Volcanic islands are often subject to flank instability, resulting from a combination of magmatic intrusions along rift zones and gravitational spreading causing extensional faulting at the surface. Here, we study the Koaʻe fault system (KFS), located south of the summit caldera of Kīlauea volcano in Hawaiʻi, one of the most active volcanoes on Earth, prone to active faulting, episodic dike intrusions, and flank instability. Two rift zones and the KFS are major structures controlling volcanic flank instability and magma propagation. Although several magmatic intrusions occurred over the KFS, the link between these faults, two nearby rift zones and the flank instability, is still poorly studied. To better characterize the KFS and its structural linkage with the surrounding fault and rift zones, we performed a detailed structural analysis of the extensional fault system, coupled with a helicopter photogrammetric survey, covering part of the south flank of Kīlauea. We generated a high-resolution DEM (~ 8 cm) and orthomosaic (~ 4 cm) to map the fracture field in detail. We also collected ~ 1000 ground structural measurements of extensional fractures during our three field missions (2019, 2022, and 2023). We observed many small, interconnected grabens, monoclines, rollover structures, and en-echelon fractures that were in part previously undocumented. We estimate the cumulative displacement rate across the KFS during the last 600 ~ 700 years and found a decrease toward the west of the horizontal component from 2 to 6 cm per year, consistent with GNSS data. Integrating morphology observations, fault mapping, and kinematic measurements, we propose a new kinematic model of the upper part of the Kīlauea’s south flank, suggesting a clockwise rotation and a translation of a triangular wedge. This wedge is bordered by the extensional structures (ERZ, SWRZ, and the KFS), largely influenced by gravitational spreading. These findings illustrate a structural linkage between the two rift zones and the KFS, the latter being episodically affected by dike intrusions.
Magnetotelluric (MT) and audiomagnetotelluric (AMT) data are used to better understand the subsurface geology and mineral resources in the San Juan–Silverton caldera complex located near Silverton, Colorado, western United States, as part of the extensive southern Rocky Mountains volcanic field that covers much of southwestern Colorado and northern New Mexico. Seven MT and AMT profiles of varying lengths image resistivity structure to depths of ~5 km. The AMT inversion models characterize geophysical responses of near-surface lithologies, structures, and mineralized systems and also help corroborate airborne electromagnetic data at shallow levels. The MT inversion models extend our depth of investigation from near the surface to great depths (~5 km) and help to form hypotheses about roots of the hydrothermal plumbing that fed shallower mineralized systems. Subsurface high resistivities occur beneath intermediate-composition lava flows and Proterozoic units. Subsurface moderate- to low-resistivity values may reflect hydrothermal plumbing that served as flow paths for mineralizing fluids and metallic ore formation. The model interpretations presented in this study could be utilized in remediation planning or mineral resource applications. The methods used could be applied to other watersheds with similar volcanic environments containing acid-generating historical mines or hydrothermally altered and mineralized source rocks.
We introduce a Python package for computing focal mechanism solutions. This algorithm, which we refer to as SKHASH, is largely based on the HASH algorithm originally written in Fortran over 20 yr ago. HASH innovated the use of suites of solutions, spanning the expected errors in polarities and takeoff angles, to estimate focal mechanism uncertainty. SKHASH benefits from new features with flexible input formats and allows users to take advantage of recent advances in constraining focal mechanisms for small magnitude or poorly recorded earthquakes. The 3D locations of earthquakes and the velocity models used are varied when finding acceptable solutions. As a result, source–receiver azimuths are reflective of errors from the earthquake locations and velocity models, in addition to the takeoff angles. Users can consider weighted P‐wave first‐motion polarities derived from traditional or machine‐learning picks, cross‐correlation consensus, and/or imputation techniques using SKHASH. Focal mechanism solutions can also be further constrained using traditional, machine learning, and/or cross‐correlation consensus S/P amplitude ratios. With improved reporting of individual and collective P polarity and S/P amplitude misfits, users can better evaluate the success of the solutions and the quality of the measurements. The reporting also makes it easier to identify potential issues with metadata, including incorrectly reported station polarity reversals. In addition, by leveraging vectorized operations, taking advantage of an efficient backend Python C Application Programming Interface, and the use of a parallel environment, the Python SKHASH routine may compute mechanisms quicker than the HASH routine.
Subtropical coastlines are impacted by both tropical and extratropical cyclones. While both may lead to substantial damage to coastal communities, it is difficult to determine the contribution of tropical cyclones to coastal flooding relative to that of extratropical cyclones. We conduct a large-scale flood hazard and impact assessment across the subtropical Southeast Atlantic Coast of the United States, from Virginia to Florida, including different flood hazards. The physics-based hydrodynamic modeling skillfully reproduces coastal water levels based on a comprehensive validation of tides, almost two hundred historical storms, and an in-depth hindcast of Hurricane Florence. We show that yearly flood impacts are two times as likely to be driven by extratropical than tropical cyclones. On the other hand, tropical cyclones are 30 times more likely to affect people during rarer 100-year events than extratropical cyclones and contribute to more than half of the regional flood risk. With increasing sea levels, more areas will be flooded, regardless of whether flooding is driven by tropical or extratropical cyclones. Most of the absolute flood risk is contained in the greater Miami metropolitan area. However, several less populous counties have the highest relative risks. The results of this study provide critical information for understanding the source and frequency of compound flooding across the Southeast Atlantic Coast of the United States.
Water quality and freshwater ecosystems are affected by river discharge and temperature. Models are frequently used to estimate river temperature on large spatial and temporal scales due to limited observations of discharge and temperature. In this study, we use physically based river routing and temperature models to simulate daily discharge and river temperature for rivers in 138 basins in Alaska, including the entire Yukon River basin, from 1990–2021. The river temperature model was optimized for ice free months using a surrogate-based model optimization method, improving model performance at uncalibrated river gages. A common statistical model relating local air and water temperature was used as a benchmark. The physically based river temperature model exhibited superior performance compared to the benchmark statistical model after optimization, suggesting river temperature model optimization could become more routine. The river temperature model demonstrated high sensitivity to air temperature and model parameterization, and lower sensitivity to discharge. Validation of the models showed a Kling-Gupta Efficiency of 0.46 for daily river discharge and a root mean square error of 2.04°C for daily river temperature, improving on the non-optimized physical model and the benchmark statistical model, which had root mean square errors of 3.24 and 2.97°C, respectively. The simulation shows that rivers in northern Alaska have higher maximum summer temperatures and more variability than rivers in the Central and Southern regions. Furthermore, this framework can be readily adapted for use across models and regions.
