A three-dimensional numerical model of groundwater flow was developed and calibrated for the unconsolidated New Jersey Coastal Plain aquifers underlying Joint Base McGuire-Dix-Lakehurst (JBMDL) and vicinity, New Jersey, to evaluate groundwater flow pathways of per- and polyfluoroalkyl substances (PFAS) contamination associated with use of aqueous film forming foam (AFFF) at the base. The regional subsurface flow model spans an area of approximately 518 square miles around JBMDL and is based on a previously developed hydrogeologic framework of the area. Steady-state flow in the unconsolidated aquifers was simulated using the MODFLOW 6 groundwater flow model, which is able to account for hydrostratigraphic pinchouts and discontinuities in the Coastal Plain aquifers underlying JBMDL. To account for local patterns of fluid flow driving advective subsurface migration of PFAS, the grid was refined using quadtree meshes spanning 21 areas where historical AFFF use was identified, five off-site reconnaissance areas identified by AFCEC as areas in which the occurrence of PFAS is most likely to pose a potential danger to local drinking water supplies, and along streams that behave as drains in the base-flow-dominated Coastal Plain.
Following grid refinement, four physical processes known to govern subsurface flow were introduced to the model. These included effective precipitation recharge, discharge to streams and stream-connected wetlands, regional inflows and outflows along the model bottom, and withdrawals from wells, each of which were incorporated into the model as either external or internal boundary conditions. To account for effective precipitation recharge, a specified-flow boundary was assigned along the top of the model. Similarly, regional flows predicted using the modified U.S Geological Survey’s New Jersey Coastal Plain Regional Aquifer System Analysis model were treated as specified-flow boundary conditions along the bottom of the model. Base-flow losses were treated as drains along streams delineated using a 10-foot LiDAR dataset. Drains were also assigned to cells falling within stream-connected National Hydrologic Database wetlands. Finally, well-pumpage data mined from the New Jersey Water Transfer database were added to the model to account for extraction of groundwater through pumping from industrial-supply and drinking-water-supply wells. Along model edges established at groundwater divides, where the net flux of water across the boundary is equal to zero, natural no-flow boundary conditions were imposed.
The refined flow model was calibrated using the parameter-estimation (PEST) program, which adjusts model parameters by performing a gradient search over the sum-of-squared-error objective function until the parameter set that produces simulated water levels and base flows most closely matches 544 water levels and 20 estimated base flows and closely adheres to initial parameter estimates. Based on the analysis of calibration residuals, the model did not appear to be affected by significant model structural error.
The MODPATH particle-tracking algorithm was used to estimate advective transport paths of PFAS in the vicinity of JBMDL. Forward tracking was used to determine paths of PFAS away from AFFF source areas to streams, wetlands, pumping wells, and geographic areas that PFAS may contaminate. Additionally, reverse tracking was used to determine particle pathlines away from off-site PFAS reconnaissance areas, or areas within which all sources of PFAS might be advectively transported into subsurface drinking-water supplies, to locations at land surface that may indicate a source of PFAS.
The coupled and calibrated groundwater flow and particle-tracking transport model provide valuable tools for predicting the relative extent of PFAS contamination from onsite legacy source areas. The calibrated model also provides measures of water-level and base-flow observation influence that can help guide future data-collection efforts related to groundwater and surface water sampling for PFAS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221112","collaboration":"Prepared in cooperation with the U.S. Air Force","usgsCitation":"Fiore, A.R., and Colarullo, S.J., 2023, Simulation of regional groundwater flow and advective transport of per- and polyfluoroalkyl substances, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018: U.S. Geological Survey Open-File Report 2022–1112, 41 p., 2 pls., https://doi.org/10.3133/ofr20221112.","productDescription":"Report: ix, 41 p.; 2 Plates: 35.00 x 45.00 inches and 45.00 x 30.00 inches; Data Release","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-129806","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":412124,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EK4CZS","text":"USGS data release","linkHelpText":"MODFLOW6 and MODPATH7 used to simulate regional groundwater flow and advective transport of per- and polyfluoroalkyl substances, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":412125,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112.XML"},{"id":412123,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221112/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1112"},{"id":412121,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1112/coverthb.jpg"},{"id":412126,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1112/images/"},{"id":412129,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112_plate1.pdf","text":"Plate 1","size":"212 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Forward particle tracks from aqueous film-forming foam source areas 1 to 15 and reverse particle tracks from per- and polyfluoroalkyl substances reconnaissance areas 4 and 14, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":412122,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112.pdf","text":"Report","size":"7.96 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1112"},{"id":412130,"rank":8,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2022/1112/ofr20221112_plate2.pdf","text":"Plate 2","size":"200 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Forward particle tracks from aqueous film-forming foam source areas 16 to 21 and reverse particle tracks from per- and polyfluoroalkyl substances reconnaissance areas 16 to 19, Joint Base McGuire-Dix-Lakehurst and vicinity, New Jersey, 2018"},{"id":499723,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114286.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.77016941849112,\n 40.156458843115274\n ],\n [\n -74.77016941849112,\n 39.93505011875061\n ],\n [\n -74.17559168378837,\n 39.93505011875061\n ],\n [\n -74.17559168378837,\n 40.156458843115274\n ],\n [\n -74.77016941849112,\n 40.156458843115274\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project quantifies the percentage cover of rangeland components across the western United States using Landsat imagery from 1985 to 2021. The RCMAP product suite consists of nine fractional components: annual herbaceous, bare ground, herbaceous, litter, nonsagebrush shrub, perennial herbaceous, sagebrush, shrub, and tree, in addition to the temporal trends of each component. Several enhancements were made to the RCMAP process relative to prior generations. First, we have trained time-series predictions directly from 331 high-resolution sites collected from 2013 to 2018 and additional field data; for example, Bureau of Land Management Assessment, Inventory, and Monitoring instead of using the 2016 “base” map as an intermediary. This removes one level of model error and allows the direct association of high-resolution derived training data to the corresponding year of Landsat imagery. Neural network models have replaced Cubist models as our classifier. Continuous Change Detection and Classification synthetic Landsat images were obtained for six monthly periods for each region and were added as predictors. These data enhance the phenologic detail of imagery, improving discrimination among components. Postprocessing has been improved with updated fire recovery equations stratified by ecosystem resistance and resilience classes. Additionally, postprocessing has been enhanced through a revised noise detection model, based on third order polynomial models for each component and each pixel. These data can be used to answer critical questions regarding the effect of climate change and the suitability of management practices. Component products can be downloaded from the Multi-Resolution Land Characteristics Consortium website at https://www.mrlc.gov/data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/fs20233004","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Rigge, M.B., 2023, Rangeland Condition Monitoring Assessment and Projection, 1985–2021: U.S. Geological Survey Fact Sheet 2023–3004, 6 p., https://doi.org/10.3133/fs20233004.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-148071","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499564,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114284.htm","linkFileType":{"id":5,"text":"html"}},{"id":412363,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233004/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":412361,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3004/images"},{"id":412360,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3004/fs20233004.XML"},{"id":412356,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3004/coverthb.jpg"},{"id":412359,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3004/fs20233004.pdf","text":"Report","size":"2.