In coarse resolution hydrological modeling we face the problem of subgrid variability, the effects of which are difficult to express and are often hidden in the parameterization and calibration. We present a numerical experiment with the physically based hydrological model ParFlow‐CLM with which we quantify the effect of subgrid heterogeneities in headwater catchments within the cell size typically used for regional hydrological applications. We simulate homogeneous domains and domains with subgrid heterogeneities in topography or soil thickness for two climates and soil types. The presence of side slope is the main error source, leading to large underestimation of runoff, and marginally also of evapotranspiration. The spatial distribution of soil saturation in the presence of subgrid variability in topography also leads to underestimation of landslide risk. Soil thickness is the second influential subgrid property, affecting soil moisture distribution and surface runoff formation. Results are consistent for the climates and the soil types considered. The topographic wetness index approach is tested as a way to downscale soil moisture simulations within the domain. Although this method is successful in reproducing some spatial variability and patterns, it fails when the coarse grid mean soil saturation is inaccurate or subgrid topography does not represent subsurface flow paths accurately. We conclude that ignoring subgrid variability in topography and soil thickness in coarse‐scale hydrological models may lead locally to underestimation of runoff and slope instability. Users of such models should be aware of these biases and consider ways to include subgrid effects in coarse‐scale hydrological predictions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027326","usgsCitation":"Leonarduzzi, E., Maxwell, R.M., Mirus, B.B., and Molnar, P., 2021, Numerical analysis of the effect of subgrid variability in a physically based hydrological model on runoff, soil moisture, and slope stability: Water Resources Research, v. 57, no. 4, e2020WR027326, 16 p., https://doi.org/10.1029/2020WR027326.","productDescription":"e2020WR027326, 16 p.","ipdsId":"IP-124808","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":453131,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020wr027326","text":"External Repository"},{"id":384495,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"57","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Leonarduzzi, E. 0000-0002-6811-9118","orcid":"https://orcid.org/0000-0002-6811-9118","contributorId":255523,"corporation":false,"usgs":false,"family":"Leonarduzzi","given":"E.","email":"","affiliations":[{"id":51571,"text":"Institute of Environmental Engineering, ETH Zurich, Switzerland; Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland","active":true,"usgs":false}],"preferred":false,"id":812489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maxwell, R. 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The Pacific Northwest presents new challenges and opportunities for ShakeAlert owing to the different types of earthquakes that occur in the Cascadia subduction zone. Many locations in the Pacific Northwest are expected to experience shaking from shallow crustal earthquakes (similar to those in California), earthquakes that occur deep within the subducted slab, and large megathrust earthquakes that occur primarily offshore. The different geometries and maximum magnitudes associated with these types of earthquakes lead to a range of warning times that are possible between when the initial ShakeAlert Message is issued and when a user experiences strong shaking. After an earthquake begins, the strategy of the ShakeAlert system for public alerting is to warn people who are located close enough to the fault that the system estimates they will experience at least weak to moderate shaking. By alerting the public at these low levels of expected shaking, it is possible to provide sufficient warning times for some users to take protective actions before strong shaking begins. In this study, we present an analysis of past ShakeAlert Messages as well as simulations of historical earthquakes and potential future Cascadia earthquakes to quantify the range of warning times that users who experience strong or worse shaking are likely to receive. Additional applications for ShakeAlert involve initiation of automatic protective actions prior to the onset of shaking, such as slowing trains, shutting water supplies, and opening firehouse doors, which are beyond the scope of this paper. Users in the Pacific Northwest should expect that the majority of alerts they receive will be from shallow crustal and intraslab earthquakes. In these cases, users will only have a few seconds of warning before strong shaking begins. This remains true even during infrequent, offshore great (magnitude ≥8) megathrust earthquakes, where warning times will generally range from seconds to tens of seconds, depending on the user’s location and the intensity of predicted shaking that a user chooses to be alerted for, with the longest warning times of 50–80 seconds possible only for users located at considerable distance from the epicenter. ShakeAlert thus requires short, readily understood alerts stating that earthquake shaking is imminent and suggesting protective actions users should take. Extensive education and outreach efforts that emphasize the need to take actions quickly will be required for ShakeAlert to successfully reduce injuries and losses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211026","usgsCitation":"McGuire, J.J., Smith, D.E., Frankel, A.D., Wirth, E.A., McBride, S.K., and de Groot, R.M., 2021, Expected warning times from the ShakeAlert earthquake early warning system for earthquakes in the Pacific Northwest (ver. 1.1, March 24, 2021): U.S. Geological Survey Open-File Report 2021–1026, 37 p., https://doi.org/10.3133/ofr20211026.","productDescription":"v, 37 p.","onlineOnly":"Y","ipdsId":"IP-125131","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":384638,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2021/1026/versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"}},{"id":384281,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1026/ofr20211026_v1.1.pdf","text":"Report","size":"12 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\"}}]}","edition":"Version 1.0: Marhc 10, 2021; Version 1.1: March 24, 2021","contact":"Earthquake Science Center—Menlo Park, Calif. Office
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
The impacts of wind power development on bat and bird populations are commonly assessed by estimating the number of fatalities at wind power facilities through post-construction monitoring (PCM) studies. Standard methodology involves periodic carcass searches on plots beneath turbines (Strickland et al. 2011, US Fish and Wildlife Service 2012). The resulting counts are adjusted to compensate for bias due to imperfect carcass detection by searchers, removal of carcasses by scavengers or other processes (Korner-Nievergelt et al. 2011), and carcasses that may have fallen outside of searched areas. To account for the bias in counts due to imperfect detection and carcass removal, investigators typically conduct bias trial experiments to inform models of carcass detection probability. Many different estimators have been proposed that combine information about the bias trial experiments to estimate a detection probability for carcasses (g) and ultimately obtain an estimate of total mortality (M). The two estimators that have seen the most widespread use in North America recently are the Huso (Huso 2011, Huso et al. 2012) and Shoenfeld (Shoenfeld 2004; also called the Erickson estimator) estimators. GenEst (Dalthorp et al. 2018a, 2018b, 2018c) is the newest statistical estimator to become available and was designed to improve upon the Huso and Shoenfeld estimators by generalizing the key assumptions in both, and to improve comparability among new PCM studies. In addition to relaxing some of the assumptions inherent to the Huso and Shoenfeld estimators, GenEst uses a parametric bootstrap applied to a novel approach to variance estimation (Madsen et al. 2019).
The current study was undertaken to document the performance of GenEst relative to the Huso and Shoenfeld estimators. We took a simulation approach to the study because simulation data provides the basis to compare mortality estimators under conditions where the “truth” is known. The estimators were compared on three metrics: 1) bias—the tendency of an estimator to over- or under-estimate actual mortality, 2) precision—the ability of an estimator to constrain an estimate to a narrow range (measured here as the width of a 90% confidence interval [CI] around the point estimate divided by the true, known mortality), and 3) CI coverage—the probability a CI with a specified level of confidence actually includes the true level of mortality.
Although our simulations were conceived and designed—and are discussed—with respect to wind power facilities, it is important to note that the estimators and results discussed here are relevant to any post-construction fatality monitoring study that may occur (such as at solar facilities) where detection is imperfect. Although our study treats the problem of mortality estimation when detection is imperfect, it is also important to note that all of the estimators considered here are Horvitz-Thompson (Horvitz and Thompson 1952) style estimators, that is, none are designed to estimate the mortality of rare species as might be necessary under an Incidental Take Permit. The Evidence of Absence estimator (Dalthorp et al. 2017) is still the most appropriate statistical tool for rare event estimation.
The simulations cover a broad range of conditions that may occur in field studies and complete results are presented without commentary in the appendix. The main body of this report does not provide a comprehensive treatment of our results; rather, we try to identify some of the more important differences among the estimators and some conditions under which reliable mortality estimates are especially challenging.
