The Osage Nation lacks a comprehensive tribal water plan to describe the quality and quantity of water resources in the Osage Nation, a 2,304-square-mile (mi2) area of rolling pastures, tallgrass prairie, and mixed woodlands in northeastern Oklahoma. A tribal water plan can be used to help manage the sustainable development of surface and groundwater resources, thereby helping to provide a better future for the Osage Nation and their neighbors, while preserving water resources for the benefit of the surrounding environment and future generations. To help meet these goals and contribute to increased knowledge of the quantity and quality of water resources and the hydrologic processes and factors affecting those resources, the U.S. Geological Survey (USGS) in cooperation with the Osage Nation began studies to evaluate the surface-water and groundwater resources of the Osage Nation. An important component of these studies is the development and application of numerical models to improve quantification and understanding of the hydrologic system. These models are needed to estimate and quantify the effects of historical and potential future water resource development for the Osage Nation.
This report describes the development and application of a precipitation-runoff model, the Osage Nation watershed model (ONWM). The ONWM is needed as a component of the Osage Nation integrated hydrologic model (ONIHM). At the time of this study, the ONIHM was being developed using the USGS computer software MODFLOW-One Water Hydrologic Flow Model (MODFLOW-OWHM). The intended use of the ONIHM is to simulate all surface-water and groundwater components of the hydrologic system for a 2,905-mi2 study area centered on the Osage Nation. The ONWM was developed using the USGS Precipitation Runoff Modeling System, version 4 (PRMS-IV) computer software, also referred to as PRMS in this report, for an 8,343-mi2 study area in northeastern Oklahoma and southeastern Kansas, centered on and including the areas of the Osage Nation and the ONIHM. The ONWM is to be used as part of the ONIHM to provide a direct coupling with spatially and temporally varying daily climate conditions affecting the ONIHM study area. As an integral part of the ONIHM, the ONWM (1) simulates the inflow boundary conditions from tributary basins in the region outside and surrounding the ONIHM area; (2) provides estimates of spatially and temporally distributed precipitation, air temperature, potential evapotranspiration (PET), actual evapotranspiration (ET), soil moisture, recharge, and streamflow in the ONIHM area; and (3) provides a preliminary water budget for the ONIHM area and the surrounding region, including tributary drainage basins outside of and next to the ONIHM.
The specific objectives of this study were to use the ONWM to (1) provide a systematic inventory of the historical distribution of water inflows from precipitation (rain or snow) falling on the land surface and flowing through the surface-water network, (2) provide a historical context of the variability and spatial and temporal distribution of these waters, and (3) provide estimates of water inflows and potential observations to the ONIHM. The application of the ONWM as a component of the ONIHM is needed for planned simulations using the ONIHM to improve the understanding of the hydrologic system and to develop a fully comprehensive water budget, including the use and movement of water across the landscape, in the surface-water network, and in groundwater aquifers under historical and potential future conditions.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Radio-collars and other radio-marking devices have been invaluable tools for wildlife managers for >40 years. These marking devices have improved our understanding of wildlife spatial ecology and demographic parameters and provided new data facilitating model development for species conservation and management. Although these tools have been used on virtually all North American ungulates, their deployment on feral horses (Equus ferus caballus) or burros (E. asinus) has been limited. To determine if radio-collars and radio-tags could be safely deployed on feral equids, we conducted a 1-year observational study in 2015 to investigate fit and wear of radio-collars on feral horses and burros kept in pastures/pens at the Bureau of Land Management contracted adoption facility in Pauls Valley, Oklahoma, USA. We assessed the impact of radio-collars and transmitter tags on individual behavior, body condition, and evaluated neck surface for effects. We tested 2 radio-collar shapes (teardrop and oval) and a radio-tag (i.e., avian backpack) braided into the mane and tail of horses. Behavior of mares did not differ between radio-collared (n = 12) and control (uncollared; n = 12) individuals. Despite the small sample size, collared burro jennies (n = 4) spent more time standing than controls (n = 4). Stallions wearing radio-collars (n = 9) fed less, moved less, and stood more than controls (n = 8). During the study, we did not detect injuries to the necks of mares or burro jennies, but stallions developed small sores (that healed while still wearing radio-collars and re-haired within 3 months). Two radio-collars occasionally flipped forward over the ears onto the foreheads of stallions. Although our study confirmed that radio-collars could be safely deployed on captive mares and jennies, stallions proved challenging for a variety of reasons. While our conclusions were optimistic, longer studies will be required to ensure radio-collar safety on free-ranging feral horses and burros.
","language":"English","publisher":"Berryman Institute","doi":"10.26077/127m-4x33","usgsCitation":"Schoenecker, K., King, S.R., and Collins, G.C., 2020, Evaluation of the impacts of radio-marking devices on feral horses and burros in a captive setting: Human Wildlife Interactions, v. 14, no. 1, p. 73-86, https://doi.org/10.26077/127m-4x33.","productDescription":"14 p.","startPage":"73","endPage":"86","ipdsId":"IP-104331","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":436958,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEUE9I","text":"USGS data release","linkHelpText":"Body condition score of horses wearing radio collars, weekly behavior data of treatments and controls, and monthly descriptive data of collar and radio tag effects, 2015-2016, Oklahoma, USA"},{"id":396430,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oklahoma","city":"Pauls Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.27432250976562,\n 34.649025753526985\n ],\n [\n -97.16033935546875,\n 34.649025753526985\n ],\n [\n -97.16033935546875,\n 34.77545980961412\n ],\n [\n -97.27432250976562,\n 34.77545980961412\n ],\n [\n -97.27432250976562,\n 34.649025753526985\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schoenecker, Kathryn A. 0000-0001-9906-911X","orcid":"https://orcid.org/0000-0001-9906-911X","contributorId":202531,"corporation":false,"usgs":true,"family":"Schoenecker","given":"Kathryn A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":835958,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"King, Sarah R. B.","contributorId":280059,"corporation":false,"usgs":false,"family":"King","given":"Sarah","email":"","middleInitial":"R. B.