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023~3004"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -102.89909633393279,\n 29.0682826215172\n ],\n [\n -102.29956963626972,\n 29.8090966774663\n ],\n [\n -100.87258967244983,\n 29.458733387985006\n ],\n [\n -100.13050111858448,\n 28.240769760178893\n ],\n [\n -100.11959574261397,\n 30.292475426842415\n ],\n [\n -101.92842251021852,\n 31.470932982781036\n ],\n [\n -103.36510762980765,\n 32.48047296916185\n ],\n [\n -100.01803379637153,\n 36.1490156792113\n ],\n [\n -99.27913819165613,\n 37.572777501564005\n ],\n [\n -103.20469410438153,\n 39.32750900585228\n ],\n [\n -104.0623305315396,\n 39.89802382204118\n ],\n [\n -101.43686566661609,\n 42.267519647308234\n ],\n [\n -100.61853110579801,\n 49.04539216233715\n ],\n [\n -123.36052981830295,\n 48.977345645871026\n ],\n [\n -123.7205891173356,\n 48.25142184919315\n ],\n [\n -124.9072340092169,\n 48.68713021246529\n ],\n [\n -124.91684175179148,\n 42.98584722268464\n ],\n [\n -124.79221124944036,\n 40.3836585578014\n ],\n [\n -122.5469831787114,\n 35.7301435211089\n ],\n [\n -120.64609875158641,\n 34.26400897631564\n ],\n [\n -117.38696400421276,\n 32.43463720185768\n ],\n [\n -114.95825417619707,\n 32.70325877979326\n ],\n [\n -110.57738226870978,\n 31.261610018918134\n ],\n [\n -108.12199560515279,\n 31.139851729510895\n ],\n [\n -108.13958017851652,\n 31.827219447629645\n ],\n [\n -106.284256297648,\n 31.694405166866275\n ],\n [\n -104.3199988686059,\n 29.50364197108891\n ],\n [\n -103.16152984843166,\n 28.840396342272896\n ],\n [\n -102.89909633393279,\n 29.0682826215172\n ],\n [\n -102.89909633393279,\n 29.0682826215172\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Keys to Clay-colored Sparrow (Spizella pallida) management include providing grasslands with a shrub or forb component or shrub-dominated edge habitat, which includes dense grass and moderately high litter cover, and avoiding disturbances that completely eliminate woody vegetation. Clay-colored Sparrows have been reported to use habitats with 20–186 centimeters (cm) average vegetation height, 3–50 cm visual obstruction reading, 15–74 percent grass cover, 5–23 percent forb cover, less than 30 percent shrub cover, 1–20 percent bare ground, 10–63 percent litter cover, and less than or equal to 5 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/pp1842Z","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Nenneman, M.P., and Euliss, B.R., 2023, The effects of management practices on grassland birds—Clay-colored Sparrow (Spizella pallida), chap. Z of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 27 p., https://doi.org/10.3133/pp1842Z.","productDescription":"v, 27 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097128","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":412290,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/z/coverthb.jpg"},{"id":412291,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/z/pp1842z.pdf","text":"Report","size":"2.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842Z"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Probabilistic predictions support public health planning and decision making, especially in infectious disease emergencies. Aggregating outputs from multiple models yields more robust predictions of outcomes and associated uncertainty. While the selection of an aggregation method can be guided by retrospective performance evaluations, this is not always possible. For example, if predictions are conditional on assumptions about how the future will unfold (e.g. possible interventions), these assumptions may never materialize, precluding any direct comparison between predictions and observations. Here, we summarize literature on aggregating probabilistic predictions, illustrate various methods for infectious disease predictions via simulation, and present a strategy for choosing an aggregation method when empirical validation cannot be used. We focus on the linear opinion pool (LOP) and Vincent average, common methods that make different assumptions about between-prediction uncertainty. We contend that assumptions of the aggregation method should align with a hypothesis about how uncertainty is expressed within and between predictions from different sources. The LOP assumes that between-prediction uncertainty is meaningful and should be retained, while the Vincent average assumes that between-prediction uncertainty is akin to sampling error and should not be preserved. We provide an R package for implementation. Given the rising importance of multi-model infectious disease hubs, our work provides useful guidance on aggregation and a deeper understanding of the benefits and risks of different approaches.
The use of moving boat ADCPs (Acoustic Doppler Current Profilers) for discharge measurements requires identification of the sources and magnitude of uncertainty to ensure accurate measurements. Recently, a tool known as QUant was developed to estimate the contribution to the uncertainty estimates for each transect of moving-boat ADCP discharge measurements, by varying different sampling configurations parameters through the use of Monte Carlo simulations. QUant is not only useful for estimating ADCP discharge measurement uncertainty, but also for identifying contributions of the various sources of uncertainty.
However, the software requires long computational times, and the method to estimate the uncertainty of multiple-transect measurements does not consider the correlation of the variables between transects. Therefore, improvements in QUant are needed to optimize its application for practical purposes by hydrographers immediately after discharge measurements.
This work presents four approaches for optimizing the performance of QUant to estimate the contribution to the uncertainty of different selected variables on ADCP discharge measurements and describes a new method of estimating multi-transect uncertainty with the QUant model that considers the correlation of errors in selected variables between transects. The approaches for optimization and the new multi-transect uncertainty method are evaluated using a dataset of 38 field measurements from a variety of riverine settings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.flowmeasinst.2023.102311","usgsCitation":"Diaz Lozada, J.M., Garcia, C.M., Oberg, K., Over, T.M., and Flores Nieto, F., 2023, Improvements to estimate ADCP uncertainty sources for discharge measurements: Flow Measurement and Instrumentation, v. 90, 102311, 12 p., https://doi.org/10.1016/j.flowmeasinst.2023.102311.","productDescription":"102311, 12 p.","ipdsId":"IP-122869","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":419175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"90","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Diaz Lozada, Jose M. 0000-0002-6735-0916","orcid":"https://orcid.org/0000-0002-6735-0916","contributorId":287571,"corporation":false,"usgs":false,"family":"Diaz Lozada","given":"Jose","email":"","middleInitial":"M.","affiliations":[{"id":61615,"text":"Institute for Advanced Studies for Engineering and Technology (IDIT CONICET/UNC) – FCEFyN, National University of Córdoba","active":true,"usgs":false}],"preferred":false,"id":878354,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Carlos M. 0000-0002-4091-6756","orcid":"https://orcid.org/0000-0002-4091-6756","contributorId":287572,"corporation":false,"usgs":false,"family":"Garcia","given":"Carlos","email":"","middleInitial":"M.","affiliations":[{"id":61615,"text":"Institute for Advanced Studies for Engineering and Technology (IDIT CONICET/UNC) – FCEFyN, National University of Córdoba","active":true,"usgs":false}],"preferred":false,"id":878355,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oberg, Kevin 0000-0002-7024-3361 kaoberg@usgs.gov","orcid":"https://orcid.org/0000-0002-7024-3361","contributorId":175229,"corporation":false,"usgs":true,"family":"Oberg","given":"Kevin","email":"kaoberg@usgs.gov","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":878356,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Over, Thomas M. 0000-0001-8280-4368","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":204650,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":878357,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Flores Nieto, Federico","contributorId":316794,"corporation":false,"usgs":false,"family":"Flores Nieto","given":"Federico","email":"","affiliations":[{"id":68697,"text":"Universidad Nacional de Córdoba, Argentina","active":true,"usgs":false}],"preferred":false,"id":878358,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70241897,"text":"70241897 - 2023 - Stochastic watershed model ensembles for long-range planning: Verification and validation","interactions":[],"lastModifiedDate":"2023-03-30T11:38:33.672469","indexId":"70241897","displayToPublicDate":"2023-01-24T06:35:30","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11438,"text":"Water Resource Research","active":true,"publicationSubtype":{"id":10}},"title":"Stochastic watershed model ensembles for long-range planning: Verification and validation","docAbstract":"Deterministic watershed models (DWMs) are used in nearly all hydrologic planning, design, and management activities, yet they cannot generate streamflow ensembles needed for hydrologic risk management (HRM). The stochastic component of DWMs is often ignored in practice, leading to a systematic bias in extreme events. Since traditional stochastic streamflow models used in HRM struggle to account for anthropogenic change, there is a need to convert DWMs into stochastic watershed models (SWMs) to generate ensembles for use in HRM. A DWM can be converted to an SWM using a post-processing (pp) approach to add error to the DWM predictions. Many pp methods advanced in the area of flood forecasting are useful in HRM and for correcting extreme event biases. Selecting a suitable error model for pp is challenging due to nonnormality, skewness, heteroscedasticity, and autocorrelation. We develop a parsimonious pp method based on an autoregressive (AR) model of the logarithm of the ratio of the observations and simulations, which leads to AR model residuals that are approximately symmetric and independent. We document the value of pp for improving flood and low flow frequency analysis and we reintroduce the concepts of verification and validation of stochastic streamflow ensembles to ensure that the SWM can reproduce both statistics it was and was not designed to reproduce, respectively. These concepts are illustrated on a Massachusetts basin using the USGS Precipitation Runoff Modeling System, with an additional analysis indicating the approach may be applicable to 1,225 other sites across the United States.
