Flathead Catfish Pylodictis olivaris are a widespread aquatic invasive species within the United States and a recent invader in the Susquehanna River basin, Pennsylvania. Flathead Catfish are piscivores known to consume native and recreationally important fish species. In the mid-Atlantic United States, it is unknown how this invader is impacting food webs and which species may be at greatest predation risk. To address this knowledge gap, we DNA barcoded stomach contents collected from Flathead Catfish to identify prey species and elucidate potential predatory impacts of Flathead Catfish in the Susquehanna River.
We used a Bayesian hierarchical multivariate probit model to investigate if the probability of prey species occurrence in the diets of Flathead Catfish varied seasonally or was a function of Flathead Catfish length.
A total of 576 Flathead Catfish were collected during 2020–2021, with 241 individuals having recoverable stomach contents. In all, we identified 47 different prey species. The most common prey species were rusty crayfish Faxonius rusticus, Margined Madtom Noturus insignis, and shiners Notropis spp. While frequency of occurrence of prey species differed across Flathead Catfish length classes (<300 mm, 301–600 mm, 601–900 mm TL), rusty crayfish were commonly found (33.7–44.0% of diets) in stomachs of all size-classes.
We found that Flathead Catfish length and seasonality did influence occurrence probability differentially for several prey species. For example, Channel Catfish Ictalurus punctatus were more likely to appear in shorter Flathead Catfish while Smallmouth Bass Micropterus dolomieu appeared in larger Flathead Catfish. We demonstrate significant variation in Flathead Catfish predation, increasing our understanding of predator–prey dynamics, which is necessary to better manage and identify future impacts to aquatic communities in the Susquehanna River basin.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10480","usgsCitation":"Stark, S., Schall, M.K., Smith, G., Maloy, A., Coombs, J.A., Wagner, T., and Avery, J., 2024, Feeding habits and ecological implications of the invasive Flathead Catfish in the Susquehanna River basin, Pennsylvania: Transactions of the American Fisheries Society, v. 153, no. 5, p. 591-610, https://doi.org/10.1002/tafs.10480.","productDescription":"20 p.","startPage":"591","endPage":"610","ipdsId":"IP-160306","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466955,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10480","text":"Publisher Index Page"},{"id":463192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","otherGeospatial":"Susquehanna River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.13792639569971,\n 42.09066504733855\n ],\n [\n -78.27638072611447,\n 42.09066504733855\n ],\n [\n -78.27638072611447,\n 39.71793162556648\n ],\n [\n -75.13792639569971,\n 39.71793162556648\n ],\n [\n -75.13792639569971,\n 42.09066504733855\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"153","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Stark, Sydney","contributorId":343364,"corporation":false,"usgs":false,"family":"Stark","given":"Sydney","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":916708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schall, Megan K.","contributorId":274359,"corporation":false,"usgs":false,"family":"Schall","given":"Megan","email":"","middleInitial":"K.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":916709,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Geoffrey D.","contributorId":224595,"corporation":false,"usgs":false,"family":"Smith","given":"Geoffrey D.","affiliations":[{"id":40898,"text":"Pennsylvania Fish & Boat Commission","active":true,"usgs":false}],"preferred":false,"id":916710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maloy, Aaron","contributorId":343773,"corporation":false,"usgs":false,"family":"Maloy","given":"Aaron","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":916711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coombs, Jason A.","contributorId":77039,"corporation":false,"usgs":true,"family":"Coombs","given":"Jason","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":916712,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":916713,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Avery, Julian","contributorId":264623,"corporation":false,"usgs":false,"family":"Avery","given":"Julian","email":"","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":916714,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70257705,"text":"70257705 - 2024 - Pre-fire assessment of post-fire debris flow hazards in the Santa Fe Municipal Watershed","interactions":[],"lastModifiedDate":"2024-08-23T15:21:25.689185","indexId":"70257705","displayToPublicDate":"2024-08-22T10:17:48","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"title":"Pre-fire assessment of post-fire debris flow hazards in the Santa Fe Municipal Watershed","docAbstract":"Wildfires are increasing in size and severity due to climate change combined with overstocked forests. Fire increases the likelihood of debris flows, posing significant threats to life, property, and water supplies.
We conducted a debris-flow hazard assessment of the Santa Fe Municipal Watershed (SFMW) to answer two questions: (1) where are debris flows most likely to occur; and (2) how much debris might they produce? We also document the influence of fuel treatments on fire severity and debris flows.
We modelled post-fire debris-flow likelihood and volume in 103 sub-basins for 2-year, 5-year, and Probable Maximum Precipitation rainfalls following modelled low-, moderate-, and high-severity wildfires.
Post-fire debris-flow likelihoods were >90% in all but the lowest fire and rain scenarios. Sub-basins with fuel treatments had the lowest burn severities, debris-flow likelihoods, and sediment volumes, but treatment effects decreased with increased fire severity and rain intensity.
Post-fire debris flows with varying debris volumes are likely to occur following wildfire in the SFMW, but fuel treatments can reduce likelihood and volume.
Future post-fire debris flows will continue to threaten water supplies, but fuel reduction treatments and debris-flow mitigation provide opportunities to minimise effects.
Hydrological drought is a pervasive and reoccurring challenge in managing water resources. Reservoirs are critical for lessening the impacts of drought on water available for many uses. We use a novel and generalized approach to identify periods of unusually low reservoir storage—via comparisons to operational rule curves and historical patterns—to investigate how droughts affect storage in 250 reservoirs across the conterminous U.S. (CONUS). We find that the maximum amount of water stored in reservoirs is decreasing, and that periods of unusually low storage are becoming longer, more severe, and more variable in (a) western and central CONUS reservoirs, and (b) reservoirs with primarily over-year storage. Results suggest that reservoir storage has become less reliable and more vulnerable to larger deviations from desired storage patterns. These changes have coincided with ongoing shifts to the hydroclimate of CONUS, and with sedimentation further reducing available reservoir storage.
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System Processes Division","active":true,"usgs":true}],"preferred":true,"id":911467,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Steyaert, Jen","contributorId":343592,"corporation":false,"usgs":false,"family":"Steyaert","given":"Jen","email":"","affiliations":[{"id":36885,"text":"Utrecht University","active":true,"usgs":false}],"preferred":false,"id":911468,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70261614,"text":"70261614 - 2024 - Aurora: An open-source Python implementation of the EMTF package for magnetotelluric data processing using MTH5 and mt-metadata","interactions":[],"lastModifiedDate":"2024-12-17T15:37:10.567627","indexId":"70261614","displayToPublicDate":"2024-08-22T09:35:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5929,"text":"Journal of Open Source Software","active":true,"publicationSubtype":{"id":10}},"title":"Aurora: An open-source Python implementation of the EMTF package for magnetotelluric data processing using MTH5 and mt-metadata","docAbstract":"The Aurora software package robustly estimates single station and remote reference electromagnetic transfer functions (TFs) from magnetotelluric (MT) time series. Aurora is part of an open-source processing workflow that leverages the self-describing data container MTH5, which in turn leverages the general mt-metadata framework to manage metadata. These pre-existing packages simplify the processing by providing managed data structures, allowing for transfer functions to be generated with only a few lines of code. The processing depends on two inputs -- a table defining the data to use for TF estimation, and a JSON file specifying the processing parameters, both of which are generated automatically, and can be modified if desired. Output TFs are returned as mt_metadata objects, and can be exported to a variety of common formats for plotting, modeling and inversion.
","language":"English","publisher":"Open Source Initiative","doi":"10.21105/joss.06832","usgsCitation":"Kappler, K., Peacock, J., Egbert, G.D., Frassetto, A., Heagy, L., Kelbert, A., Keyson, L., Oldenburg, D.W., Ronan, T., and Sweet, J., 2024, Aurora: An open-source Python implementation of the EMTF package for magnetotelluric data processing using MTH5 and mt-metadata: Journal of Open Source Software, v. 9, no. 100, 6832, 7 p., https://doi.org/10.21105/joss.06832.","productDescription":"6832, 7 p.","ipdsId":"IP-164541","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":466956,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.21105/joss.06832","text":"Publisher Index Page"},{"id":465194,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"100","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kappler, Karl 0000-0002-1877-1255","orcid":"https://orcid.org/0000-0002-1877-1255","contributorId":345189,"corporation":false,"usgs":false,"family":"Kappler","given":"Karl","email":"","affiliations":[{"id":82517,"text":"IMDEX Technology USA, LLC","active":true,"usgs":false}],"preferred":false,"id":921185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Egbert, Gary D.","contributorId":187462,"corporation":false,"usgs":false,"family":"Egbert","given":"Gary","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":921187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Frassetto, Andrew 0000-0002-8818-3731","orcid":"https://orcid.org/0000-0002-8818-3731","contributorId":345192,"corporation":false,"usgs":false,"family":"Frassetto","given":"Andrew","email":"","affiliations":[{"id":82518,"text":"Incorporated Research Institutes for Seismology","active":true,"usgs":false}],"preferred":false,"id":921327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heagy, Lindsey 0000-0002-1551-5926","orcid":"https://orcid.org/0000-0002-1551-5926","contributorId":345190,"corporation":false,"usgs":false,"family":"Heagy","given":"Lindsey","email":"","affiliations":[{"id":78772,"text":"University of British Columbia, Canada","active":true,"usgs":false}],"preferred":false,"id":921328,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":921329,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Keyson, Laura","contributorId":347262,"corporation":false,"usgs":false,"family":"Keyson","given":"Laura","email":"","affiliations":[{"id":83114,"text":"Earthscope USA","active":true,"usgs":false}],"preferred":false,"id":921188,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Oldenburg, Douglas W. 0000-0002-4327-2124","orcid":"https://orcid.org/0000-0002-4327-2124","contributorId":304117,"corporation":false,"usgs":false,"family":"Oldenburg","given":"Douglas","email":"","middleInitial":"W.","affiliations":[{"id":65972,"text":"Geophysical Inversion Facility (GIF), Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia","active":true,"usgs":false}],"preferred":false,"id":921330,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ronan, Timothy 0000-0001-8450-9573","orcid":"https://orcid.org/0000-0001-8450-9573","contributorId":345191,"corporation":false,"usgs":false,"family":"Ronan","given":"Timothy","email":"","affiliations":[{"id":82518,"text":"Incorporated Research Institutes for Seismology","active":true,"usgs":false}],"preferred":false,"id":921189,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sweet, Justin 0000-0001-7323-9758","orcid":"https://orcid.org/0000-0001-7323-9758","contributorId":347263,"corporation":false,"usgs":false,"family":"Sweet","given":"Justin","email":"","affiliations":[{"id":83114,"text":"Earthscope USA","active":true,"usgs":false}],"preferred":false,"id":921331,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70261213,"text":"70261213 - 2024 - A scaling relationship for the width of secondary deformation around strike-slip faults","interactions":[],"lastModifiedDate":"2024-12-02T14:46:56.231325","indexId":"70261213","displayToPublicDate":"2024-08-22T08:42:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3525,"text":"Tectonophysics","active":true,"publicationSubtype":{"id":10}},"title":"A scaling relationship for the width of secondary deformation around strike-slip faults","docAbstract":"Simple mechanical arguments suggest that slip along interlocked, rough faults, damages surrounding rocks. The same arguments require that the scale of secondary damage is proportional to the size of geometric irregularities along the main fault. This relationship could apply at all scales, but has, so far, been difficult to observe at the 10s to 100 s of km scales of large, natural faults, often because large-scale deformation is distributed across wide, complex plate-boundary fault systems, like the San Andreas Fault. The geometry and geology of another large-scale plate-boundary strike slip fault—the Queen Charlotte Fault (QCF)—is, in contrast, especially simple. Here, we show that observations of secondary deformation are well-aligned with predictions of stress variations caused by geometric irregularities along the QCF, suggesting a geometric relationship between primary fault geometry and secondary deformation. The analytic stress solution reveals that the highest stresses and highest likelihood of failure are confined to a zone of influence (ZOI) with a width quantified by
Large wood is an integral part of many rivers, often defining river-corridor morphology and habitat, but its occurrence, magnitude, and evolution in a river system are much less well understood than the sedimentary and hydraulic components, and due to methodological limitations, have seldom previously been mapped in substantial detail. We present a new method for this, representing a substantial advance in automated deep-learning-based image segmentation. From these maps, we measured large wood and sediment deposits from high-resolution orthoimages to explore the dynamics of large wood in two reaches of the Elwha River, Washington, USA, between 2012 and 2017 as it adjusted to upstream dam removals. The data set consists of a time series of orthoimages (12.5-cm resolution) constructed using Structure-from-Motion photogrammetry on imagery from 14 aerial surveys. Model training was optimized to yield maximum accuracy for estimated wood areas, compared to manually digitized wood, therefore model development and intended application were coupled. These fully reproducible methods and model resulted in a maximum of 15% error between observed and estimated total wood areas and wood deposit size-distributions over the full spatio-temporal extent of the data. Areal extent of wood in the channel margin approximately doubled in the years following dam removal, with greatest increases in large wood in wider, lower-gradient sections. Large-wood deposition increased between the start of dam removal (2011) and winter 2013, then plateaued. Sediment bars continued to grow up until 2016/17, assisted by a partially static wood framework deposited predominantly during the period up to winter 2013.