The frequency and extent of wildfires have increased in recent decades with immediate and cascading effects on water availability in many regions of the world. Precipitation is used as primary input to hydrologic models and is a critical driver of post-wildfire hydrologic hazards including debris flows, flash floods, water-quality effects, and reservoir sedimentation. These models are valuable tools for understanding the hydrologic response to wildfire but require accurate precipitation data at suitable spatial and temporal resolutions. Wildfires often occur in data-sparse, headwater catchments in complex terrain, and post-wildfire hydrologic effects are particularly sensitive to high-intensity, short-duration precipitation events, which are highly variable and difficult to measure or estimate. Therefore, the assessment and prediction of wildfire-induced changes to watershed hydrology, including the associated effects on ecosystems and communities, are complicated by uncertainty in precipitation data. When direct measurements of precipitation are not available, datasets of indirect measurements or estimates are often used. Choosing the most appropriate precipitation dataset can be difficult as different datasets have unique trade-offs in terms of spatial and temporal accuracy, resolution, and completeness. Here, we outline the challenges and opportunities associated with different precipitation datasets as they apply to post-wildfire hydrologic models and modeling objectives. We highlight the need for expanded precipitation gage deployment in wildfire-prone areas and discuss potential opportunities for future research and the integration of precipitation data from disparate sources into a common hydrologic modeling framework.
","language":"English","publisher":"Wiley","doi":"10.1002/wat2.1728","usgsCitation":"Partridge, T.F., Johnson, Z., Sleeter, R., Qi, S.L., Walvoord, M.A., Murphy, S.F., Peterman-Phipps, C.L., and Ebel, B., 2024, Opportunities and challenges for precipitation forcing data in post-wildfire hydrologic modeling applications: WIREs Water, v. 11, no. 5, e1728, 27 p., https://doi.org/10.1002/wat2.1728.","productDescription":"e1728, 27 p.","ipdsId":"IP-155206","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":427613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":439910,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wat2.1728","text":"Publisher Index Page"}],"volume":"11","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-04-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Partridge, Trevor Fuess 0000-0003-1589-4783","orcid":"https://orcid.org/0000-0003-1589-4783","contributorId":302668,"corporation":false,"usgs":true,"family":"Partridge","given":"Trevor","email":"","middleInitial":"Fuess","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Zachary 0000-0002-0149-5223 zjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-0149-5223","contributorId":190399,"corporation":false,"usgs":true,"family":"Johnson","given":"Zachary","email":"zjohnson@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":898395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sleeter, Rachel 0000-0003-3477-0436 rsleeter@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-0436","contributorId":666,"corporation":false,"usgs":true,"family":"Sleeter","given":"Rachel","email":"rsleeter@usgs.gov","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":898396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Qi, Sharon L. 0000-0001-7278-4498 slqi@usgs.gov","orcid":"https://orcid.org/0000-0001-7278-4498","contributorId":1130,"corporation":false,"usgs":true,"family":"Qi","given":"Sharon","email":"slqi@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":898399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peterman-Phipps, Cara L. 0000-0003-1822-2552","orcid":"https://orcid.org/0000-0003-1822-2552","contributorId":259166,"corporation":false,"usgs":true,"family":"Peterman-Phipps","given":"Cara","email":"","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":898400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":898401,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70253160,"text":"70253160 - 2024 - Predator disturbance contributed to Common Murre Uria aalge breeding failures in Cook Inlet, Alaska following the 2014–2016 Pacific marine heatwave","interactions":[],"lastModifiedDate":"2024-04-23T12:11:34.910115","indexId":"70253160","displayToPublicDate":"2024-04-07T07:07:59","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7948,"text":"Marine Onithology","active":true,"publicationSubtype":{"id":10}},"title":"Predator disturbance contributed to Common Murre Uria aalge breeding failures in Cook Inlet, Alaska following the 2014–2016 Pacific marine heatwave","docAbstract":"We developed an open source, extensible Python-based framework, that we call the Versatile Modeling of Deformation (VMOD), for forward and inverse modeling of crustal deformation sources. VMOD abstracts from specific source model implementations, data types and inversion methods. We implement the most common geodetic source models which can be combined to model and analyze multi-source deformation. VMOD supports Global Navigation Satellite System (GNSS), InSAR, electronic distance measurement, Leveling and tilt data. To infer source characteristics from observations, VMOD implements non-linear least squares and Markov Chain Monte-Carlo Bayesian inversions, including joint inversions using different sources of data. VMOD's structure allows for easy integration of new geodetic models, data types, and inversion strategies. We benchmark the forward models against other published results and the inversion approaches against other implementations. We apply VMOD to analyze deformation at Unimak Island, Alaska, observed with continuous and campaign GNSS, and ascending and descending InSAR time series generated from Sentinel-1 satellite radar acquisitions. These data show an inflation pattern at Westdahl volcano and subsidence at Fisher Caldera. We use VMOD to test a range of source models by jointly inverting the GNSS and InSAR data sets. Our final model simultaneously constrains the parameters of two sources. Our results reveal a depressurizing spheroid under Fisher Caldera ∼4–6 km deep, contracting at a rate of ∼2–3 Mm3/yr, and a pressurizing spherical source underneath Westdahl volcano ∼6–8 km deep, inflating at ∼5 Mm3/yr. This and past applications of VMOD to volcanic unrest benefit from an extensible framework which supports jointly inversions of data sets for parameters of easily composable multi-source models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2023GC011341","usgsCitation":"Angarita, M., Grapenthin, R., Henderson, S., Christoffersen, M.S., and Anderson, K.R., 2024, Versatile modeling of deformation (VMOD) inversion framework: Application to 20 years of observations at Westdahl Volcano and Fisher Caldera, Alaska, US: Geochemistry, Geophysics, Geosystems, v. 25, e2023GC011341, 19 p., https://doi.org/10.1029/2023GC011341.","productDescription":"e2023GC011341, 19 p.","ipdsId":"IP-159327","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":439914,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2023gc011341","text":"Publisher Index Page"},{"id":427557,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Unimak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166.24763757080336,\n 55.437699893466515\n ],\n [\n -166.24763757080336,\n 53.85001704010827\n ],\n [\n -162.27182660949765,\n 53.85001704010827\n ],\n [\n -162.27182660949765,\n 55.437699893466515\n ],\n [\n -166.24763757080336,\n 55.437699893466515\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2024-04-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Angarita, Mario","contributorId":215655,"corporation":false,"usgs":false,"family":"Angarita","given":"Mario","email":"","affiliations":[{"id":37066,"text":"OVSICORI","active":true,"usgs":false}],"preferred":false,"id":898366,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grapenthin, Ronni","contributorId":257035,"corporation":false,"usgs":false,"family":"Grapenthin","given":"Ronni","email":"","affiliations":[{"id":7026,"text":"New Mexico Tech","active":true,"usgs":false}],"preferred":false,"id":898367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Henderson, Scott","contributorId":206392,"corporation":false,"usgs":false,"family":"Henderson","given":"Scott","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":898368,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christoffersen, Michael S","contributorId":237038,"corporation":false,"usgs":false,"family":"Christoffersen","given":"Michael","email":"","middleInitial":"S","affiliations":[{"id":36422,"text":"University of Texas","active":true,"usgs":false}],"preferred":false,"id":898379,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Anderson, Kyle R. 0000-0001-8041-3996 kranderson@usgs.gov","orcid":"https://orcid.org/0000-0001-8041-3996","contributorId":3522,"corporation":false,"usgs":true,"family":"Anderson","given":"Kyle","email":"kranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":898370,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255672,"text":"70255672 - 2024 - Using climate-fire analog mapping to inform climate change adaptation strategies for wildland fire in protected areas of the conterminous US","interactions":[],"lastModifiedDate":"2024-06-28T11:49:44.275525","indexId":"70255672","displayToPublicDate":"2024-04-06T06:48:02","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17987,"text":"Global Environmental Change Advances","active":true,"publicationSubtype":{"id":10}},"title":"Using climate-fire analog mapping to inform climate change adaptation strategies for wildland fire in protected areas of the conterminous US","docAbstract":"Potential changes in wildland fire regimes due to anthropogenic climate change can be projected using data from climate models, but directly applying these meteorological variables to long-term planning and adaptive management activities may be difficult for decision makers. Analog mapping, in contrast, creates more intuitive assessments of changing fire regimes that also recognize the complex, multivariate, and multi-scalar nature of ecosystems. Here, we use data from 20 downscaled climate models under two climate forcing scenarios, Representative Concentration Pathways (RCP 4.5 and 8.5), to identify and map future climate-fire analogs for 655 protected areas in the conterminous U.S. based on annual temperature, cumulative precipitation amount and seasonality, and fire regime potentials derived from a simple process-based fire frequency model. Patterns of analogs were heavily influenced by gradients in latitude and topography, with longer time frames (end-of-century conditions) and the more extreme climate forcing scenario resulting in greater analog distances and more ensemble entropy (i.e., less consensus among climate models regarding the closest analog for a given management unit). Finer scale analyses for three protected areas (Yellowstone and Great Smoky Mountains National Parks, White Mountain National Forest) illustrate how climate-fire analog mapping can improve insight into the types of ecosystem responses that might occur under similar management conditions. Federally protected areas such as national parks, forests, and wildlife refuges have long served as reference sites for the study of fire regimes, a role that is likely to continue because many of these units are managed to allow at least some ecosystem processes to operate independently. The results suggest that analog mapping approaches are well-suited as part of qualitative assessments within climate- and fire-aware adaptive management processes. The use of analogs to depict relatable, real-world depictions of possible ecosystem changes in a given place, can help managers make more strategic choices about when and where to resist, accept, or direct climate change-driven ecological change.
This study presents a comparison of measured versus modeled total organic carbon (TOC) in the Upper Cretaceous Tuscaloosa marine shale (TMS) of southwestern Mississippi as a case study to evaluate the effects of mineralogy on the TOC estimated from the ΔlogR method. The ΔlogR method is utilized to calculate TOC, which involves baselining sonic transit time and resistivity log curves in a non-source rock section of the formation. In our application, the well log curves were baselined in the upper TMS, which is described as a non-source rock section, and in the lower TMS above the high resistivity zone (HRZ), which is described as having a higher carbonate content. The ΔlogR calculated TOC values from these two baselining approaches show that the lower baseline results in improved agreement between measured TOC and calculated TOC. This improvement is likely to be due to the lower baseline accounting for the increase in resistivity caused by higher carbonate content, in addition to any presence of TOC. The upper baseline, which has a lower carbonate content, does not account for this resistivity increase. Additionally, sample type appears to affect the comparison of measured and ΔlogR calculated TOC. Most of the samples used in this study are legacy cuttings that were not preserved during storage, exposing the high surface area cuttings to increased rates of oxidation, whereas geophysical logs record the rock properties in situ. To account for this oxidation effect, the difference between the medians of the TMS HRZ TOC core and cuttings values was added to each TOC measurement in this study, resulting in a median measured TOC value that is similar to the median of the lower TMS-baselined ΔlogR calculated TOC value. Overall, this study demonstrates that carbonate content and sample type can affect how well measured and ΔlogR-modeled TOC values compare.