Wastewater injection has induced earthquakes in Northeastern Colorado since 2014. We apply ambient noise correlation techniques to determine temporal changes in seismic velocities in the region. We find no clear correlation between seismic velocity fluctuations and either injection volumes or seismicity patterns. We do observe apparent annual variations in velocity that may be associated with hydrologic loading or thermoelastic strain. In addition, we model uniform and vertically localized velocity perturbations, and measure the velocity change with 1D synthetic seismograms. Our results indicate that our methods underestimate the known velocity change, especially at shorter station distances and when variations are restricted to a horizontal layer. If injection does cause measurable velocity changes, its effect is likely diluted in cross correlations due to its localized spatial extent around injection wells.
Geomagnetically induced currents (GICs) result from the interaction of the time variation of ground magnetic field during a geomagnetic disturbance with the Earth's deep electrical resistivity structure. In this study, we simulate induced GICs in a hypothetical representation of a low-latitude power transmission network located mainly over the large Paleozoic Paraná basin (PB) in southern Brazil. Two intense geomagnetic storms in June and December 2015 are chosen and geoelectric fields are calculated by convolving a three-dimensional (3-D) Earth resistivity model with recorded geomagnetic variations. The dB/dt proxy often used to characterize GIC activity fails during the June storm mainly due to the relationship of the instantaneous geoelectric field to previous magnetic field values. Precise resistances of network components are unknown, so assumptions are made for calculating GIC flows from the derived geoelectric field. The largest GICs are modeled in regions of low conductance in the 3-D resistivity model, concentrated in an isolated substation at the northern edge of the network and in a cluster of substations in its central part where the east-west (E-W) oriented transmission lines coincide with the orientation of the instantaneous geoelectric field. The maximum magnitude of the modeled GIC was obtained during the main phase of the June storm, modeled at a northern substation, while the lowest magnitudes were found over prominent crustal anomalies along the PB axis and bordering the continental margin. The simulation results will be used to prospect the optimal substations for installation of GIC monitoring equipment.
Phragmites australis (common reed) has a cosmopolitan distribution and has been suggested as a model organism for the study of invasive plant species. In North America, the non-native subspecies (ssp. australis) is widely distributed across the contiguous 48 states in the United States and large parts of Canada. Even though millions of dollars are spent annually on Phragmites management, insufficient knowledge of P. australis impeded the efficiency of management. To solve this problem, transcriptomic information generated from multiple types of tissue could be a valuable resource for future studies. Here, we constructed forty-nine P. australis transcriptomes assemblies via different assembly tools and multiple parameter settings. The optimal transcriptome assembly for functional annotation and downstream analyses was selected among these transcriptome assemblies by comprehensive assessments. For a total of 422,589 transcripts assembled in this transcriptome assembly, 319,046 transcripts (75.5%) have at least one functional annotation. Within the transcriptome assembly, we further identified 1,495 transcripts showing tissue-specific expression pattern, 10,828 putative transcription factors, and 72,165 candidates for simple sequence repeats markers. The identification and analyses of predicted transcripts related to herbicide- and salinity-resistant genes were shown as two applications of the transcriptomic information to facilitate further research on P. australis. Transcriptome assembly and selection would be important for the transcriptome annotation. With this optimal transcriptome assembly and all relative information from downstream analyses, we have helped to establish foundations for future studies on the mechanisms underlying the invasiveness of non-native P. australis subspecies.
Cycles of stress build-up and release are inherent to tectonically active planets. Such stress oscillations impart strain and damage, prompting mechanically loaded rocks and materials to fail. Here, we investigate, under uniaxial conditions, damage accumulation and weakening caused by time-dependent creep (at 60, 65, and 70% of the rocks’ expected failure stress) and repeating stress oscillations (of ± 2.5, 5.0 or 7.5% of the creep load), simulating earthquakes at a shaking frequency of ~ 1.3 Hz in volcanic rocks. The results show that stress oscillations impart more damage than constant loads, occasionally prompting sample failure. The magnitudes of the creep stresses and stress oscillations correlate with the mechanical responses of our porphyritic andesites, implicating progressive microcracking as the cause of permanent inelastic strain. Microstructural investigation reveals longer fractures and higher fracture density in the post-experimental rock. We deconvolve the inelastic strain signal caused by creep deformation to quantify the amount of damage imparted by each individual oscillation event, showing that the magnitude of strain is generally largest with the first few oscillations; in instances where pre-existing damage and/or the oscillations’ amplitude favour the coalescence of micro-cracks towards system scale failure, the strain signal recorded shows a sharp increase as the number of oscillations increases, regardless of the creep condition. We conclude that repetitive stress oscillations during earthquakes can amplify the amount of damage in otherwise mechanically loaded materials, thus accentuating their weakening, a process that may affect natural or engineered structures. We specifically discuss volcanic scenarios without wholesale failure, where stress oscillations may generate damage, which could, for example, alter pore fluid pathways, modify stress distribution and affect future vulnerability to rupture and associated hazards.
Redside Shiner Richardsonius balteatus has expanded from its native range in the Pacific Northwest region of North America to establish populations in six other western states. This expansion has fueled concerns regarding competition between Redside Shiner and native species, including salmonids. We developed a bioenergetic model for Redside Shiner, providing a powerful tool to quantify its trophic role in invaded ecosystems and evaluate potential impacts on native species.
Mass- and temperature-dependent functions for consumption and respiration were fit based on controlled laboratory experiments of maximum consumption rates and routine metabolic rates using intermittent-flow respirometry, across a range of fish sizes (0.6–27.3 g) and temperatures (5–31°C). Laboratory growth experiments were conducted to corroborate model performance across different temperatures and feeding rates.