The Redeye Bass Micropterus coosae is a piscivore introduced into California, which has become a threat to the state's endemic freshwater fishes. It has eliminated native fishes from the middle reaches of the Cosumnes River, our study stream, which is the largest stream without a major dam on its main stem in the Sacramento–San Joaquin River drainage, central California, USA. We thoroughly documented its novel life history and ecology in California to shed light on why it has been such a successful invader despite its relatively small native range.
Over 4000 stable carbon and nitrogen isotope samples were utilized to refine our understanding of fish trophic position within the river food web, along with a stable isotope mixing model that accounts for uncertainty in trophic enrichment data.
Growth was slow, with an adult size range of 9–25 cm standard length (SL), although few were larger than 15-cm SL (5–6 years old). Stable isotope analyses showed that Redeye Bass dominate the river ecosystem to the exclusion of most native fishes, occupying multiple trophic levels and microhabitats. Adults largely consumed non-native crayfish and large aquatic insects, while juveniles consumed aquatic insects, the size of prey increasing with Redeye Bass length. There was no evidence of cannibalism. Redeye Bass have effectively occupied the diverse trophic positions of at least four native fish species and have altered the trophic position of Rainbow Trout Oncorhynchus mykiss in sites where they co-occur with bass.
The introduction of Redeye Bass poses a continuing threat to native stream fishes in California and elsewhere.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10477","usgsCitation":"Long, B.C., Moyle, P.B., Young, M.J., and Crain, P.K., 2024, Age, growth, and trophic ecology of the Redeye Bass, an introduced invader of California rivers: Transactions of the American Fisheries Society, v. 153, no. 5, p. 559-575, https://doi.org/10.1002/tafs.10477.","productDescription":"17 p.","startPage":"559","endPage":"575","ipdsId":"IP-165585","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":439201,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10477","text":"Publisher Index Page"},{"id":433151,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"153","issue":"5","noUsgsAuthors":false,"publicationDate":"2024-08-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Long, Beth C.","contributorId":343631,"corporation":false,"usgs":false,"family":"Long","given":"Beth","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":911566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moyle, Peter B.","contributorId":117099,"corporation":false,"usgs":false,"family":"Moyle","given":"Peter","email":"","middleInitial":"B.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":911567,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":911568,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crain, Patrick K.","contributorId":343634,"corporation":false,"usgs":false,"family":"Crain","given":"Patrick","email":"","middleInitial":"K.","affiliations":[{"id":13109,"text":"ICF International","active":true,"usgs":false}],"preferred":false,"id":911569,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256698,"text":"sir20245026v2 - 2024 - Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington","interactions":[{"subject":{"id":70256698,"text":"sir20245026v2 - 2024 - Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington","indexId":"sir20245026v2","publicationYear":"2024","noYear":false,"chapter":"D-E","displayTitle":"Numerical Model of the Groundwater-Flow System Near the Southeastern Part of Puget Sound, Washington","title":"Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington"},"predicate":"IS_PART_OF","object":{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","indexId":"sir20245026","publicationYear":"2024","noYear":false,"title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington"},"id":1}],"isPartOf":{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","indexId":"sir20245026","publicationYear":"2024","noYear":false,"title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington"},"lastModifiedDate":"2026-02-03T18:14:38.644212","indexId":"sir20245026v2","displayToPublicDate":"2024-08-21T14:09:17","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5026","chapter":"D-E","displayTitle":"Numerical Model of the Groundwater-Flow System Near the Southeastern Part of Puget Sound, Washington","title":"Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington","docAbstract":"Groundwater flow in the active model area (AMA) was simulated using a groundwater-flow model. A steady-state model version of the model simulates equilibrium conditions, and a transient model version simulates monthly variability. The model corresponds to the physical and temporal dimensions of the conceptual model and groundwater budget. The steady-state model version represents average conditions for an 11-year period (January 1, 2005–December 31, 2015), and the transient model represents monthly hydrologic variability within that period. The 13-layer model was constructed using MODFLOW-NWT with a uniformly spaced grid consisting of 416 rows, 433 columns, and cells with a horizontal dimension of 500 feet (ft) on a side.
The model was calibrated to measured values of water levels in wells and lakes and estimated base flow for selected streamflow measurement stations, commonly referred to as streamgages. Model calibration was accomplished using a combination of manual and automatic methods, including the Model-Independent Parameter Estimation (PEST) program that adjusted model input parameters with the aim of minimizing the difference between estimated and model-simulated values of hydraulic head and base flow.
Model boundary conditions consist of all simulated groundwater inflow to and outflow from the AMA. For example, a stream reach that simulates a gain from or loss to groundwater is a boundary condition that allows water to exit or enter, respectively, the groundwater system. Other boundary conditions include springs, seeps, precipitation recharge, groundwater exchange with lakes and Puget Sound, and groundwater pumping. A comparison of the estimated groundwater budget to that simulated by the steady-state model version indicates that the relative percentages of total inflow or total outflow for six major categories of boundary conditions are similar for the two budgets.
The model was used to simulate three suites of scenarios of potential drought and water-use changes. Scenario 1 suite consisted of the steady-state model version that was run with 0, 15, 20, and 25 percent reduction of precipitation recharge to assess the corresponding reductions in base flow with decreasing recharge. The last simulation for the scenario 1 suite consisted of the transient model version simulating 3 years of consecutive seasonal drought, defined by the months of May through September, to assess the corresponding base-flow reductions. Scenario 2 suite consisted of the steady-state model version with all simulated groundwater use removed, compared with a simulation that includes current groundwater use to evaluate changes to potentiometric surfaces and base flows. Scenario 3 suite consisted of a transient model version of the model that simulated pumping increases for four different categories of water-supply wells (compared to no pumping increases) to evaluate resulting reductions in base flow. Although, these scenarios provide examples of model applications and useful insights, many other scenarios could be simulated. A description of how to download the model is described in the body of this report.
Uncertainty is associated with most model inputs. Groundwater levels, lake levels, and land-surface altitudes are relatively certain; other model inputs are far less certain, including precipitation recharge, base flow, hydraulic properties, water use, and the three-dimensional structure of subsurface hydrogeologic units. Models are useful not because of high levels of accuracy of all model inputs, but because they combine the best information and estimates available, thereby providing the best predictions available related to physical processes.
