From April-May 2024, the NASA Mentors who span eleven Distributed Active Archive Centers (DAACs) co-led the third Champions Cohort with the NASA Openscapes project team, this year focusing on, teaching lessons they adapted for geospatial and cloud analysis. The Cohort included nine international research teams from academia and government that were curious about working with NASA Earthdata in the Cloud. Many teams were interested in using data from multiple DAACs. User cloud adaption takes time, given the new conceptual mindsets and technical skillsets it requires. During the ten weeks we worked together, NASA Mentors refined and extended previous lessons to focus on thinking through and planning the transition to using the Cloud for science research and applications, and initial experiments using the Cloud through our 2i2c JupyterHub. Below are these updates and YouTube clips!
There were also recurring themes/questions that we have heard before, some of which remain as open questions and continue to remain a challenge. Importantly, Amazon Web Services (AWS) Cloud onboarding, when to use what resources, how to set them up, and how to discuss needs with organizational leadership and IT staff, which often falls outside the scope of NASA DAACs, yet it’s a key element of helping users adopt the Cloud and use NASA data in the Cloud. It is encouraging to hear some of the champions starting to have conversations with their institutions, IT departments, and making their needs known, which is likely a big part of the solution, too. We are thankful to NASA Openscapes Champions for informing and nudging these conversations! All of this work is underpinned by Openscapes and NASA’s commitment to open science practices and a kinder collaborative culture. This cohort is funded by NASA and is part of our NASA Openscapes Framework project.
","language":"English","publisher":"NASA","usgsCitation":"Thornton, M., Taglialatela, C., Lopez, L., Fisher, M., Hunzinger, A., Jami, M., Lind, B.M., Nickles, C., Teucher, A., Merrelli, A., Robinson, E., and Lowndes, J., 2024, NASA Champions 2024: Data strategies for when to use cloud, coding strategies for parallelization, & first examples of big science in the Cloud, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-167616","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":431422,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://nasa-openscapes.github.io/news/2024-07-24-2024-nasa-champions-cohort/"},{"id":431457,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thornton, 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The BLM administers, manages, and protects 19 of these trails as part of its system of national conservation lands. Various laws, regulations, and policies require that the BLM conduct and maintain an inventory to protect trail-related resources, qualities, values, associated settings, and primary use or uses. There are set procedures for conducting inventory, assessment, and monitoring (IAM) of NSHTs, as outlined in volumes 1 and 2 of BLM Technical Reference 6280-1. One of the first steps in the IAM process is deciding the area along a trail to inventory. However, volumes 1 and 2 of BLM Technical Reference 6280-1 do not specify how the land area to be inventoried should be delineated. The BLM calls these focus areas for IAM efforts “inventory analysis units” (IAUs), which are defined as the geospatial boundary for the location of an inventory along a trail. This report reviews the approach used to delineate the IAUs for an inventory effort and identifies best practices for creating initial IAUs, termed “draft IAUs.” Draft IAUs would provide standardization across multiple management jurisdictions by applying the same parameters for their delineation. These draft IAUs would provide trail managers with an area surrounding NSHTs that would trigger the need for an inventory if a project were proposed within it and are meant to be refined during localized inventory efforts. The best practices herein are for creating draft IAUs using standard parameters for performing a viewshed analysis to identify a proxy of land to include in an initial inventory effort.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245060","collaboration":"Prepared in cooperation with the Bureau of Land Management","programNote":"Land Management Research Program","usgsCitation":"Lindley, S.M., Wilkins, E.J., Farley, C., Rogers, K., and Schuster, R., 2024, Delineating draft inventory analysis units for National Scenic and Historic Trails inventory, assessment, and monitoring programs: U.S. Geological Survey Scientific Investigations Report 2024–5060, 14 p., https://doi.org/10.3133/sir20245060.","productDescription":"vi, 14 p.","onlineOnly":"Y","ipdsId":"IP-167546","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":431360,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5060/sir20245060.xml"},{"id":431359,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5060/images"},{"id":431310,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5060/sir20245060.pdf","text":"Report","size":"8.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5060"},{"id":431445,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245060/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5060"},{"id":431309,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5060/coverthb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.32269505673072,\n 37.12200862948433\n ],\n [\n -109.32269505673072,\n 31.181092298048213\n ],\n [\n -105.30169896298118,\n 31.181092298048213\n ],\n [\n -105.30169896298118,\n 37.12200862948433\n ],\n [\n -109.32269505673072,\n 37.12200862948433\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
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Decades of human activities and fire suppression have adversely affected longleaf pine (Pinus palustris) ecosystems, which are home to high levels of diversity and endemism. These iconic ecosystems also now face challenges from urbanization and climate change, which will alter conservation outcomes over the remainder of the 21st century. To explore how long-term, large-scale human influences could affect the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome, we extracted a set of 2400 longleaf pine patches ≥40 ha in size from the Florida Longleaf Pine Ecosystem Geodatabase. Projections from the FUTURES urban growth model and the Florida 2070 project indicate that development will lead to losses of existing longleaf pine habitat, reductions in longleaf pine patch size, and patches that are predominantly located in close proximity to developed areas. Finer-scale patterns of longleaf pine loss in three focal landscapes highlighted differences in land protection, ecological setting, and development pressure and the value of using of multiple urbanization iterations. The occurrence of suitable conditions to conduct prescribed fires, a crucial tool for maintaining, improving, and restoring longleaf pine ecosystems, is projected to decrease seasonally throughout the study area. As a result, the functional persistence of ecosystems is at risk due to climate changes that increase barriers to the safe and reliable application of intentional fire. The long-term viability of this critical ecosystem will warrant the evaluation of adaptive strategies that explicitly account for the individual and compounding effects of urban development and changing fire management conditions when considering options for ecosystem protection, management, and restoration.