The city of Wildwood, Missouri, has identified fluvial erosion along Caulks Creek as a management priority due to potential effects to infrastructure and property. The upper and middle reaches of Caulks Creek flow intermittently (only immediately after precipitation), whereas the lower reach flows perennially. This study examines the effects of climate change and added storage on the hydrologic and hydraulic response of the Caulks Creek Basin to design storms. The study uses hydrologic (Hydrologic Engineering Center Hydrologic Modeling System – HEC-HMS) and hydraulic (Hydrologic Engineering Center River Analysis System – HEC-RAS) models furnished by the Federal Emergency Management Agency (FEMA). HEC-HMS simulations were used to quantify the peak, volume, and timing of the flow response to a suite of design storms under both normal and wet antecedent conditions and for both the existing storage structures and new storage in the basin. The suite of design storms included all combinations of the following: (a) storm durations: 6-hour and 24-hour, (b) annual exceedance probabilities: 0.5, 0.2, 0.1, 0.04, 0.02, and 0.01, (c) climate conditions: current, 30-year, and 80-year predictions of future climate from the Coupled Model Intercomparison Project (CMIP) Climate Data Processing Tool. Additionally, for a selection of scenarios, results from the HEC-HMS simulations were used as boundary conditions for two-dimensional (2D) HEC-RAS simulations aimed at understanding how the distribution of velocity, shear stress, and stream power throughout the stream may be affected by projected changes in climate.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD-2023, Sedimentation and Hydrologic Modeling Conference","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","usgsCitation":"LeRoy, J.Z., Heimann, D.C., Burk, T.J., Cigrand, C.V., and Hix, K.D., 2023, Effects of climate change on the hydrologic and hydraulic response of the Caulks Creek basin, Wildwood, Missouri, in SEDHYD 2023, St. Louis, MO, May 8-12, 2023, 5 p.","productDescription":"5 p.","ipdsId":"IP-148407","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416240,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s26.html","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Missouri","city":"Wildwood","otherGeospatial":"Caulks Creek basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.7,\n 38.6667\n ],\n [\n -90.7,\n 38.566667\n ],\n [\n -90.55,\n 38.566667\n ],\n [\n -90.55,\n 38.6667\n ],\n [\n -90.7,\n 38.6667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"LeRoy, Jessica Z. 0000-0003-4035-6872 jzinger@usgs.gov","orcid":"https://orcid.org/0000-0003-4035-6872","contributorId":174534,"corporation":false,"usgs":true,"family":"LeRoy","given":"Jessica","email":"jzinger@usgs.gov","middleInitial":"Z.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heimann, David C. 0000-0003-0450-2545 dheimann@usgs.gov","orcid":"https://orcid.org/0000-0003-0450-2545","contributorId":3822,"corporation":false,"usgs":true,"family":"Heimann","given":"David","email":"dheimann@usgs.gov","middleInitial":"C.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burk, Tyler Joseph 0000-0002-9142-1454","orcid":"https://orcid.org/0000-0002-9142-1454","contributorId":304060,"corporation":false,"usgs":true,"family":"Burk","given":"Tyler","email":"","middleInitial":"Joseph","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869111,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cigrand, Charles V. 0000-0002-4177-7583","orcid":"https://orcid.org/0000-0002-4177-7583","contributorId":201575,"corporation":false,"usgs":true,"family":"Cigrand","given":"Charles","email":"","middleInitial":"V.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869112,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hix, Kyle D. 0000-0002-6316-7436","orcid":"https://orcid.org/0000-0002-6316-7436","contributorId":260630,"corporation":false,"usgs":true,"family":"Hix","given":"Kyle","email":"","middleInitial":"D.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":869113,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232963,"text":"70232963 - 2023 - Mode and provenance of sediment deposition on a transgressive marsh","interactions":[],"lastModifiedDate":"2025-05-13T16:02:59.729442","indexId":"70232963","displayToPublicDate":"2023-04-15T09:59:55","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Mode and provenance of sediment deposition on a transgressive marsh","docAbstract":"In this study, we use foraminifera as environmental indicators to aid in computing the historical volumetric inputs of estuarine sediments to adjacent marsh. These data can help assess the importance of estuarine sediment inputs to marsh accretion. The Grand Bay system (GBS), located on the southern coast of Alabama and Mississippi, has been described as a “self-cannibalizing bay-marsh complex” due to the disproportionately large amount of suspended sediment exported from the GBS relative to the amount of sediment imported into tidal channels and available to the marsh. Despite this sediment limitation in the marshes, mass sediment budgets along shoreline-proximal marsh sites suggest depositional fluxes on the marsh are nearly equivalent to erosional losses at decadal scales. Geochronologies and foraminiferal census data for two shore-normal transects show that estuarine sediment contributes significant portions (28-65%) of this depositional flux. Contrast in shoreline morphology (i.e., orientation, irregularity, and exposure) and adjacent environments (i.e., tidal creeks and mud flats) may influence the proportion of estuarine sediment delivered to marsh. Improvements of sediment provenance tracers in these environments will continually improve the understanding of sediment reworking and transport in transgressive marsh-estuarine systems such as GBS and the role they play in marsh resiliency to sea-level change.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The proceedings of the coastal sediments 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Coastal Sediments 2023","conferenceDate":"April 11-15, 2023","conferenceLocation":"New Orleans, LA","language":"English","publisher":"World Scientific","doi":"10.1142/9789811275135_0132","usgsCitation":"Smith, C., Ellis, A.M., and Smith, K., 2023, Mode and provenance of sediment deposition on a transgressive marsh, in The proceedings of the coastal sediments 2023, New Orleans, LA, April 11-15, 2023, p. 1426-1436, https://doi.org/10.1142/9789811275135_0132.","productDescription":"11 p.","startPage":"1426","endPage":"1436","ipdsId":"IP-142773","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416383,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Mississippi","otherGeospatial":"Grand Bay system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.0850979597876,\n 30.444219651609245\n ],\n [\n -88.83426274628478,\n 30.444219651609245\n ],\n [\n -88.83426274628478,\n 30.223813269875876\n ],\n [\n -88.0850979597876,\n 30.223813269875876\n ],\n [\n -88.0850979597876,\n 30.444219651609245\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2023-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Christopher G. 0000-0002-8075-4763","orcid":"https://orcid.org/0000-0002-8075-4763","contributorId":218439,"corporation":false,"usgs":true,"family":"Smith","given":"Christopher G.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":846572,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ellis, Alisha M. 0000-0002-1785-020X aellis@usgs.gov","orcid":"https://orcid.org/0000-0002-1785-020X","contributorId":192957,"corporation":false,"usgs":true,"family":"Ellis","given":"Alisha","email":"aellis@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":846571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Kathryn E.L. 0000-0002-7521-7875 kelsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-7521-7875","contributorId":173264,"corporation":false,"usgs":true,"family":"Smith","given":"Kathryn","email":"kelsmith@usgs.gov","middleInitial":"E.L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":846573,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243278,"text":"70243278 - 2023 - Sand- and gravel-trapping efficiencies derived for four types of pressure-difference bedload samplers","interactions":[],"lastModifiedDate":"2023-05-05T14:49:51.798384","indexId":"70243278","displayToPublicDate":"2023-04-15T09:31:22","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Sand- and gravel-trapping efficiencies derived for four types of pressure-difference bedload samplers","docAbstract":"Bedload-trapping efficiencies (coefficients) were derived for four types of pressure-difference bedload samplers at the St. Anthony Falls Laboratory, University of Minnesota during the first two phases of flume experiments in January-March, 2006, referred to as “StreamLab06.” The bedload-sampler research component was part of a series of community-led, large-scale laboratory experiments performed under the auspices of the National Center for Earth-surface Dynamics (Marr and others, 2010; Singh and others, 2013; Gray and others, 2010, 2019, 2021).\n\nA bedload-trapping coefficient is the ratio of the mass of bedload – sediment transported by rolling, sliding, or skipping in close contact with the riverbed – collected by the deployed sampler, to the mass of bedload that would have passed through the width of the sample section at the same time but in the absence of the sampler (Hubbell, 1964). A trapping coefficient of 1.0 would mean the mass of every particle-size fraction of sediment in the collected sample is in the same proportion as those in transport. \n\nFor the 2006 experiments, a Helley-Smith (intake-nozzle width of 76.2 millimeter [mm] and height of 76.2 mm), BLH-84 (76.2 mm × 76.2 mm), Elwha (203 mm × 102 mm) and Toutle River-2 (TR-2; 305 mm × 152 mm) were repeatedly deployed by a hand-held rod with a stabilizing tether line in the main flume. Six combinations of bedload sampler types and bed compositions were tested: The BLH-84, Elwha, and Helley-Smith samplers were deployed on a sand bed (d50 = 1.0 mm) during five steady flows ranging from 2.0-3.6 cubic meters per second (m3/s). The BLH-84, Elwha, and TR-2 samplers were deployed on a gravel bed (d50 = 11.2 mm) at four steady flows ranging from 4.0-5.5 m3/s.\n \nBedload samples collected manually as part of 37 trials – each associated with a unique combination of a bedload sampler type, steady-flow rate, and bed composition – and associated ancillary data were used to calculate 2,030 instantaneous, at-a-point bedload-transport rates (1,000 as part of 19 sand-bed trials, and 1,030 as part of 27 gravel-bed trials.). Five contiguous weigh drums embedded in a slot spanning the width of the flume independently and continuously weighed captured bedload on approximately 1.1-second intervals. Approximately 3.8-million individual weigh-drum time-series measurements were recorded during the bedload sampler experiments (Groten and Gray, 2021; Gray and others, 2021). \n ","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD-2023, Sedimentation and Hydrologic Modeling Conference","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","publisher":"SEDHYD","usgsCitation":"Gray, J., Groten, J.T., Czuba, J.A., Schwarz, G.E., Strom, K., and Diplas, P., 2023, Sand- and gravel-trapping efficiencies derived for four types of pressure-difference bedload samplers, in SEDHYD 2023, St. Louis, MO, May 8-12, 2023, 4 p.","productDescription":"4 p.","ipdsId":"IP-151010","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416760,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416744,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s169.html","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gray, John","contributorId":304862,"corporation":false,"usgs":false,"family":"Gray","given":"John","affiliations":[{"id":66176,"text":"Gray Sedimentology","active":true,"usgs":false}],"preferred":false,"id":871781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Groten, Joel T. 0000-0002-0441-8442 jgroten@usgs.gov","orcid":"https://orcid.org/0000-0002-0441-8442","contributorId":173464,"corporation":false,"usgs":true,"family":"Groten","given":"Joel","email":"jgroten@usgs.gov","middleInitial":"T.