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":835959,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Gail C.","contributorId":280060,"corporation":false,"usgs":false,"family":"Collins","given":"Gail","email":"","middleInitial":"C.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":835960,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211551,"text":"70211551 - 2020 - The historic events at Kilauea Volcano in 2018: Summit collapse, rift zone eruption, and Mw 6.9 earthquake: Preface to the special issue","interactions":[],"lastModifiedDate":"2020-07-30T14:53:32.42354","indexId":"70211551","displayToPublicDate":"2020-05-20T09:47:22","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":"The historic events at Kilauea Volcano in 2018: Summit collapse, rift zone eruption, and Mw 6.9 earthquake: Preface to the special issue","docAbstract":"Kīlauea Volcano, on the Island of Hawaiʻi, has had a prominent role in the science of volcanology, and a long history of generating new insights into how volcanoes operate (Tilling et al. 2014; Garcia 2015). Native Hawaiians shared ideas on the behavior of the volcano with early Western visitors to Kīlauea, addressing the basic geometry of magma supply and transport (Ellis 1825; Bishop 1827). The recognition that magma originated at the summit and was transferred at shallow levels to the flanks implied that these ideas were rooted in centuries of observation preceding Western contact. The lava lake activity at Kīlauea’s summit in the 1800s and early 1900s fascinated early geologists, such as James Dana (1890), who published one of the first inquiries into the fundamental processes of Hawaiian volcanoes. The sustained activity led to the 1912 founding of the Hawaiian Volcano Observatory, one of the world’s first volcano observatories, by Thomas Jaggar (Tilling et al. 2014). Kīlauea’s activity in the 20th century contributed to the development of many modern volcano monitoring techniques (Tilling et al. 2014), which helped refine conceptual models of how volcanoes behave (Eaton and Murata, 1960).","language":"English","publisher":"Springer","doi":"10.1007/s00445-020-01377-5","usgsCitation":"Patrick, M.R., Johanson, I.A., Shea, T., and Waite, G., 2020, The historic events at Kilauea Volcano in 2018: Summit collapse, rift zone eruption, and Mw 6.9 earthquake: Preface to the special issue: Bulletin of Volcanology, v. 82, 46, 4 p., https://doi.org/10.1007/s00445-020-01377-5.","productDescription":"46, 4 p.","ipdsId":"IP-117306","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":456682,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00445-020-01377-5","text":"Publisher Index Page"},{"id":376889,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kilauea volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3144073486328,\n 19.385000077878544\n ],\n [\n -155.22789001464844,\n 19.385000077878544\n ],\n [\n -155.22789001464844,\n 19.44652177370614\n ],\n [\n -155.3144073486328,\n 19.44652177370614\n ],\n [\n -155.3144073486328,\n 19.385000077878544\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"82","noUsgsAuthors":false,"publicationDate":"2020-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Patrick, Matthew R. 0000-0002-8042-6639 mpatrick@usgs.gov","orcid":"https://orcid.org/0000-0002-8042-6639","contributorId":2070,"corporation":false,"usgs":true,"family":"Patrick","given":"Matthew","email":"mpatrick@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":794594,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johanson, Ingrid A. 0000-0002-6049-2225","orcid":"https://orcid.org/0000-0002-6049-2225","contributorId":215613,"corporation":false,"usgs":true,"family":"Johanson","given":"Ingrid","email":"","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":794595,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shea, Thomas","contributorId":236886,"corporation":false,"usgs":false,"family":"Shea","given":"Thomas","affiliations":[{"id":47560,"text":"University of Hawaii Manoa","active":true,"usgs":false}],"preferred":false,"id":794596,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Waite, Greg 0000-0002-7092-8125","orcid":"https://orcid.org/0000-0002-7092-8125","contributorId":215624,"corporation":false,"usgs":false,"family":"Waite","given":"Greg","email":"","affiliations":[{"id":36614,"text":"Michigan Tech","active":true,"usgs":false}],"preferred":false,"id":794597,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211306,"text":"70211306 - 2020 - Hydro-morphological characterization of coral reefs for wave runup prediction","interactions":[],"lastModifiedDate":"2020-09-24T14:29:39.117352","indexId":"70211306","displayToPublicDate":"2020-05-20T08:53:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Hydro-morphological characterization of coral reefs for wave runup prediction","docAbstract":"Many coral reef-lined coasts are low-lying with elevations <4 m above mean sea level. Climate-change-driven sea-level rise, coral reef degradation, and changes in storm wave climate will lead to greater occurrence and impacts of wave-driven flooding. This poses a significant threat to their coastal communities. While greatly at risk, the complex hydrodynamics and bathymetry of reef-lined coasts make flood risk assessment and prediction costly and difficult. Here we use a large (>30,000) dataset of measured coral reef topobathymetric cross-shore profiles, statistics, machine learning, and numerical modeling to develop a set of representative cluster profiles (RCPs) that can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the large dataset is reduced by clustering cross-shore profiles based on morphology and hydrodynamic response to typical wind and swell wave conditions. By representing a large variety of coral reef morphologies with a reduced number of RCPs, a computationally feasible number of numerical model simulations can be done to obtain wave runup estimates, including setup at the shoreline and swash separated into infragravity and sea-swell components, of the entire dataset. The predictive capability of the RCPs is tested against 5,000 profiles from the dataset. The wave runup is predicted with a mean error of 9.7–13.1%, depending on the number of cluster profiles used, ranging from 312 to 50. The RCPs identified here can be combined with probabilistic tools that can provide an enhanced prediction given a multivariate wave and water level climate and reef ecology state. Such a tool can be used for climate change impact assessments and studying the effectiveness of reef restoration projects, as well as for the provision of coastal flood predictions in a simplified (global) early warning system.