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.13187","usgsCitation":"Hutchens, L., Kupfer, J.A., Gao, P., Sanchez, G.M., Meentemeyer, R.K., Terando, A., and Hiers, J.K., 2024, Projecting the long-term effects of large-scale human influence on the spatial and functional persistence of extant longleaf pine ecosystems in the Florida Flatwoods Pyrome: Conservation Science and Practice, v. 6, e13187, 17 p., https://doi.org/10.1111/csp2.13187.","productDescription":"e13187, 17 p.","ipdsId":"IP-164797","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":439258,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.13187","text":"Publisher Index Page"},{"id":433552,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.6778324075661,\n 30.937698915049367\n ],\n [\n -82.87914407681765,\n 31.52377445868447\n ],\n [\n -85.48595743814668,\n 29.870880095396842\n ],\n [\n -84.9679549020707,\n 29.721163342172446\n ],\n [\n -83.73921448060787,\n 29.975699066098443\n ],\n [\n -83.09802043717049,\n 29.013903692421295\n ],\n [\n -82.21492940678012,\n 26.67873354422413\n ],\n [\n -81.88136124157192,\n 26.33869742309807\n ],\n [\n -80.97600764340528,\n 26.60948691174565\n ],\n [\n -80.80502196139402,\n 27.153838982412836\n ],\n [\n -80.23615831927407,\n 27.044945104314294\n ],\n [\n -81.6778324075661,\n 30.937698915049367\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"6","noUsgsAuthors":false,"publicationDate":"2024-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Hutchens, Lilian","contributorId":343967,"corporation":false,"usgs":false,"family":"Hutchens","given":"Lilian","email":"","affiliations":[],"preferred":false,"id":912487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kupfer, John A.","contributorId":339801,"corporation":false,"usgs":false,"family":"Kupfer","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":912488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gao, Peng","contributorId":224731,"corporation":false,"usgs":false,"family":"Gao","given":"Peng","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":912489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanchez, Georgina M. 0000-0002-2365-6200","orcid":"https://orcid.org/0000-0002-2365-6200","contributorId":303829,"corporation":false,"usgs":false,"family":"Sanchez","given":"Georgina","email":"","middleInitial":"M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":true,"id":912490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meentemeyer, Ross K.","contributorId":179341,"corporation":false,"usgs":false,"family":"Meentemeyer","given":"Ross","email":"","middleInitial":"K.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":912491,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":912492,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hiers, J. 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Seismic studies of the mantle transition zone often use either global averaging with sparse arrays or regional sampling from a single dense array. The intermediate approach of this study utilizes many densely spaced seismic arrays distributed around the globe. We systematically compute teleseismic P-to-S receiver functions for each seismic array and invert for the 1-D seismic velocity structure of the mantle transition zone beneath each array to facilitate a comparison between densely sampled regions. We stack 3,600 receiver functions on average at 67 arrays in total. The stack is used in a probabilistic inversion to estimate the mantle transition zone interface depths and velocities beneath each array. We focus on the 410-km discontinuity (410) because it is a prominent seismic interface that is clearly linked to a single mineral phase transition between olivine and wadsleyite. The depths and velocity contrasts of the 410 are mapped to temperatures and compositions using mineral physics constraints. The depth of the 410 ranges from ∼405–440 km, which is consistent with a ∼360 K temperature range in a dry mantle and a ∼260 K temperature range in a wet mantle (2 wt. % water). The Vs contrast across the 410 ranges from ∼2.5–8 %, which is consistent with ∼20–70 vol. % olivine composition in a dry mantle and ∼25–80 vol. % in a wet mantle. The bulk composition of the upper mantle near the 410-km discontinuity is typically considered to be well-mixed because there is no thermodynamic impediment to convection at the olivine to wadsleyite phase transition. However, the wide range of inferred olivine content from our study suggests that there are large lateral variations in the bulk composition of the upper mantle near the 410-km discontinuity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2024.118889","usgsCitation":"Glasgow, M.E., Zhang, H.K., Schmandt, B., Zhou, W., and Zhang, J., 2024, Global variability of the composition and temperature at the 410-km discontinuity from receiver function analysis of dense arrays: Earth and Planetary Science Letters, v. 643, 118889, 12 p., https://doi.org/10.1016/j.epsl.2024.118889.","productDescription":"118889, 12 p.","ipdsId":"IP-162736","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489835,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2024.118889","text":"Publisher Index Page"},{"id":431352,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"643","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Glasgow, Margaret Elizabeth 0000-0001-5637-5918","orcid":"https://orcid.org/0000-0001-5637-5918","contributorId":340268,"corporation":false,"usgs":true,"family":"Glasgow","given":"Margaret","email":"","middleInitial":"Elizabeth","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":906784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Hankui K.","contributorId":211965,"corporation":false,"usgs":false,"family":"Zhang","given":"Hankui","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":906785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schmandt, Brandon","contributorId":202750,"corporation":false,"usgs":false,"family":"Schmandt","given":"Brandon","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":906786,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhou, Wen-Yi","contributorId":340269,"corporation":false,"usgs":false,"family":"Zhou","given":"Wen-Yi","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":906787,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Jinchi","contributorId":191970,"corporation":false,"usgs":false,"family":"Zhang","given":"Jinchi","email":"","affiliations":[],"preferred":false,"id":906788,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70261445,"text":"70261445 - 2024 - A semi-mechanistic model for partitioning evapotranspiration reveals transpiration dominates the water flux in drylands","interactions":[],"lastModifiedDate":"2024-12-10T14:41:05.274772","indexId":"70261445","displayToPublicDate":"2024-07-23T08:36:08","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2320,"text":"Journal of Geophysical Research: Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"A semi-mechanistic model for partitioning evapotranspiration reveals transpiration dominates the water flux in drylands","docAbstract":"Popular evapotranspiration (ET) partitioning methods make assumptions that might not be well-suited to dryland ecosystems, such as high sensitivity of plant water-use efficiency (WUE) to vapor pressure deficit (VPD). Our objectives were to (a) create an ET partitioning model that can produce fine-scale estimates of transpiration (T) in drylands, and (b) use this approach to evaluate how climate controls T and WUE across ecosystem types and timescales along a dryland aridity gradient. We developed a novel, semi-mechanistic ET partitioning method using a Bayesian approach that constrains abiotic evaporation using process-based models, and loosely constrains time-varying WUE within an autoregressive framework. We used this method to estimate daily T and weekly WUE across seven dryland ecosystem types and found that T dominates ET across the aridity gradient. Then, we applied cross-wavelet coherence analysis to evaluate the temporal