Deeper flows through bedrock in mountain watersheds could be important, but lack of data to characterize bedrock properties limits understanding. To address data scarcity, we combine a previously published integrated hydrologic model of a snow‐dominated, headwater basin of the Colorado River with a new method for dating baseflow age using dissolved gas tracers SF6, CFC‐113, N2, and Ar. The original flow model predicts the majority of groundwater flow through shallow alluvium (<8 m) sitting on top of less permeable bedrock. The water moves too quickly and is unable to reproduce observed SF6 concentrations. To match gas data, bedrock permeability is increased to allow a larger fraction of deeper and older groundwater flow (median 112 m). The updated hydrologic model indicates interannual variability in baseflow age (3–12 years) is controlled by the volume of seasonal interflow and tightly coupled to snow accumulation and monsoon rain. Deeper groundwater flow remains stable (11.7 ± 0.7 years) as a function mean historical recharge to bedrock hydraulic conductivity (R/K). A sensitivity analysis suggests that increasing bedrock K effectively moves this alpine basin away from its original conceptualization of hyperlocalized groundwater flow (high R/K) with groundwater age insensitive to changes in water inputs. Instead, this basin is situated close to the precipitation threshold defining recharge controlled groundwater flow conditions (low R/K) in which groundwater age increases with small reductions in precipitation. Work stresses the need to explore alternative methods characterizing bedrock properties in mountain basins to better quantify deeper groundwater flow and predict their hydrologic response to change.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR028161","usgsCitation":"Carroll, R.W., Manning, A.H., Niswonger, R.G., Marchetti, D.W., and Williams, K.H., 2020, Baseflow age distributions and depth of active groundwater flow in a snow‐dominated mountain headwater basin: Water Resources Research, v. 56, no. 12, e2020WR028161, 19 p., https://doi.org/10.1029/2020WR028161.","productDescription":"e2020WR028161, 19 p.","ipdsId":"IP-115011","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":454804,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr028161","text":"Publisher Index Page"},{"id":384624,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.039306640625,\n 37.00255267215955\n ],\n [\n -106.138916015625,\n 37.00255267215955\n ],\n [\n -106.138916015625,\n 40.98819156349393\n ],\n [\n -109.039306640625,\n 40.98819156349393\n ],\n [\n -109.039306640625,\n 37.00255267215955\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Carroll, Rosemary W.H. 0000-0002-9302-8074","orcid":"https://orcid.org/0000-0002-9302-8074","contributorId":178784,"corporation":false,"usgs":false,"family":"Carroll","given":"Rosemary","email":"","middleInitial":"W.H.","affiliations":[],"preferred":false,"id":812816,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manning, Andrew H. 0000-0002-6404-1237 amanning@usgs.gov","orcid":"https://orcid.org/0000-0002-6404-1237","contributorId":1305,"corporation":false,"usgs":true,"family":"Manning","given":"Andrew","email":"amanning@usgs.gov","middleInitial":"H.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812817,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":197892,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard","email":"rniswon@usgs.gov","middleInitial":"G.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":812818,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marchetti, David W 0000-0002-1246-0798","orcid":"https://orcid.org/0000-0002-1246-0798","contributorId":255716,"corporation":false,"usgs":false,"family":"Marchetti","given":"David","email":"","middleInitial":"W","affiliations":[{"id":38118,"text":"Western Colorado University","active":true,"usgs":false}],"preferred":false,"id":812819,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Kenneth H. 0000-0002-3568-1155","orcid":"https://orcid.org/0000-0002-3568-1155","contributorId":176791,"corporation":false,"usgs":false,"family":"Williams","given":"Kenneth","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":812820,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216405,"text":"ofr20201123 - 2020 - Field comparison of five in situ turbidity sensors","interactions":[],"lastModifiedDate":"2020-11-19T15:03:44.391711","indexId":"ofr20201123","displayToPublicDate":"2020-11-17T10:45:04","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1123","displayTitle":"Field Comparison of Five In Situ Turbidity Sensors","title":"Field comparison of five in situ turbidity sensors","docAbstract":"Five commercially available turbidity sensors were field tested by the U.S. Geological Survey Hydrologic Instrumentation Facility for accuracy and data comparability. The tested sensors were the Xylem EXO (EXO), the Hach Solitax sc (Solitax), the In Situ Aqua TROLL sensor installed onto a TROLL 600 sonde (TROLL 600), the Campbell Scientific OBS501 (OBS501), and the Observator ANALITE NEP–5000 (NEP–5000). The sensors were deployed at Pearl River at National Space Technology Laboratories Station, Mississippi (U.S. Geological Survey site 02492620), and were serviced weekly. In addition to the five in situ turbidity sensors, corresponding discrete samples were collected and analyzed during the evaluation on a calibrated Hach 2100N benchtop turbidimeter. The OBS501 malfunctioned early in the evaluation and eventually failed, resulting in few data from the sensor.
During this study, the four remaining sensors (minus the OBS501) changed similarly throughout the field test; however, sensor data from the EXO consistently demonstrated lower results than the Solitax, TROLL 600, and NEP–5000, possibly because of the variation in raw signal processing among manufacturers. Results from a single factor analysis of variance test and a Tukey Honestly Significant Difference test verified the low bias observed in the EXO data and indicated there was a significant difference between the EXO data and data from the Solitax, TROLL 600, and NEP–5000 but an insignificant difference among the data when the Solitax, TROLL 600, and NEP–5000 were compared to each other.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201123","usgsCitation":"Snazelle, T.T., 2020, Field comparison of five in situ turbidity sensors: U.S. Geological Survey Open-File Report 2020–1123, 15 p., https://doi.org/10.3133/ofr20201123.","productDescription":"Report: iv, 15 p.; Data Release; Dataset","numberOfPages":"24","onlineOnly":"Y","ipdsId":"IP-103944","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":380549,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1123/ofr20201123.pdf","text":"Report","size":"3.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1123"},{"id":380548,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1123/coverthb.jpg"},{"id":380550,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KDERG6","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Turbidity data collected by five in situ sensors at USGS site 02492620 Pearl River at NSTL station, Mississippi, from November 2017 to January 2018"},{"id":380551,"rank":4,"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"}],"country":"United States","state":"Mississippi","otherGeospatial":"National Space Technology Laboratories Station","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.0274658203125,\n 30.211608223816906\n ],\n [\n -89.28314208984375,\n 30.211608223816906\n ],\n [\n -89.28314208984375,\n 30.41078179084589\n ],\n [\n -90.0274658203125,\n 30.41078179084589\n ],\n [\n -90.0274658203125,\n 30.211608223816906\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"U.S. Geological Survey
Water Mission Area
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey monitored dissolved nitrate plus nitrite as nitrogen (N) and dissolved organic carbon (DOC) concentrations and calculated loads of these constituents at the W.P. Franklin Lock and Dam (S-79) from April 2014 to December 2017. Flows from Lake Okeechobee controlled by S-77, S-78 and S-79 affect water quality in the downstream Caloosahatchee River Estuary, where increased nutrients and dissolved organic matter are of concern. Numerous algal blooms have occurred in the Caloosahatchee River and downstream estuaries in recent years (2005–18) and are often attributed to eutrophication. Dissolved nitrate plus nitrite as N (hereafter, referred to as nitrate) data were collected at 15-minute intervals using a submersible ultraviolet optical nitrate sensor. The instrument data were corrected for interferences, as determined by the relation between instrument measurements and 20 concurrent laboratory values. A surrogate model, based on 36 concurrent measurements of DOC, fluorescence of chromophoric dissolved organic matter, and specific conductance, was developed to calculate DOC at 15-minute intervals.
Mean and median calculated nitrate concentrations for the study period (2014–17) were both 0.21 milligram per liter (mg/L). Monthly mean nitrate concentrations ranged from 0.04 mg/L in April 2017 to 0.48 mg/L in November 2015. Monthly mean nitrate concentrations and the proportion of water that was attributed to Lake Okeechobee discharge, released through S-79, were weakly correlated and indicate that the nitrate concentrations typically decreased as the percentage of water released from the lake increased. Annual nitrate loads were 278 metric tons in 2015, 782 metric tons in 2016, and 525 metric tons in 2017. Monthly mean nitrate loads ranged from 1.2 metric tons in April 2017 to 171.3 metric tons in February 2016. Nitrate loads increased linearly with an increase in flow and typically increased during the wet season, May to October. Monthly loads of nitrate were strongly correlated with flow at S-77 and S-79.