The model described in this report simulates groundwater flow on a regional scale, which has inherent limitations for simulating hydrologic scenarios at local scales. Model structures and inputs were generalized to be consistent with this regional scale. For example, the actual groundwater system has much greater heterogeneity of hydraulic conductivity than is possible within the model’s degrees of freedom. Variations in hydraulic gradients over distances less than 500 ft cannot be simulated. The distances between model features, such as a pumping well and a stream, must be placed at 500-ft intervals and are co-located if both features are within the same model cell.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245026v2","collaboration":"Prepared in cooperation with the Cities of Auburn, Milton, Puyallup, Sumner, and Tacoma; Pierce Conservation District; Pierce County Public Works; Washington State Department of Health; Washington State Department of Ecology; Thurston County Public Utility District; Cascade Water Alliance; Lakehaven Utility District; Lakewood Water District; Firgrove Mutual Water Company; Fruitland Mutual Water Company; Spanaway Water Company; Summit Water & Supply Company; and Mt. View-Edgewood Water Company","usgsCitation":"Long, A.J., Wright, E.E., Fuhrig, L.T., and Bright, V.A.L., 2024, Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington, v. 2 of Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington, 2 chap. (D–E): U.S. Geological Survey Scientific Investigations Report 2024–5026–D–E, [variously paged; 103 p.], https://doi.org/10.3133/sir20245026v2.","productDescription":"Report: 103 p.; 14 Tables; 2 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-140115","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":499452,"rank":22,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117217.htm","linkFileType":{"id":5,"text":"html"}},{"id":432082,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.14.csv","text":"Table 1.14","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.14","linkHelpText":"- Groundwater use applied to scenario 3 for the Spanaway Water Company and the City of Sumner, near the southeastern part of Puget Sound, Washington"},{"id":432079,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.11.csv","text":"Table 1.11","size":"8 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.11","linkHelpText":"- Supplemental hydraulic-head targets for the steady-state model version set equal to the land surface to prevent groundwater flooding and corresponding simulated values, near the southeastern part of Puget Sound, Washington"},{"id":432074,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.6.csv","text":"Table 1.6","size":"637 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.6","linkHelpText":"- Time-series records of measured and simulated hydraulic-head values (transient model version) for selected wells used, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432087,"rank":21,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/sir20245026v2.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2024-5026 Vol 2 XML"},{"id":432066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/sir20245026v2.jpg"},{"id":432067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/sir20245026v2.pdf","size":"10.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5026 Vol 2 PDF"},{"id":432069,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.1.csv","text":"Table 1.1","size":"995 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.1","linkHelpText":"- Streamflow-Routing (SFR) Package specifications by reach"},{"id":432070,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.2.csv","text":"Table 1.2","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.2","linkHelpText":"- Estimated monthly average base flow estimated for Coal, Boise, and Scatter Creeks where they enter the active model area, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432071,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.3.csv","text":"Table 1.3","size":"10 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.3","linkHelpText":"- Estimated monthly average base flow estimated for selected streams where they enter the active model area, the Buckley diversion (inflow to Lake Tapps), and outflow from Lake Tapps, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432072,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.4.csv","text":"Table 1.4","size":"6 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.4","linkHelpText":"- Monthly average water levels for American, Gravelly, Steilacoom, and Spanaway Lakes, and Lake Tapps, derived from measured and estimated values, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432073,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.5.csv","text":"Table 1.5","size":"6.7 MB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.5","linkHelpText":"- Measured water levels for American, Gravelly, and Spanaway Lakes, near the southeastern part of Puget Sound, Washington, 2000–18"},{"id":432075,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.7.csv","text":"Table 1.7","size":"398 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.7","linkHelpText":"- Averages of measured hydraulic-head values for selected wells and corresponding simulated steady-state values, near the southeastern part of Puget Sound, Washington, 2005-15"},{"id":432076,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.8.csv","text":"Table 1.8","size":"291 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.8","linkHelpText":"- Estimated and simulated monthly average base flow for selected stations, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432077,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.9.csv","text":"Table 1.9","size":"9 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.9","linkHelpText":"- Estimated and simulated base-flow values for the steady-state model version for stations with continuous records, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432078,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.10.csv","text":"Table 1.10","size":"63 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.10","linkHelpText":"- Estimated and simulated vertical hydraulic-head differences for the steady-state model version between an upper and lower model layer for selected locations, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432080,"rank":14,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.12.csv","text":"Table 1.12","size":"199 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.12","linkHelpText":"- Model calibration parameters showing input to the control file for the Model-Independent Parameter Estimation (PEST) program"},{"id":432081,"rank":15,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Table1.13.csv","text":"Table 1.13","size":"12 KB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2024-5026 Vol 2 Table 1.13","linkHelpText":"- Simulated groundwater budget for the calibrated transient model version, near the southeastern part of Puget Sound, Washington, 2005–15"},{"id":432083,"rank":17,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/data/sir20245026v2_Tables1.1-1.14.xlsx","text":"Tables 1.1-1.14","size":"5.2 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2024-5026 Vol 2 Table 1.1-1.14"},{"id":432084,"rank":18,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JFKLMG","text":"USGS data release","description":"USGS data release","linkHelpText":"Spatial data in support of the characterization of water resources near the southeastern part of Puget Sound, Washington"},{"id":432085,"rank":19,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LU1PMQ","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-NWT model to simulate the groundwater flow system near Puget Sound, Pierce and King Counties, Washington"},{"id":432086,"rank":20,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5026/v2/images"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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U.S. Geological Survey
934 Broadway, Suite 300
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More than 1 million people live within the active model area (AMA) in the southeastern part of the lowlands surrounding Puget Sound, or Puget Lowland, Washington, and groundwater is the source for approximately one-half of their public, domestic, and irrigation water demands. The 887-square-mile AMA, located in King and Pierce Counties, represents the area of analysis for the conceptual hydrogeologic framework and numerical groundwater-flow models within the study area and includes the Puyallup River and Chambers-Clover Creek watersheds. To assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources, conceptual and numerical groundwater-flow models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center in close cooperation with 18 water-resource agencies and stakeholders.
This report presents information used to characterize the groundwater-flow system and the development of a numerical model in the AMA. Included are descriptions of the geology and conceptual hydrogeologic framework, groundwater levels and flow directions, groundwater recharge and discharge, numerical groundwater-flow model construction and results, and model limitations. The study area encompasses the western part of Pierce County and the southwestern part of King County, Washington. The study area extends south to the Nisqually River, southwest to Tanwax Creek, northeast to the Green River, and north through the valley near Auburn and adjacent uplands. It is bounded on the east by foothills of the Cascade Range, and on the northwest by Puget Sound.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245026v1","collaboration":"Prepared in cooperation with the Cities of Auburn, Milton, Puyallup, Sumner, and Tacoma; Pierce Conservation District; Pierce County Public Works; Washington State Department of Health; Washington State Department of Ecology; Thurston County Public Utility District; Cascade Water Alliance; Lakehaven Utility District; Lakewood Water District; Firgrove Mutual Water Company; Fruitland Mutual Water Company; Spanaway Water Company; Summit Water & Supply Company; and Mt. View-Edgewood Water Company","usgsCitation":"Welch, W.B., Bright, V.A.L., Gendaszek, A.S., Dunn, S.B., Headman, A.O., and Fasser, E.T., 2024, Conceptual hydrogeologic framework and groundwater budget near the southeastern part of Puget Sound, Washington, v. 1 of Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington, 3 chap. (A–C): U.S. Geological Survey Scientific Investigations Report 2024–5026–A–C, [variously paged; 71 p.], 1 pl., https://doi.org/10.3133/sir20245026v1.","productDescription":"Report: 71 p.; Data Release: 1 Plate: 50.67 × 37.33 inches","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-135626","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":499451,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117216.htm","linkFileType":{"id":5,"text":"html"}},{"id":432059,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.jpg"},{"id":432065,"rank":7,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1_plate.pdf","text":"Plate 1","size":"5.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5026 Vol 1 Plate 1"},{"id":432064,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.XML"},{"id":432063,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/images"},{"id":433528,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1_versionHist.txt","description":"SIR 2024-5026 Vol 1 Version History"},{"id":432062,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JFKLMG","text":"USGS data release","description":"USGS data release","linkHelpText":"Spatial data in support of the characterization of water resources near the southeastern part of Puget Sound, Washington"},{"id":432060,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5026/v1/sir20245026v1.pdf","text":"Report","size":"57.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2025-5026 Vol 1"}],"country":"United States","state":"Washington","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.8989970759464,\n 47.52614250846048\n ],\n [\n -122.8989970759464,\n 46.60885290293453\n ],\n [\n -121.43135900005484,\n 46.60885290293453\n ],\n [\n -121.43135900005484,\n 47.52614250846048\n ],\n [\n -122.8989970759464,\n 47.52614250846048\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
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Submerged macrophytes have complex effects on spatiotemporal characteristics of river ecosystems, including trout habitat. We investigated the impact of submerged macrophyte coverage on trout habitat in the Henrys Fork of the Snake River, Idaho, USA. We hypothesized that higher submerged macrophyte coverage would create new habitat types beneficial for trout growth. We assessed river physical and biotic attributes, trout habitat preferences, and estimated trout growth potential with bioenergetics models across a gradient of submerged macrophyte coverage (32–94%). We identified four distinct habitat types within the riverscape shaped by submerged macrophyte coverage. Increased submerged macrophyte coverage increased the frequency of habitat types with higher trout growth potential but reduced the occurrence of preferred habitat types. We observed no relationship between reach-scale trout growth potential and submerged macrophyte coverage. However, an outlier of very high trout growth potential at 94% submerged macrophyte coverage suggests a potential threshold effect. More study is required but our observations suggest macrophyte growth homogenized physical habitat characteristics, reduced flow velocities, and increased invertebrate drift, thereby enhancing trout growth potential. Our findings underscore the complex interplay between submerged macrophytes and trout habitat dynamics across scales, emphasizing the importance of considering both physical and biological effects on trout habitat.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-024-05671-7","usgsCitation":"McLaren, J.S., Van Kirk, R.W., Budy, P., and Brothers, S., 2024, The reach-scale biogeomorphic effect of submerged macrophytes on trout habitat suitability: Hydrobiologia, v. 851, p. 5167-5180, https://doi.org/10.1007/s10750-024-05671-7.","productDescription":"14 p.","startPage":"5167","endPage":"5180","ipdsId":"IP-157131","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485716,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Henrys Fork, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.18272502007463,\n 44.565942435149395\n ],\n [\n -111.69541678783352,\n 44.565942435149395\n ],\n [\n -111.69541678783352,\n 44.271529079239684\n ],\n [\n -111.18272502007463,\n 44.271529079239684\n ],\n [\n -111.18272502007463,\n 44.565942435149395\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"851","noUsgsAuthors":false,"publicationDate":"2024-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"McLaren, John S.","contributorId":337322,"corporation":false,"usgs":false,"family":"McLaren","given":"John","email":"","middleInitial":"S.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":936626,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Kirk, Robert W.","contributorId":337326,"corporation":false,"usgs":false,"family":"Van Kirk","given":"Robert","email":"","middleInitial":"W.","affiliations":[{"id":81016,"text":"Henrys Fork Foundation","active":true,"usgs":false}],"preferred":false,"id":936627,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":936628,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brothers, Soren","contributorId":339019,"corporation":false,"usgs":false,"family":"Brothers","given":"Soren","email":"","affiliations":[{"id":81013,"text":"Department of Natural History","active":true,"usgs":false}],"preferred":false,"id":936629,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","interactions":[{"subject":{"id":70256697,"text":"sir20245026v1 - 2024 - Conceptual hydrogeologic framework and groundwater budget near the southeastern part of Puget Sound, Washington","indexId":"sir20245026v1","publicationYear":"2024","noYear":false,"chapter":"A-C","displayTitle":"Conceptual Hydrogeologic Framework and Groundwater Budget Near the Southeastern Part of Puget Sound, Washington","title":"Conceptual hydrogeologic framework and groundwater budget near the southeastern part of Puget Sound, Washington"},"predicate":"IS_PART_OF","object":{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","indexId":"sir20245026","publicationYear":"2024","noYear":false,"title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington"},"id":1},{"subject":{"id":70256698,"text":"sir20245026v2 - 2024 - Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington","indexId":"sir20245026v2","publicationYear":"2024","noYear":false,"chapter":"D-E","displayTitle":"Numerical Model of the Groundwater-Flow System Near the Southeastern Part of Puget Sound, Washington","title":"Numerical model of the groundwater-flow system near the southeastern part of Puget Sound, Washington"},"predicate":"IS_PART_OF","object":{"id":70256874,"text":"sir20245026 - 2024 - Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","indexId":"sir20245026","publicationYear":"2024","noYear":false,"title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington"},"id":2}],"lastModifiedDate":"2024-09-06T18:14:28.690362","indexId":"sir20245026","displayToPublicDate":"2024-08-21T09:59:52","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5026","displayTitle":"Characterization of Groundwater Resources Near the Southeastern Part of Puget Sound, Washington","title":"Characterization of groundwater resources near the southeastern part of Puget Sound, Washington","docAbstract":"More than 1 million people live within the active model area (AMA) in the southeastern part of the lowlands surrounding Puget Sound, or Puget Lowland, Washington, and groundwater is the source for approximately one-half of their public, domestic, and irrigation water demands. The 887-square-mile AMA, located in King and Pierce Counties, represents the area of analysis for the conceptual hydrogeologic framework and numerical groundwater-flow models within the study area and includes the Puyallup River and Chambers-Clover Creek watersheds. To assess the potential hydrologic and anthropogenic impacts to groundwater and the connected surface-water resources, conceptual and numerical groundwater-flow models of groundwater flow were developed by the U.S. Geological Survey Washington Water Science Center in close cooperation with 18 water-resource agencies and stakeholders.