coherence between focal response variables (WUE and T/ET) and environmental variables. At yearly scales, we found that WUE at less arid, higher elevation sites was primarily limited by atmospheric moisture demand, and WUE at more arid, lower elevation sites was primarily limited by moisture supply. At sub-yearly timescales, WUE and VPD were sporadically correlated. Hence, ecosystem-scale dryland WUE is not always sensitive to changes in VPD at short timescales, despite this being a common assumption in many ET partitioning models. This new ET partitioning method can be used in dryland ecosystems to better understand how climate influences physically and biologically driven water fluxes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023JG007914","usgsCitation":"Reich, E., Samuels-Crow, K., Bradford, J., Litvak, M., Schlaepfer, D.R., and Ogle, K., 2024, A semi-mechanistic model for partitioning evapotranspiration reveals transpiration dominates the water flux in drylands: Journal of Geophysical Research: Biogeosciences, v. 129, no. 7, e2023JG007914, 18 p., https://doi.org/10.1029/2023JG007914.","productDescription":"e2023JG007914, 18 p.","ipdsId":"IP-166030","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":464941,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New 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Mexico\",\"nation\":\"USA \"}}]}","volume":"129","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Reich, E.G.","contributorId":347030,"corporation":false,"usgs":false,"family":"Reich","given":"E.G.","email":"","affiliations":[{"id":83041,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":920581,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Samuels-Crow, K.","contributorId":347031,"corporation":false,"usgs":false,"family":"Samuels-Crow","given":"K.","affiliations":[{"id":83041,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":920582,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":920583,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Litvak, M.","contributorId":127830,"corporation":false,"usgs":false,"family":"Litvak","given":"M.","email":"","affiliations":[{"id":7164,"text":"Department of Biology, University of New Mexico, Albuquerque, NM 87131 USA","active":true,"usgs":false}],"preferred":false,"id":920584,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schlaepfer, Daniel Rodolphe 0000-0001-9973-2065","orcid":"https://orcid.org/0000-0001-9973-2065","contributorId":225569,"corporation":false,"usgs":true,"family":"Schlaepfer","given":"Daniel","email":"","middleInitial":"Rodolphe","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":920585,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ogle, K.","contributorId":347032,"corporation":false,"usgs":false,"family":"Ogle","given":"K.","affiliations":[{"id":83043,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA; Center of Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":920586,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259218,"text":"70259218 - 2024 - Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better","interactions":[],"lastModifiedDate":"2024-10-02T13:35:21.858349","indexId":"70259218","displayToPublicDate":"2024-07-23T08:15:54","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better","docAbstract":"Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub-meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management-relevant aspects of the hydrograph using non-parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer-resolution simulations are useful.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023WR035982","usgsCitation":"Barnhart, T.B., Putman, A.L., Heldmyer, A.J., Rey, D., Hammond, J., Driscoll, J.M., and Sexstone, G., 2024, Evaluating distributed snow model resolution and meteorology parameterizations against streamflow observations: Finer Is not always better: Water Resources Research, v. 60, no. 7, e2023WR035982, 21 p., https://doi.org/10.1029/2023WR035982.","productDescription":"e2023WR035982, 21 p.","ipdsId":"IP-154162","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":466979,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023wr035982","text":"Publisher Index Page"},{"id":462477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -105.35,\n 40\n ],\n [\n -107.5,\n 40\n ],\n [\n -107.5,\n 37.75\n ],\n [\n -105.35,\n 37.75\n ],\n [\n -105.35,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"60","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Barnhart, Theodore B. 0000-0002-9682-3217","orcid":"https://orcid.org/0000-0002-9682-3217","contributorId":219010,"corporation":false,"usgs":true,"family":"Barnhart","given":"Theodore","email":"","middleInitial":"B.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914512,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Putman, Annie L. 0000-0002-9424-1707","orcid":"https://orcid.org/0000-0002-9424-1707","contributorId":225134,"corporation":false,"usgs":true,"family":"Putman","given":"Annie","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Heldmyer, Aaron Joseph 0000-0001-8608-4927","orcid":"https://orcid.org/0000-0001-8608-4927","contributorId":302944,"corporation":false,"usgs":true,"family":"Heldmyer","given":"Aaron","email":"","middleInitial":"Joseph","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914514,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":914515,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914516,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Driscoll, Jessica M. 0000-0003-3097-9603 jdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0003-3097-9603","contributorId":167585,"corporation":false,"usgs":true,"family":"Driscoll","given":"Jessica","email":"jdriscoll@usgs.gov","middleInitial":"M.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914517,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":914518,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70256166,"text":"70256166 - 2024 - Isotopic evidence against North Pacific Deep Water formation during late Pliocene warmth","interactions":[],"lastModifiedDate":"2024-08-13T14:40:37.63952","indexId":"70256166","displayToPublicDate":"2024-07-23T07:28:38","publicationYear":"2024","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":"Isotopic evidence against North Pacific Deep Water formation during late Pliocene warmth","docAbstract":"Several modelling and observational studies suggest deep water formation in the subpolar North Pacific as a possible alternative mode of thermohaline circulation that occurred in the warm Pliocene, a time when global atmospheric partial pressure of carbon dioxide was like the modern atmosphere (~400 ppm). We test this hypothesis by measuring the δ13C of the benthic foraminifer Cibicidoides wuellerstorfi collected from northernmost Pacific mid-Piacenzian Warm Period (3.264–3.025 Myr ago) sediments. The data reveal progressively more isotopically negative dissolved inorganic carbon along a northward Equator-to-pole transect, the opposite of the expected Pliocene Pacific meridional overturning circulation signal. C. wuellerstorfi δ13C is also often more positive at the deeper Ocean Drilling Program (ODP) site 887 compared with the shallower ODP site 883, suggesting ‘bottom-up’ ventilation of the deep Pacific Ocean. We then present alkenone sea surface temperature and export-productivity data from ODP site 883, which suggest that late Pliocene subarctic North Pacific carbonate sedimentation was, at least in part, probably due to higher coccolithophore export production, rather than North Pacific Deep Water formation as previously argued. Therefore, we suggest it is unlikely that North Pacific Deep Water formation occurred in the mid-Piacenzian Warm Period, although a shallower overturning cell cannot be ruled out.
