The U.S. Geological Survey, in cooperation with Federal, State, and local agencies, maintains a long-term network of hydrologic monitoring stations in Kansas. This network included 219 real-time streamgages, 12 real-time reservoir-level monitoring stations, and 20 groundwater monitoring stations in water year (WY) 2020. A WY is a 12-month period from October 1 to September 30 and is designated by the calendar year in which it ends. Real-time data are verified by U.S. Geological Survey personnel throughout the year with regular measurements of streamflow, lake levels, and groundwater levels. Hydrologic data collected in real time aid in the understanding of, and decisions made involving, water resources of Kansas. Hydrologic conditions are assessed annually by comparing statistical analyses of current and past WY data for the period of record. The monitoring of hydrologic conditions in Kansas can provide critical information to meet several needs including water-resources management, protection of life and property, reservoir operations, agricultural practices, public supply, ecological assessments, and industrial and recreational purposes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213045","usgsCitation":"Davis, C., 2021, Hydrologic conditions in Kansas, water year 2020: U.S. Geological Survey Fact Sheet 2021–3045, 6 p., https://doi.org/10.3133/fs20213045.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-125662","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":388515,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS Water Data for the Nation"},{"id":388514,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3045/fs20213045.pdf","text":"Report","size":"4.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 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\"}}]}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
This report addresses system characterization of the China-Brazil Earth Resources Satellite-4A (CBERS–4A) multispectral remote sensing satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
CBERS–4A is a joint Chinese-Brazilian medium-resolution satellite launched in December 2019 by the China National Space Agency/National Institute for Space Research (Brazil) on a Chang Zheng 4B rocket from the Taiyuan Satellite Launch Center for Earth resources monitoring. The CBERS–4A mission continues the CBERS mission that has been in continual operation since the launch of CBERS–1 in 1999.
The CBERS–4A satellite was designed and built by Academia Chinesa de Tecnologia Espacial/National Institute for Space Research and uses the Phoenix-Eye bus. CBERS–4A carries the multispectral camera and wide field imager sensors for medium-resolution land imaging and the wide swath panchromatic and multispectral camera sensor for high-resolution land imaging. This assessment focused on the multispectral camera sensor only. More information on CBERS sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and at https://www.gov.br/pt-br/servicos/obter-imagens-de-sensoriamento-remoto-da-terra-geradas-pelo-satelite-cbers-04a.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that CBERS–4A provides an interior (band-to-band) geometric performance in the range of −0.02 to −0.16 pixel; an exterior geometric accuracy performance of −22.02 (−1.47 pixels) to −16.06 meters (−1.07 pixels); a radiometric accuracy performance of –0.006 to 0.925 (offset and slope); and a spatial performance for relative edge response in the range of 0.39 to 0.44, for full width at half maximum in the range of 2.38 to 2.56 pixels, and for a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.013.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030J","usgsCitation":"Vrabel, J.C., Stensaas, G.L., Anderson, C., Christopherson, J., Kim, M., Park, S., and Cantrell, S., 2021, System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A), chap. J of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 35 p., https://doi.org/10.3133/ofr20211030J.","productDescription":"v, 35 p.","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130782","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":388510,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/j/ofr20211030j.pdf","text":"Report","size":"12.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030J"},{"id":388509,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/j/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Within the forests of Pictured Rocks National Lakeshore, biologists are trying to understand the effects beech bark disease has on wildlife species, especially species that need forest connectivity to thrive. This project used aerial imagery collected in 2005, shortly after beech bark disease infestation, and satellite imagery from 2018. The 2018 imagery represents present day conditions and was used to locate forest canopy gaps through object-based image analysis. Forest canopy gaps were identified using the multiresolution segmentation algorithm within Trimble’s eCognition software. A time change analysis was completed to understand how the forest canopy had changed from 2005 to 2018. The analysis showed areas that had maintained forest canopy, maintained a forest canopy gap, created a new canopy gap (closed forest canopy in 2005 but open canopy gap in 2018), or created new forest canopy (open canopy gap in 2005 but closed forest canopy in 2018). There were 9,127 acres of forest canopy lost, and 72.8 percent of that lost canopy occurred in a forest type where Fagus grandifolia Ehrh. (American beech) is a common tree species. The datasets developed through this project can enhance knowledge of where canopy gaps exist and help place focus on certain areas for wildlife studies. In addition, these datasets can be used in future studies to monitor the health of the forest and conduct additional change analyses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211069","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Sattler, S.R., 2021, Changes in forest connectivity from beech bark disease in Pictured Rocks National Lakeshore in the Upper Peninsula of Michigan: U.S. Geological Survey Open-File Report 2021–1069, 12 p., https://doi.org/10.3133/ofr20211069.","productDescription":"Report: vi, 12 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-124452","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":388432,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1069/images"},{"id":388429,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1069/coverthb.jpg"},{"id":388430,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1069/ofr20211069.pdf","text":"Report","size":"6.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1069"},{"id":388431,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EZEAYD","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Effects of beech bark disease on forest connectivity in Pictured Rocks National Lakeshore from 2005 to 2018"}],"country":"United States","state":"Michigan","otherGeospatial":"Pictures Rocks National Lakeshore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.62307739257812,\n 46.42176587242696\n ],\n [\n -86.4349365234375,\n 46.45961954102543\n ],\n [\n -86.20147705078125,\n 46.57585481240773\n ],\n [\n -86.02706909179688,\n 46.619261036171515\n ],\n [\n -86.00509643554686,\n 46.669229446893404\n ],\n [\n -86.08612060546875,\n 46.66545985627255\n ],\n [\n -86.14105224609375,\n 46.677710064644344\n ],\n [\n -86.4459228515625,\n 46.557916007595786\n ],\n [\n -86.48712158203125,\n 46.55602736725248\n ],\n [\n -86.6217041015625,\n 46.44826620185314\n ],\n [\n -86.62307739257812,\n 46.42176587242696\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
In recent years, earthquake felt reports contributed via online systems have provided increasingly valuable sources of data to characterize earthquakes and their effects. Contributed felt reports are accompanied by increases in website traffic, which are themselves potentially useful for the early detection of seismic events. In February 2017 the European‐Mediterranean Seismic Centre detected an unusual surge in traffic from the Punjab region in northwestern India, although no nearby seismic event was detected instrumentally. Had crowdsourcing detected a felt earthquake that instruments had missed? Or did Punjab cry wolf? In this Earthquake Lites report, we describe the sleuthing endeavor undertaken to find an answer.
