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.
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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 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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":"Prevéy, Janet S. 0000-0003-2879-6453","orcid":"https://orcid.org/0000-0003-2879-6453","contributorId":222702,"corporation":false,"usgs":true,"family":"Prevéy","given":"Janet","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 R. 0000-0002-9450-5223","orcid":"https://orcid.org/0000-0002-9450-5223","contributorId":216681,"corporation":false,"usgs":true,"family":"Sofaer","given":"Helen","middleInitial":"R.","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 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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}]}} ,{"id":70240165,"text":"70240165 - 2023 - Moving Aircraft River Velocimetry (MARV): Framework and proof-of-concept on the Tanana River","interactions":[],"lastModifiedDate":"2023-01-31T13:13:29.009178","indexId":"70240165","displayToPublicDate":"2023-01-17T07:11:28","publicationYear":"2023","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":"Moving Aircraft River Velocimetry (MARV): Framework and proof-of-concept on the Tanana River","docAbstract":"Information on velocity fields in rivers is critical for designing infrastructure, modeling contaminant transport, and assessing habitat. Although non-contact approaches to measuring flow velocity are well established, these methods assume a stationary imaging platform. This study eliminates this constraint by introducing a framework for moving aircraft river velocimetry (MARV). The workflow takes as input images acquired from an airplane and involves orthorectification, frame overlap analysis, image enhancement, particle image velocimetry (PIV), and aggregation of the resulting velocity vectors onto a prediction grid. We also use new metrics to quantify the agreement between image-derived and field-measured velocity vectors in terms of both orientation and magnitude. The potential of MARV was evaluated using data from two Alaskan rivers: a large, highly turbid channel and its smaller, clearer tributary. Sediment boil vortices on the mainstem provided natural features trackable via PIV and estimated velocities corresponded closely with field measurements (R2 up to 0.911). We compared an exhaustive approach that evaluates overlap for all frame combinations to a simpler rolling window implementation and found that the more efficient algorithm did not compromise accuracy. Sensitivity analysis suggested that the method was robust to window parameterization. Comparing PIV output from different flying heights and imaging systems indicated that larger pixels led to higher accuracy and that a more advanced dual-camera system provided superior performance. Results from the tributary were less encouraging, presumably due to a lack of trackable features in visible images. Testing across a range of rivers is needed to assess the generality of MARV.
Globally, Marine Protected Areas are an important tool in the conservation of large marine vertebrates. Recent studies have highlighted the use of protected areas by imperiled green turtles (Chelonia mydas) in the southern Gulf of Mexico. To identify and characterize inter-nesting, migratory, and foraging areas for green turtles that nest in the northern Gulf of Mexico, we deployed 14 satellite tags on 13 individual green turtles after nesting in Northwest Florida. We used switching state-space modeling to highlight turtle use in the Florida Keys National Marine Sanctuary and in habitat outside of protected areas such as near Cape Sable, Florida and off the Yucatán Peninsula, Mexico. Turtles were tracked for 21–217 days and migrated for a mean of 22 days. Five individuals used stopover sites during migration; these sites were in areas of dense seagrass habitat, often within boundaries of existing Aquatic Preserves. Turtles established mean foraging home ranges of 118.0 km2 (50% kernel density estimate) with foraging centroids that were 0.33–7.3 km apart. The area off Cape Sable, Florida, which lies outside of currently protected area boundaries, appears to be a hotspot for green turtles that nest throughout the Gulf of Mexico. While protected areas in the Gulf of Mexico are used by this subset of nesting green turtles, several key sites remain unprotected. These findings are relevant when considering expansion of currently protected areas and in defining critical habitat for this species.