","language":"English","publisher":"American Wind Wildlife Institute","usgsCitation":"Rabie, P., Riser-Espinoza, D., Studyvin, J., Dalthorp, D., and Huso, M., 2021, Performance of the GenEst Mortality Estimator Compared to The Huso and Shoenfeld Estimators: AWWI Technical Report, 29 p.","productDescription":"29 p.","ipdsId":"IP-119710","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":385197,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385196,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://awwi.org/resources/genest/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rabie, Paul","contributorId":248699,"corporation":false,"usgs":false,"family":"Rabie","given":"Paul","affiliations":[{"id":49982,"text":"WEST, Inc.","active":true,"usgs":false}],"preferred":false,"id":809635,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Riser-Espinoza, Daniel","contributorId":248700,"corporation":false,"usgs":false,"family":"Riser-Espinoza","given":"Daniel","email":"","affiliations":[{"id":49982,"text":"WEST, Inc.","active":true,"usgs":false}],"preferred":false,"id":809636,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Studyvin, Jared","contributorId":248701,"corporation":false,"usgs":false,"family":"Studyvin","given":"Jared","affiliations":[{"id":49982,"text":"WEST, Inc.","active":true,"usgs":false}],"preferred":false,"id":809637,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dalthorp, Daniel 0000-0002-4815-6309 ddalthorp@usgs.gov","orcid":"https://orcid.org/0000-0002-4815-6309","contributorId":4902,"corporation":false,"usgs":true,"family":"Dalthorp","given":"Daniel","email":"ddalthorp@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":809638,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huso, Manuela 0000-0003-4687-6625 mhuso@usgs.gov","orcid":"https://orcid.org/0000-0003-4687-6625","contributorId":223969,"corporation":false,"usgs":true,"family":"Huso","given":"Manuela","email":"mhuso@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":809639,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220298,"text":"70220298 - 2021 - Seasonal movements of muskellunge in the St. Clair – Detroit River System: Implications for multi-jurisdictional fisheries management","interactions":[],"lastModifiedDate":"2021-05-03T15:39:09.580626","indexId":"70220298","displayToPublicDate":"2021-03-10T10:33:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal movements of muskellunge in the St. Clair – Detroit River System: Implications for multi-jurisdictional fisheries management","docAbstract":"The St. Clair-Detroit River System contains a world-class Great Lakes muskellunge (Esox masquinongy) fishery that has avoided the declines observed in many Great Lakes muskellunge populations. Muskellunge are an upper trophic level predator, and therefore a naturally low-density species. Limited fishery-independent data exist on which to base management decisions. To remedy this, we initiated an acoustic telemetry study in May of 2016, in collaboration with the Great Lakes Acoustic Telemetry Observation System. Our objective was to describe patterns of movement of muskellunge in this large and open system to better understand their spatial ecology. We acoustically tagged 133 muskellunge in the Detroit River and Lake St. Clair, and movements of 58 fish that passed our data quality control screens were analyzed. We utilized mixed modelling to assess the effects of sex, length, release location, and season on daily movement rates. We found that movement rates only differed among seasons, with highest movement rates occurring in the fall and lowest movement rates in the winter. Muskellunge tagged at different locations exhibited distinct residency patterns, and fish frequently crossed jurisdictional and waterbody boundaries. Ultimately our study highlights the scope and patterns of muskellunge movement in a large, unimpounded system and demonstrates that management of these fish would benefit from consideration of their full distribution covering multiple management jurisdictions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2020.12.006","usgsCitation":"Hessenauer, J., Harris, C., Marklevitz, S., Faust, M.D., Thorn, M.W., Utrup, B., and Hondorp, D.W., 2021, Seasonal movements of muskellunge in the St. Clair – Detroit River System: Implications for multi-jurisdictional fisheries management: Journal of Great Lakes Research, v. 47, no. 2, p. 475-485, https://doi.org/10.1016/j.jglr.2020.12.006.","productDescription":"11 p.","startPage":"475","endPage":"485","ipdsId":"IP-121684","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":385423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"St. Clair – Detroit River System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.16650390625,\n 42.039094188385945\n ],\n [\n -82.99072265625,\n 42.285437007491545\n ],\n [\n -82.37548828125,\n 42.28746890196628\n ],\n [\n -82.42218017578125,\n 42.5733097370664\n ],\n [\n -82.38372802734375,\n 43.022721607058344\n ],\n [\n -82.496337890625,\n 43.022721607058344\n ],\n [\n -82.63641357421875,\n 42.7349091465156\n ],\n [\n -82.95501708984375,\n 42.64608143458068\n ],\n [\n -83.045654296875,\n 42.49235259142821\n ],\n [\n -83.232421875,\n 42.28340504748081\n ],\n [\n -83.22418212890625,\n 42.071723466810774\n ],\n [\n -83.16650390625,\n 42.039094188385945\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hessenauer, Jan-Michael","contributorId":257795,"corporation":false,"usgs":false,"family":"Hessenauer","given":"Jan-Michael","email":"","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815040,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harris, Cleyo","contributorId":257796,"corporation":false,"usgs":false,"family":"Harris","given":"Cleyo","email":"","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815041,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marklevitz, Stephen","contributorId":257797,"corporation":false,"usgs":false,"family":"Marklevitz","given":"Stephen","email":"","affiliations":[{"id":52125,"text":"Ontario Ministry of Natural Resources & Forestry","active":true,"usgs":false}],"preferred":false,"id":815042,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faust, Matthew D.","contributorId":257798,"corporation":false,"usgs":false,"family":"Faust","given":"Matthew","email":"","middleInitial":"D.","affiliations":[{"id":16232,"text":"Ohio Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815043,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thorn, Michael W.","contributorId":257799,"corporation":false,"usgs":false,"family":"Thorn","given":"Michael","email":"","middleInitial":"W.","affiliations":[{"id":52125,"text":"Ontario Ministry of Natural Resources & Forestry","active":true,"usgs":false}],"preferred":false,"id":815044,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Utrup, Brad","contributorId":257800,"corporation":false,"usgs":false,"family":"Utrup","given":"Brad","email":"","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":815045,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":815046,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70219014,"text":"70219014 - 2021 - Argentine Black and White Tegu (Salvator merianae) can survive the winter under semi-natural conditions well beyond their current invasive range","interactions":[],"lastModifiedDate":"2021-03-22T11:53:52.300696","indexId":"70219014","displayToPublicDate":"2021-03-10T07:31:30","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Argentine Black and White Tegu (Salvator merianae) can survive the winter under semi-natural conditions well beyond their current invasive range","docAbstract":"The Argentine Black and White Tegu (Salvator merianae, formerly Tupinambis merianae) is a large lizard from South America. Now established and invasive in southern Florida, and it poses threats to populations of many native species. Models suggest much of the southern United States may contain suitable temperature regimes for this species, yet there is considerable uncertainty regarding either the potential for range expansion northward out of tropical and subtropical zones or the potential for the species establishing elsewhere following additional independent introductions. We evaluated survival, body temperature, duration and timing of winter dormancy, and health of wild-caught tegus from southern Florida held in semi-natural enclosures for over a year in Auburn, Alabama (> 900 km northwest of capture location). Nine of twelve lizards emerged from winter dormancy and seven survived the greater-than-one-year duration of the study. Average length of dormancy (176 d) was greater than that reported in the native range or for invasive populations in southern Florida and females remained dormant longer than males. Tegus grew rapidly throughout the study and the presence of sperm in the testes of males and previtellogenic or early vitellogenic follicles in female ovaries at the end of our study suggest the animals would have been capable of reproduction the following spring. The survival and overall health of the majority of adult tegus in our study suggests weather and climate patterns are unlikely to prevent survival following introduction in many areas of the United States far from their current invasive range.