This multichapter volume documents the development of the conceptual and numerical groundwater-flow models of groundwater flow. Chapters A, B, and C provide an overall introduction to the multichapter volume (Chapter A), the conceptual hydrogeologic framework (Chapter B), and the groundwater budget (Chapter C). Chapters D and E describe numerical groundwater-flow model construction and calibration (Chapter D) and the numerical groundwater-flow model results (Chapter E). Collectively, these reports present a characterization and simulation tool for groundwater resources near the southeastern part of Puget Sound, Washington.
","doi":"10.3133/sir20245026","usgsCitation":"Welch, W.B., and Long, A.J., eds., Characterization of groundwater resources near the southeastern part of Puget Sound, Washington: U.S. Geological Survey Scientific Investigations Report 2024–5026, https://doi.org/10.3133/sir20245026.","onlineOnly":"Y","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":433028,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -125.74462161441157,\n 49.512370286103504\n ],\n [\n -125.74462161441157,\n 46.69617446727628\n ],\n [\n -120.97655520816149,\n 46.69617446727628\n ],\n [\n -120.97655520816149,\n 49.512370286103504\n ],\n [\n -125.74462161441157,\n 49.512370286103504\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Traditionally, local-scale habitat-relationship models are developed over small spatial extents, limiting model transferability and inference outside the study area. Thus, habitat managers frequently lack fine-scale information regarding the influence of vegetation composition and structure on site suitability or species abundance. Gymnorhinus cyanocephalus (Pinyon Jay) represents one declining species for which managers have limited information regarding the influence that vegetation composition and structure have on abundance at broad scales. To address this need, we developed a hierarchical Bayesian abundance model using summertime bird and vegetation data collected under the Integrated Monitoring in Bird Conservation Regions program to explain jay abundance as a function of local conditions. Our G. cyanocephalus abundance model allowed abundance relationships with pinyon pine (Pinus edulis and P. monophylla) and juniper (Juniperus spp.) to vary by ecoregion, thereby accounting for potential regional differences in habitat associations. We found G. cyanocephalus abundance was generally positively associated with pinyon pine and juniper cover; however, habitat relationships varied by ecoregion. Additionally, we found positive associations between jay abundance and grass cover, sagebrush cover, and percent bare ground. Our results agree with prior research suggesting mechanical removal of pinyon pine and juniper trees for sagebrush restoration or fuel treatments may negatively affect G. cyanocephalus. Managers wishing to reduce pinyon and juniper tree cover without negatively affecting G. cyanocephalus may benefit from targeting sites where both large-scale distribution models and our local habitat relationships suggest G. cyanocephalus are likely to occur in low numbers. Additionally, our modeled relationships indicate restoration that increases grass cover, sagebrush cover, and bare ground, while maintaining pinyon and (or) juniper cover, may lead to increased local densities of G. cyanocephalus.
","language":"English","publisher":"Oxford University Press","doi":"10.1093/ornithapp/duae036","usgsCitation":"Van Lanen, N.J., Monroe, A., and Aldridge, C.L., 2024, Despite regional variation, Gymnorhinus cyanocephalus (Pinyon Jay) densities generally increase with local pinyon–juniper cover and heterogeneous ground cover: Ornithological Applications, v. 126, no. 4, duae036, https://doi.org/10.1093/ornithapp/duae036.","productDescription":"duae036","ipdsId":"IP-158852","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":439204,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duae036","text":"Publisher Index Page"},{"id":433154,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"126","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Lanen, Nicholas J. 0000-0003-0871-0261","orcid":"https://orcid.org/0000-0003-0871-0261","contributorId":302927,"corporation":false,"usgs":true,"family":"Van Lanen","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":911621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":911622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":911623,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257808,"text":"70257808 - 2024 - On the uncertain intensity estimate of the 1859 Carrington storm","interactions":[],"lastModifiedDate":"2024-08-28T11:58:46.249223","indexId":"70257808","displayToPublicDate":"2024-08-21T06:57:28","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18352,"text":"Journal of Space Weather and Space Climate","active":true,"publicationSubtype":{"id":10}},"title":"On the uncertain intensity estimate of the 1859 Carrington storm","docAbstract":"A study is made of the intensity of the Carrington magnetic storm of September 1859 as inferred from visual measurements of horizontal-component geomagnetic disturbance made at the Colaba observatory in India. Using data from modern observatories, a lognormal statistical model of storm intensity is developed, to characterize the maximum-negative value of the storm-time disturbance index (maximum –Dst) versus geomagnetic disturbance recorded at low-latitude observatories during magnetic storms. With this model and a recently published presentation of the Colaba data, the most likely maximum –Dst of the Carrington storm and its credibility interval are estimated. A related model is used to examine individual Colaba disturbance values reported for the Carrington storm. Results indicate that only about one in a million storms with maximum –Dst like the Carrington storm would result in local disturbance greater than that reported from Colaba. This indicates that either the Colaba data were affected by magnetospheric-ionospheric current systems in addition to the ring current, or there might be something wrong with the Colaba data. If the most extreme Colaba disturbance value is included in the analysis, then, of all hypothetical storms generating the hourly average disturbance recorded at Colaba during the Carrington storm, the median maximum –Dst = 964 nT, with a 68% credibility interval of [855,1087] nT. If the most extreme Colaba disturbance value is excluded from the analysis, then the median maximum –Dst = 866 nT, with a 68% credibility interval of [768,977] nT. The widths of these intervals indicate that estimates of the occurrence frequency of Carrington-class storms are very uncertain, as are related estimates of risk for modern technological systems.
","language":"English","publisher":"EcoSciences","doi":"10.1051/swsc/2024015","usgsCitation":"Love, J.J., Rigler, E.J., Hayakawa, H., and Mursula, K., 2024, On the uncertain intensity estimate of the 1859 Carrington storm: Journal of Space Weather and Space Climate, v. 14, https://doi.org/10.1051/swsc/2024015.","productDescription":"21, 16 p.","startPage":"21","ipdsId":"IP-153188","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":439205,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1051/swsc/2024015","text":"External Repository"},{"id":433242,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2024-08-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Love, Jeffrey J. 0000-0002-3324-0348 jlove@usgs.gov","orcid":"https://orcid.org/0000-0002-3324-0348","contributorId":760,"corporation":false,"usgs":true,"family":"Love","given":"Jeffrey","email":"jlove@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":911733,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rigler, E. Joshua 0000-0003-4850-3953 erigler@usgs.gov","orcid":"https://orcid.org/0000-0003-4850-3953","contributorId":4367,"corporation":false,"usgs":true,"family":"Rigler","given":"E.","email":"erigler@usgs.gov","middleInitial":"Joshua","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":911734,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayakawa, H. 0000-0001-5370-3365","orcid":"https://orcid.org/0000-0001-5370-3365","contributorId":261775,"corporation":false,"usgs":false,"family":"Hayakawa","given":"H.","email":"","affiliations":[{"id":53009,"text":"Nagoya University, Rutherford Appleton Laboratory, Nishina Center","active":true,"usgs":false}],"preferred":false,"id":911735,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mursula, Kalevi 0000-0003-4892-5056","orcid":"https://orcid.org/0000-0003-4892-5056","contributorId":343695,"corporation":false,"usgs":false,"family":"Mursula","given":"Kalevi","email":"","affiliations":[{"id":82163,"text":"Oulu University","active":true,"usgs":false}],"preferred":false,"id":911736,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256145,"text":"sir20245044 - 2024 - Simulation of groundwater flow in the Long Island, New York regional aquifer system for pumping and recharge conditions from 1900 to 2019","interactions":[],"lastModifiedDate":"2026-02-03T19:24:36.091129","indexId":"sir20245044","displayToPublicDate":"2024-08-20T11:40:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5044","displayTitle":"Simulation of Groundwater Flow in the Long Island, New York Regional Aquifer System for Pumping and Recharge Conditions From 1900 To 2019","title":"Simulation of groundwater flow in the Long Island, New York regional aquifer system for pumping and recharge conditions from 1900 to 2019","docAbstract":"The U.S. Geological Survey has developed a transient, groundwater-flow model that simulates hydrologic conditions in the Long Island aquifer system as part of an ongoing (since 2016) multiyear, cooperative investigation with the New York State Department of Environmental Conservation. The goals of this investigation are to assist stakeholders and resource managers to evaluate the response of the hydrologic system to changes in future hydraulic stresses. Responses in the hydrologic system include changes in water levels in the hydrogeologic units; discharge to streams, coastal waters, and subsurface infrastructure; and the extent of saline groundwater in the aquifers. Hydraulic stresses include future water-supply management and changes in land use and infrastructure.
