{"pageNumber":"4","pageRowStart":"75","pageSize":"25","recordCount":676,"records":[{"id":70240353,"text":"70240353 - 2022 - Invasion of annual grasses following wildfire corresponds to maladaptive habitat selection by a sagebrush ecosystem indicator species","interactions":[],"lastModifiedDate":"2023-02-06T15:55:04.895367","indexId":"70240353","displayToPublicDate":"2022-05-05T09:50:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Invasion of annual grasses following wildfire corresponds to maladaptive habitat selection by a sagebrush ecosystem indicator species","docAbstract":"

Numerous wildlife species within semi-arid shrubland ecosystems across western North America are experiencing substantial habitat loss and fragmentation. These changes in habitat are often attributed to a diverse suite of factors including prolonged and increasingly severe droughts, conifer expansion, anthropogenic development, domestic and feral livestock grazing, and invasion of exotic annual grasses, which promotes increased wildfire frequency and severity. Greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) are considered an indicator of sagebrush ecosystem health and have experienced widespread population decline associated with habitat loss and degradation, as well as changes in predator communities. Our objectives were to model and map sage-grouse habitat selection and survival during the important brood-rearing life stage in relation to landscape-scale environmental predictors. Furthermore, we sought to understand impacts of wildfire and annual grass invasion on brood habitat, as these accelerated disturbance regimes are a primary cause of habitat loss within the Great Basin region of the USA. We used a hierarchical Bayesian modeling framework to estimate resource selection functions and survival for early and late brood-rearing stages of sage-grouse in relation to a broad suite of habitat characteristics evaluated at multiple spatial scales within the Great Basin from 2009 to 2019. Sage-grouse selected for greater perennial grass cover, higher relative elevations, and areas closer to springs and wet meadows during both early and late brood-rearing. Terrain characteristics, including heat load and aspect, were important in survival models, as was variation in shrub height. We also found strong evidence for higher survival for both early and late broods within previously burned areas, but survival within burned areas decreased as annual grass cover (i.e. cheatgrass, Bromus tectorum) increased. This interaction effect demonstrates how invasion of annual grasses into burned areas, which has become prevalent in Great Basin sagebrush ecosystems, can lead to maladaptive habitat selection by brood-rearing greater sage-grouse. Understanding these complex relationships aids wildlife conservation and habitat management as wildfire and annual grass cycles continue to accelerate across western ecosystems.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02147","usgsCitation":"Brussee, B.E., Coates, P.S., O’Neil, S.T., Casazza, M.L., Espinosa, S.P., Boone, J., Ammon, E., Gardner, S.C., and Delehanty, D.J., 2022, Invasion of annual grasses following wildfire corresponds to maladaptive habitat selection by a sagebrush ecosystem indicator species: Global Ecology and Conservation, v. 37, e02147, 19 p., https://doi.org/10.1016/j.gecco.2022.e02147.","productDescription":"e02147, 19 p.","ipdsId":"IP-133908","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447908,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02147","text":"Publisher Index Page"},{"id":435856,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B593DZ","text":"USGS data release","linkHelpText":"Spatially-Explicit Predictive Maps of Greater Sage-Grouse Brood Selection Integrated with Brood Survival in Nevada and Northeastern California, USA"},{"id":412740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.11100469577016,\n 37.65679718030911\n ],\n [\n -114.01367957532398,\n 37.81079149850166\n ],\n [\n -114.06841886178916,\n 41.950546575009156\n ],\n [\n -120.51366289783158,\n 41.94877001660362\n ],\n [\n -120.95831043718897,\n 40.25557232247007\n ],\n [\n -120.37729928858255,\n 39.092072629058066\n ],\n [\n -119.0529053921718,\n 39.74527180686712\n ],\n [\n -117.0729066045891,\n 38.04894793599695\n ],\n [\n -115.11005550665487,\n 37.598613857563834\n ],\n [\n -115.11100469577016,\n 37.65679718030911\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"37","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863533,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863534,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863535,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863536,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Espinosa, Shawn P.","contributorId":195583,"corporation":false,"usgs":false,"family":"Espinosa","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":863537,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Boone, John D.","contributorId":300334,"corporation":false,"usgs":false,"family":"Boone","given":"John D.","affiliations":[{"id":65087,"text":"Great Basin Bird Observatory, Reno, Nevada, USA","active":true,"usgs":false}],"preferred":false,"id":863538,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ammon, Elisabeth M.","contributorId":302116,"corporation":false,"usgs":false,"family":"Ammon","given":"Elisabeth M.","affiliations":[{"id":65418,"text":"Great Basin Bird Observatory, 1755 E. Plumb Ln Ste 256 A, Reno, NV 89502, USA","active":true,"usgs":false}],"preferred":false,"id":863539,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gardner, Scott C.","contributorId":192081,"corporation":false,"usgs":false,"family":"Gardner","given":"Scott","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":863540,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":863541,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70229185,"text":"70229185 - 2022 - The impact of future climate on wetland habitat in a critical migratory waterfowl corridor of the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2022-03-03T15:34:56.28404","indexId":"70229185","displayToPublicDate":"2022-01-12T09:27:17","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":251,"text":"Final Report","active":false,"publicationSubtype":{"id":4}},"title":"The impact of future climate on wetland habitat in a critical migratory waterfowl corridor of the Prairie Pothole Region","docAbstract":"

Depressional wetlands are extremely sensitive to changes in temperature and precipitation, so understanding how wetland inundation dynamics respond to changes in climate is essential for describing potential effects on wildlife breeding habitat. Millions of depressional basins make up the largest wetland complex in North America known as the Prairie Pothole Region (PPR). The wetland ecosystems that have formed in these basins provide important migratory-bird breeding habitat. The southeast portion of the U.S. PPR in Minnesota and Iowa has faced some of the greatest challenges in wetland conservation. Many existing prairie-pothole wetlands are small (<1 ha) and shallow (<2 m) and are typically not inundated with surface water year-round. Our goal with this project is to increase the efficacy of mapping tools used by management agencies to predict future changes in water levels in the PPR. We accomplish this goal by improving the link between existing data (about wetland water characteristics) and existing tools (mapping products). Our results successfully validated (2009-2021) the current mapping tool (a wetland hydrology model) used by the U.S. Fish and Wildlife Service (USFWS) to manage 22 wetlands in Minnesota. We were able to hindcast wetland water levels to 1984 and assess the accuracy of a satellite-derived surface water product and forecast water levels through 2099 using a suite of modeled climate data. This newly refined link between monitoring data and remote sensing tools will increase understanding and prediction for other wetlands beyond our study sites and through the Minnesota and Iowa portions of the PPR. Through conference presentations, publications, and development of an interactive climate change dashboard we are now working with managers to determine how we can help incorporate these predicted changes to waterfowl breeding habitat into their future management, acquisition, and restoration strategy.


","language":"English","publisher":"USGS MIdwest Climate Adaptation Science Center","collaboration":"USGS MIdwest Climate Adaptation Science Center","usgsCitation":"McKenna, O.P., 2022, The impact of future climate on wetland habitat in a critical migratory waterfowl corridor of the Prairie Pothole Region: Final Report, 19 p.","productDescription":"19 p.","ipdsId":"IP-137085","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":396701,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396700,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/5e2f3f59e4b0a79317d422af/5f29c43982cef313ed9edb1d"}],"country":"Canada, United States","state":"Alberta, Iowa, Manitoba, Minnesota, Montana, Nebraska, North Dakota, Saskatchewan, South Dakota","otherGeospatial":"Prairie Potholes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.8896484375,\n 41.96765920367816\n ],\n [\n -93.1640625,\n 44.5278427984555\n ],\n [\n -95.8447265625,\n 47.27922900257082\n ],\n [\n -96.240234375,\n 49.210420445650286\n ],\n [\n -99.5361328125,\n 51.01375465718821\n ],\n [\n -100.2392578125,\n 51.590722643120145\n ],\n [\n -101.513671875,\n 51.31688050404585\n ],\n [\n -107.8857421875,\n 52.669720383688166\n ],\n [\n -114.697265625,\n 52.72298552457069\n ],\n [\n -117.333984375,\n 52.45600939264076\n ],\n [\n -113.37890625,\n 48.019324184801185\n ],\n [\n -111.884765625,\n 46.649436163350245\n ],\n [\n -109.9072265625,\n 47.96050238891509\n ],\n [\n -106.34765625,\n 48.04870994288686\n ],\n [\n -102.6123046875,\n 47.931066347509784\n ],\n [\n -100.72265625,\n 45.767522962149876\n ],\n [\n -100.8544921875,\n 44.465151013519616\n ],\n [\n -99.0966796875,\n 43.739352079154706\n ],\n [\n -99.00878906249999,\n 41.96765920367816\n ],\n [\n -97.9541015625,\n 41.21172151054787\n ],\n [\n -97.6025390625,\n 40.78054143186033\n ],\n [\n -96.064453125,\n 42.13082130188811\n ],\n [\n -95.9326171875,\n 42.68243539838623\n ],\n [\n -93.4716796875,\n 41.541477666790286\n ],\n [\n -93.1201171875,\n 40.97989806962013\n ],\n [\n -91.8896484375,\n 41.21172151054787\n ],\n [\n -91.8896484375,\n 41.96765920367816\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":836894,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70226879,"text":"70226879 - 2022 - Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials","interactions":[],"lastModifiedDate":"2021-12-17T15:12:03.166515","indexId":"70226879","displayToPublicDate":"2021-12-17T08:50:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2954,"text":"Ore Geology Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials","docAbstract":"

Demand for critical raw materials is expected to accelerate over the next few decades due to continued population growth and the shifting consumption patterns of the global economy. Sedimentary basins are important sources for critical raw materials and new discoveries of sediment–hosted Mississippi Valley–type (MVT) and/or clastic–dominated (CD) Zn–Pb deposits are likely required to mitigate future supply chain disruptions for Zn, Pb, Ag, Cd, Ga, Ge, Sb, and In. Herein we integrate public geoscience datasets using a discrete global grid to system to model the mineral potential for MVT and CD deposits across Canada, the United States of America, and Australia. Statistical analysis of the model results demonstrates that surface–wave tomography and derivative products from satellite gravity datasets can be used to map the most favourable paleo–tectonic settings of MVT and CD deposits inboard of orogenic belts and at the rifted edges of cratonic lithosphere, respectively. Basin development at pre–existing crustal boundaries was likely important for maintaining the low geothermal–gradients that are favourable for metal transport and generating the crustal fluid pathways that were reactivated during ore–formation, as suggested by the statistical association of both sediment–hosted mineral deposit types with the edges of upward–continued gravity and long–wavelength magnetic anomalies. Multivariate statistical analysis demonstrates that the most prospective combination of these geophysical datasets varies for each geological region and deposit type. We further demonstrate that maximum and minimum geological ages, coupled with Phanerozoic paleogeographic reconstructions, represent mappable proxies for the availability of oxidized, brine–generating regions that are the most likely source of ore–forming fluids (e.g., low– to mid–latitude carbonate platforms and evaporites). Ore deposition was likely controlled by interaction between oxidized, low–temperature brines and sulfidic and/or carbonaceous rocks, which, in some cases, can be mapped at the exposed surface or identified using the available rock descriptions. Baseline weights–of–evidence models are based on regional geophysics and are the least impacted by missing surface information but yield relatively poor results, as demonstrated by the low area–under–the–curve (AUC) for the spatially independent test set on the success–rate plot (AUC = 0.787 for MVT and AUC = 0.870 for CD). Model performance can be improved by: (1) using advanced methods that were trained and validated during a series of semi–automated machine learning competitions; and/or (2) incorporating geological and geophysical datasets that are proxies for each component of the mineral system. The best–performing gradient boosting machine models yield higher AUC for the test set (AUC = 0.983 for MVT and AUC = 0.991 for CD) and reduce the search space by >94%. The model results highlight the potential benefits of mapping sediment–hosted mineral systems at continental scale to improve mineral exploration targeting for critical raw materials.

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Canada","active":true,"usgs":false}],"preferred":false,"id":828594,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70222374,"text":"70222374 - 2022 - Shrub influence on soil carbon and nitrogen in a semi-arid grassland is mediated by precipitation and largely insensitive to livestock grazing","interactions":[],"lastModifiedDate":"2022-02-15T15:33:21.112862","indexId":"70222374","displayToPublicDate":"2021-06-22T07:35:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":904,"text":"Arid Land Research and Management","active":true,"publicationSubtype":{"id":10}},"title":"Shrub influence on soil carbon and nitrogen in a semi-arid grassland is mediated by precipitation and largely insensitive to livestock grazing","docAbstract":"

Dryland (arid and semi-arid) ecosystems globally provide more than half of livestock production and store roughly one-third of soil organic carbon (SOC). Biogeochemical pools are changing due to shrub encroachment, livestock grazing, and climate change. We assessed how vegetation microsite, grazing, and precipitation interacted to affect SOC and total nitrogen (TN) at a site with long-term grazing manipulations and well-described patterns of shrub encroachment across elevation and mean annual precipitation (MAP) gradients. We analyzed SOC and TN in the context of vegetation cover at ungrazed locations within livestock exclosures, high-intensity grazing locations near water sources, and moderate-intensity grazing locations away from water. SOC was enhanced by MAP (p < 0.0001), but grazing intensity had little effect regardless of MAP (p = 0.12). Shrubs enhanced SOC (300–1279 g C m−2) and TN (27–122 g N m−2), except at high MAP where the contribution or stabilization of shrub inputs relative to grassland inputs was likely diminished. Cover of perennial herbaceous plants and litter were significant predictors of SOC (r2 = 0.63 and 0.34, respectively) and TN (r2 = 0.64 and 0.30, respectively). Our results suggest that continued shrub encroachment in drylands can increase SOC storage when grass production remains high, although this response may saturate with higher MAP. In contrast, grazing – at least at the intensities of our sites – has a lesser effect. These effects underscore the need to understand how future climate and grazing may interact to influence dryland biogeochemical cycling.