","language":"English","publisher":"PLoS ONE","doi":"10.1371/journal.pone.0245877","usgsCitation":"Goetz, S.M., Steen, D.A., Miller, M.A., Guyer, C., Kottwitz, J., Roberts, J.F., Blankenship, E., Pearson, P.R., Warner, D., and Reed, R., 2021, Argentine Black and White Tegu (Salvator merianae) can survive the winter under semi-natural conditions well beyond their current invasive range: PLoS ONE, v. 16, no. 3, e0245877, 17 p., https://doi.org/10.1371/journal.pone.0245877.","productDescription":"e0245877, 17 p.","ipdsId":"IP-120439","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":453143,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0245877","text":"Publisher Index Page"},{"id":436466,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YLV6IF","text":"USGS data release","linkHelpText":"Temperature data for tegu lizard study in Auburn, Alabama 2017-2018"},{"id":384501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Goetz, Scott Michael 0000-0002-8705-5316","orcid":"https://orcid.org/0000-0002-8705-5316","contributorId":255507,"corporation":false,"usgs":true,"family":"Goetz","given":"Scott","email":"","middleInitial":"Michael","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":812460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steen, David A. 0000-0002-3031-7557","orcid":"https://orcid.org/0000-0002-3031-7557","contributorId":255509,"corporation":false,"usgs":false,"family":"Steen","given":"David","email":"","middleInitial":"A.","affiliations":[{"id":51560,"text":"Fish and Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission, Gainesville, Florida, United States of America","active":true,"usgs":false}],"preferred":false,"id":812461,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Melissa A.","contributorId":57701,"corporation":false,"usgs":false,"family":"Miller","given":"Melissa","email":"","middleInitial":"A.","affiliations":[{"id":39007,"text":"CA Dept of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":812462,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guyer, Craig","contributorId":104800,"corporation":false,"usgs":false,"family":"Guyer","given":"Craig","email":"","affiliations":[],"preferred":false,"id":812463,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kottwitz, Jack","contributorId":255510,"corporation":false,"usgs":false,"family":"Kottwitz","given":"Jack","email":"","affiliations":[{"id":51562,"text":"Department of Anatomy, Physiology, and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, Alabama, United States of America","active":true,"usgs":false}],"preferred":false,"id":812464,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roberts, John F.","contributorId":255511,"corporation":false,"usgs":false,"family":"Roberts","given":"John","email":"","middleInitial":"F.","affiliations":[{"id":51563,"text":"Department of Comparative, Diagnostic, and Population Medicine, College of Veterinary Medicine, University of Florida, Gainesville, Florida, United States of America","active":true,"usgs":false}],"preferred":false,"id":812465,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Blankenship, Emmett","contributorId":255512,"corporation":false,"usgs":false,"family":"Blankenship","given":"Emmett","email":"","affiliations":[{"id":51564,"text":"All Pets Emergency and Referral Center, Alpharetta, Georgia, United States of America","active":true,"usgs":false}],"preferred":false,"id":812466,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pearson, Phillip R.","contributorId":255513,"corporation":false,"usgs":false,"family":"Pearson","given":"Phillip","email":"","middleInitial":"R.","affiliations":[{"id":51565,"text":"Centre for Conservation, Ecology, and Genetics, Institute for Applied Ecology, University of Canberra, Bruce, Australian Capital City, Australia","active":true,"usgs":false}],"preferred":false,"id":812467,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Warner, Daniel A.","contributorId":255514,"corporation":false,"usgs":false,"family":"Warner","given":"Daniel A.","affiliations":[{"id":51566,"text":"Department of Biological Sciences, College of Sciences and Mathematics, Auburn University, Auburn, Alabama, United States of America","active":true,"usgs":false}],"preferred":false,"id":812468,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":812469,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70220157,"text":"70220157 - 2021 - Probabilities of detecting submersed aquatic vegetation species using a rake method may vary with biomass","interactions":[],"lastModifiedDate":"2021-04-22T14:31:36.304212","indexId":"70220157","displayToPublicDate":"2021-03-09T09:29:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":861,"text":"Aquatic Botany","active":true,"publicationSubtype":{"id":10}},"title":"Probabilities of detecting submersed aquatic vegetation species using a rake method may vary with biomass","docAbstract":"Levels of submersed aquatic vegetation (SAV) are commonly assessed using a modified garden rake. However, the utility of the rake sampling method relative to methods that are typically viewed as more definitive (and expensive) such as snorkeling and coring remains a matter of debate. This study explores whether probabilities of species detections for four SAV species varied among sampling units in a rake-biomass study and, if so, whether such variation reflected variation in species abundance. Variation in detection probabilities, when unaddressed, may yield biased estimators of percent frequency of occurrence (“occupancy”) and of occurrence-habitat associations. Biomass-driven variation in detection probabilities is important because such variation may not be explainable using covariates typically measured when sampling using the rake method. This study found substantial among-unit variation in detection probabilities, with majorities of that variation on the logit or modeling scale being associated with biomass but not with the non-biomass covariates substrate type, water depth and day of study. The study closes by exploring sampling protocols and modeling methods that may yield improved SAV occupancy estimates.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.aquabot.2021.103375","usgsCitation":"Gray, B.R., 2021, Probabilities of detecting submersed aquatic vegetation species using a rake method may vary with biomass: Aquatic Botany, v. 171, 103375, 7 p., https://doi.org/10.1016/j.aquabot.2021.103375.","productDescription":"103375, 7 p.","ipdsId":"IP-123221","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":436468,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZM11FY","text":"USGS data release","linkHelpText":"SAS Code: Estimating probabilities of detecting submersed aquatic vegetation species using a rake method may vary with biomass."},{"id":385277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"171","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gray, Brian R. 0000-0001-7682-9550 brgray@usgs.gov","orcid":"https://orcid.org/0000-0001-7682-9550","contributorId":2615,"corporation":false,"usgs":true,"family":"Gray","given":"Brian","email":"brgray@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":814599,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237781,"text":"70237781 - 2021 - Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","interactions":[],"lastModifiedDate":"2022-10-24T14:38:38.267353","indexId":"70237781","displayToPublicDate":"2021-03-09T09:28:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","title":"Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement","docAbstract":"African elephants (Loxodonta africana) are well-studied and inhabit diverse landscapes that are being transformed by both humans and natural forces. Most tools currently in use are limited in their ability to predict how elephants will respond to novel changes in the environment. Individual-, or agent-based modeling (ABM), may extend current methods in addressing and predicting spatial responses to environmental conditions over time. We developed a spatially explicit agent-based model to simulate elephant space use and validated the model with movement data from elephants in Kruger National Park (KNP) and Chobe National Park (CNP). We simulated movement at an hourly scale, as this scale can reflect switches in elephant behavior due to changes in internal states and short-term responses to the local availability and distribution of critical resources, including forage, water, and shade. Known internal drivers of elephant movement, including perceived temperature and the time since an individual last visited a water source, were linked to the external environment through behavior-based movement rules. Simulations were run on model landscapes representing the wet season and the hot, dry season for both parks. The model outputs, including home range size, daily displacement distance, net displacement distance, and maximum distance traveled from a permanent water source, were evaluated through qualitative and quantitative comparisons to actual elephant movement data from both KNP and CNP. The ABM was successful in reproducing the differences in daily displacements between seasons in each park, and in distances traveled from a permanent water source between parks and seasons. Other movement characteristics, including differences in home range sizes and net daily displacements, were partially reproduced. Out of the all the statistical comparisons made between the empirical and simulated movement patterns, the majority were classified as discrepancies of medium or small effect size. We have shown that a resource-driven model with relatively simple decision rules generates trajectories with movement characteristics that are mostly comparable to those calculated from empirical data. Simulating hourly movement (as our model does) may be useful in predicting how finer-scale patterns of space use, such as those created by foraging movements, are influenced by finer spatio-temporal changes in the environment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2021.109499","usgsCitation":"Diaz, S.G., DeAngelis, D.L., Gaines, M.S., Purdon, A., Mole, M.A., and van Aarde, R.J., 2021, Development and validation of a spatially-explicit agent-based model for space utilization by African savanna elephants (Loxodonta africana) based on determinants of movement: Ecological Modelling, v. 447, 109499, 27 p., https://doi.org/10.1016/j.ecolmodel.2021.109499.","productDescription":"109499, 27 p.","ipdsId":"IP-124073","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":408643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Botswana, Mozambique, South Africa","otherGeospatial":"Chobe National Park, Kruger National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 25.26908811865576,\n -17.811236528794907\n ],\n [\n 23.696375701212872,\n -17.811236528794907\n ],\n [\n 23.696375701212872,\n -19.291477668581805\n ],\n [\n 25.26908811865576,\n -19.291477668581805\n ],\n [\n 25.26908811865576,\n -17.811236528794907\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 30.337765815705552,\n -22.153479707969097\n ],\n [\n 30.337765815705552,\n -25.725433227433996\n ],\n [\n 33.00624860561675,\n -25.725433227433996\n ],\n [\n 33.00624860561675,\n -22.153479707969097\n ],\n [\n 30.337765815705552,\n -22.153479707969097\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"447","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diaz, Stephanie G.","contributorId":212228,"corporation":false,"usgs":false,"family":"Diaz","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":855617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057 don_deangelis@usgs.gov","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":148065,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald","email":"don_deangelis@usgs.gov","middleInitial":"L.","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":855618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gaines, Michael S.","contributorId":298435,"corporation":false,"usgs":false,"family":"Gaines","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":5112,"text":"University of Miami","active":true,"usgs":false}],"preferred":false,"id":855619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Purdon, Andrew","contributorId":298436,"corporation":false,"usgs":false,"family":"Purdon","given":"Andrew","email":"","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855620,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mole, Michael A.","contributorId":298438,"corporation":false,"usgs":false,"family":"Mole","given":"Michael","email":"","middleInitial":"A.","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855621,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"van Aarde, Rudi J.","contributorId":298440,"corporation":false,"usgs":false,"family":"van Aarde","given":"Rudi","email":"","middleInitial":"J.","affiliations":[{"id":48053,"text":"University