Initial bioenergetic simulations of long-term growth experiments indicated large model error for predicted consumption and growth, and deviations from observed responses varied systematically as a function of daily consumption rate (J·g−1·d−1) and water temperature. A growth rate error correction function was developed and included in the bioenergetics model framework on a daily time step, resulting in decreased absolute model error in all experimental groups. Predicted values from the corrected model were highly correlated with observed values (�2; consumption = 0.97, final weight = 0.99) and unbiased. These results show that the optimal temperature for Redside Shiner growth (18°C) exceeds that of Pacific salmon Oncorhynchus spp. by 2–6°C under a scenario of high food availability and moderate food quality.
Consequently, increases in water temperature associated with climate change may favor growth and expansion of Redside Shiner populations, while negatively affecting some salmonids. The bioenergetics model presented here provides the necessary first step in quantifying trophic impacts in sensitive ecosystems where Redside Shiner have invaded or in ecosystems where anadromous salmonid reintroductions are being considered.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10392","usgsCitation":"Johnson, R.C., Beauchamp, D., and Olden, J., 2023, Bioenergetics model for the nonnative Redside Shiner: Transactions of the American Fisheries Society, v. 152, no. 1, p. 94-113, https://doi.org/10.1002/tafs.10392.","productDescription":"20 p.","startPage":"94","endPage":"113","ipdsId":"IP-140159","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":444746,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10392","text":"Publisher Index Page"},{"id":435494,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NAIACL","text":"USGS data release","linkHelpText":"Data used to parameterize and evaluate a bioenergetics model for Redside Shiner (Richardsonius balteatus)"},{"id":412271,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"152","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-01-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Rachelle Carina 0000-0003-1480-4088","orcid":"https://orcid.org/0000-0003-1480-4088","contributorId":241962,"corporation":false,"usgs":true,"family":"Johnson","given":"Rachelle","email":"","middleInitial":"Carina","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":862274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beauchamp, David 0000-0002-3592-8381","orcid":"https://orcid.org/0000-0002-3592-8381","contributorId":217816,"corporation":false,"usgs":true,"family":"Beauchamp","given":"David","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":862275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Olden, Julian D.","contributorId":202893,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":862276,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70266472,"text":"70266472 - 2023 - Habitat selection of a migratory freshwater fish in response to seasonal hypoxia as revealed by acoustic telemetry","interactions":[],"lastModifiedDate":"2025-05-07T18:10:32.226189","indexId":"70266472","displayToPublicDate":"2023-01-21T00:00:00","publicationYear":"2023","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":"Habitat selection of a migratory freshwater fish in response to seasonal hypoxia as revealed by acoustic telemetry","docAbstract":"Adaptive efforts to achieve water quality objectives by modifying nutrient loading can have attendant impacts on fish habitats and fisheries. Thus, coordinating fishery and water quality management depends on knowledge of fish behavioral responses to habitat change. This study combined acoustic telemetry of fish with water quality modeling to understand how water quality management might impact fishery management. We examined habitat use of a native demersal fish, lake whitefish Coregonus clupeaformis, in Lake Erie. We focused on the summer stratified period when habitat was expected to be most limiting and used a forecast model to predict temperature and oxygen in the hypolimnion when fish were detected. As hypothesized, lake whitefish occupied a subset of available conditions with occupied habitats characterized by a cool, normoxic, hypolimnion. On some occasions fish were detected when the hypolimnion was predicted to be hypoxic, suggesting that fish were either displaced vertically or horizontally into marginal habitats or uncertainty in model predictions was high. Still, when hypolimnetic conditions were hypoxic, fish tended to move toward normoxia as expected, but when initial conditions were cold with high dissolved oxygen, fish movements were toward lower oxygen (but still normoxic) conditions. We also observed a high affinity for fish to remain near the southern shore in eastern Ohio, Pennsylvania, and New York. If current nutrient reduction objectives are achieved and the extent and severity of hypoxia is reduced, an expansion of lake whitefish habitat and distribution may have significance to the spatial regulation of fishing effort in Lake Erie.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2023.01.004","usgsCitation":"Kraus, R., Cook, H., Faust, M., Schmitt, J., Rowe, M., and Vandergoot, C., 2023, Habitat selection of a migratory freshwater fish in response to seasonal hypoxia as revealed by acoustic telemetry: Journal of Great Lakes Research, v. 49, no. 5, p. 1004-1014, https://doi.org/10.1016/j.jglr.2023.01.004.","productDescription":"11 p.","startPage":"1004","endPage":"1014","ipdsId":"IP-144704","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":485514,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, New York, Ohio, Pennsylvania","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.45746247368997,\n 42.19332204270543\n ],\n [\n -83.58088952321158,\n 41.37751990998936\n ],\n [\n -81.36793483137687,\n 41.36610477953545\n ],\n [\n -79.12723694634781,\n 42.41574902379864\n ],\n [\n -78.7500162167698,\n 43.007471194229566\n ],\n [\n -81.13424345938826,\n 42.76252432461877\n ],\n [\n -82.2940150129951,\n 42.35073159163453\n ],\n [\n -83.45746247368997,\n 42.19332204270543\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kraus, Richard 0000-0003-4494-1841","orcid":"https://orcid.org/0000-0003-4494-1841","contributorId":216548,"corporation":false,"usgs":true,"family":"Kraus","given":"Richard","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cook, H. Andrew","contributorId":354648,"corporation":false,"usgs":false,"family":"Cook","given":"H. Andrew","affiliations":[{"id":65742,"text":"Ontario Ministry of Northern Development, Mines, Natural Resources and Forestry","active":true,"usgs":false}],"preferred":false,"id":936070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Faust, Matthew D.","contributorId":354649,"corporation":false,"usgs":false,"family":"Faust","given":"Matthew D.","affiliations":[{"id":16232,"text":"Ohio Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":936071,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmitt, Joseph 0000-0002-8354-4067","orcid":"https://orcid.org/0000-0002-8354-4067","contributorId":221020,"corporation":false,"usgs":true,"family":"Schmitt","given":"Joseph","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936072,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rowe, Mark D.","contributorId":354650,"corporation":false,"usgs":false,"family":"Rowe","given":"Mark D.","affiliations":[{"id":34438,"text":"NOAA-GLERL","active":true,"usgs":false}],"preferred":false,"id":936073,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vandergoot, Christopher S.","contributorId":354651,"corporation":false,"usgs":false,"family":"Vandergoot","given":"Christopher S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936074,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240774,"text":"70240774 - 2023 - A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments","interactions":[],"lastModifiedDate":"2023-02-22T13:23:47.409683","indexId":"70240774","displayToPublicDate":"2023-01-20T07:21:18","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments","docAbstract":"The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe “Coast Train,” a multi-labeler dataset of orthomosaic and satellite images of coastal environments and corresponding labels. These data include imagery that are diverse in space and time, and contain 1.2 billion labeled pixels, representing over 3.6 million hectares. We use a human-in-the-loop tool especially designed for rapid and reproducible Earth surface image segmentation. Our approach permits image labeling by multiple labelers, in turn enabling quantification of pixel-level agreement over individual and collections of images.