","language":"English","publisher":"Taylor and Francis","doi":"10.1080/15324982.2021.1952660","usgsCitation":"Throop, H.L., Munson, S.M., Hornslein, N., and McClaran, M., 2022, Shrub influence on soil carbon and nitrogen in a semi-arid grassland is mediated by precipitation and largely insensitive to livestock grazing: Arid Land Research and Management, v. 36, no. 1, p. 27-46, https://doi.org/10.1080/15324982.2021.1952660.","productDescription":"20 p.","startPage":"27","endPage":"46","ipdsId":"IP-126222","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":502623,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":387410,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-07-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Throop, Heather L. 0000-0002-7963-4342","orcid":"https://orcid.org/0000-0002-7963-4342","contributorId":139051,"corporation":false,"usgs":false,"family":"Throop","given":"Heather","email":"","middleInitial":"L.","affiliations":[{"id":12633,"text":"Biology Department, New Mexico State University, Las Cruces, NM","active":true,"usgs":false}],"preferred":false,"id":819848,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":819849,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hornslein, Nicole","contributorId":261340,"corporation":false,"usgs":false,"family":"Hornslein","given":"Nicole","email":"","affiliations":[{"id":52828,"text":"School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287, USA","active":true,"usgs":false}],"preferred":false,"id":819850,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McClaran, Mitchel P","contributorId":261341,"corporation":false,"usgs":false,"family":"McClaran","given":"Mitchel P","affiliations":[{"id":52829,"text":"School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721-0043, USA","active":true,"usgs":false}],"preferred":false,"id":819851,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70240354,"text":"70240354 - 2021 - SMaRT: A science-based tiered framework for common ravens","interactions":[],"lastModifiedDate":"2023-02-06T15:23:17.560502","indexId":"70240354","displayToPublicDate":"2021-12-31T09:18:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"SMaRT: A science-based tiered framework for common ravens","docAbstract":"

Large-scale increases and expansion of common raven (Corvus corax; raven) populations are occurring across much of North America, leading to increased negative consequences for livestock and agriculture, human health and safety, and sensitive species conservation. We describe a science-based adaptive management framework that incorporates recent quantitative analyses and mapping products for addressing areas with elevated raven numbers and minimizing potential adverse impacts to sensitive species, agricultural damage, and human safety. The framework comprises 5 steps: (1) desktop analysis; (2) field assessments; (3) comparison of raven density estimates to an ecological threshold (in terms of either density or density plus distance to nearest active or previous nest); (4) prescribing management options using a 3-tiered process (i.e., habitat improvements, subsidy reductions, and direct actions using StallPOPd.V4 software); and (5) post-management monitoring. The framework is integrated within the Science-based Management of Ravens Tool (SMaRT), a web-based application outfitted with a user-friendly interface that guides managers through each step to develop a fully customized adaptive plan for raven management. In the SMaRT interface, users can: (1) interact with pre-loaded maps of raven occurrence and density and define their own areas of interest within the Great Basin to delineate proposed survey or treatment sites; (2) enter site-level density estimates from distance sampling methods or perform estimation of raven densities using the rapid assessment protocol that we provide; (3) compare site-level density estimates to an identified ecological threshold; and (4) produce a list of potential management options for their consideration. The SMaRT supports decision-making by operationalizing scientific products for raven management and facilitates realization of diverse management goals including sensitive species conservation, protection of livestock and agriculture, safeguarding human health, and addressing raven overabundance and expansion. We illustrate the use of the framework through SMaRT using an example of greater sage-grouse (Centrocercus urophasianus) conservation efforts within the Great Basin, USA.

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lava-lake dynamics revealed through multitemporal terrestrial lidar scanning at Kīlauea Volcano, Hawaiʻi","interactions":[{"subject":{"id":70226828,"text":"pp1867C - 2021 - Crater growth and lava-lake dynamics revealed through multitemporal terrestrial lidar scanning at Kīlauea Volcano, Hawaiʻi","indexId":"pp1867C","publicationYear":"2021","noYear":false,"chapter":"C","displayTitle":"Crater Growth and Lava-Lake Dynamics Revealed Through Multitemporal Terrestrial Lidar Scanning at Kīlauea Volcano, Hawaiʻi","title":"Crater growth and lava-lake dynamics revealed through multitemporal terrestrial lidar scanning at Kīlauea Volcano, Hawaiʻi"},"predicate":"IS_PART_OF","object":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"id":1}],"isPartOf":{"id":70217129,"text":"pp1867 - 2021 - The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i","indexId":"pp1867","publicationYear":"2021","noYear":false,"title":"The 2008–2018 summit lava lake at Kīlauea Volcano, Hawai‘i"},"lastModifiedDate":"2024-06-26T15:52:46.463119","indexId":"pp1867C","displayToPublicDate":"2021-12-14T10:04:56","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1867","chapter":"C","displayTitle":"Crater Growth and Lava-Lake Dynamics Revealed Through Multitemporal Terrestrial Lidar Scanning at Kīlauea Volcano, Hawaiʻi","title":"Crater growth and lava-lake dynamics revealed through multitemporal terrestrial lidar scanning at Kīlauea Volcano, Hawaiʻi","docAbstract":"

Lava lake surfaces display the tops of active magma columns and respond to eruption variables such as magmatic pressure, convection, degassing, and cooling, as well as interactions with the craters that contain them. However, they are challenging to study owing to the numerous hazards that accompany these eruptions, and they are typically difficult to observe because the emitted gas plumes obscure the lava lake surfaces. The 2008–2018 Overlook crater and lava lake at Kīlauea Volcano, Hawaiʻi, provided a remarkable opportunity to study several high-resolution data streams of eruption variables that impacted the lava lake. To investigate how the crater and associated lava lake responded to changes in these eruption variables, we acquired terrestrial light detection and ranging (lidar) surveys of the Overlook crater and lava lake surface from February 2012 through December 2013, supplemented with several earlier terrestrial and airborne lidar datasets, to quantitatively track changes in the shape of the lava lake surface and the crater walls. Lidar captures high-resolution data even when the lake is completely obscured by thick gas plumes. We used a novel “unrolling technique” to map volumetric changes in crater shape, because standard elevation differencing fails to capture all topographic changes on the nearly vertical, and sometimes overhanging, crater walls. We measured crater perimeter growth rates of approximately 52 meters per year from 2009 to 2013, with the greatest growth occurring along a line linking areas of persistent upwelling and downwelling. We suggest that the development of an oblong crater with a perimeter that grows linearly is best explained by a model where degradation is favored at the sites of persistent upwelling and downwelling and where growth is controlled by a lithology that varies little with respect to rock strength. We also found that most of the Overlook crater growth occurred during a relatively small number of significant rockfall events (~16) over this period. Additional lidar datasets revealed that the lava lake surface has a measurable slope from the areas of persistent upwelling to downwelling, although rockfalls from the crater walls temporarily changed the direction of crustal plate movement along with the magnitude and direction of the lava lake surface slope. Our study demonstrates that lidar is an effective tool for tracking the topography of an active volcanic crater when heavy outgassing renders other tools, such as structure from motion, ineffective.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1867C","usgsCitation":"LeWinter, A.L., Anderson, S.W., Finnegan, D.C., Patrick, M.R., and Orr, T.R., 2021, Crater growth and lava-lake dynamics revealed through multitemporal terrestrial lidar scanning at Kīlauea Volcano, Hawaiʻi, chap. C of Patrick, M., Orr, T., Swanson, D., and Houghton, B., eds., The 2008–2018 summit lava lake at Kīlauea Volcano, Hawaiʻi: U.S. Geological Survey Professional Paper 1867, 26 p., https://doi.org/10.3133/pp1867C.","productDescription":"viii, 26 p.","numberOfPages":"26","onlineOnly":"N","ipdsId":"IP-121567","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":392860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1867/c/covrthb.jpg"},{"id":392861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1867/c/pp1867c.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3192138671875,\n 19.37593175537523\n ],\n [\n -155.21896362304685,\n 19.37593175537523\n ],\n [\n -155.21896362304685,\n 19.460118162137714\n ],\n [\n -155.3192138671875,\n 19.460118162137714\n ],\n [\n -155.3192138671875,\n 19.37593175537523\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Contact HVO
Hawaiian Volcano Observatory
U.S. Geological Survey
1266 Kamehameha Avenue
Suite A-8
Hilo, HI 96720

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2021-12-14","noUsgsAuthors":false,"publicationDate":"2021-12-14","publicationStatus":"PW","contributors":{"authors":[{"text":"LeWinter, Adam L.","contributorId":241892,"corporation":false,"usgs":false,"family":"LeWinter","given":"Adam","email":"","middleInitial":"L.","affiliations":[{"id":48447,"text":"U.S. Army Corps of Engineers Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":828408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Steve W.","contributorId":192765,"corporation":false,"usgs":false,"family":"Anderson","given":"Steve","email":"","middleInitial":"W.","affiliations":[],"preferred":true,"id":828409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finnegan, David C.","contributorId":192073,"corporation":false,"usgs":false,"family":"Finnegan","given":"David","email":"","middleInitial":"C.","affiliations":[],"preferred":true,"id":828410,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":828411,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Orr, Tim R. 0000-0003-1157-7588","orcid":"https://orcid.org/0000-0003-1157-7588","contributorId":214065,"corporation":false,"usgs":true,"family":"Orr","given":"Tim R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":828412,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241052,"text":"70241052 - 2021 - Spatial modeling of common raven density and occurrence helps guide landscape management within Great Basin sagebrush ecosystems","interactions":[],"lastModifiedDate":"2023-03-08T15:03:20.772821","indexId":"70241052","displayToPublicDate":"2021-12-01T08:55:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Spatial modeling of common raven density and occurrence helps guide landscape management within Great Basin sagebrush ecosystems","docAbstract":"

Common ravens (Corvus corax; ravens) are a behaviorally flexible nest predator of several avian species, including species of conservation concern. Movement patterns based on life history phases, particularly territoriality of breeding birds and transiency of nonbreeding birds, are thought to influence the frequency and efficacy of nest predation. As such, predicting where on the landscape territorial resident and non-territorial transient birds may be found in relation to the distribution of sensitive prey is of increasing importance to managers and conservationists. From 2007 to 2019, we conducted raven point count surveys between mid-March and mid-September across 43 different field sites representing typical sagebrush (Artemisia spp.) ecosystems of the Great Basin, USA. The surveys conducted during 2007–2016 were used in previously published maps of raven occurrence and density. Here, we examined the relationship between occurrence and density of ravens using spatially explicit predictions from 2 previously published studies and differentiate areas occupied by higher concentrations of resident ravens as opposed to transients. Surveys conducted during 2017–2019 were subsequently used to evaluate the predicted trends from our analytical approach. Specifically, we used residuals from a generalized linear regression to establish the relationship between occurrence and density, which ultimately resulted in a spatially explicit categorical map that identifies areas of resident versus transient ravens. We evaluated mapped categories using independently collected observed raven group sizes from the 2017–2019 survey data, as well as an independent dataset of global positioning system locations of resident and transient individuals monitored during 2019–2020. We observed moderate agreement between the mapped categories and independent datasets for both evaluation approaches. Our map provides broad inference about spatial variation in potential predation risk from ravens for species such as greater sage-grouse (Centrocercus urophasianus) and can be used as a valuable spatial layer for decision support tools aimed at guiding raven management decisions and, ultimately, improving survival and reproduction of sensitive prey within the Great Basin.

","language":"English","publisher":"Berryman Institute","doi":"10.26077/djza-3976","usgsCitation":"Webster, S.C., O’Neil, S.T., Brussee, B.E., Coates, P.S., Jackson, P.J., Tull, J.C., and Delehanty, D.J., 2021, Spatial modeling of common raven density and occurrence helps guide landscape management within Great Basin sagebrush ecosystems: Human–Wildlife Interactions, v. 15, no. 3, 10, 19 p., https://doi.org/10.26077/djza-3976.","productDescription":"10, 19 p.","ipdsId":"IP-130899","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436107,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P900CRGI","text":"USGS data release","linkHelpText":"Raven Occurrence and Density in the Great Basin Region of the Western United States (2007-2019)"},{"id":413854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Idaho, Montana, Nevada, Oregon, Utah, Wyoming","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.34468329014176,\n 34.70098212463705\n ],\n [\n -114.04075025505519,\n 35.8909084565023\n ],\n [\n -113.16542551306084,\n 37.230073866537396\n ],\n [\n -112.47946810222676,\n 37.386067115551796\n ],\n [\n -111.1464418743502,\n 39.05064247472356\n ],\n [\n -110.68528003854762,\n 41.12528608167821\n ],\n [\n -111.00237117235294,\n 42.26628304547768\n ],\n [\n -111.68989801144276,\n 42.8686045283136\n ],\n [\n -111.47814016329308,\n 43.59652326820736\n ],\n [\n -111.75325040068994,\n 44.7964787747967\n ],\n [\n -112.71726362386148,\n 44.73453532393705\n ],\n [\n -113.65013416063762,\n 45.57183439950148\n ],\n [\n -115.06168907025786,\n 44.889399700106594\n ],\n [\n -115.29104427203676,\n 43.254541948308145\n ],\n [\n 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0000-0003-4981-2010","orcid":"https://orcid.org/0000-0003-4981-2010","contributorId":302117,"corporation":false,"usgs":true,"family":"Webster","given":"Sarah","email":"","middleInitial":"C.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865871,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865872,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865873,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865874,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackson, Pat J.","contributorId":206602,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","email":"","middleInitial":"J.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":865875,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tull, John C. 0000-0002-0680-008X","orcid":"https://orcid.org/0000-0002-0680-008X","contributorId":201650,"corporation":false,"usgs":false,"family":"Tull","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":865876,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":865877,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70226582,"text":"sim3484 - 2021 - Maps of the Arctic Alaska boundary area as defined by the U.S. Arctic Research and Policy Act—Including geospatial characteristics of select marine and terrestrial features","interactions":[],"lastModifiedDate":"2022-11-28T23:28:53.710888","indexId":"sim3484","displayToPublicDate":"2021-11-30T13:04:42","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3484","displayTitle":"Maps of the Arctic Alaska Boundary Area as Defined by the U.S. Arctic Research and Policy Act—Including Geospatial Characteristics of Select Marine and Terrestrial Features","title":"Maps of the Arctic Alaska boundary area as defined by the U.S. Arctic Research and Policy Act—Including geospatial characteristics of select marine and terrestrial features","docAbstract":"