of Pretoria","active":true,"usgs":false}],"preferred":false,"id":855622,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219424,"text":"70219424 - 2021 - UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence","interactions":[],"lastModifiedDate":"2021-04-05T13:40:13.157647","indexId":"70219424","displayToPublicDate":"2021-03-09T08:18:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence","docAbstract":"Seasonal snow cover in the dry forests of the American West provides essential water resources to both human and natural systems. The structure of trees and their arrangement across the landscape are important drivers of snow cover distribution across these forests, varying widely in both space and time. We used unmanned aerial vehicle (UAV) multispectral imagery and Structure-from-Motion (SfM) models to quantify rapidly melting snow cover dynamics and examine the effects of forest structure shading on persistent snow cover in a recently thinned ponderosa pine forest. Using repeat UAV multispectral imagery (n = 11 dates) across the 76 ha forest, we first developed a rapid and effective method for identifying persistent snow cover with 90.2% overall accuracy. The SfM model correctly identified 98% (n = 1280) of the trees, when compared with terrestrial laser scanner validation data. Using the SfM-derived forest structure variables, we then found that canopy shading associated with the vertical and horizontal metrics was a significant driver of persistent snow cover patches (R2 = 0.70). The results indicate that UAV image-derived forest structure metrics can be used to accurately predict snow patch size and persistence. Our results provide insight into the importance of forest structure, specifically canopy shading, in the amount and distribution of persistent seasonal snow cover in a typical dry forest environment. An operational understanding of forest structure effects on snow cover will help drive forest management that can target snow cover dynamics in addition to forest health.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13051036","usgsCitation":"Belmonte, A., Sankey, T.T., Biedermann, J., Bradford, J., Goetz, S.J., and Kolb, T., 2021, UAV-based estimate of snow cover dynamics: Optimizing semi-arid forest structure for snow persistence: Remote Sensing, v. 13, no. 5, 1036, 20 p., https://doi.org/10.3390/rs13051036.","productDescription":"1036, 20 p.","ipdsId":"IP-126824","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453149,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13051036","text":"Publisher Index Page"},{"id":384871,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.78863525390625,\n 34.5235300339023\n ],\n [\n -111.23382568359374,\n 34.5235300339023\n ],\n [\n -111.23382568359374,\n 35.15135442846945\n ],\n [\n -111.78863525390625,\n 35.15135442846945\n ],\n [\n -111.78863525390625,\n 34.5235300339023\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Belmonte, Adam","contributorId":222546,"corporation":false,"usgs":false,"family":"Belmonte","given":"Adam","email":"","affiliations":[{"id":40559,"text":"School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":813495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Temuulen T.","contributorId":173297,"corporation":false,"usgs":false,"family":"Sankey","given":"Temuulen","email":"","middleInitial":"T.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":813496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Biedermann, Joel","contributorId":256936,"corporation":false,"usgs":false,"family":"Biedermann","given":"Joel","email":"","affiliations":[{"id":51904,"text":"USDA Agricultural Research Service Southwest Watershed Research Center, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":813497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goetz, Scott J 0000-0002-6326-4308","orcid":"https://orcid.org/0000-0002-6326-4308","contributorId":210734,"corporation":false,"usgs":false,"family":"Goetz","given":"Scott","email":"","middleInitial":"J","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":813499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolb, Thomas","contributorId":174381,"corporation":false,"usgs":false,"family":"Kolb","given":"Thomas","affiliations":[],"preferred":false,"id":813500,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70218794,"text":"70218794 - 2021 - Temperature‐associated decreases in demographic rates of Afrotropical bird species over 30 years","interactions":[],"lastModifiedDate":"2021-04-22T16:10:48.189211","indexId":"70218794","displayToPublicDate":"2021-03-09T07:14:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Temperature‐associated decreases in demographic rates of Afrotropical bird species over 30 years","docAbstract":"Tropical mountains harbor globally significant levels of biodiversity and endemism. Climate change threatens many tropical montane species, yet little research has assessed the effects of climate change on the demographic rates of tropical species, particularly in the Afrotropics. Here, we report on the demographic rates of 21 Afrotropical bird species over 30 years in montane forests in Tanzania. We used mark–recapture analyses to model rates of population growth, recruitment, and apparent survival as functions of annual mean temperature and annual precipitation. For over one‐half of focal species, decreasing population growth rates were associated with increasing temperature. Due to the trend in temperature over time, we substituted a time covariate for the temperature covariate in top‐ranked population growth rate models. Temperature was a better explanatory covariate than time for 6 of the 12 species, or 29% of all focal species. Population growth rates were also lower for species found further below their elevational midpoint and for smaller‐bodied species. Changes in population growth rates were more closely tied to changes in recruitment than to changes in apparent survival. There were no consistent associations between demographic rates and precipitation. This study demonstrates temperature‐associated demographic impacts for 6 (29%) of 21 focal species in an Afrotropical understory bird community and highlights the need to incorporate the impacts of climate change on demographic rates into conservation planning across the tropics.
We present a comprehensive critical review of well-established satellite remote sensing water indices and offer a novel, robust Augmented Normalized Difference Water Index (ANDWI). ANDWI employs an expanded set of spectral bands, RGB, NIR, and SWIR1-2, to maximize the contrast between water and non-water pixels. Further, we implement a dynamic thresholding method, the Otsu algorithm, to enhance ANDWI's performance. Applied to a variety of environmental conditions, ANDWI with Otsu-thresholding offered the highest overall accuracy (accuracy = 0.98, F1 = 0.98, and Kappa = 0.96) compared to other indices (NDWI, MNDWI, AWEI, WI). We also propose a novel cloud filtering algorithm that substantially increases the number of useable images compared to the conventional cloud-free composites (124% increased observations in the studied area) and resolves inappropriate masking of water bodies and hot sands as clouds by conventional methods. Finally, we develop a Google Earth Engine App to readily delineate 16-day surface water bodies across the globe.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105030","usgsCitation":"Rad, A.M., Kreitler, J.R., and Sadegh, M., 2021, Augmented normalized difference water index for improved monitoring of surface water: Environmental Modeling and Software, v. 140, 105030, 15 p., https://doi.org/10.1016/j.envsoft.2021.105030.","productDescription":"105030, 15 p.","ipdsId":"IP-121583","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":453156,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105030","text":"Publisher Index Page"},{"id":385213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"140","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rad, Arash Modaresi","contributorId":257536,"corporation":false,"usgs":false,"family":"Rad","given":"Arash","email":"","middleInitial":"Modaresi","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":814521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kreitler, Jason R. 0000-0002-0243-5281 jkreitler@usgs.gov","orcid":"https://orcid.org/0000-0002-0243-5281","contributorId":4050,"corporation":false,"usgs":true,"family":"Kreitler","given":"Jason","email":"jkreitler@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":814522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sadegh, Mojitaba","contributorId":257538,"corporation":false,"usgs":false,"family":"Sadegh","given":"Mojitaba","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":814523,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70255557,"text":"70255557 - 2021 - Global Changes in 20-year, 50-year and 100-year River Floods","interactions":[],"lastModifiedDate":"2024-06-24T11:35:50.509067","indexId":"70255557","displayToPublicDate":"2021-03-09T06:30:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Global Changes in 20-year, 50-year and 100-year River Floods","docAbstract":"Concepts like the 100-year flood event can be misleading if they are not updated to reflect significant changes over time. Here, we model observed annual maximum daily streamflow using a nonstationary approach to provide the first global picture of changes in: (a) the magnitudes of the 20-, 50-, and 100-year floods (i.e., flows of a given exceedance probability in each year); (b) the return periods of the 20-, 50-, and 100-year floods, as assessed in 1970 (i.e., flows of a fixed magnitude); and (c) corresponding flood probabilities. Empirically, we find the 20-/50-year floods have mostly increased in temperate climate zones, but decreased in arid, tropical, polar, and cold zones. In contrast, 100-year floods have mostly decreased in arid/temperate zones and exhibit mixed trends in cold zones, but results are influenced by the small number of stations with long records, and highlight the need for continued updating of hazard assessments.
Indirect climate effects on tree fecundity that come through variation in size and growth (climate-condition interactions) are not currently part of models used to predict future forests. Trends in species abundances predicted from meta-analyses and species distribution models will be misleading if they depend on the conditions of individuals. Here we find from a synthesis of tree species in North America that climate-condition interactions dominate responses through two pathways, i) effects of growth that depend on climate, and ii) effects of climate that depend on tree size. Because tree fecundity first increases and then declines with size, climate change that stimulates growth promotes a shift of small trees to more fecund sizes, but the opposite can be true for large sizes. Change the depresses growth also affects fecundity. We find a biogeographic divide, with these interactions reducing fecundity in the West and increasing it in the East. Continental-scale responses of these forests are thus driven largely by indirect effects, recommending management for climate change that considers multiple demographic rates.
Invasive predators are known to have negative consumptive and non-consumptive effects on native species, but few examples show how the abundance of native prey may influence an established invasive predator. We compared invasive brown treesnakes (Boiga irregularis; BTS) found in caves occupied by endangered Mariana swiftlets (Aerodramus bartschi) to snakes found in nearby forests and caves without birds to quantify how the abundance of native avian prey impacts BTS abundance and behavior on Guam. From 2011 to 2017 we removed 151 BTS in caves occupied by swiftlets and never observed BTS in caves without birds. Notable locations included snakes foraging near swiftlets and in holes that allowed cave access and escape from capture. Of 43 BTS with gut contents, 27 (63%) contained swiftlets. BTS in swiftlet-occupied caves had greater fat mass compared to forests, indicating access to swiftlets may increase body condition and promote reproduction. Number of ovarian follicles was significantly greater in female snakes from swiftlet-occupied caves compared to those from ravine, but not limestone forests; evidence of male BTS being more capable of reproduction was limited (i.e., fewer non-discernible but not significantly larger testes in snakes from caves). Assuming other limiting factors are considered, altering the functional response of predators through the modification of caves or interdiction lures to exclude or hinder the largest BTS could bolster swiftlet populations by increasing nesting refugia in currently-occupied caves and facilitate recolonization of historical caves.