Mean and median calculated DOC concentrations for the study period were 18.3 mg/L and 18.9 mg/L, respectively. Monthly mean DOC concentrations ranged from 12.6 mg/L in May 2017 to 21.5 mg/L in September 2015. Generally, DOC concentrations were lower during the dry season months (November to April) and higher during the wet season months. Monthly mean DOC concentrations were moderately correlated with monthly mean flow volumes at S-79. There was a strong correlation between monthly mean DOC concentrations and the proportion of water released at S-79 that can be attributed directly to Lake Okeechobee, indicating that contributions between Moore Haven Lock and Dam (S-77) and S-79 have a higher DOC concentration than water released from Lake Okeechobee. Monthly mean nitrate concentrations and monthly mean DOC concentrations were strongly correlated. Annual loads of DOC were 23,960 metric tons in 2015 and 65,610 metric tons in 2016 (2014 and 2017 data were incomplete). Monthly loads of DOC ranged from 284 metric tons in May 2017 to 15,122 metric tons in September 2017, the latter corresponding to the effects from Hurricane Irma. Monthly loads of DOC were strongly correlated with flow at S-77 and S-79.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201094","collaboration":"USGS Greater Everglades Priority Ecosystem Science Program","usgsCitation":"Booth, A., 2020, Measured and calculated nitrate and dissolved organic carbon concentrations and loads at the W.P. Franklin Lock and Dam, S-79, south Florida, 2014-17: U.S. Geological Survey Open-File Report 2020-1094, 37 p., https://doi.org/10.3133/ofr20201094.","productDescription":"Report: vi, 37 p.; Data Release","numberOfPages":"37","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-091619","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":380478,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1094/coverthb.jpg"},{"id":380479,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1094/ofr20201094.pdf","text":"Report","size":"3.50 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1094"},{"id":380480,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V4ZGWU","text":"USGS data release","linkHelpText":"Calculated carbon concentrations, Franklin Lock and Dam (S-79) southern Florida, 2014-2017"}],"country":"United States","state":"Florida","otherGeospatial":"W.P. Franklin Lock and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.7437744140625,\n 26.701452590314368\n ],\n [\n -81.47735595703125,\n 26.701452590314368\n ],\n [\n -81.47735595703125,\n 26.74683674289727\n ],\n [\n -81.7437744140625,\n 26.74683674289727\n ],\n [\n -81.7437744140625,\n 26.701452590314368\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
Many canyon‐bound rivers have been dammed and downstream flow and water temperatures modified. Climate change is expected to cause lower storage in reservoirs and warmer release temperatures, which may further alter downstream flow and thermal regimes. To anticipate potential future changes, we first need to understand the dominant heat transfer mechanisms in canyon‐bound river systems. Towards this end, we adapt a dynamic process‐based river routing and temperature model to account for complex shading and radiation characteristics found in canyon‐bound rivers. We apply the model to a 362 km segment of the Colorado River in Grand Canyon National Park, USA to simulate temperature over an 18‐year period. Extensive temperature and flow datasets from within the canyon were used to assess model performance. At the most downstream gaging location, root mean square errors of hourly flow routing and temperature predictions were 11.5 m3/s and 0.93 °C, respectively. We found that heat fluxes controlling temperatures were highly variable over space and time, primarily due to shortwave radiation dynamics and hydropeaking flow conditions. Additionally, the large differences between air and water temperature during summer periods resulted in high sensible and latent heat fluxes. Sensitivity analyses indicate that reservoir release temperatures are most influential above the RM88 gage (141 kilometers below Glen Canyon Dam), while a combination of discharge, shortwave radiation, and air temperature become more important farther downstream. This study illustrates the importance of understanding the spatial and temporal variability of topographic shading when predicting water temperatures in canyon‐bound rivers.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027566","usgsCitation":"Mihalevich, B.A., Neilson, B., Buahin, C.A., Yackulic, C., and Schmidt, J.C., 2020, Water temperature controls for regulated canyon-bound rivers: Water Resources Research, v. 56, e2020WR027566, 24 p., https://doi.org/10.1029/2020WR027566.","productDescription":"e2020WR027566, 24 p.","ipdsId":"IP-117871","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":381103,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.961181640625,\n 35.639441068973944\n ],\n [\n -111.29150390625,\n 35.639441068973944\n ],\n [\n -111.29150390625,\n 36.923547681089296\n ],\n [\n -113.961181640625,\n 36.923547681089296\n ],\n [\n -113.961181640625,\n 35.639441068973944\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","noUsgsAuthors":false,"publicationDate":"2020-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Mihalevich, Bryce A.","contributorId":245512,"corporation":false,"usgs":false,"family":"Mihalevich","given":"Bryce","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":806340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Neilson, Bethany","contributorId":178798,"corporation":false,"usgs":false,"family":"Neilson","given":"Bethany","affiliations":[],"preferred":false,"id":806341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buahin, Caleb A.","contributorId":245514,"corporation":false,"usgs":false,"family":"Buahin","given":"Caleb","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":806342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":806343,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schmidt, John C.","contributorId":207751,"corporation":false,"usgs":false,"family":"Schmidt","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":37627,"text":"Department of Watershed Sciences, Utah State University, Logan, UT, USA","active":true,"usgs":false}],"preferred":false,"id":806344,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230646,"text":"70230646 - 2020 - Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring","interactions":[],"lastModifiedDate":"2022-04-20T11:49:11.923065","indexId":"70230646","displayToPublicDate":"2020-11-16T06:42:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10567,"text":"Proceedings of the National Academy of Sciences of the USA","active":true,"publicationSubtype":{"id":10}},"title":"Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring","docAbstract":"Considerable effort and resources have been placed into conservation programs designed to reduce or alleviate negative environmental effects of crop production and into evaluation of the benefits of these programs. Wetlands are an important source of ecosystem services, but modeling wetland plants is an emerging science. To date, wetland plant growth has not been explicitly accounted for in ecosystem service models that quantify conservation program effects. As part of an effort to more accurately simulate wetland plants within process-based models, we expanded upon plant growth data collected in an earlier effort with additional sampling at two of four previously sampled areas, and included a fifth sampling site. We then used data from the five sites spanning five years as wetland plant parameters at both the species and functional group levels for the Agricultural Land Management Alternative with Numerical Assessment Criteria (ALMANAC) model. In addition to individual species, modelers are interested in functional groups representing a collection of species because it is unrealistic to model every species occurring in an ecosystem. ALMANAC simulations were completed at three sites for both individual wetland plant species and functional groups. At each site, simulated plant yields were within 1 Mg ha–1 (±7%) of measured values (r2 = 0.99). Multisite species simulated yields were within 37% of measured values (r2 = 0.95). Functional groups performed as well as individual species simulations. Functional group simulated yields were within 1 Mg ha–1 (±5%) of measured yields. Plant growth is a major component of these wetland ecosystems, and ALMANAC verified wetland plant parameters support more accurate assessments of conservation programs and practices on the influence of wetland ecosystems embedded within agricultural fields. The improved plant parameters we provide here will be transferred to other process-based models that focus on other ecosystem components such as soil and water effects, facilitating wetland evaluations across the United States and elsewhere.
Across the western United States, many ungulate herds must migrate seasonally to access resources and avoid harsh winter conditions. Because these migration paths cover vast landscapes (in other words migration distances up to 150 miles [241 kilometers]), they are increasingly threatened by roads, fencing, subdivisions, and other development. Over the last decade, many new tracking studies have been conducted on migratory herds, and analytical methods have been developed that allow for population-level corridors and stopovers to be mapped and prioritized. In 2018, the U.S. Geological Survey assembled a Corridor Mapping Team to provide technical assistance to western states working to map bison, elk, moose, mule deer, and pronghorn migrations using existing Global Positioning System data. Led by the Wyoming Cooperative Fish and Wildlife Research Unit, the team consists of federal scientists, university researchers, and biologists and analysts from participating state agencies.