","language":"English","publisher":"Seismological Society of America","doi":"https://doi.org/10.1785/0220210130","usgsCitation":"Martin, S., Bossu, R., Steed, R., Landes, M., Srinagesh, D., Srinivas, D., and Hough, S.E., 2021, When Punjab cried wolf: How a rumor triggered an “earthquake” in India: Seismological Research Letters, v. 92, no. 6, p. 3887-3898, https://doi.org/https://doi.org/10.1785/0220210130.","productDescription":"12 p.","startPage":"3887","endPage":"3898","ipdsId":"IP-130668","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","otherGeospatial":"Punjab","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 72.75,\n 32.15\n ],\n [\n 72.75,\n 28\n ],\n [\n 78,\n 28\n ],\n [\n 78,\n 32.15\n ],\n [\n 72.75,\n 32.15\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"92","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Martin, S.S.","contributorId":350980,"corporation":false,"usgs":false,"family":"Martin","given":"S.S.","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":927631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bossu, Remy","contributorId":198780,"corporation":false,"usgs":false,"family":"Bossu","given":"Remy","email":"","affiliations":[],"preferred":false,"id":927632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steed, R.","contributorId":350981,"corporation":false,"usgs":false,"family":"Steed","given":"R.","affiliations":[{"id":35319,"text":"EMSC","active":true,"usgs":false}],"preferred":false,"id":927633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Landes, Matthieu","contributorId":198781,"corporation":false,"usgs":false,"family":"Landes","given":"Matthieu","email":"","affiliations":[],"preferred":false,"id":927634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Srinagesh, D.","contributorId":18631,"corporation":false,"usgs":true,"family":"Srinagesh","given":"D.","email":"","affiliations":[],"preferred":false,"id":927635,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Srinivas, D.","contributorId":350983,"corporation":false,"usgs":false,"family":"Srinivas","given":"D.","affiliations":[{"id":83893,"text":"NGRI, Hyderabad","active":true,"usgs":false}],"preferred":false,"id":927636,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hough, Susan E. 0000-0002-5980-2986 hough@usgs.gov","orcid":"https://orcid.org/0000-0002-5980-2986","contributorId":587,"corporation":false,"usgs":true,"family":"Hough","given":"Susan","email":"hough@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":927637,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223424,"text":"70223424 - 2021 - Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi)","interactions":[],"lastModifiedDate":"2022-01-07T15:57:22.646685","indexId":"70223424","displayToPublicDate":"2021-08-25T10:21:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi)","title":"Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi)","docAbstract":"Fish population structure in previously glaciated regions is often influenced by natural colonization processes and human-mediated dispersal, including fish stocking. Endemic populations are of conservation interest because they may contain rare and unique genetic variation. While coregonines are native to certain Michigan inland lakes, some were stocked with fish from Great Lakes sources, calling into question the origin of extant populations. While most stocking targeted lake whitefish (Coregonus clupeaformis), cisco (C. artedi) were also stocked from the Great Lakes to inland waterbodies. We used population genetic data (microsatellite genotypes and mitochondrial (mt)DNA sequences), coalescent modeling, and approximate Bayesian computation to investigate the origins of 12 inland Michigan cisco populations. The spatial distribution of mtDNA haplotypes suggests Michigan is an introgression zone for two ancestral cisco lineages associated with separate glacial refugia. Low levels of genetic diversity and high levels of genetic divergence were observed for populations located well inland of the Great Lakes relative to populations occupying waterbodies near the Great Lakes. Estimates of recent Great Lakes gene flow ranged from 27 to 48% for populations near the Great Lakes shoreline but were substantially lower (under 8%) for populations further inland. Inland lakes with elevated recent gene flow estimates may have been recipients of stocked coregonine fry, including cisco. Low levels of genetic diversity paired with a high likelihood of endemism as indicated by strong genetic divergence and low Great Lakes population inputs suggest the analyzed cisco populations occupying southern Michigan kettle lakes are of elevated conservation interest.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.08.008","usgsCitation":"Homola, J.J., Robinson, J.D., Kanefsky, J., Stott, W., Whelan, G., and Scribner, K.T., 2021, Coalescent methods reconstruct contributions of natural colonization and stocking to origins of Michigan inland Cisco (Coregonus artedi): Journal of Great Lakes Research, v. 47, no. 6, p. 1781-1792, https://doi.org/10.1016/j.jglr.2021.08.008.","productDescription":"12 p.","startPage":"1781","endPage":"1792","ipdsId":"IP-124168","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":388588,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.505859375,\n 41.47566020027821\n ],\n [\n -81.38671875,\n 41.47566020027821\n ],\n [\n -81.38671875,\n 46.830133640447386\n ],\n [\n -88.505859375,\n 46.830133640447386\n ],\n [\n -88.505859375,\n 41.47566020027821\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Homola, Jared J.","contributorId":264547,"corporation":false,"usgs":false,"family":"Homola","given":"Jared","email":"","middleInitial":"J.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":822012,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robinson, John D","contributorId":264810,"corporation":false,"usgs":false,"family":"Robinson","given":"John","email":"","middleInitial":"D","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822013,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kanefsky, Jeannette","contributorId":243198,"corporation":false,"usgs":false,"family":"Kanefsky","given":"Jeannette","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822014,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stott, Wendylee 0000-0002-5252-4901 wstott@usgs.gov","orcid":"https://orcid.org/0000-0002-5252-4901","contributorId":191249,"corporation":false,"usgs":true,"family":"Stott","given":"Wendylee","email":"wstott@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":822015,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whelan, Gary","contributorId":146115,"corporation":false,"usgs":false,"family":"Whelan","given":"Gary","email":"","affiliations":[{"id":16584,"text":"Fisheries Division, Michigan Department of Natural Resources, P.O. Box 30446, Lansing, MI 48909","active":true,"usgs":false}],"preferred":false,"id":822016,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scribner, Kim T","contributorId":264811,"corporation":false,"usgs":false,"family":"Scribner","given":"Kim","email":"","middleInitial":"T","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":822017,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249720,"text":"70249720 - 2021 - Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis","interactions":[],"lastModifiedDate":"2023-10-25T11:59:54.29182","indexId":"70249720","displayToPublicDate":"2021-08-25T06:52:34","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7170,"text":"Frontiers in Water","active":true,"publicationSubtype":{"id":10}},"title":"Exploring environmental factors that drive diel variations in tree water storage using wavelet analysis","docAbstract":"Internal water storage within trees can be a critical reservoir that helps trees overcome both short- and long-duration environmental stresses. We monitored changes in internal tree water storage in a ponderosa pine on daily and seasonal scales using moisture probes, a dendrometer, and time-lapse electrical resistivity imaging (ERI). These data were used to investigate how patterns of in-tree water storage are affected by changes in sapflow rates, soil moisture, and meteorologic factors such as vapor pressure deficit. Measurements of xylem fluid electrical conductivity were constant in the early growing season while inverted sapwood electrical conductivity steadily increased, suggesting that increases in sapwood electrical conductivity did not result from an increase in xylem fluid electrical conductivity. Seasonal increases in stem electrical conductivity corresponded with seasonal increases in trunk diameter, suggesting that increased electrical conductivity may result from new growth. On the daily scale, changes in inverted sapwood electrical conductivity correspond to changes in sapwood moisture. Wavelet analyses indicated that lag times between inverted electrical conductivity and sapflow increased after storm events, suggesting that as soils wetted, reliance on internal water storage decreased, as did the time required to refill daily deficits in internal water storage. We found short time lags between sapflow and inverted electrical conductivity with dry conditions, when ponderosa pine are known to reduce stomatal conductance to avoid xylem cavitation. A decrease in diel amplitudes of inverted sapwood electrical conductivity during dry periods suggest that the ponderosa pine relied on internal water storage to supplement transpiration demands, but as drought conditions progressed, tree water storage contributions to transpiration decreased. Time-lapse ERI- and wavelet-analysis results highlight the important role internal tree water storage plays in supporting transpiration throughout a day and during periods of declining subsurface moisture.
Evapotranspiration (ET) is the water-vapor flux transported from the surface of the Earth into the atmosphere and is the sum of surface water directly evaporated and subsurface water transpired by plants. ET rates are commonly estimated by using potential or reference ET, which might differ from actual ET rates. Actual evapotranspiration (ETa) rates can be estimated by using the Operational Simplified Surface Energy Balance (SSEBop) model. This report evaluates SSEBop ETa rates at the point and basin scales in Florida and parts of Alabama and Georgia for 2000–17. ETa rates computed by using data from 24 micrometeorological stations in Florida are referred to as mETa rates and were used to quantify biases in the SSEBop ETa rates, stratified by generalized land-use type. Bias was computed as mETa minus SSEBop ETa rates for given generalized land-use types, and bias-correction equations were computed by using least-squares regressions. In addition to mETa rates at station locations, annual average ETa rates calculated from the application of a water-balance method to 55 basins in Florida and parts of Alabama and Georgia were used to assess the accuracy of the annual SSEBop ETa rates at the basin scale. Another independent model used to simulate ETa rates was based on monthly reference ET from the statewide daily reference evapotranspiration (ETo) gridded dataset for Florida computed by using Geostationary Operational Environmental Satellite estimates of solar radiation (GOES ETo). ETa at grid points was computed as monthly GOES ETo multiplied by ratios of monthly mETa to GOES ETo, computed at micrometeorological stations and stratified by each generalized land-use type.
The coefficient of determination (R2) between monthly mETa and SSEBop ETa rates for all stations combined improved from 0.37 before bias correction of SSEBop ETa rates to 0.79 after the bias correction stratified by land-use type. For individual land-uses types, R2 varied from 0.59 for the monthly mETa at a station in the land-use type forest to 0.82 for the monthly mETa at stations in the land-use type shallow-water-table pasture. Root-mean-square error (RMSE) was computed as a function of the difference between SSEBop ETa rates and mETa rates. RMSE of monthly SSEBop ETa rates was 1.27 inches per month before the bias corrections improved to 0.73 inch per month after the bias corrections. RMSE for bias-corrected annual SSEBop ETa rates based on micrometeorological stations with complete years of records ranged from 2.01 inches per year (in/yr) for the land-use type of agriculture to 5.73 in/yr for the land-use type of deep water-table pasture, or 4.96 and 21.21 percent errors relative to annual mETa rates, respectively. Bias-corrected annual SSEBop ETa rates were also compared to annual ETa rates computed by using a water-balance method (wbETa) for 55 basins in Florida. Differences in bias-corrected average annual SSEBop ETa rates and average annual wbETa rates for the 55 basins ranged from −3.67 to 5.29 in/yr (−9.24 to 17.36 percent). RMSE when computed as a function of the differences between annual SSEBop ETa rates and wbETa rates decreased, on average, from 4.13 in/yr for the uncorrected bias SSEBop ETa rates to 1.95 in/yr for the bias-corrected SSEBop rates. The average annual bias-corrected SSEBop ETa rates, from all basins, was 36.46 in/yr or 3.41 percent lower than the average annual wbETa rate of 37.79 inches.