Given widespread population declines of birds breeding in North American grasslands, management that sustains wildlife while supporting rancher livelihoods is needed. However, management effects vary across landscapes, and identifying areas with the greatest potential bird response to conservation is a pressing research need.
We developed a hierarchical modeling approach to study grassland bird response to habitat factors at multiple scales and levels. We then identified areas to prioritize for implementing a bird-friendly ranching program.
Using bird survey data from grassland passerine species and 175 sites (2009–2018) across northeast Wyoming, USA, we fit hierarchical community distance sampling models and evaluated drivers of site-level density and regional-level distribution. We then created spatially-explicit predictions of bird density and distribution for the study area and predicted outcomes from pasture-scale management scenarios.
Cumulative overlap of species distributions revealed areas with greater potential community response to management. Within each species’ potential regional-level distribution, the grassland bird community generally responded negatively to cropland cover and vegetation productivity at local scales (up to 10 km of survey sites). Multiple species declined with increasing bare ground and litter cover, shrub cover, and grass height measured within sites.
We demonstrated a novel approach to multi-scale and multi-level prioritization for grassland bird conservation based on hierarchical community models and extensive population monitoring. Pasture-scale management scenarios also suggested the examined community may benefit from less bare ground cover and shorter grass height. Our approach could be extended to other bird guilds in this region and beyond.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-021-01211-z","usgsCitation":"Monroe, A.P., Edmunds, D.R., Aldridge, C.L., Holloran, M.J., Assal, T.J., and Holloran, A., 2021, Prioritizing landscapes for grassland bird conservation with hierarchical community models: Landscape Ecology, v. 36, p. 1023-1038, https://doi.org/10.1007/s10980-021-01211-z.","productDescription":"16 p.","startPage":"1023","endPage":"1038","ipdsId":"IP-122010","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":453170,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-021-01211-z","text":"Publisher Index Page"},{"id":384245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Bird Conservation Region 17","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.5341796875,\n 45.02695045318546\n ],\n [\n -107.05078125,\n 44.715513732021336\n ],\n [\n -106.69921875,\n 44.37098696297173\n ],\n [\n -106.61132812499999,\n 43.78695837311561\n ],\n [\n -106.435546875,\n 43.052833917627936\n ],\n [\n -105.57861328125,\n 42.79540065303723\n ],\n [\n -104.83154296875,\n 42.4234565179383\n ],\n [\n -104.0185546875,\n 42.52069952914966\n ],\n [\n -104.0625,\n 45.042478050891546\n ],\n [\n -107.5341796875,\n 45.02695045318546\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","noUsgsAuthors":false,"publicationDate":"2021-03-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Monroe, Adrian Pierre-Frederic 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":254952,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian","email":"amonroe@usgs.gov","middleInitial":"Pierre-Frederic","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811524,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811525,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":811526,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holloran, Matthew J 0000-0001-5244-770X","orcid":"https://orcid.org/0000-0001-5244-770X","contributorId":254954,"corporation":false,"usgs":false,"family":"Holloran","given":"Matthew","email":"","middleInitial":"J","affiliations":[{"id":51367,"text":"Operational Conservation LLC","active":true,"usgs":false}],"preferred":false,"id":811527,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Assal, Timothy J","contributorId":238085,"corporation":false,"usgs":false,"family":"Assal","given":"Timothy","email":"","middleInitial":"J","affiliations":[{"id":18142,"text":"Kent State University","active":true,"usgs":false}],"preferred":false,"id":811528,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holloran, Alison G","contributorId":254955,"corporation":false,"usgs":false,"family":"Holloran","given":"Alison G","affiliations":[{"id":51369,"text":"Audubon Rockies","active":true,"usgs":false}],"preferred":false,"id":811529,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70222570,"text":"70222570 - 2021 - Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data","interactions":[],"lastModifiedDate":"2021-08-05T12:13:48.433134","indexId":"70222570","displayToPublicDate":"2021-03-06T07:09:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3071,"text":"Physics of the Earth and Planetary Interiors","active":true,"publicationSubtype":{"id":10}},"title":"Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data","docAbstract":"We use combined teleseismic and strong motion data sets to investigate finite-fault slip models for a double of earthquakes that occurred on August 11, 2012, in northwestern Iran near the cities of Ahar and Varzaghan. The data include teleseismic P-waveforms retrieved from broadband seismic stations located between 30°–94° from the earthquakes and local strong motion data recorded by the Iran Strong Motion Network, installed and operated by the Building and Housing Research Centre. We first invert teleseismic P-waveforms and local strong motion data separately. For the first event (12:23 UTC), the teleseismic broadband inversion yields a somewhat deeper and simpler distribution of slip than the local strong motion inversion. The strong motion inversion results in a more complex distribution because of higher frequency content but can also be influenced by complexities in the propagation path. For the second event (12:34 UTC), the slip distribution from strong motion data is more similar to the teleseismic result and shows a simple slip area with a small relative movement to the west. To resolve the differences between the results of these two data sets and obtain a better constrained slip model, we perform a joint inversion of teleseismic broadband and local strong motion data.
The joint inversion for the first event shows two asperities with a maximum slip of 3.9 m up- dip from the hypocenter and extending to the west between depths of 1 and 5 km. A second narrower high-slip area is seen just above the hypocenter from 6 to 10 km depth. The total moment for this earthquake is calculated to be Mo = 3.8 × 1025 dyn-cm (3.8 × 1018 N.m) (Mw 6.4). For the second event, the results of the joint inversion show a simple slip distribution that is mainly confined in a single patch around the hypocenter with a depth range from about 10 to 13 km and maximum slip of 1.9 m. We compute a total seismic moment of Mo = 1.6 × 1025 dyn-cm (1.6 × 1018 N.m) (Mw 6.1) for the second event. The largest stress drops for the first event occur above the hypocenter with an average stress drop over the rupture area of 120 bar (12 Mpa). For the second event, the maximum stress drop occurs at the reported focal depth with an average stress drop over the rupture area of 80 bar (8 Mpa).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.pepi.2021.106688","usgsCitation":"Saltanatpouri, A., Hartzell, S.H., Rahimi, H., Rouhollahi, R., and Amiri Fard, R., 2021, Slip distribution and rupture history of the August 11, 2012, double earthquakes in Ahar – Varzaghan, Iran, using joint inversion of teleseismic broadband and local strong motion data: Physics of the Earth and Planetary Interiors, v. 313, 106688, 15 p., https://doi.org/10.1016/j.pepi.2021.106688.","productDescription":"106688, 15 p.","ipdsId":"IP-124948","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":387702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Turkey","otherGeospatial":"East Anatolian Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 42.626953125,\n 39.65011210186371\n ],\n [\n 43.1982421875,\n 39.65011210186371\n ],\n [\n 43.1982421875,\n 39.94975340768179\n ],\n [\n 42.626953125,\n 39.94975340768179\n ],\n [\n 42.626953125,\n 39.65011210186371\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"313","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Saltanatpouri, Atefeh","contributorId":261761,"corporation":false,"usgs":false,"family":"Saltanatpouri","given":"Atefeh","email":"","affiliations":[{"id":52998,"text":"Institute of Geophysics, University of Tehran, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hartzell, Stephen H. 0000-0003-0858-9043 shartzell@usgs.gov","orcid":"https://orcid.org/0000-0003-0858-9043","contributorId":2594,"corporation":false,"usgs":true,"family":"Hartzell","given":"Stephen","email":"shartzell@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rahimi, Habib","contributorId":261762,"corporation":false,"usgs":false,"family":"Rahimi","given":"Habib","email":"","affiliations":[{"id":52998,"text":"Institute of Geophysics, University of Tehran, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rouhollahi, Rahmatollah","contributorId":261763,"corporation":false,"usgs":false,"family":"Rouhollahi","given":"Rahmatollah","email":"","affiliations":[{"id":53001,"text":"Babol Noshirvani University of Technology, Babol, Iran","active":true,"usgs":false}],"preferred":false,"id":820606,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Amiri Fard, Rouholla","contributorId":261764,"corporation":false,"usgs":false,"family":"Amiri Fard","given":"Rouholla","email":"","affiliations":[{"id":53002,"text":"International Institute of Earthquake Engineering and Seismology, Tehran, Iran","active":true,"usgs":false}],"preferred":false,"id":820607,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219437,"text":"70219437 - 2021 - Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk","interactions":[],"lastModifiedDate":"2021-04-06T11:58:58.749804","indexId":"70219437","displayToPublicDate":"2021-03-06T06:53:33","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2331,"text":"Journal of Hazardous Materials","active":true,"publicationSubtype":{"id":10}},"title":"Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk","docAbstract":"Toxic cyanobacterial blooms, often containing multiple toxins, are a serious public health issue. However, there are no known models that predict a cyanotoxin mixture (anatoxin-a, microcystin, saxitoxin). This paper presents two cyanotoxin mixture models (MIX) and compares them to two microcystin (MC) models from data collected in 2016–2017 from three recurring cyanobacterial bloom locations in Kabetogama Lake, Voyageurs National Park (Minnesota, USA). Models include those using near-real-time environmental variables (readily available) and those using additional comprehensive variables (based on laboratory analyses). Comprehensive models (R2 = 0.87 MC; R2 = 0.86 MIX) explained more variability than the environmental models (R2 = 0.58 MC; R2 = 0.57 MIX). Although neither MIX model was a better fit than the MC models, the MIX models produced no false negatives in the calibration dataset, indicating that all observations above regulatory guidelines were simulated by the MIX models. This is the first known use of Virtual Beach software for a cyanotoxin mixture model, and the methods used in this paper may be applicable to other lakes or beaches.