","language":"English","publisher":"Frontiers","doi":"10.3389/fmars.2020.00361","usgsCitation":"Scott, F., Antolinez, J.A., McCall, R.T., Storlazzi, C.D., Reiners, A., and Pearson, S., 2020, Hydro-morphological characterization of coral reefs for wave runup prediction: Frontiers in Marine Science, v. 7, 361, 20 p., https://doi.org/10.3389/fmars.2020.00361.","productDescription":"361, 20 p.","ipdsId":"IP-116940","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456686,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2020.00361","text":"Publisher Index Page"},{"id":436960,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C39WNE","text":"USGS data release","linkHelpText":"Coral reef profiles for wave-runup prediction"},{"id":376661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","noUsgsAuthors":false,"publicationDate":"2020-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Fred","contributorId":229615,"corporation":false,"usgs":false,"family":"Scott","given":"Fred","email":"","affiliations":[{"id":27619,"text":"TU Delft","active":true,"usgs":false}],"preferred":false,"id":793676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Antolinez, Jose A.A.","contributorId":177510,"corporation":false,"usgs":false,"family":"Antolinez","given":"Jose","email":"","middleInitial":"A.A.","affiliations":[],"preferred":false,"id":793677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCall, Robert T.","contributorId":148986,"corporation":false,"usgs":false,"family":"McCall","given":"Robert","email":"","middleInitial":"T.","affiliations":[{"id":12474,"text":"Deltares, Netherlands","active":true,"usgs":false}],"preferred":false,"id":793678,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":793679,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reiners, Ad","contributorId":229616,"corporation":false,"usgs":false,"family":"Reiners","given":"Ad","email":"","affiliations":[{"id":27619,"text":"TU Delft","active":true,"usgs":false}],"preferred":false,"id":793680,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pearson, Stuart","contributorId":193835,"corporation":false,"usgs":false,"family":"Pearson","given":"Stuart","affiliations":[],"preferred":false,"id":793681,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208929,"text":"sir20205053 - 2020 - Using remotely sensed data to map Joshua Tree distributions at Naval Air Weapons Station China Lake, California, 2018","interactions":[],"lastModifiedDate":"2020-05-21T11:43:03.892535","indexId":"sir20205053","displayToPublicDate":"2020-05-20T08:23:12","publicationYear":"2020","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":"2020-5053","displayTitle":"Using Remotely Sensed Data to Map Joshua Tree Distributions at Naval Air Weapons Station China Lake, California, 2018","title":"Using remotely sensed data to map Joshua Tree distributions at Naval Air Weapons Station China Lake, California, 2018","docAbstract":"Species distribution models (SDMs) that are derived through inference have been used to provide important insights toward species distributions. Their inferences can be robust in relation to known presences, but SDMs have error rates that cannot be quantified with certainty. For large plant species with unique signatures and in sparsely vegetated habitats, object-oriented satellite image interpretation provides a useful alternative to the more commonly used SDM approach. We tested visual image interpretation techniques in a pilot project to map the distribution of the Joshua tree (Yucca brevifolia), an arborescent succulent plant endemic to the Mojave Desert of North America. Naval Air Weapons Station China Lake (NAWS–CL) required assistance in mapping the distribution of Joshua trees across the 4,715 square kilometer (km2) military installation in support of their national security mission. Joshua trees were present on 1,307 1-km2 cells in the species distribution model, or 27.7 percent of the military installation. This increases the published range of Joshua trees at NAWS–CL by 90 percent and corrects for two stands of Joshua trees that were previously identified but do not exist. Remotely sensed satellite data in combination with ground surveys of Joshua trees produced a more accurate distribution map at a 1-kilometer resolution than did previous SDMs based on correlative modeling (area under the curve [AUC] 0.9064 versus 0.5848, respectively). Ancillary comparison with light detection and ranging (lidar) data indicated that satellite and lidar data were equally successful with slightly different sources of error, but that using them in combination produced the best results.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205053","usgsCitation":"Esque, T.C., Baird, P.E., Chen, F.C., Housman, D., and Holton, J.T., 2020, Using remotely sensed data to map Joshua Tree distributions at Naval Air Weapons Station China Lake, California, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5053, 13 p., https://doi.org/10.3133/sir20205053.","productDescription":"vi, 13 p.","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-106778","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":374807,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5053/sir20205053.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":374806,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5053/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Naval Air Weapons Station China Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.0316162109375,\n 35.04573815523954\n ],\n [\n -116.66931152343749,\n 34.99625375979014\n ],\n [\n -116.79565429687499,\n 36.461054075054314\n ],\n [\n -117.99316406249999,\n 36.474306755095235\n ],\n [\n -118.0316162109375,\n 35.04573815523954\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Model‐estimated monthly water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff (R)) for 146 United States (U.S.) Geological Survey 8‐digit hydrologic units located in the Colorado River Basin (CRB) are used to examine the temporal and spatial variability of the CRB water balance for water years 1901 through 2014 (a water year is the period from October 1 of one year through September 30 of the following year). Results indicate that the CRB can be divided into six subregions with similar temporal variability in monthly R. The water balance analyses indicated that approximately 75% of total water‐year R is generated by just one CRB subregion and that most of the R in the basin is derived from surplus (S) water generated during the months of October through April. Furthermore, the analyses show that temporal variability in S is largely controlled by the occurrence of negative atmospheric pressure anomalies over the northwestern conterminous U.S. (CONUS) and positive atmospheric pressure anomalies over the southeastern CONUS. This combination of atmospheric pressure anomalies results in an anomalous flow of moist air from the North Pacific Ocean into the CRB, particularly the Upper CRB. Additionally, the occurrence of extreme dry and wet periods in the CRB appears to be related to variability of the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation.