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":871782,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Czuba, Jonathan A. 0000-0002-9485-2604","orcid":"https://orcid.org/0000-0002-9485-2604","contributorId":301942,"corporation":false,"usgs":false,"family":"Czuba","given":"Jonathan","email":"","middleInitial":"A.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":871783,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwarz, Gregory E. 0000-0002-9239-4566 gschwarz@usgs.gov","orcid":"https://orcid.org/0000-0002-9239-4566","contributorId":213621,"corporation":false,"usgs":true,"family":"Schwarz","given":"Gregory","email":"gschwarz@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":871784,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Strom, Kyle","contributorId":304863,"corporation":false,"usgs":false,"family":"Strom","given":"Kyle","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":871785,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Diplas, Panayiotis","contributorId":304864,"corporation":false,"usgs":false,"family":"Diplas","given":"Panayiotis","email":"","affiliations":[{"id":16160,"text":"Lehigh University","active":true,"usgs":false}],"preferred":false,"id":871786,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243262,"text":"70243262 - 2023 - How machine learning can improve predictions and provide insight into fluvial sediment transport in Minnesota","interactions":[],"lastModifiedDate":"2023-05-05T14:30:38.523906","indexId":"70243262","displayToPublicDate":"2023-04-15T09:25:15","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"How machine learning can improve predictions and provide insight into fluvial sediment transport in Minnesota","docAbstract":"Understanding fluvial sediment transport is critical to addressing many environmental concerns such as exacerbated flooding, degradation of aquatic habitat, excess nutrients, and the economic challenges of restoring aquatic systems. However, fluvial sediment transport is difficult to understand because of the multitude of factors controlling the potential sources, delivery, mechanics, and storage of sediment in aquatic systems. While physical fluvial sediment samples are an integral part of developing solutions for these environmental concerns, samples cannot be collected at every river and time of interest. Therefore, accurate and cost-effective estimates of sediment loading are needed to manage riverine sediment transport at a multitude of scales (Ellison et al. 2016); also needed are methods to estimate sediment transport at sites where little or no physical samples have been collected (Gray & Simes 2008). The application of machine learning (ML) approaches to estimate sediment transport has grown over the past two decades (Afan et al. 2016). ML used in sediment transport research has shown multiple benefits over traditional approaches, such as increased prediction accuracy, the ability to learn complex linear and non-linear relations amongst the dataset and providing the ability to interpret these complex relations with important features used in the model (Cisty et al. 2021; Francke et al. 2008; Khan et al. 2021; Zounemat-Kermani et al. 2020; Cutler et al. 2007).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD-2023, Sedimentation and Hydrologic Modeling Conference","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","publisher":"SEDHYD","usgsCitation":"Lund, J., Groten, J.T., Karwan, D.L., and Babcock, C., 2023, How machine learning can improve predictions and provide insight into fluvial sediment transport in Minnesota, in SEDHYD 2023, St. Louis, MO, May 8-12, 2023, 5 p.","productDescription":"5 p.","ipdsId":"IP-148054","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416740,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s18.html"}],"country":"United 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Civil Engineering, Istanbul Bilgi University, Istanbul, Türkiye","active":true,"usgs":false}],"preferred":false,"id":870406,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243286,"text":"70243286 - 2023 - Comparing empirical sediment transport modeling approaches in Michigan rivers","interactions":[],"lastModifiedDate":"2023-05-05T14:04:32.563372","indexId":"70243286","displayToPublicDate":"2023-04-15T08:51:14","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Comparing empirical sediment transport modeling approaches in Michigan rivers","docAbstract":"Excess or limited fluvial sediment transport can contribute to and exacerbate many environmental issues including nutrient loading, aquatic habitat degradation, flooding, channel navigation dredging, dam operation, and stream degradation or aggradation. However, fluvial sediment transport is difficult and expensive to comprehensively characterize because it can vary substantially both temporally and spatially. Having better estimates of fluvial sediment transport is important for understanding and solving these environmental issues when it is not possible to collect fluvial sediment samples. Different modeling approaches can be used to help estimate suspended sediment when sampling data are limited or unavailable. This study compared dimensionless sediment rating curves (DSRCs) developed in Pagosa Springs Colorado, Minnesota, and Michigan to determine if these DSRCs were suitable to make predictions of suspended sediment for Michigan rivers.
Approximately 3,000 suspended sediment samples collected in or near Michigan from the mid-1960s through August 2022 were used to develop two DSRC models. The DSRCs developed in Michigan include a pooled DSRC model which uses nonlinear least squares regression, and a mixed-effects DSRC model which uses a mixed-effects modeling approach. In general, there was not a noticeable improvement in the performance of the Michigan mixed-effects DSRC model over the Michigan pooled DSRC model. The two Michigan DSRCs were evaluated against DSRCs developed for Pagosa Springs and Minnesota. The results showed DSRC models developed from Minnesota and Michigan were similar to each other. In contrast, the Pagosa Springs DSRC predicts higher suspended-sediment concentration (SSC) at low flows and increases at a higher rate due to having a greater exponent. The Pagosa Springs DSRC produces higher SSC predictions that do not approximate the observed data well at most of the Michigan sites in the study. The results suggest that the Pagosa Springs DSRC was not suitable to make predictions of suspended sediment for Michigan rivers. The similarity of the DSRC equations developed for Minnesota and Michigan compared to the Pagosa Springs DSRC equation suggest that there may be regional patterns of SSC in the upper Midwest rivers that differ from those in other areas of the country like Pagosa Springs. A regionally applicable model could be developed and strengthened by combining data from additional midwestern states. Since the Michigan DSRCs goodness-of-fit metrics were comparable to the site-specific simple linear regressions (SLRs) and outperformed them in the aggregate goodness-of-fit metrics, the Michigan DSRCs are suitable to make predictions of suspended sediment in Michigan rivers with limited data. However, the availability of the DSRCs from this study should not diminish the value of collecting physical samples and exploring alternative modeling approaches because of the uncertainty associated with using DSRCs. Approximately 3,000 suspended sediment samples collected in or near Michigan from the mid-1960s through August 2022 were used to develop two DSRC models. The DSRCs developed in Michigan include a pooled DSRC model which uses nonlinear least squares regression, and a mixed-effects DSRC model which uses a mixed-effects modeling approach. In general, there was not a noticeable improvement in the performance of the Michigan mixed-effects DSRC model over the Michigan pooled DSRC model. The two Michigan DSRCs were evaluated against DSRCs developed for Pagosa Springs and Minnesota. The results showed DSRC models developed from Minnesota and Michigan were similar to each other. In contrast, the Pagosa Springs DSRC predicts higher suspended-sediment concentration (SSC) at low flows and increases at a higher rate due to having a greater exponent. The Pagosa Springs DSRC produces higher SSC predictions that do not approximate the observed data well at most of the Michigan sites in the study. The results suggest that the Pagosa Springs DSRC was not suitable to make predictions of suspended sediment for Michigan rivers. The similarity of the DSRC equations developed for Minnesota and Michigan compared to the Pagosa Springs DSRC equation suggest that there may be regional patterns of SSC in the upper Midwest rivers that differ from those in other areas of the country like Pagosa Springs. A regionally applicable model could be developed and strengthened by combining data from additional midwestern states. Since the Michigan DSRCs goodness-of-fit metrics were comparable to the site-specific simple linear regressions (SLRs) and outperformed them in the aggregate goodness-of-fit metrics, the Michigan DSRCs are suitable to make predictions of suspended sediment in Michigan rivers with limited data. However, the availability of the DSRCs from this study should not diminish the value of collecting physical samples and exploring alternative modeling approaches because of the uncertainty associated with using DSRCs.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD-2023, Sedimentation and Hydrologic Modeling Conference","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","publisher":"SEDHYD","usgsCitation":"Groten, J.T., Levin, S., Coenen, E., Lund, J., and Matousek, B., 2023, Comparing empirical sediment transport modeling approaches in Michigan rivers, in SEDHYD 2023, St. Louis, MO, May 8-12, 2023.","ipdsId":"IP-127397","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416758,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":416746,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s17.html"}],"country":"United 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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Groten, Joel T. 0000-0002-0441-8442 jgroten@usgs.gov","orcid":"https://orcid.org/0000-0002-0441-8442","contributorId":173464,"corporation":false,"usgs":true,"family":"Groten","given":"Joel","email":"jgroten@usgs.gov","middleInitial":"T.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":871849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Levin, Sara B. 0000-0002-2448-3129","orcid":"https://orcid.org/0000-0002-2448-3129","contributorId":209947,"corporation":false,"usgs":true,"family":"Levin","given":"Sara B.