Bias in SSEBop ETa rates varies based on time step (monthly versus annual), scale (point, basin, statewide), and land-use type. Applications to hydrologic models should consider bias relative to the inherent error in models. Bias-corrected SSEBop ETa rates could be used as calibration targets in models of hydrologic processes, such as groundwater models. Annual bias in SSEBop ETa introduced to the model calibration is typically below the margin of error associated with typical residuals in model simulations, depending on scale. Surface-water and groundwater-flow models with RMSEs on the order of a few feet could benefit from bias-corrected SSEBop values of ETa.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215072","collaboration":"Prepared in cooperation with Northwest Florida Water Management District, Suwannee River Water Management District, St. Johns River Water Management District, South Florida Water Management District, Southwest Florida Water Management District, and Tampa Bay Water","usgsCitation":"Sepúlveda, N., 2021, Evaluation of actual evapotranspiration rates from the Operational Simplified Surface Energy Balance (SSEBop) model in Florida and parts of Alabama and Georgia, 2000–17: U.S. Geological Survey Scientific Investigations Report 2021–5072, 66 p., https://doi.org/10.3133/sir20215072.","productDescription":"Report: x, 66 p.; Data Release","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-112971","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":388346,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5072/coverthb.jpg"},{"id":388349,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5072/images"},{"id":388347,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5072/sir20215072.pdf","text":"Report","size":"12.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5072"},{"id":388348,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99AB3X4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets of actual evapotranspiration rates from 2000 to 2017 for basins in Florida and parts of Alabama and Georgia, calculated using the water-balance method, the bias-corrected Operational Simplified Surface Energy Balance (SSEBop) model, and the land-use crop coefficients model"}],"country":"United States","state":"Alabama, Florida, Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.71484375,\n 25.005972656239187\n ],\n [\n -79.98046875,\n 25.005972656239187\n ],\n [\n -79.98046875,\n 31.98944183792288\n ],\n [\n -87.71484375,\n 31.98944183792288\n ],\n [\n -87.71484375,\n 25.005972656239187\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Multiple linear regression models were developed to estimate dissolved-solids concentrations in water at four sites in the Yuma area between Imperial Dam, Arizona and California and the southerly international boundary with Mexico at San Luis, Arizona. Continuous and discrete water-quality data were collected at gaging stations in the Colorado River upstream from Imperial Dam, Arizona-California, the Colorado River below Cooper wasteway near Yuma, Arizona, the Yuma Main Drain above Arizona–Sonora, Mexico boundary, and the 242 lateral above Main Drain at the Arizona–Sonora boundary. Continuous specific conductance and water temperature data were collected at each site between January 2017 and March 2019. Bi-weekly to monthly dissolved-solids water samples were collected during the same period. Continuous specific conductance data collected at the Colorado River below Cooper wasteway were affected by poorly mixed streamflow during periods when the Pilot Knob Hydro-electric Plant was releasing water to the river. The continuous specific conductance data for the site downstream from Cooper wasteway were corrected using mean specific conductance values computed from cross-section measurements collected during site visits. Continuous specific conductance data were affected by sensor fouling issues at the 242 lateral site, and continued operation at the site would require more frequent visits for cleaning and service to ensure data quality.
During the study, instream specific conductance readings ranged from 966 to 3,030 microsiemens per centimeter (μS/cm) at 25 degrees Celsius. Computed dissolved-solids concentrations from discrete samples ranged from 690 to 2,580 milligrams per liter (mg/L). Dissolved-solids concentrations were estimated from regression models using the optimal relation between dissolved solids and environmental factors, such as specific conductance, water temperature, dissolved oxygen, streamflow, and seasonality. Specific conductance was the primary factor at all four sites and explained 87.6 to 94 percent of variation in dissolved solids. Water temperature, as an indicator of seasonality, was determined to be a statistically significant secondary factor at both the Colorado River above Imperial Dam and Colorado River below Cooper wasteway sites explaining an additional 6.9 and 2.1 percent of variation in dissolved solids, respectively. Regression models explained 87.6 to 96.9 percent of the variation in dissolved solids; the root mean square error in the modeled data ranged between about 6 and 27 mg/L.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215080","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Cederberg, J.R., Paretti, N.V., Coes, A.L., Hermosillo, E., Andrade, L., 2021, Estimation of dissolved-solids concentrations using continuous water-quality monitoring and regression models at four sites in the Yuma area, Arizona and California, January 2017 through March 2019: U.S. Geological Survey Scientific Investigations Report 2021–5080, 26 p., https://doi.org/10.3133/sir20215080.","productDescription":"Report: vii, 26 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-111110","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":436228,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SMK908","text":"USGS data release","linkHelpText":"Water-Quality Field Blank and Replicate Sample Data, Instantaneous and Mean Daily Discharge Data, and Dissolved-Solids Concentrations Data Collected in Four Waterways of Southwest Arizona, January 2017-March 2019"},{"id":388445,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/p9SMK908","linkHelpText":"Supplemental streamflow, quality-assurance, and dissolved-solids concentration datasets used for regression model development at four sites in the Yuma area, Arizona and California, January 2017 through March 2019"},{"id":388447,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5080/covrthb.jpg"},{"id":388448,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5080/sir20215080.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona, California","otherGeospatial":"Yuma area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.873046875,\n 32.58384932565662\n ],\n [\n -114.3896484375,\n 32.58384932565662\n ],\n [\n -114.3896484375,\n 32.88881315761995\n ],\n [\n -114.873046875,\n 32.88881315761995\n ],\n [\n -114.873046875,\n 32.58384932565662\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
We present an updated ground‐motion model (GMM) for
Conducting managed species translocations and establishing climate change refugia are adaptation strategies to cope with projected consequences of global warming, but successful implementation requires on-the-ground validation of demographic responses to transient climate conditions. Here we estimated the effect of nine abiotic and biotic factors on local occupancy and an index of abundance (few or chorus) for four amphibian species (Eleutherodactylus wightmanae, E. brittoni, E. antillensis, and E. coqui) in Puerto Rico, USA. We also assessed how the same factors influenced reproductive activity of E. coqui and how species responded to hurricane María (20 September 2017). As predicted, occupancy and abundance of E. wightmanae, E. brittoni and E. coqui were positively and strongly influenced by abiotic covariates (e.g., relative humidity) that characterize high elevation, mesic habitats. E. antillensis exhibited the opposite pattern, with highest probabilities (≥0.6) recorded at ≤300 m and with average relative humidity<75%. Biotic covariates (e.g., canopy cover) had a weak influence on both parameters, regardless of species. High probabilities (≥0.9) of detecting an E. coqui chorus and active nests occurred at sites experiencing average relative humidity of>80% and temperature of ≤26 °C. Moderate to high probabilities of detecting a chorus (0.4–0.7) were recorded at sites with average temperatures>26 °C, but no reproductive activity was detected, implying that monitoring abundance alone could misrepresent the capacity of a local population to sustain itself. The possibility underscores the importance of understanding the interplay between local demographic and environmental parameters in the advent of global warming to help guide monitoring and management decisions, especially for high elevation specialists. Hurricanes can inflict marked reductions in population numbers, but impacts vary by location and species. We found that the abundance (chorus) of E. antillensis and E. brittoni increased after the hurricane, but the abundance of the other two species did not differ between years. Lack of impacts was probably mediated by low structural damage to forest tracts (e.g., 9% canopy loss). Our findings help assess habitat suitability in terms of parameters that foster local population growth, which provides a basis for testing spatio-temporal predictions about demographic rates in potential climate refugia and for designing criteria to help guide managed translocations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2021.e01624","usgsCitation":"Rivera-Burgos, A., Collazo, J.A., Terando, A., and Pacifici, K., 2021, Linking demographic rates to local environmental conditions: Empirical data to support climate adaptation strategies for Eleutherodactylus frogs: Global Ecology and Conservation, v. 28, e01624,16 p., https://doi.org/10.1016/j.gecco.2021.e01624.","productDescription":"e01624,16 p.","ipdsId":"IP-119108","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":451095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2021.e01624","text":"Publisher Index Page"},{"id":396461,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto 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0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":835886,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pacifici, Krishna","contributorId":244494,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835887,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223332,"text":"ds1140 - 2021 - Distribution and abundance of Southwestern Willow Flycatchers (Empidonax traillii extimus) on the upper San Luis Rey River, San Diego County, California—2020 data summary","interactions":[],"lastModifiedDate":"2021-08-24T11:44:52.091294","indexId":"ds1140","displayToPublicDate":"2021-08-23T12:42:39","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1140","displayTitle":"Distribution and Abundance of Southwestern Willow Flycatchers (Empidonax traillii extimus) on the Upper San Luis Rey River, San Diego County, California—2020 Data Summary","title":"Distribution and abundance of Southwestern Willow Flycatchers (Empidonax traillii extimus) on the upper San Luis Rey River, San Diego County, California—2020 data summary","docAbstract":"We surveyed for Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) along the upper San Luis Rey River, near Lake Henshaw, in Santa Ysabel, California, in 2020. Surveys were completed at four locations: three downstream from Lake Henshaw, where nest monitoring occurred from 2015 to 2019 (Rey River Ranch [RRR], Cleveland National Forest [CNF], Vista Irrigation District [VID]), and one at VID Lake Henshaw (VLH) that was previously surveyed in 2018 and 2019. There were 62 territorial flycatchers detected at 3 locations (RRR, CNF, VLH); no flycatchers were detected at VID. Within the former nest monitoring study area, 11 flycatchers, including 5 males and 6 females, were detected at RRR and CNF. In total, seven territories were established, consisting of six pairs (two polygynous groups consisting of two males each pairing with two different females) and one male of undetermined breeding status. Upstream from the former nest monitoring study area at VLH, we detected 51 flycatchers, including 24 males and 27 females. There were 28 territories established, containing 27 pairs (23 monogamous pairings and 4 confirmed polygynous pairings consisting of 1 male and 2 females) and 1 male of undetermined breeding status. Brown-headed cowbirds (Molothrus ater; cowbird) were detected at all four survey locations.