In the United States and globally, contaminant exposure in unregulated private-well point-of-use tapwater (TW) is a recognized public-health data gap and an obstacle to both risk-management and homeowner decision making. To help address the lack of data on broad contaminant exposures in private-well TW from hydrologically-vulnerable (alluvial, karst) aquifers in agriculturally-intensive landscapes, samples were collected in 2018–2019 from 47 northeast Iowa farms and analyzed for 35 inorganics, 437 unique organics, 5 in vitro bioassays, and 11 microbial assays. Twenty-six inorganics and 51 organics, dominated by pesticides and related transformation products (35 herbicide-, 5 insecticide-, and 2 fungicide-related), were observed in TW. Heterotrophic bacteria detections were near ubiquitous (94 % of the samples), with detection of total coliform bacteria in 28 % of the samples and growth on at least one putative-pathogen selective media across all TW samples. Health-based hazard index screening levels were exceeded frequently in private-well TW and attributed primarily to inorganics (nitrate, uranium). Results support incorporation of residential treatment systems to protect against contaminant exposure and the need for increased monitoring of rural private-well homes. Continued assessment of unmonitored and unregulated private-supply TW is needed to model contaminant exposures and human-health risks.
In the Greater Yellowstone Ecosystem, counts of female grizzly bears (Ursus arctos) with cubs-of-the-year (females with cubs) from systematic aerial surveys and opportunistic ground sightings are combined with demographic data to derive annual population estimates. We addressed 2 limitations to the monitoring approach. As part of a rule set, a conservative distance of >30 km currently is used as a threshold to assign sightings to unique females with cubs, resulting in underestimation bias. Using telemetry locations of females with cubs collected during 1997–2019, we created 1,000 data sets for each of 5 levels of simulated number of females with cubs, simulated sightings by selecting among these locations, and evaluated the classification performance of alternative distance criteria (12–30 km). Under all scenarios, 12–16-km criteria maximized classification performance and minimized estimation bias; the 16-km criterion was optimal for current conditions and sampling efforts. Our second objective was to test generalized additive models (GAMs) as a flexible trend analysis technique. We simulated 1,000 time series for each of 10 scenarios (10, 15, and 20% decline over periods of 5, 10, and 15 yrs, plus stability), applied GAMs, and assessed metrics associated with the posterior distribution of the instantaneous rate of change. We detected declines among >99.6% of replicates under the 15 and 20% decline scenarios and in 84.7–94.7% of replicates under the 10% decline scenario. From decline onset to first detection, periods ranged from 3.7 (20% decline over 5 yrs) to 11.1 (10% decline over 15 yrs), with 3.9–8.8 years mean duration of detection events. The GAM approach allows detection of directional changes in population trend, including early warning metrics, and stabilization after such changes. Retrospective application of the 16-km distance criterion and GAMs resulted in higher population estimates and growth rates than are reported using current methods.
","language":"English","publisher":"International Association for Bear Research and Management","doi":"10.2192/URSUS-D-22-00002.2","usgsCitation":"van Manen, F.T., Ebinger, M., Costello, C., Bjornlie, D., Clapp, J., Thompson, D., Haroldson, M.A., Frey, K.L., Hendricks, C., Nicholson, J., Gunther, K.A., Wilmot, K.R., Cooley, H., Fortin-Noreus, J., Hnilicka, P., and Tyers, D.B., 2023, Enhancements to population monitoring of Yellowstone grizzly bears: Ursus, v. 33, e17, 19 p., https://doi.org/10.2192/URSUS-D-22-00002.2.","productDescription":"e17, 19 p.","ipdsId":"IP-137270","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":444815,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2192/ursus-d-22-00002.2","text":"Publisher Index Page"},{"id":411958,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Box 168, Yellowstone National Park, WY 82190","active":true,"usgs":false}],"preferred":false,"id":861651,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Wilmot, Katharine R.","contributorId":244265,"corporation":false,"usgs":false,"family":"Wilmot","given":"Katharine","email":"","middleInitial":"R.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":861652,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cooley, Hilary","contributorId":205414,"corporation":false,"usgs":false,"family":"Cooley","given":"Hilary","affiliations":[],"preferred":false,"id":861653,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Fortin-Noreus, Jennifer","contributorId":200746,"corporation":false,"usgs":false,"family":"Fortin-Noreus","given":"Jennifer","email":"","affiliations":[],"preferred":false,"id":861654,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hnilicka, Pat","contributorId":289731,"corporation":false,"usgs":false,"family":"Hnilicka","given":"Pat","affiliations":[],"preferred":false,"id":861655,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Tyers, Daniel B.","contributorId":124587,"corporation":false,"usgs":false,"family":"Tyers","given":"Daniel","email":"","middleInitial":"B.","affiliations":[{"id":5129,"text":"U.S. Forest Service, 2327 University Way, Bozeman, MT 59715, USA","active":true,"usgs":false}],"preferred":false,"id":861656,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70239810,"text":"70239810 - 2023 - Watershed- and reach-scale drivers of phosphorus retention and release by streambed sediment in a western Lake Erie watershed during summer","interactions":[],"lastModifiedDate":"2023-01-20T13:09:14.687072","indexId":"70239810","displayToPublicDate":"2023-01-16T07:06:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Watershed- and reach-scale drivers of phosphorus retention and release by streambed sediment in a western Lake Erie watershed during summer","docAbstract":"Reducing phosphorus (P) concentrations in aquatic ecosystems, is necessary to improve water quality and reduce the occurrence of harmful cyanobacterial algal blooms. Managing P reduction requires information on the role rivers play in P transport from land to downstream water bodies, but we have a poor understanding of when and where river systems are P sources or sinks. During the summers of 2019 and 2021, we sampled streambed sediment at 78 sites throughout the Maumee River network (a major source of P loads to Lake Erie) focusing on the zero equilibrium P concentration (EPC0), the soluble reactive phosphorus (SRP) concentration at which sediment neither sorbs nor desorbs P. We used structural equation modeling to identify direct and indirect drivers of EPC0. Stream sediment was a P sink at 40 % and 67 % of sites in 2019 and 2021, respectively. During both years, spatial variation in EPC0 was shaped by stream water SRP concentrations, sediment P saturation, and sediment physicochemical characteristics. In turn, SRP concentrations and sediment P saturation (PSR) were influenced by agricultural land use and stream size. Effect of stream size differed among years with stream size having a greater effect on SRP in 2019 and on PSR in 2021. Streambed sediment is currently a net P sink across the sites sampled in the Maumee River network during summer, but sediment at these locations, especially sites in headwater streams, may become a P source if stream water SRP concentrations decrease. Our results improve the understanding of watershed- and reach-scale controls on EPC0 but also indicate the need for further research on how changes in SRP concentration as a result of conservation management implementation influences the role of streambed sediment in P transport to Lake Erie.
In arid and Mediterranean regions, landscape-scale wetland conservation requires understanding how wildlife responds to dynamic freshwater availability and conservation actions to enhance wetland habitat. Taking advantage of Landsat satellite data and structured and community science bird survey data, we built species distribution models to describe how three duck species, the Northern Pintail (Anas acuta), Green-winged Teal (Anas crecca), and Northern Shoveler (Anas clypeata), respond to freshwater supply and food resources on different flooded land cover types in the Central Valley of California. Specifically, our models compared duck habitat suitability between the wettest and driest conditions in each month from September through April. Using abundance-weighted boosted regression trees, we created three sets of species occurrence models based on different covariates: (1) near real-time (hereafter “real-time”) covariates in which duck observations were matched to the water availability within the 16-day window of a Landsat observation, (2) a combination of real-time covariates and waterfowl food resource covariates describing annual corn and rice biomass and managed wetland moist soil seed yield estimates derived from Landsat data, and (3) long-term average covariates—the most common approach to species distribution modeling—in which long-term average surface water availability was used. We modeled the monthly occurrence of three duck species as a function of surface water availability, land cover type, road density, temperature, and bird data source. We found that dry conditions result in reduced habitat suitability, with the biggest reductions in November through January and in agricultural fields; in contrast, suitability of flooded wetland habitat was relatively robust to surface water availability. When models of habitat suitability based on long-term average climate conditions were compared to models based on real-time conditions, the highest long-term suitability values occurred in areas where suitability was high regardless of whether it was a wet or a dry year. While all models performed well, the inclusion of crop and wetland plant yield covariates resulted in slightly higher model performance. Overall, species distribution models created using data on the environmental conditions present at the time of bird observations can aid conservation efforts under extreme conditions over large spatial scales.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4367","usgsCitation":"Conlisk, E.E., Byrd, K.B., Matchett, E., Lorenz, A., Casazza, M.L., Golet, G.H., Reynolds, M.D., Sesser, K.A., and Reiter, M.E., 2023, Changes in habitat suitability for wintering dabbling ducks during dry conditions in the Central Valley of California: Ecosphere, v. 14, e4367, 19 p., https://doi.org/10.1002/ecs2.4367.","productDescription":"e4367, 19 p.","ipdsId":"IP-144890","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":444827,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4367","text":"Publisher Index Page"},{"id":412024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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E.","contributorId":301022,"corporation":false,"usgs":false,"family":"Conlisk","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":861775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":861776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Matchett, Elliott 