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhazmat.2021.125560","usgsCitation":"Christensen, V., Stelzer, E., Eikenberry, B., Olds, H., LeDuc, J.F., Maki, R., Norland, J.E., and Khan, E., 2021, Cyanotoxin mixture models: Relating environmental variables and toxin co-occurrence to human exposure risk: Journal of Hazardous Materials, v. 415, 125560, 13 p., https://doi.org/10.1016/j.jhazmat.2021.125560.","productDescription":"125560, 13 p.","ipdsId":"IP-123013","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":436472,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X7EO1K","text":"USGS data release","linkHelpText":"Data and model archive for multiple linear regression models for prediction of weighted cyanotoxin mixture concentrations and microcystin concentrations at three recurring bloom sites in Kabetogama Lake in Minnesota"},{"id":384883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","otherGeospatial":"Kabetogama Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.3453369140625,\n 48.21735290928554\n ],\n [\n -92.48291015625,\n 48.21735290928554\n ],\n [\n -92.48291015625,\n 48.622016428468385\n ],\n [\n -93.3453369140625,\n 48.622016428468385\n ],\n [\n -93.3453369140625,\n 48.21735290928554\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"415","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stelzer, Erin A. 0000-0001-7645-7603","orcid":"https://orcid.org/0000-0001-7645-7603","contributorId":220549,"corporation":false,"usgs":true,"family":"Stelzer","given":"Erin A.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813549,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Eikenberry, Barbara C. Scudder 0000-0001-8058-1201 beikenberry@usgs.gov","orcid":"https://orcid.org/0000-0001-8058-1201","contributorId":172148,"corporation":false,"usgs":true,"family":"Eikenberry","given":"Barbara C. Scudder","email":"beikenberry@usgs.gov","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":813550,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olds, Hayley T. 0000-0002-6701-6459 htemplar@usgs.gov","orcid":"https://orcid.org/0000-0002-6701-6459","contributorId":5002,"corporation":false,"usgs":true,"family":"Olds","given":"Hayley T.","email":"htemplar@usgs.gov","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":false,"id":813551,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LeDuc, Jaime F.","contributorId":190132,"corporation":false,"usgs":false,"family":"LeDuc","given":"Jaime","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":813552,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Maki, Ryan P.","contributorId":190131,"corporation":false,"usgs":false,"family":"Maki","given":"Ryan P.","affiliations":[],"preferred":false,"id":813553,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Norland, Jack E.","contributorId":214257,"corporation":false,"usgs":false,"family":"Norland","given":"Jack","email":"","middleInitial":"E.","affiliations":[{"id":39001,"text":"School of Natural Resources Sciences, North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":813554,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Khan, Eakalak","contributorId":220550,"corporation":false,"usgs":false,"family":"Khan","given":"Eakalak","email":"","affiliations":[{"id":40182,"text":"University of Nevada Las Vegas","active":true,"usgs":false}],"preferred":false,"id":813555,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70221894,"text":"70221894 - 2021 - Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine","interactions":[],"lastModifiedDate":"2021-07-13T18:35:29.258188","indexId":"70221894","displayToPublicDate":"2021-03-05T13:30:16","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3892,"text":"Ecohydrology & Hydrobiology","active":true,"publicationSubtype":{"id":10}},"title":"Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine","docAbstract":"Dissolved organic carbon (DOC) is an important component of the carbon cycle as a measure of the hydrological transport of carbon between terrestrial carbon pools into soil pools and eventually into streams. As a result, changes in DOC in rivers and streams may indicate alterations in the storage of terrestrial carbon. Exploring the complex interactions between biogeochemical cycling and hydrologic processes, as well as the micro-climate variabilities that impact the rate of DOC fluxes, are challenging because the information is not readily available from in-situ measurements or from empirical models alone. This is particularly true of large-scale watersheds. The Penobscot Watershed is the largest watershed of the Gulf of Maine and the second largest in New England. Its typical soils, with high organic matter and a large forested and wetland landscape, result in higher DOC fluxes than what has been observed previously for most rivers in the northern temperate or boreal zones (Hope et al., 1994; Mulholland, 1997; Aitkenhead and McDowell, 2000).
In this study, we emphasized the simulation of streamflow and DOC fluxes from the Penobscot Watershed (and several tributaries within the Penobscot Watershed) using the spatially distributed process-based Regional Hydro-Ecological Simulation System (RHESSys) model. Simulated results were evaluated using field measurements (streamflow, DOC fluxes) and remotely sensed products (Net Primary Production (NPP) and Leaf Area Index (LAI) from Moderate Resolution Imaging Spectroradiometer (MODIS). The average DOC flux for the Penobscot Watershed during 2004-2012 using the RHESSys model was 69 kg C/ha/year. The RHESSys simulated DOC flux is shown to correlate well with observed values, as well as with results previously reported from the empirical Load Estimator (LOADEST) model (71 kg C/ha/year) for 2004-2007 (Huntington and Aiken, 2013).
Our simulated results also show a temporal variation in the amount of DOC flux, indicating that the antecedent DOC concentration from one year can impact the DOC export in following years. Thus, DOC concentration is positively correlated with streamflow and antecedent precipitation, in agreement with previous studies (Ågren et al., 2010; Huntington and Aiken, 2013; Tian et al., 2013). The successful application of the rigorous RHESSys model in the Penobscot Watershed makes it a reasonable platform to test future scenarios impacting the hydrology and biogeochemistry within similar large complex watersheds.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecohyd.2021.02.005","usgsCitation":"Rouhani, S., Schaaf, C.B., Huntington, T., and Choate, J., 2021, Simulation of dissolved organic carbon flux in the Penobscot Watershed, Maine: Ecohydrology & Hydrobiology, v. 21, no. 23-24, p. 256-270, https://doi.org/10.1016/j.ecohyd.2021.02.005.","productDescription":"15 p.","startPage":"256","endPage":"270","ipdsId":"IP-106391","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":453173,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecohyd.2021.02.005","text":"Publisher Index Page"},{"id":387156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","otherGeospatial":"Penobscot watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.73046875,\n 44.48866833139464\n ],\n [\n -67.576904296875,\n 45.57560020947802\n ],\n [\n -68.5986328125,\n 46.255846818480315\n ],\n [\n -70.15869140625,\n 46.430285240839964\n ],\n [\n -70.37841796875,\n 45.78284835197676\n ],\n [\n -69.43359375,\n 45.874712248904764\n ],\n [\n -69.60937499999999,\n 45.36758436884978\n ],\n [\n -70.11474609375,\n 45.213003555993964\n ],\n [\n -69.345703125,\n 44.6061127451739\n ],\n [\n -68.73046875,\n 44.48866833139464\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"23-24","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rouhani, Shabnam","contributorId":260994,"corporation":false,"usgs":false,"family":"Rouhani","given":"Shabnam","email":"","affiliations":[{"id":52735,"text":"University of Massachusetts, Boston, MA","active":true,"usgs":false}],"preferred":false,"id":819233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schaaf, Crystal B.","contributorId":149538,"corporation":false,"usgs":false,"family":"Schaaf","given":"Crystal","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":819234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huntington, Thomas G. 0000-0002-9427-3530","orcid":"https://orcid.org/0000-0002-9427-3530","contributorId":218737,"corporation":false,"usgs":true,"family":"Huntington","given":"Thomas G.","affiliations":[{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Choate, Janet","contributorId":260995,"corporation":false,"usgs":false,"family":"Choate","given":"Janet","email":"","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":819236,"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.
Incorporating the influence of soil layering and local variability into the parameterizations of physics-based numerical models for distributed landslide susceptibility assessments remains a challenge. Typical applications employ substantial simplifications including homogeneous soil units and soil-hydraulic properties assigned based only on average textural classifications; the potential impact of these assumptions is usually disregarded. We present a multi-scale approach for parameterizing the distributed Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model that accounts for site-specific spatial variations in both soil thickness and complex layering properties by defining homogeneous soil properties that vary spatially for each model grid cell. These effective properties allow TRIGRS to accurately simulate the timing and distribution of slope failures without any modification of the model structure. We implemented this approach for the carbonate ridge of Sarno Mountains (southern Italy) whose slopes are mantled by complex layered soils of pyroclastic origin. The urbanized foot slopes enveloping these mountains are among the most landslide-prone areas of Italy and have been subjected to repeated occurrences of damaging and deadly rainfall-induced flow-type shallow landslides. At this scope, a primary local-scale application of TRIGRS was calibrated on physics-based rainfall thresholds, previously determined by a coupled VS2D (version 1.3) hydrological modeling and slope stability analysis. Subsequently, by taking into account the spatial distribution of soil thickness and vertical heterogeneity of soil hydrological and mechanical properties, a distributed assessment of landslide hazard was carried out by means of TRIGRS. The combination of these approaches led to the spatial assessment of landslide hazard under different hypothetical rainfall intensities and antecedent hydrological conditions. This approach to parameterizing TRIGRS can be adapted to other spatially variable soil layering and thickness to improve hazard assessments.