This study created digital terrain models (DTMs) from historical aerial images using Structure from Motion (SfM) for a variety of image dates, resolutions, and photo scales. Accuracy assessments were performed on the SfM DTMs, and they were compared to the United States Geological Survey’s three-dimensional digital elevation program (3DEP) light detection and ranging (LiDAR) DTMs to evaluate geomorphic change thresholds based on vertical accuracy assessments and elevation change methodologies. The results of this study document a relationship between historical aerial photo scales and predicted vertical accuracy of the resultant DTMs. The results may be used to assess geomorphic change thresholds over multi-decadal timescales depending on spatial scale, resolution, and accuracy requirements. This study shows that if elevation changes of approximately ±1 m are to be mapped, historical aerial photography collected at 1:20,000 scale or larger would be required for comparison to contemporary LiDAR derived DTMs.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12101625","usgsCitation":"Chirico, P.G., DeWitt, J.D., and Bergstresser, S.E., 2020, Evaluating elevation change thresholds between structure-from-motion DEMs derived from historical aerial photos and 3DEP LiDAR data: Remote Sensing, v. 10, no. 12, 1625, 16 p., https://doi.org/10.3390/rs12101625.","productDescription":"1625, 16 p.","ipdsId":"IP-118392","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":456696,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12101625","text":"Publisher Index Page"},{"id":376482,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","county":"Fairfax County","otherGeospatial":"Piney Branch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.30409622192383,\n 38.8538792131213\n ],\n [\n -77.23749160766602,\n 38.8538792131213\n ],\n [\n -77.23749160766602,\n 38.93230667504973\n ],\n [\n -77.30409622192383,\n 38.93230667504973\n ],\n [\n -77.30409622192383,\n 38.8538792131213\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Chirico, Peter G. 0000-0001-8375-5342","orcid":"https://orcid.org/0000-0001-8375-5342","contributorId":63838,"corporation":false,"usgs":true,"family":"Chirico","given":"Peter","email":"","middleInitial":"G.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":793216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeWitt, Jessica D. 0000-0002-8281-8134 jdewitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8281-8134","contributorId":5804,"corporation":false,"usgs":true,"family":"DeWitt","given":"Jessica","email":"jdewitt@usgs.gov","middleInitial":"D.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":793217,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bergstresser, Sarah E. 0000-0003-0182-5779 sbergstresser@usgs.gov","orcid":"https://orcid.org/0000-0003-0182-5779","contributorId":195556,"corporation":false,"usgs":true,"family":"Bergstresser","given":"Sarah","email":"sbergstresser@usgs.gov","middleInitial":"E.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":793218,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210166,"text":"70210166 - 2020 - Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling","interactions":[],"lastModifiedDate":"2020-05-19T14:46:15.127607","indexId":"70210166","displayToPublicDate":"2020-05-19T09:41:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling","docAbstract":"This study employed a novel combination of data (winter cover crop cost-share enrollment records, satellite remote sensing of wintertime vegetation, and results of Soil and Water Assessment Tool (SWAT) water quality simulations) to estimate the environmental performance of winter cover crops (WCC) at the watershed scale, from 2008 through 2017, within the Tuckahoe sub-watershed of the Choptank River. The Choptank is a river basin within the Chesapeake Bay watershed and, as a focus watershed for the U.S. Department of Agriculture’s Conservation Effects Assessment Project (CEAP), has been the subject of considerable study assessing linkages between land use and water quality. Farm enrollment data from the Maryland Agricultural Cost Share (MACS) program documented a strong increase in the use of WCC within the Tuckahoe watershed during the study period, from 27% of corn fields and 9% of soybean fields in 2008 to 89% of corn fields and 46% of soybean fields in 2016. Satellite remote sensing of wintertime ground cover detected increased wintertime vegetation following corn crops, in comparison to full season and double cropped soybean, consistent with patterns of cover crop implementation. Although inter-annual variation in climate strongly affected observed levels of vegetation, with warm winters resulting in increased vegetative cover, a 30-year analysis of wintertime greenness revealed significant increases in wintertime vegetation associated increased adoption of WCC. The predominant WCC species recorded by the MACS program as planted in the Tuckahoe watershed were wheat (68.1%), barley (16.1%), and rye (7.2%). The MACS WCC enrollment data were combined with output from the SWAT model, calibrated to streamflow and nutrient loading from the Tuckahoe watershed, to estimate water quality impacts based on known distribution of cover crop species and planting dates (2008 to 2017). Results indicated a 25% overall 10-year reduction in nitrate leaching from cropland resulting from cover crop adoption, rising to an estimated 38% load reduction in 2016 when 64% of fields were planted to cover crops. A large portion of WCC (39.3%) were planted late (after October 15) and planted to wheat (68.1%). Increased environmental benefits would be achieved by shifting agronomic methods away from late-planted wheat.","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.75.3.362","usgsCitation":"Hively, W.D., Lee, S., Sadeghi, A.M., McCarty, G.W., Lamb, B.T., Soroka, A.M., Keppler, J., Yeo, I., and Moglen, G.E., 2020, Estimating the effect of winter cover crops on nitrogen leaching using cost-share enrollment data, satellite remote sensing, and Soil and Water Assessment Tool (SWAT) modeling: Journal of Soil and Water Conservation, v. 75, no. 3, p. 362-375, https://doi.org/10.2489/jswc.75.3.362.","productDescription":"14 p.","startPage":"362","endPage":"375","ipdsId":"IP-106326","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":456701,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.75.3.362","text":"Publisher Index Page"},{"id":374921,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.783203125,\n 36.98500309285596\n ],\n [\n -75.0146484375,\n 36.98500309285596\n ],\n [\n -75.0146484375,\n 39.57182223734374\n ],\n [\n -77.783203125,\n 39.57182223734374\n ],\n [\n -77.783203125,\n 36.98500309285596\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789371,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Sangchul","contributorId":201237,"corporation":false,"usgs":false,"family":"Lee","given":"Sangchul","email":"","affiliations":[],"preferred":false,"id":789372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadeghi, Ali M.","contributorId":131147,"corporation":false,"usgs":false,"family":"Sadeghi","given":"Ali","email":"","middleInitial":"M.","affiliations":[{"id":7262,"text":"USDA-ARS, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705","active":true,"usgs":false}],"preferred":false,"id":789373,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCarty, Gregory W.","contributorId":192367,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":789374,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lamb, Brian T.","contributorId":211092,"corporation":false,"usgs":false,"family":"Lamb","given":"Brian","email":"","middleInitial":"T.","affiliations":[{"id":38178,"text":"City College of New York","active":true,"usgs":false}],"preferred":false,"id":789375,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Soroka, Alexander M. 0000-0002-8002-5229","orcid":"https://orcid.org/0000-0002-8002-5229","contributorId":201664,"corporation":false,"usgs":true,"family":"Soroka","given":"Alexander","email":"","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789376,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Keppler, Jason","contributorId":218039,"corporation":false,"usgs":false,"family":"Keppler","given":"Jason","email":"","affiliations":[{"id":39731,"text":"Maryland Department of Agriculture, Office of Resource Conservation","active":true,"usgs":false}],"preferred":false,"id":789377,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yeo, In-Young","contributorId":131145,"corporation":false,"usgs":false,"family":"Yeo","given":"In-Young","email":"","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":789378,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moglen, Glenn E.","contributorId":106585,"corporation":false,"usgs":false,"family":"Moglen","given":"Glenn","email":"","middleInitial":"E.","affiliations":[{"id":13220,"text":"The Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":789379,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70210258,"text":"70210258 - 2020 - Local to landscape-level controls of water fluxes through Hawaiian