0000-0001-5095-2884 ematchett@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-2884","contributorId":5541,"corporation":false,"usgs":true,"family":"Matchett","given":"Elliott","email":"ematchett@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":861777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lorenz, Austen 0000-0003-3657-5941","orcid":"https://orcid.org/0000-0003-3657-5941","contributorId":222610,"corporation":false,"usgs":true,"family":"Lorenz","given":"Austen","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":861778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":861779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Golet, Gregory H.","contributorId":89844,"corporation":false,"usgs":false,"family":"Golet","given":"Gregory","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":861780,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reynolds, Mark D.","contributorId":301023,"corporation":false,"usgs":false,"family":"Reynolds","given":"Mark","email":"","middleInitial":"D.","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":861781,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sesser, Kristin A.","contributorId":215294,"corporation":false,"usgs":false,"family":"Sesser","given":"Kristin","email":"","middleInitial":"A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":861782,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Reiter, Matthew E. 0000-0002-0587-786X","orcid":"https://orcid.org/0000-0002-0587-786X","contributorId":271031,"corporation":false,"usgs":false,"family":"Reiter","given":"Matthew","email":"","middleInitial":"E.","affiliations":[{"id":56258,"text":"Point Blue","active":true,"usgs":false}],"preferred":false,"id":861783,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70239935,"text":"70239935 - 2023 - Nest-site selection model for endangered Everglade snail kites to inform ecosystem restoration","interactions":[],"lastModifiedDate":"2023-03-28T14:38:56.423278","indexId":"70239935","displayToPublicDate":"2023-01-15T07:09:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Nest-site selection model for endangered Everglade snail kites to inform ecosystem restoration","docAbstract":"dictors of nesting for snail kites in south Florida. The results of our modeling indicate that hydrology, percent canopy cover, and proximity to recently burned areas were the most important factors associated with nest-site selection for snail kites. Water depths between 75 and 100 cm, water recession rates between 0 and 1.25 cm/day, percent canopy covers <20%, and areas <10 km from recently burned habitat were associated with the greatest likelihood of nest-site selection. KiteNest is applicable to natural resource management decisions in the Everglades and may be useful independently or in conjunction with other ecological models for restoration decision support.
Irregularly flooded wetlands are found above the mean high water tidal datum and are exposed to tides and saltwater less frequently than daily. These wetlands provide important ecosystem services, such as providing habitat for fish and wildlife, enhancing water quality, ameliorating flooding impacts, supporting coastal food webs, and protecting upslope areas from erosion. Mapping irregularly flooded wetlands is challenging given their expansive coverage and dynamic nature. Furthermore, coastal wetlands are expected to change over the coming century due to sea-level rise and changes in the frequency and intensity of extreme storms. Consequently, coastal managers need baseline information on the spatial distribution of wetlands along with efficient and repeatable methods for observing changes. In this study, we used coastal wetlands from existing land use land cover data, best available lidar-derived digital elevation models, and Monte Carlo simulations to incorporate elevation uncertainty to create a probabilistic map of irregularly flooded wetlands along the northern Gulf of Mexico coast (USA). Our approach integrated findings from a review of coastal wetland elevation error in lidar datasets and an analysis of spatial autocorrelations of wetland elevation. We found a positive correlation (r = 0.563, p < 0.0001) when comparing the probability estimated from a digital elevation model and in situ elevation observations. The differences in probability had a mean bias error of −0.04 (i.e., digital elevation model-based probability tends to be slightly lower), a mean absolute error of 0.20, and a root mean square error of 0.26. Beyond this overall validation, we explored error metrics for land cover classes and lidar collection details. To quantify areal coverage of the probabilistic output, we classified the probability values into equal bins using an interval of 0.33. The areal coverage of the lowest probability bin (“unlikely”; probability ≤0.33) was separated into the upper and lower portions of the irregularly flooded wetland zone. Of the coastal wetlands along the northern Gulf of Mexico coast about 38% were classified as unlikely and low with the greatest coverage in south Louisiana and the Everglades and around 33% were classified as unlikely and high with the greatest coverage in the Everglades and Texas. The relative coverage within the highest probability bin (“likely”; probability >0.66) covered around 13%, with the greatest coverage in south Florida, south Louisiana, and Texas. The framework developed in this study can be transferred to other coastal wetland areas and updated to observe changes with sea-level rise.
