillation and rupture of magmatic gas bubbles that initially formed from submerged vents, but that grew and burst above sea level. We model the low-frequency signals as overpressurized gas bubbles that grow near the water–air interface, which require bubble radii of 50–220 m. Bubbles of this size and larger have been described in explosive subaqueous eruptions for more than a century, but we present a unique geophysical record of this phenomenon. We propose that the dominant role of seawater during the effusion of gas-rich magma into shallow water is to repeatedly produce a gas-tight seal near the vent. This resealing mechanism leads to sequences of violent explosions and the release of large, bubble-forming volumes of gas—activity we describe as hydrovulcanian.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41561-019-0461-0","usgsCitation":"Lyons, J.J., Haney, M.M., Fee, D., Wech, A., and Waythomas, C.F., 2019, Infrasound from giant bubbles during explosive submarine eruptions: Nature Geoscience, v. 12, p. 952-958, https://doi.org/10.1038/s41561-019-0461-0.","productDescription":"7 p.","startPage":"952","endPage":"958","ipdsId":"IP-104588","costCenters":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":383394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Bogoslof volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -168.78295898437497,\n 53.1928702436326\n ],\n [\n -166.1737060546875,\n 53.1928702436326\n ],\n [\n -166.1737060546875,\n 54.08517342088679\n ],\n [\n -168.78295898437497,\n 54.08517342088679\n ],\n [\n -168.78295898437497,\n 53.1928702436326\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2019-10-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Lyons, John J. 0000-0001-5409-1698 jlyons@usgs.gov","orcid":"https://orcid.org/0000-0001-5409-1698","contributorId":5394,"corporation":false,"usgs":true,"family":"Lyons","given":"John","email":"jlyons@usgs.gov","middleInitial":"J.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810690,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haney, Matthew M. 0000-0003-3317-7884 mhaney@usgs.gov","orcid":"https://orcid.org/0000-0003-3317-7884","contributorId":172948,"corporation":false,"usgs":true,"family":"Haney","given":"Matthew","email":"mhaney@usgs.gov","middleInitial":"M.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fee, David","contributorId":251816,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[{"id":6695,"text":"UAF","active":true,"usgs":false}],"preferred":false,"id":810692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wech, Aaron 0000-0003-4983-1991","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":202561,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810693,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Waythomas, Christopher F. 0000-0002-3898-272X cwaythomas@usgs.gov","orcid":"https://orcid.org/0000-0002-3898-272X","contributorId":640,"corporation":false,"usgs":true,"family":"Waythomas","given":"Christopher","email":"cwaythomas@usgs.gov","middleInitial":"F.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":810694,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208993,"text":"70208993 - 2019 - Calibration of the USGS National Hydrologic Model in ungauged basins using statistical at-site streamflow simulations","interactions":[],"lastModifiedDate":"2020-03-10T14:20:54","indexId":"70208993","displayToPublicDate":"2019-10-14T13:57:26","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Calibration of the USGS National Hydrologic Model in ungauged basins using statistical at-site streamflow simulations","docAbstract":"In the absence of measured streamflow, statistically simulated daily streamflow can be used to support the ability of physical models to represent hydrologic processes at ungauged locations. The objective of this study was to determine the feasibility of using statistical simulations in place of measured streamflow to calibrate physical models in ungauged basins. Daily streamflow was simulated at each of the 1,410 gauged watersheds using a cross-validated implementation of pooled ordinary kriging (POK). In this manner, the streamflow at each gauge was simulated as if no at-site streamflow information were available. The National Hydrologic Model application of the Precipitation-Runoff Modeling System was then calibrated through two separate procedures: (1) with measured streamflow, and (2) with statistically simulated streamflow in lieu of measured streamflow. Calibrating with statistically simulated streamflow produced performances within 23% of applications with knowledge of at-site measurements. Furthermore, statistically generated streamflow produced accurate timing information, which, when combined with alternative data sets (e.g., evapotranspiration, recharge, etc.), can be used to improve representation of hydrologic processes at ungauged locations.","language":"English","publisher":"ASCE","doi":"10.1061/(ASCE)HE.1943-5584.0001854","usgsCitation":"Farmer, W., LaFontaine, J., and Hay, L., 2019, Calibration of the USGS National Hydrologic Model in ungauged basins using statistical at-site streamflow simulations: Journal of Hydrologic Engineering, v. 24, no. 11, 04019049, 13 p., https://doi.org/10.1061/(ASCE)HE.1943-5584.0001854.","productDescription":"04019049, 13 p.","ipdsId":"IP-101969","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":437307,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U2A2KU","text":"USGS data release","linkHelpText":"Physical and 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PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Farmer, William 0000-0002-2865-2196 wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223175,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":784444,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"LaFontaine, Jacob 0000-0003-4923-2630 jlafonta@usgs.gov","orcid":"https://orcid.org/0000-0003-4923-2630","contributorId":223176,"corporation":false,"usgs":true,"family":"LaFontaine","given":"Jacob","email":"jlafonta@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784445,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hay, Lauren 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":223177,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":784446,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212818,"text":"70212818 - 2019 - Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities","interactions":[],"lastModifiedDate":"2024-05-16T14:56:12.436485","indexId":"70212818","displayToPublicDate":"2019-10-14T08:20:20","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities","docAbstract":"Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.