","language":"English","publisher":"MDPI","doi":"10.3390/w13050713","usgsCitation":"Fusco, F., Mirus, B.B., Baum, R.L., Calcaterra, D., and De Vita, P., 2021, Incorporating the effects of complex soil layering and thickness local variability into distributed landslide susceptibility assessments: Water, v. 13, no. 5, 27 p., https://doi.org/10.3390/w13050713.","productDescription":"27 p.","ipdsId":"IP-120315","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":453185,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13050713","text":"Publisher Index Page"},{"id":384490,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Mount Vesuvius","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 14.327545166015625,\n 40.75974059207392\n ],\n [\n 14.53765869140625,\n 40.75974059207392\n ],\n [\n 14.53765869140625,\n 40.90832339902113\n ],\n [\n 14.327545166015625,\n 40.90832339902113\n ],\n [\n 14.327545166015625,\n 40.75974059207392\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Fusco, F. 0000-0002-6271-2228","orcid":"https://orcid.org/0000-0002-6271-2228","contributorId":219005,"corporation":false,"usgs":false,"family":"Fusco","given":"F.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812515,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true}],"preferred":true,"id":812516,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":812517,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Calcaterra, D. 0000-0002-3480-3667","orcid":"https://orcid.org/0000-0002-3480-3667","contributorId":219008,"corporation":false,"usgs":false,"family":"Calcaterra","given":"D.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812518,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"De Vita, P.","contributorId":219006,"corporation":false,"usgs":false,"family":"De Vita","given":"P.","email":"","affiliations":[{"id":39950,"text":"University of Napoli Federico II, Italy","active":true,"usgs":false}],"preferred":false,"id":812519,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218517,"text":"ofr20201145 - 2021 - Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018","interactions":[],"lastModifiedDate":"2021-03-05T12:53:46.034292","indexId":"ofr20201145","displayToPublicDate":"2021-03-04T15:39:22","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":"2020-1145","displayTitle":"Estimated Total Phosphorus Loads for Selected Sites on Great Lakes Tributaries, Water Years 2014–2018","title":"Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018","docAbstract":"Monthly and annual total phosphorus loads were estimated for water years 2014 through 2018 for 23 streamgaged (gaged) sites on tributaries to the Great Lakes. Processing and regression methods described by Robertson and others (2018) were used with discrete and continuous data collected during water years 2011 and 2018 to update regression models for estimating instantaneous flux with the same form of equations as published by Robertson and others (2018). Monthly and water year average fluxes for all but two of the 23 gage sites were estimated using a weighted combination of results from surrogate models (which have streamflow, turbidity, and seasonal indicators as explanatory variables) and unit-value (UV)-flow models which have only UV streamflow and seasonal indicators as explanatory variables. Two of the gage sites had extensive periods of missing turbidity records, so average flux estimates for those stations were based solely on results from UV-flow models.
For most sites, estimated loads of total phosphorus were computed and summed for water years 2014–2018. The cumulative loads were used to compute yields and flow-weighted mean concentrations for water years 2014–2018. The estimated cumulative total phosphorus loads for water years 2014–2018 ranged from 112 to 11,500 metric tons. The Maumee River site (U.S. Geological Survey gage number 04193500) had the largest estimated cumulative load for water years 2014–2018 and the third largest estimated flow-weighted mean concentration. In fact, the estimated cumulative load at the Maumee River site was more than three times larger than the second largest estimated cumulative load.
Estimated average annual total phosphorus yields and flow-weighted mean concentrations for water years 2014–2018 ranged from 0.016 metric tons per square kilometer to 0.771 metric tons per square kilometer and 0.033 milligram per liter to 0.466 milligram per liter, respectively. The Cattaraugus Creek gage site (U.S. Geological Survey gage number 04213500) had the highest estimated average annual total phosphorus yield and flow-weighted mean concentration. The average annual total phosphorus yield at the Cattaraugus Creek gage site was almost twice as large as the second largest estimated yield.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201145","collaboration":"Prepared in cooperation with the Great Lakes Restoration Initiative","usgsCitation":"Koltun, G.F., 2021, Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018: U.S. Geological Survey Open-File Report 2020–1145, 13 p., https://doi.org/10.3133/ofr20201145.","productDescription":"Report: v, 13 p.; 2 Appendixes; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122090","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":383717,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145//ofr20201145_appendix_2.csv","text":"Appendix 2","size":"64.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2020–1145 Appendix 2","linkHelpText":"— Estimated monthly total phosphorus loads at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383712,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1145/coverthb.jpg"},{"id":383713,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145.pdf","text":"Report","size":"1.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1145"},{"id":383714,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145_appendix_1.xlsx","text":"Appendix 1","size":"16.4 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1145 Appendix 1","linkHelpText":"— Estimated annual total phosphorus loads and flow-weighted mean concentrations at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383715,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145_appendix_1.csv","text":"Appendix 1","size":"8.45 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2020–1145 Appendix 1","linkHelpText":"— Estimated annual total phosphorus loads and flow-weighted mean concentrations at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383716,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1145/ofr20201145_appendix_2.xlsx","text":"Appendix 2","size":"66.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1145 Appendix 2","linkHelpText":"— Estimated monthly total phosphorus loads at selected U.S. Geological Survey gage sites on Great Lakes tributaries"},{"id":383718,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WEW32M","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Model archive—Estimated total phosphorus loads for selected sites on Great Lakes tributaries, water years 2014–2018"}],"country":"United States","state":"Illinois, Indiana, Michigan, 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U.S. Geological Survey
6460 Busch Boulevard Ste 100
Columbus, OH 43229-1737
Simulating long-term, landscape level changes in forest composition requires estimates of stand age to initialize succession models. Detailed stand ages are rarely available, and even general information on stand history often is lacking. We used data from USDA Forest Service Forest Inventory and Analysis (FIA) database to estimate broad age classes for a forested landscape to simulate changes in landscape composition and structure relative to climate change at Fort Drum, a 43,000 ha U.S. Army installation in northwestern New York. Using simple linear regression, we developed relationships between tree diameter and age for FIA site trees from the host and adjacent ecoregions and applied those relationships to forest stands at Fort Drum. We observed that approximately half of the variation in age was explained by diameter breast height (DBH) across all species studied (r2 = 0.42 for sugar maple Acer saccharum to 0.63 for white ash Fraxinus americana). We then used age-diameter relationships from published research on northern hardwood species to calibrate results from the FIA-based analysis. With predicted stand age, we used tree species life histories and environmental conditions represented by ecological site types to parameterize a stochastic forest landscape model (LANDIS-II) to spatially and temporally model successional changes in forest communities at Fort Drum. Forest stands modeled over 100 years without significant disturbance appeared to reflect expected patterns of increasing dominance by shade-tolerant mesophytic tree species such as sugar maple, red maple (Acer rubrum), and eastern hemlock (Tsuga canadensis) where soil moisture was sufficient. On drier sandy soils, eastern white pine (Pinus strobus), red pine (P. resinosa), northern red oak (Quercus rubra), and white oak (Q. alba) continued to be important components throughout the modeling period with no net loss at the landscape scale. Our results suggest that despite abundant precipitation and relatively low evapotranspiration rates for the region, low soil water holding capacity and fertility may be limiting factors for the spread of mesophytic species on excessively drained soils in the region. Increasing atmospheric temperatures projected for the region could alter moisture regimes for many coarse-textured soils providing a possible mechanism for expansion of xerophytic tree species.