Flycatchers used four different habitat types in the survey area: (1) mixed willow riparian, (2) willow-oak, (3) willow-ash, and (4) willow-sycamore. Eighty percent of the flycatchers were detected in habitat characterized as mixed willow riparian, and 83 percent of the flycatchers were detected in habitat with greater than 50-percent native plant cover. Exotic vegetation was not prevalent in the survey area.
There were 17 flycatcher nests incidentally located during surveys: 2 were successful, 3 were seen with nestlings on the last visit, 10 failed, and the outcome of the remaining 2 nests was unknown. Five of these nests were parasitized by cowbirds. There were 10 juveniles detected during surveys: 2 at RRR and 8 at VLH.
Of the 17 banded flycatchers detected during surveys, 8 were resighted and confirmed to be adults that held territories in previous years. Seven flycatchers with a single dark blue federal band, indicating that they were banded as nestlings in the former nest monitoring study area downstream from Lake Henshaw, were resighted during surveys; 86 percent of these “natal” flycatchers held territories at VLH.
In 2020, we documented both adult and natal flycatchers moving from the former nest monitoring study area downstream from Lake Henshaw upstream to the habitat surrounding Lake Henshaw. Six natal flycatchers that were originally banded as nestlings and two adults that previously held territories downstream dispersed to Lake Henshaw in 2020.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1140","usgsCitation":"Howell, S.L., and Kus, B.E., 2021, Distribution and abundance of Southwestern Willow Flycatchers (Empidonax traillii extimus) on the upper San Luis Rey River, San Diego County, California—2020 data summary: U.S. Geological Survey Data Series 1140, 11 p., https://doi.org/10.3133/ds1140.","productDescription":"vi, 11 p.","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-126374","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":388356,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1140/covrthb.jpg"},{"id":388357,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1140/ds1140.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":388359,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/ds/1140/images"},{"id":388358,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/ds/1140/ds1140.xml"}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.9769287109375,\n 33.15364887320581\n ],\n [\n -116.69403076171875,\n 33.15364887320581\n ],\n [\n -116.69403076171875,\n 33.29150775159364\n ],\n [\n -116.9769287109375,\n 33.29150775159364\n ],\n [\n -116.9769287109375,\n 33.15364887320581\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Diel cycles are known to occur in all types of waters, and increasing studies indicate routine water samples may not provide an accurate snapshot in concentrations of trace elements and nutrients. Diel behavior in neutral to alkaline pH ranges is independent of streamflow variability and concentration. Extensive historical U.S. Geological Survey (USGS) water-quality data have been collected in the Eagle River Basin during daylight hours, which is defined as the period of time between one-half hour prior to sunrise and one-half hour after sunset. However, no USGS data have been collected throughout the nighttime, defined as the time between one-half hour after sunset and one-half hour prior to sunrise, making the evaluation of diel cycles impossible. To assess the importance of diel cycling within the Eagle River Basin, the USGS, in cooperation with Eagle River Watershed Council, developed a study to assess the mechanisms, patterns, and magnitude of change during the diel cycle for selected constituents. Water-quality monitors at five USGS streamgage sites (09065500, Gore Creek at Upper Station, near Minturn, Colorado, 09063000, Eagle River at Red Cliff, Colorado, 09064600, Eagle River near Minturn, Colorado, 09066325, Gore Creek above Red Sandstone Creek at Vail, Colorado, and 394220106431500, Eagle River below Milk Creek near Wolcott, Colorado) were deployed in 2017 to evaluate the water-quality field parameters and to determine if water conditions were favorable for the diel cycling of nutrients and trace elements. Based on the evaluation of water-quality parameters, three of the five sites were sampled for nutrient and trace-element concentrations in 2018 to confirm the presence and magnitude of diel cycling. Historical data were also analyzed to assess the effect of time of day on measured nutrient and trace-element concentrations. An assessment of the effect of land use on diel cycling was also investigated.
Measurable nutrients displayed a diel cycle at all three sites with the largest percentage change at the most downstream site (394220106431500), located on the Eagle River. More notable diel cycles at this site include filtered nitrate plus nitrite, which varied 179 percent, with concentrations from 0.24 to 0.67 milligrams per liter (mg/L) and filtered orthophosphate, which varied 71 percent, with concentrations from 0.07 to 0.12 mg/L. Filtered nitrate plus nitrite at site 09066325 varied 57 percent, ranging from 0.14 to 0.22 mg/L. Maximum concentrations occurred prior to noon, decreased through the afternoon (between noon and sunset), and increased during the night (between sunset and sunrise). That pattern is consistent with nutrient uptake in response to daytime (between sunrise and sunset) photosynthesis along with biologically driven denitrification and nitrification cycles. Nutrient concentrations at sites 09064600 and 09066325 were generally low and below laboratory reporting limits, which is the smallest measured concentration that nutrients could be measured by a given analytical method.
Trace-element concentrations were detectable at all sites with the largest percentage change at the most downstream site (394220106431500) and exhibited diel concentration variation from 11.6 to 284 percent. Appreciable diel cycles included filtered copper (0.98–1.40 micrograms per liter [µg/L], 42.9 percent), filtered zinc (less than [<] 4.00–5.50 µg/L, greater than [>] 37.5 percent), total manganese (9.70–19.5 µg/L, 101 percent), and total arsenic (0.30–0.40 µg/L, 33.3 percent). The largest percentage change in concentration was filtered manganese (2.84–10.9 µg/L, 284 percent). Diel cycles at site 09064600 ranged from 9.1 to 64.5 percent across the trace elements measured. Dissolved trace elements with appreciable diel cycles during the sampling period include filtered cadmium (0.09–0.12 µg/L, 33.3 percent), filtered copper (0.99–1.40 µg/L, 41.4 percent), and total arsenic (0.20–0.30 µg/L, 50 percent). The largest percentage change was filtered zinc (38.3–63.0 µg/L, 65 percent). Trace-element concentrations at site 09066325 were below laboratory reporting limits for many parameters, and no diel cycle could be assessed for these parameters. However, total recoverable iron, filtered barium, filtered manganese, and filtered selenium exhibited changes in concentrations of <10.0–19.4 µg/L (>94 percent), 115–121 µg/L (5 percent), 1.44–1.72 µg/L (19.4 percent), and 0.25–0.28 µg/L (12 percent), respectively. At sites 09064600 and 394220106431500, maximum trace-element concentrations occurred during nighttime with some variation regarding the timing of the peak. The exceptions to this were filtered copper, total arsenic, and filtered selenium, which had maximum concentrations around noon or as the sun disappeared below the horizon. The timing of minimum concentrations occurred in the afternoon for many trace elements, with filtered copper, total arsenic, and filtered selenium having minimum concentrations in the morning or just prior to the appearance of the sun.
Analysis of historical data also showed evidence of diel cycling. Historical samples collected from July through October were used to identify diel cycling in base-flow conditions. The resulting diel pattern in the median concentration for filtered manganese, filtered zinc at water-quality site 09064600, and filtered manganese and filtered nitrate plus nitrite at water-quality site 39422016431500 were consistent with the diel pattern in the September 2018 samples, and indicate time of day can bias sampling results even during daylight hours.