forests: Effects of invasive animals and plants on soil infiltration capacity across substrate and moisture gradients","interactions":[],"lastModifiedDate":"2020-05-27T14:25:26.537122","indexId":"70210258","displayToPublicDate":"2020-05-19T09:20:21","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Local to landscape-level controls of water fluxes through Hawaiian forests: Effects of invasive animals and plants on soil infiltration capacity across substrate and moisture gradients","docAbstract":"Given the potential effect of invasive plants and animals to water fluxes through forests, the invasive-driven degradation of native ecosystems is a topic of great concern for many downstream land and water managers. The infiltration rate determines the partitioning between runoff and infiltration into soil in Hawaiian forests and beyond. Thus, to explore the ecohydrological effects of plant and animal invasion in mesic and wet forests in Hawaii, we measured soil infiltration capacity in multiple fenced (i.e., ungulate-free)/unfenced and native/invaded forest sites along moisture and substrate age gradients across the islands of Hawai‘i and Kaua‘i. We also characterized forest composition and structure and soil characteristics at these sites to assess the direct and vegetation-mediated impacts of invasive species on infiltration capacity.\nInfiltration capacity is highly variable across forested sites and the wider landscape. Much of this variability is determined by a complex set of soil, vegetation, and disturbance factors that affect infiltration capacity at the immediate surrounding of measurement plots. Consequently, the effect of any given factor can be masked by variability in other factors. However, by controlling for variability in soil and vegetation conditions at a local plot level, we found that the presence of invasive species in forests has complex and sometimes non-intuitive effects on infiltration.\nOur final models showed that invasive ungulates negatively affect soil infiltration capacity consistently across the wide moisture and substrate age gradients considered. Additionally, because several soil characteristics known to be affected by ungulates were associated with local infiltration rates (e.g., soil organic matter, bare soil cover, soil depth), the long-term secondary effects of high ungulate densities in Hawaiian forests may be higher than effects observed in this study. These results provide clear evidence for land managers that ungulate control efforts likely improve ecohydrologic function to mesic and wet forest systems critical to protecting downstream and nearshore resources and maintaining groundwater recharge.\nCompared to ungulate effects, the effect of invasive plants on water infiltration capacity in Hawaiian forests appeared much more complex. In general, elements of forest structure including increased canopy, understory and floor cover, greater presence of large roots, and lower grass and bare soil covers were positively associated with water infiltration. Whether native or not, a plant species’ potential to alter infiltration rates in Hawaiian forests was likely to depend on its physiognomy and how it affects forest community structure. For instance, while the cover of native dominant tree ‘ōhi‘a, Metrosideros polymorpha, was found to be positively associated with infiltration capacity (perhaps as an indicator of overall forest integrity), invasive Himalayan ginger, Hedychium gardnerianum, was also positively correlated with infiltration capacity, possibly due to preferential flow channels created by the presence of large root mats.\nFew studies have conducted comprehensive integrated ecological and hydrological sampling in forests of high conservation value. While we show there are large benefits to understanding how conservation efforts may help shape water fluxes, we also found that the commonly used study design for infiltration studies used here and elsewhere (i.e., adjacent paired sites) could be modified to provide more accurate effects of invasion in future studies for ecosystems in Hawaii and beyond.","language":"English","publisher":"Hawai‘i Cooperative Studies Unit","collaboration":"Kohala Watershed Partnership; Three Mountain Alliance; Hawaii Water Resource Commissioner; The Nature Conservancy, Hawaii Department of Land and Natural Resources; USGS PIWSC; University of California – Santa Barbara; University of Hawai‘i at Hilo – Hawai‘i Cooperative Studies Unit","usgsCitation":"Fortini, L., Leopold, C., Perkins, K., Chadwick, O.A., Yelenik, S.G., Jacobi, J.D., Bishaw, K., Gregg, M., and Rosa, S.N., 2020, Local to landscape-level controls of water fluxes through Hawaiian forests: Effects of invasive animals and plants on soil 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Ridgecrest Earthquakes","docAbstract":"Strong seismic waves from the July 2019 Ridgecrest, California, earthquakes displaced rocks in proximity to the M 7.1 mainshock fault trace at several locations. In this report, we document large boulders that were displaced at the Wagon Wheel Staging Area (WWSA), approximately 4.5 km southeast of the southern terminus of the large M 6.4 foreshock rupture (hereafter “the large foreshock”) and 9 km southwest of the nearest approach of the M 7.1 mainshock surface rupture. Some boulders appear to have slid along essentially flat surfaces, which suggest that dynamic stresses overcame the coefficient of friction. Other boulders appear to have rocked within their sockets. In both cases, we use simple mechanical models to estimate total peak dynamic accelerations between 0.5 and 1g, commensurate with modified Mercalli intensity 9. It is unclear if the strongest shaking at this location occurred during the large foreshock or the M 7.1 mainshock. The inferred accelerations are higher than predicted mainshock ground motions at WWSA, although local high accelerations could have been generated by path, site, or source effects. Gaps between boulders and their sockets are easily visible in the immediate aftermath of earthquakes and provide a quick indication of strong shaking. More importantly, the gaps quickly fill with surficial organic debris, including seeds and leaves of the year, that quickly become entombed. Boulders may thus potentially be extracted to examine gap fillings associated with past earthquakes, providing a new datable paleoseismic method.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200029","usgsCitation":"Sleep, N., and Hough, S.E., 2020, Mild displacements of boulders during the 2019 Ridgecrest Earthquakes: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1579-1588, https://doi.org/10.1785/0120200029.","productDescription":"10 p.","startPage":"1579","endPage":"1588","ipdsId":"IP-119088","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.71929444649237,\n 35.663769671293025\n ],\n [\n -117.71929444649237,\n 35.5783607422547\n ],\n [\n -117.59492272074345,\n 35.5783607422547\n ],\n [\n -117.59492272074345,\n 35.663769671293025\n ],\n [\n -117.71929444649237,\n 35.663769671293025\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Sleep, Norman","contributorId":245424,"corporation":false,"usgs":false,"family":"Sleep","given":"Norman","affiliations":[{"id":49192,"text":"Stanford","active":true,"usgs":false}],"preferred":false,"id":927666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hough, Susan E. 0000-0002-5980-2986","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":263442,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927667,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210172,"text":"70210172 - 2020 - Representing the function and sensitivity of coastal interfaces in Earth system models","interactions":[],"lastModifiedDate":"2020-05-19T14:12:47.917987","indexId":"70210172","displayToPublicDate":"2020-05-18T08:44:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"Representing the function and sensitivity of coastal interfaces in Earth system models","docAbstract":"Along coastal interfaces, components of the Earth system interact to regulate ecosystem functions and Earth’s climate. Between the land and ocean, diverse