In its first year, the team has worked to develop standardized analytical and computational methods and a workflow applicable to datasets typically collected by state agencies. In 2019, the team completed analyses necessary to map corridors, stopovers, routes and winter ranges in Arizona, Idaho, Nevada, Utah, and Wyoming. A total of 26 corridors, 16 migration routes, 25 stopovers, and 9 winter ranges were mapped across these states and are included in this report. This report and associated data release provide the means for the habitats required for migration to be taken into account by state and federal transportation officials, land and wildlife managers, planners, and other conservationists working to maintain big-game migration in the western states.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205101","issn":"2328-031X; 2328-0328","isbn":"978-1-4113-4379-5","usgsCitation":"Kauffman, M.J., Copeland, H.E., Berg, J., Bergen, S., Cole, E., Cuzzocreo, M., Dewey, S., Fattebert, J., Gagnon, Gelzer, E., Geremia, C., Graves, T., Hersey, K., Hurley, M., Kaiser, J., Meacham, J., Merkle, J., Middleton, A., Nuñez, T., Oates, B., Olson, D., Olson, L., Sawyer, H., Schroeder, C., Sprague, S., Steingisser, A., Thonhoff, M., 2020, Ungulate migrations of the western United States, Volume 1 (ver. 1.1, December 2023): U.S. Geological Survey Scientific Investigations Report 2020–5101, 119 p., https://doi.org/10.3133/sir20205101.","productDescription":"Report: xiv, 119 p.; Data Release","onlineOnly":"N","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":482390,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245006","text":"Ungulate Migrations of the Western United States, Volume 4"},{"id":423443,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2020/5101/versionHist.txt","size":"1.0 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2020-5101 version history"},{"id":380307,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5101/sir20205101.pdf","text":"Report","size":"34.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5101"},{"id":482391,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20245111","text":"Ungulate Migrations of the Western United States, Volume 5"},{"id":482389,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225088","text":"Ungulate Migrations of the Western United States, Volume 3"},{"id":482388,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20225008","text":"Ungulate Migrations of the Western United States, Volume 2"},{"id":380308,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O2YM6I","text":"USGS data release","description":"USGS data release","linkHelpText":"Ungulate Migrations of the Western United States, Volume 1"},{"id":380306,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5101/coverthb2.jpg"}],"country":"Canada, Mexico, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.03515625,\n 22.51255695405145\n ],\n [\n -95.361328125,\n 22.51255695405145\n ],\n [\n -95.361328125,\n 53.225768435790194\n ],\n [\n -126.03515625,\n 53.225768435790194\n ],\n [\n -126.03515625,\n 22.51255695405145\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Verion 1.1: December 203; Version 1.0: November 2020","contact":"Associate Director, Ecosystems Mission Area
U.S. Geological Survey
Mail Stop 300, 12201 Sunrise Valley Drive
Reston, VA 20192
Groundwater is a critical resource in the Grand Canyon region, supplying nearly all water needs for residents and millions of visitors. Additionally, groundwater discharging at hundreds of spring locations in and near Grand Canyon supports important ecosystems in this mostly arid environment. The security of groundwater supplies is of critical importance for both people and ecosystems in the region and the potential for changes to groundwater systems from projected climate change is a cause for concern. In this study, we analyze recent historical and projected precipitation and temperature data for the Grand Canyon region. Projected climate scenarios are then used in Soil Water Balance groundwater infiltration simulations to understand the state-of-the-science on projected changes to groundwater resources in the area. Historical climate data from 1896 through 2019 indicate multi-decadal cyclical patterns in both precipitation and temperature for most of the time period. Since the 1970s, however, a significant rising trend in temperature is observed in the area. All 10-year periods since 1993 are characterized by both below average precipitation and above average temperature. Downscaled and bias-corrected precipitation and temperature output from 97 CMIP5 global climate models for the water-year 2020–2099 time period indicate projected precipitation patterns similar to recent historical (water-year 1951–2015) data. Projected temperature for the Grand Canyon area, however, is expected to rise by as much as 3.4 °C by the end of the century, relative to the recent historical average. Integrating the effects of projected precipitation and temperature changes on groundwater infiltration, simulation results indicate that > 76% of future decades will experience average potential groundwater infiltration less than that of the recent historical period.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-020-76743-6","usgsCitation":"Tillman, F.D., Gangopadhyay, S., and Pruitt, T., 2020, Recent and projected precipitation and temperature changes in the Grand Canyon area with implications for groundwater resources: Scientific Reports, v. 10, 19740, 11 p., https://doi.org/10.1038/s41598-020-76743-6.","productDescription":"19740, 11 p.","ipdsId":"IP-117188","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":454837,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-76743-6","text":"Publisher Index Page"},{"id":381030,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Utah","otherGeospatial":"Colorado Plateau, Grand Canyon, Kaibab Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.97216796875,\n 35.60371874069731\n ],\n [\n -109.53369140625,\n 35.60371874069731\n ],\n [\n -109.53369140625,\n 38.35888785866677\n ],\n [\n -113.97216796875,\n 38.35888785866677\n ],\n [\n -113.97216796875,\n 35.60371874069731\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-11-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806235,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":806236,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pruitt, Tom 0000-0002-3543-1324","orcid":"https://orcid.org/0000-0002-3543-1324","contributorId":173440,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","email":"","affiliations":[{"id":27228,"text":"Reclamation","active":true,"usgs":false}],"preferred":false,"id":806237,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70248052,"text":"70248052 - 2020 - Using remote sensing products to predict recovery of vegetation across space and time following energy development","interactions":[],"lastModifiedDate":"2024-05-16T15:37:39.154563","indexId":"70248052","displayToPublicDate":"2020-11-12T09:16:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Using remote sensing products to predict recovery of vegetation across space and time following energy development","docAbstract":"Using localized studies to understand how ecosystems recover can create uncertainty in recovery predictions across landscapes. 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","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2019.105872","usgsCitation":"Monroe, A., Aldridge, C.L., O’Donnell, M.S., Manier, D., Homer, C., and Anderson, P.J., 2020, Using remote sensing products to predict recovery of vegetation across space and time following energy development: Ecological Indicators, v. 110, 105872, 15 p.; 2 Data Releases, https://doi.org/10.1016/j.ecolind.2019.105872.","productDescription":"105872, 15 p.; 2 Data Releases","ipdsId":"IP-101503","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":454840,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2019.105872","text":"Publisher Index Page"},{"id":436724,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XV8GH7","text":"USGS data 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Jersey\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
Understanding recent historical and projected trends in precipitation and temperature in the Colorado River Basin, and estimating what the projected changes in these climate parameters may mean for groundwater resources in the region, is important for water managers and policymakers to sustainably manage water resources in the basin. Historical (1896–2019) precipitation and temperature data for the upper and lower Colorado River Basins were analyzed to better understand recent trends in climate data that may affect groundwater resources in the area. Historical data indicate multidecadal-scale cyclical patterns in precipitation in both the upper and lower basins. Although upper basin precipitation had no statistical trend over the recent historical period, the lower basin had a weak negative trend over this period. Multidecadal-scale cyclical patterns in temperature also are observed in historical climate data in both the upper and lower basins, at least until the early 1970s. Beginning at that time, both the upper and lower basins experienced strong, monotonic positive trends in temperature. Basic principles of hydrology indicate that periods of decreasing precipitation as well as increasing temperature would have a negative effect, that is, reduction in groundwater infiltration and hence, reduced recharge of aquifer systems.
Projected climate data from 97 Coupled Model Intercomparison Project phase 5 (CMIP5) ensemble members across the full range of Representative Concentration Pathway (RCPs) from water years 1951 through 2099 were evaluated to understand what current global climate models are projecting about future conditions in the Colorado River Basin, and what this might mean for groundwater systems in the region. Precipitation in the upper basin is projected to increase throughout the rest of the century, rising to 6 percent above the 1951–2015 historical period by mid-century and to 9 percent above the historical period by the end of the century. Temperature in the upper basin also is projected to be above the recent historical median throughout the rest of the century, with steady warming in decadal average temperatures expected until the last quarter of this century. In contrast to projected precipitation in the upper basin, precipitation in the lower basin is projected to be the same as, or slightly less than, the historical period throughout most of the rest of this century. Like projected temperature in the upper basin, temperature in the lower basin also is projected to be above the recent historical median throughout the rest of the century. Comparing median projections for all future decades with median results from all historical decades, future precipitation is expected to be greater than that of the past in the upper basin, though no significant difference is projected for precipitation in the lower basin. Significant increases (p-value<0.05) are expected in temperature in both the upper and lower basins.