Unknown causes behind the loss of freshwater mussel populations have prompted population restoration as a tool to recover these imperiled species. However, water quality conditions that support mussel species within natural environments and potential causes of water quality impairment in systems with declining populations are typically unknown and may be critical knowledge needed before reintroducing mussels. Our objective was to relate the growth and survival of declining freshwater mussel populations of the Brook Floater Alasmidonta varicosa (Lamarck, 1819) to water quality parameters within 4 Massachusetts, USA, rivers containing extant populations. We deployed propagated age-1 and age-2 Brook Floater in contained systems (silos) for 1 growing season (June–October). Through biweekly sampling, we tracked the growth and survival of mussels then modeled their relationships with water quality variables. Mussels had a higher growth rate at a higher chlorophyll a (Chl a) value (2.82 µg/L) over the temperature range measured (biweekly mean = 16–26°C) when compared with lower Chl a values (0.61 and 1.17 µg/L). Age-1 mussel growth rate was negatively affected by low Chl a concentration (0.61 µg/L) across the temperature range, but age-2 mussel growth rate was not negatively affected until temperatures were above ~22°C. Na+ limited the growth rate of mussels, with the rate of change in growth rate for age-1 mussels greater than for age-2 mussels. Other cations (Mg2+, K+, and Ca2+)—potentially linked to road deicers—also negatively affected growth rate in all 4 rivers but may have had a greater impact on mussels in rivers with reduced growth rates from lower temperatures and Chl a. However, survival was uniformly high across all rivers, indicating water quality parameters may have sublethal but not lethal effects. Additional assessments for chronic water quality stressors along with changing land cover, land management, and climates are important considerations for restoration potential and the long-term persistence of populations.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/730247","usgsCitation":"Skorupa, A., Roy, A.H., Hazelton, P., Perkins, D., Timothy Warren, T., and Cheng, B.S., 2024, Food, water quality, and the growth of a freshwater mussel: Implications for population restoration: Freshwater Science, v. 43, no. 2, p. 107-123, https://doi.org/10.1086/730247.","productDescription":"7 p.","startPage":"107","endPage":"123","ipdsId":"IP-154695","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":432035,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.92801492613863,\n 42.333950606928596\n ],\n [\n -71.9376914557071,\n 42.33232039224919\n ],\n [\n -73.077586638902,\n 42.32546816564172\n ],\n [\n -73.17435193458931,\n 42.06443189078277\n ],\n [\n -71.94349737344804,\n 42.081670744671186\n ],\n [\n -71.92801492613863,\n 42.333950606928596\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -71.62300789778979,\n 42.70452095814687\n ],\n [\n -71.62300789778979,\n 42.65160665862743\n ],\n [\n -71.5465897901165,\n 42.65160665862743\n ],\n [\n -71.5465897901165,\n 42.70452095814687\n ],\n [\n -71.62300789778979,\n 42.70452095814687\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Skorupa, Ayla J.","contributorId":340492,"corporation":false,"usgs":false,"family":"Skorupa","given":"Ayla J.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":907293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907294,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hazelton, Peter D.","contributorId":340493,"corporation":false,"usgs":false,"family":"Hazelton","given":"Peter D.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":907295,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Perkins, David","contributorId":340494,"corporation":false,"usgs":false,"family":"Perkins","given":"David","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907296,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Timothy Warren, Timothy","contributorId":340495,"corporation":false,"usgs":false,"family":"Timothy Warren","given":"Timothy","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":907297,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cheng, Brian S.","contributorId":340496,"corporation":false,"usgs":false,"family":"Cheng","given":"Brian","email":"","middleInitial":"S.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":907298,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70253194,"text":"70253194 - 2024 - Tracking data highlight the importance of human-induced mortality for large migratory birds at a flyway scale","interactions":[],"lastModifiedDate":"2024-04-26T12:06:30.467982","indexId":"70253194","displayToPublicDate":"2024-04-05T07:05:03","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Tracking data highlight the importance of human-induced mortality for large migratory birds at a flyway scale","docAbstract":"Human-induced direct mortality affects huge numbers of birds each year, threatening hundreds of species worldwide. Tracking technologies can be an important tool to investigate temporal and spatial patterns of bird mortality as well as their drivers. We compiled 1704 mortality records from tracking studies across the African-Eurasian flyway for 45 species, including raptors, storks, and cranes, covering the period from 2003 to 2021. Our results show a higher frequency of human-induced causes of mortality than natural causes across taxonomic groups, geographical areas, and age classes. Moreover, we found that the frequency of human-induced mortality remained stable over the study period. From the human-induced mortality events with a known cause (n = 637), three main causes were identified: electrocution (40.5 %), illegal killing (21.7 %), and poisoning (16.3 %). Additionally, combined energy infrastructure-related mortality (i.e., electrocution, power line collision, and wind-farm collision) represented 49 % of all human-induced mortality events. Using a random forest model, the main predictors of human-induced mortality were found to be taxonomic group, geographic location (latitude and longitude), and human footprint index value at the location of mortality. Despite conservation efforts, human drivers of bird mortality in the African-Eurasian flyway do not appear to have declined over the last 15 years for the studied group of species. Results suggest that stronger conservation actions to address these threats across the flyway can reduce their impacts on species. In particular, projected future development of energy infrastructure is a representative example where application of planning, operation, and mitigation measures can enhance bird conservation.
Carbon dioxide (CO2) is gaining interest as a tool to combat aquatic invasive species, including zebra mussels (Dreissena polymorpha). However, the effects of water chemistry on CO2 efficacy are not well described. We conducted five trials in which we exposed adult zebra mussels to a range of CO2 in water with adjusted total hardness and specific conductance. We compared dose–responses and found differences in lethal concentration to 50% of organisms (LC50) estimates ranging from 108.3 to 179.3 mg/L CO2 and lethal concentration to 90% of organisms (LC90) estimates ranging from 163.7 to 216.6 mg/L CO2. We modeled LC50 and LC90 estimates with measured water chemistry variables from the trials. We found sodium (Na+) concentration to have the strongest correlation to changes in the LC50 and specific conductance to have the strongest correlation to changes in the LC90. Our results identify water chemistry as an important factor in considering efficacious CO2 concentrations for zebra mussel control. Additional research into the physiological responses of zebra mussels exposed to CO2 may be warranted to further explain mode of action and reported selectivity. Further study could likely develop a robust and relevant model to refine CO2 applications for a wider range of water chemistries. Environ Toxicol Chem 2024;00:1–8. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
An understanding of oceanographic conditions and processes important to marine animal ecology is fundamental to the development of effective management and conservation actions. Longfin Smelt (Spirinchus thaleichthys) is a pelagic forage fish found in coastal and estuarine waters along the Pacific coast of North America from Alaska to central California. Substantial population declines in California’s San Francisco Estuary, where Longfin Smelt are protected under California’s Endangered Species Act, have prompted extensive study of estuarine factors associated with the decline. However, coastal factors that affect up to two-thirds of the Longfin Smelt life cycle are poorly understood and may be important drivers of population dynamics. We compiled coastal observations from numerous sources to estimate the range-wide coastal marine distribution of Longfin Smelt and assess habitat factors affecting distribution in the northeast Pacific Ocean. Based on maximum entropy species distribution models, Longfin Smelt distribution was correlated with depth, distance from the nearest estuary, sea surface temperature, and sea surface chlorophyll. Longfin Smelt were found in shallow, higher productivity coastal waters closer to estuaries, with depth and temperature the most consistent factors influencing distribution. Habitat suitability was highly variable at the southern extent of the range, particularly off the California coast, and was largely driven by habitat contractions associated with warm-water conditions. Study results provide insights into the habitat and range-wide distribution of an at-risk estuarine-reliant forage fish and are the first step toward identifying processes that affect the marine portion of the Longfin Smelt life cycle.
Salinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state-of-the-art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L−1 Cl− isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere-Wave-Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process-based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.
He‘eia and ‘Ioleka‘a Streams in the He‘eia watershed on O‘ahu, Hawai‘i, receive substantial discharge from dike-impounded groundwater. Previous studies indicated that groundwater withdrawals from the watershed affect streamflow. Resource managers and users seek information that can be used to balance the needs of competing uses of groundwater and streamflow in the watershed.
In this study, analyses of historical streamflow and withdrawal data indicate that when groundwater withdrawals from Haiku Tunnel (a groundwater development tunnel built in the 1940s in the watershed) of 1.73–1.87 million gallons per day (Mgal/d) were introduced in the first few decades of the tunnel’s operation, base flow at a gage on He‘eia Stream decreased by 1.37–1.40 Mgal/d. Changes in rainfall during this period were not sufficient to account for the changes in base flow. The tunnel withdrawal also affected ‘Ioleka‘a Stream, but the effect was less. In the 1980s, average withdrawal from the tunnel decreased by 0.73–1.00 Mgal/d and base flow at the He‘eia streamgage increased by 0.15–0.21 Mgal/d; a concurrent rainfall increase may partly account for the base-flow increase. Withdrawal from another well (Haiku well) starting in the late 1980s had a much smaller effect than the tunnel did on flow at the He‘eia streamgage.
Numerical groundwater-model simulations indicate that shutting down withdrawals from Haiku Tunnel and Haiku well would increase base flows in streams inside and outside of the He‘eia watershed. Simulated shutdown of 0.35 Mgal/d withdrawal from Haiku well caused base flow of streams in the He‘eia watershed to increase by 0.09 Mgal/d or 26 percent of the withdrawal reduction, and shutdown of 0.60 Mgal/d withdrawal from Haiku Tunnel caused base flow of streams within the watershed to increase by 0.12 Mgal/d or 20 percent of withdrawal reduction. Shutdown of a combined 0.95 Mgal/d withdrawal from the tunnel and well caused base flow of streams within the watershed to increase by 0.22 Mgal/d or 23 percent of the withdrawal reduction.
The model simulations and analyses of streamflow data demonstrate that, climate changes notwithstanding, reducing or shutting down withdrawal from Haiku Tunnel has not in the past, and will not in the future, restore base flow to predevelopment rates. The nearly pristine condition that existed prior to the construction of the Haiku Tunnel no longer exists because other large-producing tunnels and wells near the He‘eia watershed have since begun withdrawing water from the same dike-impounded aquifer. Reduction or shutdown of withdrawals from the wells and tunnel in the He‘eia watershed cannot restore streamflow to predevelopment rates if withdrawals from all other wells and tunnels continue.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245020","collaboration":"Prepared in cooperation with the Honolulu Board of Water Supply","usgsCitation":"Izuka, S.K., Kāne, H.L., and Rotzoll, K., 2024, Groundwater and surface-water interactions in the He‘eia watershed, O‘ahu, Hawai‘i—Insights from analysis of historical data and numerical groundwater-model simulations: U.S. Geological Survey Scientific Investigations Report 2024–5020, 22 p., https://doi.org/10.3133/sir20245020.","productDescription":"Report: v, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-149791","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":499449,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116223.htm","linkFileType":{"id":5,"text":"html"}},{"id":427359,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5020/sir20245020.pdf","text":"Report","size":"7 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":427358,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5020/covrthb.jpg"},{"id":427357,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91JM5FZ","text":"USGS Data Release","description":"Rotzoll, K., 2024, MODFLOW-2005 and SWI2 models for assessing groundwater and surface-water interactions in the Heeia Watershed, Oahu, Hawaii: U.S. Geological Survey data release, https://doi.org/10.5066/P91JM5FZ.","linkHelpText":"MODFLOW-2005 and SWI2 models for assessing groundwater and surface-water interactions in the Heeia Watershed, Oahu, Hawaii"}],"country":"United States","state":"Hawaii","otherGeospatial":"He‘eia Watershed, O‘ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.841667,\n 21.441667\n ],\n [\n -157.841667,\n 21.391667\n ],\n [\n -157.791667,\n 21.391667\n ],\n [\n -157.791667,\n 21.441667\n ],\n [\n -157.841667,\n 21.441667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Ungulates commonly select habitat with higher forage biomass and or nutritional quality to improve body condition and fitness. However, predation risk can alter ungulate habitat selection and foraging behavior and may affect their nutritional condition. Ungulates often choose areas with lower predation risk, sometimes sacrificing higher quality forage. This forage–predation risk trade-off can be important for life history strategies and influences individual nutritional condition and population vital rates. We used GPS collar data from adult female mule deer (Odocoileus hemionus) and mountain lions (Puma concolor) to model mule deer habitat selection in relation to forage conditions, stalking cover and predation risk from mountain lions to determine if a forage-predation risk trade-off existed for mule deer in central New Mexico. We also examined mountain lion kill sites and mule deer foraging locations to assess trade-offs at a finer scale. Forage biomass and protein content were inversely correlated with horizontal visibility, hence associated with higher stalking cover for mountain lions, suggesting a forage-predation risk trade-off for mule deer. Mule deer habitat selection was influenced by forage biomass and protein content at the landscape and within home range spatial scales, with forage protein being related to habitat selection during spring and summer and forage biomass during winter. However, mule deer selection for areas with better foraging conditions was constrained by landscape-scale encounter risk for mountain lions, such that increasing encounter risk was associated with diminished selection for areas with better foraging conditions. Mule deer also selected for areas with higher visibility when mountain lion predation risk was higher. Mountain lion kill sites were best explained by decreasing horizontal visibility and available forage protein, suggesting that deer may be selecting for forage quality at the cost of predation risk. A site was 1.5 times more likely to be a kill site with each 1-meter decrease in visibility (i.e., increased stalking cover). Mule deer selection of foraging sites was related to increased forage biomass, further supporting the potential for a trade-off scenario. Mule deer utilized spatio-temporal strategies and risk-conditional behavior to reduce predation risk, and at times selected suboptimal foraging areas with lower predation risk.