Urban land cover types influence the urban microclimates. However, recent work indicates the magnitude of land cover's microclimate influence is affected by aridity. Moreover, this variation in cooling and warming potentials of urban land cover types can substantially alter the exposure of urban areas to extreme heat. Our goal is to understand both the relative influences of urban land cover on local air temperature, as well as how these influences vary during periods of extreme heat. To do so we apply predictive machine learning models to an extensive in-situ microclimate and 1 m land cover dataset across eight U.S. cities spanning a wide aridity gradient during typical and extreme heat conditions. We demonstrate how the cooling influence of tree canopy and the warming influence of buildings on microclimate linearly scales with regional aridity, while the influence of turf and impervious surfaces does not. These interactions lead tree canopy to consistently mitigate to air temperature increases during periods extreme heat in arid cities, while the influence of urban tree canopy on extreme heat in humid regions is varied, suggesting that mitigation is possible, but tree canopy can also aggravate extreme heat or have no significant effect.
In 2020, natural resource managers at Camp Edwards, Barnstable County, MA, observed Terrapene carolina carolina (Eastern Box Turtle) individuals infected by myiasis, where parasitic flesh flies larviposit into the living tissue of a host. The hypothesized parasite was Dexosarcophaga cistudinis, but its impacts on the host's body condition, movement, and habitat use were unknown. Our objectives were to identify the parasite at Camp Edwards and to compare the body condition, movement, and habitat characteristics at capture locations of Eastern Box Turtles for infected and noninfected individuals. We radio-tracked turtles weekly and encountered 48 individuals from May to August 2022 at Camp Edwards, MA. Upon capture, we recorded turtle infection status, mass, carapace length, shell surface temperature, GPS location, and habitat characteristics of the capture location. We confirmed D. cistudinis as the parasite and found that myiasis-infected turtles had a significantly higher shell temperature (27.92 ± 5.28 °C) than noninfected turtles (26.77 ± 5.64 °C). However, we did not find an effect of myiasis on body condition, habitat use, or average daily distance moved. Collectively, our results suggest that infected turtles may exhibit behavioral fever, a mechanism by which ectotherms move to warmer microclimates to raise their body temperature in response to infections. Eastern Box Turtles at Camp Edwards may be able to use behavioral fever in response to myiasis infection because of the habitat mosaic made available through detailed habitat-management regimes.
In this paper, we review the derivation of the Gauss–Levenberg–Marquardt (GLM) algorithm and its extension to ensemble parameter estimation. We explore the use of graphical methods to provide insights into how the algorithm works in practice and discuss the implications of both algorithm tuning parameters and objective function construction in performance. Some insights include understanding the control of both parameter trajectory and step size for GLM as a function of tuning parameters. Furthermore, for the iterative Ensemble Smoother (iES), we discuss the importance of noise on observations and show how iES can cope with non-unique outcomes based on objective function construction. These insights are valuable for modelers using PEST, PEST++, or similar parameter estimation tools.
The U.S. Geological Survey Cascades Volcano Observatory (CVO) recently expanded its continuous geophysical monitoring at Mount Rainier, an active stratovolcano in Washington state. CVO monitors volcanoes in Oregon, Washington, and Idaho to characterize volcanic systems and detect unrest. Mount Rainier has a history of large lahar occurrences in the Holocene, including at least one that may not have been associated with volcanic activity. Pierce County, Washington, is one of the areas most at risk from large lahars. In the 1990s, CVO collaborated with Pierce County to install the Rainier lahar detection system (RLDS), an automated system designed to detect large lahars in high‐risk drainages and mitigate hazards to heavily populated areas. The system was designed to detect lahars within 5–10 min of their occurrence and alert authorities of the need to evacuate populated low‐lying areas before lahar arrival. In addition, CVO and the Pacific Northwest Seismic Network (PNSN) maintained and expanded a network of seismic and geodetic monitoring stations on and near the edifice to provide adequate volcano monitoring capabilities. Since 2016, CVO has worked to upgrade the existing RLDS and to expand its capabilities into other drainages around Mount Rainier. This expansion includes installation of 25 new broadband seismic stations with many including infrasound along high‐risk drainages, as well as support for equipment upgrades at existing PNSN and CVO volcano monitoring sites. All stations transmit continuous, near‐real‐time data with dramatically improved spatial coverage for volcano monitoring and lahar hazard mitigation compared to the previous system.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220240112","usgsCitation":"Kramer, R., Thelen, W., Iezzi, A.M., Moran, S.C., and Pauk, B., 2024, Recent expansion of the Cascades Volcano Observatory geophysical network at Mount Rainier for improved volcano and lahar monitoring: Seismological Research Letters, v. 95, no. 5, p. 2707-2721, https://doi.org/10.1785/0220240112.","productDescription":"15 p.","startPage":"2707","endPage":"2721","ipdsId":"IP-163746","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":484133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Mount Ranier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.99784760683576,\n 47.05336015194894\n ],\n [\n -121.99784760683576,\n 46.64802462725589\n ],\n [\n -121.42808425851092,\n 46.64802462725589\n ],\n [\n -121.42808425851092,\n 47.05336015194894\n ],\n [\n -121.99784760683576,\n 47.05336015194894\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"95","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Kramer, Rebecca 0000-0002-4873-1983 rkramer@usgs.gov","orcid":"https://orcid.org/0000-0002-4873-1983","contributorId":195070,"corporation":false,"usgs":true,"family":"Kramer","given":"Rebecca","email":"rkramer@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932568,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thelen, Weston 0000-0003-2534-5577","orcid":"https://orcid.org/0000-0003-2534-5577","contributorId":215530,"corporation":false,"usgs":true,"family":"Thelen","given":"Weston","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932569,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":304206,"corporation":false,"usgs":true,"family":"Iezzi","given":"Alexandra","email":"","middleInitial":"M.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932570,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moran, Seth C. 0000-0001-7308-9649 smoran@usgs.gov","orcid":"https://orcid.org/0000-0001-7308-9649","contributorId":224629,"corporation":false,"usgs":true,"family":"Moran","given":"Seth","email":"smoran@usgs.gov","middleInitial":"C.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":932571,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pauk, Benjamin 0000-0003-3036-5927 bpauk@usgs.gov","orcid":"https://orcid.org/0000-0003-3036-5927","contributorId":195069,"corporation":false,"usgs":true,"family":"Pauk","given":"Benjamin","email":"bpauk@usgs.gov","affiliations":[],"preferred":true,"id":932572,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70257027,"text":"70257027 - 2024 - Feedbacks: A new synthesis of causal loops across ecology","interactions":[],"lastModifiedDate":"2024-11-22T15:56:13.864419","indexId":"70257027","displayToPublicDate":"2024-07-22T08:28:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Feedbacks: A new synthesis of causal loops across ecology","docAbstract":"Feedbacks are the basic linkages of living systems. In organisms, they regulate the processes of growth and homeostasis, as well as their interactions with their world. Feedback, which Judson (1980) called ‘one of the chief themes of scientific understanding,' is equally important in ecological systems. The ecological literature is rich in papers dealing with the role of feedback in various phenomena. However, we know of no comprehensive synthesis of feedbacks in ecology. Pichon et al. (2024) accomplish this, and for the first time show that ecological feedbacks can be categorized in terms of a small number of fundamental attributes. The paper brings the array of different types of feedbacks into a manageable order, providing not only the relevant theoretical framework but also guidance on methods for applying understanding to practical issues.