This pamphlet presents a series of general reference maps showing relevant geospatial features of the U.S. Arctic boundary as defined by the U.S. Congress since 1984. The first generation of the U.S. Arctic Research and Policy Act (ARPA) boundary maps was originally formatted and published in 2009 by a private firm contracted with the National Science Foundation and the U.S. Arctic Research Commission. Recognizing the steadily increasing relevance of Arctic issues to national and global affairs that requires more functional projections and online tools, the U.S. Geological Survey (USGS) Alaska Regional Office and the National Geospatial Technical Operations Center developed this updated series of ARPA boundary maps. Map sheet 1 shows the ARPA boundary as it relates to Alaska and marine features of the Bering Sea. Map sheet 2 shows the ARPA boundary from a circumpolar perspective. Map sheet 3 shows the national boundary of the U.S. 200-nautical-mile Exclusive Economic Zone through the Bering, Chukchi, and Beaufort Seas, facilitating Arctic domain awareness and more consistent territorial assessments of the U.S. Arctic. Map sheet 4 shows, in poster-size detail, the ARPA boundary as it relates to terrestrial features of Arctic Alaska north of the Yukon and Kuskokwim Rivers. Map sheet 5 shows, in poster-size detail, the ARPA boundary as it relates to marine and terrestrial features of the Aleutian Islands. These new maps collectively illustrate several value-added attributes, including updated bathymetry and shoreline refinements, demographic information, international borders and offshore territorial claims, Alaska conservation areas, Alaska land cover, Alaska terrestrial shaded relief, annual sea ice maximum extent, annual circumpolar 10-degree-Celsius isotherm, location of active volcanoes, and updated geospatial information. The static PDF-file maps offer value as standalone products but are intended for use with a potential interactive website that can be sourced by annual data updates, allowing users to access the various map layers in a dynamic up-to-date environment.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3484","usgsCitation":"Williams, D.M., and Richmond, C.L., 2021, Maps of the Arctic Alaska boundary area as defined by the U.S. Arctic Research and Policy Act—Including geospatial characteristics of select marine and terrestrial features: U.S. Geological Survey Scientific Investigations Map 3484, 7 p., 5 sheets, https://doi.org/10.3133/sim3484.","productDescription":"Pamphlet: vi, 7 p.; 5 Sheets: 47.50 × 33.50 inches or smaller","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-125844","costCenters":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"links":[{"id":392220,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet5.pdf","text":"Map sheet 5","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 5","linkHelpText":"— The Arctic Research and Policy Act Region—Aleutian Islands"},{"id":392219,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet4.pdf","text":"Map sheet 4","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 4","linkHelpText":"— The Arctic Research and Policy Act Region—Mainland Alaska"},{"id":392218,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet3.pdf","text":"Map sheet 3","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 3","linkHelpText":"— The Arctic Research and Policy Act Region—U.S. Territorial Limits"},{"id":392217,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet2.pdf","text":"Map sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 2","linkHelpText":"— The Arctic Research and Policy Act Region—Circumpolar Perspective"},{"id":392216,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_sheet1.pdf","text":"Map sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Map sheet 1","linkHelpText":"— The Arctic Research and Policy Act Region—Bering Sea"},{"id":392249,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3484/sim3484_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3484 – Pamphlet"},{"id":392268,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3484/coverthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.50390625,\n 69.83962194067463\n ],\n [\n -149.94140625,\n 70.55417853776078\n ],\n [\n -155.91796874999997,\n 71.41317683396566\n ],\n [\n -162.421875,\n 70.19999407534661\n ],\n [\n -166.11328125000003,\n 68.52823492039876\n ],\n [\n -166.640625,\n 67.20403234340081\n ],\n [\n -165.9375,\n 66.93006025862448\n ],\n [\n -168.22265625,\n 65.58572002329473\n ],\n [\n -166.11328125,\n 61.270232790000634\n ],\n [\n -165.58593749999997,\n 60.06484046010452\n ],\n [\n -164.35546875,\n 59.265880628258095\n ],\n [\n -161.3671875,\n 58.81374171570782\n ],\n [\n -147.83203125,\n 65.2198939361321\n ],\n [\n -140.625,\n 65.94647177615738\n ],\n [\n -141.50390625,\n 69.83962194067463\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Regional Director, Alaska
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508-4560

","tableOfContents":"","publishedDate":"2021-11-30","noUsgsAuthors":false,"publicationDate":"2021-11-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Dee M. 0000-0003-0400-479X dmwilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-0400-479X","contributorId":224715,"corporation":false,"usgs":true,"family":"Williams","given":"Dee M.","email":"dmwilliams@usgs.gov","affiliations":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"preferred":false,"id":827518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Richmond, Christopher L. 0000-0003-0474-6224","orcid":"https://orcid.org/0000-0003-0474-6224","contributorId":269602,"corporation":false,"usgs":true,"family":"Richmond","given":"Christopher","email":"","middleInitial":"L.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":827519,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70226847,"text":"70226847 - 2021 - Remotely sensed fine-fuel changes from wildfire and prescribed fire in a semi-arid grassland","interactions":[],"lastModifiedDate":"2021-12-15T12:40:09.70423","indexId":"70226847","displayToPublicDate":"2021-11-11T06:37:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Remotely sensed fine-fuel changes from wildfire and prescribed fire in a semi-arid grassland","docAbstract":"

The spread of flammable invasive grasses, woody plant encroachment, and enhanced aridity have interacted in many grasslands globally to increase wildfire activity and risk to valued assets. Annual variation in the abundance and distribution of fine-fuel present challenges to land managers implementing prescribed burns and mitigating wildfire, although methods to produce high-resolution fuel estimates are still under development. To further understand how prescribed fire and wildfire influence fine-fuels in a semi-arid grassland invaded by non-native perennial grasses, we combined high-resolution Sentinel-2A imagery with in situ vegetation data and machine learning to estimate yearly fine-fuel loads from 2015 to 2020. The resulting model of fine-fuel corresponded to field-based validation measurements taken in the first (R2\">2 = 0.52, RMSE = 218 kg/ha) and last year (R2\">2 = 0.63, RMSE = 196 kg/ha) of this 6-year study. Serial prediction of the fine-fuel model allowed for an assessment of the effect of prescribed fire (average reduction of −80 kg/ha 1-year post fire) and wildfire (−260 kg/ha 1-year post fire) on fuel conditions. Post-fire fine-fuel loads were significantly lower than in unburned control areas sampled just outside fire perimeters from 2015 to 2020 across all fires (t = 1.67, p < 0.0001); however, fine-fuel recovery occurred within 3–5 years, depending upon burn and climate conditions. When coupled with detailed fuels data from field measurements, Sentinel-2A imagery provided a means for evaluating grassland fine-fuels at yearly time steps and shows high potential for extended monitoring of dryland fuels. Our approach provides land managers with a systematic analysis of the effects of fire management treatments on fine-fuel conditions and provides an accurate, updateable, and expandable solution for mapping fine-fuels over yearly time steps across drylands throughout the world

","language":"English","publisher":"MDPI","doi":"10.3390/fire4040084","usgsCitation":"Wells, A.G., Munson, S.M., Sesnie, S., and Villarreal, M.L., 2021, Remotely sensed fine-fuel changes from wildfire and prescribed fire in a semi-arid grassland: Fire, v. 4, no. 4, 84, 22 p., https://doi.org/10.3390/fire4040084.","productDescription":"84, 22 p.","ipdsId":"IP-134126","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":450231,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fire4040084","text":"Publisher Index Page"},{"id":436120,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91U530P","text":"USGS data release","linkHelpText":"Remotely sensed fine-fuel data for Buenos Aires National Wildlife Refuge (BANWR) from 2015 to 2020"},{"id":436119,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9347I2H","text":"USGS data release","linkHelpText":"Remotely sensed fine fuel data for Buenos Aires National Wildlife Refuge (BANWR) from 2015 to 2020"},{"id":392940,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Buenos Aires National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.61285400390625,\n 31.302021690136105\n ],\n [\n -110.92071533203125,\n 31.302021690136105\n ],\n [\n -110.92071533203125,\n 31.88921859876096\n ],\n [\n -111.61285400390625,\n 31.88921859876096\n ],\n [\n -111.61285400390625,\n 31.302021690136105\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-11-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Wells, Adam Gerhard 0000-0001-9675-4963","orcid":"https://orcid.org/0000-0001-9675-4963","contributorId":270137,"corporation":false,"usgs":true,"family":"Wells","given":"Adam","email":"","middleInitial":"Gerhard","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":828474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":828475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sesnie, Steven","contributorId":239687,"corporation":false,"usgs":false,"family":"Sesnie","given":"Steven","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":true,"id":828476,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Villarreal, Miguel L. 0000-0003-0720-1422 mvillarreal@usgs.gov","orcid":"https://orcid.org/0000-0003-0720-1422","contributorId":1424,"corporation":false,"usgs":true,"family":"Villarreal","given":"Miguel","email":"mvillarreal@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":828477,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225636,"text":"sir20215038 - 2021 - Groundwater/surface-water interactions in the Partridge River Basin and evaluation of hypothetical future mine pits, Minnesota","interactions":[],"lastModifiedDate":"2022-03-23T13:15:47.763523","indexId":"sir20215038","displayToPublicDate":"2021-11-04T10:55:00","publicationYear":"2021","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":"2021-5038","displayTitle":"Groundwater/Surface-Water Interactions in the Partridge River Basin and Evaluation of Hypothetical Future Mine Pits, Minnesota","title":"Groundwater/surface-water interactions in the Partridge River Basin and evaluation of hypothetical future mine pits, Minnesota","docAbstract":"

The Partridge River Basin (PRB) covers 156 square miles in northeastern Minnesota with headwaters in the Mesabi Iron Range. The basin is characterized by extensive wetlands, lakes, and streams in poorly drained and often thin glacial material overlying Proterozoic bedrock. To better understand the interaction between these extensive surface water features and the groundwater system, a three-dimensional, steady-state, groundwater-flow model of the PRB was developed by the U.S. Geological Survey in cooperation with the Great Lakes Indian Fish & Wildlife Commission using the finite-difference computer code MODFLOW-NWT. The model simulates steady-state base flow in streams and groundwater interactions using the streamflow routing (SFR2) package. Existing mining features including tailings basins, stockpiles, pumped mine pits, and flooded mine pits were simulated using either high hydraulic conductivity zones or the drain (DRN) package. The unsaturated zone flow (UZF) package was used to better represent the groundwater system in areas with a high water table and for wetlands often associated with such areas. UZF typically is used to represent unsaturated zone processes but also can simulate the rejection of recharge and groundwater discharge to the land surface when the water table is near land surface. The steady-state model used data from the 2011 to 2013 period when 2011 high-resolution land surface (light detecting and ranging [lidar]) data were available that reflected land-surface and water elevations from mining activity in the basin. The parameter-estimation software suite PEST_HP was used to obtain a best fit of the modeled to measured groundwater levels, streamflow, pit inflow rates, and mapped peat deposits. The PEST calibration used the target residuals from two models with the same model parameters and targets from two separate periods: (1) a 1995–2015 calibration model, which provided a larger number of calibration targets, and (2) a 2011–2013 mining conditions model, which included calibration targets that reflected conditions consistent with the modeled mine-workings topography.

Calibration of the PRB model resulted in ranges of glacial horizontal hydraulic conductivity parameters that generally agreed with literature values and other models of the region. Horizontal hydraulic conductivity of the bedrock was higher in the upper bedrock layers where numerous and continuous fractures have been observed and lower in the deeper bedrock layers. Average basin-wide calibrated infiltration was 5.3 inches per year. An average of 4.6 inches per year of infiltration crosses the water table and becomes recharge and 0.7 inch per year is rejected by UZF due to saturated conditions at the land surface. Simulated groundwater runoff (the sum of rejected recharge and groundwater seepage to the land surface) can either be routed to streams or removed from the model as evapotranspiration. The calibrated model indicates relatively shallow groundwater-flow paths dominating and approximately 50 percent of the stream base flow coming from groundwater runoff.

The 2011–2013 mining conditions model was then used to develop five model scenarios simulating the response of the groundwater and surface-water system to potential hydrologic stress. The purpose of these mine pit scenarios is to present a possible workflow to quantify a model’s uncertainty for a given model forecast and serve as a possible guide for initial data collection that may improve a future model’s ability to make such a forecast. The scenarios included one scenario with the currently existing Peter Mitchell pit at final buildout and flooded to an elevation of 1,500 feet, and four scenarios with a hypothetical, new mine pit plus the flooded Peter Mitchell at final buildout. The five model scenarios were used to forecast streamflow at six locations in the PRB, pit inflow rates for the new mine pits and the flooded Peter Mitchell pit, and the average depth to water in 12 wetlands. A linear uncertainty analysis was performed using information from the PEST calibration and tools in the PyEMU python package to assess model uncertainty propagation to the model forecasts. Streamflows generally were reduced with future mining and the greatest streamflow reductions occurred from the flooded Peter Mitchell Pit, probably due to its large size. Average depth to groundwater in wetlands was most affected the closer the wetland was to a new mine pit.

Linear uncertainty methods were also used to evaluate data worth, which is the ability for potential new groundwater elevation observations to reduce the uncertainty in scenario forecasts. Data worth was performed for a grid of new hydraulic head observations. Overall, areas with nonnegligible data worth generally corresponded to wetland areas with no groundwater seepage to land surface from UZF. These model behaviors indicated that the land-surface boundary condition simulated by the UZF package was pinning the groundwater elevations to the land surface in areas with groundwater seepage (33 percent of the 2011–2013 base conditions model) such that the sensitivity to new observations in these areas was minimal. Therefore, representing wetlands as boundary conditions minimized the usefulness of data worth calculations because wetland areas were present over a large part of the model domain.

Probabilistic capture zones were estimated for each of the mines in the model scenarios. A capture zone represents the area contributing recharge to a model feature, like a well or a mine pit, and can be calculated by forward tracking particles from the water table. By using Monte Carlo techniques, it is possible to generate estimated capture zones that include the probability of recharge capture given the uncertainty present in the model. Monte Carlo techniques use randomly generated model parameter sets sampled from a plausible parameter range to create many possible realizations. The resulting capture zone arrays were calculated by tallying the total number of realizations in which a particle from a model cell was captured by the feature. Probabilities from the Monte Carlo runs ranged from 1 (captured in 100 percent of the runs) near the pits to 0 (captured in 0 percent of the runs) at the edges of the capture zone. Capture zones were not always spatially continuous; for example, the capture zone for the proposed mine pits south of the flooded Peter Mitchell pit was discontinuous with capture surrounding the proposed mine pit and north of the flooded Peter Mitchell pit. This northern section represents deeper groundwater flow paths that originate in the topographic high, move under the flooded pit, and discharge into the proposed pit. This pattern of capture indicates the possibility of some deeper flow through the upper fractured bedrock when the shallow groundwater flow system is modified. These results underscore that future site-specific applications of the base condition model require the input of site-specific data and recalibration to focus on the site of interest.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215038","collaboration":"Prepared in cooperation with the Great Lakes Indian Fish & Wildlife Commission","usgsCitation":"Haserodt, M.J., Hunt, R.J., Fienen, M.N., and Feinstein, D.T., 2021, Groundwater/surface-water interactions in the Partridge River Basin and evaluation of hypothetical future mine pits, Minnesota: U.S. Geological Survey Scientific Investigations Report 2021–5038, 94 p., https://doi.org/10.3133/sir20215038.","productDescription":"Report: ix, 87 p.; Data Release; Dataset","numberOfPages":"102","onlineOnly":"Y","ipdsId":"IP-123210","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391131,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5038/sir20215038.xml","text":"Report xml","size":"277 kB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2021–5038 xml"},{"id":391130,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":391132,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5038/images"},{"id":391129,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VODOU8","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT and MODPATH models, capture zones and uncertainty data analysis for the Partridge River Basin, Minnesota"},{"id":391127,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5038/coverthb.jpg"},{"id":391128,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5038/sir20215038.pdf","text":"Report","size":"69.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5038"}],"country":"United States","state":"Minnesota","otherGeospatial":"Partridge River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.25,\n 47.4\n ],\n [\n -91.75,\n 47.4\n ],\n [\n -91.75,\n 47.8\n ],\n [\n -92.25,\n 47.8\n ],\n [\n -92.25,\n 47.4\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive,
Madison, WI 53726

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"publishedDate":"2021-11-04","noUsgsAuthors":false,"publicationDate":"2021-11-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Haserodt, Megan J. 0000-0002-8304-090X mhaserodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8304-090X","contributorId":174791,"corporation":false,"usgs":true,"family":"Haserodt","given":"Megan","email":"mhaserodt@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":16118,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Feinstein, Daniel T. 0000-0003-1151-2530","orcid":"https://orcid.org/0000-0003-1151-2530","contributorId":203888,"corporation":false,"usgs":true,"family":"Feinstein","given":"Daniel T.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826024,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70224989,"text":"70224989 - 2021 - Past, present, and future of Mars Polar Science: Outcomes and outlook from the 7th International Conference on Mars Polar Science and Exploration","interactions":[],"lastModifiedDate":"2021-10-13T12:15:32.552149","indexId":"70224989","displayToPublicDate":"2021-10-11T07:12:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8607,"text":"The Planetary Science Journal","active":true,"publicationSubtype":{"id":10}},"title":"Past, present, and future of Mars Polar Science: Outcomes and outlook from the 7th International Conference on Mars Polar Science and Exploration","docAbstract":"

Mars Polar Science is a subfield of Mars science that encompasses all studies of the cryosphere of Mars and its interaction with the Martian environment. Every 4 yr, the community of scientists dedicated to this subfield meets to discuss new findings and debate open issues in the International Conference on Mars Polar Science and Exploration (ICMPSE). This paper summarizes the proceedings of the seventh ICMPSE and the progress made since the sixth edition. We highlight the most important advances and present the most salient open questions in the field today, as discussed and agreed upon by the participants of the conference. We also feature agreed-upon suggestions for future methods, measurements, instruments, and missions that would be essential to answering the main open questions presented. This work is thus an overview of the current status of Mars Polar Science and is intended to serve as a road map for the direction of the field during the next 4 yr and beyond, helping to shape its contribution within the larger context of planetary science and exploration.