To estimate the effects of projected precipitation and temperature on groundwater systems in the region, results from the 97 member CMIP5 climate projection ensemble were used as input in a Soil-Water Balance (SWB) groundwater infiltration model for the Colorado River Basin. SWB simulation results indicate that the upper Colorado River Basin is expected to experience decades of above-historical-average groundwater infiltration through the end of the century. For the lower Colorado River Basin, simulated groundwater infiltration is projected to be consistently less than the recent (1951–2015) historical period for most of the remaining century. A comparison of the distribution of all median simulated groundwater infiltration results between recent historical and future periods indicates projected groundwater infiltration in the upper basin is significantly (p-value<0.05) greater over the combined 2020–2099 future period than the recent (1951–2015) historical period. Moreover, in 41 of 71 (58 percent) possible future decades in this century, groundwater infiltration is projected to be greater than the 75th percentile of historical simulated groundwater infiltration. Projected groundwater infiltration in the lower Colorado River Basin across all future decades is significantly less than in the historical period. Of the 71 future decades in the century, projected groundwater infiltration in the lower basin is expected to be less than the 25th percentile of historical infiltration in 55 (77 percent) of the 10-year periods. Important differences in projected precipitation between the upper (increasing precipitation) and lower (decreasing precipitation) basins largely drive the different responses of simulated groundwater infiltration in the upper (increasing infiltration) and lower (decreasing infiltration) basins. It will be useful to revisit projections in groundwater infiltration in the Colorado River Basin when more up-to-date projections of precipitation become available from the next Coupled Model Intercomparison Project phases or by using climate input developments through Regional Climate Modeling efforts and stochastic weather generators.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205107","collaboration":"Prepared in cooperation with Bureau of Reclamation","usgsCitation":"Tillman, F.D., Gangopadhyay, S., and Pruitt, T., 2020, Trends in recent historical and projected climate data for the Colorado River Basin and potential effects on groundwater availability: U.S. Geological Survey Scientific Investigations Report 2020–5107, 24 p., https://doi.org/10.3133/sir20205107.","productDescription":"Report: vii, 24 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-117191","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":380358,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5107/coverthb.jpg"},{"id":380361,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7ST7MX7","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-water balance groundwater recharge model results for the Upper Colorado River Basin (ver. 2.0, April 2017)"},{"id":380359,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5107/sir20205107.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5107"},{"id":380360,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VLU0O6","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-water balance groundwater infiltration model results for the Lower Colorado River Basin"}],"country":"Mexico, United States","state":"Arizona, California, Colorado, Nevada, New Mexico, Utah, Wyoming","otherGeospatial":"Colorado River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.5,\n 30.088107753367257\n ],\n [\n -108.984375,\n 30.221101852485987\n ],\n [\n -108.21533203125,\n 31.39115752282472\n ],\n [\n -107.16064453125,\n 35.08395557927643\n ],\n [\n -105.35888671875,\n 36.12012758978146\n ],\n [\n -104.6337890625,\n 36.40359962073253\n ],\n [\n -104.96337890625,\n 37.735969208590504\n ],\n [\n -105.3369140625,\n 38.58252615935333\n ],\n [\n -104.83154296875,\n 39.027718840211605\n ],\n [\n -104.87548828125,\n 41.19518982948959\n ],\n [\n -105.05126953124999,\n 41.934976500546604\n ],\n [\n -106.3916015625,\n 43.16512263158296\n ],\n [\n -110.91796875,\n 43.100982876188546\n ],\n [\n -111.09374999999999,\n 40.64730356252251\n ],\n [\n -111.533203125,\n 39.07890809706475\n ],\n [\n -113.04931640625,\n 37.405073750176925\n ],\n [\n -114.14794921875,\n 37.10776507118514\n ],\n [\n -114.54345703125,\n 37.54457732085582\n ],\n [\n -115.46630859375,\n 38.839707613545144\n ],\n [\n -116.806640625,\n 38.805470223177466\n ],\n [\n -116.19140625,\n 37.3002752813443\n ],\n [\n -115.3564453125,\n 36.421282443649496\n ],\n [\n -116.1474609375,\n 35.55010533588552\n ],\n [\n -115.4443359375,\n 32.491230287947594\n ],\n [\n -114.93896484374999,\n 31.3348710339506\n ],\n [\n -114.2578125,\n 31.57853542647338\n ],\n [\n -113.291015625,\n 31.316101383495624\n ],\n [\n -113.04931640625,\n 30.732392734006083\n ],\n [\n -112.7197265625,\n 30.012030680358613\n ],\n [\n -112.47802734375,\n 30.012030680358613\n ],\n [\n -112.5,\n 30.088107753367257\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
The U.S. Geological Survey (USGS), a bureau within the U.S. Department of the Interior, has valued and used a scientific peer review and approval process since its creation in 1879. Bureau approval, formerly called Director’s approval, has been described in several USGS documents since 1900, and peer review has been codified in policy since 1959. Peer review of USGS manuscripts is intended to ensure the accuracy of data, the scientific validity of interpretations, and the consideration of alternative interpretations. This rigorous quality assurance process is considered deliberative because of the iterative exchange of ideas and opinions among the involved parties.
Peer review practices differed between USGS organizational units until implementation of USGS Fundamental Science Practices (FSP) in 2006, which formalized Bureau-wide science practices, including peer review and approval, for all Bureau scientific information products released to the public or other Federal agencies. FSP policies also address review and approval requirements pertaining to the release of USGS-funded data and software and endorse quality-control standards for USGS laboratories. Bureau approval signifies the scientific excellence of information products, validates and ensures that all necessary reviews have been conducted, and confirms that information products meet USGS science quality standards and have the full backing of the Bureau. The extent, scope, and history of the peer review and approval process within the USGS are documented herein, so future USGS scientists and the public understand how consistent approaches in developing, reviewing, and publishing USGS scientific information have been and continue to be essential in maintaining the reputation of the Bureau for reliable and impartial Earth science research and data collection.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203050","usgsCitation":"Kirk, K.G., Reid, C.L., Cooper, S.C., 2020, History of U.S. Geological Survey scientific peer review and approval, 1879–2019: U.S. Geological Survey Fact Sheet 2020–3050, 4 p., https://doi.org/10.3133/fs20203050","productDescription":"4 p.","ipdsId":"IP-110012","costCenters":[{"id":5066,"text":"Office of the Director USGS","active":true,"usgs":true}],"links":[{"id":380355,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3050/fs20203050.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3050"},{"id":380354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3050/coverthb.jpg"}],"contact":"Contacts, Office of Scientific Quality and Integrity
This study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark beetle and wildfire disturbances. Annual summary values of simulated snowpack properties [snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation] were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade−1, and downward trends in winter precipitation were −52 mm decade−1. Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20% decade−1, peak SWE decreased by 14% decade−1, and total snowmelt quantity decreased by 13% decade−1. These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-20-0077.1","usgsCitation":"Sexstone, G., Penn, C.A., Liston, G., Gleason, K., Moeser, C.D., and Clow, D.W., 2020, Spatial variability in seasonal snowpack trends across the Rio Grande headwaters (1984 - 2017): Journal of Hydrometeorology, v. 21, no. 11, p. 2713-2733, https://doi.org/10.1175/JHM-D-20-0077.1.","productDescription":"21 p.","startPage":"2713","endPage":"2733","ipdsId":"IP-114071","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":454846,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jhm-d-20-0077.1","text":"Publisher Index Page"},{"id":436725,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q8PYX1","text":"USGS data release","linkHelpText":"SnowModel simulations and supporting observations for the Rio Grande Headwaters, southwestern Colorado, United States, 1984 - 2017"},{"id":380501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rio Grande headwaters","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.65777587890625,\n 37.267495764381856\n ],\n [\n -105.83404541015625,\n 37.267495764381856\n ],\n [\n -105.83404541015625,\n 37.91603433975963\n ],\n [\n -107.65777587890625,\n 37.91603433975963\n ],\n [\n -107.65777587890625,\n 37.267495764381856\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sexstone, Graham A. 0000-0001-8913-0546","orcid":"https://orcid.org/0000-0001-8913-0546","contributorId":203850,"corporation":false,"usgs":true,"family":"Sexstone","given":"Graham A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Penn, Colin A. 0000-0002-5195-2744","orcid":"https://orcid.org/0000-0002-5195-2744","contributorId":203851,"corporation":false,"usgs":true,"family":"Penn","given":"Colin","email":"","middleInitial":"A.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liston, Glen","contributorId":244889,"corporation":false,"usgs":false,"family":"Liston","given":"Glen","affiliations":[{"id":36729,"text":"Cooperative Institute for Research in the Atmosphere","active":true,"usgs":false}],"preferred":false,"id":804853,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gleason, Kelly","contributorId":244890,"corporation":false,"usgs":false,"family":"Gleason","given":"Kelly","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":804854,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804855,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clow, David W. 0000-0001-6183-4824 dwclow@usgs.gov","orcid":"https://orcid.org/0000-0001-6183-4824","contributorId":1671,"corporation":false,"usgs":true,"family":"Clow","given":"David","email":"dwclow@usgs.gov","middleInitial":"W.