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2024.1121439","usgsCitation":"Cain, J.W., Kay, J.H., Liley, S.G., and Gedir, J.V., 2024, Mule deer (Odocoileus hemionus) resource selection: Trade-offs between forage and predation risk: Frontiers in Ecology and Evolution, v. 12, 1121439, 17 p., https://doi.org/10.3389/fevo.2024.1121439.","productDescription":"1121439, 17 p.","ipdsId":"IP-148026","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":439940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2024.1121439","text":"Publisher Index Page"},{"id":429764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Cibola National Forest, Gallinas Mountains area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.59348062065897,\n 34.340783560738174\n ],\n [\n -107.59348062065897,\n 34.265500399112824\n ],\n [\n -107.493643710218,\n 34.265500399112824\n ],\n [\n -107.493643710218,\n 34.340783560738174\n ],\n [\n -107.59348062065897,\n 34.340783560738174\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2024-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kay, Jacob H.","contributorId":337909,"corporation":false,"usgs":false,"family":"Kay","given":"Jacob","email":"","middleInitial":"H.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":902777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liley, Stewart G.","contributorId":337910,"corporation":false,"usgs":false,"family":"Liley","given":"Stewart","email":"","middleInitial":"G.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":902778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gedir, Jay V.","contributorId":337911,"corporation":false,"usgs":false,"family":"Gedir","given":"Jay","email":"","middleInitial":"V.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":902779,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252794,"text":"70252794 - 2024 - Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images","interactions":[],"lastModifiedDate":"2024-04-05T15:19:37.198461","indexId":"70252794","displayToPublicDate":"2024-04-04T10:14:30","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17157,"text":"Frontiers in Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images","docAbstract":"Introduction: Information on spatial patterns of water depth in river channels is valuable for numerous applications, but such data can be difficult to obtain via traditional field methods. Ongoing developments in remote sensing technology have enabled various image-based approaches for mapping river bathymetry; this study evaluated the potential to retrieve depth from multispectral images acquired by an uncrewed aircraft system (UAS).
Methods: More specifically, we produced depth maps for a 4 km reach of a clear-flowing, relatively shallow river using an established spectrally based algorithm, Optimal Band Ratio Analysis. To assess accuracy, we compared image-derived estimates to direct measurements of water depth. The field data were collected by wading and from a boat equipped with an echo sounder and used to survey cross sections and a longitudinal profile. We partitioned our study area along the Sacramento River, California, USA, into three distinct sub-reaches and acquired a separate image for each one. In addition to the typical, self-contained, per-image depth retrieval workflow, we also explored the possibility of exporting a relationship between depth and reflectance calibrated using data from one site to the other two sub-reaches. Moreover, we evaluated whether sampling configurations progressively more sparse than our full field survey could still provide sufficient calibration data for developing robust depth retrieval models.
Results: Our results indicate that under favorable environmental conditions like those observed on the Sacramento River during low flow, accurate, precise depth maps can be derived from images acquired by UAS, not only within a sub-reach but also across multiple, adjacent sub-reaches of the same river.
Discussion: Moreover, our findings imply that the level of effort invested in obtaining field data for calibration could be significantly reduced. In aggregate, this investigation suggests that UAS-based remote sensing could facilitate highly efficient, cost-effective, operational mapping of river bathymetry at the reach scale in clear-flowing streams.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/frsen.2024.1305991","usgsCitation":"Legleiter, C.J., and Harrison, L.R., 2024, Evaluating the potential for efficient, UAS-based reach-scale mapping of river channel bathymetry from multispectral images: Frontiers in Remote Sensing, v. 5, 1305991, 16 p., https://doi.org/10.3389/frsen.2024.1305991.","productDescription":"1305991, 16 p.","ipdsId":"IP-156864","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":439943,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/frsen.2024.1305991","text":"Publisher Index Page"},{"id":434995,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KEXVAR","text":"USGS data release","linkHelpText":"Multispectral images and field measurements of water depth from the Sacramento River near Glenn, California, acquired September 14-16, 2021"},{"id":427518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5,\n 40\n ],\n [\n -122.5,\n 39\n ],\n [\n -121.75,\n 39\n ],\n [\n -121.75,\n 40\n ],\n [\n -122.5,\n 40\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","noUsgsAuthors":false,"publicationDate":"2024-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":898242,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":898243,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70252803,"text":"70252803 - 2024 - Apparent non-double-couple components as artifacts of moment tensor inversion","interactions":[],"lastModifiedDate":"2024-04-05T15:09:16.834651","indexId":"70252803","displayToPublicDate":"2024-04-04T10:05:07","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17454,"text":"Seismica","active":true,"publicationSubtype":{"id":10}},"title":"Apparent non-double-couple components as artifacts of moment tensor inversion","docAbstract":"Compilations of earthquake moment tensors from global and regional catalogs find pervasive non-double-couple (NDC) components with a mean deviation from a double-couple (DC) source of around 20%. Their distributions vary only slightly with magnitude, faulting mechanism, or geologic environments. This consistency suggests that for most earthquakes, especially smaller ones whose rupture processes are expected to be simpler, the NDC components are largely artifacts of the moment tensor inversion procedure. This possibility is also supported by the fact that NDC components for individual earthquakes with Mw<6.5 are only weakly correlated between catalogs. We explore this possibility by generating synthetic seismograms for the double-couple components of earthquakes around the world using one Earth model and inverting them with a different Earth model. To match the waveforms with a different Earth model, the inversion changes the mechanisms to include a substantial NDC component while largely preserving the fault geometry (DC component). The resulting NDC components have a size and distribution similar to those reported for the earthquakes in the Global Centroid Moment Tensor (GCMT) catalog. The fact that numerical experiments replicate general features of the pervasive NDC components reported in moment tensor catalogs implies that these components are largely artifacts of the inversions not adequately accounting for the effects of laterally varying Earth structure.