Concurrent, distribution-wide abundance declines of some Pacific salmon species, including Chinook salmon (Oncorhynchus tshawytscha), highlights the need to understand how vulnerability at different life stages to climate stressors affects population dynamics and fisheries sustainability. Yukon River Chinook salmon stocks are among the largest subarctic populations, near the northernmost extent of the species range. Existing research suggests that Yukon River Chinook salmon population dynamics are largely driven by factors occurring between the adult spawner life stage and their offspring's first summer at sea (second year post-hatching). However, specific mechanisms sustaining chronic poor productivity are unknown, and there is a tremendous sense of urgency to understand causes, as declines of these stocks have taken a serious toll on commercial, recreational, and indigenous subsistence fisheries. Therefore, we leveraged multiple existing datasets spanning parent and juvenile stages of life history in freshwater and marine habitats. We analyzed environmental data in association with the production of offspring that survive to the marine juvenile stage (juveniles per spawner). These analyses suggest more than 45% of the variability in the production of juvenile Chinook salmon is associated with river temperatures or water discharge levels during the parent spawning migration. Over the past two decades, parents that experienced warmer water temperatures and lower discharge in the mainstem Yukon River produced fewer juveniles per spawning adult. We propose the adult spawner life stage as a critical period regulating population dynamics. We also propose a conceptual model that can explain associations between population dynamics and climate stressors using independent data focused on marine nutrition and freshwater heat stress. It is sobering to consider that some of the northernmost Pacific salmon habitats may already be unfavorable to these cold-water species. Our findings have immediate implications, given the common assumption that northern ranges of Pacific salmon offer refugia from climate stressors.
Migratory birds employ a variety of mechanisms to ensure appropriate timing of migration based on integration of endogenous and exogenous information. The cues to fatten and depart from the non-breeding area are often linked to exogenous cues such as temperature or precipitation and the endogenous program. Shorter distance migrants should rely heavily on environmental information when initiating migration given relatively close proximity to the breeding area. However, the ability to fatten and subsequently depart may be linked to individual circumstances, including current fuel load and body size. For early and late departing migrants, we investigate effects of temperature, precipitation, lean body mass, fuel load and day of year on the initiation of migration (i.e. fuel load and departure timing) from the non-breeding region by analyzing 21 years of banding data for four species of short- and medium-distance migrants. Temperatures at the non-breeding area were related to temperatures at potential stopover areas. Despite local cues being predictive of conditions further north, the amount variation explained by local weather conditions in our models differed by species and temporal period but was low overall (< 33% variation explained). For each species, we also compared lean body mass and fuel load between early and late departing migrants, which showed mixed results. Our combined results suggest that most individuals migrating short or medium distances in our study did not time the initiation of migration with local predictive cues alone, but rather other factors such as lean body mass, fuel load, day of year, which may be a proxy for the endogenous program, and those beyond the scope of our study also influenced the initiation of migration. Our study contributes to understanding which factors influence departure decisions of short- and medium-distance migrants as they transition from the non-breeding to the migratory phase of the annual cycle.
Debris flow runout poses a hazard to life and infrastructure. The expansion of human population into mountainous areas and onto alluvial fans increases the need to predict and mitigate debris flow runout hazards. Debris flows on unconfined alluvial fans can exhibit spontaneous self-channelization through levee formation that reduces lateral spreading and extends runout distances compared to unchannelized flows. Here we modify the D-Claw shallow flow model in two ways that are hypothesized to generate levees. We evaluate these modifications with observations from a large-scale flume experiment. We investigate model performance when including the effect of two different friction sub-models, as well as the inclusion of segregation effects on granular permeability. Results show that, for a wide range of plausible model input parameters, simulations including the effects of segregation promoted modeled levee formation, whereas simulations without the effects of segregation did not create levees. Further, using a forward predictive framework, simulations with the effects of segregation were more likely to better model the magnitude of debris flow depth and runout distance, whereas simulation timing of the debris flow was affected by the choice of friction sub-model. Our results indicate that including the effects of segregation on granular permeability can improve the likelihood of better predictions of debris flow depth and runout prior to an event occurring.
Watch lists of invasive species that threaten a particular land management unit are useful tools because they can draw attention to invasive species at the very early stages of invasion when early detection and rapid response efforts are often most successful. However, watch lists typically rely on the subjective selection of invasive species by experts or on the use of spotty occurrence records. Further, incomplete records of invasive plant occurrences bias these watch lists towards the inclusion of invasive plant species that may already be present in a land management unit, because the occurrences have not been formally integrated into publicly accessible biodiversity databases. However, these problems may be overcome by an iterative approach that guides more complete detection and compilation of invasive plant species records within land management units. To address issues from unobserved or unrecorded occurrences, we combined predicted suitable habitat from species distribution models and aggregated invasive plant occurrence records to develop ranked watch lists of 146 priority invasive plant species on >4000 land management units from five different administrative types within the United States. Based on this analysis, we determined that on average 84% of priority invasive plants with suitable habitat within a given land management unit were as yet unobserved, and that 41% of those were ‘doorstep species’ – found within 50 miles of the unit boundary yet not detected within the unit. Two case studies, developed in collaboration with staff at U.S. Fish and Wildlife Service Refuges, showed that by combining both habitat suitability models and invasive plant occurrence records, we could identify additional problematic invasive plants that had been previously overlooked. Model-based watch lists of ‘doorstep species’ are useful tools because they can objectively alert land managers to threats from invasive plants with high likelihood of establishment.