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":804856,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70248354,"text":"70248354 - 2020 - Trihalomethane precursors: Land use hot spots, persistence during transport, and management options","interactions":[],"lastModifiedDate":"2023-09-08T13:03:29.070379","indexId":"70248354","displayToPublicDate":"2020-11-10T07:54:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Trihalomethane precursors: Land use hot spots, persistence during transport, and management options","docAbstract":"To meet drinking water regulations, rather than investing in costly treatment plant operations, managers can look for ways to improve source water quality; this requires understanding watershed sources and fates of constituents of concern. Trihalomethanes (THMs) are one of the major classes of regulated disinfection byproducts, formed when a specific fraction of the organic carbon pool—referred to as THM precursors—reacts with chorine and/or bromine during treatment. Understanding the source, fate, timing and duration of the organic compounds that react to form THMs will allow identification of targeted and effective management actions. In this study we evaluated THM precursor contributions from multiple land use categories and hydrologic contexts, including novel data for urban land uses that demonstrate strong potential to release water with high THM formation potential (THMFP; median 618 μg L−1): greater than storm runoff integrated across a mixed-use (1/3 natural, 2/3 agricultural) watershed (median 460 μg L−1), irrigation runoff from agricultural systems (357 μg L−1), or runoff from a natural forested (median 123 μg L−1) and shrubland/grassland (median 259 μg L−1) watersheds. While individual storm events released high THM precursor concentrations over short periods, dry season agricultural irrigation as well as urban landscapes have the potential to release water high in THM precursors for several months. Experimental bioassays and sampling along 333 miles of the California Aqueduct confirmed bioavailability and photooxidation potential of less than 10% for THM precursors, suggesting that rivers with residence times of days to weeks may act as THM precursor conduits, shuttling THM precursors from hundreds of miles away to drinking water intakes with minimal degradation. This finding has considerable implications for water managers, who may therefore consider THM precursor management strategies that target even sources located far upstream.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.140571","usgsCitation":"Eckard, R.S., Bergamaschi, B.A., Pellerin, B., Kraus, T.E., and Hernes, P.J., 2020, Trihalomethane precursors: Land use hot spots, persistence during transport, and management options: Science of the Total Environment, v. 742, 140571, 9 p., https://doi.org/10.1016/j.scitotenv.2020.140571.","productDescription":"140571, 9 p.","ipdsId":"IP-119566","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":420660,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River, Willow Slough Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.17412069648752,\n 38.72735441792287\n ],\n [\n -122.17412069648752,\n 38.49482301341115\n ],\n [\n -121.67887201941832,\n 38.49482301341115\n ],\n [\n -121.67887201941832,\n 38.72735441792287\n ],\n [\n -122.17412069648752,\n 38.72735441792287\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"742","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eckard, Robert S.","contributorId":88863,"corporation":false,"usgs":true,"family":"Eckard","given":"Robert","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":882660,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":882661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pellerin, Brian A. 0000-0003-3712-7884","orcid":"https://orcid.org/0000-0003-3712-7884","contributorId":204324,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian A.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":882662,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kraus, Tamara E. C. 0000-0002-5187-8644 tkraus@usgs.gov","orcid":"https://orcid.org/0000-0002-5187-8644","contributorId":147560,"corporation":false,"usgs":true,"family":"Kraus","given":"Tamara","email":"tkraus@usgs.gov","middleInitial":"E. C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":882663,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hernes, Peter J.","contributorId":139730,"corporation":false,"usgs":false,"family":"Hernes","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":12894,"text":"Department of Land, Air, and Water Resources, University of California, One Shields Avenue, Davis, CA, 95616, USA","active":true,"usgs":false}],"preferred":false,"id":882664,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216216,"text":"70216216 - 2020 - Shorebird reproductive response to exceptionally early and late springs varies across sites in Arctic Alaska","interactions":[],"lastModifiedDate":"2020-11-10T12:45:10.570893","indexId":"70216216","displayToPublicDate":"2020-11-09T06:40:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Shorebird reproductive response to exceptionally early and late springs varies across sites in Arctic Alaska","docAbstract":"While increases in overall temperatures are widely reported in the Arctic, large inter-annual variation in spring weather, with extreme early and late conditions, is also occurring. Using data collected from three sites in Arctic Alaska, we explored how shorebird breeding density, nest initiation, nest synchrony, nest survival, and phenological mismatch varied between two exceptionally early (2015 and 2016) and late (2017 and 2018) springs. We assessed these differences in the context of long-term data from each site and whether species exhibited conservative or opportunistic reproductive strategies. Conservative shorebirds typically display nest-site fidelity and territoriality, consistent population densities, relatively even individual spacing, and monogamous mating systems with bi-parental incubation. In contrast, opportunistic shorebirds display the opposite traits, and a polygamous mating system with uniparental incubation. In this study, we evaluated 2,239 nests from 13 shorebird species, 2015–2018, and found that shorebirds of both strategies bred earlier and in higher numbers in early, warm springs relative to historic levels (based on 3,789 nests, 2005–2014); opposite trends were observed in late springs. In early springs, nests were initiated less synchronously than in late springs. Nest survival was unrelated to spring type, but was greater in earlier laid nests overall. Invertebrate food resources emerged earlier in early springs, resulting in a greater temporal asynchrony between invertebrate emergence and chick hatching in early than late springs. However, invertebrate abundance was quite variable among sites and years regardless of spring type. Overall, our results were generally consistent with predicted relationships between spring conditions and reproductive parameters. However, we detected differences among sites that could not be explained by other ecological factors (e.g., predators or alternative prey). Differences in shorebird community composition and other subtler methodological/ecological differences among sites highlight the difficulty of understanding the complex nature of these ecological systems and the importance of evaluating questions at multiple sites across multiple years. Our study demonstrates that shorebirds exhibit a high degree of behavioral flexibility in response to variable Arctic conditions, but whether this flexibility is enough to allow them to optimally track changing environmental conditions or if evolutionary adjustments will be necessary is unknown.
This report provides the best estimates of riparian area evapotranspiration (ET) on the rivers and streams of the Navajo Nation by (1) quantifying the natural riparian vegetation water use within the Little Colorado River watershed using a literature search for comparable riparian ET estimates, and (2) in conjunction with the given area of stream-side plant cover on the Navajo Nation, provides the best estimate of consumptive use, the total water requirement (in acre-feet). This report includes riparian water use information only from the literature for riparian areas that are in similar dryland ecosystems in the Southwest, and not specific to the perennial tributaries and springs on the Navajo Nation within the Little Colorado River watershed. The report also includes any information found regarding the location of Navajo Nation weather station variables, such as where we can derive required data inputs from the Navajo Nation to estimate actual ET rates (in millimeters per day or millimeters per year). We provide estimates of annual riparian plant water use and calculations that include reference ET (potential ET or ETo), precipitation (in millimeters), and the calculations of consumptive water requirements of riparian vegetation. We cite our data sources and provide references used to determine the consumptive water requirement (acre-feet).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201129","collaboration":"Prepared in cooperation with Fred Phillips Consulting","usgsCitation":"Nagler, P.L., 2020, Literature reviewed estimates of riparian consumptive water use in the drylands of Northeast Arizona, USA: U.S. Geological Survey Open-File Report 2020–1129, 9 p., https://doi.org/10.3133/ofr20201129.","productDescription":"v, 9 p.","onlineOnly":"Y","ipdsId":"IP-122975","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":380268,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1129/coverthb.jpg"},{"id":380269,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1129/ofr20201129.pdf","text":"Report","size":"1.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1129"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.26855468749999,\n 32.30570601389429\n ],\n [\n -113.51074218749999,\n 32.30570601389429\n ],\n [\n -113.51074218749999,\n 35.71083783530009\n ],\n [\n -115.26855468749999,\n 35.71083783530009\n ],\n [\n -115.26855468749999,\n 32.30570601389429\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.90673828125,\n 34.45221847282654\n ],\n [\n -108.984375,\n 34.45221847282654\n ],\n [\n -108.984375,\n 36.96744946416934\n ],\n [\n -111.90673828125,\n 36.96744946416934\n ],\n [\n -111.90673828125,\n 34.45221847282654\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"SBSC Staff, Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
Recent surveys of white perch Morone americana from Chesapeake Bay, USA, revealed a high prevalence of hepatic and biliary lesions, including neoplasia, and bile duct parasites. Here, we describe lesions in the liver and gallbladder and evaluate for statistical associations among lesions, parasites, and biomarkers of chemical exposure in fish from 2 tributaries of Chesapeake Bay. Fish were collected from an estuarine site in the Choptank River (n = 122, ages 3-11), a tributary with extensive agriculture within the watershed, and the Severn River (n = 131, ages 2-16), a tributary with extensive urban development. Passive integrative samplers were deployed at the fish collection site and an upstream, non-tidal site in each river for 30 d. Intrahepatic biliary lesions observed in fish from both rivers included neoplasia (23.3%), dysplasia (16.2%), hyperplasia (46.6%), cholangitis (24.9%), and dilated ducts containing plasmodia of Myxidium sp. (24.9%). Hepatocellular lesions included foci of hepatocellular alteration (FHA, 15.8%) and neoplasia in 4 Severn River fish (2.3%). Age of fish and Myxidium sp. infections were significant risk factors for proliferative and neoplastic biliary lesions, age alone was a risk factor for FHA, and Goussia bayae infections were associated with cholangitis and cholecystitis. Lesion prevalence was higher in fish from the Severn River, which contained higher concentrations of PAHs, organochlorine pesticides, and brominated diphenyl ethers. Metabolite biomarkers indicated higher PAH exposures in Severn River fish. This study suggests Myxidium sp. as a promoter of bile duct tumors, but more data are needed to evaluate the biological effects of environmental contaminants in this species.