Diel cycling in the Eagle River Basin appears to be driven primarily by instream, biological processes. However, land use, particularly human effects downstream from urban areas, mining, and agriculture, may affect these processes. At some locations, diel variations in nutrient and trace-element concentrations are small enough to be of low concern. At other locations, however, variations in concentrations up to 284 percent in the data collected for this study and 214 percent in base-flow historical data, indicate daytime-only sampling, particularly in late afternoon, can underestimate daily average nutrient and trace-element concentrations. When feasible, the potential of diel cycling warrants consideration in sample design to account for the potential of diel cycles, or at a minimum, be recognized as a component of the river dynamic and the potential consequences that diel cycles may have in data interpretation and river management decisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215066","collaboration":"Prepared in cooperation with Eagle River Watershed Council","usgsCitation":"Richards, R.J., and Henneberg, M.F., 2021, Assessment of diel cycling in nutrients and trace elements in the Eagle River Basin, 2017–18: U.S. Geological Survey Scientific Investigations Report 2021–5066, 36 p., \nhttps://doi.org/ 10.3133/ sir20215066.","productDescription":"Report: viii, 36 p.; 3 Databases","onlineOnly":"Y","ipdsId":"IP-116765","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":388128,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5066/coverthb.jpg"},{"id":388129,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5066/sir20215066.pdf","text":"Report","size":"5.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225
The avian conservation community struggles to design and implement large scale, long-term coordinated bird monitoring programs within the northern Gulf of Mexico due to the complexity of the conservation enterprise in the region; this complexity arises from the diverse stakeholders, multiple jurisdictions, complex ecological processes, myriad habitats, and over 500 species of birds using the region for at least some part of their annual cycle. In addition, long-term monitoring over large spatial scales is difficult because of the need for monitoring data to both (1) evaluate management and restoration outcomes, and (2) provide reliable information about the status and trends of bird populations over time.
To address these challenges, the Gulf of Mexico Avian Monitoring Network developed a problem statement:
“How can a cost-effective monitoring strategy for the Gulf Coast bird community and ecosystem be developed that evaluates ongoing conservation activities and chronic and acute threats; maximizes learning; and is flexible and holistic enough to detect novel ecological threats and evaluate new and emerging conservation activities?”
A structured decision-making framework was then used to articulate and quantify stakeholder values related to the problem statement. One use of the stakeholder values was to develop a regional, strategic plan for bird monitoring, which is presented elsewhere. A formal and complete decision support tool for conservation investments in monitoring and research guided by the stakeholder values is presented in this report. The technical aspects of the stakeholder value model and a portfolio analysis that could be used to guide decision making when allocating resources for monitoring activities is described. Whereas the decision analysis presented here could be useful to any decision maker faced with difficult choices about resource allocation, it is designed for decision makers who request monitoring study proposals and then determine which combination of proposals to fund. The portfolio decision support tool is designed to help funding agencies and organizations identify resource allocation strategies to maximize stated objectives.
To begin the decision analysis, an objectives hierarchy and quantitative performance metrics from the values of the Gulf of Mexico bird conservation community were created by a panel of regional stakeholders. Each fundamental objective and sub-objective in the hierarchy is composed of several performance metrics. To test the decision support tool, the authors evaluated a combination of monitoring study proposals written for the region and simulated proposals. Each proposal was scored against the performance metrics and used multi-attribute utility theory to combine the multiple objectives into a measure of total monitoring benefit. The total monitoring benefit and costs of each proposal were then used in a constrained optimization routine to identify optimal monitoring portfolios, that is, a combination of activities that maximizes monitoring benefits while meeting cost and other constraints of interest to stakeholders. A graphical solution based on the concept of Pareto efficiency, which is useful in situations when cost constraints and exact budgets are not known, is also provided. Finally, an evaluation of the sensitivity of the decision-making framework to the weights assigned to objectives by stakeholders is included. This decision support tool allows decision makers to identify an optimal suite of monitoring proposals with a transparent portfolio analysis that includes user-defined constraints (such as costs).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201122","collaboration":"Prepared in Cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Fournier, A.M.V., Wilson, R.R., Lyons, J.E., Gleason, J.S., Adams, E.M., Barnhill, L.M., Brush, J.M., Cooper, R.J., DeMaso, S.J., Driscoll, M.J.L., Eaton, M.J., Frederick, P.C., Just, M.G., Seymour, M.A., Tirpak, J.M, and Woodrey, M.S., 2021, Structured decision making and optimal bird monitoring in the northern Gulf of Mexico: U.S. Geological Survey Open-File Report 2020–1122, 62 p., https://doi.org/10.3133/ofr20201122.","productDescription":"Report: ix, 62 p.; 6 Companion Files","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-100582","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":387878,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/sdm_tool_excel_version_2019_12_22.xlsm","text":"2. Portfolio Analysis Spreadsheet","size":"139 KB"},{"id":387871,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1122/coverthb.jpg"},{"id":387876,"rank":10,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_matrix.xlsx","text":"5. Matrix of Management Actions and Bird Species","size":"45.5 KB"},{"id":387874,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_birds.xlsx","text":"1. Gulf of Mexico Avian Monitoring Network Birds of Conservation Concern","size":"727 KB"},{"id":387872,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122.pdf","text":"Report","size":"5.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1122"},{"id":387875,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1122/ofr20201122_birds_csv.zip","text":"1. 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U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708
The U.S. Geological Survey, in cooperation with the Montana Department of Transportation, developed regression equations based on channel width to estimate peak-flow frequencies at ungaged sites in Montana. The equations are based on peak-flow data at streamgages through September 2011 (end of water year 2011), and channel widths measured in the field and from aerial photographs.
Active-channel width and bankfull width (channel widths) were measured in the field at 64 sites across Montana in 2017. Channel widths also were measured near 515 streamgages from aerial photographs. These new channel-width data, along with more than 438 historical channel-width measurements, are published in a separate data release.
Regression equations were developed using generalized least squares regression or weighted least squares regression. The channel-width regression equations can be used to estimate peak-flow frequencies (peak-flow magnitudes associated with annual exceedance probabilities of 66.7, 50, 42.9, 20, 10, 4, 2, 1, 0.5, and 0.2 percent) at ungaged sites in each of the eight hydrologic regions in Montana. Methods are presented for weighting estimates from the channel-width equations with estimates from equations using basin characteristics. The weighting technique can be used to reduce the standard error of prediction relative to that obtained using a single method. Several example problems covering a range of estimation scenarios also are included.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205142","collaboration":"Prepared in cooperation with Montana Department of Transportation","usgsCitation":"Chase, K.J., Sando, R., Armstrong, D.W., and McCarthy, P., 2021, Regional regression equations based on channel-width characteristics to estimate peak-flow frequencies at ungaged sites in Montana using peak-flow frequency data through water year 2011 (ver. 1.1, September 2021): U.S. Geological Survey Scientific Investigations Report 2020–5142, 49 p., https://doi.org/10.3133/sir20205142.","productDescription":"Report: vi, 49 p.; Data Release; Dataset; Version History","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-102009","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":436235,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D7MEB1","text":"USGS data 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\"}}]}","edition":"Version 1.0: August 19, 2021; Version 1.1: September 20, 2021","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
The U.S. Geological Survey, with the support of the International Joint Commission, and in cooperation with Alberta Environment and Parks, Blackfeet Nation, Environment and Climate Change Canada, and Montana Department of Natural Resources and Conservation, is leading a project that should improve information available to apportion water between Canada and the United States in the St. Mary and Milk River Basins. One component of the water budget, consumptive use of irrigation water (the amount of supplemental water used by crops), can be estimated at 100-meter resolution almost every week using imagery recorded by satellites from 1985 to present (2021) and weather data, when conditions permit. Better estimates of consumptive water use should improve understanding of water availability and use in the basin and should assist with water apportionment procedures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213042","collaboration":"Prepared in cooperation with Alberta Environment and Parks, Blackfeet Nation, Environment and Climate Change Canada, and Montana Department of Natural Resources and Conservation","usgsCitation":"Sando, R., Friedrichs, M., and Senay, G.B., 2021, Using satellite imagery to estimate consumptive water use from irrigated lands in the Milk River Basin, United States and Canada: U.S. Geological Survey Fact Sheet 2021–3042, 2 p., https://doi.org/10.3133/fs20213042.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-130575","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":388101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3042/coverthb.jpg"},{"id":388102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3042/fs20213042.pdf","text":"Report","size":"11.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"fs 2021–3042"}],"country":"Canada, United States","state":"Alberta, Montana, Saskatchewan","otherGeospatial":"Milk River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.851318359375,\n 48.07807894349862\n ],\n [\n -106.424560546875,\n 48.07807894349862\n ],\n [\n -106.424560546875,\n 49.7173764049358\n ],\n [\n -113.851318359375,\n 49.7173764049358\n ],\n [\n -113.851318359375,\n 48.07807894349862\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
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3162 Bozeman Avenue
Helena, MT 59601
Along the coast of the U.S. Gulf of Mexico, the eastern oyster (Crassostrea virginica) plays important ecological and economic roles. Commercial landings from this region account for more than 50 percent of all U.S. landings; these oyster reefs also provide varied ecosystem services, including nursery habitat for many fish and macroinvertebrate species, shoreline protection, and water-quality maintenance. Declining trends in both total oyster production and functional reef area across this region have spurred investment in restoration of oyster resources, with specific calls for restoration projects to develop a network of reefs and identify broodstock and sanctuary reef restoration sites. Decision making related to restoration and establishment of a network of oyster reefs in the Gulf of Mexico requires information on both the environment and the effects of the environment on the oyster life cycle (including larval movement, survival, oyster recruitment, reproduction, growth, and mortality). Here, we examined the current state of data and model development in this region with the goal of providing an overview of oyster modeling approaches and an inventory of available data and existing oyster models. This report is meant to provide an overview to managers for understanding existing efforts and identify a path forward to most efficiently inform oyster resource management and restoration planning in moving from a single reef management approach to a reef network management approach.