coastal ecosystem types transform, store, and transport material. A dynamic two-way exchange of energy and matter is driven by hydrological and marine processes such as river and groundwater discharge, tides, waves, and storms. Global models lack representation of coastal processes and related feedbacks on Earth’s climate and ecosystems, impeding their predictions of coastal and global response to change. We recommend leveraging existing monitoring networks and regional models to develop and validate global models that include the coastal interface.","language":"English","publisher":"Nature","doi":"10.1038/s41467-020-16236-2","usgsCitation":"Ward, N., Megonigal, J.P., Bond-Lamberty, B., Bailey, V., Butman, D., Canuel, E., Diefenderfer, H., Ganju, N., Goni, M., Graham, E.B., Hopkinson, C., Khangaonkar, T., Langley, A., McDowell, N., Myers-Pigg, A., Neumann, R., Osburn, C., Price, R., Rowland, J., Sengupta, A., Simard, M., Thornton, P.E., Tzortziou, M., Vargas, R., Weisenhorn, P., and Windham-Myers, L., 2020, Representing the function and sensitivity of coastal interfaces in Earth system models: Nature Communications, v. 11, 2458, 14 p., https://doi.org/10.1038/s41467-020-16236-2.","productDescription":"2458, 14 p.","ipdsId":"IP-115692","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456725,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-020-16236-2","text":"Publisher Index Page"},{"id":374918,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2020-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Ward, Nicholas","contributorId":224751,"corporation":false,"usgs":false,"family":"Ward","given":"Nicholas","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":789400,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Megonigal, J. 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Delaware","active":true,"usgs":false}],"preferred":false,"id":789420,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Weisenhorn, Pamela","contributorId":224771,"corporation":false,"usgs":false,"family":"Weisenhorn","given":"Pamela","email":"","affiliations":[{"id":17946,"text":"Argonne National Laboratory","active":true,"usgs":false}],"preferred":false,"id":789421,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","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},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789422,"contributorType":{"id":1,"text":"Authors"},"rank":26}]}} ,{"id":70215085,"text":"70215085 - 2020 - Aligning climate models with stakeholder needs: Advances in communicating future rainfall uncertainties for south Florida decision makers","interactions":[],"lastModifiedDate":"2020-10-07T13:12:42.218246","indexId":"70215085","displayToPublicDate":"2020-05-18T08:08:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Aligning climate models with stakeholder needs: Advances in communicating future rainfall uncertainties for south Florida decision makers","docAbstract":"Changes in future precipitation are of great importance to climate data users in South Florida. A recent U.S. Geological Survey workshop, “Increasing Confidence in Precipitation Projections for Everglades Restoration,” highlighted a gap between standard climate model outputs and the climate information needs of some key Florida natural resource managers. These natural resource managers (hereafter broadly defined as “climate data users”) need more tailored output than is commonly provided by the climate modeling community. This study responds to these user needs by outlining and testing an adaptable methodology to select output from ensemble climate‐model simulations based on user‐defined precipitation drivers, using statistical methods common across scientific disciplines. This methodology is developed to provide a “decision matrix” that guides climate data users to specify the subset of models most important to their work based on each user's season (winter, summer, and annual) and the condition (dry, wet, neutral, and no threshold events) of interest. The decision matrix is intended to better communicate the subset of models best representing precipitation drivers. This information could increase users' confidence in climate models as a resource for natural resource planning and can be used to direct future dynamical downscaling efforts. This methodology is based in dynamical processes controlling precipitation via remote and local teleconnections. We also suggest that future climate studies in South Florida include high‐resolution climate model runs (i.e., ocean eddy resolving) in conjunction with dynamical downscaling to adequately capture precipitation variability.
Barrier islands, headlands, and coastal shorelines provide numerous valuable ecosystem goods and services, including storm protection and erosion control for the mainland, habitat for fish and wildlife, salinity regulation in estuaries, carbon sequestration in marshes, and areas for recreation and tourism. These coastal features are dynamic environments because of their position at the land-sea interface. Storms, wave energy, tides, currents, and relative sea-level rise are powerful forces that shape local geomorphology and habitat distribution. In order to make more informed decisions, coastal resource managers require insights into how these dynamic systems are changing through time.
In 2005, Louisiana’s Coastal Protection and Restoration Authority, in partnership with the University of New Orleans and the U.S. Geological Survey, developed the Barrier Island Comprehensive Monitoring (BICM) Program. The goal of the BICM Program is to develop long-term datasets for habitat coverage, shoreline assessments, shoreline position, topobathymetric changes, and sediment characterization to assist with planning, designing, evaluating, and maintaining current and future barrier shorelines. The overall objectives of the study described in this report were to (1) map habitats for 2008 and 2015–16 for BICM coastal reaches and (2) map habitat change between these two time periods.
This report highlights the second phase of habitat analyses for the BICM Program. This work builds on a previous habitat analysis conducted by the University of New Orleans, which included the development of habitat maps for 1996/1998, 2001, 2004, and 2005, along with habitat change maps. For this current effort, a new 15-class habitat scheme was developed from the original BICM scheme to further delineate various dune habitats, including meadow habitat found along the backslopes of dunes, to distinguish between marsh and mangrove, and to distinguish between beach and unvegetated barrier flat habitats. Additionally, a geographic object-based image analysis-based mapping framework was used to incorporate relative topography and address elevation uncertainty in light detection and ranging data to assist with mapping dune and intertidal habitats.
For the entire BICM region, the area experiencing a change in a land/water category (that is, land gain or land loss) was 3.4 percent, of which, 59.2 percent was land gain and 40.8 percent was land loss. Areal coverages of meadow, mangrove, scrub/shrub, and vegetated dune increased from 2008 to 2015–16, whereas areal coverages of beach, grassland, and intertidal decreased. The decrease in intertidal, however, was largely due to differing water levels in the orthophotography between the two time periods. Regional analyses of habitat coverage and habitat change captured the dynamic nature of these systems and the effects of restoration efforts, most notably in the Late Lafourche Delta, Modern Delta, and Chandeleur Islands regions. For instance, in the Modern Delta region there was a marked increase in unvegetated flat, meadow, mangrove, scrub/shrub, beach, unvegetated dune, and vegetated dune. As a result, this region experienced the highest percent change for land/water classes (6.6 percent) with land gain accounting for much of this change (70.8 percent). In contrast, the Acadiana Bays region had the highest relative percent loss of all regions. The region had a percent change for land/water classes of 2.8 percent, of which, 79.7 percent was land loss.