The numerical model synthesizes a diverse set of physiographic, geologic, climatic, land-use, and historical population, water use, and infrastructure data to physically represent the Long Island aquifer system from land surface to bedrock and to simulate annual hydrologic conditions between 1900 and 2019. A three-dimensional hydrogeologic framework was developed from existing and recently collected borehole geologic and geophysical data collected as part of a companion drilling program. Water-transmitting properties of the principal aquifer sediments were defined in three dimensions from new and existing lithologic logs. The distribution of recharge from precipitation was estimated from landscape characteristics and climate data. Anthropogenic recharge from wastewater, leaky infrastructure, and storm runoff were estimated from population, infrastructure, and pumping data.
Water-use data, including well locations, depths, and pumping rates, were obtained from historical sources and records and used to estimate pumping stresses continuously in time and space, at an annual average time scale. The data were incorporated into a three-dimensional numerical model using the U.S. Geological Survey finite difference modeling code MODFLOW 6; the model encompassed all of Long Island and surrounding surface waters and simulated historical hydrologic conditions from 1900 to 2019.
The calibration process involved trial and error adjustments using prior knowledge to improve general fit to observations followed by an inverse calibration to update and optimize input parameters, using an iterative ensemble smoother algorithm implemented in PEST++ version 5.0. This resulted in a model that generally was in good agreement with observed, dynamically varying hydrologic conditions from 1900 to 2019. The calibrated model was used to develop two base-case models for scenario testing of future, hypothetical conditions where one represented average-annual conditions, and one represented average-seasonal conditions from 2010 to 2019. The model representing average-annual conditions was modified further to represent an alternate sea-level position of 6 feet above the North American Vertical Datum of 1988, and the model representing average-seasonal conditions was modified to represent the average seasonal effects of a 5-year drought imposed upon current hydrologic conditions.
Recharge is the sole source of water to the aquifer system; groundwater discharges to coastal water and streams and is withdrawn by pumped wells. Model-estimated annual recharge ranged from about 11 inches in 1965 to 41 inches in 1983. On average, from 2010 to 2019, about 23 percent of water was pumped from wells, and about 47 and 27 percent discharged to coastal waters and streams, respectively; the remaining 4 percent was water that moved into storage in the aquifer matrix.
Water levels on Long Island vary naturally during time in response to changes in recharge; the amount of variation is largest in the interior of the island, in areas with highest water table altitudes near groundwater divides and lowest near streams and the coastal waters. The total range of water table altitudes on Long Island between 1900 and 2019 ranged from near 0 to more than 70 feet in western parts of Long Island. The largest range in altitudes is in New York City and is associated with areas of large historical withdrawals between the 1920s and the late 1980s. Water table altitudes generally varied by less than 10 feet in eastern Suffolk County, where the aquifer is under more natural conditions.
Saltwater intrusion is of great concern on Long Island, particularly in western Long Island where both the unconfined and confined parts of the aquifer system have been intruded in response to large-scale groundwater withdrawals; however, the volume of freshwater in the islandwide aquifer system only has changed by about 5 percent between 1900 and 2019. The decadal change in the freshwater volume was largest during the early and mid-20th century, corresponding to the largest historical pumping, but that volume change did not exceed 1 percent.
The negligible change in freshwater volume suggests that saltwater intrusion as of 2019 was limited at an islandwide scale but continues to occur in local areas of Queens and Nassau Counties, adversely affecting current water supplies and limiting future water supplies for affected communities. The regional groundwater model developed for this investigation is a tool that can be used to help determine the viability of current and future water supplies at a regional scale and can be used to support development of additional models at finer scale to support more focused assessments of groundwater sustainability.
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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
In 2016, the U.S. Geological Survey began a multiyear cooperative study with the New York State Department of Environmental Conservation to evaluate the sustainability of Long Island’s sole-source aquifer system through hydrogeologic mapping, compilation of groundwater chloride concentrations, and groundwater flow modeling. In the initial phase of the islandwide study, the hydrogeologic framework and extent of saltwater intrusion in aquifers in Kings, Queens, and Nassau Counties on western Long Island, N.Y., were investigated. The aquifer system underlying western Long Island has been under stress from pumping of public, irrigation (golf course), and industrial supply wells. Saltwater intrusion has occurred from surrounding embayments (East River, Long Island Sound, Jamaica Bay, and the Atlantic Ocean) due to pumping.
Eighteen boreholes were drilled and cores taken during 2019–21 to collect hydrogeologic, geochemical, and geophysical data to delineate the complex subsurface hydrogeology and extent of saltwater intrusion within the study area. Evaluation of the new cores, reexamination of legacy core descriptions, and analysis of borehole geophysical logs was used to refine the previously published hydrogeologic framework of Pleistocene and Cretaceous unconsolidated sediments in the area, including delineation of a previously undefined hydrogeologic unit between the Magothy aquifer and the Raritan confining unit, herein named the “upper Raritan aquifer.” The upper Raritan aquifer was first recognized in southeastern Nassau County from an analysis of about 50 closely spaced boreholes with high-resolution core descriptions and gamma-ray (gamma) logs. Further analysis of borehole logs across the study area indicated that the upper Raritan aquifer was also present in Kings and Queens Counties.
Nuclear magnetic resonance (NMR) logging was used for the first time on Long Island to provide estimates of the hydraulic properties of the major aquifer and confining units. Unlike other geophysical logs that record responses to the rock matrix and fluid properties and are strongly dependent on mineralogy, NMR logs record responses to the presence of hydrogen protons in the formation fluid to determine water fraction and pore-size distribution. NMR log analysis provided estimates of the clay-bound, capillary-bound, and mobile water fractions and hydraulic conductivity of aquifers and confining units penetrated by five wells in Nassau County.
Pumpage for public-supply and industrial wells on Long Island began in the 1870s with small, localized suppliers of populated areas in Kings and Queens Counties. By 1904–16, pumpage for public water supply in Kings County averaged 21 million gallons per day, and averaged 37 million gallons per day in Queens County, mostly from the upper glacial aquifer. Saltwater intrusion was reported as early as the beginning of the 20th century and included the upper glacial-Jameco-Magothy and Lloyd-North Shore aquifer systems. By 1936, pumping in central Kings County created a major cone of depression in the water table extending to the south shore of much of Kings County and into southwestern Queens County. Saltwater intrusion has caused the shutdown of public-supply wells in Kings, Queens, and Nassau Counties. A large saltwater intrusion wedge in the Lloyd aquifer was indicated in southern Queens County in the early part of the 20th century, and the saltwater interface may have been onshore predevelopment. Most of Kings and Queens Counties are intruded with saltwater in both the upper glacial-Jameco-Magothy and Lloyd-North Shore aquifers systems. Saltwater increased during the 20th century and continues to increase to the present (2023) in the Lloyd-North Shore aquifer system in Great Neck and Manhasset Neck in northern Nassau County. A major wedge of saltwater intrusion in the upper glacial-Jameco-Magothy aquifer in southwestern Nassau County appears to be increasing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245048","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Stumm, F., Finkelstein, J.S., Williams, J.H., and Lange, A.D., 2024, Hydrogeologic framework and extent of saltwater intrusion in Kings, Queens, and Nassau Counties, Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2024–5048, 83 p., https://doi.org/10.3133/sir20245048.","productDescription":"Report: ix, 83 p.; 3 Data Releases; Interactive Geospatial Data Viewer","numberOfPages":"83","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-147125","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":431252,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97WN72B","text":"USGS data release","linkHelpText":"Hydrogeologic framework and 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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Sedimentary landforms on Earth and other planetary bodies are built through scour, transport, and deposition of sediment. Sediment connectivity refers to the hypothesis that pathways of sediment transport do not occur in isolation, but rather are mechanistically linked. In dryland river systems, one such example of sediment connectivity is the transport of fluvially deposited sediment by wind. However, predictive tools that can forecast fluvial-aeolian sediment connectivity at meaningful scales are rare. Here we develop a suite of models for quantifying the availability of river-sourced sediment for aeolian transport as a function of river flow, wind regime, and land cover across 168 km of the Colorado River in Grand Canyon, USA. We compare and validate these models using topographic changes observed over 10 years in a coupled river sandbar-aeolian dunefield setting. The models provide a path forward for directly linking fluvial hydrology with the management and understanding of aeolian landscapes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2024GL110106","usgsCitation":"Kasprak, A., Sankey, J., and Caster, J., 2024, Landscape-scale modeling to forecast fluvial-aeolian sediment connectivity in river valleys: Geophysical Research Letters, v. 51, no. 6, e2024GL110106, 10 p., https://doi.org/10.1029/2024GL110106.","productDescription":"e2024GL110106, 10 p.","ipdsId":"IP-165471","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":490135,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024gl110106","text":"Publisher Index Page"},{"id":486217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.41686436104216,\n 36.95682017793375\n ],\n [\n -112.32831221274077,\n 36.95682017793375\n ],\n [\n -112.32831221274077,\n 36.00005971677052\n ],\n [\n -111.41686436104216,\n 36.00005971677052\n ],\n [\n -111.41686436104216,\n 36.95682017793375\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-08-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":937704,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937705,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caster, Joshua 0000-0002-2858-1228 jcaster@usgs.gov","orcid":"https://orcid.org/0000-0002-2858-1228","contributorId":199033,"corporation":false,"usgs":true,"family":"Caster","given":"Joshua","email":"jcaster@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":937706,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259729,"text":"70259729 - 2024 - A conterminous United States–Wide validation of relative tidal elevation products","interactions":[],"lastModifiedDate":"2024-10-22T12:15:37.822664","indexId":"70259729","displayToPublicDate":"2024-08-20T07:14:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"A conterminous United States–Wide validation of relative tidal elevation products","docAbstract":"Recent large-scale spatial products have been developed to assess wetland position in the tidal frame, but nationwide comparisons and validations are missing for these products. Wetland position within the tidal frame is a commonly used characteristic to compare wetlands across biogeomorphic gradients and factors heavily into wetland vulnerability models. We utilize a dataset of 365 surface elevation table stations across the conterminous USA containing ground-surveyed tidal datum and elevation data to validate two gridded, conterminous USA–wide relative tidal elevation products. We identified substantial differences between our ground-surveyed dataset and the gridded products, with the Gulf coast exhibiting the greatest error (p < 0.0001, n = 140). Error in relative tidal elevation products varied by coast, tidal range, and latitude. These differences in errors indicate that gridded relative tidal elevation products may be more accurate in coastal wetlands with larger tidal ranges (> 30 cm) and are less accurate in freshwater wetlands near the coast. This paper makes advances in understanding why relative tidal elevation differences occur among national datasets and identifies areas of future work that could support more robust vulnerability models.