","language":"English","publisher":"Nordic Society Oikos","doi":"10.1111/ecog.07460","usgsCitation":"DeAngelis, D.L., and Xu, L., 2024, Feedbacks: A new synthesis of causal loops across ecology: Ecography, v. 2024, no. 11, e07460, 3 p., https://doi.org/10.1111/ecog.07460.","productDescription":"e07460, 3 p.","ipdsId":"IP-164923","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":439260,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ecog.07460","text":"Publisher Index Page"},{"id":432334,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2024","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":909198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xu, Linhao","contributorId":221358,"corporation":false,"usgs":false,"family":"Xu","given":"Linhao","email":"","affiliations":[{"id":40353,"text":"Co-Innovation Center for Sustainable Forestry in Southern China, Jiangsu Province Key","active":true,"usgs":false}],"preferred":false,"id":909199,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70257094,"text":"70257094 - 2024 - Predictor importance in habitat suitability models for invasive terrestrial plants","interactions":[],"lastModifiedDate":"2024-09-16T16:12:33.446909","indexId":"70257094","displayToPublicDate":"2024-07-22T07:13:31","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Predictor importance in habitat suitability models for invasive terrestrial plants","docAbstract":"Due to the socioeconomic and environmental damages caused by invasive species, predicting the distribution of invasive plants is fundamental for effectively targeting management efforts. A habitat suitability model (HSM) is a powerful tool to predict potential habitat of invasive species to help guide the early detection of invasive plants. Despite numerous studies of the predictors used in HSMs, there is little consensus about the most appropriate predictors to use in creating ecologically realistic predictions from HSMs.
The contiguous United States.
We explore 220 invasive terrestrial plant species' existing HSMs constructed with consistent modelling algorithms, background generation methods, predictor resolution, and geographic extent, and calculate the relative importance of predictors for each species. We sort predictors into eight groups (topography, temperature, disturbance, atmospheric water, landscape water, substrate, biotic interaction, and radiation) and compare the importance of predictor groups by plant lifeforms and phylogenetic relatedness.
Human modification and minimum winter temperature were generally the two highest performing individual predictors across the species studied. The highest-performing predictor groups were disturbance, temperature, and atmospheric water. Across lifeforms, there were minimal differences in the influences of predictor groups, although woody plant models exhibited the largest differences in predictor importance when compared with non-woody plant models. Additionally, we found no significant relationship between the importance of predictor groups and phylogenetic relatedness.
This study has implications for informing predictor selection in invasive plant HSMs, leading to more reliable and accurate models of invasive terrestrial plants. Our results emphasize the need to critically select predictors included in HSMs, with special consideration to temperature and disturbance predictors, to accurately predict habitat of invasive plant for detection and response of invasive plant species. With more accurate predictions, managers will be better prepared to address invasive species and reduce their threats to landscapes.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13906","usgsCitation":"Williams, D.A., Shadwell, K.S., Pearse, I., Prevey, J.S., Engelstad, P., Henderson, G., and Jarnevich, C.S., 2024, Predictor importance in habitat suitability models for invasive terrestrial plants: Diversity and Distributions, v. 30, no. 9, e13906, 13 p., https://doi.org/10.1111/ddi.13906.","productDescription":"e13906, 13 p.","ipdsId":"IP-160521","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439261,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13906","text":"Publisher Index Page"},{"id":432432,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINN) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatiotemporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.","language":"English","publisher":"Elsevier","doi":"10.1016/j.spasta.2024.100850","usgsCitation":"Reyes, J., Ma, T., McGahan, I., Storm, D., Walsh, D.P., and Zhu, J., 2024, Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease: Spatial Statistics, v. 62, 100850, 15 p., https://doi.org/10.1016/j.spasta.2024.100850.","productDescription":"100850, 15 p.","ipdsId":"IP-155343","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490107,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.1016/j.spasta.2024.100850","text":"Publisher Index Page"},{"id":485559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reyes, Juan Francisco Mandujano","contributorId":354688,"corporation":false,"usgs":false,"family":"Reyes","given":"Juan Francisco Mandujano","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ma, Ting Fung","contributorId":354689,"corporation":false,"usgs":false,"family":"Ma","given":"Ting Fung","affiliations":[{"id":37804,"text":"University of South Carolina","active":true,"usgs":false}],"preferred":false,"id":936164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGahan, Ian P.","contributorId":354690,"corporation":false,"usgs":false,"family":"McGahan","given":"Ian P.","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936165,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Storm, Daniel J.","contributorId":354692,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":936166,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":936167,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhu, Jun","contributorId":354695,"corporation":false,"usgs":false,"family":"Zhu","given":"Jun","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":936168,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70270064,"text":"70270064 - 2024 - Utilization of stochastic ground motion simulations for scenario-based performance assessment of geo-structures","interactions":[],"lastModifiedDate":"2025-08-18T15:27:11.945136","indexId":"70270064","displayToPublicDate":"2024-07-22T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":22165,"text":"Reliability Engineering and System Safety (RESS)","active":true,"publicationSubtype":{"id":10}},"title":"Utilization of stochastic ground motion simulations for scenario-based performance assessment of geo-structures","docAbstract":"Probabilistic seismic performance assessments of engineered structures can be highly sensitive to the seismic input excitation and its variability. In the present study, the scenario-based performance assessment recommended by Federal Emergency Management Agency (FEMA) P-58 guidelines is adopted to estimate seismic fragility of concrete dams for various seismic hazard scenarios. Due to the scarcity of recorded ground motions and thereby their poor representation of uncertainties, stochastic ground motion simulation methods are utilized to obtain the required input excitations. Moreover, to understand the uncertainty in ground motion simulation models, two broadband stochastic simulation models are used to generate input excitations representing six seismic hazard scenarios defined by earthquake magnitude, source-to-site distance, and soil conditions.