The results of this study provide information about the areal coverage and distribution of habitats for two recent time periods and change over about an 8-year period. These data can be used to evaluate changes along the Louisiana Gulf of Mexico shoreline, including gradual changes caused by coastal processes, restoration actions, and (or) episodic events, such as hurricanes and extreme storms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201030","collaboration":"Prepared in cooperation with the Louisiana Coastal Protection and Restoration Authority","usgsCitation":"Enwright, N.M., SooHoo, W.M., Dugas, J.L., Conzelmann, C.P., Laurenzano, C., Lee, D.M., Mouton, K., and Stelly, S.J., 2020, Louisiana Barrier Island Comprehensive Monitoring Program—Mapping habitats in beach, dune, and intertidal environments along the Louisiana Gulf of Mexico shoreline, 2008 and 2015–16: U.S. Geological Survey Open-File Report 2020–1030, 57 p., https://doi.org/10.3133/ofr20201030.","productDescription":"Report: ix, 57 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","ipdsId":"IP-114268","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":436983,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRT54Z","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 habitat map, Chandeleur Islands Region"},{"id":436982,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E94E33","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2016 habitat map, Chandeleur Islands Region"},{"id":436981,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UBUO7C","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2016 habitat change, Chandeleur Islands Region"},{"id":436980,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LUPB9N","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2015 habitat change, East Chenier Region"},{"id":436979,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N0GKPB","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2016 habitat change, Acadiana Bays Region"},{"id":436978,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ABPHMC","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 Habitat Map, Acadiana Bays Region"},{"id":436977,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91F6GQY","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 habitat map, East Chenier Region"},{"id":436976,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KSG6WX","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2015 Habitat Map, East Chenier Region"},{"id":436975,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SKS31W","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2015/16 Habitat Map, Acadiana Bays Region"},{"id":436974,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DW2Y25","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008-2016 habitat change, Late Lafourche Delta Region"},{"id":436973,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UDQ0U0","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 to 2016 habitat change, Modern Delta Region"},{"id":436972,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L2GU4R","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2008 habitat map, West Chenier Region"},{"id":436971,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95TQ72U","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 2015 habitat map, West Chenier Region"},{"id":436970,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ERLQ1V","text":"USGS data release","linkHelpText":"Louisiana Barrier Island Comprehensive Monitoring Program - 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U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506–3152
To track the illegal pangolin trade from Africa to Asia, we analyzed 1800 DNA samples from 30 seizures of African pangolin scales in Hong Kong during the period 2012–2016. We concluded that all four African pangolin species were present in trade, and that the white-bellied pangolin (Phataginus tricuspis) appeared most frequently (88.5%) in our samples. All six previously described phylogeographic lineages originating from the entire distribution range of P. tricuspis were found in the seizures, and the western central African lineage alone accounted for 67.1% of the samples of this species. Confirmed by modelling data, high DNA haplotype richness was present in most of the pangolin scale seizures, including those contained in small air parcels and large-volume sea shipments. Results suggest that African pangolins were hunted across large areas of their natural range and then delivered to a small number of trade transit hubs. Our study illustrates the utility of genetic analysis for characterizing the illegal pangolin trade and identifying the geographic origin of poaching hotspots.
Sedimentation and turbidity have effects on habitat suitability in the San Francisco Bay‐Delta (Bay‐Delta), concerning key species in the bay as well as the ability of the delta marshes to keep pace with sea level rise. A daily rainfall runoff and transport model of the Sacramento River Basin of northern California was developed to simulate streamflow and suspended sediment transport to the Bay‐Delta for the next century (water years, WY2010–2099). The model was calibrated to historical streamflow and sediment data and applied using 10 Global Climate Models with two representative concentration pathways (RCP) each for WY1980–2099 from the IPCC 5th Assessment Report. Results indicate average increases in peak streamflow of +58% and +66% for the RCP 4.5 and 8.5 ensembles, respectively, by mid‐century and +62 and +96% by end‐of‐century. Sediment loads increased by +39% and +69% by end‐of‐century. Suspended sediment concentrations (SSC) increased on average by +4.6% and +6.7% for RCP 4.5 and 8.5, respectively, by end‐of‐century. Individual scenario results varied, and statistically significant increasing trends of sediment loads to the Bay‐Delta were found for the RCP 4.5 and 8.5 ensembles and five individual scenarios. Increased suspended sediment loads may have negative effects such as contaminant transport but also have positive effects that help protect against sea level rise, increase turbidity and fish habitat, and sustain wetland habitats in the Bay‐Delta.
This study examined the activity of the endemic Hawaiian hoary bat (Lasiurus cinereus semotus) at wind turbines operated by Auwahi Wind Energy, LLC, on southern Maui Island, from August to November 2018. The research was conducted to assess the potential effect of wind speed and turbine operation on bat presence and behavior and compared information obtained from both acoustic monitoring and thermal videography.
|
Chemical gradients between fresh, brackish and saline waters shape biogeochemical reactions and organic matter transformation within subterranean estuaries. In the Yucatán Peninsula’s karst subterranean estuary (KSE), methane and dissolved organic matter generated during the anaerobic decomposition of tropical forest vegetation are transported into flooded cave networks where microbial consumption greatly reduces their concentrations in the groundwater. To test the hypothesis that chemoclines associated with salinity gradients of the KSE are sites of methane oxidation, we obtained methane concentration and δ13C profiles of unprecedented vertical resolution from within a fully-submerged cave system located 6.6 km inland from the coastline using the ‘OctoPiPi’ (OPP) water sampler. Along a 12–24 cm thick low-salinity-halocline at ∼4.5 m water depth, salinity increased from fresh to brackish (0.2–1.8 psu), methane concentrations decreased, and δ13C values increased, as expected for microbial methane oxidation. The underlying brackish water had elevated oxygen concentrations compared to the always anoxic freshwater, suggesting that aerobic methane oxidation is the dominant process facilitating methane consumption. By contrast, as salinity increased from 1.8 to 36 psu through a 24–36 cm thick high-salinity-halocline between the meteoric lens and the saline groundwater at ∼20 m water depth, methane concentrations and δ13C values were constant. Conservative mixing and kinetic isotope models incorporating the methane data confirm a hotspot for microbial methane oxidation at the low-salinity-halocline. At least 98% of methane originating in the anoxic freshwaters was removed before its transport via channelized flow towards the coastline. These findings provide novel insight into the spatial constraints of methane dynamics within a karst subterranean estuary.
Context
Development of systematic methods for conservation planning has improved effectiveness and efficiency of implementing such plans. The lesser prairie-chicken (Tympanuchus pallidicinctus) is a grouse species of conservation concern native to the southwestern Great Plains of the United States. Recent lesser prairie-chicken conservation planning has involved identifying ecologically important areas but has not incorporated economic data into prioritization of areas to target for conservation management.
Objectives
We used the program Marxan to develop a decision-support tool for managers in Kansas to prioritize tracts for improving lesser prairie-chicken habitat quality and increasing habitat availability. We developed three different conservation scenarios and evaluated the tradeoffs among multiple planning objectives in these scenarios.
Methods
We incorporated population targets from an existing conservation plan and agricultural economic data to help select land with maximum ecological value and minimum economic productivity to prioritize for lesser prairie-chicken conservation. We compared potential conservation plans and incorporated a post hoc connectivity model to test potential for individuals to travel among habitat patches in these plans during dispersal events.
Results
We found that different conservation scenarios led to different solutions, though differences varied by ecoregion. Potential solutions for all scenarios contained habitat patches not currently included in existing conservation plans and had high connectivity potential.
Conclusions
These results provide context for spatial prioritization of lesser prairie-chicken habitat management in Kansas. Application of this approach to species of conservation interest could help managers incorporate socioeconomic factors into planning methods and identify important tracts for conservation currently overlooked by existing planning methods.