Salinity, or total dissolved solids (TDS), of the Colorado River is a major concern in the southwestern United States where the river provides water to about 40 million people for municipal and industrial use and is used to irrigate about 5.5 million acres of land. Much of the salinity in the Colorado River Basin is derived from natural interactions of surface water and groundwater with various geologic materials (rocks, soils, and alluvial deposits). The Dolores River in southwest Colorado is a major tributary of the Colorado River that historically accounts for about 6 percent of the salinity load to the Upper Colorado River Basin with the Paradox Valley being the primary source of salinity to the Dolores River. The Paradox Valley, one of several salt-anticline valleys in the region, is a fault-bounded topographic basin aligned with and exposing an underlying salt-anticline core. Salt deposits in the Pennsylvanian Paradox Formation of the Hermosa Group form an elongated salt diapir oriented northwest to southeast that is up to 12,000 feet (ft) thick beneath the present valley floor. Surface erosion, groundwater circulation, and weathering during Tertiary and Quaternary valley formation contributed to development of a cap rock, collapse features, breccia, and brine at the top of the exposed salt diapir. Today (2023), brine occurring in the brecciated cap rock and underlying salt deposits is in hydraulic connection with an overlying freshwater alluvial aquifer, and depending on seasonal river stage and hydrologic conditions, the brine discharges to the Dolores River causing the observed increase in salinity as the river crosses the Paradox Valley.
To reduce salinity concentrations in the Dolores River, the Bureau of Reclamation (Reclamation) operates the Paradox Valley Unit (PVU). The PVU project consists of nine shallow brine pumping wells near the Dolores River and one deep disposal well where the brine is injected for disposal. When operational, the PVU pumping wells extract brine from the base of the alluvial aquifer that is piped and injected into a deep disposal well about 3 miles southwest of the PVU. The PVU became fully operational July 1, 1996, and by 2015, operation of the PVU had reduced salinity concentrations in the Dolores River by as much as 70 percent compared to pre-PVU conditions. In response to a 4.5 magnitude earthquake, injection operations, and thus PVU pumping, were ceased from March 2019 to June 2022. A trial period of PVU operation began in June 2022 with a reduced injection rate, and thus PVU pumping rate, of about two-thirds capacity to gather additional information and guide future operational decisions.
In cooperation with Reclamation, the U.S. Geological Survey (USGS) developed this report to present the current (2023) understanding of groundwater and brine occurrence and discharge to the Dolores River in the Paradox Valley. Results from the compilation of spatial datasets, groundwater sampling and age dating, and aquifer tests are presented to provide improved understanding of the Paradox Valley hydrogeology, to supply datasets for a numerical groundwater-flow and brine-transport model, and to support future operations of the PVU. The hydrogeologic data provided herein, along with the most recent loading analysis for the Dolores River in the Paradox Valley, and a previous conceptual model for brine discharge to the river are used to present a conceptual understanding of groundwater occurrence in the Paradox Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235094","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Paschke, S.S., Mast, M.A., Gardner, P.M., Newman, C.P., and Watts, K.R., 2024, Hydrogeologic conceptual model of groundwater occurrence and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado: U.S. Geological Survey Scientific Investigations Report 2023–5094, 58 p., https://doi.org/10.3133/sir20235094.","productDescription":"Report: x, 54 p.; 2 Data Releases; Database","onlineOnly":"Y","ipdsId":"IP-125569","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":432678,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245038","text":"Simulation of Groundwater Flow and Brine Discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado"},{"id":432677,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS database—","linkHelpText":"USGS water data for the nation: U.S. Geological Survey National Water Information System database"},{"id":432676,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CJQDDU","text":"USGS data release","linkHelpText":"Geospatial datasets developed for a hydrogeologic conceptual model of brine discharge to the Dolores River, Paradox Valley, Colorado"},{"id":432675,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NV5U6F","text":"USGS data release","linkHelpText":"Water-level and pumping data, water-level models, and estimated hydraulic properties for the Paradox Valley alluvial aquifer in Montrose County, Colorado, 2013"},{"id":432674,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FMWX2J","text":"USGS data release","linkHelpText":"Recharge temperatures and groundwater-age models for the Paradox Valley alluvial aquifer, 2011, Colorado"},{"id":432669,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5094/sir20235094.pdf","text":"Report","size":"9.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5094"},{"id":499380,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117218.htm","linkFileType":{"id":5,"text":"html"}},{"id":432668,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5094/coverthb.jpg"}],"country":"United States","state":"Colorado","county":"Montrose County","otherGeospatial":"Paradox Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.23469392755278,\n 38.62004006715256\n ],\n [\n -109.23469392755278,\n 38.0400613431201\n ],\n [\n -108.48695698861,\n 38.0400613431201\n ],\n [\n -108.48695698861,\n 38.62004006715256\n ],\n [\n -109.23469392755278,\n 38.62004006715256\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, Colorado 80225
Salinity, or total dissolved solids (TDS), of the Colorado River affects agricultural, municipal, and industrial water users and is an important concern in the Western United States. In the Paradox Valley of southwestern Colorado, natural discharge of sodium-chloride brine to the Dolores River from the underlying core of a salt-valley anticline accounts for about 6 percent of the salinity load to the Colorado River. Formation of the Paradox Valley began during the Miocene, and subsequent erosion exposed the Pennsylvania Paradox Formation in the core of the anticline where a cap rock, collapse features, breccia, and sodium-chloride saturated brine developed at the top of the exposed salt diapir. The discharge of brine to the Dolores River is affected by these dissolution features, along with seasonal hydrologic conditions and density-dependent flow between older dense brine and the younger fresh groundwater in the overlying alluvial aquifer. To reduce TDS concentrations in the Dolores River through the Paradox Valley, the Bureau of Reclamation has pumped brine from a series of shallow wells adjacent to the river since July 1996. The pumped brine is collected and piped to a deep disposal well where it is injected into the Mississippian Leadville Limestone at a depth of about 4,570-meters below land surface. The pumping and injection operation is collectively known as the Paradox Valley Unit (PVU), and by 2015, the PVU had substantially reduced TDS concentrations in the Dolores River by about 70 percent. Since 2019, injection-pressure limits and related seismic activity have constrained deep-well injection and thus brine pumping at the PVU.
In cooperation with the Bureau of Reclamation, the U.S. Geological Survey developed a MODFLOW-6 three-dimensional, variable-density groundwater flow and TDS transport model of the Paradox Valley to evaluate the effects of PVU pumping operations on brine discharge to the Dolores River and to guide additional research. The finite-difference model grid consists of 76 rows and 48 columns oriented from northwest to southeast in alignment with valley topography and groundwater-flow directions in the near-surface freshwater alluvial aquifer. A 7-layer hydrogeologic framework was developed from existing datasets to represent the alluvial aquifer, cap rock, collapse breccia, and groundwater flow and TDS transport from the underlying Paradox Formation salt to the Dolores River. The model represents a 33-year transient calibration period from 1987 through 2020 that includes pre-PVU conditions from 1987 through June 1996 and post-PVU conditions from July 1996 through 2020. A 1,000-year simulation of groundwater flow and coupled TDS transport computed the initial conditions for the subsequent 33-year transient simulation. Observations of precipitation, streamflow, evaporation, agricultural land use, and PVU brine pumping rates were used to specify appropriate boundary conditions to the model representing time-varying recharge, tributary streamflow, groundwater underflow, evapotranspiration (ET), and PVU pumping. Values for average monthly streamflow and TDS concentration at the upstream streamgage, the Dolores River at Bedrock (USGS streamgage 09169500), were specified as model input where the Dolores River enters Paradox Valley. Observed pumping from the PVU, water levels and TDS concentrations in groundwater, and streamflow and estimated TDS concentrations at the downstream streamgage, the Dolores River near Bedrock (USGS streamgage 09171100), were calibration targets that constrained the manual calibration of model parameters representing aquifer hydraulic conductivity, storage, streambed conductance, recharge, and (ET).
Two primary model-calibration targets were the match between observed and simulated TDS mass flux from PVU pumping wells and the match between estimated and simulated TDS mass flux to the Dolores River. The simulated TDS mass withdrawn by pumping wells is calculated by the model as the product of the assigned pumping rate and simulated groundwater TDS concentrations. Because actual pumping rates were assigned as simulated values, the total simulated PVU pumping for the 33-year calibration is within 0.5 percent of the observed values. However, simulated concentrations and thus mass flux of TDS withdrawn by the PVU pumping wells were consistently about 26 percent less than observed values for all the simulated time periods (33-year simulation, pre-PVU, and post-PVU). The representation of brine inflow was explored through additional modeling to evaluate the effect of the simulated brine source on groundwater TDS concentrations. Results indicated that a saturated-salt constant-flux brine source best replicated the magnitude and transient pattern observed for TDS mass flux from PVU pumping wells.
The simulated TDS mass flux to the Dolores River is compared to estimates based on observed streamflow and specific conductance (SC) data for the downstream streamgage. The calibrated model provided a close fit of simulated to measured streamflow at the downstream streamgage, and the calibrated model fit to estimated TDS concentrations at the downstream streamgage was reasonable. The greatest differences between simulated and estimated values occurred during drought periods from June 2000 to March 2003, May 2012 to June 2013, and October 2013 to October 2014, when simulated TDS concentrations in the river were greater than estimated concentrations. In general, simulated TDS mass flux to the river for the pre-PVU period is in good agreement with estimated values (2-percent difference), but the model overestimated TDS mass flux to the river by about 41 percent during the post-PVU period. The model uncertainty with respect to TDS mass flux to the river indicates other processes or model parameters not well represented by the model are affecting the system, especially during drought. During model calibration, the most sensitive parameters were identified as vertical hydraulic conductivity of the alluvial aquifer, conductance of the Dolores River streambed, ET extinction depth and rate, and recharge rate.