","language":"English","publisher":"AAS","doi":"10.3847/PSJ/ac19a5","usgsCitation":"Becerra, P., Smith, I.B., Hibbard, S.M., Andres, C., Bapst, J., Bramson, A., Buhler, P., Coronato, A., Diniega, S., Emmett, J., Grau Galofre, A., Herny, C., Kahre, M., Knightly, J.P., Nerozzi, S., Pascuzzo, A., Portyankina, G., Rabassa, J., Tamppari, L., Titus, T.N., Whitten, J., and Yoldi, Z., 2021, Past, present, and future of Mars Polar Science: Outcomes and outlook from the 7th International Conference on Mars Polar Science and Exploration: The Planetary Science Journal, v. 2, no. 5, 209, 22 p., https://doi.org/10.3847/PSJ/ac19a5.","productDescription":"209, 22 p.","ipdsId":"IP-129449","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":450492,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3847/psj/ac19a5","text":"Publisher Index Page"},{"id":390463,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"2","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-10-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Becerra, Patricio","contributorId":173341,"corporation":false,"usgs":false,"family":"Becerra","given":"Patricio","email":"","affiliations":[],"preferred":false,"id":825087,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Isaac B.","contributorId":200695,"corporation":false,"usgs":false,"family":"Smith","given":"Isaac","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":825088,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hibbard, Shannon M","contributorId":229484,"corporation":false,"usgs":false,"family":"Hibbard","given":"Shannon","email":"","middleInitial":"M","affiliations":[{"id":41656,"text":"U. Western Ontario","active":true,"usgs":false}],"preferred":false,"id":825098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andres, Chimira","contributorId":229481,"corporation":false,"usgs":false,"family":"Andres","given":"Chimira","email":"","affiliations":[{"id":41656,"text":"U. Western Ontario","active":true,"usgs":false}],"preferred":false,"id":825089,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bapst, Jonathan","contributorId":229482,"corporation":false,"usgs":false,"family":"Bapst","given":"Jonathan","email":"","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":825090,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bramson, Ali","contributorId":189477,"corporation":false,"usgs":false,"family":"Bramson","given":"Ali","email":"","affiliations":[],"preferred":false,"id":825091,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Buhler, Peter","contributorId":258300,"corporation":false,"usgs":false,"family":"Buhler","given":"Peter","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":825092,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Coronato, Andrea","contributorId":267377,"corporation":false,"usgs":false,"family":"Coronato","given":"Andrea","email":"","affiliations":[{"id":55484,"text":"Centro Austral de Investigaciones Científicas - CONICET","active":true,"usgs":false}],"preferred":false,"id":825093,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Diniega, Serina","contributorId":212017,"corporation":false,"usgs":false,"family":"Diniega","given":"Serina","email":"","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":825094,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Emmett, Jeremy","contributorId":267378,"corporation":false,"usgs":false,"family":"Emmett","given":"Jeremy","email":"","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":825095,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Grau Galofre, Anna","contributorId":267379,"corporation":false,"usgs":false,"family":"Grau Galofre","given":"Anna","email":"","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":825096,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Herny, Clemence","contributorId":267380,"corporation":false,"usgs":false,"family":"Herny","given":"Clemence","email":"","affiliations":[{"id":55485,"text":"Centre d'Etude de la Neige, CNRM/CNRS, Météo France","active":true,"usgs":false}],"preferred":false,"id":825097,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kahre, Melinda","contributorId":237031,"corporation":false,"usgs":false,"family":"Kahre","given":"Melinda","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":825099,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Knightly, J. Paul","contributorId":267381,"corporation":false,"usgs":false,"family":"Knightly","given":"J.","email":"","middleInitial":"Paul","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":825100,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Nerozzi, Stefano","contributorId":267382,"corporation":false,"usgs":false,"family":"Nerozzi","given":"Stefano","email":"","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":825101,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Pascuzzo, Alyssa","contributorId":267383,"corporation":false,"usgs":false,"family":"Pascuzzo","given":"Alyssa","email":"","affiliations":[{"id":16929,"text":"Brown University","active":true,"usgs":false}],"preferred":false,"id":825102,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Portyankina, Ganna","contributorId":200703,"corporation":false,"usgs":false,"family":"Portyankina","given":"Ganna","email":"","affiliations":[],"preferred":false,"id":825103,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Rabassa, Jorge","contributorId":267384,"corporation":false,"usgs":false,"family":"Rabassa","given":"Jorge","email":"","affiliations":[{"id":55484,"text":"Centro Austral de Investigaciones Científicas - CONICET","active":true,"usgs":false}],"preferred":false,"id":825104,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Tamppari, Leslie","contributorId":237035,"corporation":false,"usgs":false,"family":"Tamppari","given":"Leslie","affiliations":[{"id":36276,"text":"JPL","active":true,"usgs":false}],"preferred":false,"id":825105,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":825106,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Whitten, Jennifer L","contributorId":237951,"corporation":false,"usgs":false,"family":"Whitten","given":"Jennifer L","affiliations":[{"id":47657,"text":"National Air and Space Museum, Smithsonian Institution","active":true,"usgs":false}],"preferred":false,"id":825107,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Yoldi, Zurine","contributorId":267385,"corporation":false,"usgs":false,"family":"Yoldi","given":"Zurine","email":"","affiliations":[{"id":27198,"text":"Niels Bohr Institute, University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":825108,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70226855,"text":"70226855 - 2021 - A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping","interactions":[],"lastModifiedDate":"2023-11-08T16:32:06.862608","indexId":"70226855","displayToPublicDate":"2021-09-15T06:59:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping","docAbstract":"

The long record of Landsat imagery, which is the cornerstone of Earth observation, provides an opportunity to monitor land use and land cover (LULC) change and understand the interactions between the climate and earth system through time. A few change detection algorithms such as Continuous Change Detection and Classification (CCDC) have been developed to utilize all available Landsat images for change detection and characterization at local or global scales. However, the reliable, rapid, and reproducible collection of training samples have become a challenge for time series land cover classification at a large scale. To meet the challenge, we proposed an automatic phenology learning (APL) method with the assumption that the temporal profiles of samples within the same land cover type are the same or similar at a local scale to generate evenly distributed training samples automatically. We designed the method to build land cover patterns for each category based on consensus samples derived from multiple existing scientific datasets including LANDFIRE's (LF) Existing Vegetation Type (EVT), USGS National Land Cover Database (NLCD), National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL), and National Wetlands Inventory (NWI). Then we calculated the Time-Weighted Dynamic Time Warping (twDTW) distance between any undefined samples and land cover patterns in the same geographical region as prior knowledge. Finally, we selected the optimal land cover category for each undefined sample from the land cover products based on the designed criteria iteratively using the twDTW distance as an indicator. The method was applied in the footprint of 10 selected Landsat Analysis Ready Data (ARD) tiles in the eastern and western conterminous United States (CONUS) to produce annual land cover maps from 1985 to 2017. The accuracy assessment and visual comparison revealed that the APL method can generate reliable training samples without any manual interpretation, producing better land cover results especially for the grass/shrub and wetland land cover classes. Applying the APL method, the overall accuracy of the annual land cover maps was improved by 2% over the accuracy of Land Change Monitoring, Assessment, and Projection (LCMAP) Collection 1.0 Science Products in the research regions. Our results also indicate that the APL method provides an approach for best use of different land cover products and meets the requirement of intensive sampling for training data collection.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2021.112670","usgsCitation":"Li, C., Xian, G.Z., Zhou, Q., and Pengra, B., 2021, A novel automatic phenology learning (APL) method of training sample selection using multiple datasets for time-series land cover mapping: Remote Sensing of Environment, v. 266, 112670, 19 p., https://doi.org/10.1016/j.rse.2021.112670.","productDescription":"112670, 19 p.","ipdsId":"IP-123712","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":450816,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2021.112670","text":"Publisher Index Page"},{"id":393007,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"266","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":828505,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":828506,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":828507,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pengra, Bruce 0000-0003-2497-8284","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":264539,"corporation":false,"usgs":false,"family":"Pengra","given":"Bruce","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":828508,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229777,"text":"70229777 - 2021 - Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate","interactions":[],"lastModifiedDate":"2022-03-17T15:32:45.548565","indexId":"70229777","displayToPublicDate":"2021-09-07T10:17:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2163,"text":"Journal of Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate","docAbstract":"
  1. Translocations are essential for re-establishing wildlife populations. As they sometimes fail, it is critical to assess factors that influence their success pre-translocation.
  2. Socioecological suitability models (SESMs) integrate social acceptance and ecological suitability to enable identification of areas where wildlife populations will expand, which makes it likely that SESMs will also be useful for predicting translocation success.
  3. To inform site selection for potential elk Cervus canadensis reintroduction to north-eastern Minnesota, United States, we developed broadscale maps of social acceptance from surveys of local residents and landowners, animal use equivalence (AUE) from forage measured in the field and empirical conflict risk from geospatial data. Resulting SESMs integrated social acceptance favourability scores, AUE and conflict risk, and weighted SESMs showed the relative influences of acceptance and conflict.
  4. Social acceptance was positive for local residents and landowners (mean ≥ 5.4; scale of 1–7). AUE (scaled to an elk home range) ranged between 1 and 9 elk/16 km2 during winter, and from 14 to 83 elk/16 km2 during summer. Human–elk conflict risk was low (mean ≤ 0.10; scaled 0–1), increasing from north to south. Geographical distributions differed for social acceptance, AUE and conflict risk, and weighted SESMs revealed unsuitable areas that were otherwise obscured.
  5. Synthesis and applications. Integrating human–wildlife conflict risk into SESMs shows where social acceptance of translocated species is likely to erode, even where viewed favourably pre-translocation, to inform translocation planning by highlighting interactions between key factors. Such integrated models supplement existing reintroduction biology frameworks by supporting decision-making and knowledge development. In north-eastern Minnesota, natural resource managers who are considering elk reintroductions are using SESMs reported here to identify where human–elk conflict is unlikely to result in an isolated elk population and where addressing concerns for area residents about conflict risk is essential.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.14021","usgsCitation":"McCann, N.P., Walberg, E.M., Forester, J., Schrage, M.W., Fulton, D.C., and Ditmer, M., 2021, Integrating socioecological suitability with human-wildlife conflict risk: Case study for translocation of a large ungulate: Journal of Applied Ecology, v. 58, no. 12, p. 2810-2820, https://doi.org/10.1111/1365-2664.14021.","productDescription":"11 p.","startPage":"2810","endPage":"2820","ipdsId":"IP-127289","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":502433,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":397248,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Cloquet Valley Study Area, Fond du Lac Study Area, Nemadji Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.33984375,\n 46.195042108660154\n ],\n [\n -92.10937499999999,\n 46.195042108660154\n ],\n [\n -92.10937499999999,\n 47.338822694822\n ],\n [\n -93.33984375,\n 47.338822694822\n ],\n [\n -93.33984375,\n 46.195042108660154\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-09-17","publicationStatus":"PW","contributors":{"authors":[{"text":"McCann, Nicholas P.","contributorId":288723,"corporation":false,"usgs":false,"family":"McCann","given":"Nicholas","email":"","middleInitial":"P.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":838246,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walberg, Eric M.","contributorId":288724,"corporation":false,"usgs":false,"family":"Walberg","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":838247,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forester, James D.","contributorId":288725,"corporation":false,"usgs":false,"family":"Forester","given":"James D.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":838248,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schrage, Michael W.","contributorId":288729,"corporation":false,"usgs":false,"family":"Schrage","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":61835,"text":"Fond du Lac Band of Lake Superior Chippewa","active":true,"usgs":false}],"preferred":false,"id":838249,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fulton, David C. 0000-0001-5763-7887 dcf@usgs.gov","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":2208,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"dcf@usgs.gov","middleInitial":"C.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":838245,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ditmer, Mark A.","contributorId":288732,"corporation":false,"usgs":false,"family":"Ditmer","given":"Mark A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":838250,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70223725,"text":"70223725 - 2021 - Identification of the Gulf of Mexico as an important high-use habitat for leatherback turtles from Central America","interactions":[],"lastModifiedDate":"2021-09-03T12:55:54.51574","indexId":"70223725","displayToPublicDate":"2021-08-11T07:52:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Identification of the Gulf of Mexico as an important high-use habitat for leatherback turtles from Central America","docAbstract":"

Endangered leatherback sea turtles (Dermochelys coriacea) are wide-ranging, long-distance migrants whose movements are often associated with environmental cues. We examined the spatial distribution and habitat use for 33 satellite-tracked leatherbacks from nesting beaches on the Caribbean coast of Costa Rica and Panama from 2004 to 2018, an important nesting population for the leatherback Northwest Atlantic Distinct Population Segment. Tracking revealed the use of two distinct regions, the Gulf of Mexico (GoM, n = 18) and the North Atlantic Ocean (NAO, n = 15). We developed density utilization maps to elucidate high-use habitats, migration pathways, and seasonal movements. GoM leatherbacks were found in three concentrated high-use habitats connected by a migration pathway, while NAO leatherbacks were primarily found in a single, large high-use habitat. Leatherbacks in both regions have the potential to interact with Atlantic pelagic longline fisheries based on seasonal overlap with high fishing effort. Our findings suggest that the GoM is an important destination for leatherbacks from the Caribbean coast of Central America with seasonal movements between high-use habitats within the GoM. While leatherbacks are utilizing high-use habitats in both the NAO and the GoM, the proportion of individuals migrating into the GoM increased over the study period. Additionally, NAO leatherbacks have increased the distance they travel in the first 90 d. Regional differences in movement and spatial distribution of high-use habitats are important considerations when developing conservation plans for the Northwest Atlantic leatherback population.