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":871850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coenen, Erin N. 0000-0003-2470-3854","orcid":"https://orcid.org/0000-0003-2470-3854","contributorId":211159,"corporation":false,"usgs":true,"family":"Coenen","given":"Erin N.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":871851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lund, J. William 0000-0002-8830-4468","orcid":"https://orcid.org/0000-0002-8830-4468","contributorId":289132,"corporation":false,"usgs":true,"family":"Lund","given":"J. William","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":871852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Matousek, Bethany","contributorId":304881,"corporation":false,"usgs":false,"family":"Matousek","given":"Bethany","email":"","affiliations":[{"id":66186,"text":"Michigan Department of Environment, Great Lakes, and Energy","active":true,"usgs":false}],"preferred":false,"id":871853,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247427,"text":"70247427 - 2023 - Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models","interactions":[],"lastModifiedDate":"2023-08-07T14:02:02.242741","indexId":"70247427","displayToPublicDate":"2023-04-15T08:48:17","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models","docAbstract":"Process-based, large-scale (e.g., conterminous United States [CONUS]) hydrologic models have struggled to achieve reliable streamflow drought performance in arid regions and for low-flow periods. Deep learning has recently seen broad implementation in streamflow prediction and forecasting research projects throughout the world with performance often equaling or exceeding that of process-based models. Deep learning models are a possible approach to increase the accuracy of streamflow drought predictions and to expand the spatial coverage of river locations with available streamflow drought forecasts.
As part of a multi-component Data-Driven Drought Prediction project, the U.S. Geological Survey is developing and testing deep learning models for streamflow drought forecasting. In this work, we present preliminary results of a deep learning model capable of predicting streamflow drought occurrence at ungaged locations for the Colorado River Basin (CRB). A long short-term memory (LSTM) neural network model was trained using 40 years (1980-2020) of daily streamflow data from 425 streamgages within and surrounding the CRB using static watershed attributes as well as meteorological and remotely sensed dynamic forcing inputs. Model tests were performed to evaluate model accuracy for now-casting streamflow drought conditions at ungaged locations and for forecasting drought conditions at lead times ranging from 0 to 14 days. Nearly all model configurations showed behavioral performance for predicting daily streamflow percentiles. Comparisons of LSTM model performance for predicting drought using fixed drought thresholds (calculated over all days and years) and variable drought thresholds (unique threshold calculated for each day of the year) identify differences in model skill between locations with implications for model design.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD","conferenceDate":"May 8-12, 2023","conferenceLocation":"St. Louis, MO","language":"English","publisher":"SEDHYD","usgsCitation":"Hamshaw, S.D., Goodling, P.J., Hafen, K., Hammond, J., McShane, R., Sando, R., Shastry, A.R., Simeone, C.E., Watkins, D., White, E., and Wieczorek, M., 2023, Regional streamflow drought forecasting in the Colorado River Basin using Deep Neural Network models, in Proceedings of SEDHYD 2023, St. Louis, MO, May 8-12, 2023, 15 p.","productDescription":"15 p.","ipdsId":"IP-151973","costCenters":[{"id":227,"text":"Earth Surface Dynamics Program","active":true,"usgs":true},{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":419560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":419548,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s181.html"}],"country":"United States","otherGeospatial":"Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.60045372623478,\n 31.258330936607123\n ],\n [\n -110.59254302741863,\n 31.054063075754513\n ],\n [\n -108.59802952983767,\n 31.359920476818175\n ],\n [\n -107.77329728728904,\n 32.667013027627036\n ],\n [\n -105.33291815667258,\n 38.21996621737378\n ],\n [\n -105.64401667449579,\n 40.61584869706027\n ],\n [\n -108.25906974720414,\n 42.992213339755665\n ],\n [\n -110.41906640637575,\n 43.12924273929244\n ],\n [\n -111.27275669412631,\n 41.39663030950132\n ],\n [\n -112.47810183593663,\n 38.504465490675386\n ],\n [\n -113.09970923969854,\n 37.353465042204334\n ],\n [\n -114.3929667988645,\n 37.49906345159148\n ],\n [\n -114.6266795454161,\n 38.107130943367025\n ],\n [\n -115.48463166591264,\n 39.43479765478551\n ],\n [\n -115.68353845853977,\n 37.41554450267796\n ],\n [\n -115.1115276804997,\n 33.75507968847421\n ],\n [\n -115.55059550089697,\n 31.937316150454635\n ],\n [\n -114.60045372623478,\n 31.258330936607123\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hamshaw, Scott Douglas 0000-0002-0583-4237","orcid":"https://orcid.org/0000-0002-0583-4237","contributorId":305601,"corporation":false,"usgs":true,"family":"Hamshaw","given":"Scott","email":"","middleInitial":"Douglas","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":879573,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879574,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hafen, Konrad 0000-0002-1451-362X","orcid":"https://orcid.org/0000-0002-1451-362X","contributorId":215959,"corporation":false,"usgs":true,"family":"Hafen","given":"Konrad","email":"","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879575,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879576,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McShane, Ryan R. 0000-0002-3128-0039","orcid":"https://orcid.org/0000-0002-3128-0039","contributorId":219009,"corporation":false,"usgs":true,"family":"McShane","given":"Ryan R.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879577,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sando, Roy 0000-0003-0704-6258","orcid":"https://orcid.org/0000-0003-0704-6258","contributorId":3874,"corporation":false,"usgs":true,"family":"Sando","given":"Roy","email":"","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":879578,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shastry, Apoorva Ramesh 0000-0002-3996-4857","orcid":"https://orcid.org/0000-0002-3996-4857","contributorId":317867,"corporation":false,"usgs":true,"family":"Shastry","given":"Apoorva","email":"","middleInitial":"Ramesh","affiliations":[{"id":227,"text":"Earth Surface Dynamics Program","active":true,"usgs":true}],"preferred":true,"id":879579,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Simeone, Caelan E. 0000-0003-3263-6452 csimeone@usgs.gov","orcid":"https://orcid.org/0000-0003-3263-6452","contributorId":221126,"corporation":false,"usgs":true,"family":"Simeone","given":"Caelan","email":"csimeone@usgs.gov","middleInitial":"E.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879580,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Watkins, David 0000-0002-7544-0700","orcid":"https://orcid.org/0000-0002-7544-0700","contributorId":317375,"corporation":false,"usgs":true,"family":"Watkins","given":"David","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":879581,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"White, Elaheh 0000-0003-1248-5247","orcid":"https://orcid.org/0000-0003-1248-5247","contributorId":295260,"corporation":false,"usgs":true,"family":"White","given":"Elaheh","email":"","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":879582,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wieczorek, Michael 0000-0003-0999-5457","orcid":"https://orcid.org/0000-0003-0999-5457","contributorId":207911,"corporation":false,"usgs":true,"family":"Wieczorek","given":"Michael","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":879583,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70244030,"text":"70244030 - 2023 - Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time","interactions":[],"lastModifiedDate":"2023-05-31T13:46:59.201583","indexId":"70244030","displayToPublicDate":"2023-04-15T08:43:15","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time","docAbstract":"Sediment connectivity is a framework for transfer and storage of sediment among different geomorphic compartments across upland and channel network of the catchment sediment cascade. Sediment connectivity and dysconnectivity (i.e., source delivery and storage processes) are linked to the water cycle and hydrologic systems with the associated multiscale interactions with climate, soil, topography, ecology, and landuse/landcover under natural variability and human intervention. We review the sediment connectivity concept and frameworks developed in the last few decades to examine and quantify water and sediment transfer in catchment systems. Past conceptual models of connectivity have attempted to integrate multiple processes into sediment domain, including geomorphic, hydrologic, and ecological processes (i.e., “holistic approach to connectivity”). In particular, multiple studies highlight the importance of sediment and water interaction in defining landscape connectivity. There are also efforts to quantify the topographic controls on sediment connectivity, in the advent of increasingly high-resolution digital terrain models. More recent modeling efforts have integrated structural and functional connectivity through coupling topographic information with hydrologic simulation models. Though this recent modeling development is encouraging, a comprehensive sediment connectivity framework that integrates geomorphic and hydrologic processes across spatiotemporal scales is yet to be conceived. Such an effort will require understanding the governing hydrologic and geomorphic processes that control sediment source, storage, and transport. A conceptual model is proposed to describe dominant hydrologic-sediment connectivity regimes through spatial-temporal feedbacks between hydrologic processes (rainfall, flow routing, and water residence time) and geomorphic drivers (upland soil erosion and deposition, and geomorphic channel erosion and deposition response). Recent advancements in landscape monitoring techniques using geochemical tracers, remote-sensing, increasing availability of hydrologic monitoring data, and the integration of various analytic methods (e.g., isotopic hydrograph separation, stormflow concentration-discharge, hysteretic behavior analysis) have the potential to broaden the spatial and temporal scales of geomorphic observations and understanding of landscape sediment connectivity. Using the conceptual model as a “thinking” space, we examine sediment and hydrologic interactions in real world examples of watershed studies using multiple lines of evidence and modeling techniques.