Factors affecting iron fouling in wet areas adjacent to roadways were investigated by collecting field rock cut and aqueous physicochemical data; developing exploratory predictive models; and developing geochemical models. Basic data included the identification of iron fouling from aerial imagery and field visits at 374 New Hampshire rock cut locations, and their associated rock-fill sites. Based on field water quality measurements from wet areas at 36 of the rock-fill sites, the occurrence of iron fouling was associated with higher values of specific conductance, lower concentrations of dissolved oxygen and lower pH compared to areas without iron fouling. A statistical model, using boosted regression trees, was developed to predict the occurrence of iron fouling in wet areas adjacent to roadways where rock-fill from nearby rock cuts was used in roadway construction. The model was used to develop a continuous iron fouling probability map for the state of New Hampshire that can be used to better understand the occurrence of iron fouling. Geochemical models illustrate how iron fouling of waters increases along roadways built with fill from sulfidic rock cuts as a result of acid generation from pyrite dissolution and ferrous iron (Fe2+) oxidation and increases in areas with greater specific conductance from deicing runoff caused by cation exchange. More iron is precipitated as goethite in simulations that include pyrite, and in simulations with deicing salts added, indicating that rock-fill sites with rocks that contain pyrite and water with greater salt content could have enhanced iron fouling.
","language":"English","publisher":"Springer","doi":"10.1007/s42452-020-2849-2","usgsCitation":"Lombard, M.A., Lombard, P.J., Brown, C., and Degnan, J., 2020, A multi-model approach toward understanding iron fouling at rock-fill drainage sites along roadways in New Hampshire, USA: SN Applied Sciences, 1073, 16 p., https://doi.org/10.1007/s42452-020-2849-2.","productDescription":"1073, 16 p.","ipdsId":"IP-100784","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":456777,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s42452-020-2849-2","text":"Publisher Index Page"},{"id":436987,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TUOH95","text":"USGS data release","linkHelpText":"Iron fouling data associated with drainage from roadway sites constructed with rock fill in New 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Melissa A. 0000-0001-5924-6556 mlombard@usgs.gov","orcid":"https://orcid.org/0000-0001-5924-6556","contributorId":198254,"corporation":false,"usgs":true,"family":"Lombard","given":"Melissa","email":"mlombard@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":794079,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lombard, Pamela J. 0000-0002-0983-1906","orcid":"https://orcid.org/0000-0002-0983-1906","contributorId":203509,"corporation":false,"usgs":true,"family":"Lombard","given":"Pamela","email":"","middleInitial":"J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794080,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, Craig J. 0000-0002-3858-3964","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":210450,"corporation":false,"usgs":true,"family":"Brown","given":"Craig J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794081,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Degnan, James R. 0000-0002-5665-9010","orcid":"https://orcid.org/0000-0002-5665-9010","contributorId":218796,"corporation":false,"usgs":true,"family":"Degnan","given":"James R.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794082,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210180,"text":"70210180 - 2020 - Do empirical observations support commonly-held climate change range shift hypotheses? A systematic review protocol","interactions":[],"lastModifiedDate":"2020-05-19T12:35:26.049125","indexId":"70210180","displayToPublicDate":"2020-05-14T07:27:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5897,"text":"Environmental Evidence","active":true,"publicationSubtype":{"id":10}},"title":"Do empirical observations support commonly-held climate change range shift hypotheses? A systematic review protocol","docAbstract":"Background \nAmong the most widely anticipated climate-related impacts to biodiversity are geographic range shifts, whereby species shift their spatial distribution in response to changing climate conditions. In particular, a series of commonly articulated hypotheses have emerged: species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to climate change, reflecting an underlying hypothesis that species will move to cooler locations to track spatial changes in the temperature of their current range. Yet, many species are not demonstrating range shifts consistent with these hypotheses. Resolving this discrepancy and providing effective explanations for the observed variability in species’ range shifts is urgently needed to help support a range of natural resource management decisions. Here, we propose a protocol to review the body of evidence for commonly-held climate change range shift hypotheses at the species level focusing on observed latitudinal, longitudinal, elevational, and depth shifts in response to temperature and precipitation changes. We aim to answer the question: what is the impact of anthropogenic climate change (specifically changes in temperature and precipitation) on species ranges?\n \nMethods \nIn this review protocol, we propose to conduct a systematic search of literature from internet databases and search engines in English. Articles will be screened in a two-stage process (title/abstract and full text) to evaluate whether they meet a list of eligibility criteria (e.g., presents species-level data, compares >1 time period). Initial data coding and extraction will be completed by four reviewers and checked by a secondary reviewer from among our co-authors. We will perform a formal meta-analysis to document estimated effect size using the subset of available range-shift data expressed in distance per time (e.g., km/decade). We will also use multinomial logistic regression models to assess the probability that species are shifting in a direction that supports our hypotheses (i.e. towards higher latitudes, greater elevations, and deeper depths). We will account for study methodology as a potential source of variation.","language":"English","publisher":"Springer Nature","doi":"10.1186/s13750-020-00194-9","collaboration":"","usgsCitation":"Rubenstein, M.A., Weiskopf, S.R., Carter, S., Eaton, M.J., Johnson, C., Lynch, A., Miller, B.W., Morelli, T.L., Rodriguez, M.A., Terando, A., and Thompson, L., 2020, Do empirical observations support commonly-held climate change range shift hypotheses? A systematic review protocol: Environmental Evidence, v. 9, 10, 10 p., https://doi.org/10.1186/s13750-020-00194-9.","productDescription":"10, 10 p.","ipdsId":"IP-113427","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":456784,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13750-020-00194-9","text":"Publisher Index Page"},{"id":374913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2020-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Rubenstein, Madeleine A. 0000-0001-8569-781X mrubenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-8569-781X","contributorId":203206,"corporation":false,"usgs":true,"family":"Rubenstein","given":"Madeleine","email":"mrubenstein@usgs.gov","middleInitial":"A.","affiliations":[{"id":411,"text":"National Climate 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The R–QWTREND package is a collection of functions written in R, an open source language and a general environment for statistical computing and graphics. The package uses a parametric time-series model to express logarithmically transformed concentration in terms of flow-related variability, trend, and serially correlated model errors. Flow-related variability captures natural variability in concentration on the basis of concurrent and antecedent streamflow. The trend identifies systematic changes in concentration in terms of potential step trends, piecewise monotonic trends, or user-specified trends. Maximum likelihood estimation is used to estimate model parameters and determine the best-fit trend model. This report describes the time-series model and statistical methodology behind R–QWTREND and provides formal documentation for installing and using the package.
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U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
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Rapid City, SD 57702