","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3722","usgsCitation":"Evans, D., Valverde, R., Ordonez, C., and Carthy, R.R., 2021, Identification of the Gulf of Mexico as an important high-use habitat for leatherback turtles from Central America: Ecosphere, v. 12, no. 8, e03722, 14 p., https://doi.org/10.1002/ecs2.3722.","productDescription":"e03722, 14 p.","ipdsId":"IP-104471","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":451214,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3722","text":"Publisher Index Page"},{"id":388835,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.85937499999999,\n 27.059125784374068\n ],\n [\n -84.72656249999999,\n 32.54681317351514\n ],\n [\n -94.5703125,\n 32.54681317351514\n ],\n [\n -99.49218749999999,\n 29.53522956294847\n ],\n [\n -100.1953125,\n 21.289374355860424\n ],\n [\n -91.7578125,\n 17.308687886770034\n ],\n [\n -88.24218749999999,\n 19.642587534013032\n ],\n [\n -82.265625,\n 23.241346102386135\n ],\n [\n -80.85937499999999,\n 27.059125784374068\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, D.R.","contributorId":265164,"corporation":false,"usgs":false,"family":"Evans","given":"D.R.","email":"","affiliations":[{"id":54616,"text":"Sea Turtle Conservancy","active":true,"usgs":false}],"preferred":false,"id":822495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Valverde, R.A.","contributorId":265267,"corporation":false,"usgs":false,"family":"Valverde","given":"R.A.","email":"","affiliations":[{"id":54640,"text":"Ordoñez","active":true,"usgs":false}],"preferred":false,"id":822496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ordonez, C.","contributorId":265268,"corporation":false,"usgs":false,"family":"Ordonez","given":"C.","email":"","affiliations":[{"id":54640,"text":"Ordoñez","active":true,"usgs":false}],"preferred":false,"id":822497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carthy, Raymond R. 0000-0001-8978-5083 rayc@usgs.gov","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":3685,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","email":"rayc@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":822498,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236722,"text":"70236722 - 2021 - Late Holocene slip rate of the Mojave section of the San Andreas Fault near Palmdale, California","interactions":[],"lastModifiedDate":"2022-09-16T12:28:22.314471","indexId":"70236722","displayToPublicDate":"2021-07-29T07:25:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Late Holocene slip rate of the Mojave section of the San Andreas Fault near Palmdale, California","docAbstract":"

The geologic slip rate on the Mojave section of the San Andreas fault is poorly constrained, despite its importance for understanding earthquake hazard, apparent discrepancies between geologic and geodetic slip rates along this fault section, and long‐term fault interactions in southern California. Here, we use surficial geologic mapping, excavations, and radiocarbon and luminescence dating to quantify the displacements and ages of late Holocene landforms offset by the fault at three sites. At the Ranch Center site, the slip rate is determined using the base of a fan marking incision and deflection of an ephemeral channel. At the adjacent Key Slide site, the margin of a landslide deposited on indigenous fire hearths provides a minimum rate. At the X‐12 site, the slip rate is determined from a channel that incised into a broad fan surface, and is deflected and beheaded by the fault. We use maximum–minimum bounds on both the displacement and age of each offset feature to calculate slip rate for each site independently. Overlap of the three independent rate ranges yields a rate of 33–39 mm/yr over the last 3 ka, under the assumption that the sites share a common history, given their proximity. Considered in sequence, site‐level epistemic uncertainties in the data permit but do not require a rate increase since ∼1200 cal B.P. Modest rate changes can be explained by aleatory variability in earthquake timing and magnitude; larger changes could suggest a shared regional variation with the Garlock and other faults. The new late Holocene slip rates are consistent with geodetic model estimates that include a viscoelastic crust and earthquake cycle effects. The geologic slip rates also provide average slip over dozens of earthquake cycles—a key constraint for long‐term earthquake rupture forecasts.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200278","usgsCitation":"Young, E., Cowgill, E., Scharer, K., Anderson-Merritt, E., Keen-Zebert, A., and Weldon, R.J., 2021, Late Holocene slip rate of the Mojave section of the San Andreas Fault near Palmdale, California: Bulletin of the Seismological Society of America, v. 111, no. 6, p. 3204-3225, https://doi.org/10.1785/0120200278.","productDescription":"22 p.","startPage":"3204","endPage":"3225","ipdsId":"IP-126803","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":406830,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Palmdale","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.97918701171876,\n 34.252676117101515\n ],\n [\n -117.333984375,\n 34.252676117101515\n ],\n [\n -117.333984375,\n 35.07046911981966\n ],\n [\n -118.97918701171876,\n 35.07046911981966\n ],\n [\n -118.97918701171876,\n 34.252676117101515\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"111","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Young, Elaine","contributorId":296630,"corporation":false,"usgs":false,"family":"Young","given":"Elaine","email":"","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":852009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cowgill, Eric","contributorId":192850,"corporation":false,"usgs":false,"family":"Cowgill","given":"Eric","affiliations":[],"preferred":false,"id":852010,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scharer, Katherine M. 0000-0003-2811-2496","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":217361,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":852011,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson-Merritt, Emery","contributorId":296632,"corporation":false,"usgs":false,"family":"Anderson-Merritt","given":"Emery","email":"","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":852012,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keen-Zebert, Amanda","contributorId":224228,"corporation":false,"usgs":false,"family":"Keen-Zebert","given":"Amanda","email":"","affiliations":[{"id":40841,"text":"University of Nevada Reno / Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":852013,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Weldon, Ray J.","contributorId":175463,"corporation":false,"usgs":false,"family":"Weldon","given":"Ray","email":"","middleInitial":"J.","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":852014,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70221226,"text":"ofr20211021 - 2021 - Cape Romain partnership for coastal protection","interactions":[],"lastModifiedDate":"2021-06-09T15:41:26.952716","indexId":"ofr20211021","displayToPublicDate":"2021-06-08T16:20:09","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1021","displayTitle":"Cape Romain Partnership for Coastal Protection","title":"Cape Romain partnership for coastal protection","docAbstract":"

This final report summarizes activities, outcomes, and lessons learned from a 3-year project titled “Climate Change Adaptation for Coastal National Wildlife Refuges” with the Cape Romain National Wildlife Refuge (NWR) and local partners in the surrounding South Carolina Lowcountry. The Lowcountry is classified as the 10-county area encompassing the coastal plain of South Carolina (this report specifically focuses on Berkeley, Charleston, and Georgetown Counties). The goals of this work, sponsored by the U.S. Geological Survey’s Southeast Climate Adaptation Science Center (SECASC), were to foster active engagement with stakeholders; to develop a comprehensive definition of adaptation problems faced by agencies, organizations, and individuals near the Cape Romain NWR that accounts for global change, local values, knowledge and perceptions; and to encourage social learning and building of effective networks and trust across South Carolina Lowcountry organizations and individuals. Although project scoping began at the scale of the Atlantic seaboard, by engaging with NWRs from Massachusetts to Florida, participating refuge personnel eventually selected the Cape Romain NWR to serve as a case study for testing our goals. The Cape Romain Partnership for Coastal Conservation was established to address global change impacts at a regional level and includes representation from Federal and State resource agencies, local conservation nongovernmental organizations, and organizations representing underserved community interests. Research topics, originating from discussions with Cape Romain Partnership for Coastal Conservation members, focused on quantifying key drivers of change including localized sea-level rise (SLR) predictions, estimates of coastal hurricane inundation as amplified by SLR, and urban growth trends and forecasts. These key drivers provided a foundation to engage stakeholders in planning exercises to begin a process of collective understanding and collaborative decision making. The goal of this process was to develop collective strategies of adaptation to enhance community and ecosystem resilience in the South Carolina Lowcountry.

South Carolina’s Lowcountry is experiencing rapid environmental and social transformation because of SLR rates approaching twice the global average, chronic tidal flooding and catastrophic storm surges, erosion and loss of habitats that provide essential services to wildlife and humans, and increasing social polarization fueled by aggressive low-density urban growth and other forms of land conversion. To support characterizations of plausible future scenarios, we used available or, in some cases, developed new models to project future conditions of key environmental and social-economic drivers. Because of the imprecision of mean global SLR projections, the SECASC commissioned a climatological study to account for local conditions and multiple representative concentration pathways to project a tailored distribution of future sea levels. These projections were matched to SLR scenarios provided by existing models to anticipate the range of future coastal habitat changes in the South Carolina Lowcountry. SLR scenarios were also incorporated into existing storm-surge models, which do not account for alternate baseline sea levels, to project the local effects of future hurricanes. To evaluate the extent and effects of population growth and urban expansion, we relied on an existing urban-growth model to map the spatial distribution of land-conversion probabilities, the total area of which is predicted to increase twofold to threefold over the next 60 years. In addition to this simplified model, an econometric model is in development to account for nonlinear feedback dynamics in land value, land use, and ecosystem service production. Although not yet completed, the goals of this model are to produce more-detailed projections of growth dynamics and to allow predictions of development patterns resulting from alternate land-use planning policies and incentives.

Collaborative planning for an uncertain future requires more than providing decision makers with information on future physical and ecological conditions; developing effective and consensual strategies must also integrate sociological values, multiple cultural perspectives, and an understanding of human behavior. To support broad stakeholder engagement in integrative approaches to adaptation planning, emphasis was placed on the importance of considering differences in how individuals perceive their environment and create meaning. Because cultural frameworks form the basis for perceptions and, ultimately, the behaviors of individuals and institutions, we describe a model of human behavior and how it can be used to understand the effect of cultural complexity and variation in perception on choices, behavioral change, and long-term maintenance of behaviors. We consider a model commonly used in the field of behavioral health that accommodates variation in human perception when describing stages of behavior and the dynamics of behavioral change. Tailoring communication and engagement activities to targeted stakeholders is likely to benefit from increased understanding of behavioral change processes.

The complex nature of this problem limited the usefulness of a traditional decision-analytic approach, we explored alternative methods for engagement, collaborative learning and decision making. Recognizing that project partners and Lowcountry stakeholders may be at different stages of preparedness and interest level for modifying behavior as a function of global change, we facilitated a scenario-planning exercise to familiarize partners with this well-established approach for communicating the opportunities and threats arising under alternative, plausible futures. We developed narratives for four alternative South Carolina Lowcountry scenarios to be used in later strategic planning that focus on quantitative trends for three primary drivers with high impact and high uncertainty: manifestations of climate change, social-political shifts at a global level, and forces of local value and power structures. This scenario-planning exercise underscored the complex relation between the temporospatial scale of the production of ecological goods and services and the institutional scale at which they are managed. We then guided the partners through an assessment of the relevant strengths and weaknesses of the Cape Romain Partnership for Coastal Protection, using the threats and opportunities characterized by each scenario to understand how the partnership might respond when attempting to meet conservation and societal objectives. The partnership identified key strengths including partnership experience, outreach and technical capacities, a substantial conservation land base, and high social cohesion in the South Carolina Lowcountry. Limited communication expertise, institutional inertia, and insufficient staffing and funding were recognized as important weaknesses across the partnership. By examining and scoring combinations of internal strengths and weaknesses and external threats and opportunities, the partnership developed sets of prioritized strategies to consider in the context of a given scenario. Although we had insufficient time to examine all scenarios in detail, the intent was to identify a portfolio of strategic actions to address threats and opportunities represented in multiple plausible futures. Top-ranking strategies encompassed a range of actions that focused on strengthening the conservation community and communicating the benefits of nature (that is, ecosystem services) to leveraging partnerships to expand land protection.

This report also details the methods and preliminary results of several models developed or applied in support of this project. Two parcel-selection algorithms were used to evaluate anticipated habitat changes and patterns of urban growth to guide decisions on optimal conservation reserve design to protect habitat communities. One approach used a widely available planning software (MARXAN) to maximize conservation benefits near the Cape Romain NWR, whereas the other approach was a novel application of economic theory to account for uncertainty in future conditions and for the risks of unanticipated habitat loss. This latter model applies modern portfolio theory to estimate the risk of investing in any portfolio of land parcels (that is, candidate “reserves”) under climate-change uncertainty by quantifying the variation and spatial correlation of conservation benefits derived from each portfolio. We expanded the range of actions beyond simply whether or not to invest in a set of land parcels, an approach commonly used in spatial conservation planning, to also include consideration of divestment from currently protected lands. Such refinements allow for better accounting of system dynamics and can evaluate the benefits of flexible conservation tools such as rolling easements. Model results were conditional on a decision maker’s risk tolerance but highlighted general strategies of land conservation to increase future habitat representation beyond what is expected under the current protected land base. We built models that may help inform coastal planning by estimating salinity dynamics and the performance of oyster reef restoration efforts to predict the combined effects of global change and management of freshwater flows on coastal habitats and the processes that contribute to their resilience. These models can support restoration decisions by evaluating the expected benefits of site locations for shoreline protection and fisheries production. Lastly, we developed a spatially explicit economic model that predicts feedback dynamics among land value, land-use change, and effects on ecosystem service provision to explore zoning policies and incentives on urban growth and ecosystem services.

We summarize these efforts with insights and considerations for the Cape Romain Partnership for Coastal Protection to continue to engage stakeholders in effective adaptation planning. First, notions of place attachment (referred to as sense of place), and the role of culture in social discourse are increasingly being used to understand the complex interactions between society and the environment and how societies respond and adapt to climate change. Sense of place was a unifying theme whenever the future of the South Carolina Lowcountry was discussed. The contribution of the South Carolina Lowcountry’s environmental wealth, rich cultural heritage, and quality of life to sense of place has important implications for how adaptation planning might best be pursued. More community-based governance of the commons (in other words, natural and cultural resources held in common), in which broad stakeholder participation and power sharing are key elements, is considered important. This devolution of governance is characterized by polycentric institutions and self-organizing social networks that promote a local culture of knowledge sharing, problem solving, and learning. These so-called bridging organizations (or individuals) often provide the leadership necessary to bring together potentially disparate Government agencies and institutions, private organizations, and individuals in a collective process of problem solving. Our observations also suggest that the conservation community in the South Carolina Lowcountry views its activities as integral to the broader governance of social-ecological systems, in which responses to the forces of global change are mediated through culture, economics, and politics. Rather than directly competing with other interests, the South Carolina Lowcountry conservation community seems to embrace an interpretation of conservation in which the fundamental objective is the quality of human life rather than environmental protection.