","language":"English","publisher":"Seismica","doi":"10.26443/seismica.v3i1.1157","usgsCitation":"Rosler, B., Stein, S., Ringler, A.T., and Vackar, J., 2024, Apparent non-double-couple components as artifacts of moment tensor inversion: Seismica, v. 3, no. 1, 1157, 11 p., https://doi.org/10.26443/seismica.v3i1.1157.","productDescription":"1157, 11 p.","ipdsId":"IP-140079","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":439944,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.26443/seismica.v3i1.1157","text":"Publisher Index Page"},{"id":427517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosler, Boris","contributorId":335403,"corporation":false,"usgs":false,"family":"Rosler","given":"Boris","email":"","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":898273,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stein, Seth","contributorId":263457,"corporation":false,"usgs":false,"family":"Stein","given":"Seth","affiliations":[{"id":25254,"text":"Northwestern University","active":true,"usgs":false}],"preferred":false,"id":898274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":898275,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vackar, Jiri","contributorId":335404,"corporation":false,"usgs":false,"family":"Vackar","given":"Jiri","email":"","affiliations":[{"id":80396,"text":"The Czech Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":898276,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252873,"text":"70252873 - 2024 - Preliminary implications of viscoelastic ray theory for anelastic seismic tomography models","interactions":[],"lastModifiedDate":"2024-06-03T14:58:44.548945","indexId":"70252873","displayToPublicDate":"2024-04-04T07:02:30","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Preliminary implications of viscoelastic ray theory for anelastic seismic tomography models","docAbstract":"The recent developments in general viscoelastic ray theory provide a rigorous mathematical framework for anelastic seismic tomography. They provide closed‐form solutions of forward ray‐tracing and simple inverse problems for anelastic horizontal and spherical layered media with material gradients. They provide ray‐tracing computation algorithms valid for all angles of incidence that account for changes in wave speed, attenuation, and trajectory of anelastic P and S body waves induced by anelastic boundaries. They account for theoretical predictions that seismic waves refract as inhomogeneous waves across anelastic boundaries for all angles of incidence, which in turn accounts for energy carried by plane waves along seismic boundaries at head wave critical angles and wide‐angle refracted (WAR) ray paths that are not predicted by elastic models. Exact viscoelastic ray‐tracing numerical results for various models provide examples that illustrate the effects of anelastic boundaries on the travel times and amplitudes of seismic waves. They show the effects are strongly dependent on angle of incidence. For near‐critical and wide angles of incidence the anelastic effects on travel times and amplitudes can be large and are not explained by elastic ray theory, but the effects on travel times can be relatively small and difficult to distinguish from those for elastic media for pre‐near‐critical angles of incidence. The results for some models indicate that reflected anelastic WAR waves may be observable at the surface and possibly account for some prominent seismic arrivals not explained by elasticity. These preliminary results suggest that the application of exact viscoelastic ray‐tracing computation algorithms to exploration and teleseismic data sets can reveal new insights regarding the properties and distribution of anelastic materials in the Earth.
From 2018 through 2020, the U.S. Geological Survey, in cooperation with the Air Force Civil Engineering Center, conducted an integrated study of the Fountain Creek alluvial aquifer located near Colorado Springs, Colorado. The objective of the study was to characterize hydrologic conditions for the alluvial aquifer pertinent to the potential for transport of solutes. Specific goals of this report were to characterize the groundwater hydrology of the area, to quantify groundwater and surface-water interactions, to estimate hydraulic properties of the aquifer using aquifer testing, and to complete numerical simulations of groundwater flow.
Synoptic groundwater-level elevation measurements completed throughout this study, and as part of other U.S. Geological Survey programs between 1994 and 2020, indicate groundwater-level elevations fluctuate on annual and interannual timeframes. Groundwater-level fluctuations likely were caused by temporally variable groundwater recharge and discharge components in the area, with many wells showing maximum groundwater-level elevations during the winter months (November through March). From an interannual perspective, groundwater-level fluctuations appear to have reached maximum values during 2000 to 2003, decreased during 2003 to 2006, and remained relatively constant since that time, with the exception of several wells which have displayed rising groundwater-level elevations since 2018. Spatial evaluation of groundwater-level elevations indicates groundwater flow is generally from northeast to southwest within the vicinity of several alluvial paleochannels occurring along the northeastern margin of the aquifer. Within the center of the aquifer along Fountain Creek, groundwater flow is generally from north to south, approximately paralleling surface-water flow. To quantitatively understand the potential effect of groundwater recharge and groundwater pumping on fluctuations in groundwater-level elevation, a statistical transfer-function-noise model was applied. Results of the statistical model indicate throughout most of the aquifer, fluctuations were primarily the result of recharge seasonality. In the main stem of the aquifer where groundwater pumping wells were more concentrated, however, groundwater-level elevation fluctuations were also attributable to groundwater pumping through time.
Three-dimensional evaluation of the aquifer geometry near Fountain Creek was combined with synoptic streamflow measurement and accounting of stream gains and losses to evaluate groundwater and surface-water interactions in the study area. Streamflow gain or loss calculations indicate Fountain Creek both gains from and loses flow to the alluvial aquifer, and gaining or losing reaches of the stream may be partially controlled by the depth to bedrock near the stream. Reaches with streamflow gains tend to coincide with areas where the estimated depth to bedrock is decreasing, meaning the alluvial aquifer is likely thinning in these areas and groundwater-flow paths may be converging and discharging groundwater to the stream. Losing reaches tended to coincide with locally greater depth to bedrock where the alluvial aquifer is likely thicker and has greater storage potential for surface water lost from Fountain Creek.