Numerous models related to some aspect of the oyster life cycle have been built, calibrated, and validated for various Gulf of Mexico estuaries over the last few decades (over 30 models identified). These models, which could inform site restoration, can be classified into four approaches: (1) oyster Habitat Suitability Index (HSI) models; (2) larval transport models; (3) on-reef oyster models that may include oyster growth, mortality and reproduction, and substrate persistence; and (4) coupled larval transport on-reef metapopulation models that simulate the entire oyster life cycle. The data requirements, model complexity and assumptions, and transferability vary by approach. Specifically, some approaches may offer greater accessibility, flexibility, and transferability spatially or temporally, with minimal data input, but only provide broad information to support site selection. In contrast, other approaches may require significant site-specific data for their construction and validation but may provide more accurate and location-specific data to support site selection for broodstock reefs.
Regardless of modeling approach used, data on environmental drivers, such as salinity, water temperature, or water flow impacting oyster metabolism and movement, are required at appropriate spatial and temporal scales. While numerous data collection platforms, environmental models, and research products exist within Gulf of Mexico estuaries to provide important environmental data to use as drivers in the oyster models, significant variability in temporal and spatial coverage of the data, and variation in the availability of future condition models, exists across estuaries. This variation influences the spatial and temporal scales at which oyster models may be developed and impacts the calibration and validation of the oyster models within a given estuary, affecting its potential ability to address specific management or restoration questions.
While multiple modeling approaches exist for informing site selection of broodstock or sanctuary oyster reefs, the development, calibration, and validation of a single modeling platform presents the most efficient, transferable, and useful tool for managers across the Gulf of Mexico. The development of a single modeling platform would involve using standardized input variables, governing equations, and assumptions for the modeled oyster processes and outputs, and for standardized calibration and validation procedures that could be applied within each estuary. The differences among estuary applications would require substituting only estuary-specific environmental data, and calibrating and validating the modeling approach with local oyster data.
Two modeling approaches likely to be useful include (1) development of a general geospatial HSI modeling framework that could be applied consistently across estuaries and (2) a mechanistic coupled larval transport on-reef metapopulation model requiring only estuarine specific calibration and hydrodynamic models. Both approaches benefit from existing work across multiple Gulf of Mexico estuaries and could provide valuable support for oyster restoration, but may differ in their ability to address specific questions related to oyster restoration. HSI models specifically guide restoration practitioners in determining suitable habitat based on available data. The HSI approach, while currently more widely used and accessible, requires more development of larval suitability and larval input and output components in order to inform reef connectivity. A metapopulation approach considering the full oyster life cycle that simulates both on-reef oyster growth, mortality, reproduction, substrate persistence, and larval transport (ideally with larval growth and mortality) would provide the greatest detail and level of understanding but requires significant up-front investment. The larval oyster model and on-reef oyster model are usually developed independently for systems, although the two approaches can be coupled to represent the entire oyster life cycle in order to characterize and assess a reef metapopulation. This approach may be less accessible and much more data-intensive, however, and it requires some expertise to run and apply to inform oyster resource management.
Ultimately, the development of single modeling platforms for each of these approaches would provide flexible tools applicable across all Gulf of Mexico oyster supporting estuaries. By using a single platform for model development, testing, calibrating and validating, and evaluation of modeled future scenarios, oyster restoration scientists and managers would not only be able to examine different scenario outcomes within a single estuary, but could also have comparable modeled results to evaluate potential outcomes, across estuaries and regions, that are not confounded by varying modeled data inputs, governing equations, assumptions, or user judgement.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211063","usgsCitation":"La Peyre, M.K., Marshall, D.A., and Sable, S.E., 2021, Oyster model inventory: Identifying critical data and modeling approaches to support restoration of oyster reefs in coastal U.S. Gulf of Mexico waters: U.S. Geological Survey\nOpen-File Report 2021–1063, 40 p., https://doi.org/10.3133/ofr20211063.","productDescription":"Report: viii, 40p.; 3 Appendix Tables","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-126014","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":388074,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1063/images"},{"id":388041,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1063/coverthb.jpg"},{"id":388042,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063.pdf","text":"Report","size":"37.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1063"},{"id":388043,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.csv","text":"Table 1.1 (.csv)","size":"5.07 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388044,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.csv","text":"Table 2.1 (.csv)","size":"5.40 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388045,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.csv","text":"Table 3.1 (.csv)","size":"63.8 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"},{"id":388046,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_1.1.xlsx","text":"Table 1.1 (.xlsx)","size":"14.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 1.1","linkHelpText":"— Discrete water-quality data sources"},{"id":388047,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_2.1.xlsx","text":"Table 2.1 (.xlsx)","size":"13.0 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 2.1","linkHelpText":"— Modeled water quality and physical data sources"},{"id":388048,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2021/1063/ofr20211063_table_3.1.xlsx","text":"Table 3.1 (.xlsx)","size":"46.9 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2021–1063 Table 3.1","linkHelpText":"— Oyster model inventory"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf Coast","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.177734375,\n 27.527758206861886\n ],\n [\n -82.6171875,\n 29.267232865200878\n ],\n [\n -83.8916015625,\n 30.372875188118016\n ],\n [\n -85.078125,\n 30.259067203213018\n ],\n [\n -86.7919921875,\n 30.826780904779774\n ],\n [\n -88.681640625,\n 30.675715404167743\n ],\n [\n -90.263671875,\n 30.486550842588485\n ],\n [\n -90.3076171875,\n 29.649868677972304\n ],\n [\n -91.62597656249999,\n 30.14512718337613\n ],\n [\n -94.3505859375,\n 29.916852233070173\n ],\n [\n -96.45996093749999,\n 29.036960648558267\n ],\n [\n -97.470703125,\n 27.800209937418252\n ],\n [\n -97.734375,\n 26.78484736105119\n ],\n [\n -97.0751953125,\n 25.918526162075153\n ],\n [\n -96.767578125,\n 27.01998400798257\n ],\n [\n -96.0205078125,\n 28.34306490482549\n ],\n [\n -94.39453125,\n 29.075375179558346\n ],\n [\n -92.94433593749999,\n 29.34387539941801\n ],\n [\n -92.2412109375,\n 29.075375179558346\n ],\n [\n -90.9228515625,\n 28.806173508854776\n ],\n [\n -89.033203125,\n 28.613459424004414\n ],\n [\n -88.59374999999999,\n 29.267232865200878\n ],\n [\n -88.9013671875,\n 29.916852233070173\n ],\n [\n -87.4951171875,\n 29.80251790576445\n ],\n [\n -86.17675781249999,\n 29.99300228455108\n ],\n [\n -85.341796875,\n 29.19053283229458\n ],\n [\n -84.0673828125,\n 29.57345707301757\n ],\n [\n -83.4521484375,\n 28.998531814051795\n ],\n [\n -83.3642578125,\n 27.72243591897343\n ],\n [\n -82.4853515625,\n 26.667095801104814\n ],\n [\n -82.177734375,\n 27.527758206861886\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Cooperative Fish and Wildlife Research Units
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12201 Sunrise Valley Drive
Reston, VA 20192