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecoinf.2023.101997","usgsCitation":"Jarnevich, C.S., Engelstad, P., LaRoe, J., Hays, B., Hogan, T., Jirak, J., Pearse, I.S., Prevey, J.S., Sieraki, J., Simpson, A., Wenick, J., Young, N., and Sofaer, H., 2023, Invaders at the doorstep: Using species distribution modeling to enhance invasive plant watch lists: Ecological Informatics, v. 75, 101997, 8 p., https://doi.org/10.1016/j.ecoinf.2023.101997.","productDescription":"101997, 8 p.","ipdsId":"IP-145316","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":412734,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"contiguous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"75","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jarnevich, Catherine S. 0000-0002-9699-2336 jarnevichc@usgs.gov","orcid":"https://orcid.org/0000-0002-9699-2336","contributorId":3424,"corporation":false,"usgs":true,"family":"Jarnevich","given":"Catherine","email":"jarnevichc@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":863466,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engelstad, Peder","contributorId":238758,"corporation":false,"usgs":false,"family":"Engelstad","given":"Peder","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":863467,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LaRoe, Jillian 0000-0002-1429-9811","orcid":"https://orcid.org/0000-0002-1429-9811","contributorId":299950,"corporation":false,"usgs":false,"family":"LaRoe","given":"Jillian","affiliations":[{"id":64987,"text":"Student contractor to USGS Fort Collins Science Center","active":true,"usgs":false}],"preferred":false,"id":863468,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hays, Brandon 0000-0001-9499-3717","orcid":"https://orcid.org/0000-0001-9499-3717","contributorId":302088,"corporation":false,"usgs":false,"family":"Hays","given":"Brandon","email":"","affiliations":[{"id":64897,"text":"Student Contractor to the USGS Fort Collins Science Center","active":true,"usgs":false}],"preferred":false,"id":863469,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hogan, Terri","contributorId":240929,"corporation":false,"usgs":false,"family":"Hogan","given":"Terri","email":"","affiliations":[{"id":48162,"text":"National Park Service, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":863470,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jirak, Jeremy","contributorId":302095,"corporation":false,"usgs":false,"family":"Jirak","given":"Jeremy","email":"","affiliations":[{"id":27594,"text":"Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":863471,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pearse, Ian S. 0000-0001-7098-0495","orcid":"https://orcid.org/0000-0001-7098-0495","contributorId":216680,"corporation":false,"usgs":true,"family":"Pearse","given":"Ian","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":863472,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Prevey, Janet S. 0000-0003-2879-6453","orcid":"https://orcid.org/0000-0003-2879-6453","contributorId":222702,"corporation":false,"usgs":true,"family":"Prevey","given":"Janet","email":"","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":863473,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sieraki, Jennifer","contributorId":302096,"corporation":false,"usgs":false,"family":"Sieraki","given":"Jennifer","email":"","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":863474,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Simpson, Annie 0000-0001-8338-5134","orcid":"https://orcid.org/0000-0001-8338-5134","contributorId":206062,"corporation":false,"usgs":true,"family":"Simpson","given":"Annie","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":863475,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wenick, Jess","contributorId":302097,"corporation":false,"usgs":false,"family":"Wenick","given":"Jess","email":"","affiliations":[{"id":27594,"text":"Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":863476,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Young, Nicholas","contributorId":279993,"corporation":false,"usgs":false,"family":"Young","given":"Nicholas","affiliations":[{"id":7230,"text":"Natural Resource Ecology Laboratory, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":863477,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sofaer, Helen 0000-0002-9450-5223","orcid":"https://orcid.org/0000-0002-9450-5223","contributorId":216681,"corporation":false,"usgs":true,"family":"Sofaer","given":"Helen","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":863478,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70240151,"text":"70240151 - 2023 - A model of transmissivity and hydraulic conductivity from electrical resistivity distribution derived from airborne electromagnetic surveys of the Mississippi River Valley Alluvial Aquifer, Midwest USA","interactions":[],"lastModifiedDate":"2023-03-31T15:16:16.810474","indexId":"70240151","displayToPublicDate":"2023-01-19T06:50:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"A model of transmissivity and hydraulic conductivity from electrical resistivity distribution derived from airborne electromagnetic surveys of the Mississippi River Valley Alluvial Aquifer, Midwest USA","docAbstract":"Groundwater-flow models require the spatial distribution of the hydraulic conductivity parameter. One approach to defining this spatial distribution in groundwater-flow model grids is to map the electrical resistivity distribution by airborne electromagnetic (AEM) survey and establish a petrophysical relation between mean resistivity calculated as a nonlinear function of the resistivity layering and thicknesses of the layers and aquifer transmissivity compiled from historical aquifer tests completed within the AEM survey area. The petrophysical relation is used to transform AEM resistivity to transmissivity and to hydraulic conductivity over areas where the saturated thickness of the aquifer is known. The US Geological Survey applied this approach to a gain better understanding of the aquifer properties of the Mississippi River Valley alluvial aquifer. Alluvial-aquifer transmissivity data, compiled from 160 historical aquifer tests in the Mississippi Alluvial Plain (MAP), were correlated to mean resistivity calculated from 16,816 line-kilometers (km) of inverted resistivity soundings produced from a frequency-domain AEM survey of 95,000 km2 of the MAP. Correlated data were used to define petrophysical relations between transmissivity and mean resistivity by omitting from the correlations the aquifer-test and AEM sounding data that were separated by distances greater than 1 km and manually calibrating the relation coefficients to slug-test data. The petrophysical relation yielding the minimum residual error between simulated and slug-test data was applied to 2,364 line-km of AEM soundings in the 1,000-km2 Shellmound (Mississippi) study area to calculate hydraulic property distributions of the alluvial aquifer for use in future groundwater-flow models.
Big sagebrush ecosystems are widespread across drylands of western North America and provide numerous services, but the abundance of these ecosystems has declined substantially and the future of these ecosystems is uncertain. As a result, characterizing potential areas for expansion of these ecosystems is important. Species distribution models of the big sagebrush suggest areas of increasing climatic habitat suitability at northern latitudes under climate change scenarios. This implies the formation of a leading edge during a future big sagebrush range expansion. Such an expansion requires that current nearby range margin big sagebrush populations are stable and serve as future seed sources. Our goal was to quantify the impacts of future climate conditions on the plant community composition and biomass in the in range margin big sagebrush plant communities adjacent to the leading edge. We did this using an individual-based soil water and plant growth simulation model, STEPWAT2. We assessed community dynamics throughout the twenty-first century using 13 climate models under two representative concentration pathways to capture the variability among projections. Our results show minimal overall change in plant community composition and little change in biomass, suggesting that range margin big sagebrush plant communities adjacent to the leading edge will remain stable to serve as essential dispersal sources for future range expansion, assuming no other relevant changes such as changes in disturbance regimes. These assessments of plant community responses to shifts in climate and characterization of variability in future projections will help inform conservation planning and management of the big sagebrush ecosystem.
Background: Wildland–urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated owing to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex, making them challenging for end-users, such as those who use or create WUI maps, to apply.
Aims: We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs and Woolsey.
Methods: We evaluated a CNN-based building dataset and a CNN model from a separate commercial vendor to detect buildings from high-resolution imagery. This dataset and model represent to end-users the state of the art of what is readily available for potential WUI mapping.
Key results: We found moderate accuracies for the building dataset and the CNN model and a severe underestimation of buildings and their destruction rates where trees occluded buildings. The CNN model performed best post-fire with accuracies ≥73%.
Conclusions: Existing CNNs may be used with moderate accuracy for identifying individual buildings post-fire and mapping the extent of the WUI. The implications are, however, that CNNs are too inaccurate for post-fire damage assessments or building counts in the WUI.