Results of aquifer testing indicate hydraulic conductivity, estimated from slug tests and single-well pumping tests, ranged from 0.32 to 1,410 feet per day (ft/d) and 4.13 to 664 ft/d, respectively. These results are similar to the range of values from previous aquifer tests in the study area. Hydraulic conductivities from aquifer testing for this study were generally greater than the estimates of previous slug tests and had a mean value less than the estimates from previous pumping tests. Spatial evaluation of aquifer testing results indicates hydraulic conductivity tends to be greater in the main stem of the alluvial aquifer and lower in paleochannels upgradient from the main stem of the aquifer. The spatial variation in hydraulic conductivity may be attributed to the geomorphologic processes that formed the alluvial aquifer. Compacted sediment in the paleochannels has not been potentially transported sufficient distance to cause grain-size sorting, resulting in a poorly sorted deposit and lower hydraulic conductivities. In the central portion of the alluvial aquifer, near Fountain Creek, the sediments have been transported farther from their source areas and are likely better sorted, removing finer grained sediments that would cause lower hydraulic conductivity.
A numerical groundwater-flow model was calibrated for the Fountain Creek alluvial aquifer for 2000–19 to simulate water-budget components, groundwater-flow directions, and groundwater-flow paths. The model simulated precipitation recharge, groundwater and surface-water interactions, evapotranspiration, high-volume groundwater pumping by pumping wells, and external inflows and outflows occurring along the boundaries of the alluvial aquifer. Model calibration was completed using manual and automated approaches, the latter of which assisted in quantifying model results sensitivity to input parameters. The calibrated model corresponds well with groundwater-level elevation observations, with a mean residual (observed minus simulated groundwater-level elevation) equal to −0.60 feet. Simulated groundwater base flow to streams was typically within 10 percent of base flow estimated by independent methods. Groundwater and surface-water interactions represented the largest water-budget components of the aquifer, with the second largest groundwater discharge component coming from pumping wells. Groundwater and surface-water interactions represent both the largest gain and loss terms in the water budget, because these interactions differ spatially, meaning in some areas of the model domain groundwater is being recharged by streams, whereas in other areas, groundwater is discharged to streams. Estimates of advective groundwater-flow paths indicate pumping wells may capture groundwater recharged from losing streams and groundwater that flows into the main stem of the alluvial aquifer from paleochannels.
Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, Colorado 80225
Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16071264","usgsCitation":"Geisz, J.K., Wernette, P., and Esselman, P., 2024, Classification of lakebed geologic substrate in autonomously collected benthic imagery using machine learning: Remote Sensing, v. 16, no. 7, 1264, 29 p., https://doi.org/10.3390/rs16071264.","productDescription":"1264, 29 p.","ipdsId":"IP-152592","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":439948,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16071264","text":"Publisher Index Page"},{"id":434996,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N32CV7","text":"USGS data release","linkHelpText":"Autonomously Collected Benthic Imagery for Substrate Prediction, Lake Michigan 2020-2021"},{"id":433724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.90204822016032,\n 42.674502983776904\n ],\n [\n -87.65489307014272,\n 42.76669269166209\n ],\n [\n -86.26871023108912,\n 45.83189647310439\n ],\n [\n -86.71489901589742,\n 45.86861211740754\n ],\n [\n -87.71542062998205,\n 44.51147169699496\n ],\n [\n -88.08342598535049,\n 43.233909567544146\n ],\n [\n -87.90204822016032,\n 42.674502983776904\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.61587060463187,\n 45.628545449737345\n ],\n [\n -84.87703701182424,\n 45.79764011104598\n ],\n [\n -85.13854737456055,\n 45.75496839185476\n ],\n [\n -85.7086983940175,\n 44.97892485981876\n ],\n [\n -85.62680073631381,\n 44.81109395544547\n ],\n [\n -84.61587060463187,\n 45.628545449737345\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"7","noUsgsAuthors":false,"publicationDate":"2024-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Geisz, Joseph K. 0000-0001-6783-7057","orcid":"https://orcid.org/0000-0001-6783-7057","contributorId":342270,"corporation":false,"usgs":false,"family":"Geisz","given":"Joseph","email":"","middleInitial":"K.","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":912943,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wernette, Phillipe Alan 0000-0002-8902-5575","orcid":"https://orcid.org/0000-0002-8902-5575","contributorId":259274,"corporation":false,"usgs":true,"family":"Wernette","given":"Phillipe Alan","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":912944,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":912945,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252786,"text":"70252786 - 2024 - Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys","interactions":[],"lastModifiedDate":"2024-04-05T14:33:25.164806","indexId":"70252786","displayToPublicDate":"2024-04-03T09:27:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys","docAbstract":"Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitations and the need for processing efficiencies to support the envisioned in-flight workflow. We developed an annotated dataset for a suite of marine birds from high-resolution digital aerial imagery collected over open water environments to train the models. The proposed 3-stage workflow for automated, in-flight data processing includes: 1) image filtering based on the probability of any bird occurrence, 2) bird instance detection, and 3) bird instance classification. For image filtering, we compared the performance of a binary classifier with Mask Region-based Convolutional Neural Network (Mask R-CNN) as a means of sub-setting large volumes of imagery based on the probability of at least one bird occurrence in an image. On both the validation and test datasets, the binary classifier achieved higher performance than Mask R-CNN for predicting bird occurrence at the image-level. We recommend the binary classifier over Mask R-CNN for workflow first-stage filtering. For bird instance detection, we leveraged Mask R-CNN as our detection framework and proposed an iterative refinement method to bootstrap our predicted detections from loose ground-truth annotations. We also discuss future work to address the taxonomic classification phase of the envisioned workflow.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0288121","usgsCitation":"Ke, T., Yu, S.X., Koneff, M.D., Fronczak, D.L., Fara, L., Harrison, T., Landolt, K.L., Hlavacek, E., Lubinski, B.R., and White, T., 2024, Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys: PLoS ONE, v. 19, no. 4, e0288121, 19 p., https://doi.org/10.1371/journal.pone.0288121.","productDescription":"e0288121, 19 p.","ipdsId":"IP-154866","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":439949,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0288121","text":"Publisher Index Page"},{"id":434997,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CBZQV1","text":"USGS data release","linkHelpText":"Code, imagery, and annotations for training a deep learning model to detect 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