Strandlines of peak-stage indicators (such as driftwood logs, woody debris, and trash) provide valuable data for understanding the maximum stage and extent of inundation during floods. A series of seven strandlines have been preserved along the Colorado River in Grand Canyon National Park, Arizona, USA. A survey and analysis of these strandlines was completed from the Colorado River at Lees Ferry, Ariz., gaging station to the Colorado River near Grand Canyon, Ariz., gaging station. Owing to the longitudinally discontinuous nature of the strandlines, several lines of evidence were used to determine the year of the flood associated with each strandline segment. This evidence included strandline relative vertical position, degree of peak-stage indicator weathering, datable trash drift, and map-view location. The seven distinct strandlines identified were deposited during floods with the following peak discharges (in cubic feet per second [ft3/s]) at the Colorado River at Lees Ferry, Ariz., gaging station (year of flood in parentheses): 210,000 ft3/s (1884), 170,000 ft3/s (1921), 125,000 ft3/s (1957), 108,000 ft3/s (1958), 97,000 ft3/s (1983), 52,500 ft3/s (1986), and 45,000 ft3/s (multiple events between 1996 and 2012). Stage-discharge relations were developed in areas where all, or most of the strandlines were present, and were compared to predicted stage-discharge relations from a one-dimensional flow model. River width exerted a strong control on these relations, with much greater stage change occurring for a given discharge change in narrower bedrock-dominated reaches than in wider reaches with more extensive channel-margin alluvium. This comprehensive dataset allows for the verification of model-predicted flood stage along the Colorado River in Grand Canyon National Park.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215048","usgsCitation":"Sabol, T.A., Griffiths, R.E., Topping, D.J., Mueller, E.R., Tusso, R.B., and Hazel, J.E., Jr., 2021, Strandlines from large floods on the Colorado River in Grand Canyon National Park, Arizona: U.S. Geological Survey Scientific Investigations Report 2021-5048, 41 p., https://doi.org/10.3133/sir20215048.","productDescription":"Report: vi, 41 p.; Data Release; Version History","numberOfPages":"41","onlineOnly":"Y","ipdsId":"IP-118687","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":388103,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GIQ9ZN","linkHelpText":"Surveyed peak-stage elevations, coordinates, and indicator data of strandlines from large floods on the Colorado River in Grand Canyon National Park, Arizona"},{"id":388754,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5048/versionhist.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}},{"id":387949,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5048/covrthb.jpg"},{"id":388753,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5048/sir20215048.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.027099609375,\n 35.78662688467009\n ],\n [\n -111.37390136718749,\n 35.78662688467009\n ],\n [\n -111.37390136718749,\n 36.98500309285596\n ],\n [\n -114.027099609375,\n 36.98500309285596\n ],\n [\n -114.027099609375,\n 35.78662688467009\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Since 2017, the orbit of Landsat 7 has drifted outside its nominal mission requirement toward an earlier acquisition time because of limited onboard fuel resources. This makes quantitative analyses from Landsat 7 data potentially unreliable for many scientific studies. To comprehensively understand the effect of ongoing (2018–2020) orbit drift on Landsat 7 data, we compared surface reflectance and Top-Of-Atmosphere (TOA) reflectance of growing season observations (July 1 ± 30 days) from Landsat 7 with orbit drift and Landsat 8 with nominal orbit using a total of 10,000 randomly selected Northern Hemisphere (0–750 N) terrestrial pixels. To evaluate the future (2021–2023) effect of Landsat 7's orbit drift, we analyzed the historical Northern Hemisphere terrestrial growing season Earth Observing-1 (EO-1) TOA reflectance images, which shared a similar orbit drift as Landsat 7 but occurred much earlier. Results suggest that Landsat 7's orbit drift has already led to a general decrease in surface reflectance and TOA reflectance in 2019 and 2020, with a limited impact (overall reflectance changes less than 0.007). The influence of orbit drift is more substantial for the two shortwave infrared (SWIR) bands and the near infrared (NIR) band, but less for the three visible bands (i.e., Red, Green, and Blue). The Normalized Difference Vegetation Index (NDVI), derived from either surface reflectance or TOA reflectance, increased less than 0.003 in 2020. According to the historical EO-1 TOA reflectance data, we estimate that the effect of Landsat 7's orbit drift will be much more dramatic in the future (e.g., the NIR and SWIR bands will decrease more than 0.015 since July 1, 2021), and for different land cover types, the effects of orbit drift are also quite different. To reduce this influence, we examined the c-factor Bidirectional Reflectance Distribution Function (BRDF) normalization approach to correct the orbit drift impact for Landsat 7 surface reflectance data collected between 2019 and 2020. We found that the c-factor BRDF can reduce the data difference substantially, but how this approach works after Landsat 7's orbit drifts further still requires more investigation. Therefore, we determined that Landsat 7 can preserve its science capability until 2020, but will be less reliable for remote sensing applications that need accurate absolute radiometric values after 2020. Correction methods such as c-factor BRDF could be a potential viable approach to maintain its science capability going forward.
Noble gases record fluid interactions in multiphase subsurface environments through fractionation processes during fluid equilibration. Water in the presence of hydrocarbons at the subsurface acquires a distinct elemental signature due to the difference in solubility between these two fluids. We find the atmospheric noble gas signature in produced water is partially preserved after hydrocarbons production and water disposal to unlined ponds at the surface. This signature is distinct from meteoric water and can be used to trace oil-field water seepage into groundwater aquifers. We analyse groundwater (n = 30) and fluid disposal pond (n = 2) samples from areas overlying or adjacent to the Fruitvale, Lost Hills, and South Belridge Oil Fields in the San Joaquin Basin, California, USA. Methane (2.8 × 10−7 to 3 × 10−2 cm3 STP/cm3) was detected in 27 of 30 groundwater samples. Using atmospheric noble gas signatures, the presence of oil-field water was identified in 3 samples, which had equilibrated with thermogenic hydrocarbons in the reservoir. Two (of the three) samples also had a shallow microbial methane component, acquired when produced water was deposited in a disposal pond at the surface. An additional 6 samples contained benzene and toluene, indicative of interaction with oil-field water; however, the noble gas signatures of these samples are not anomalous. Based on low tritium and 14C contents (≤ 0.3 TU and 0.87–6.9 pcm, respectively), the source of oil-field water is likely deep, which could include both anthropogenic and natural processes. Incorporating noble gas analytical techniques into the groundwater monitoring programme allows us to 1) differentiate between thermogenic and microbial hydrocarbon gas sources in instances when methane isotope data are unavailable, 2) identify the carrier phase of oil-field constituents in the aquifer (gas, oil-field water, or a combination), and 3) differentiate between leakage from a surface source (disposal ponds) and from the hydrocarbon reservoir (either along natural or anthropogenic pathways such as faulty wells).
The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, collects data pertaining to the surface-water resources of Missouri. Established in 1964, the Ambient Water-Quality Monitoring Network (AWQMN) consisted of 69 sites in 2017. Two additional sites from the National Water-Quality Program are included with the AWQMN sites for the analyses in this report. The sites are sampled typically from 2 to 12 times per year for physical properties, total suspended solids, nutrients, fecal indicator bacteria, and trace elements.
The period of analysis for this study was from 1993 through 2017 and data analysis included 71 sites and 15 water-quality constituents plus discharge. Data analysis involved retrieving the data, conditioning the data for analysis, analyzing the data for trends, and analyzing the monitoring network to determine if potential data gaps or data redundancies exist in the network. Results from these analyses can be used to help manage the monitoring network into the future.
Water-quality data were analyzed using several software packages to provide graphical and statistical information for interpretation of trends in the data at selected sites. Discharge data at selected sites were analyzed to determine the general trends during the analysis period and how the water-quality samples represented the range of daily mean discharges at each site. Water-quality data also were analyzed at selected sites to determine the relative sensitivity of selected sites and constituents to changes in data collection frequency. Trend analysis at selected sites using a simulated reduction in sampling frequency was completed to compare to trends obtained using monthly data to determine the potential degradation in the ability of determining trends from a reduced sampling frequency. The viability of using estimated discharge to evaluate long-term trends for sites with no continuous discharge was investigated. Data from sites were statistically compared in groups to determine the relative similarity (or difference) between sites for each water-quality constituent to identify potentially redundant sites in the monitoring network.
Discharge-weighted long-term trends during 1993 through 2017 were analyzed for 15 water-quality constituents at 58 sites and results indicated there were significant single- or two-period trends in about 17 percent of the analyses. Some trends indicated improvement and some trends indicated deterioration of the general water quality at some sites in the AWQMN. No trend was indicated in about 31 percent of the analyses. The constituents pH, specific conductance, and total phosphorus showed the most frequent significant trends, and each of the 15 constituents examined had a significant trend at one or more sites. A total of 42 sites indicated at least 1 constituent with a significant single- or two-period trend, and 10 sites indicated 6 or more significant trends.