","language":"English","publisher":"Inter Research Science Publisher","doi":"10.3354/dao03510","usgsCitation":"Matsche, M.A., Blazer, V., Pulster, E., and Mazik, P.M., 2020, High prevalence of biliary neoplasia in white perch Morone americana: Potential roles of bile duct parasites and environmental contaminants: Diseases of Aquatic Organisms, v. 141, p. 195-224, https://doi.org/10.3354/dao03510.","productDescription":"20 p.","startPage":"195","endPage":"224","ipdsId":"IP-119255","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":377084,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.2666015625,\n 36.58024660149866\n ],\n [\n -74.92675781249999,\n 36.58024660149866\n ],\n [\n -74.92675781249999,\n 39.757879992021756\n ],\n [\n -78.2666015625,\n 39.757879992021756\n ],\n [\n -78.2666015625,\n 36.58024660149866\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"141","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Matsche, Mark A","contributorId":194275,"corporation":false,"usgs":false,"family":"Matsche","given":"Mark","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":794927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pulster, Erin","contributorId":236999,"corporation":false,"usgs":false,"family":"Pulster","given":"Erin","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":794929,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":794930,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70255619,"text":"70255619 - 2020 - Comparison of groundwater storage changes from GRACE satellites with monitoring and modeling of major U.S. aquifers","interactions":[],"lastModifiedDate":"2024-06-26T12:26:49.454901","indexId":"70255619","displayToPublicDate":"2020-11-05T07:20:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of groundwater storage changes from GRACE satellites with monitoring and modeling of major U.S. aquifers","docAbstract":"GRACE satellite data are widely used to estimate groundwater storage (GWS) changes in aquifers globally; however, comparisons with GW monitoring and modeling data are limited. Here we compared GWS changes from GRACE over 15 yr (2002–2017) in 14 major U.S. aquifers with groundwater-level (GWL) monitoring data in ~23,000 wells and with regional and global hydrologic and land surface models. Results show declining GWS trends from GRACE data in the six southwestern and south-central U.S. aquifers, totaling −90 km3 over 15 yr, related to long-term (5–15 yr) droughts, and exceeding Lake Mead volume by ~2.5×. GWS trends in most remaining aquifers were stable or slightly rising. GRACE-derived GWS changes agree with GWL monitoring data in most aquifers (correlation coefficients, R = 0.52–0.95), showing that GRACE satellites capture groundwater (GW) dynamics. Regional GW models (eight models) generally show similar or greater GWS trends than those from GRACE. Large discrepancies in the Mississippi Embayment aquifer, with modeled GWS decline approximately four times that of GRACE, may reflect uncertainties in model storage parameters, stream capture, pumpage, and/or recharge rates. Global hydrologic models (2003–2014), which include GW pumping, generally overestimate GRACE GWS depletion (total: approximately −172 to −186 km3) in heavily exploited aquifers in southwestern and south-central U.S. by ~2.4× (GRACE: −74 km3), underscoring needed modeling improvements relative to anthropogenic impacts. Global land surface models tend to track GRACE GWS dynamics better than global hydrologic models. Intercomparing remote sensing, monitoring, and modeling data underscores the importance of considering all data sources to constrain GWS uncertainties.
New, non-invasive methods for detecting and monitoring species presence are being developed to aid in fisheries and wildlife conservation management. The use of environmental DNA (eDNA) samples for detecting macrobiota is one such group of methods that is rapidly becoming popular and being implemented in national management programs. Here we focus on the development of species-specific targeted assays for probe-based quantitative PCR (qPCR) applications. Using probe-based qPCR offers greater specificity than is possible with primers alone. Furthermore, the ability to quantify the amount of DNA in a sample can be useful in our understanding of the ecology of eDNA and the interpretation of eDNA detection patterns in the field. Careful consideration is needed in the development and testing of these assays to ensure the sensitivity and specificity of detecting the target species from an environmental sample. In this protocol we will delineate the steps needed to design and test probe-based assays for the detection of a target species; including creation of sequence databases, assay design, assay selection and optimization, testing assay performance, and field validation. Following these steps will help achieve an efficient, sensitive, and specific assay that can be used with confidence. We demonstrate this process with our assay designed for populations of the mucket (Actinonaias ligamentina), a freshwater mussel species found in the Clinch River, USA.
","language":"English","publisher":"JoVE Journal","doi":"10.3791/61825","usgsCitation":"Klymus, K.E., Ruiz-Ramos, D.V., Thompson, N., and Richter, C.A., 2020, Development and testing of species-specific quantitative PCR assays for environmental DNA applications: JOVE Journal Of Visualized Experiments, v. 165, e61825, 25 p., https://doi.org/10.3791/61825.","productDescription":"e61825, 25 p.","ipdsId":"IP-120373","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":454875,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3791/61825","text":"Publisher Index Page"},{"id":436727,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BIGOS5","text":"USGS data release","linkHelpText":"Mucket eDNA detection in Wallen's Bend, Clinch river, Tennessee, September 2019"},{"id":381495,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"165","noUsgsAuthors":false,"publicationDate":"2020-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Klymus, Katy E. 0000-0002-8843-6241 kklymus@usgs.gov","orcid":"https://orcid.org/0000-0002-8843-6241","contributorId":5043,"corporation":false,"usgs":true,"family":"Klymus","given":"Katy","email":"kklymus@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":807105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruiz-Ramos, Dannise Vannesa 0000-0001-7282-0380","orcid":"https://orcid.org/0000-0001-7282-0380","contributorId":245827,"corporation":false,"usgs":true,"family":"Ruiz-Ramos","given":"Dannise","email":"","middleInitial":"Vannesa","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":807106,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":807107,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richter, Catherine A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":138994,"corporation":false,"usgs":true,"family":"Richter","given":"Catherine","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":807108,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216425,"text":"70216425 - 2020 - Soil moisture product validation good practices protocol, version 1.0","interactions":[],"lastModifiedDate":"2020-11-18T00:40:03.432418","indexId":"70216425","displayToPublicDate":"2020-11-03T18:32:29","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Soil moisture product validation good practices protocol, version 1.0","docAbstract":"The Global Climate Observing System (GCOS) included soil moisture in the list of Essential\nClimate Variables (ECVs) to express its important role in Earth’s water, energy and carbon cycle.\nSoil moisture has a major impact on agriculture, land surface hydrology, weather, and climate\nforecasting. This document is a community-based effort to provide recommendations on good\npractices for the validation of global to regional soil moisture products. \n\nDefinitions are given and metrics to adequately describe the quality of soil moisture products are presented. Spaceborne active and passive microwave sensors are listed with their characteristics, and the typical soil moisture retrieval methods are explained, including dielectric mixing models and optical methods. Spatial scaling, root zone soil moisture estimation, and operational implementations are addressed, as these issues continue to gain more and more importance. Standard and advanced in situ measurement techniques are described as well as sensor calibration, spatial representativity, sampling strategies, and the benefit of airborne campaigns.The community has agreed upon the utilization of the International Soil Moisture Network (ISMN) as the main online repository for in situ soil moisture measurements. Different validation methods such as ground-based validation, satellite product intercomparison, and time series analyses are presented. We provide strategies to evaluate the long-term quality of soil moisture products, and give advice on how to handle typical temporal and spatial-scale mismatches and how to effectively report validation results. Moreover, the benefit of blind tests is discussed to gain objective validation results.\n\nWe encourage data providers, scientists and practitioners to use this Soil Moisture Product Validation Good Practices Protocol to provide, analyze, and improve high quality Earth Observation results.","language":"English","publisher":"NASA","doi":"10.5067/doc/ceoswgcv/lpv/sm.001","collaboration":"NASA, USDA, ESA","usgsCitation":"Montzka, C., Cosh, M.H., Bayat, B., Al Bitar, A., Berg, A., Bindlish, R., Bogena, H.R., Bolton, J.D., Cabot, F., Caldwell, T., Chan, S., Colliander, A., Crow, W., Das, N., De Lannoy, G., Dorigo, W., Evett, S.R., Gruber, A., Hahn, S., Jagdhuber, T., Jones, S., Kerr, Y., Kim, S., Koyama, C., Kurum, M., Lopez-Baeza, E., Mattia, F., McColl, K.A., Mecklenburg, S., Mohanty, B., O’Neill, P., Or, D., Pellarin, T., Petropoulos, G.P., Piles, M., Reichle, R.H., Rodriguez-Fernandez, N., Rudiger, C., Scanlon, T., Schwartz, R.C., Spengler, D., Srivastava, P.K., Suman, S., van der Schalie, R., Wagner, W., Wegmuller, U., Wigneron, J., Camacho, F., and Nickeson, J., 2020, Soil moisture product validation good practices protocol, version 1.0, 123 p., https://doi.org/10.5067/doc/ceoswgcv/lpv/sm.001.","productDescription":"123 p.","ipdsId":"IP-123352","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":380569,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"editors":[{"text":"Montzka, Carsten 0000-0003-0812-8570","orcid":"https://orcid.org/0000-0003-0812-8570","contributorId":244977,"corporation":false,"usgs":false,"family":"Montzka","given":"Carsten","email":"","affiliations":[{"id":49033,"text":"Jülich Research Center GmbH","active":true,"usgs":false}],"preferred":false,"id":805048,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Cosh, Michael H.","contributorId":146998,"corporation":false,"usgs":false,"family":"Cosh","given":"Michael","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":805049,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Camacho, Fernando","contributorId":244978,"corporation":false,"usgs":false,"family":"Camacho","given":"Fernando","email":"","affiliations":[{"id":49048,"text":"University of Valencia","active":true,"usgs":false}],"preferred":false,"id":805050,"contributorType":{"id":2,"text":"Editors"},"rank":46},{"text":"Nickeson, Jaime","contributorId":244979,"corporation":false,"usgs":false,"family":"Nickeson","given":"Jaime","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":805051,"contributorType":{"id":2,"text":"Editors"},"rank":47}],"authors":[{"text":"Montzka, Carsten 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Mineral Resources Program
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
A crude-oil spill occurred in 1979 when a pipeline burst near Bemidji, Minnesota. More than 70 percent of the 1.7 million liters of spilled crude oil was removed shortly thereafter. In response to a requirement by the State regulatory agency to remove the remaining crude to a sheen in all wells, in 1998, the pipeline company installed a dual-pump recovery system at the site. This additional remediation from 1999 to 2003 resulted in removal of about 115,000 liters of crude oil, representing between 36 and 41 percent of the volume of oil (281,000–317,000 liters) estimated to be present in 1998. Effects of the 1999–2003 remediation on groundwater plumes and unsaturated-zone vapor concentrations were evaluated by the U.S. Geological Survey using several methods including measurements of oil thicknesses in wells; field water-quality properties of dissolved oxygen, specific conductance, temperature, and pH in groundwater; and vapor concentrations of methane, carbon dioxide, nitrogen, and oxygen in the unsaturated zone.