","language":"English","publisher":"CSIRO","doi":"10.1071/WF22181","usgsCitation":"Kasraee, N.K., Hawbaker, T., and Radeloff, V., 2023, Identifying building locations in the wildland–urban interface before and after fires with convolutional neural networks: International Journal of Wildland Fire, v. 32, no. 4, p. 610-621, https://doi.org/10.1071/WF22181.","productDescription":"12 p.","startPage":"610","endPage":"621","ipdsId":"IP-141304","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":435496,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VWV2IO","text":"USGS data release","linkHelpText":"Building locations identified before and after the Camp, Tubbs, and Woolsey wildfires"},{"id":412207,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"32","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Kasraee, Neda K.","contributorId":301130,"corporation":false,"usgs":false,"family":"Kasraee","given":"Neda","email":"","middleInitial":"K.","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":862137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":862138,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Radeloff, Volker C.","contributorId":294405,"corporation":false,"usgs":false,"family":"Radeloff","given":"Volker C.","affiliations":[{"id":34113,"text":"University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":862139,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240290,"text":"70240290 - 2023 - Drivers of survival of translocated tortoises","interactions":[],"lastModifiedDate":"2023-02-03T14:52:44.17437","indexId":"70240290","displayToPublicDate":"2023-01-18T08:43:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Drivers of survival of translocated tortoises","docAbstract":"Translocation of animals, especially for threatened and endangered species, is a currently popular but very challenging activity. We translocated 158 adult Agassiz's desert tortoises (Gopherus agassizii), a threatened species, from the National Training Center, Fort Irwin, in the central Mojave Desert in California, USA, to 4 plots as part of a long-distance, hard-release, mitigation-driven translocation to prevent deaths from planned military maneuvers. We monitored demographic and behavioral variables of tortoises fitted with radio-transmitters from 2008 to 2018. By the end of the project, 17.72% of tortoises were alive, 65.82% were dead, 15.19% were missing, and 1.27% were removed from the study because they returned to Fort Irwin. Mortality was high during the first 3 years: >50% of the released animals died, primarily from predation. Thereafter, mortality declined but remained high. After 10.5 years, survival was highest, 37.50% (15/40), on the plot closest to original home sites, whereas from 2.56% to 23.68% remained alive on the other 3 release plots. Surviving tortoises settled early, repeatedly using locations where they constructed burrows, compared with tortoises that died or disappeared. Models of behavioral and other variables indicated that numbers of repeatedly used locations (burrows) were a driver of survival throughout the study, although plot location, size and sex of tortoises, and distance traveled were contributors, especially during early years. Because >50% mortality occurred, we considered this translocation unsuccessful. The study area appeared to be an ecological sink with historical and current anthropogenic uses contributing to habitat degradation and a decline in both the resident and released tortoises. Our findings will benefit design and selection of future translocation areas.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22352","usgsCitation":"Mack, J., and Berry, K.H., 2023, Drivers of survival of translocated tortoises: Journal of Wildlife Management, v. 87, e22352, 27 p, https://doi.org/10.1002/jwmg.22352.","productDescription":"e22352, 27 p","ipdsId":"IP-144211","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":444783,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22352","text":"Publisher Index Page"},{"id":435497,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9USRU0T","text":"USGS data release","linkHelpText":"Demographic and Movement Data for Adult Desert Tortoises Translocated from Fort Irwin, 2008-2018"},{"id":412671,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Fort Irwin National Training Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.35309032036193,\n 35.83722728738816\n ],\n [\n -117.35309032036193,\n 35.1178564871942\n ],\n [\n -116.11268639625317,\n 35.1178564871942\n ],\n [\n -116.11268639625317,\n 35.83722728738816\n ],\n [\n -117.35309032036193,\n 35.83722728738816\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"87","noUsgsAuthors":false,"publicationDate":"2023-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Mack, Jeremy S 0000-0002-3394-8493","orcid":"https://orcid.org/0000-0002-3394-8493","contributorId":206166,"corporation":false,"usgs":false,"family":"Mack","given":"Jeremy S","affiliations":[{"id":37269,"text":"Crater Lake National Park (formerly USGS - WERC)","active":true,"usgs":false}],"preferred":false,"id":863258,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863259,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70239772,"text":"70239772 - 2023 - Incorporating temperature into seepage loss estimates for a large unlined irrigation canal","interactions":[],"lastModifiedDate":"2025-05-14T17:36:22.285243","indexId":"70239772","displayToPublicDate":"2023-01-18T06:52:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Incorporating temperature into seepage loss estimates for a large unlined irrigation canal","docAbstract":"Quantifying seepage losses from unlined irrigation canals is necessary to improve water use and conservation. The use of heat as a tracer is widely used in quantifying seepage rates across the sediment–water interface. In this study, field observations and two-dimensional numerical models were used to simulate seepage losses during the 2018 and 2019 irrigation season in the Truckee Canal system. Nineteen transects were instrumented with temperature probes and stage recording devices for inverse modeling to derive seepage flux and volumetric losses over the 39 km length of canal. The numerical models for each transect were calibrated and validated using the two-year dataset. Soil zones and observation data were used in each numerical model to help guide calibration of vertical and lateral heat and fluid fluxes. Model simulations were used to derive multivariable regression equations that consider stage, temperature, and hydraulic gradient. The results demonstrate the value of long-term datasets that illustrate the seasonality of groundwater levels, siltation, stage, and temperature on seepage rates. Seepage rates estimated by the numerical models range from 0.16 to 4.6 m3/d m−1. Total annual volumetric losses estimated for 2018 and 2019 were 1.6 × 10-2 to 1.2 × 10-2 km3, respectively. The seepage losses estimated by this study account for 32 % to 41 % of the inflow volumes. Regression models were able to reproduce seepage time-series simulated by the numerical models reasonably well. In arid environments, water diverted into irrigation canals may be influenced by seasonal variations in temperature sufficient to influence the water accounting of conveyed surface flows.
Non-native species maps are important tools for understanding and managing biological invasions. We demonstrate a novel approach to extend presence modeling to map fractional cover (FC) of non-native yellow sweet clover Melilotus officinalis in the Northern Great Plains, USA. We used ensembles of MaxEnt models to map FC across landscapes from satellite imagery trained from regional aerial imagery that was trained by local unmanned aerial vehicle (UAV) imagery. Clover cover from field surveys and classified UAV imagery were nearly identical (n = 22, R2 = 0.99). Two classified UAV images provided training data to map clover presence with MaxEnt and National Agricultural Imagery Program (NAIP) aerial imagery. We binned cover predictions from NAIP imagery within each Sentinel-2 pixel into eight cover classes to create pure (100%) and FC (20%–95%) training data and modeled each class separately using MaxEnt and Sentinel-2 imagery. We mapped pure clover with one classification threshold and compared its performance to 15 candidate maps that included FC predictions outside pure predictions. Each FC map represented alternative combinations of five MaxEnt thresholds and three approaches to assign cover to pixels with multiple predictions from the FC ensemble. Evaluations of performance with independent datasets revealed maps including FC corresponded to field (n = 32, R2 range: 0.39–0.68) and UAV (n = 20, R2 range: 0.61–0.84) data better than pure clover maps (R2 = 0.15 and 0.31, respectively). Overall, the pure clover map predicted 3.2% cover, whereas the three best performing FC maps predicted 6.6%–8.0% cover. Including FC predictions increased accuracy and cover predictions which can improve ecological understanding of invasions. Our method allows efficient FC mapping for vegetative species discernible in UAV imagery and may be especially useful for mapping rare, irruptive or patchily distributed species with poor representation in field data, which challenges landscape-level mapping.