","language":"English","publisher":"Hindawi","doi":"10.1155/2021/6650821","usgsCitation":"Odom, R.H., and Ford, W., 2021, Developing species-age cohorts from forest inventory and analysis data to parameterize a forest landscape model: International Journal of Forestry Research, v. 2021, 6650821, 16 p., https://doi.org/10.1155/2021/6650821.","productDescription":"6650821, 16 p.","ipdsId":"IP-111053","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":453196,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1155/2021/6650821","text":"Publisher Index Page"},{"id":393572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fort Drum","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.0089111328125,\n 43.95921358836687\n ],\n [\n -75.509033203125,\n 43.95921358836687\n ],\n [\n -75.509033203125,\n 44.209772586984485\n ],\n [\n -76.0089111328125,\n 44.209772586984485\n ],\n [\n -76.0089111328125,\n 43.95921358836687\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2021","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Odom, Richard H.","contributorId":171659,"corporation":false,"usgs":false,"family":"Odom","given":"Richard","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":829633,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":829632,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70218590,"text":"ofr20201146 - 2021 - Practical field survey operations for flood insurance rate maps","interactions":[],"lastModifiedDate":"2021-03-05T12:41:18.272992","indexId":"ofr20201146","displayToPublicDate":"2021-03-04T08:00:00","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":"2020-1146","displayTitle":"Practical Field Survey Operations for Flood Insurance Rate Maps","title":"Practical field survey operations for flood insurance rate maps","docAbstract":"The U.S. Geological Survey assists the Federal Emergency Management Agency in its mission to identify flood hazards and zones for risk premiums for communities nationwide, by creating flood insurance rate maps through updating hydraulic models that use river geometry data. The data collected consist of elevations of river channels, banks, and structures, such as bridges, dams, and weirs that can affect flow. To account for the model complexity of river structure hydraulics and the fidelity between river channel and structure geometry, two distinct standards for collecting geometry data are presented, both using global navigation satellite system real-time network surveying. This method is adapted from U.S. Geological Survey manuals and is foundational in hydraulic surveying for flood insurance rate maps.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201146","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Taylor, N.J., and Simeone, C.E., 2021, Practical field survey operations for flood insurance rate maps: U.S. Geological Survey Open-File Report 2020–1146, 8 p., https://doi.org/10.3133/ofr20201146.","productDescription":"iv, 8 p.","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114316","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":383741,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/tm11D1","text":"Techniques and Methods 11-D1","linkHelpText":"- Methods of practice and guidelines for using survey-grade global navigation satellite systems (GNSS) to establish vertical datum in the United States Geological Survey"},{"id":383723,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1146/coverthb.jpg"},{"id":383724,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1146/ofr20201146.pdf","text":"Report","size":"662 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1146"},{"id":383725,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm11D3","text":"Techniques and Methods 11-D3","linkHelpText":"- Procedures and Best Practices for Trigonometric Leveling in the U.S. Geological Survey"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
We concur with Azizi‐Rad et al. (2021) that it is vital to critically evaluate and compare different soil carbon models, and we welcome the opportunity to further describe the unique contribution of the PROMISE model (Waring et al. 2020) to this literature. The PROMISE framework does share many features with established biogeochemical models, as our original manuscript highlighted in Table 1, and our work builds upon model innovations developed by many different groups, including that of Azizi‐Rad and colleagues. Yet, the PROMISE framework is distinctive due to where it places mechanistic emphasis, and how these mechanisms are formalized in the mathematical model structure.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.15580","usgsCitation":"Waring, B.G., Sulman, B.N., Reed, S., Smith, A.P., Averill, C., Creamer, C., Cusack, D.F., Hall, S.J., Jastrow, J.D., Jilling, A., Kemner, K.M., Kleber, M., Allen Liu, X., Pett-Ridge, J., and Schulz, M., 2021, Response to ‘Stochastic and deterministic interpretation of pool models’: Global Change Biology, v. 27, no. 11, p. e11-e12, https://doi.org/10.1111/gcb.15580.","productDescription":"2 p.","startPage":"e11","endPage":"e12","ipdsId":"IP-127051","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":453205,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.15580","text":"Publisher Index Page"},{"id":384273,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Waring, Bonnie G. 0000-0002-8457-5164","orcid":"https://orcid.org/0000-0002-8457-5164","contributorId":245284,"corporation":false,"usgs":false,"family":"Waring","given":"Bonnie","email":"","middleInitial":"G.","affiliations":[{"id":49130,"text":"Utah State University, Department of Biology and Ecology Center, Logan UT 84322","active":true,"usgs":false}],"preferred":false,"id":811560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sulman, Benjamin N. 0000-0002-3265-6691","orcid":"https://orcid.org/0000-0002-3265-6691","contributorId":209890,"corporation":false,"usgs":false,"family":"Sulman","given":"Benjamin","email":"","middleInitial":"N.","affiliations":[{"id":7108,"text":"Princeton Univ.","active":true,"usgs":false}],"preferred":false,"id":811561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":811562,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, A. Peyton","contributorId":245298,"corporation":false,"usgs":false,"family":"Smith","given":"A.","email":"","middleInitial":"Peyton","affiliations":[],"preferred":false,"id":811563,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Averill, Colin","contributorId":245299,"corporation":false,"usgs":false,"family":"Averill","given":"Colin","email":"","affiliations":[],"preferred":false,"id":811564,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Creamer, Courtney 0000-0001-8270-9387","orcid":"https://orcid.org/0000-0001-8270-9387","contributorId":201952,"corporation":false,"usgs":true,"family":"Creamer","given":"Courtney","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":811565,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cusack, Daniela F. 0000-0003-4681-7449","orcid":"https://orcid.org/0000-0003-4681-7449","contributorId":245300,"corporation":false,"usgs":false,"family":"Cusack","given":"Daniela","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":811566,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hall, Steven J. 0000-0002-7841-2019","orcid":"https://orcid.org/0000-0002-7841-2019","contributorId":244336,"corporation":false,"usgs":false,"family":"Hall","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":811567,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jastrow, Julie D.","contributorId":254970,"corporation":false,"usgs":false,"family":"Jastrow","given":"Julie","email":"","middleInitial":"D.","affiliations":[{"id":51371,"text":"Environmental Science Division, Argonne National Laboratory, Lemont IL 60439","active":true,"usgs":false}],"preferred":false,"id":811568,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jilling, Andrea","contributorId":254971,"corporation":false,"usgs":false,"family":"Jilling","given":"Andrea","email":"","affiliations":[{"id":51372,"text":"Department of Plant and Soil Sciences, Oklahoma State University, Stillwater OK 74078","active":true,"usgs":false}],"preferred":false,"id":811569,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kemner, Kenneth M.","contributorId":245301,"corporation":false,"usgs":false,"family":"Kemner","given":"Kenneth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":811570,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kleber, Markus","contributorId":254972,"corporation":false,"usgs":false,"family":"Kleber","given":"Markus","affiliations":[{"id":51374,"text":"Department of Crop and Soil Science, Oregon State University, Corvallis OR 97331","active":true,"usgs":false}],"preferred":false,"id":811571,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Allen Liu, Xiao-Jun","contributorId":254973,"corporation":false,"usgs":false,"family":"Allen Liu","given":"Xiao-Jun","affiliations":[{"id":51375,"text":"Department of Microbiology, University of Massachusetts, Amherst MA 01003","active":true,"usgs":false}],"preferred":false,"id":811572,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Pett-Ridge, Jennifer","contributorId":254974,"corporation":false,"usgs":false,"family":"Pett-Ridge","given":"Jennifer","affiliations":[{"id":51376,"text":"Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore CA 94551","active":true,"usgs":false}],"preferred":false,"id":811573,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schulz, Marjorie S. 0000-0001-5597-6447 mschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-6447","contributorId":3720,"corporation":false,"usgs":true,"family":"Schulz","given":"Marjorie S.","email":"mschulz@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":811574,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70219458,"text":"70219458 - 2021 - Characterization of groundwater recharge and flow in California's San Joaquin Valley from InSAR-observed surface deformation","interactions":[],"lastModifiedDate":"2021-04-08T12:47:24.059845","indexId":"70219458","displayToPublicDate":"2021-03-04T07:44:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of groundwater recharge and flow in California's San Joaquin Valley from InSAR-observed surface deformation","docAbstract":"Surface deformation in California's Central Valley (CV) has long been linked to changes in groundwater storage. Recent advances in remote sensing have enabled the mapping of CV deformation and associated changes in groundwater resources at increasingly higher spatiotemporal resolution. Here, we use interferometric synthetic aperture radar (InSAR) from the Sentinel‐1 missions, augmented by continuous Global Positioning System (cGPS) positioning, to characterize the surface deformation of the San Joaquin Valley (SJV, southern two‐thirds of the CV) for consecutive dry (2016) and wet (2017) water years. We separate trends and seasonal oscillations in deformation time series and interpret them in the context of surface and groundwater hydrology. We find that subsidence rates in 2016 (mean −42.0 mm/yr; peak −345 mm/yr) are twice that in 2017 (mean −20.4 mm/yr; peak −177 mm/yr), consistent with increased groundwater pumping in 2016 to offset the loss of surface‐water deliveries. Locations of greatest subsidence migrated outwards from the valley axis in the wetter 2017 water year, possibly reflecting a surplus of surface‐water supplies in the lowest portions of the SJV. Patterns in the amplitude of seasonal deformation and the timing of peak seasonal uplift reveal entry points and potential pathways for groundwater recharge into the SJV and subsequent groundwater flow within the aquifer. This study provides novel insight into the SJV aquifer system that can be used to constrain groundwater flow and subsidence models, which has relevance to groundwater management in the context of California's 2014 Sustainable Groundwater Management Act (SGMA).