Although the recovery system decreased oil thicknesses near the remediation wells, average oil thicknesses measured in all wells at the site were not reduced substantially. Dissolved oxygen and specific conductance measurements indicate that a secondary plume was created during the remediation, caused by the disposal of pumped water from the remediation wells in an upgradient infiltration gallery. This plume expanded rapidly immediately after the start of the remediation in 1999, resulting in expansion of the anoxic zone of groundwater upgradient and beneath the existing natural attenuation plume. Beginning in 2000–1, for example, specific conductance concentrations noticeably increased in many wells at the north oil pool from about 400 to more than 700 microsiemens per centimeter. The rapid expansion of the anoxic and elevated specific conductance plume indicates that the remediation contributed substantial amounts of biodegradable dissolved organic carbon to groundwater through the infiltration gallery. The trends in vapor data collected before, during, and after the remediation generally support the research hypothesis that crude-oil removal would have an insignificant effect on vapor concentrations in the unsaturated zone. Although there were some small changes in the concentration of methane, carbon dioxide, nitrogen, and oxygen in the unsaturated zone, these changes were not coincident with the beginning or cessation of the remediation and are therefore thought to be the result of other factors affecting biodegradation rates. A decrease in methane concentrations in one representative well, for example, is thought to be the result of reduced rates of biodegradation and methane production from the increasingly more weathered crude oil. Oil-phase recovery at this site was determined to be challenging and resulted in considerable volumes of mobile and entrapped oil remaining in the subsurface despite remediation efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205111","collaboration":"Toxic Substances Hydrology Program","usgsCitation":"Delin, G.N., Herkelrath, W.N., and Trost, J.J., 2020, Effects of a crude-oil recovery remediation system operated 1999–2003 on groundwater plumes and unsaturated-zone vapor concentrations at a crude-oil spill site near Bemidji, Minnesota: U.S. Geological Survey Scientific Investigations Report 2020–5111, 31 p., https://doi.org/10.3133/sir20205111.","productDescription":"Report: vii, 31 p.; Data Releases","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-112190","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5111/coverthb.jpg"},{"id":380006,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","description":"USGS Data Release","linkHelpText":"USGS water data for the Nation"},{"id":380005,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FJ8I0P","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets from the National Crude Oil Spill Fate and Natural Attenuation Research site near Bemidji, Minnesota, USA (ver 3.0, April 2020)"},{"id":380004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5111/sir20205111.pdf","text":"Report","size":"1.60 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5111"}],"country":"United States","state":"Minnesota","city":"Bemidji","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.98779296875,\n 47.416937456635445\n ],\n [\n -94.7900390625,\n 47.416937456635445\n ],\n [\n -94.7900390625,\n 47.537601245618134\n ],\n [\n -94.98779296875,\n 47.537601245618134\n ],\n [\n -94.98779296875,\n 47.416937456635445\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
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Urbanization substantially alters the landscape in ways that can impact stream hydrology, water chemistry, and the health of aquatic communities. Stormwater best management practices (BMPs) are the primary tools used to mitigate the effects of urban stressors such as increased runoff, decreased baseflow, and increased nutrient and sediment transport. To date, Fairfax County Virginia’s stormwater management program has made substantial investments into the implementation of both structural and nonstructural BMPs aimed at restoring and protecting watersheds. The U.S. Geological Survey (USGS), in cooperation with Fairfax County, Virginia, established a long-term water-resources monitoring program to evaluate the watershed-scale effects of these investments. Monitoring began at 14 stations in 2007 and was expanded to 20 stations in 2013. This report utilized the first 10 years of data collection to (1) assess water quantity and quality, as well as ecological condition; (2) compute annual nutrient and sediment loads; and (3) evaluate trends in streamflow, water quality, and ecological condition. Efforts are underway to link the biotic and abiotic patterns described herein to watershed management practices as well as factors such as land use change, public works infrastructure, and climate.
Hydrologic, chemical, and benthic macroinvertebrate community conditions in the streams monitored were similar to those observed in other studies of urban streams. Multidecadal trends in baseflow indices and runoff ratios at long-term Chesapeake Bay Non-tidal Network streamgages (CB-NTN) indicate a decrease in groundwater recharge and increase in storm runoff as a result of urbanization. Streamflow yields varied spatially with land cover, geology, and soil characteristics, whereas flashiness was positively related to impervious area. Dissolved oxygen typically was lowest in the Coastal Plain and across all Triassic Lowlands streams, and highest in the Piedmont. Dissolved oxygen concentrations generally were above Virginia’s minimum criterion of 4.0 milligrams per liter (mg/L), most violations occurred at Paul Spring Branch in the Coastal Plain during the warmest months of the year owing to increased chemical and biological oxygen demand. Typical pH values of the monitored streams centered on neutrality (pH = 7); however, diurnal fluctuations were most prevalent in the continuous pH data at Flatlick Branch (FLAT; a Triassic Lowlands station), as a result of increased photosynthesis catalyzed by phosphorus-rich geology. Specific conductance (SC) varied spatially owing to geology (highest at Triassic Lowlands stations) and anthropogenic disturbance (watersheds with high impervious land cover). Specific conductance typically was inversely related to streamflow except in winter months following deicing road salt applications, when values increased by several orders of magnitude. A significant increase in SC of about 2 percent per year was observed from the combined trend result of all monitoring stations over the 10-year period. Significant SC increases occurred at nearly all monitoring stations. Increasing trends were observed during winter and nonwinter months, which suggests that salts applied to deice roadways and other impervious surfaces are stored in the environment and released year-round.
Suspended-sediment (SS) concentrations in monthly samples did not vary significantly between most stations, but typically were highest in the spring and lowest in the fall as a result of seasonal differences in streamflow and climate. Suspended-sediment yields ranged from 62 to 1,428 tons per square mile (ton/mi2), with a median of 302 ton/mi2. Annual loads were greatest during the wettest water years (October 1-September 30; 2008, 2011, and 2014), with the greatest interannual variability occurring at Difficult Run above Fox Lake (DIFF) and South Fork Little Difficult Run (SFLIL). Suspended sediment was primarily composed of silts and clays; however, the proportion of sand in suspended sediment was related positively to streamflow. Cross-correlation analyses suggested the dominant sources of SS were streambank erosion and resuspension of in-channel material at DIFF and FLAT; whereas, upland sources and erosion of upper streambanks were more common at Dead Run (DEAD), Long Branch (LONG), and SFLIL.
Median total phosphorus (TP) concentrations ranged from 0.016 to 0.077 mg/L, with a networkwide median of 0.022 mg/L, were highest in the warm season (April-September), and were composed primarily of dissolved phosphorous. Although TP concentrations were relatively low across the network, the highest concentrations were consistently at stations located in the Triassic Lowlands, owing to phosphorous-rich geology, and in the Coastal Plain, owing to the low-phosphorous sorptive capacity of those soils. A significant increase in TP concentration occurred in a few stations, but the combined trend results from all stations demonstrated a significant increase of about 4 percent per year. Networkwide increases were also observed in total dissolved phosphorus, orthophosphate, and total particulate phosphorus. The composition of TP shifted from dissolved to particulate as streamflow increased and for this reason loads primarily were composed of particulate phosphorous. Median annual TP loads were highest at FLAT and DEAD and ranged from 247 to 642 pounds per square mile (lbs/mi2) networkwide. Interannual variability in phosphorous yields was apparent at most stations; the highest loading years were also the wettest years during the study period and coincident with the highest peak annual flows.