Fundamental to the types of governance reforms described above is the notion of coproduction, in which experts and users collaborate to develop a shared body of knowledge. In this approach, scientists work with stakeholders to help frame questions, design research, and collect and analyze data. Such sustained collaborations are increasingly believed to be an effective way to produce useable (or actionable) science. The emphasis on social learning, leveraging strong social networks, coordinating and deliberating among diverse stakeholders, and applying principles of adaptive management is an essential contribution to adaptive capacity. The diverse and robust set of scientific approaches, methods to help stakeholders collaborate in effective and goal-driven planning processes, and decision tools resulting from this project hopefully will assist Cape Romain NWR and its partners prepare for climatic, ecological, and social changes over the coming decades.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211021","usgsCitation":"Eaton, M.J., Johnson, F.A., Mikels-Carrasco, J., Case, D.J., Martin, J., Stith, B., Yurek, S., Udell, B., Villegas, L., Taylor, L., Haider, Z., Charkhgard, H., and Kwon, C., 2021, Cape Romain Partnership for Coastal Protection: U.S. Geological Survey Open-File Report 2021–1021, 158 p., https://doi.org/10.3133/ofr20211021.","productDescription":"xii, 158 p.","numberOfPages":"174","onlineOnly":"Y","ipdsId":"IP-100705","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":386276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1021/coverthb.jpg"},{"id":386277,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1021/ofr20211021.pdf","text":"Report","size":"33.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1021"}],"country":"United States","state":"South Carolina","otherGeospatial":"Cape Romain National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.8431396484375,\n 32.78842902722552\n ],\n [\n -79.815673828125,\n 32.765336175015776\n ],\n [\n -79.63577270507811,\n 32.85421076375021\n ],\n [\n -79.55886840820312,\n 32.92455477363828\n ],\n [\n -79.47784423828125,\n 33.00981511270531\n ],\n [\n -79.3487548828125,\n 33.0063602132054\n ],\n [\n -79.27047729492188,\n 33.12490094278685\n ],\n [\n -79.34600830078125,\n 33.16169660598766\n ],\n [\n -79.50393676757812,\n 33.060471419708115\n ],\n [\n -79.60968017578125,\n 32.99599470276581\n ],\n [\n -79.6673583984375,\n 32.93838636388491\n ],\n [\n -79.68658447265625,\n 32.91533251206152\n ],\n [\n -79.8431396484375,\n 32.78842902722552\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Southeast Climate Adaptation Science Center
U.S. Geological Survey
127 David Clark Labs
Raleigh, NC 27695

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2021-06-08","noUsgsAuthors":false,"publicationDate":"2021-06-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Eaton, Mitchell J. 0000-0001-7324-6333","orcid":"https://orcid.org/0000-0001-7324-6333","contributorId":216712,"corporation":false,"usgs":true,"family":"Eaton","given":"Mitchell J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":817128,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Fred A. 0000-0002-5854-3695","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":213877,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":817129,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mikels-Carrasco, Jessica","contributorId":245520,"corporation":false,"usgs":false,"family":"Mikels-Carrasco","given":"Jessica","email":"","affiliations":[{"id":49215,"text":"D.J. Case & Assoc.","active":true,"usgs":false}],"preferred":false,"id":817130,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Case, David J.","contributorId":140653,"corporation":false,"usgs":false,"family":"Case","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":13543,"text":"DJ Case & Associates","active":true,"usgs":false}],"preferred":false,"id":817131,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martin, Julien 0000-0002-7375-129X","orcid":"https://orcid.org/0000-0002-7375-129X","contributorId":216722,"corporation":false,"usgs":true,"family":"Martin","given":"Julien","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":817132,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stith, Bradley bstith@usgs.gov","contributorId":3596,"corporation":false,"usgs":true,"family":"Stith","given":"Bradley","email":"bstith@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":817133,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yurek, Simeon 0000-0002-6209-7915","orcid":"https://orcid.org/0000-0002-6209-7915","contributorId":216729,"corporation":false,"usgs":true,"family":"Yurek","given":"Simeon","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":817134,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Udell, Bradley","contributorId":216709,"corporation":false,"usgs":false,"family":"Udell","given":"Bradley","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":817135,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Villegas, Laura","contributorId":238524,"corporation":false,"usgs":false,"family":"Villegas","given":"Laura","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":817136,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Taylor, Laura","contributorId":169433,"corporation":false,"usgs":false,"family":"Taylor","given":"Laura","email":"","affiliations":[{"id":25510,"text":"NC State University","active":true,"usgs":false}],"preferred":false,"id":817137,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Haider, Zulquarnain","contributorId":216706,"corporation":false,"usgs":false,"family":"Haider","given":"Zulquarnain","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":817138,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Charkhgard, Hadi","contributorId":216710,"corporation":false,"usgs":false,"family":"Charkhgard","given":"Hadi","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":817139,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kwon, Changhyun","contributorId":216711,"corporation":false,"usgs":false,"family":"Kwon","given":"Changhyun","email":"","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":817140,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70219519,"text":"70219519 - 2021 - A roadmap for sampling and scaling biological nitrogen fixation in terrestrial ecosystems","interactions":[],"lastModifiedDate":"2021-06-30T18:00:05.022669","indexId":"70219519","displayToPublicDate":"2021-03-21T08:48:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A roadmap for sampling and scaling biological nitrogen fixation in terrestrial ecosystems","docAbstract":"
  1. Accurately quantifying rates and patterns of biological nitrogen fixation (BNF) in terrestrial ecosystems is essential to characterize ecological and biogeochemical interactions, identify mechanistic controls, improve BNF representation in conceptual and numerical modelling, and forecast nitrogen limitation constraints on future carbon (C) cycling.
  2. While many resources address the technical advantages and limitations of different methods for measuring BNF, less systematic consideration has been given to the broader decisions involved in planning studies, interpreting data, and extrapolating results. Here, we present a conceptual and practical road map to study design, study execution, data analysis and scaling, outlining key considerations at each step.
  3. We address issues including defining N‐fixing niches of interest, identifying important sources of temporal and spatial heterogeneity, designing a sampling scheme (including method selection, measurement conditions, replication, and consideration of hotspots and hot moments), and approaches to analysing, scaling and reporting BNF. We also review the comparability of estimates derived using different approaches in the literature, and provide sample R code for simulating symbiotic BNF data frames and upscaling.
  4. Improving and standardizing study design at each of these stages will improve the accuracy and interpretability of data, define limits of extrapolation, and facilitate broader use of BNF data for downstream applications. We highlight aspects—such as quantifying scales of heterogeneity, statistical approaches for dealing with non‐normality, and consideration of rates versus ecological significance—that are ripe for further development.
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U.S. Geological Survey (USGS) Mission: The USGS national mission is to monitor, analyze, and predict current and evolving dynamics of complex human and natural Earth-system interactions and to deliver actionable information at scales and timeframes relevant to decision-makers. Consistent with the national mission, the USGS in Alaska provides timely and objective scientific information to help address issues and inform management decisions across five inter-connected themes:

The USGS in Alaska consists of approximately 350 scientists and support staff working in three Alaska-based science centers, a Cooperative Research Unit, and USGS centers outside Alaska, with a combined annual science budget of about $60 million. In the last 5 years, USGS research in Alaska has produced many scientific benefits resulting from more than 1,100 publications. Publications relevant to Alaska can be conveniently searched by keyword through the USGS Publications Warehouse at https://pubs.er.usgs.gov/search?q=Alaska.

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Regional Director, Alaska
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508-4560

  

","tableOfContents":"","publishedDate":"2021-03-16","noUsgsAuthors":false,"publicationDate":"2021-03-16","publicationStatus":"PW","contributors":{"editors":[{"text":"Powers, Elizabeth M. 0000-0002-4688-1195","orcid":"https://orcid.org/0000-0002-4688-1195","contributorId":255448,"corporation":false,"usgs":false,"family":"Powers","given":"Elizabeth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":812370,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Williams, Dee M. 0000-0003-0400-479X dmwilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-0400-479X","contributorId":224715,"corporation":false,"usgs":true,"family":"Williams","given":"Dee M.","email":"dmwilliams@usgs.gov","affiliations":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"preferred":false,"id":812371,"contributorType":{"id":2,"text":"Editors"},"rank":2}]}} ,{"id":70218781,"text":"sir20205141 - 2021 - Assessment of water availability in the Osage Nation using an integrated hydrologic-flow model","interactions":[],"lastModifiedDate":"2021-03-15T16:09:57.254165","indexId":"sir20205141","displayToPublicDate":"2021-03-15T07:54:17","publicationYear":"2021","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":"2020-5141","displayTitle":"Assessment of Water Availability in the Osage Nation Using an Integrated Hydrologic-Flow Model","title":"Assessment of water availability in the Osage Nation using an integrated hydrologic-flow model","docAbstract":"

The Osage Nation of northeastern Oklahoma, conterminous with Osage County, covers about 2,900 square miles. The area is primarily rural with 62 percent of the land being native prairie grass, and much of the area is used for cattle ranching and extraction of petroleum and natural gas. Protection of water rights are important to the Osage Nation because of its reliance on cattle ranching and the potential for impairment of water quality by petroleum extraction. Additionally, the potential for future population increases, demands for water from neighboring areas such as the Tulsa metropolitan area, and expansion of petroleum and natural-gas extraction on water resources of this area further the need for the Osage Nation to better understand its water availability. Therefore, the U.S. Geological Survey, in cooperation with the Osage Nation, completed a hydrologic investigation to assess the status and availability of surface-water and groundwater resources in the Osage Nation.

A transient integrated hydrologic-flow model was constructed using the U.S. Geological Survey fully integrated hydrologic-flow model called the MODFLOW One-Water Hydrologic Model. The integrated hydrologic-flow model, called the Osage Nation Integrated Hydrologic Model (ONIHM), was constructed and uses an orthogonal grid of 276 rows and 289 columns, and each grid cell measures 1,312.34 feet (ft; 400 meters) per side, with eight variably thick vertical layers that represented the alluvial and bedrock aquifers within the study area, including the alluvial aquifer, the Vamoosa-Ada aquifer, and the minor Pennsylvanian bedrock aquifers, and the confining units. Landscape and groundwater-flow processes were simulated for two periods: (1) the 1950–2014 period from January 1950 through September 2014 and (2) the forecast period from October 2014 through December 2099. The 1950–2014 period ONIHM simulated past conditions using measured or estimated inputs, and the forecast-period ONIHM simulated three separate potential forecast conditions under constant dry, average, or wet climate conditions using calibrated input values from the 1950–2014 period ONIHM.

The 1950–2014 period ONIHM was calibrated by linking the Parameter Estimation software (PEST) with the MODFLOW One-Water Hydrologic Model. PEST uses statistical parameter estimation techniques to identify the best set of parameter values to minimize the difference between measured or estimated calibration targets and their simulated equivalent values (residuals). Tikhonov regularization and singular-value decomposition-assist features of PEST were used during the calibration process. The 1950–2014 period ONIHM was calibrated to 713 measured groundwater levels at 195 wells; 95,636 estimated monthly mean groundwater levels at 124 wells; 5,307 measured streamflows at 13 streamgages; and 8,679 simulated mean monthly streamflows at 10 streamgages extracted from a surface-water model by adjusting 231 parameters. The estimated groundwater-level observations and streamflows were included as observations to improve the spatial and temporal density of observation targets during calibration. The best set of parameter values obtained during the calibration process of the 1950–2014 model was then used as the input parameter values for the forecast model simulations. A comparison of the calibration targets to their corresponding simulated values indicated that the model adequately reproduced streamflows and groundwater levels for some streamgages and wells and underestimated streamflows and groundwater levels at other locations. Measured and simulated streamflows correlated adequately with a coefficient of determination of 0.938, as did water levels with a coefficient of determination of 0.795. The 1950–2014 period ONIHM underestimated certain groundwater levels and streamflows, but generally measured or estimated calibration targets correlated well with simulated equivalents, which indicated that the model can adequately simulate the response of the hydrologic system to stresses in the 1950–2014 and forecast periods.

In the 1950–2014 period ONIHM, the calibrated mean horizontal hydraulic conductivity for layer 1 alluvial aquifer was 30.7 feet per day, and the seven lower layers had a calibrated mean horizontal hydraulic conductivity of less than 3.3 feet per day. The mean calibrated groundwater-level residual was 16.6 ft, and the mean calibrated streamflow residual of the Arkansas River at Ralston, Oklahoma, streamgage (U.S. Geological Survey station 07152500) was within 6 percent (373 cubic feet per second) of mean measured streamflow for the 1950–2014 period ONIHM.

The ONIHM simulated landscape fluxes of precipitation; groundwater applied by irrigation wells; evapotranspiration from precipitation, groundwater, and irrigation; runoff from precipitation; and deep percolation from precipitation. The largest loss of water from the landscape was evapotranspiration from precipitation with a calibrated mean annual outflow of 32 inches (in.): mean annual precipitation was about 36 in. Calibrated mean annual runoff and deep percolation (recharge to the water table) rates were 4.7 inches per year (in/yr) and 0.70 in/yr, respectively, for the 1950–2014 period ONIHM.

The calibrated 1950–2014 period ONIHM groundwater fluxes included net farm net recharge (calculated as the difference between the inflow of recharge to the water table and the outflow of evapotranspiration from the water table such that negative values indicate that evapotranspiration from the water table was greater than deep percolation [recharge to the water table] and vice versa). Net farm net recharge was the largest flux from the groundwater system with a mean annual net outflow of 153.4 cubic feet per second. Stream leakage was the largest flux to the groundwater system with a mean annual net inflow of 152.5 cubic feet per second, indicating that, on average, the groundwater/surface-water interaction was a “losing” system where stream water leaked into the subsurface and recharged the water table. Simulated monthly trends demonstrated that net stream leakage was the largest inflow to the groundwater-flow system for 10 of the 12 months; for the other 2 months (January and March), farm net recharge (January) and net storage (March) were the largest inflow to the groundwater-flow system.

A saline groundwater interface map was created for the study and compared to the water levels from the final stress period of the 1950–2014 model to identify the presence of fresh/marginal groundwater throughout the study area. Fresh/marginal groundwater was characterized as groundwater with less than 1,500 milligrams per liter of total dissolved solids. Fresh/marginal groundwater thickness ranged from 0 to 438.2 ft within the study area. The thickest regions of fresh/marginal groundwater were in the eastern part of the study area near Sand Creek, Bird Creek, and Hominy Creek and in the Arkansas River alluvial aquifer in the region downstream from the Arkansas River at Ralston, Okla.

Like the 1950–2014 model, forecast model results for the landscape indicated that transpiration from precipitation was the largest flux out of the landscape for all three forecasts, constituting 77, 73, and 58 percent of precipitation for the dry, average, and wet forecasts, respectively. The dry and average forecast landscape fluxes demonstrated similar trends and magnitudes, whereas the wet forecast landscape fluxes indicated the largest changes compared to the average forecast fluxes. Most notably, runoff increased from a mean of 1.1 and 1.6 in/yr for the dry and average forecasts, respectively, to 10 in/yr for the wet forecast. Similar changes occurred for the other wet forecast landscape fluxes.

The calibrated 1950–2014 period ONIHM simulated three forecasts to assess the effects of potential climatic changes on the hydrologic system from October 2014 to December 2099. The three forecasts simulated theoretical dry, average, and wet conditions using precipitation and potential evapotranspiration datasets from selected years in the calibrated 1950–2014 period ONIHM. Annual precipitation amounts were 26.89, 35.47, and 50.73 in. for the dry, average, and wet forecasts, respectively. Groundwater-flow component forecast results indicated that stream leakage is always a net inflow to the groundwater-flow system for dry, average, and wet conditions, meaning the study area stream network is always predominantly a “losing” regime where stream water infiltrates into the underlying aquifer. Storage was only a net outflow from the groundwater-flow system and indicated a replenishment to groundwater storage that resulted in an increase in groundwater levels only during the wet forecast. Further, these gains in groundwater storage for the wet forecast occurred only during February through June.

Mean fresh/marginal groundwater saturated thicknesses were 125 and 126 ft for the dry and average forecast conditions, respectively, and wet forecast average thickness was 145 ft and ranged from 0 to 443 ft. The spatial extents of fresh/marginal groundwater at the end of the dry, average, and wet forecast model periods (December 2099) did not change substantially from the end of the 1950–2014 model period (September 2014).