As a part of the Texas Water Development Board groundwater availability modeling program, the U.S. Geological Survey developed the Gulf Coast Land Subsidence and Groundwater-Flow model (hereinafter, the “GULF model”) and ensemble to simulate groundwater flow and land-surface subsidence in the northern part of the Gulf Coast aquifer system (the study area) in Texas from predevelopment (1897) through 2018. Since the publication of a previous groundwater model for the greater Houston area in 2012, there have been changes to the distribution of groundwater withdrawals and advances in modeling tools. To reflect these changes and to simulate more recent conditions, the GULF model was developed in cooperation with the Harris-Galveston and Fort Bend Subsidence Districts to provide an updated Groundwater Availability Model.
Since the early 1900s, most of the groundwater withdrawals in the study area have been from three of the hydrogeologic units that compose the Gulf Coast aquifer system—the Chicot, Evangeline, and Jasper aquifers and, more recently, from the Catahoula confining unit. Withdrawals from these hydrogeologic units are used for municipal supply, commercial and industrial use, and irrigation purposes. Withdrawals of large quantities of groundwater in the greater Houston area have caused widespread groundwater-level declines in the Chicot, Evangeline, and Jasper aquifers of more than 300 feet (ft). Early development of the aquifer system, which began before 1900, resulted in nearly 50 percent of the eventual historical groundwater-level minimums having been reached as early as 1946 in some areas. These groundwater-level declines led to more than 9 ft of land-surface subsidence—historically in central and southeastern Harris County and Galveston County, but more recently in northern, northwestern, and western Harris County, Montgomery County, and northern Fort Bend County—from depressurization and compaction of clay and silt layers interbedded in the aquifer sediments.
In a generalized conceptual model of the Gulf Coast aquifer system, water enters the groundwater system in topographically high outcrops of the hydrogeologic units in the northwestern part of the aquifer system. Groundwater that does not discharge to streams flows to intermediate and deep zones of the aquifer system southeastward of the outcrop areas where it is discharged by wells and by upward leakage in topographically low areas near the coast. The uppermost parts of the aquifer system, which include outcrop areas, are under water-table (unconfined) conditions where the groundwater is not confined under pressure. As depth increases in the aquifer system and interbedded clay and silt layers accumulate, water-table conditions evolve into confined conditions where the groundwater is under pressure.
Groundwater flow and land-surface subsidence in the GULF model and ensemble were simulated by using MODFLOW 6 with the Skeletal Storage, Compaction, and Subsidence package. The model consists of six layers, one for each of the five hydrogeologic units in the northern part of the Gulf Coast aquifer system and a surficial top layer that includes part of each hydrogeologic unit. Transient groundwater flow was simulated during 1897–2018 by using a combination of multiyear, annual, and monthly stress periods. An initial steady-state stress period was configured to represent predevelopment mean annual inflows and outflows. The subsidence package used in the GULF model and ensemble uses a head-based subsidence formulation that simulates the delayed drainage response from clay and silt sediment to changes in groundwater levels.
The GULF model and ensemble were history matched to groundwater-level observations at selected wells, land-surface subsidence at benchmarks, aquifer compaction at borehole extensometers, and vertical displacement from Global Positioning System stations. A Bayesian framework was used to represent uncertainty in modeled parameters and simulated outputs of interest. History matching and uncertainty quantification were performed by using a Monte Carlo approach enabled through iterative ensemble smoother software to produce an ensemble of models fit to historical data. The iterative ensemble smoother substantially reduced the computational demand of parameter estimation by approximating the first-order relation between model inputs and outputs, thereby allowing 183,207 adjustable parameters to be used for history matching at a relatively low computational and time cost.