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SEDHYD 2023","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"SEDHYD-2023, Sedimentation and Hydrologic Modeling Conference","language":"English","publisher":"SEDHYD","usgsCitation":"Cho, J., Karwan, D., Skalak, K., Pizzuto, J., and Huffman, M., 2023, Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time, in SEDHYD 2023, 14 p.","productDescription":"14 p.","ipdsId":"IP-150563","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":417576,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417575,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sedhyd.org/2023Program/s252.html","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cho, Jong 0000-0001-5514-6056","orcid":"https://orcid.org/0000-0001-5514-6056","contributorId":291384,"corporation":false,"usgs":true,"family":"Cho","given":"Jong","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":874198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karwan, Diana","contributorId":305967,"corporation":false,"usgs":false,"family":"Karwan","given":"Diana","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":874199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Skalak, Katherine 0000-0003-4122-1240 kskalak@usgs.gov","orcid":"https://orcid.org/0000-0003-4122-1240","contributorId":3990,"corporation":false,"usgs":true,"family":"Skalak","given":"Katherine","email":"kskalak@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":874200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pizzuto, James","contributorId":305968,"corporation":false,"usgs":false,"family":"Pizzuto","given":"James","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":874201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Huffman, Max","contributorId":305969,"corporation":false,"usgs":false,"family":"Huffman","given":"Max","email":"","affiliations":[{"id":13359,"text":"University of Delaware","active":true,"usgs":false}],"preferred":false,"id":874202,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70242708,"text":"fs20233009 - 2023 - Potential drivers of change in fluxes of nutrients and total suspended solids in the upper White River Basin, Indiana, Water Years 1997–2019","interactions":[],"lastModifiedDate":"2026-02-06T21:50:49.581974","indexId":"fs20233009","displayToPublicDate":"2023-04-14T13:32:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3009","displayTitle":"Potential Drivers of Change in Fluxes of Nutrients and Total Suspended Solids in the Upper White River Basin, Indiana, Water Years 1997–2019","title":"Potential drivers of change in fluxes of nutrients and total suspended solids in the upper White River Basin, Indiana, Water Years 1997–2019","docAbstract":"The U.S. Geological Survey and The Nature Conservancy previously collaborated to evaluate changes and trends in the concentrations and flux of nutrients (total phosphorus, as phosphorus; nitrate plus nitrite, as nitrogen; and total Kjeldahl nitrogen, as nitrogen) and total suspended solids (TSS) at three study gages located on the upper White River at Muncie, near Nora, and near Centerton, Indiana. That work is extended and updated using 3 additional years of data (through 2020) and newer estimation methods. In addition, information is provided about climatic and anthropogenic factors that could influence the concentrations and fluxes of nutrients and TSS in the upper White River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233009","usgsCitation":"Koltun, G.F. and Hauswald, C., 2023, Potential drivers of change in fluxes of nutrients and total suspended solids in the upper White River Basin, Indiana, Water Years 1997–2019: U.S. Geological Survey Fact Sheet 2023–3009, https://doi.org/10.3133/fs20233009.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-144087","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":415714,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233009/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2023-3009"},{"id":415713,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3009/fs20233009.pdf","text":"Report","size":"2.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3009"},{"id":415717,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.er.usgs.gov/publication/sir20235025","text":"Scientific Investigations Report 2023–5025","linkHelpText":"- Trends in Environmental, Anthropogenic, and Water-Quality Characteristics in the Upper White River Basin, Indiana"},{"id":415715,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3009/images/"},{"id":415712,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3009/coverthb2.jpg"},{"id":415716,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3009/fs20233009.XML"},{"id":499659,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114665.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Indiana","otherGeospatial":"Upper White River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.808361236281,\n 40.56108404734479\n ],\n [\n -86.60385353569629,\n 40.56108404734479\n ],\n [\n -86.60385353569629,\n 39.19167595806789\n ],\n [\n -84.808361236281,\n 39.19167595806789\n ],\n [\n -84.808361236281,\n 40.56108404734479\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
6460 Busch Blvd, Suite 100
Columbus, OH 43229
The U.S. Geological Survey (USGS), in cooperation with The Nature Conservancy, undertook a study to update and extend results from a previous study (Koltun, 2019, https://doi.org/10.3133/sir20195119), using data from 3 additional years and newer estimation methods. Koltun (2019) assessed trends in streamflow, precipitation, and estimated annual mean concentrations and flux of nitrate plus nitrite, total Kjeldahl nitrogen, total phosphorus, and total suspended solids (TSS) for USGS streamflow gages on the upper White River at Muncie, near Nora, and near Centerton, Indiana. Annual mean and maximum daily streamflows had statistically significant upward trends at all study gages between water years 1978 and 2020. An abrupt increase in streamflow occurred around water year 2001. Annual total precipitation at the Indianapolis International Airport increased between calendar years 1932 and 2020 at an average rate of 0.089 inches per year.
The current study assessed the magnitude, direction, and likelihood of change in flow-normalized concentrations and flux of TSS, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen between water years 1997 and 2019. With two exceptions, concentration and flux changes that were statistically significant in Koltun (2019, https://doi.org/10.3133/sir20195119), which reported changes between water years 1997 and 2017, still have the same statistically significant change directions. The reliability of the current trend result for TSS is uncertain because of a large gap in the TSS record for the Centerton gage.
For each constituent, spatial patterns were examined in the sampled distribution of nutrient and TSS concentration data from 20 mainstem, tributary, and distributary locations in the upper White River Basin. The largest median concentrations of TSS, total phosphorus, and total Kjeldahl nitrogen were associated with mainstem upper White River sites downstream from Indianapolis. The median total phosphorus and total Kjeldahl nitrogen concentrations were elevated relative to bracketing upstream/downstream mainstem sites at the upper White River site immediately downstream from Muncie.
Data on several anthropogenic factors that could influence the concentrations and fluxes of nutrients and TSS were gathered and analyzed to better understand the factors’ spatial and temporal variations. Those anthropogenic factors included population, land cover, cropping and operational tillage practices, fertilizer application, and upgrades to wastewater treatment systems and delivery processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235025","collaboration":"Prepared in cooperation with The Nature Conservancy with generous support from the Nina Mason Pulliam Charitable Trust","usgsCitation":"Koltun, G.F., 2023, Trends in environmental, anthropogenic, and water-quality characteristics in the upper White River Basin, Indiana: U.S. Geological Survey Scientific Investigations Report 2023–5025, 46 p., https://doi.org/10.3133/sir20235025.","productDescription":"Report: x, 46 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-139275","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":415439,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O6C9L3","text":"USGS data release","linkHelpText":"Model data archive—Trends in selected environmental, anthropogenic, and water-quality characteristics in the upper White River Basin, Indiana, 1991–2020"},{"id":415441,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5025/images/"},{"id":415438,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235025/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5025"},{"id":415437,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5025/sir20235025.pdf","text":"Report","size":"5.05 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5025"},{"id":415436,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5025/coverthb.jpg"},{"id":415440,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5025/sir20235025.XML"},{"id":415718,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20233009","text":"Fact Sheet 2023–3009","linkHelpText":"- Potential Drivers of Change in Fluxes of Nutrients and Total Suspended Solids in the Upper White River Basin, Indiana, Water Years 1997–2019"},{"id":500879,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114664.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Indiana","otherGeospatial":"Upper White River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.808361236281,\n 40.56108404734479\n ],\n [\n -86.60385353569629,\n 40.56108404734479\n ],\n [\n -86.60385353569629,\n 39.19167595806789\n ],\n [\n -84.808361236281,\n 39.19167595806789\n ],\n [\n -84.808361236281,\n 40.56108404734479\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278-1996
Data collected during well-observed eruptions can lead to dramatic increases in our understanding of volcanic processes. However, the necessary prioritization of public safety and hazard mitigation during a crisis means that scientific opportunities may be sacrificed. Thus, maximizing the scientific gains from eruptions requires improved planning and coordinating science activities among governmental organizations and academia before and during volcanic eruptions. One tool to facilitate this coordination is a Scientific Advisory Committee (SAC). In the USA, the Community Network for Volcanic Eruption Response (CONVERSE) has been developing and testing this concept during workshops and scenario-based activities. The December 2020 eruption of Kīlauea volcano, Hawaii, provided an opportunity to test and refine this model in real-time and in a real-world setting. We present here the working model of a SAC developed during this eruption. Successes of the Kīlauea SAC (K-SAC) included broadening the pool of scientists involved in eruption response and developing and codifying procedures that may form the basis of operation for future SACs. Challenges encountered by the K-SAC included a process of review and facilitation of research proposals that was too slow to include outside participation in the early parts of the eruption and a decision process that fell on a small number of individuals at the responding volcano observatory. Possible ways to address these challenges include (1) supporting community-building activities between eruptions that make connections among scientists within and outside formal observatories, (2) identifying key science questions and pre-planning science activities, which would facilitate more rapid implementation across a broader scientific group, and (3) continued dialog among observatory scientists, emergency responders, and non-observatory scientists about the role of SACs. The SAC model holds promise to become an integral part of future efforts, leading in the short and longer term to more effective hazard response and greater scientific discovery and understanding.