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205141","collaboration":"Prepared in cooperation with the Osage Nation","usgsCitation":"Traylor, J.P., Mashburn, S.L., Hanson, R.T., and Peterson, S.M., 2021, Assessment of water availability in the Osage Nation using an integrated hydrologic-flow model: U.S. Geological Survey Scientific Investigations Report 2020–5141, 96 p., https://doi.org/10.3133/sir20205141.","productDescription":"Report: xiii, 96 p.; 2 Interactive Figures; Data Release; Dataset","numberOfPages":"114","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102662","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":384320,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5141/coverthb.jpg"},{"id":384321,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141.pdf","text":"Report","size":"9.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141"},{"id":384322,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure8.pdf","text":"Figure 8 (layered)","size":"626 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 8","linkHelpText":"— Supergroups for the Osage Nation Integrated Hydrologic Model (note: some supergroups are hidden; in order to see a given supergroup, the reader may need to turn off layers for the overlying supergroups)."},{"id":384324,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91OKQ2C","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-One Water Hydrologic Model integrated hydrologic-flow model used to evaluate water availability in the Osage Nation"},{"id":384323,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2020/5141/sir20205141_figure14.pdf","text":"Figure 14 (layered)","size":"711 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5141 Figure 14","linkHelpText":"— Simulated groundwater-level altitude contours for the final stress period of the calibrated Osage Nation Integrated Hydrologic Model (September 30, 2014), dry forecast (December 31, 2099), average forecast (December 31, 2099), and wet forecast (December 31, 2099). This figure is a layered PDF."},{"id":384325,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Kansas, Oklahoma","otherGeospatial":"Osage Nation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.99578857421875,\n 36.13565654678543\n ],\n [\n -95.99853515625,\n 37.00035919622158\n ],\n [\n -95.97930908203125,\n 37.081475648860525\n ],\n [\n -96.29241943359375,\n 37.13623498442895\n ],\n [\n -96.48193359375,\n 36.96306042436515\n ],\n [\n -96.9873046875,\n 36.94989178681327\n ],\n [\n -97.12188720703125,\n 36.6992553955527\n ],\n [\n -97.14385986328125,\n 36.36822190085111\n ],\n [\n -96.6412353515625,\n 36.213255233061844\n ],\n [\n -96.26220703125,\n 36.11125252076156\n ],\n [\n -95.99578857421875,\n 36.13565654678543\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512 

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2021-03-15","noUsgsAuthors":false,"publicationDate":"2021-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Traylor, Jonathan P. 0000-0002-2008-1923 jtraylor@usgs.gov","orcid":"https://orcid.org/0000-0002-2008-1923","contributorId":5322,"corporation":false,"usgs":true,"family":"Traylor","given":"Jonathan","email":"jtraylor@usgs.gov","middleInitial":"P.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811834,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mashburn, Shana L. 0000-0001-5163-778X shanam@usgs.gov","orcid":"https://orcid.org/0000-0001-5163-778X","contributorId":2140,"corporation":false,"usgs":true,"family":"Mashburn","given":"Shana","email":"shanam@usgs.gov","middleInitial":"L.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811835,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hanson, Randall T. 0000-0002-9819-7141 rthanson@usgs.gov","orcid":"https://orcid.org/0000-0002-9819-7141","contributorId":801,"corporation":false,"usgs":true,"family":"Hanson","given":"Randall","email":"rthanson@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811836,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peterson, Steven M. 0000-0002-9130-1284 speterson@usgs.gov","orcid":"https://orcid.org/0000-0002-9130-1284","contributorId":847,"corporation":false,"usgs":true,"family":"Peterson","given":"Steven","email":"speterson@usgs.gov","middleInitial":"M.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":811837,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70218813,"text":"70218813 - 2021 - The making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)","interactions":[],"lastModifiedDate":"2021-03-15T13:59:10.529639","indexId":"70218813","displayToPublicDate":"2021-03-05T07:53:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"The making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18)","docAbstract":"

The NEAM Tsunami Hazard Model 2018 (NEAMTHM18) is a probabilistic hazard model for tsunamis generated by earthquakes. It covers the coastlines of the North-eastern Atlantic, the Mediterranean, and connected seas (NEAM). NEAMTHM18 was designed as a three-phase project. The first two phases were dedicated to the model development and hazard calculations, following a formalized decision-making process based on a multiple-expert protocol. The third phase was dedicated to documentation and dissemination. The hazard assessment workflow was structured in Steps and Levels. There are four Steps: Step-1) probabilistic earthquake model; Step-2) tsunami generation and modeling in deep water; Step-3) shoaling and inundation; Step-4) hazard aggregation and uncertainty quantification. Each Step includes a different number of Levels. Level-0 always describes the input data; the other Levels describe the intermediate results needed to proceed from one Step to another. Alternative datasets and models were considered in the implementation. The epistemic hazard uncertainty was quantified through an ensemble modeling technique accounting for alternative models’ weights and yielding a distribution of hazard curves represented by the mean and various percentiles. Hazard curves were calculated at 2,343 Points of Interest (POI) distributed at an average spacing of ∼20 km. Precalculated probability maps for five maximum inundation heights (MIH) and hazard intensity maps for five average return periods (ARP) were produced from hazard curves. In the entire NEAM Region, MIHs of several meters are rare but not impossible. Considering a 2% probability of exceedance in 50 years (ARP≈2,475 years), the POIs with MIH >5 m are fewer than 1% and are all in the Mediterranean on Libya, Egypt, Cyprus, and Greece coasts. In the North-East Atlantic, POIs with MIH >3 m are on the coasts of Mauritania and Gulf of Cadiz. Overall, 30% of the POIs have MIH >1 m. NEAMTHM18 results and documentation are available through the TSUMAPS-NEAM project website (http://www.tsumaps-neam.eu/), featuring an interactive web mapper. Although the NEAMTHM18 cannot substitute in-depth analyses at local scales, it represents the first action to start local and more detailed hazard and risk assessments and contributes to designing evacuation maps for tsunami early warning.

","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.616594","usgsCitation":"Basili, R., Brizuela, B., Herrero, A., Iqbal, S., Lorito, S., Maesano, F.E., Murphy, S., Perfetti, P., Romano, F., Scala, A., Selva, J., Taroni, M., Tiberti, M.M., Thio, H., Tonini, R., Volpe, M., Glimsdal, S., Harbitz, C.B., Lovholt, F., Baptista, M.A., Carrilho, F., Matias, L.M., Omira, R., Babeyko, A., Hoechner, A., Gurbuz, M., Pekcan, O., Yalciner, A., Canals, M., Lastras, G., Agalos, A., Papadapoulos, G., Triantafyllou, I., Benchekroun, S., Jaouadi, H.A., Abdallah, S.B., Bouallegue, A., Hamdi, H., Oueslati, F., Amato, A., Armigliato, A., Behrens, J., Davies, G., Di Bucci, D., Dolce, M., Geist, E.L., Gonzalez Vida, J.M., Gonzalez, M., Sanchez, J.M., Meletti, C., Sozdinler, C.O., Pagani, M., Parsons, T., Polet, J., Power, W., Sorensen, M., and Zaytsev, A., 2021, The making of the NEAM Tsunami Hazard Model 2018 (NEAMTHM18): Frontiers in Earth Science, v. 8, 616594, 29 p., 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Jascha","contributorId":201793,"corporation":false,"usgs":false,"family":"Polet","given":"Jascha","email":"","affiliations":[{"id":12635,"text":"California Polytechnic State University, College of Science, Pomona, CA","active":true,"usgs":false}],"preferred":false,"id":812222,"contributorType":{"id":1,"text":"Authors"},"rank":54},{"text":"Power, William","contributorId":201794,"corporation":false,"usgs":false,"family":"Power","given":"William","email":"","affiliations":[{"id":36364,"text":"Institute of Geological and Nuclear Sciences (GNS), Lower Hutt, New Zealand","active":true,"usgs":false}],"preferred":false,"id":812223,"contributorType":{"id":1,"text":"Authors"},"rank":55},{"text":"Sorensen, Mathilde B.","contributorId":236700,"corporation":false,"usgs":false,"family":"Sorensen","given":"Mathilde B.","affiliations":[{"id":47532,"text":"Dept. of Earth Science, University of Bergen, Postboks 7803, 5020 Bergen, Norway","active":true,"usgs":false}],"preferred":false,"id":812224,"contributorType":{"id":1,"text":"Authors"},"rank":56},{"text":"Zaytsev, Andrey","contributorId":255365,"corporation":false,"usgs":false,"family":"Zaytsev","given":"Andrey","email":"","affiliations":[],"preferred":false,"id":812225,"contributorType":{"id":1,"text":"Authors"},"rank":57}]}} ,{"id":70216363,"text":"70216363 - 2021 - A lagrangian-to-eulerian metric to identify estuarine pelagic habitats","interactions":[],"lastModifiedDate":"2021-06-01T17:01:46.95513","indexId":"70216363","displayToPublicDate":"2020-11-11T09:23:39","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"A lagrangian-to-eulerian metric to identify estuarine pelagic habitats","docAbstract":"

Estuaries are among the world’s most productive ecosystems, but recent natural and anthropogenic changes have stressed these ecosystems. Tools to assess estuarine pelagic habitats are important to support and maintain healthy ecosystem function. In this work, we demonstrate that estuarine pelagic habitats can be identified by a simple ratio, termed the LE ratio, that takes into account the tidal excursion along a channel (a Lagrangian length scale) and the distance along that channel (an Eulerian length scale). To develop and assess this concept, numerical simulations of the 1D advection–dispersion equation of a conservative tracer and tidal excursion estimates based on data were used to formulize a conceptual model and to define exchange zones within a tidal channel. This conceptual model was then used to predict the extent of pelagic habitats in a terminal channel network in the Sacramento–San Joaquin Delta. Exchange zones mapped onto these channels were found to be in good agreement with independent estimates of residence time. Sensitivity analyses of the numerical model suggest that productive pelagic habitats can be expanded by a factor of 2 by either increasing dispersion or increasing spring–neap variability in mean tidal velocity. Such changes can also enhance flushing in upper channel reaches. These findings are relevant for tidal marsh restoration projects that aim to expand beneficial aquatic habitats by varying exchange or residence time over the spring–neap cycle, because this variability may interact synergistically with varying rates of phytoplankton growth due to spatiotemporal changes in environmental conditions.

","language":"English","publisher":"Springer","doi":"10.1007/s12237-020-00861-7","usgsCitation":"Stumpner, P., Burau, J.R., and Forrest, A.L., 2021, A lagrangian-to-eulerian metric to identify estuarine pelagic habitats: Estuaries and Coasts, v. 44, p. 1231-1249, https://doi.org/10.1007/s12237-020-00861-7.","productDescription":"19 p.","startPage":"1231","endPage":"1249","ipdsId":"IP-113036","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":436645,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VR4EHS","text":"USGS data release","linkHelpText":"Numerical Simulation of 1D Advection-Dispersion Equation of Conservative Tracer with Oscillating Tidal Flows"},{"id":380509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","noUsgsAuthors":false,"publicationDate":"2020-11-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Stumpner, Paul 0000-0002-0933-7895 pstump@usgs.gov","orcid":"https://orcid.org/0000-0002-0933-7895","contributorId":5667,"corporation":false,"usgs":true,"family":"Stumpner","given":"Paul","email":"pstump@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804809,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burau, Jon R. 0000-0002-5196-5035 jrburau@usgs.gov","orcid":"https://orcid.org/0000-0002-5196-5035","contributorId":1500,"corporation":false,"usgs":true,"family":"Burau","given":"Jon","email":"jrburau@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804810,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forrest, Alexander L. 0000-0002-7853-9765","orcid":"https://orcid.org/0000-0002-7853-9765","contributorId":244855,"corporation":false,"usgs":false,"family":"Forrest","given":"Alexander","email":"","middleInitial":"L.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":804811,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216472,"text":"70216472 - 2021 - Stress gradients interact with disturbance to reveal alternative states in salt marsh: Multivariate resilience at the landscape scale","interactions":[],"lastModifiedDate":"2021-10-04T16:46:47.880285","indexId":"70216472","displayToPublicDate":"2020-11-09T07:45:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2242,"text":"Journal of Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Stress gradients interact with disturbance to reveal alternative states in salt marsh: Multivariate resilience at the landscape scale","docAbstract":"
  1. Stress gradients influence many ecosystem processes and properties, including ecosystem recovery from and resistance to disturbance. While recent analytical approaches have advanced multivariate metrics of ecosystem resilience that allow quantification of conceptual resilience models and identification of thresholds of state change, these approaches are not often translated to landscape scales.
  2. Using natural and restored salt marshes in Louisiana, USA, we quantified plant community recovery and resistance metrics along flooding stress gradients. n‐dimensional hypervolumes of plant community biomass and structure were simulated using field data collected from disturbance‐recovery experiments. The relationships between multivariate resilience metrics and flooding stress gradients were then mapped at community‐ and landscape‐relevant scales by scaling with airborne‐derived data across the region.
  3. Greater pre‐disturbance abiotic stress decreased live belowground, but not aboveground, biomass, and ultimately led to lower post‐disturbance total recovery, recovery rates, and resistance of plant communities. Vegetated plots flooded >52% of the time transitioned to an alternative, unvegetated state after disturbance. Mapping revealed differences in spatial patterns of resilience‐ highlighting low, interior marsh edges as especially vulnerable to the combination of chronic flooding stress and acute disturbance. At the landscape scale, approximately half of the area (48%) is vulnerable to state change after pulse disturbances.


Synthesis. Ultimately, we quantify the ball‐and‐cup conceptual model for a salt marsh ecosystem and its alternative state, mudflat. We find that increasing abiotic stress due to climate change diminishes ecosystem resilience, but the interaction with common episodic disturbances is necessary to reveal transitions to alternative states and quantify state change thresholds. Quantifying and mapping resilience and where alternative states may exist in this fashion improves ecologists’ ability to investigate the mechanisms of stress gradient control on emergent ecosystem properties, while providing spatially explicit resources for managing ecosystems according to their projected resilience.