The history-matched parameter values are within the ranges of previously published values and agree with the current understanding of the spatial and temporal patterns of parameter uncertainty for the Gulf Coast aquifer system. A good agreement between the observed (or estimated) and simulated groundwater levels, land-surface subsidence, compaction, and vertical displacement was obtained across the modeled area based on qualitative and quantitative comparisons. Ensemble mean annual groundwater-flow rates to the Chicot, Evangeline, Jasper aquifers and Catahoula confining unit were 0.0–0.49 inch (in.), 0.09–0.33 in., 0.01–0.07 in., and 0.01–0.05 in., respectively. GULF model mean annual groundwater-flow rates to the Chicot, Evangeline, and Jasper aquifers and Catahoula confining unit were 0.31 in., 0.19 in., 0.03 in., and 0.03 in., respectively.
The GULF-model-simulated recharge to the outcrop area was the largest inflow (75 percent), and recharge to other areas was 25 percent of the model inflow. The simulated outflows included (1) net surface-water/groundwater exchange with study area streams (50 percent), (2) groundwater use (49 percent), and (3) net surface-water/groundwater exchange with the Gulf of Mexico (1 percent). The sum of the simulated values of the outflows (1,041,973 acre-feet per year [acre-ft/yr]) and the elastic expansion of the fine-grained sediment and numerical solver error (339 acre-ft/yr) minus the inflows (654,172 acre-ft/yr) represents the reduction of storage from the Gulf Coast aquifer system (388,140 acre-ft/yr). Most of the storage depletion is caused by the long-term groundwater-level declines that have resulted primarily in inelastic compaction.
The GULF model was used to estimate Jasper aquifer compaction at selected benchmarks in Montgomery County and northern Harris County, which are the primary locations of Jasper aquifer groundwater use. Simulated Jasper aquifer compaction in northern Harris County was between 0.2 and 0.5 ft, or between about 5 and 16 percent of simulated subsidence at the benchmark locations. Simulated Jasper aquifer compaction in Montgomery County was between 0.8 and 1.2 ft, or between about 33 and 57 percent of simulated subsidence at the benchmark locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1877","issn":"ISSN 2330-7102","collaboration":"Prepared in cooperation with the Harris-Galveston Subsidence District and the Fort Bend Subsidence District","usgsCitation":"Ellis, J.H., Knight, J.E., White, J.T., Sneed, M., Hughes, J.D., Ramage, J.K., Braun, C.L., Teeple, A., Foster, L., Rendon, S.H., and Brandt, J., 2023, Hydrogeology, land-surface subsidence, and documentation of the Gulf Coast Land Subsidence and Groundwater-Flow (GULF) model, southeast Texas, 1897–2018 (ver. 1.1, November 2023): U.S. Geological Survey Professional Paper 1877, 425 p., https://doi.org/10.3133/pp1877.","productDescription":"Report: xx, 425 p., 8 Appendixes; Data Release","numberOfPages":"450","onlineOnly":"Y","ipdsId":"IP-127938","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":500160,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114230.htm","linkFileType":{"id":5,"text":"html"}},{"id":422702,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/pp/pp1877/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":411889,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XM8A1P","text":"USGS Data Release","linkHelpText":"MODFLOW 6 model and ensemble used in the simulation of groundwater flow and land-surface subsidence in the northern part of the Gulf Coast aquifer system, 1897–2018"},{"id":422705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/pp1877/coverthb2.jpg"},{"id":411888,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/pp1877/pp1877.pdf","text":"Report","size":"184 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.54245107965883,\n 31.199747848944256\n ],\n [\n -96.33297842340923,\n 30.997489619299927\n ],\n [\n -96.79440420465926,\n 30.136679255787612\n ],\n [\n -96.02536123590899,\n 28.551820525825022\n ],\n [\n -95.36068838434645,\n 28.86498475853952\n ],\n [\n -94.72348135309639,\n 29.28746086219381\n ],\n [\n -94.65207022028405,\n 29.402380282489133\n ],\n [\n -94.23458975153387,\n 29.574516044800063\n ],\n [\n -93.82809561090883,\n 29.670020494605353\n ],\n [\n -93.89401357965892,\n 29.803574466610613\n ],\n [\n -93.6907665093464,\n 30.05113045792723\n ],\n [\n -93.67428701715903,\n 30.307554456695556\n ],\n [\n -93.6687938530968,\n 30.563309394138372\n ],\n [\n -93.49301260309677,\n 30.841976559030968\n ],\n [\n -93.4765331109094,\n 31.077503645282718\n ],\n [\n -93.54245107965883,\n 31.199747848944256\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: January 13, 2023; Version 1.1: November 28, 2023","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754-4501