Dams have negatively affected freshwater biodiversity throughout the world. These negative effects tend to be exacerbated for aquatic taxa with migratory life histories, and for taxa whose habitat is fundamentally altered by the formation of large reservoirs. Sauger (Sander candadensis; Percidae), large-bodied migratory fishes native to North America, have seen population declines over much of the species' range, and dams are often implicated for their role in blocking access to spawning habitat and otherwise negatively affecting river habitat. Furthermore, hybridization appears to be more frequent between sauger and walleye in the reservoirs formed by large dams. In this study, we examine the role of dams in altering sauger population connectivity and facilitating hybridization with introduced walleye in Wyoming's Wind River and Bighorn River systems. We collected genomic data from individuals sampled over a large spatial scale and replicated sampling throughout the spawning season, with the intent to capture potential variation in hybridization prevalence or genomic divergence between sauger with different life histories. The timing of sampling was not related to hybridization prevalence or population divergence, suggesting limited genetic differences between sauger spawning in different time and places. Overall, there was limited hybridization detected, however, hybridization was most prevalent in Boysen Reservoir (a large impounded section of the Wind River). Dams in the lower Wind River and upper Bighorn River were associated with population divergence between sauger upstream and downstream of the dams, and demographic models suggest that this divergence has occurred in concordance with the construction of the dam. Sauger upstream of the dams exhibited substantially lower estimates of genetic diversity, which implies that disrupted connectivity between Wind River and Bighorn River sauger populations may already be causing negative demographic effects. This research points towards the importance of considering the evolutionary consequences of dams on fish populations in addition to the threats they pose to population persistence.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.11706","usgsCitation":"Rosenthal, W., Mandeville, E., Pilkerton, A., Gerrity, P.C., Skorupski, J., Walters, A.W., and Wagner, C., 2024, Influence of dams on sauger population structure and hybridization with introduced walleye, v. 14, no. 7, e11706, 16 p., https://doi.org/10.1002/ece3.11706.","productDescription":"e11706, 16 p.","ipdsId":"IP-145629","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":488124,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.11706","text":"Publisher Index Page"},{"id":485447,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bighorn River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.2,\n 45\n ],\n [\n -109.2,\n 42.8\n ],\n [\n -107.6,\n 42.8\n ],\n [\n -107.6,\n 45\n ],\n [\n -109.2,\n 45\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenthal, William C.","contributorId":244630,"corporation":false,"usgs":false,"family":"Rosenthal","given":"William C.","affiliations":[{"id":34113,"text":"University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":935772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mandeville, Elizabeth G.","contributorId":270691,"corporation":false,"usgs":false,"family":"Mandeville","given":"Elizabeth G.","affiliations":[{"id":56198,"text":"uwyo","active":true,"usgs":false}],"preferred":false,"id":935773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pilkerton, Ashleigh","contributorId":346434,"corporation":false,"usgs":false,"family":"Pilkerton","given":"Ashleigh","affiliations":[{"id":63974,"text":"Wyoming Cooperative Fish and Wildlife Research Unit","active":true,"usgs":false}],"preferred":false,"id":935774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gerrity, Paul C.","contributorId":104198,"corporation":false,"usgs":true,"family":"Gerrity","given":"Paul","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":935775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Skorupski, Joseph A.","contributorId":354495,"corporation":false,"usgs":false,"family":"Skorupski","given":"Joseph A.","affiliations":[],"preferred":false,"id":935776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935777,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wagner, Catherine E.","contributorId":337377,"corporation":false,"usgs":false,"family":"Wagner","given":"Catherine E.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":935778,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70259268,"text":"70259268 - 2024 - Experimental assessment of egg mat gear retention and collection efficacy","interactions":[],"lastModifiedDate":"2024-12-10T15:25:10.409709","indexId":"70259268","displayToPublicDate":"2024-07-21T06:46:30","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Experimental assessment of egg mat gear retention and collection efficacy","docAbstract":"Assessment of egg deposition is widely used to provide an index of spawning efforts for lithophilic spawning fishes. However, little is known about the collection efficacy and bias of fish egg collection methods. We conducted a two-phased study consisting of a simulated-river flume study (two-part design), and a field study (an egg drift comparison with capture on mats) to assess egg collection methods and evaluate egg retention and capture on egg mats. Lake whitefish Coregonus clupeaformis egg retention on seeded mats decreased with increasing velocity and walleye Sander vitreus egg retention was variable as velocity increased. Fewer lake whitefish eggs were collected on egg mats when limestone reef rock was present in the flume study during the simulated spawned trials, but the inverse was true for walleye. Similarly, during field collections more lake whitefish eggs were collected in benthic D-shaped frame (D-frame) drift nets set near a known spawning reef compared to egg mats set on the reef, indicating lake whitefish eggs were drifting downstream along the river bottom. In contrast, fewer walleye eggs were observed in D-frame drift nets compared to number of eggs captured on the egg mats. Therefore, egg mats are an informative tool for evaluating walleye egg deposition in an immediate area but may underestimate egg deposition of lake whitefish, especially in lotic systems. Compared to other egg collection methods in the current literature, our study indicates that egg mats are useful for assessing egg deposition by lithophilic spawning fishes, but that the collection and retention efficacy and bias of this gear may vary between species and habitat types.