Five 5-year scenarios of conditions for 2021–25 were simulated to assist evaluation of alternative strategies to manage the discharge of brine into the Dolores River. The first scenario simulates no PVU pumping and serves as a base case for comparison to the other scenarios. Two scenarios simulate the effects of varying withdrawal timing at an annual rate about one-third less than during 2010 through 2018. During high-flow spring snowmelt runoff periods when brine discharge is naturally minimized, PVU pumping does not substantially affect salinity in the Dolores River, and comparison of these two scenarios indicates that scheduling brine withdrawals during times of low river stage is nearly as effective at reducing TDS mass flux to the river as pumping brine year-round. Cessation of pumping during periods of high river stage may be advantageous for system maintenance, brine injection, and seismic-risk reduction. The fourth scenario tested the effect of reducing irrigation-return flow on brine discharge and predicted a slight reduction of TDS mass flux to the Dolores River, but not as great a reduction as that of using the PVU to remove brine. The fifth scenario simulated 5 years of drought conditions without PVU pumping and indicates brine discharge during drought about 15 percent greater than during average hydrologic conditions. Results from scenario 5 are consistent with the calibrated model results and indicate that aquifer properties and ET processes and parameters may be affecting simulation results during drought.
The Paradox Valley groundwater model provides a reasonable overall match to observed conditions in the Dolores River. The model is useful for evaluating relative differences between brine management scenarios to inform PVU operational decisions and to identify gaps in data and process understanding. Representation of the brine source, hydraulic-conductivity parameters, and recharge and ET processes were identified as potential areas for additional field and modeling research. Additional research in the Paradox Valley might include field-data collection that provides additional information on the hydrogeologic framework, groundwater levels, groundwater TDS concentrations, stream characteristics, and aquifer properties. Additional modeling efforts could benefit from applying advanced tools for model development, calibration, and visualization including parameter-estimation and sensitivity analysis. Statistical evaluation of known model uncertainties such as hydraulic conductivity, streambed conductance, representations of the brine source, recharge, and ET could improve the match between simulated and estimated TDS mass flux from PVU pumping wells and to the Dolores River further informing model predictions and system understanding for the Paradox Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245038","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Heywood, C.E., Paschke, S.S., Mast, M.A., and Watts, K.R., 2024, Simulation of groundwater flow and brine discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado: U.S. Geological Survey Scientific Investigations Report 2024–5038, 47 p., https://doi.org/10.3133/sir20245038.","productDescription":"Report: viii, 47 p.; Data Release; 3 Databases","onlineOnly":"Y","ipdsId":"IP-130109","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":432768,"rank":6,"type":{"id":9,"text":"Database"},"url":"https://waterdata.usgs.gov/monitoring-location/09171100/all-graphs/#period=P7D","text":"USGS database site information —","linkHelpText":"USGS 09171100 Dolores River near Bedrock, CO, in USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432764,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS database—","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432759,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5038/coverthb.jpg"},{"id":432763,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZW0FH5","text":"USGS data release","linkHelpText":"MODFLOW-6 model of variable-density groundwater flow and brine discharge to the Dolores River in the Paradox Valley, Colorado"},{"id":432766,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://waterdata.usgs.gov/monitoring-location/09169500/all-graphs/#period=P7D","text":"USGS database site information —","linkHelpText":"USGS 09169500 Dolores River at Bedrock, CO, in USGS water data for the Nation: U.S. Geological Survey National Water Information System"},{"id":432760,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5038/sir20245038.pdf","text":"Report","size":"9.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024-5038"},{"id":499462,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117219.htm","linkFileType":{"id":5,"text":"html"}},{"id":432769,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235094","text":"Hydrogeologic Conceptual Model of Groundwater Occurrence and Brine Discharge to the Dolores River in the Paradox Valley, Montrose County, Colorado"}],"country":"United States","state":"Colorado","county":"Montrose County","otherGeospatial":"Paradox Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.10372373203981,\n 38.51805273423872\n ],\n [\n -109.10372373203981,\n 38.1119253984173\n ],\n [\n -108.49529764389875,\n 38.1119253984173\n ],\n [\n -108.49529764389875,\n 38.51805273423872\n ],\n [\n -109.10372373203981,\n 38.51805273423872\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
Denver, Colorado 80225
Long-term wildlife research and monitoring programs strive to maintain consistent data collections and analytical methods. Incorporating new techniques is important but can render data sets incongruent and limit their potential to discern trends in demographic parameters. Integrated population models (IPMs) can address these limitations by combining data sources that may span different periods into a unified statistical framework while providing a holistic view of population dynamics. We developed an IPM in a Bayesian framework for grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem. We coupled demographic data with multiple, independent population count data to link annual changes in abundance with vital rates over 4 decades (1983–2023). Abundance increased threefold from an estimated 270 individuals in 1984 to 1030 individuals in 2023. Parameter estimates indicated survival of bears ≥2 years of age was high, contributing to robust population growth during the 1980s (λ = 1.023 [50 % interquartile range = 0.993–1.082]) and 1990s (λ = 1.064 [1.023–1.103]). A slowing of population growth started around 2000 (2000s: λ = 1.030 [0.989–1.068]) and continued into the 2010s (λ = 1.021 [0.985–1.057]), due primarily to reductions in survival of bears <2 years of age. These findings corroborate previous research that identified density-dependent effects as a likely cause. The IPM framework provided greater certainty and understanding regarding the dynamic demographic characteristics of the population and serves as a powerful monitoring tool for this long-lived species. Implementation of the IPM allows timely dissemination of demographic data to help inform adaptive management strategies and policy decisions necessary for the continued management and conservation of this population. This robust and flexible monitoring system allows scientists to investigate the effects of a changing ecosystem on population dynamics, incorporate new data sources and statistical models, and respond to changes in monitoring needs for the population. We highlight the efficacy of the IPM in estimating and tracking demographic parameters for a long-lived species, while accommodating shifts in monitoring techniques and data collections typical of long-term wildlife conservation programs worldwide.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2024.e03133","usgsCitation":"Gould, M.J., Clapp, J., Haroldson, M.A., Costello, C., Nowak, J.J., Martin, H., Ebinger, M., Bjornlie, D., Thompson, D., Dellinger, J.A., Mumma, M., Lukacs, P., and van Manen, F.T., 2024, A unified approach to long-term population monitoring of grizzly bears in the Greater Yellowstone Ecosystem: Global Ecology and Conservation, v. 54, e03133, 16 p., https://doi.org/10.1016/j.gecco.2024.e03133.","productDescription":"e03133, 16 p.","ipdsId":"IP-166249","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":466961,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2024.e03133","text":"Publisher Index Page"},{"id":432938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Greater Yellowstone Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.19553511360566,\n 45.48126054576713\n ],\n [\n -112.23427309687703,\n 44.53619573717927\n ],\n [\n -110.92847041406486,\n 43.38122138835186\n ],\n [\n -109.24643599937531,\n 43.18040812667567\n ],\n [\n -109.13267230664917,\n 45.518110186630736\n ],\n [\n -112.19553511360566,\n 45.48126054576713\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"54","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gould, Matthew J.","contributorId":201504,"corporation":false,"usgs":false,"family":"Gould","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":911000,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Clapp, Justin","contributorId":256932,"corporation":false,"usgs":false,"family":"Clapp","given":"Justin","email":"","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":911001,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haroldson, Mark A. 0000-0002-7457-7676 mharoldson@usgs.gov","orcid":"https://orcid.org/0000-0002-7457-7676","contributorId":1773,"corporation":false,"usgs":true,"family":"Haroldson","given":"Mark","email":"mharoldson@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":911002,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Costello, Cecily M.","contributorId":145510,"corporation":false,"usgs":false,"family":"Costello","given":"Cecily M.","affiliations":[{"id":5117,"text":"University of Montana, College of Forestry and Conservation, University Hall, Room 309, Missoula, MT 59812, USA","active":true,"usgs":false}],"preferred":false,"id":911003,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nowak, J. Joshua","contributorId":171707,"corporation":false,"usgs":false,"family":"Nowak","given":"J.","email":"","middleInitial":"Joshua","affiliations":[],"preferred":false,"id":911004,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martin, Hans","contributorId":331216,"corporation":false,"usgs":false,"family":"Martin","given":"Hans","email":"","affiliations":[{"id":79153,"text":"Univ. of Minnesota, St. Paul, MN","active":true,"usgs":false}],"preferred":false,"id":911005,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ebinger, Michael","contributorId":300973,"corporation":false,"usgs":false,"family":"Ebinger","given":"Michael","affiliations":[{"id":35211,"text":"Montana Department of Fish, Wildlife and Parks","active":true,"usgs":false}],"preferred":false,"id":911006,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Bjornlie, Daniel D.","contributorId":145512,"corporation":false,"usgs":false,"family":"Bjornlie","given":"Daniel D.","affiliations":[{"id":16140,"text":"Wyoming Game & Fish Department, Large Carnivore Section, Lander, Wyoming 82520, USA","active":true,"usgs":false}],"preferred":false,"id":911007,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Thompson, Daniel","contributorId":225736,"corporation":false,"usgs":false,"family":"Thompson","given":"Daniel","affiliations":[{"id":13584,"text":"Natural Resources Canada, Canadian Forest Service","active":true,"usgs":false}],"preferred":false,"id":911008,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dellinger, Justin A.","contributorId":190532,"corporation":false,"usgs":false,"family":"Dellinger","given":"Justin","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":911009,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mumma, Matthew A.","contributorId":202351,"corporation":false,"usgs":false,"family":"Mumma","given":"Matthew","middleInitial":"A.","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":911010,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lukacs, Paul","contributorId":189208,"corporation":false,"usgs":false,"family":"Lukacs","given":"Paul","affiliations":[],"preferred":false,"id":911011,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":911012,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70259350,"text":"70259350 - 2024 - Skill assessment of a total water level and coastal change forecast during the landfall of a hurricane","interactions":[],"lastModifiedDate":"2024-10-04T14:11:40.777116","indexId":"70259350","displayToPublicDate":"2024-08-19T09:07:37","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Skill assessment of a total water level and coastal change forecast during the landfall of a hurricane","docAbstract":"The Total Water Level and Coastal Change Forecast (TWL&CC Forecast) provides coastal communities with 6-day notice of potential elevated water levels and coastal change (i.e., dune erosion, overwash, or inundation) on sandy beaches that threatens safety, infrastructure, or