Groundwater is the primary source of drinking water in the Coachella Valley in the desert region of southern California. Although most people in Coachella Valley are served by public drinking-water systems, about 20,000 people rely on private domestic or small-system wells (referred to herein as domestic wells). Recently, the U.S. Geological Survey (USGS) found that 39 percent of the groundwater resources used by domestic wells in Coachella Valley contained arsenic, fluoride, or both constituents at concentrations greater than the maximum contaminant levels established for public drinking-water systems. Uranium, chromium, nitrate, and perchlorate were detected at moderate concentrations below maximum contaminant levels. Elevated (above background) perchlorate concentrations in some areas indicate that domestic wells may receive recharge from Colorado River water used for irrigation or aquifer replenishment. Moderate total dissolved solids (TDS) concentrations throughout the study area and the co-occurrence of high concentrations of TDS and perchlorate indicates that Colorado River water is a source of recharge in the southeastern Indio groundwater subbasin. Four volatile organic compounds were detected at low concentrations, and pesticides and per- and polyfluoroalkyl substances were not detected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221122","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Soldavini, A.L., Harkness, J.S., Levy, Z.F., and Fram, M.S., 2023, Quality of groundwater used for domestic drinking-water supply in the Coachella Valley, 2020: U.S. Geological Survey Open-File Report 2022-1122, 6 p., https://doi.org/10.3133/ofr20221122.","productDescription":"Report: 6 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-127493","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":411823,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UYXI95","text":"USGS data release","description":"USGS data release","linkHelpText":"Groundwater-quality data in the Coachella Valley Domestic Supply Aquifer Study Unit, 2020: Results from the California GAMA Priority Basin 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U.S. Geological Survey
California Water Science Center
6000 J Street, Placer Hall
Sacramento, CA 95819
Telephone number: (916) 278-3000
Unit Chief State Water Resources Control Board Division of Water Quality
P.O. Box 2231, Sacramento, CA 95812
Telephone number: (916) 341-5779
Snow Lake, a glacially dammed lake on the Snow Glacier near Seward, Alaska, drains rapidly every 14 months–3 years, causing flooding along the Snow River. Highway, railroad, and utility infrastructure on the lower Snow River floodplain is vulnerable to flood damage. Historical hydrology, geomorphology, and two-dimensional hydraulic and sediment transport modeling were used to assess the flood risks from Snow Lake outburst floods. Floods have become more frequent, peaked more rapidly, and have had generally higher peaks over the last 20 years as the Snow Glacier has thinned, translating to a greater potential for flood damage. Rapidly shifting channel locations and the occasional introduction of large volumes of debris to the river also threaten infrastructure on the floodplain and in the channel. An assessment of the historical channel planform between 1951 and 2019 showed that there have been more and less stable segments along the lower Snow River and that channel migration has generally been toward the east. An analysis of floodplain elevations using 2008 light detection and ranging (lidar) showed that the main channel is relatively high compared to floodplain channels that carry floodwaters along the railroad grade, so that once the main channel banks are overtopped water rapidly disperses throughout the floodplain. A two-dimensional flow and sediment transport model was developed, and its simulation results were compared to three past outburst floods from 2007, 2017, and 2019. Despite the complex floodplain and channel geometry, coarse resolution of the mesh, and sediment input data, the model successfully simulated areas of observed scour along the railroad grade and at the guidebank to the highway bridge. The modeled water-surface elevations generally replicated peak elevations recorded at a streamgage in the middle of the model domain and at pressure transducers installed on the floodplain and main channel, although there were discrepancies on the rising limb and some locations had a poorer fit than others. A model of a hypothetical check flood, approximately 150 percent of the largest recorded outburst flood, was developed to provide hydraulic variables to use when planning for infrastructure upgrades.
Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508