Changes in riparian vegetation cover and composition occur in relation to flow regime, geomorphic template, and climate, and can have cascading effects on aquatic and terrestrial ecosystems. Tracking such changes over time is therefore an important part of monitoring the condition and trajectory of riparian ecosystems. Maintaining diverse, self-sustaining riparian vegetation comprised of mostly native species is identified in the Glen Canyon Dam Long-Term Experimental and Management Plan as a key resource objective for the section of the Colorado River between Glen Canyon Dam and Lake Mead. The U.S. Geological Survey Grand Canyon Monitoring and Research Center implemented an annual monitoring program in 2014 to assess the status and trends of riparian vegetation along this section of river, particularly as they relate to flow regime. In this report, we summarize plant species composition and cover data collected under the annual monitoring program from 2014 to 2019, with special consideration given to the hydrologic position, associated geomorphic feature class, local climate patterns, native and nonnative species, and floristic region for key vegetation metrics and species. We divided the study area into four river segments (referred to as Glen Canyon, Marble Canyon, eastern Grand Canyon, and western Grand Canyon) on the basis of geography and floristic composition and calculated each recorded plant species’ relative frequency and foliar cover by river segment. These data were then used to evaluate species composition relationships among river segments, hydrologic zones, geomorphic features, and sampling years through ordination analysis. Temporal trends in our focal resource objectives—species richness, total foliar cover, proportion of native to nonnative species richness, proportion of native to nonnative species cover, Tamarix cover, Pluchea sericea cover, and Baccharis species cover—were assessed using mixed-effects models. Four patterns related to species composition emerged: (1) species composition of fixed-site sandbars differed from that of randomly selected sites (including randomly selected sandbars), (2) species composition of Glen Canyon sites differed from that of other previously identified floristic regions, (3) species composition differed across hydrologic zones related to dam operations, and (4) species composition within river segments did not change across years. For temporal patterns, four main findings emerged: (1) trends differed between fixed-sites and randomly selected sites; (2) although few directional changes were observed from 2014 to 2019, Baccharis species cover increased at randomly selected sites in areas influenced by daily water fluctuations; (3) native species cover and richness were greater than nonnative species cover and richness across all hydrologic zones; and (4) the temporal trend metrics used here can be used across floristic groups, enabling assessment of the Colorado River ecosystem as a whole. In addition to these findings, lists of recorded plant species are included as appendixes. The variations and patterns in vegetation status and trends presented in this report can be used as a baseline against which future monitoring can be compared.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231026","collaboration":"Prepared in cooperation with the Bureau of Reclamation Glen Canyon Adaptive Management Program","usgsCitation":"Palmquist, E.C., Butterfield, B.J., and Ralston, B.E., 2023, Assessment of riparian vegetation patterns and change downstream from Glen Canyon Dam from 2014 to 2019: U.S. Geological Survey Open-File Report 2023–1026, 55 p., https://doi.org/10.3133/ofr20231026.","productDescription":"Report: vii, 55 p.; Data Release","numberOfPages":"55","onlineOnly":"Y","ipdsId":"IP-132835","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":499774,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114661.htm","linkFileType":{"id":5,"text":"html"}},{"id":415675,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1026/images"},{"id":415674,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1026/ofr20231026.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":415673,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1026/covrthb.jpg"},{"id":415672,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KEHY2S","text":"Riparian vegetation data downstream of Glen Canyon Dam in Glen Canyon National Recreation Area and Grand Canyon National Park, AZ from 2014 to 2019","description":"Palmquist, E.C., Butterfield, B.J., and Ralston, B.E., 2022, Riparian vegetation data downstream of Glen Canyon Dam in Glen Canyon National Recreation Area and Grand Canyon National Park, AZ from 2014 to 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9KEHY2S."}],"country":"United States","state":"Arizona","otherGeospatial":"Glen Canyon Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.06028247701303,\n 36.94784441270309\n ],\n [\n -114.06028247701303,\n 35.55756259875736\n ],\n [\n -111.24899178190306,\n 35.55756259875736\n ],\n [\n -111.24899178190306,\n 36.94784441270309\n ],\n [\n -114.06028247701303,\n 36.94784441270309\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Phytoplankton are an important and limiting food source in the Sacramento-San Joaquin Delta and San Francisco Bay in California. Decreasing phytoplankton biomass is one possible factor for the pelagic organism decline and the decline of the protected Hypomesus transpacificus (delta smelt). Bivalves Corbicula fluminea and Potamocorbula amurensis (hereafter C. fluminea and P. amurensis, respectively) have been shown to control phytoplankton biomass throughout San Francisco Bay and the Sacramento-San Joaquin Delta; therefore, their distribution and population dynamics are of great interest.
We describe the distribution and dynamics of bivalve biomass using samples from California Department of Water Resources’ (DWR) 2019 benthic monitoring program. As one element of DWR’s and the Bureau of Reclamation’s Environmental Monitoring Program (EMP), the DWR benthic monitoring program examines the effect of water project operations on the estuary as prescribed by a series of Water Rights Decisions mandated by the California State Water Resources Control Board (SWRCB).
The biomass and grazing rate values of both bivalves had similar patterns, therefore, comments on biomass distribution can be applied to grazing rate data. Biomass and recruitment values of C. fluminea were too low at station C9 (Old River upstream from Clift on Court Forebay Intake) to describe a temporal pattern. Corbicula fluminea biomass values were consistently high at station D24 (Sacramento River). Station D4L (confluence of San Joaquin and Sacramento Rivers) biomass values were low during the first half of the year and high the rest of the year. Corbicula fluminea biomass values at station P8 (San Joaquin River) were the highest and most consistent on that river. Station D16 (San Joaquin River) and station D28A (central delta) biomass values were near zero with a small peak in May.
Potamocorbula amurensis biomass values were near zero at station D4L (confluence of San Joaquin and Sacramento Rivers). Biomass values were strongly seasonal at station D6 (Suisun Bay). Station D41 (San Pablo Bay) had the highest P. amurensis biomass values. Station D7 (Grizzly Bay) and station D41A (San Pablo Bay) had low biomass values in January-June or July and maximum biomass values in August.
Corbicula fluminea recruits in the Sacramento River stations peaked twice, from January to June and from September to December. At the San Joaquin River stations, C. fluminea recruitment peaked from May to July or August and from November to December. Peak recruit abundance was higher on the Sacramento River than the San Joaquin River.