resources. This continuously operating model provides hourly information for select regions along U.S. Gulf of Mexico and Atlantic Ocean coastlines. The objective of this work is to assess the skill of forecasts during a period of elevated water levels along the coasts of North Carolina (NC) and South Carolina, USA caused by Hurricane Isaias in August 2020, using a combination of observations and model hindcasts. Water levels and waves were observed throughout the storm at three locations near Wrightsville Beach, NC, which provided information to assess forecast skill; a wave buoy offshore, a tide gage at a local pier, and a pressure sensor deployed at the pier. In addition to observations, the non-hydrostatic phase-resolving model SWASH (Simulating WAves till SHore) was forced with hourly wave energy spectra derived from a coupled Delft3D-SWAN simulation during the peak of Isaias, to complement observations by computing nearshore wave height and wave-induced setup and runup at the shoreline. During the storm peak, SWASH-simulated water levels at the sensor position were comparable to those at the maximum landward extent (bias = −0.05 m; gain = 0.26; r2 = 0.99), suggesting that observations at the USGS sensor location were a useful proxy for total water level (TWL; sum of tide, surge and wave runup) at the shoreline that are predicted by the TWL&CC Forecast. The TWL forecast at Wrightsville Beach was consistent with observations from the USGS sensor (bias = −0.38 m and −0.74 m, scatter index = 0.22 and 0.28 for the two forecast model grids considered, respectively; weighted regression considering model uncertainty explained 95 percent of variability in observed TWL). Observed TWL was within the confidence interval of the TWL&CC Forecast for the 5 h at the storm peak. Forecast mean water levels (MWL; sum of tide, surge and wave setup) and tide gage observations were also consistent (bias = 0.07 m and 0.02 m for the forecast model grids; scatter index = 0.46; r2 = 0.80). Forecast MWL at the storm peak was within 0.06 m of the observed MWL from the tide gage for both sites. In the region where Isaias made landfall, eight additional pressure sensors were compared to the peak TWL forecast (bias = 0.14 m; scatter index = 0.18). Forecast TWL explained 90 percent of observed variability in TWL when considering uncertainty of the forecast with a weighted regression. The results demonstrate that wave-driven water levels contributed a significant portion of the forecast TWL during Isaias (52 percent during the three peak hours of the storm), and that TWL were represented using the forecast model. Mean absolute error of the coastal change forecast and observed overwash is 0.4 and 0.14 for the two forecast model grids considered. The skill demonstrated by this computationally efficient method indicates that the forecasting system can provide fast and reliable predictions of TWL across hundreds of km of coastline at sub-km resolution, days to hours in advance of when storms threaten coastal regions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2024.104590","usgsCitation":"Birchler, J.J., Palmsten, M.L., Doran, K., Karwandyar, S., Pardun, J.M., Oades, E.M., Mulligan, R.P., and Whitehead-Zimmers, E.S., 2024, Skill assessment of a total water level and coastal change forecast during the landfall of a hurricane: Coastal Engineering, v. 193, 104590, 19 p., https://doi.org/10.1016/j.coastaleng.2024.104590.","productDescription":"104590, 19 p.","ipdsId":"IP-154794","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":466962,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2024.104590","text":"Publisher Index Page"},{"id":462596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"193","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Birchler, Justin J. 0000-0002-0379-2192 jbirchler@usgs.gov","orcid":"https://orcid.org/0000-0002-0379-2192","contributorId":169117,"corporation":false,"usgs":true,"family":"Birchler","given":"Justin","email":"jbirchler@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915008,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doran, Kara S. 0000-0001-8050-5727","orcid":"https://orcid.org/0000-0001-8050-5727","contributorId":292448,"corporation":false,"usgs":true,"family":"Doran","given":"Kara S.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915009,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karwandyar, Sharifa 0000-0002-1531-2360","orcid":"https://orcid.org/0000-0002-1531-2360","contributorId":343816,"corporation":false,"usgs":false,"family":"Karwandyar","given":"Sharifa","email":"","affiliations":[{"id":82201,"text":"Department of Earth Sciences, Binghamton University","active":true,"usgs":false}],"preferred":false,"id":915010,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pardun, Joshua Michael 0000-0003-4633-3970","orcid":"https://orcid.org/0000-0003-4633-3970","contributorId":335148,"corporation":false,"usgs":true,"family":"Pardun","given":"Joshua","email":"","middleInitial":"Michael","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":915011,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oades, Elora M.","contributorId":343817,"corporation":false,"usgs":false,"family":"Oades","given":"Elora","email":"","middleInitial":"M.","affiliations":[{"id":82202,"text":"Department of Civil Engineering, Queen’s University","active":true,"usgs":false}],"preferred":false,"id":915012,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mulligan, Ryan P.","contributorId":194423,"corporation":false,"usgs":false,"family":"Mulligan","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":35723,"text":"Queen's University - Kingston, Ontario","active":true,"usgs":false}],"preferred":false,"id":915013,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Whitehead-Zimmers, Eli Sawyer 0000-0002-8925-3498","orcid":"https://orcid.org/0000-0002-8925-3498","contributorId":339930,"corporation":false,"usgs":true,"family":"Whitehead-Zimmers","given":"Eli","email":"","middleInitial":"Sawyer","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":915014,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266827,"text":"70266827 - 2024 - Abiotic and demographic drivers of flea parasitism on deer mice in a recovering mixed-conifer forest a decade postfire","interactions":[],"lastModifiedDate":"2025-05-14T13:20:58.690457","indexId":"70266827","displayToPublicDate":"2024-08-19T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2414,"text":"Journal of Parasitology","active":true,"publicationSubtype":{"id":10}},"title":"Abiotic and demographic drivers of flea parasitism on deer mice in a recovering mixed-conifer forest a decade postfire","docAbstract":"With the intensity and frequency of wildfires increasing rapidly, the need to study the ecological effects of these wildfires is also growing. An understudied aspect of fire ecology is the effect fires have on parasite–host interactions, including ectoparasites that might be pathogen vectors. Although some studies have examined the impacts of fire on ticks, studies on other ectoparasites, including pathogen vectors, are rare. To help address this knowledge gap, we examined the abiotic and biotic factors that predict the likelihood and extent of parasitism of deer mice (Peromyscus maniculatus) by fleas within a landscape of unburned and recovering burned (>9 yr postfire) mixed conifer forests. We sampled 227 individual deer mice across 27 sites within the Jemez Mountains of northern New Mexico in 2022 and quantified measures of parasitism by fleas (primarily Aetheca wagneri). These sites were distributed in both unburned areas (n = 15) and recovering burned areas (n = 12), with the latter derived from 2 large fires, the Las Conchas fire (2011) and the Thompson Ridge fire (2013). Using these data, we tested for differences in prevalence, mean abundance, and mean intensity of fleas on deer mice, focusing on the predictive importance of host sex and fire history. We also created generalized linear mixed-effects models to investigate the best host and environmental predictors of parasitism by fleas. Approximately a decade postfire, we found minimal evidence to suggest that fire history influenced either the presence or intensity of fleas on deer mice. Rather, at the current forest-regeneration stage, the extent of parasitism by fleas was best predicted by measures of host sex, body condition, and the trapline's ability to accumulate water, as measured through topography. As host body condition increased, the probability of males being parasitized increased, whereas the opposite pattern was seen for females. Male mice also had significantly greater flea loads. Among potential abiotic predictors, the topographic wetness index or compound topographic index (a proxy for soil moisture) was positively related to flea intensity, suggesting larger flea populations in burrows with higher relative humidity. In summary, although fire may potentially have short-term impacts on the likelihood and extent of host parasitism by fleas, in this recovering study system, host characteristics and topographic wetness index are the primary predictors of parasitism by fleas.
","language":"English","publisher":"BioOne","doi":"10.1645/23-45","usgsCitation":"Padilla, C., Martin, J., Cain, J.W., and Gompper, M., 2024, Abiotic and demographic drivers of flea parasitism on deer mice in a recovering mixed-conifer forest a decade postfire: Journal of Parasitology, v. 110, no. 4, p. 375-385, https://doi.org/10.1645/23-45.","productDescription":"11 p.","startPage":"375","endPage":"385","ipdsId":"IP-153592","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485844,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Jemez Ranger District of Santa Fe National Forest, Valles Caldera National Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -107.30445858820687,\n 36.653454747968524\n ],\n [\n -107.30445858820687,\n 35.654968715553636\n ],\n [\n -105.9993414935517,\n 35.654968715553636\n ],\n [\n -105.9993414935517,\n 36.653454747968524\n ],\n [\n -107.30445858820687,\n 36.653454747968524\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Padilla, Colton J.","contributorId":353982,"corporation":false,"usgs":false,"family":"Padilla","given":"Colton J.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":936849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Jessica T.","contributorId":355088,"corporation":false,"usgs":false,"family":"Martin","given":"Jessica T.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":936850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":936851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gompper, Matthew E.","contributorId":353984,"corporation":false,"usgs":false,"family":"Gompper","given":"Matthew E.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":936852,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261644,"text":"70261644 - 2024 - Testing tree-ring cellulose δ18O with water isotopes for Holocene lake δ18O interpretations in the central Rocky Mountains USA","interactions":[],"lastModifiedDate":"2024-12-18T14:28:08.044274","indexId":"70261644","displayToPublicDate":"2024-08-17T15:55:33","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3562,"text":"The Holocene","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Testing tree-ring cellulose δ18O with water isotopes for Holocene lake δ18O interpretations in the central Rocky Mountains USA","title":"Testing tree-ring cellulose δ18O with water isotopes for Holocene lake δ18O interpretations in the central Rocky Mountains USA","docAbstract":"Stable isotopes of water preserved in geologic archives, primarily as oxygen (δ18O), have proven critical for documenting Earth’s climatic and hydrologic
systems past and present. However, timescale differences of water isotope inputs to proxy systems and the signal embedded in long paleorecords often
confound translation to observed hydroclimatic metrics. Here, a unique 20-year dataset of meteorology, hydrology, and the isotopic composition of
weekly meteoric and surface water samples (δ18O, δ2 H) are combined with paleoclimate δ18O data from tree-ring cellulose and lake carbonate to better
understand proxy signals of Upper Colorado river basin drought. Annual tree-ring cellulose δ18O from Picea engelmannii growing within a glacier-fed creek
and a spring discharge area were used to derive annual source water δ18O using a cellulose source-water isotope model. Comparisons with the monitoring
record indicates that tree-ring cellulose δ18O tracks variations in wet and dry hydroclimatic extremes. Source water isotopes are shown to reflect the
hydroclimate of the current year and some number of previous years as an effective moisture-discharge proxy rather than a precipitation isotope proxy.
Results contextualize Holocene lake carbonate δ18O data. The contemporary-to-paleo comparison identifies changes in seasonal precipitation extremes
during recent millennia and several earlier arid and monsoon-dominated Holocene periods that exceed the arid maximum of the calibration period.