","language":"English","publisher":"Wiley","doi":"10.1002/rse2.325","usgsCitation":"Preston, T.M., Johnston, A.N., Ebenhoch, K.G., and Diehl, R.H., 2023, Beyond presence mapping: Predicting fractional cover of non-native vegetation in Sentinel-2 imagery using an ensemble of MaxEnt models: Remote Sensing in Ecology and Conservation, v. 9, no. 4, p. 512-526, https://doi.org/10.1002/rse2.325.","productDescription":"15 p.","startPage":"512","endPage":"526","ipdsId":"IP-135782","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":444792,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.325","text":"Publisher Index Page"},{"id":435499,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91X4EPQ","text":"USGS data release","linkHelpText":"Fractional cover estimates of sweet clover derived from UAV, aerial, and Sentinel-2 imagery for central Montana and northwest South Dakota, 2019"},{"id":412216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, South Dakota","county":"Butte County, Harding County, Musselshell County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-108.6276,46.7487],[-108.5911,46.7491],[-108.5699,46.7489],[-108.5494,46.7486],[-108.5163,46.7481],[-108.4991,46.7483],[-108.4772,46.7489],[-108.4567,46.7491],[-108.3586,46.7503],[-108.3373,46.7509],[-108.2618,46.7524],[-108.2399,46.7525],[-108.2187,46.7526],[-108.1981,46.7532],[-108.1763,46.7533],[-107.8833,46.7568],[-107.878,46.7571],[-107.8276,46.7566],[-107.8279,46.7502],[-107.8221,46.7455],[-107.8162,46.7444],[-107.8164,46.7394],[-107.8118,46.7375],[-107.8101,46.7306],[-107.8155,46.7279],[-107.8295,46.7254],[-107.829,46.7218],[-107.8212,46.7184],[-107.8287,46.7126],[-107.819,46.7083],[-107.8238,46.7038],[-107.8186,46.7],[-107.811,46.693],[-107.8043,46.6942],[-107.8026,46.6873],[-107.7955,46.6835],[-107.7957,46.6784],[-107.7998,46.6758],[-107.802,46.6707],[-107.7983,46.6643],[-107.8024,46.6602],[-107.8044,46.6607],[-107.8082,46.6631],[-107.8111,46.659],[-107.8073,46.6539],[-107.8153,46.6522],[-107.8217,46.6413],[-107.8224,46.639],[-107.8152,46.638],[-107.8148,46.6325],[-107.8184,46.6243],[-107.829,46.6231],[-107.8298,46.6204],[-107.8188,46.6147],[-107.8367,46.5976],[-107.7971,46.5954],[-107.7986,46.495],[-107.7781,46.4951],[-107.7829,46.3948],[-107.9102,46.3931],[-107.9299,46.3935],[-107.9299,46.3779],[-107.947,46.3773],[-107.9482,46.3649],[-107.9693,46.3644],[-107.9712,46.3493],[-107.9915,46.3502],[-107.9901,46.335],[-108.0099,46.3358],[-108.0112,46.3171],[-108.0116,46.3065],[-108.0254,46.3068],[-108.0266,46.2761],[-108.0271,46.2624],[-108.0896,46.2626],[-108.1113,46.263],[-108.131,46.2628],[-108.3197,46.2632],[-108.3195,46.2504],[-108.3622,46.2502],[-108.3627,46.2351],[-108.3838,46.2354],[-108.4035,46.2352],[-108.4033,46.2196],[-108.4035,46.1949],[-108.4032,46.1812],[-108.4037,46.1665],[-108.4035,46.1528],[-108.4035,46.1326],[-108.5301,46.1327],[-108.5518,46.133],[-108.6574,46.1331],[-108.7782,46.1328],[-108.7778,46.2762],[-108.7988,46.2765],[-108.7993,46.3072],[-108.8211,46.307],[-108.822,46.3216],[-108.8325,46.3222],[-108.8318,46.3511],[-108.8423,46.3517],[-108.8419,46.3668],[-108.8636,46.3666],[-108.8634,46.3781],[-108.8642,46.4509],[-108.8846,46.4525],[-108.8856,46.4915],[-108.9074,46.4918],[-108.906,46.5775],[-108.9912,46.5775],[-108.9902,46.622],[-109.0101,46.6209],[-109.0104,46.6649],[-109.0094,46.7378],[-109.0091,46.7516],[-108.9038,46.7504],[-108.8647,46.7504],[-108.817,46.7507],[-108.7567,46.75],[-108.7382,46.7497],[-108.6958,46.7496],[-108.6276,46.7487]]],[[[-102.9587,45.2128],[-102.958,45.1251],[-102.9581,45.0388],[-102.9576,44.7781],[-102.9589,44.69],[-102.9653,44.6898],[-102.966,44.6036],[-103.1861,44.6039],[-103.2066,44.6039],[-103.3273,44.6042],[-103.4467,44.6053],[-103.5666,44.6044],[-103.8156,44.6048],[-103.8258,44.6023],[-103.8256,44.5982],[-103.8234,44.5937],[-103.8324,44.5939],[-103.8309,44.5889],[-103.8373,44.5888],[-103.8384,44.586],[-103.8417,44.5877],[-103.8464,44.5913],[-103.8533,44.5884],[-103.8567,44.5915],[-103.8641,44.5854],[-103.8702,44.5925],[-103.884,44.5985],[-103.8883,44.5952],[-103.8934,44.5942],[-103.8973,44.595],[-103.9018,44.5954],[-103.9061,44.5916],[-103.9053,44.5889],[-103.9105,44.5892],[-103.9144,44.59],[-103.9179,44.5849],[-103.9262,44.5838],[-103.9344,44.5799],[-103.9396,44.5812],[-103.9446,44.5783],[-103.9454,44.5819],[-103.9511,44.5808],[-103.9549,44.5789],[-103.9671,44.5791],[-103.9761,44.5811],[-103.9813,44.5814],[-103.983,44.5777],[-103.9997,44.5773],[-104.0183,44.5773],[-104.0229,44.5799],[-104.035,44.5782],[-104.0399,44.574],[-104.0457,44.5734],[-104.0564,44.5717],[-104.0571,44.9818],[-104.0571,44.9987],[-104.0397,44.9986],[-104.0399,45.0602],[-104.0402,45.1563],[-104.0403,45.169],[-104.0403,45.1774],[-104.0403,45.1832],[-104.0406,45.2143],[-104.041,45.2639],[-104.0425,45.5572],[-104.0426,45.5736],[-104.0424,45.6245],[-104.0425,45.6437],[-104.0425,45.6578],[-104.0425,45.6656],[-104.0426,45.6717],[-104.0426,45.6835],[-104.0433,45.7735],[-104.0434,45.7951],[-104.0435,45.8098],[-104.0437,45.8405],[-104.0439,45.8799],[-104.0441,45.9063],[-104.0443,45.9438],[-102.9956,45.944],[-102.9425,45.944],[-102.9445,45.8189],[-102.9439,45.7311],[-102.955,45.7318],[-102.9558,45.5584],[-102.9565,45.4711],[-102.9539,45.3852],[-102.9578,45.3851],[-102.9605,45.2982],[-102.9587,45.2128]]]]},\"properties\":{\"name\":\"Musselshell\",\"state\":\"MT\"}}]}","volume":"9","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-01-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Preston, Todd M. 0000-0002-8812-9233","orcid":"https://orcid.org/0000-0002-8812-9233","contributorId":204676,"corporation":false,"usgs":true,"family":"Preston","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnston, Aaron N. 0000-0003-4659-0504","orcid":"https://orcid.org/0000-0003-4659-0504","contributorId":201768,"corporation":false,"usgs":true,"family":"Johnston","given":"Aaron","email":"","middleInitial":"N.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ebenhoch, Kyle Gregory 0000-0001-7046-5557","orcid":"https://orcid.org/0000-0001-7046-5557","contributorId":299946,"corporation":false,"usgs":true,"family":"Ebenhoch","given":"Kyle","email":"","middleInitial":"Gregory","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diehl, Robert H. 0000-0001-9141-1734 rhdiehl@usgs.gov","orcid":"https://orcid.org/0000-0001-9141-1734","contributorId":3396,"corporation":false,"usgs":true,"family":"Diehl","given":"Robert","email":"rhdiehl@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862050,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}