Wildfires and climate change are having transformative effects on vegetation composition and structure, and post-fire management may have long-lasting impacts on ecosystem reorganization. Post-fire aerial seeding treatments are commonly used to reduce runoff and soil erosion, but little is known about how seeding treatments affect native vegetation recovery over long periods of time, particularly in type-converted forests which have been dramatically transformed by the effects of repeated, high-severity fire. In this study, we analyze and report on a rare long-term (23-year) dataset that documents vegetation dynamics following a 1996 post-fire aerial seed treatment and subsequent 2011 high-severity reburn in a dry conifer forest of northern New Mexico in the southwestern United States. Repeated surveys between 1997 – 2019 of 49 permanent transects were used to test for differences in vegetation cover, richness, and diversity between seeded and unseeded areas, and to characterize the development of seeded and unseeded vegetation communities through time and across gradients of burn severity, elevation, and soil-available water capacity. Post-fire seeding led to a clear and sustained divergence in herbaceous community composition. Seeded plots had much higher cover of non-native graminoids, primarily Bromus inermis, a likely contaminant in the seed mix. High-severity reburning in all plots in 2011 reduced native graminoid cover by half at seeded plots compared to both pre-fire levels and to plots that were unseeded following the initial 1996 fire. In addition, increased fire severity was associated with increased non-native graminoid cover and reduced native graminoid cover, native species richness, and species diversity. This study documents a fire-driven ecosystem transformation from a former conifer forest into a shrub-grass system, reinforced by aerial seeding of grasses and high-severity reburning. This unique long-term dataset illustrates that post-fire seeding carries significant risk of unwanted non-native species invasions that persist through subsequent fires – indicating that alternative post-fire management actions merit consideration to better support native ecosystem resilience in the face of emergent climate change and increasing disturbance. Lastly, this study highlights the importance of long-term monitoring of post-fire vegetation dynamics, as short-term assessments will miss key elements of the full complexity of ecosystem responses to fire and post-fire management actions.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.3008","usgsCitation":"Wion, A.P., Stevens, J., Beeley, K., Oertel, R., Margolis, E.Q., and Allen, C., 2024, Multi-decadal vegetation transformations of a New Mexico ponderosa pine landscape after severe fires and aerial seeding: Ecological Applications, v. 34, no. 6, e3008, 21 p., https://doi.org/10.1002/eap.3008.","productDescription":"e3008, 21 p.","ipdsId":"IP-158911","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":498298,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.3008","text":"Publisher Index Page"},{"id":431346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Mountains, San Miguel Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.61072300933778,\n 36.66992633929999\n ],\n [\n -107.61072300933778,\n 35.363708672581055\n ],\n [\n -105.69910191558768,\n 35.363708672581055\n ],\n [\n -105.69910191558768,\n 36.66992633929999\n ],\n [\n -107.61072300933778,\n 36.66992633929999\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Wion, Andreas Paul 0000-0002-0701-2843","orcid":"https://orcid.org/0000-0002-0701-2843","contributorId":335166,"corporation":false,"usgs":true,"family":"Wion","given":"Andreas","email":"","middleInitial":"Paul","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":906778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stevens, Jens T. 0000-0002-2234-1960","orcid":"https://orcid.org/0000-0002-2234-1960","contributorId":289230,"corporation":false,"usgs":false,"family":"Stevens","given":"Jens T.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":906779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beeley, Kay","contributorId":340264,"corporation":false,"usgs":false,"family":"Beeley","given":"Kay","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":906780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oertel, Rebecca","contributorId":340265,"corporation":false,"usgs":false,"family":"Oertel","given":"Rebecca","email":"","affiliations":[{"id":81531,"text":"Fort Collins Science Center *retired","active":true,"usgs":false}],"preferred":false,"id":906781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Margolis, Ellis Q. 0000-0002-0595-9005 emargolis@usgs.gov","orcid":"https://orcid.org/0000-0002-0595-9005","contributorId":173538,"corporation":false,"usgs":true,"family":"Margolis","given":"Ellis","email":"emargolis@usgs.gov","middleInitial":"Q.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":906782,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allen, Craig D.","contributorId":289211,"corporation":false,"usgs":false,"family":"Allen","given":"Craig D.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":906783,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256109,"text":"70256109 - 2024 - Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model","interactions":[],"lastModifiedDate":"2024-07-22T11:47:51.294971","indexId":"70256109","displayToPublicDate":"2024-07-20T06:43:33","publicationYear":"2024","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":"Post-fire sediment yield from a central California watershed: Field measurements and validation of the WEPP model","docAbstract":"In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post-fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km2), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post-fire flooding was 854–1,100 t/km2/yr, a factor of 3.5–4.6 greater than the long-term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large-scale field validation test of the WEPPcloud/wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post-fire hillslope erosion even across 100-km2 scales under conditions without debris flows.