Potential data gaps identified for computing discharge-weighted long-term trends in the monitoring network included the lack of collection of continuous discharge at 23 sites, insufficient sampling frequency for some constituents (dissolved chloride and total and dissolved lead and zinc) at some sites, insufficient temporal sample distribution (lack of at least one sample in each season per year) at some sites, and insufficient sampling frequency for some highly censored constituents (nutrients and total and dissolved lead and zinc) at some sites. Potential data gaps based on site spatial distribution were identified in 7 basins greater than 800 square miles.
Potential site redundancies were identified in 4 basins that had an area greater than 500 square miles with a site density greater than 2 sites per 1,000 square miles. Potential site redundancies also were identified for nine site pairs by observing statistical similarities in the constituent data distributions. Sampling frequency was investigated to determine if reducing the sampling frequency of select constituents could provide a statistically similar data distribution. At 28 of 71 sites, 11 constituents had sufficient data collection frequency (approximately monthly) to allow for the creation of simulated datasets of various reduced data collection frequency. For the selected monitoring network sites analyzed, the data distribution of a simulated sampling frequency of four times per year or greater, roughly evenly distributed over the year, was not significantly different than the data distribution of the original monthly sampling frequency. Sites analyzed using varying simulated sampling frequencies tended to be more sensitive to sampling frequency changes if they were in basins classified as large or very large size and tended to be least sensitive in basins classified as small and medium size in the Ozark Plateaus Province. Simulated reduced frequency sampling analysis indicated that the constituents and measurements most sensitive to changes in sampling frequencies were water temperature, dissolved oxygen, discharge, and dissolved nitrate, and least sensitive were pH, total suspended solids, dissolved phosphorus, and total phosphorus. Discharge-weighted long-term trend analysis was repeated at 22 sites for 11 constituents using a simulated quarterly sampling frequency, and matched about 46 percent of the significant single-period trends identified using monthly data and about 65 percent of the analyses that indicated no trend using the monthly data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215079","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Richards, J.M., and Barr, M.N., 2021, General water-quality conditions, long-term trends, and network analysis at selected sites within the Ambient Water-Quality Monitoring Network in Missouri, water years 1993–2017: U.S. Geological Survey Scientific Investigations Report 2021–5079, 75 p., https://doi.org/10.3133/sir20215079.","productDescription":"Report: xi, 75 p.; Data Release; Dataset; 11 Tables","numberOfPages":"91","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-123674","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science 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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Winters are changing rapidly across the globe but the implications for aquatic productivity and food webs are not well understood. In addition, the degree to which winter dynamics in aquatic systems respond to large-scale climate versus ecosystem-level factors is unclear but important for understanding and managing potential changes. We used a unique winter data set from the Upper Mississippi River System to explore spatial and temporal patterns in phytoplankton biomass (chlorophyll a, CHL) and associated environmental covariates across 25 years and ∼1,500 river km. To assess the role of regional climate versus site-specific drivers of winter CHL, we evaluated whether there were coherent long-term CHL dynamics from north to south and across lotic-lentic areas. We then estimated the degree to which these patterns were associated with climate variability (i.e., the Multivariate El Nino-Southern Oscillation Index), winter severity (freezing degree days), river discharge, or site-specific environmental variables (ice depth, snow depth, and nutrient concentrations). We found that winter CHL was typically highest in ice-free reaches and backwater lakes, occasionally exceeding summer values. We did not find highly synchronous CHL dynamics across the basin, but instead show that temporal trends were independent among river reaches and lotic-lentic areas of the river. Moreover, after accounting for these spatial dynamics, we found that CHL was most responsive to winter air temperature, being consistently higher in years with warmer winters across the basin. These results indicate that although productivity dynamics are highly dynamic within large river ecosystems, changes in the duration and severity of winter may uniformly increase wintertime productivity.
Southern sea otters (Enhydra lutris nereis) have recovered slowly from their near extinction a century ago, and their continued recovery has been challenged by multiple natural and anthropogenic factors. Development of an integrated population model (IPM) for southern sea otters has been identified as a management priority, to help in evaluating the relative impacts of known threats and guide best management options for species recovery. An IPM represents an analytical modeling framework where various types of data relevant to animal health, population trends, and survival can be evaluated collectively to project future population dynamics under different resource management scenarios. Here, we describe the development of a spatially explicit IPM for southern sea otters that is fit by using Bayesian methods to multiple datasets including a time series of range-wide survey counts, estimated survival rates of tagged animals from telemetry-based population studies, and cause-of-death data from comprehensive necropsies of beach-cast carcasses. The core of the model is a stage-structured matrix, in which survival rates for a given life history stage, year, and location are computed as the outcome of multiple ‘competing risks,’ or hazards, allowing for spatiotemporal variation in each hazard, density-dependence, and stochasticity. The parameterized IPM was used to (1) examine how age and sex-specific hazards vary over space and time, (2) gain insights into density-dependent variation in specific hazards, (3) assess population-level effects of known mortality hazards in the past and in future projections, and (4) evaluate the relative benefits of various potential management actions to address these hazards.
Our results indicated that different types of hazards have variable impacts at different life history stages of sea otters; for example, shark-bite mortality had a strong impact on mortality of subadult females but relatively low impacts on aged adult female survival, whereas End Lactation Syndrome showed just the opposite age-based pattern. There also was spatial and temporal variation in exposure to different hazards; for example, shark-bite mortality generally was highest at the north and south ends of the sea otter range, End Lactation Syndrome and cardiac disease were highest in the center part of the range, and harmful algal bloom intoxication and protozoal infection mortalities were highest around Morro Bay. The relative impacts of hazards depended on population density; for example, shark-bite mortality had the greatest effect on male survival when population abundance was low, but as densities increased the impacts of cardiac disease (for aged adults) and acanthocephalan peritonitis (for subadults) exceeded the effects of shark-bite mortality. Sensitivity analyses showed that modifying certain hazard rates can have substantial impacts on future population growth; for example, if the shark-bite hazard rate were to decrease by 20 percent, projected abundance after 50 years is predicted to be 18-percent higher, on average, than under baseline conditions. We used the IPM to evaluate the possible impacts of a potential management action: the reintroduction of sea otters to currently unoccupied parts of their historical range. We found that there were large increases in expected growth potential associated with reintroduction programs to various locations to the north and south of the currently occupied range, although a reintroduction to San Francisco Bay was projected to have the greatest potential impacts on future population growth.
The IPM for southern sea otters presented here provides resource managers with a useful tool for evaluating the impacts of specific hazards, forecasting future population dynamics and range expansion, and evaluating alternative management scenarios.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211076","programNote":"Wildlife Program","usgsCitation":"Tinker, M.T., Carswell, L.P., Tomoleoni, J.A., Hatfield, B.B., Harris, M.D., Miller, M.A., Moriarty, M.E., Johnson, C.K., Young, C., Henkel, L.A., Staedler, M.M., Miles, A.K., and Yee, J.L., 2021, An integrated population model for southern sea otters: U.S. Geological Survey Open-File Report 2021–1076, 50 p., https://doi.org/10.3133/ofr20211076.","productDescription":"vii, 50 p.","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-126237","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":387937,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1076/images"},{"id":387936,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.xml"},{"id":387935,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1076/ofr20211076.pdf","text":"Report","size":"6.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":387934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1076/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.23388671874999,\n 37.125286284966805\n ],\n [\n -121.59667968749999,\n 37.37015718405753\n ],\n [\n -121.55273437499999,\n 37.666429212090605\n ],\n [\n -122.1240234375,\n 38.61687046392973\n ],\n [\n -122.84912109375,\n 39.30029918615029\n ],\n [\n -123.37646484374999,\n 40.329795743702064\n ],\n [\n -123.37646484374999,\n 40.84706035607122\n ],\n [\n -123.3544921875,\n 41.705728515237524\n ],\n [\n -123.22265625000001,\n 42.00032514831621\n ],\n [\n -124.49707031249999,\n 42.01665183556825\n ],\n [\n -124.98046874999999,\n 40.94671366508002\n ],\n [\n -124.67285156250001,\n 39.90973623453719\n ],\n [\n -124.18945312500001,\n 38.92522904714054\n ],\n [\n -123.3544921875,\n 37.579412513438385\n ],\n [\n -122.9150390625,\n 37.23032838760387\n ],\n [\n -122.23388671874999,\n 37.125286284966805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819