Potamocorbula amurensis recruitment was more seasonal than C. fluminea, with a high number of recruits followed by periods with no recruits. Station D4L had few recruits except in January. Station D6 had low recruitment from January to February, increased in August, and peaked from November to December. Station D7 had fewer recruits than station D6 but had a similar temporal pattern, although winter recruits continued into April instead of February. Station D41 recruits were sparce and present only from May to July. Station D41A had the most recruits from January to July, and again in September.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221101","collaboration":"Prepared in cooperation with California Department of Water Resources and Bureau of Reclamation","usgsCitation":"Zierdt Smith, E.L., Shrader, K.H., Thompson, J.K., Parchaso, F., Gehrts, K., and Wells, E., 2023, Bivalve effects on the food web supporting delta smelt—A long-term study of bivalve recruitment, biomass, and grazing rate patterns with varying freshwater outflow: U.S. Geological Survey Open-File Report 2022–1101, 13 p., https://doi.org/10.3133/ofr20221101.","productDescription":"Report: vi, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","ipdsId":"IP-123598","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":415676,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1101/covrthb.jpg"},{"id":415677,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1101/ofr20221101.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":415678,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20221102","text":"Open-File Report 2022-1102","description":"Zierdt Smith, E.L., Shrader, K.H., Thompson, J.K., Parchaso, F., Gehrts, K., and Wells, E., 2023, Bivalve effects on the food web supporting delta smelt—A spatially intensive study of bivalve recruitment, biomass, and grazing rate patterns with varying freshwater outflow in 2019: U.S. Geological Survey Open-File Report 2022–1102, 15 p., http://doi.org/10.3133/ofr20221102.","linkHelpText":"- Bivalve Effects on the Food Web Supporting Delta Smelt—A Spatially Intensive Study of Bivalve Recruitment, Biomass, and Grazing Rate Patterns with Varying Freshwater Outflow in 2019"},{"id":415679,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q57NL0","text":"Bivalve metrics in the North San Francisco Bay and Sacramento-San Joaquin Delta","description":"Zierdt Smith, E.L., Shrader, K.H., Pearson S.A., Crauder, J.S., Parchaso, F., and Thompson, J.K., 2021, Bivalve metrics in the North San Francisco Bay and Sacramento-San Joaquin Delta: U.S. Geological Survey data release, https://doi.org/10.5066/P9Q57NL0."}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.87943679633386,\n 38.21596776184356\n ],\n [\n -122.87943679633386,\n 37.37464201681941\n ],\n [\n -121.47296304678717,\n 37.37464201681941\n ],\n [\n -121.47296304678717,\n 38.21596776184356\n ],\n [\n -122.87943679633386,\n 38.21596776184356\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Water Resources, Earth System Processes Division
U.S. Geological Survey
411 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Nature-based features, also called living shorelines, are increasingly applied in coastal protection and restoration. However, the processes and mechanisms (feedbacks and interactions) of wave attenuation, current velocity change, and sediment deposition and erosion along the living shoreline remain unclear, thus limiting the adaptive management of living shoreline restoration projects for coastal shoreline resilience under future storm conditions. In this study, wave, current, and sediment dynamics along the Little Toms Cove living shoreline, Chincoteague National Wildlife Refuge, Virginia, a low wave energy environment, were investigated during a 2-month winter period in 2019 to examine the effects of living shoreline structures on shoreline protection and oyster habitat enhancement. It was found that wave attenuation by the living shoreline structures (oyster castles or constructed oyster reefs) is dependent on water depth, wind speed, wind direction, and local bathymetry. Analysis of observed data indicate that the oyster castles along the Little Toms Cove living shoreline play a limited role in wave attenuation in this low wave energy environment. During the 2-month winter period, wave energy was attenuated by 39.7 percent when oyster castles were emergent or slightly submerged with southwest winds. In contrast, when the oyster castles were fully submerged, wave energy behind the oyster castles increased by 38.6 percent. The construction of oyster castles affected circulation patterns with increase or decrease in velocity at nearshore waters protected by the castles depending on locations of measurements in relation to the oyster castles. Bottom shear stress analysis indicates that tidal currents play a larger role than waves on shoreline and marsh edge erosion along the Little Toms Cove shoreline during the 2 months of field monitoring. The oyster castles protecting the marsh edge and tidal flat from erosion resulted in higher fine sediment concentration in the water column landward of the castles because more sediment was retained in the lee side of the castles. It is important to maximize sediment within the wetlands and adjacent mudflats behind the oyster castles. Erosion from the marsh edge and interior serves as the major source of sediment for this wetland system due to the limited sediment supply in Assateague Channel. Furthermore, it was found that the oyster castles along the Little Toms Cove living shoreline were inundated more than 60 percent of the time, leading to the enhanced oyster habitat as evidenced by suitable velocity (less than 10 centimeters per second) and mean grain size (less than 0.08 millimeters) for oyster feeding and the increased oyster shell density and growth in the intertidal zone protected by the castles than in the control area. More field data (for example, concurrent monitoring of sediment concentration and salinity) over other seasons (for example, summer) could help examine the long term and combined engineering and ecological benefits of living shoreline restoration projects under seasonal and enhanced future storm conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231020","issn":"2331-1258","collaboration":"Prepared in collaboration with Northeastern University, U.S. Fish and Wildlife Service, The Nature Conservancy, Louisiana State University, and Shippensburg University","usgsCitation":"Wang, H., Chen, Q., Wang, N., Capurso, W.D., Niemoczynski, L.M., Zhu, L., Snedden, G.A., Holcomb, K.S., Lusk, B.W., Wilson, C.A., and Cornell, S.R., 2023, Monitoring of wave, current, and sediment dynamics along the Chincoteague living shoreline, Virginia: U.S. Geological Survey Open-File Report 2023–1020, 32 p., https://doi.org/10.3133/ofr20231020.","productDescription":"Report: viii, 32 p.; 2 Data Releases","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-147055","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":415665,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1020/ofr20231020.pdf","text":"Report","size":"3.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1020 pdf"},{"id":415664,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1020/coverthb.jpg"},{"id":415667,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1020/images"},{"id":415669,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CUF4UZ","text":"U.S. Geological Survey data release—Field observation of wind waves (2019) along the Chincoteague Living Shoreline, Virginia"},{"id":415670,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P903FIH7","text":"U.S. Geological Survey data release—Field observation of current velocities (2019) along the Chincoteague Living Shoreline, Virginia"},{"id":415736,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1020/ofr20231020.XML","linkFileType":{"id":8,"text":"xml"},"description":"OFR 2023-1020 XML"},{"id":415737,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20231020/full","description":"OFR 2023-1020 HTML"},{"id":499771,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114662.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Virginia","otherGeospatial":"Assateague Bay, Chincoteague National Wildlife Refuge, Little Tom's Cove","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.32920735620297,\n 37.90602658439137\n ],\n [\n -75.43123250187875,\n 37.90602658439137\n ],\n [\n -75.43123250187875,\n 37.83932564463906\n ],\n [\n -75.32920735620297,\n 37.83932564463906\n ],\n [\n -75.32920735620297,\n 37.90602658439137\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
7920 NW 71st St.
Gainesville, FL 32653
For additional information, visit
https://www.usgs.gov/centers/wetland-and-aquatic-research-center-warc
Incorporating species distributions into conservation planning has traditionally involved long-term representations of habitat use where temporal variation is averaged to reveal habitats that are most suitable across time. Advances in remote sensing and analytical tools have allowed for the integration of dynamic processes into species distribution modeling. Our objective was to develop a spatiotemporal model of breeding habitat use for a federally threatened shorebird (piping plover, Charadrius melodus). Piping plovers are an ideal candidate species for dynamic habitat models because they depend on habitat created and maintained by variable hydrological processes and disturbance. We integrated a 20-year (2000–2019) nesting dataset with volunteer-collected sightings (eBird) using point process modeling. Our analysis incorporated spatiotemporal autocorrelation, differential observation processes within data streams, and dynamic environmental covariates. We evaluated the transferability of this model in space and time and the contribution of the eBird dataset. eBird data provided more complete spatial coverage in our study system than nest monitoring data. Patterns of observed breeding density depended on both dynamic (e.g., surface water levels) and long-term (e.g., proximity to permanent wetland basins) environmental processes. Our study provides a framework for quantifying dynamic spatiotemporal patterns of breeding density. This assessment can be iteratively updated with additional data to improve conservation and management efforts, because reducing temporal variability to average patterns of use may cause a loss in precision for such actions.