Total nitrogen (TN) concentrations typically were low throughout the network with exceptions occurring at stations located in watersheds with a high density of septic infrastructure. Elevated TN concentrations also were observed in some watersheds without a high density of septic systems and may be attributable to geologic and soil properties that limit denitrification as well as other unknown anthropogenic inputs. Total nitrogen typically was dominated by nitrate during baseflows; however, the proportion of particulate nitrogen increased during stormflows. Total nitrogen yields were similar across stations, with medians ranging from about 3,600 to 6,300 lbs/mi2 and were related to annual streamflow volume. Total nitrogen concentrations and flow-normalized concentrations decreased over the 10-year period at 7 stations, with median reductions of about 2.5 percent. Increasing trends were observed at the two stations with the highest median TN concentration (Captain Hickory Run and SFLIL, 3–5 mg/L), both watersheds contain a high density of septic infrastructure. The combined trend results from all stations revealed no trend in TN and a declining trend in nitrate of about 2 percent per year.
Overall, benthic community metrics indicated that streams throughout Fairfax County were initially of poor health; however, many metrics show an improving trend (from poor to fair based on the Fairfax County Index of Biological Integrity [IBI]). Significant increasing trends in IBI occurred at the network-scale and at 4 individual stations; additionally, scores improved by at least 1 qualitative category (for example, poor to fair, fair to good) at 11 of the 14 stations between 2009 (the first year all 14 stations were sampled) and 2017. Changes in all metrics suggest that the biodiversity, function, and condition of streams in Fairfax County are improving, but some of these improvements are driven by increased diversity and percent composition of organisms that are tolerant of the urban environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205061","collaboration":"Prepared in cooperation with Fairfax County, Virginia","usgsCitation":"Porter, A.J., Webber, J.S., Witt, J.W., and Jastram, J.D., 2020, Spatial and temporal patterns in streamflow, water chemistry, and aquatic macroinvertebrates of selected streams in Fairfax County, Virginia, 2007–18: U.S. Geological Survey Scientific Investigations Report 2020–5061, 106 p., https://doi.org/10.3133/sir20205061.","productDescription":"Report: xii, 106 p.; Data Release","numberOfPages":"106","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-113872","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":378258,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95S9RFV","text":"USGS data release","linkHelpText":"Inputs and selected outputs used to assess spatial and temporal patterns in streamflow, water-chemistry, and aquatic macroinvertebrates of selected streams in Fairfax County, Virginia, 2007-2018"},{"id":378256,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5061/coverthb.gif"},{"id":378257,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5061/sir20205061.pdf","text":"Report","size":"7.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5061"}],"country":"United States","state":"Virginia","county":"Fairfax 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Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 E. Parham Road
Richmond, VA 23228
Fluid seepage along obliquely deforming plate boundaries can be an important indicator of crustal permeability and influence on fault-zone mechanics and hydrocarbon migration. The ~850-km-long Queen Charlotte fault (QCF) is the dominant structure along the right-lateral transform boundary that separates the Pacific and North American tectonic plates offshore southeastern Alaska (USA) and western British Columbia (Canada). Indications for fluid seepage along the QCF margin include gas bubbles originating from the seafloor and imaged in the water column, chemosynthetic communities, precipitates of authigenic carbonates, mud volcanoes, and changes in the acoustic character of seismic reflection data. Cold seeps sampled in this study preferentially occur along the crests of ridgelines associated with uplift and folding and between submarine canyons that incise the continental slope strata. With carbonate stable carbon isotope (δ13C) values ranging from −46‰ to −3‰, there is evidence of both microbial and thermal degradation of organic matter of continental-margin sediments along the QCF. Both active and dormant venting on ridge crests indicate that the development of anticlines is a key feature along the QCF that facilitates both trapping and focused fluid flow. Geochemical analyses of methane-derived authigenic carbonates are evidence of fluid seepage along the QCF since the Last Glacial Maximum. These cold seeps sustain vibrant chemosynthetic communities such as clams and bacterial mats, providing further evidence of venting of reduced chemical fluids such as methane and sulfide along the QCF.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02269.1","usgsCitation":"Prouty, N.G., Brothers, D.S., Kluesner, J.W., Barrie, J., Andrews, B.D., Lauer, R., Greene, G., Conrad, J.E., Lorenson, T., Law, M.D., Sahy, D., Conway, K., McGann, M., and Dartnell, P., 2020, Focused fluid flow and methane venting along the Queen Charlotte fault, offshore Alaska (USA) and British Columbia (Canada): Geosphere, v. 16, no. 6, p. 1336-1357, https://doi.org/10.1130/GES02269.1.","productDescription":"22 p.","startPage":"1336","endPage":"1357","ipdsId":"IP-111343","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454893,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02269.1","text":"Publisher Index Page"},{"id":380375,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia","otherGeospatial":"Queen Charlotte Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -138.9111328125,\n 51.83577752045248\n ],\n [\n -129.5068359375,\n 51.83577752045248\n ],\n [\n -129.5068359375,\n 58.07787626787517\n ],\n [\n -138.9111328125,\n 58.07787626787517\n ],\n [\n -138.9111328125,\n 51.83577752045248\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":167089,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":800716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kluesner, Jared W. 0000-0003-1701-8832 jkluesner@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":201261,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared","email":"jkluesner@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barrie, J. 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2020 - Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas","interactions":[],"lastModifiedDate":"2020-11-03T12:40:42.087231","indexId":"sir20205086","displayToPublicDate":"2020-11-02T14:07:21","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5086","displayTitle":"Regional Regression Equations for Estimation of Four Hydraulic Properties of Streams at Approximate Bankfull Conditions for Different Ecoregions in Texas","title":"Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas","docAbstract":"The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, assessed statistical relations between hydraulic properties of streams at approximate bankfull conditions for different ecological regions (ecoregions) in Texas. Data from more than 103,000 records of measured discharge and ancillary hydraulic properties were assembled from summaries of discharge measurements for 424 U.S. Geological Survey streamgages in Texas. The data were subsequently subsetted at each streamgage for a streamgage-specific discharge interval centered on the estimated median annual peak discharge (0.5 annual exceedance probability) obtained from previously published regional regression equations in Texas in conjunction with the streamgage-specific sample median annual peak discharge for the period of record for each streamgage. Discharge measurements at gaged locations representing bankfull conditions (approximated from a discharge interval centered on the estimated median annual peak discharge at a given site) and associated watershed properties were subjected to rigorous statistical analysis. For most discharge measurements (where discharge is symbolically represented as Q), the following hydraulic properties are available: cross-section area (A), water-surface top width (B), and reported mean velocity (V). Statewide summary statistics were computed by using these four hydraulic properties (Q, A, B, and V) and the following five watershed properties: (1) watershed area (contributing drainage area), (2) a multiple of main-channel slope (1,000 times main-channel slope), (3) mean annual precipitation, (4) drainage density, and (5) sinuosity ratio. From the initial set of 424 streamgages, summary statistics were computed for 372 selected streamgages in Texas and constitute the subsetted measurements dataset described in this report. Eight of the 10 ecoregions in Texas are represented in the statewide summary statistics.
The resulting statistical relations, expressed as regression equations, can be used to estimate cross-section area, water-surface top width, discharge, and mean velocity of streams in different Texas ecoregions, at approximate bankfull conditions. In the regression equations, watershed properties were the independent variables for applicable watersheds, and predictions from the equations might be useful for estimating the four hydraulic properties at ungaged or unmonitored locations from selected characteristics measured at both the ungaged locations and gaged locations.
Four regression equations to estimate the four hydraulic properties were identified as the preferred equations from this study. The four preferred equations use watershed area, mean annual precipitation, and aggregated ecoregion (treated as a categorical variable) to estimate the hydraulic properties, and justification is provided for this preference. For the four equations, the proportions of variance explained by the regression equations as measured by Nash-Sutcliffe efficiency are about 71 percent for cross-section area, 36 percent for top width, 76 percent for discharge, and 25 percent for mean velocity. Residual standard error (RSEs) of the regression equations are 0.252 log10 square feet for cross-section area, 0.319 log10 feet for top width, 0.247 log10 cubic feet per second for discharge, and 0.190 log10 feet per second for mean velocity, and the corresponding standard deviations of response are 0.465 log10 square feet, 0.397 log10 feet, 0.507 log10 cubic feet per second, and 0.220 log10 feet per second, respectively. The residual standard errors are less than the standard deviations as anticipated but show that the uncertainty reduction (percent change) for cross-section area is about −46 percent, about −20 percent for top width, about −51 percent for discharge, and about −14 percent for mean velocity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205086","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Asquith, W.H., Gordon, J.D., and Wallace, D.S., 2020, Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas: U.S. Geological Survey Scientific Investigations Report 2020–5086, 45 p., https://doi.org/10.3133/sir20205086.","productDescription":"Report: vi, 45 p.; Companion File","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-081456","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":436735,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96WY938","text":"USGS data release","linkHelpText":"Source code in R for creation of regional regression equations for estimation of four 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\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501