Vesicularity of individual pyroclasts from airfall tephra deposits is an important parameter that is commonly measured at basaltic volcanoes. Conventional methods used to determine pyroclast vesicularity on a large number of clasts has the potential to be time consuming, particularly when rapid analysis is required. Here we propose dynamic image analysis on two-dimensional (2D) projection shapes of crushed pyroclasts from tephra deposits as a new method to estimate vesicularity. This method relies on the influence of vesicles and uses grain morphology as a proxy for vesicle size and abundance. Pyroclasts from a variety of basaltic tephra deposits from the volcanoes of Mauna Loa and Kīlauea were analyzed. Vesicularities between 52–98% were measured via nitrogen-gas pycnometry. The same pyroclasts were then crushed and sieved, and their grain shapes measured using dynamic image analysis on a CAMSIZER®. This yields values for the mean sphericity, elongation, compactness, and Krumbein roundness of the grains. Our data show that grains become increasingly irregular with increasing vesicularity, with the degree of correlation between shape parameters and vesicularity depending on the size of measured grains. Shape irregularities in small grains (60–250 µm) are mostly area-based, with elongation being the best vesicularity indicator, whereas shape irregularities in large grains (250–700 µm) are mostly perimeter-based, with Krumbein roundness as the best vesicularity indicator. Using mean shape parameter values with all grain sizes included, grain elongation is the most well-correlated shape parameter with vesicularity, with the best fitted model explaining 76% of variation in the observations. Microscope images of thin sections of intact pyroclasts, as well as from crushed pyroclasts, were analyzed using CSDCorrections 1.6 software in ImageJ to find local vesicularity, vesicle size, grain size, grain elongation, and vesicle spatial distribution by stereological conversion. Observed correlation between grain shape and vesicularity can be explained by the local effect of vesicles on the shape of the solid structure in between those vesicles. Grain shape depends not only on vesicularity, but also on vesicle to grain size ratio and the spatial distribution of vesicles. The influence of vesicles on grain shape is best captured by grains with the size of the solid structure in between vesicles, which generally increases with decreasing vesicularity. Dynamic image analysis is a useful tool to quickly gauge vesicularity, which could be used in near-real-time during an eruption response. However, this method is best suited for highly vesicular (> 80%) basaltic pyroclasts from tephra deposits with few microlites and phenocrysts. Further research on crushing techniques, optimum grain size for shape measurements, and Krumbein roundness measurements for the grain size range of 250–700 µm might enable application of this method to lower vesicularity pyroclasts.
Remote sensing can be an effective tool for mapping river bathymetry, but the need for direct measurements to calibrate image-derived depth estimates impedes broader application of this approach. One way to circumvent the need for field campaigns dedicated to calibration is to capitalize upon existing data. In this study, we introduce a framework for Bathymetric Mapping using Gage Records and Image Databases (BaMGRID). This workflow involves retrieving depth measurements made during gaging station site visits, downloading archived multispectral images, and then combining these two data sets to establish a relationship between depth and reflectance. We developed a processing chain that involves using application programming interfaces to obtain both depth measurements made during site visits and images centered on the gage and then linking depth to reflectance via an optimal band ratio analysis (OBRA) algorithm modified for small sample sizes. Applying this workflow to selected gages within two river basins indicated that depth retrieval from multispectral satellite images could be highly accurate, but with variable results from one image to the next at a given site. High resolution aerial photography was less conducive to bathymetric mapping in one of the basin considered. Of the four predictors of depth retrieval performance we evaluated (mean and standard deviation of depth, width, and an index of water clarity), only width was consistently significantly correlated with OBRA R2 (p < 0.026). Currently, BaMGRID is best-suited for site-by-site analysis to support practical applications at the reach scale; continuous, basin-wide mapping of river bathymetry will require additional research.
To explain the drivers of historical water use in the public water systems (PWSs) that serve populations in Providence, Rhode Island, and surrounding areas, and to forecast future water use, a machine-learning model (cubist regression) was developed by the U.S. Geological Survey in cooperation with Providence Water to model daily per capita rates of domestic, commercial, and industrial water use. The PWSs in this area form a connected network that sources water from the Scituate Reservoir in Rhode Island. The cubist regression model was trained and tested on daily per capita rates for three categories of water use (domestic, commercial, and industrial) that were developed from quarterly water sales data and U.S. Census Bureau population estimates within each PWS service area from January 2005 through December 2021. The model was then used to make forecasts of future water use under varying scenarios of climate change, population growth, and economic growth for the years 2030 and 2040.
The resulting daily per capita rates, which were modeled from the historical data, had an r2 value of 0.94 and root mean square error of 6.7 gallons per capita daily. Results of the model were used to estimate total water use (the product of daily per capita rates and population) for all public water systems over the historical study period. Daily per capita rates in the study area decreased from 2005 to 2021, while population increased during that same period. “Category of water use” was the variable with the greatest explanatory power for modeling daily per capita rates. Overall, both daily per capita rates and total water use were projected to decrease in 2030 and 2040, in comparison to historical values from 2005 to 2021. Daily per capita rates and total water use were forecasted to decrease as economic growth rates increase. Daily per capita rates were expected to decrease as population growth rates increase; however, total water use was less sensitive to population growth rates than daily per capita rates. Effects of climate change were minimal over the 2030 and 2040 forecasting horizon for the scenarios tested.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245052","collaboration":"Prepared in cooperation with Providence Water","usgsCitation":"Chamberlin, C.A., 2024, A predictive analysis of water use for Providence, Rhode Island: U.S. Geological Survey Scientific Investigations Report 2024–5052, 36 p., https://doi.org/10.3133/sir20245052.","productDescription":"Report: viii, 36 p.; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-152679","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":499474,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117188.htm","linkFileType":{"id":5,"text":"html"}},{"id":431062,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94XIQ7W","text":"USGS data release","linkHelpText":"Model archive, input data, modeled estimates of water use 2005-2021, and forecasts of water use in 2030 and 2040 in Providence, Rhode Island"},{"id":431061,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5052/sir20245052.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5052 XML"},{"id":431060,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5052/images/"},{"id":431059,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245052/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2024-5052 HTML"},{"id":431058,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5052/sir20245052.pdf","text":"Report","size":"4.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5052 PDF"},{"id":431057,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5052/coverthb.jpg"}],"country":"United States","state":"Rhode Island","city":"Providence","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.6320030327788,\n 41.56235835697041\n ],\n [\n -71.17676197086665,\n 41.56235835697041\n ],\n [\n -71.17676197086665,\n 42.025783641742635\n ],\n [\n -71.6320030327788,\n 42.025783641742635\n ],\n [\n -71.6320030327788,\n 41.56235835697041\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532