Fossils in the order Sirenia (family Dugongidae) from Santa Rosa Island, part of Channel Islands National Park in southern California, provide rare temporal and spatial links between earlier and later evolutionary forms of dugongids, and add information about their dispersal into the northeastern Pacific region. Marine sedimentary rocks containing these fossils have characteristics of both the late Oligocene to middle Miocene Vaqueros Sandstone and the early to middle Miocene Rincon formation observed elsewhere. To determine a more precise age of the fossils, marine invertebrate shells were collected from the same exposures as the sirenian fossils for chronostratigraphic assessment using strontium isotope compositions and the well-calibrated seawater strontium evolution curve. Shells used for analysis were from bivalve mollusks (Pycnodonte sp. [oyster] and Lyropecten sp. [scallop]) and crustaceans (Balanus sp. [barnacle]). Results show a wide range of 87Sr/86Sr values, indicating that shell materials experienced varying degrees of diagenetic alteration. Strontium concentrations and 87Sr/86Sr values in subsamples of Pycnodonte shell show correlations between original shell material and a secondary component having lower strontium concentrations and less radiogenic (lower) 87Sr/86Sr. In contrast, all Lyropecten shell analyses yielded a uniform 87Sr/86Sr value (0.708440±0.000010 [2× standard deviation]) over a wide range of strontium concentrations (around 900 to 1,800 micrograms per gram [μg/g]). Results for Balanus shell subsamples show a range of strontium compositional behavior between the other two types of shell. Acetic acid leachates of sandy matrix confirm that diagenetic fluids had low 87Sr/86Sr values consistent with the least radiogenic values in Pycnodonte subsamples. A simple mixing model between two calcite end-members can explain observed Pycnodonte data, although actual diagenetic processes likely involved secondary dissolution/reprecipitation or strontium ion exchange between shell material and pore fluid. Data indicate that only Lyropecten subsamples have retained their original 87Sr/86Sr compositions, resulting in a best-estimate age of 20.08±0.11 million years ago (Ma) (±95-percent confidence interval [CI]). Although Dugongidae fossils have been found in Miocene and younger sediments along the west coast of North America, the Santa Rosa Island specimens represent some of the earliest and most accurately dated sirenian fossils in the region. Chronostratigraphic results also constrain the timing of the transgressional processes represented by shallow-water (Vaqueros Sandstone) to deep-water (Rincon formation) depositional environments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235026","collaboration":"Prepared in cooperation with the U.S. National Park Service","usgsCitation":"Paces, J.B., Minor, S.A., Schmidt, K.M., and Hoffman, J., 2023, Strontium isotope chronostratigraphic age of a sirenian fossil site on Santa Rosa Island, Channel Islands National Park, California: U.S. Geological Survey Scientific Investigations Report 2023–5026, 27 p., https://doi.org/10.3133/sir20235026.","productDescription":"Report: vii, 27 p.; Data Release","numberOfPages":"27","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-135728","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":415211,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GG6NB5","text":"USGS data release","linkHelpText":"Sr concentrations and 87Sr/86Sr data used to determine the Sr-chronostratigraphic age of sirenian fossils on Santa Rosa Island, Channel Islands National Park: California, USA"},{"id":415210,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5026/images/"},{"id":415208,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235026/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5026"},{"id":415206,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5026/coverthb.jpg"},{"id":415207,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5026/sir20235026.pdf","text":"Report","size":"42.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5026"},{"id":415209,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5026/sir20235026.XML"},{"id":500882,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114663.htm"}],"country":"United States","state":"California","otherGeospatial":"Santa Rosa Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -120.27493358761777,\n 34.050843485499456\n ],\n [\n -120.27493358761777,\n 33.868674166486116\n ],\n [\n -119.93999465714583,\n 33.868674166486116\n ],\n [\n -119.93999465714583,\n 34.050843485499456\n ],\n [\n -120.27493358761777,\n 34.050843485499456\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Center Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225
The Sacramento–San Joaquin Delta (hereafter, “the Delta”) is one of the estuaries with the most invasive species in the world, and nonnative predators may be a major factor in the observed decline of Central Valley Chinook Salmon Oncorhynchus tshawytscha over recent decades. In order for managers to take actions that might reduce predation-related mortality for these ecologically, culturally, and economically valuable fish, it is important to understand the factors influencing the distribution and abundance of piscivores in the Delta. In this study, we used a dual-frequency identification sonar (i.e., DIDSON) to conduct mobile surveys to quantify the abundances of piscivores in the Delta. We then used these data to identify the habitat features that are correlated with the abundance of piscivores. Prior to conducting the surveys, we used DIDSON data from captured fish to develop an algorithm to distinguish piscivores from nonpiscivores with high confidence (98% accuracy). A generalized linear mixed-effects model fit to these survey data indicated that predator abundances were most associated with areas of increased submerged aquatic vegetation patches, and channels that are straighter, with increased bathymetric complexity. When applied to the entire survey area, this model was successfully able to predict known areas of high predator densities. These results indicate that one approach to reduce predator densities in key locations throughout the Delta, and improve juvenile salmonid outmigration survival, is to reduce the extent of invasive submerged aquatic vegetation. Because experimental predator removals have been largely ineffective in the Delta, efforts to manipulate habitat to discourage nonnative predator recruitment and favor native species recruitment may provide a more effective solution to improve salmonid survival rates.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10873","usgsCitation":"Henderson, M., Loomis, C., Michel, C., Smith, J., Iglesias, I., Lehman, B., and Huff, D., 2023, Estimates of predator densities using mobile DIDSON surveys: Implications for survival of Central Valley Chinook Salmon: North American Journal of Fisheries Management, v. 43, no. 3, p. 628-645, https://doi.org/10.1002/nafm.10873.","productDescription":"18 p.","startPage":"628","endPage":"645","ipdsId":"IP-145482","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":443880,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10873","text":"Publisher Index Page"},{"id":433250,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento–San Joaquin Delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122,\n 38.5\n ],\n [\n -122,\n 37.25\n ],\n [\n -121,\n 37.25\n ],\n [\n -121,\n 38.5\n ],\n [\n -122,\n 38.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Henderson, Mark J. 0000-0002-2861-8668 mhenderson@usgs.gov","orcid":"https://orcid.org/0000-0002-2861-8668","contributorId":198609,"corporation":false,"usgs":true,"family":"Henderson","given":"Mark J.","email":"mhenderson@usgs.gov","affiliations":[],"preferred":false,"id":909952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loomis, Chris","contributorId":342274,"corporation":false,"usgs":false,"family":"Loomis","given":"Chris","email":"","affiliations":[{"id":26936,"text":"Humbolt State University","active":true,"usgs":false}],"preferred":false,"id":909953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michel, Cyril","contributorId":342275,"corporation":false,"usgs":false,"family":"Michel","given":"Cyril","affiliations":[{"id":81849,"text":"NOAA-SWFSC Fisheries Ecology Division","active":true,"usgs":false}],"preferred":false,"id":909954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Joe","contributorId":342276,"corporation":false,"usgs":false,"family":"Smith","given":"Joe","affiliations":[{"id":81850,"text":"NOAA-NWFSC Fish Ecology Division","active":true,"usgs":false}],"preferred":false,"id":909955,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Iglesias, Ilysa","contributorId":342277,"corporation":false,"usgs":false,"family":"Iglesias","given":"Ilysa","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":909956,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lehman, Brendan","contributorId":342279,"corporation":false,"usgs":false,"family":"Lehman","given":"Brendan","affiliations":[{"id":81849,"text":"NOAA-SWFSC Fisheries Ecology Division","active":true,"usgs":false}],"preferred":false,"id":909957,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Huff, David","contributorId":342281,"corporation":false,"usgs":false,"family":"Huff","given":"David","affiliations":[{"id":81850,"text":"NOAA-NWFSC Fish Ecology Division","active":true,"usgs":false}],"preferred":false,"id":909958,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243983,"text":"70243983 - 2023 - Using neutral landscape models to evaluate the umbrella species concept in an ecotone","interactions":[],"lastModifiedDate":"2023-05-30T14:51:00.989431","indexId":"70243983","displayToPublicDate":"2023-04-11T09:36:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Using neutral landscape models to evaluate the umbrella species concept in an ecotone","docAbstract":"Steep declines in North American rangeland biodiversity have prompted researchers and managers to use umbrella species as a tool to manage diverse suites of co-occurring wildlife, but efficacy of this method has been variable. Evaluation of prairie and shrubland grouse as umbrellas is typically restricted to observed overlap between umbrella and background species, but this approach does not distinguish between overlap due to ubiquity or niche overlap.
We demonstrate a novel application of neutral landscape models (NLMs) to test the effectiveness of greater sage-grouse (Centrocercus urophasianus) as an umbrella species for grassland songbirds at a grassland-sagebrush ecotone in northeastern Wyoming, USA.
We leveraged existing spatial data representing sage-grouse habitat in two distinct seasons (nesting and late brood-rearing) and density and distribution of eight grassland songbirds. We applied a permutation-based analysis using NLMs to determine whether overlap between background species and greater sage-grouse was greater than expected by chance.
Three species (western meadowlark Sturnella neglecta, loggerhead shrike Lanius ludovicianus, and lark bunting Calamospiza melanocorys) had greater overlap than expected with at least one type of greater sage-grouse habitat, while western kingbirds (Tyrannus verticalis) indicated avoidance of all sage-grouse habitat assessed.