","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2745.13552","usgsCitation":"Jones, S., Stagg, C., Yando, E.S., James, W.R., Buffington, K., and Hester, M.W., 2021, Stress gradients interact with disturbance to reveal alternative states in salt marsh: Multivariate resilience at the landscape scale: Journal of Ecology, v. 109, no. 9, p. 3211-3223, https://doi.org/10.1111/1365-2745.13552.","productDescription":"13 p.","startPage":"3211","endPage":"3223","ipdsId":"IP-121938","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":436646,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FNH7F6","text":"USGS data release","linkHelpText":"Field and simulated data to construct hypervolumes of coastal wetland plant states for resilience quantification, Louisiana, USA 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\"}}]}","volume":"109","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-11-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Scott 0000-0002-1056-3785","orcid":"https://orcid.org/0000-0002-1056-3785","contributorId":215602,"corporation":false,"usgs":true,"family":"Jones","given":"Scott","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805229,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":222380,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":805230,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yando, Erik S.","contributorId":127788,"corporation":false,"usgs":false,"family":"Yando","given":"Erik","email":"","middleInitial":"S.","affiliations":[{"id":7155,"text":"University of Louisiana at Lafayette","active":true,"usgs":false}],"preferred":false,"id":805231,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"James, W. Ryan","contributorId":245037,"corporation":false,"usgs":false,"family":"James","given":"W.","email":"","middleInitial":"Ryan","affiliations":[{"id":13722,"text":"University of Louisiana-Lafayette","active":true,"usgs":false}],"preferred":false,"id":805232,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Buffington, Kevin J. 0000-0001-9741-1241 kbuffington@usgs.gov","orcid":"https://orcid.org/0000-0001-9741-1241","contributorId":4775,"corporation":false,"usgs":true,"family":"Buffington","given":"Kevin","email":"kbuffington@usgs.gov","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805233,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hester, Mark W.","contributorId":195572,"corporation":false,"usgs":false,"family":"Hester","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":34316,"text":"University of Louisiana at Lafayette, Lafayette, LA, USA","active":true,"usgs":false}],"preferred":false,"id":805234,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217747,"text":"70217747 - 2021 - Transport and speciation of uranium in groundwater-surface water systems impacted by legacy milling operations","interactions":[],"lastModifiedDate":"2021-02-01T14:29:48.935866","indexId":"70217747","displayToPublicDate":"2020-11-02T06:35:59","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Transport and speciation of uranium in groundwater-surface water systems impacted by legacy milling operations","docAbstract":"

Growing worldwide concern over uranium contamination of groundwater resources has placed an emphasis on understanding uranium transport dynamics and potential toxicity in groundwater-surface water systems. In this study, we utilized novel in-situ sampling methods to establish the location and magnitude of contaminated groundwater entry into a receiving surface water environment, and to investigate the speciation and potential bioavailability of uranium in groundwater and surface water. Streambed temperature mapping successfully identified the location of groundwater entry to the Little Wind River, downgradient from the former Riverton uranium mill site, Wyoming, USA. Diffusive equilibrium in thin-film (DET) samplers further constrained the groundwater plume and established sediment pore water solute concentrations and patterns. In this system, evidence is presented for attenuation of uranium-rich groundwater in the shallow sediments where surface water and groundwater interaction occurs. Surface water grab and DET sampling successfully detected an increase in river uranium concentrations where the groundwater plume enters the Little Wind River; however, concentrations remained below environmental guideline levels. Uranium speciation was investigated using diffusive gradients in thin-film (DGT) samplers and geochemical speciation modelling. Together, these investigations indicate uranium may have limited bioavailability to organisms in the Little Wind River and, possibly, in other similar sites in the western U.S.A. This could be due to ion competition effects or the presence of non- or partially labile uranium complexes. Development of methods to establish the location of contaminated (uranium) groundwater entry to surface water environments, and the potential effects on ecosystems, is crucial to develop both site-specific and general conceptual models of uranium behavior and potential toxicity in affected ground and surface water environments.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.143314","usgsCitation":"Byrne, P.A., Fuller, C.C., Naftz, D.L., Runkel, R.L., Lehto, N.J., and Dam, W., 2021, Transport and speciation of uranium in groundwater-surface water systems impacted by legacy milling operations: Science of the Total Environment, v. 761, 143314, 11 p., https://doi.org/10.1016/j.scitotenv.2020.143314.","productDescription":"143314, 11 p.","ipdsId":"IP-121496","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":454315,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1776309","text":"Publisher Index Page"},{"id":382831,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","city":"Riverton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.39832305908203,\n 43.00816202648563\n ],\n [\n -108.33995819091797,\n 43.00816202648563\n ],\n [\n -108.33995819091797,\n 43.03175685183966\n ],\n [\n -108.39832305908203,\n 43.03175685183966\n ],\n [\n -108.39832305908203,\n 43.00816202648563\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"761","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byrne, Patrick A.","contributorId":247578,"corporation":false,"usgs":false,"family":"Byrne","given":"Patrick","email":"","middleInitial":"A.","affiliations":[{"id":49583,"text":"Liverpool John Moores University","active":true,"usgs":false}],"preferred":false,"id":809453,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809454,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809455,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809456,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lehto, Niklas J","contributorId":248588,"corporation":false,"usgs":false,"family":"Lehto","given":"Niklas","email":"","middleInitial":"J","affiliations":[{"id":49952,"text":"Lincoln University","active":true,"usgs":false}],"preferred":false,"id":809457,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dam, William L","contributorId":248589,"corporation":false,"usgs":false,"family":"Dam","given":"William L","affiliations":[{"id":49955,"text":"Conserve-Prosper LLC","active":true,"usgs":false}],"preferred":false,"id":809458,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217892,"text":"70217892 - 2021 - Quantifying and mapping inundation regimes within a large river‐floodplain ecosystem for ecological and management applications","interactions":[],"lastModifiedDate":"2021-02-11T17:40:25.289588","indexId":"70217892","displayToPublicDate":"2020-04-17T06:32:29","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying and mapping inundation regimes within a large river‐floodplain ecosystem for ecological and management applications","docAbstract":"

Spatial information on the distribution of ecosystem patterns and processes can be a critical component of designing and implementing effective management programs in river‐floodplain ecosystems. For example, translating how flood pulses detected within a stream gauge record are spatially manifested across a river‐valley bottom can be used to evaluate whether the current distribution of physical conditions has the potential to support priority habitats or if intervention is needed to meet desired goals. The size and complexity of large river‐floodplain systems can make mapping inundation dynamics a challenging task. We used a geospatial model to simulate 40 years (1972–2011) of daily surface‐water inundation depths for 11,331 km2 of the Upper Mississippi River System floodplain. We identified discrete inundation events at each 4‐m × 4‐m pixel in the model as sequential days of submergence. We then quantified and mapped four aspects of inundation regime – event frequency, duration, magnitude, and timing – for each pixel. The spatial distribution of inundation regime attributes varied within and among multiple levels of river organization, including navigation pools and geomorphic reaches, but only event timing exhibited a strong down‐river trend. Non‐linear relations among inundation attributes and their geospatial distributions likely reflect complex interactions among topographic, hydrologic, and anthropogenic constraints on flooding dynamics. Together, our results reveal spatial gradients in inundation dynamics not captured by hydrologic data alone. Characterizing such diversity in inundation dynamics is important for testing hypotheses about ecological processes, developing models of ecosystem functions, and informing management actions.

","language":"English","publisher":"Wiley","doi":"10.1002/rra.3628","usgsCitation":"Van Appledorn, M., De Jager, N.R., and Rohweder, J.J., 2021, Quantifying and mapping inundation regimes within a large river‐floodplain ecosystem for ecological and management applications: River Research and Applications, v. 37, no. 2, p. 241-255, https://doi.org/10.1002/rra.3628.","productDescription":"15 p.","startPage":"241","endPage":"255","ipdsId":"IP-113745","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":383139,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.3955078125,\n 38.51378825951165\n ],\n [\n -88.154296875,\n 40.245991504199026\n ],\n [\n -86.572265625,\n 41.07935114946899\n ],\n [\n -86.7919921875,\n 41.50857729743935\n ],\n [\n -88.0224609375,\n 42.45588764197166\n ],\n [\n -89.3408203125,\n 44.213709909702054\n ],\n [\n -91.7578125,\n 45.85941212790755\n ],\n [\n -92.63671875,\n 46.195042108660154\n ],\n [\n -92.59277343749999,\n 47.724544549099676\n ],\n [\n -94.8779296875,\n 47.249406957888446\n ],\n [\n -95.9326171875,\n 47.30903424774781\n ],\n [\n -95.4052734375,\n 44.84029065139799\n ],\n [\n -93.6474609375,\n 42.13082130188811\n ],\n [\n -93.515625,\n 39.605688178320804\n ],\n [\n -90.3955078125,\n 38.51378825951165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"37","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":810091,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210826,"text":"70210826 - 2020 - Recent planform changes in the Upper Mississippi River","interactions":[],"lastModifiedDate":"2021-11-03T14:42:36.620726","indexId":"70210826","displayToPublicDate":"2020-12-31T09:03:48","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5000,"text":"Long Term Resource Monitoring Technical Report","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"LTRM-2019GC8","title":"Recent planform changes in the Upper Mississippi River","docAbstract":"Geomorphic changes in the Upper Mississippi River (UMR) have long been a concern of river agencies charged with maintaining and restoring river habitat (GREAT 1980; Jackson et al. 1981; USFWS 1992). Large meandering alluvial rivers like the UMR are expected to constantly change and adjust their fluvial landforms within their riparian corridors as a result of the natural interaction of hydrologic processes, sediment movement, and vegetation over time. However, present geomorphic changes in the UMR reflect altered hydrologic, hydraulic, and sediment conditions caused by regulated flows, constructed agricultural levees and navigation dams, altered land use in the watershed, and climate change. Levees reduce lateral hydrologic and sediment connectivity between channels and floodplains on many tributaries and on the Mississippi River downstream of Pool 13. Between each of the dams are a repeating series of landforms associated with tailwater, intermediate, and impounded conditions. The dams maintain a minimum water level, thus creating many off-channel areas that act as sediment traps. Whereas high-head dams cut off sedimentological connectivity longitudinally through the river corridor (Skalak et al., 2013), low head dams on the UMR only slightly altered transport longitudinally. Deltaic-like sedimentation can be common in the impounded sections of dammed rivers. Erosion of relict land surfaces that remained above the raised impounded water levels has been the dominant change in UMR impounded sections due to increased wind fetch leading to increased wave action. Even though upland sources of sediment from tributaries have decreased over the middle to late 20th century, increased annual precipitation, the interplay of increased variability in flood magnitudes from year to year, and more fall and winter flooding have likely changed erosion and sedimentation patterns in the UMR (Belby, et al., 2019). Paradoxically, monitoring and research indicates that the concentration of some water column constituents like total suspended solids and phosphorous has decreased during the 1991 to 2014 time period (Kreiling and Houser, 2016). In areas prone to increased sedimentation, bed elevations rise and thereby water depths are reduced at a given discharge, resulting in loss of fish habitat. Sediment deposition or erosion further influences water exchange rates between main channel and off-channel areas in the river by increasing resistance in connecting channels or enlarging existing connecting channels. Water depth and water exchange rates are the most prominent features describing habitat quality in the UMR (De Jager et al. 2018), and in some cases, the trajectory of planform change from heightened deposition promises to threaten deep backwater habitats particularly important for overwintering fish.\n\nAlthough information on the rate of vertical change in bed elevation is needed for a complete assessment of geomorphic change associated with the loss of deep backwater habitats, mapping planform changes over time (i.e., lateral changes between the land-water boundary) provide needed information on the location, potential cause, and progressive direction of deposition, especially in the mid sections between dams where deltaic processes are the most pronounced. Several types of planform changes have been observed and identified as concerns. For example, island loss in the large impounded areas of the upper part of the UMR was one of the concerns identified by river managers in the 1980s and 90s, and subsequently island construction became a common form of restoration implemented by the Upper Mississippi River Restoration (UMRR) Program (USACE 2012). Other subtler planform changes, such as channel bank erosion and delta formation in backwaters, are perceived to be important, but have largely gone unquantified. A systemwide reconnaissance of the UMR and IWW conducted in 1998 concluded that 14-percent of the river banks were eroding (Nakato and Anderson 1998). However, stabilization of existing river banks has never been widely pursued as a restoration measure, due to the high cost and uncertain benefits. Delta formation reduces the amount of backwater habitat; however, the deltas maintain and create a mix of riparian and aquatic habitats, and that is generally considered to be beneficial for wildlife and fish. If recent hydrologic trends of more frequent and longer duration flood events continue, a better understanding of planform changes can help in describing past changes, and then be used to forecast potential future trajectories of change. If UMR resource managers determine that past and forecasted conditions are undesirable, then UMRR projects could be identified and prioritized to address those concerns.\n\nVegetative cover associations with landform changes have been used to detect and quantify planform changes in many rivers (Johnson 1985; Hiatt 2015; Volte et al. 2015). Freyer and Jefferson (2013) completed such a study in Pool 6 of the UMR using the landcover data from 12 dates over a 115-yr period, including the 1989, 2000, and 2010/2011 landcover/use (LCU) data from the UMRR Program. Planform change detected over the last 20 years represented by the UMRR Program data best reflect present-day geomorphic patterns, rates and processes. Changes occurring prior to dam construction and changes occurring soon after dam construction are likely not the same as those happening now, 50-70 years after dam construction and creation of the impoundments (McHenry et al., 1984; Bhowmik and Adams, 1986; WEST Consultants, 2000). \n\nThe LCU data from each of the 1989, 2000, and 2010/2011 imagery was developed using similar methods and is available in a Geographical Information System (GIS) for the entire UMR and therefore provides the opportunity for a more comprehensive planform change analysis. This study used GIS overlays of LCU classes to map and quantify changes in planform features over two periods, looking specifically for depositional areas where terrestrial and wetland vegetation expanded at the expense of open water. The land expansion was grouped into four possible process-based types common in large floodplain rivers, some following that used by Lewin et al. (2017). The four types include: crevasse deltas emanating from a breach from a main channel through a natural levee or narrow floodplain into backwaters (crevasse deltas), tributary deltas expanding into backwaters (tributary deltas), deltaic bars at the upstream end of impoundments (impounded deltas), and linear-like bars extending from the downstream ends of narrow levees and remnant floodplains (bar-tail limbs). The methods deployed for change detection addressed possible errors from a variety of sources.","language":"English","publisher":"US Army Corps of Engineers, Upper Mississippi River Restoration (UMRR) Program","usgsCitation":"Rogala, J.T., Fitzpatrick, F., and Hendrickson, J.S., 2020, Recent planform changes in the Upper Mississippi River: Long Term Resource Monitoring Technical Report LTRM-2019GC8, 33 p.","productDescription":"33 p.","ipdsId":"IP-113610","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":391325,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":391323,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://umesc.usgs.gov/documents/publications/2020/rogala_a_2020.html"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90,\n 38.58252615935333\n ],\n [\n -91.0546875,\n 40.07807142745009\n ],\n [\n -90,\n 41.86956082699455\n ],\n [\n -90.8349609375,\n 43.29320031385282\n ],\n [\n -91.2744140625,\n 44.465151013519616\n ],\n [\n -93.55957031249999,\n 46.01222384063236\n ],\n [\n -93.4716796875,\n 46.619261036171515\n ],\n [\n -95.1416015625,\n 46.46813299215554\n ],\n [\n -94.52636718749999,\n 45.24395342262324\n ],\n [\n -93.251953125,\n 44.55916341529182\n ],\n [\n -91.93359375,\n 43.866218006556394\n ],\n [\n -91.1865234375,\n 42.4234565179383\n ],\n [\n -90.791015625,\n 42.22851735620852\n ],\n [\n -91.14257812499999,\n 41.705728515237524\n ],\n [\n -91.669921875,\n 41.07935114946899\n ],\n [\n -91.97753906249999,\n 39.842286020743394\n ],\n [\n -91.318359375,\n 38.89103282648846\n ],\n [\n -90,\n 38.58252615935333\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rogala, James T. 0000-0002-1954-4097 jrogala@usgs.gov","orcid":"https://orcid.org/0000-0002-1954-4097","contributorId":2651,"corporation":false,"usgs":true,"family":"Rogala","given":"James","email":"jrogala@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":791606,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fitzpatrick, Faith A. 0000-0002-9748-7075","orcid":"https://orcid.org/0000-0002-9748-7075","contributorId":209612,"corporation":false,"usgs":true,"family":"Fitzpatrick","given":"Faith A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791607,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hendrickson, Jon S.","contributorId":177520,"corporation":false,"usgs":false,"family":"Hendrickson","given":"Jon","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":791608,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}