NLMs provided a more nuanced evaluation of the umbrella species concept than previously available and allowed us to differentiate between overlap due to ubiquity (e.g., vesper sparrow; Pooecetes gramineus) rather than overlap in habitat use. All grassland passerine species with greater than expected overlap with sage-grouse habitat either nest in sagebrush (loggerhead shrike) or often select nest locations underneath small shrubs (western meadowlark, lark bunting). These results indicate that nesting substrate is a potential niche axis to consider when evaluating the umbrella species concept, especially within sagebrush-grassland ecotones.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01586-7","usgsCitation":"Duchardt, C.J., Monroe, A., Edmunds, D.R., Holloran, M.J., Holloran, A.G., and Aldridge, C.L., 2023, Using neutral landscape models to evaluate the umbrella species concept in an ecotone: Landscape Ecology, v. 38, p. 1447-1462, https://doi.org/10.1007/s10980-022-01586-7.","productDescription":"16 p.","startPage":"1447","endPage":"1462","ipdsId":"IP-135208","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":435377,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MLURH7","text":"USGS data release","linkHelpText":"A neutral landscape approach to evaluating the umbrella species concept for greater sage-grouse in northeast Wyoming, USA"},{"id":417531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.00304481605505,\n 44.99130028797805\n ],\n [\n -111.00304481605505,\n 40.92596776661196\n ],\n [\n -104.05240845017104,\n 40.92596776661196\n ],\n [\n -104.05240845017104,\n 44.99130028797805\n ],\n [\n -111.00304481605505,\n 44.99130028797805\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationDate":"2023-04-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Duchardt, Courtney J. 0000-0003-4563-0199","orcid":"https://orcid.org/0000-0003-4563-0199","contributorId":239754,"corporation":false,"usgs":false,"family":"Duchardt","given":"Courtney","middleInitial":"J.","affiliations":[{"id":48000,"text":"U Wyoming","active":true,"usgs":false}],"preferred":false,"id":874007,"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":874008,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":874009,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holloran, Matthew James 0000-0001-5244-770X","orcid":"https://orcid.org/0000-0001-5244-770X","contributorId":305854,"corporation":false,"usgs":true,"family":"Holloran","given":"Matthew","email":"","middleInitial":"James","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":874010,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Holloran, Alison G.","contributorId":305855,"corporation":false,"usgs":false,"family":"Holloran","given":"Alison","email":"","middleInitial":"G.","affiliations":[{"id":51369,"text":"Audubon Rockies","active":true,"usgs":false}],"preferred":false,"id":874011,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":874012,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243529,"text":"70243529 - 2023 - Inferring pathogen presence when sample misclassification and partial observation occur","interactions":[],"lastModifiedDate":"2023-05-11T11:57:40.081025","indexId":"70243529","displayToPublicDate":"2023-04-11T06:55:13","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Inferring pathogen presence when sample misclassification and partial observation occur","docAbstract":"Water nutrient management efforts are frequently coordinated across thousands of water bodies, leading to a need for spatially extensive information to facilitate decision making. Here we explore potential applications of a machine learning model of river low-flow total phosphorus (TP) concentrations to support landscape nutrient management. The model was trained, validated, and then applied for all rivers of Michigan, USA to identify potential drivers of nutrient variation, predict alteration in nutrient concentrations from minimally disturbed conditions, and explore reach specific sensitivity to riparian agricultural change. A boosted regression tree model of low-flow TP concentrations trained on natural and anthropogenic landscape predictors accounted for 53 % of variation in cross-validation data, had good accuracy, little bias, and plausible relationships between predictors and response. Percent riparian agricultural cover accounted for the greatest root mean square error reduction in the modeled response (33.2 %), followed by riparian soil permeability (12.9 %), watershed slope (9.6 %), and percent urban cover (9.6 %). An apparent non-linear relationship between TP concentrations and percent riparian agricultural cover suggested steep positive increases in stream TP concentrations between 10 and 30 % upstream riparian agricultural cover. Predicted minimally disturbed TP concentrations were spatially variable and ranged from 7.0 to 48.5 μg l−1, with the highest concentrations in watersheds draining low-permeability lake plain soils. Comparison of minimally disturbed predictions to those from the early 2000s suggested that much of northern Michigan existed close to the reference condition, while lower Michigan streams were often substantially enriched. Our predicted values of minimally disturbed condition generally agree with previous studies but offer greater geographic specificity. Expanded application of machine learning modeling with landscape predictor data have great potential to inform large scale strategy development in landscapes with sparse reference data.
Among the most widely predicted climate change-related impacts to biodiversity are geographic range shifts, whereby species shift their spatial distribution to track their climate niches. A series of commonly articulated hypotheses have emerged in the scientific literature suggesting species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to rising temperatures associated with climate change. Yet, many species are not demonstrating range shifts consistent with these expectations. Here, we evaluate the impact of anthropogenic climate change (specifically, changes in temperature and precipitation) on species’ ranges, and assess whether expected range shifts are supported by the body of empirical evidence.
We conducted a Systematic Review, searching online databases and search engines in English. Studies were screened in a two-stage process (title/abstract review, followed by full-text review) to evaluate whether they met a list of eligibility criteria. Data coding, extraction, and study validity assessment was completed by a team of trained reviewers and each entry was validated by at least one secondary reviewer. We used logistic regression models to assess whether the direction of shift supported common range-shift expectations (i.e., shifts to higher latitudes and elevations, and deeper depths). We also estimated the magnitude of shifts for the subset of available range-shift data expressed in distance per time (i.e., km/decade). We accounted for methodological attributes at the study level as potential sources of variation. This allowed us to answer two questions: (1) are most species shifting in the direction we expect (i.e., each observation is assessed as support/fail to support our expectation); and (2) what is the average speed of range shifts?
We found that less than half of all range-shift observations (46.60%) documented shifts towards higher latitudes, higher elevations, and greater marine depths, demonstrating significant variation in the empirical evidence for general range shift expectations. For the subset of studies looking at range shift rates, we found that species demonstrated significant average shifts towards higher latitudes (average = 11.8 km/dec) and higher elevations (average = 9 m/dec), although we failed to find significant evidence for shifts to greater marine depths. We found that methodological factors in individual range-shift studies had a significant impact on the reported direction and magnitude of shifts. Finally, we identified important variation across dimensions of range shifts (e.g., greater support for latitude and elevation shifts than depth), parameters (e.g., leading edge shifts faster than trailing edge for latitude), and taxonomic groups (e.g., faster latitudinal shifts for insects than plants).
Despite growing evidence that species are shifting their ranges in response to climate change, substantial variation exists in the extent to which definitively empirical observations confirm these expectations. Even though on average, rates of shift show significant movement to higher elevations and latitudes for many taxa, most species are not shifting in expected directions. Variation across dimensions and parameters of range shifts, as well as differences across taxonomic groups and variation driven by methodological factors, should be considered when assessing overall confidence in range-shift hypotheses. In order for managers to effectively plan for species redistribution, we need to better account for and predict which species will shift and by how much. The dataset produced for this analysis can be used for future research to explore additional hypotheses to better understand species range shifts.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s13750-023-00296-0","usgsCitation":"Rubenstein, M.A., Weiskopf, S.R., Bertrand, R., Carter, S., Comte, L., Eaton, M.J., Johnson, C.G., Lenoir, J., Lynch, A., Miller, B.W., Morelli, T.L., Rodriguez, M.A., Terando, A., and Thompson, L., 2023, Climate change and the global redistribution of biodiversity: Substantial variation in empirical support for expected range shifts: Journal of Environmental Evidence, v. 12, 7, 21 p., https://doi.org/10.1186/s13750-023-00296-0.","productDescription":"7, 21 p.","ipdsId":"IP-138082","costCenters":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":443888,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13750-023-00296-0","text":"Publisher Index Page"},{"id":435380,"rank":0,"type":{"id":30,"text":"Data 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feasibility of in situ K-Ar dating for use in future landing planetary missions. One drawback of these laboratory demonstrations is the insufficient analogy of the analyzed analog samples with expected future targets. We present the results obtained using the K-Ar laser experiment (KArLE) on two old and K-poor chondritic samples, Pułtusk and Hvittis, as better lunar analogs. The KArLE instrument uses laser ablation to vaporize rock samples and quantifies K content by laser-induced breakdown spectroscopy (LIBS), Ar by quadrupole mass spectrometry (QMS), and ablated mass by laser profilometry. We performed 64 laser ablations on the chondrites to measure spots with a range of K2O and Ar content and used the data to construct isochrons to determine the chondrite formation age. The KArLE isochron ages on Pułtusk and Hvittis are 5059 ± 892 Ma and 4721 ± 793 Ma, respectively, which is within the uncertainty of published reference ages, and interpreted as the age of their formation. The uncertainty (2σ) on the KArLE ages obtained in this study is better than 20% (18% for Pułtusk and 17% for Hvittis). The precision, which compares our obtained ages to the reference ages, is also better than 20% (11% for Pułtusk and 4% for Hvittis). These results are encouraging for understanding the limits of this technique to measure ancient planetary samples and for guiding future improvements to the instrument.
Billions of animals migrate to track seasonal pulses in resources. Optimally timing migration is a key strategy, yet the ability of animals to compensate for phenological mismatches en route is largely unknown. Using GPS movement data collected from 72 adult female deer over a 10-year duration, we study a population of mule deer (Odocoileus hemionus) in Wyoming that lack reliable cues on their desert winter range, causing them to start migration 70 days ahead to 52 days behind the wave of spring green-up. We show that individual deer arrive at their summer range within an average 6-day window by adjusting movement speed and stopover use. Late migrants move 2.5 times faster and spend 72% less time on stopovers than early migrants, which allows them to catch the green wave. Our findings suggest that ungulates, and potentially other migratory species, possess cognitive abilities to recognize where they are in space and time relative to key resources. Such behavioral capacity may allow migratory taxa to maintain foraging benefits amid rapidly changing phenology.
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