Chaco Culture National Historical Park (CCNHP) is in the San Juan Basin of northwestern New Mexico, U.S.A. Its only water supply is in Gallup Sandstone aquifer, stratigraphically surrounded by layers long targeted for oil and natural gas extraction.
To assess groundwater flow direction, age, mixing between aquifers, and whether hydrocarbons extraction may affect water quality, we completed a geochemical groundwater sampling campaign. Groundwater at 11 sites was analyzed for major ions, hydrocarbon associated volatile organic carbon (VOC) compounds, noble gases, and the isotope systems δ2H, δ18O, 87Sr/86Sr, δ13C, and 14C.
Results demonstrate that all sampled groundwaters are exceedingly old and geochemically evolved, with a median 14C age of ∼41,000 years before present and a north flowing path. Three lines of evidence suggest mixing between aquifers through relatively impermeable shale units and mixing with hydrocarbons: 1) noble gases are fractionated likely through mixing with connate water expelled during hydrocarbon genesis; 2) several wells—including the park’s main supply well—contained trace amounts of hydrocarbon related VOC compounds; and 3) major ion analysis shows mixing trends between aquifers. We hypothesize that cross-aquifer mixing may be facilitated through the region’s numerous hydrocarbon related boreholes. Whether our findings are the result of oil and gas extraction or represent the natural state of the aquifers will require more research.
The availability of reliable gridded precipitation datasets is limited around the world, especially in arid regions. In this study, we utilized observations from satellite-based precipitation data and in situ rain gauge observations to determine a suitable precipitation dataset in the Middle East & North Africa (MENA) region. First, we evaluated seven different precipitation products using rain gauge observations. The validation was conducted at the daily, monthly, and annual time scales. Results indicated a weaker correlation between in situ rain gauge observation and satellite precipitation data at the daily time step (r: 0.02 to 0.44), mainly due to the lack of range in precipitation distribution. However, the agreement between precipitation estimates and in situ gauge observations improved at monthly (r: 0.02 to 0.66) and annual time scales (r: −0.22 to 0.57), indicating greater reliability of satellite-based precipitation at monthly and annual time scales. Based on the results and dataset availability, the Multi-Source Weighted-Ensemble Precipitation (MSWEP) was deemed suitable to create a bias-corrected new precipitation dataset for the MENA region. This study highlights the benefits of an adjusted regional precipitation product for hydrologic applications in the MENA region, such as streamflow or runoff estimation, to improve the reliability of the model outputs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jaridenv.2023.105010","usgsCitation":"Kagone, S., Velpuri, N., Khand, K., Senay, G.B., Van der Valk, M.R., Goode, D.J., Hantash, S.A., Al-Momani, T.M., Momejian, N., and Eggleston, J., 2023, Satellite precipitation bias estimation and correction using in situ observations and climatology isohyets for the MENA region: Journal of Arid Environments, v. 215, 105010, 14 p., https://doi.org/10.1016/j.jaridenv.2023.105010.","productDescription":"105010, 14 p.","ipdsId":"IP-126383","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":443334,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jaridenv.2023.105010","text":"Publisher Index Page"},{"id":417535,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Jordan, Lebanon","otherGeospatial":"West Bank","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 39.30649160058408,\n 35.36856869578705\n ],\n [\n 32.084784344491425,\n 35.36856869578705\n ],\n [\n 32.084784344491425,\n 28.71772323486526\n ],\n [\n 39.30649160058408,\n 28.71772323486526\n ],\n [\n 39.30649160058408,\n 35.36856869578705\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"215","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kagone, Stefanie 0000-0002-2979-4655","orcid":"https://orcid.org/0000-0002-2979-4655","contributorId":216913,"corporation":false,"usgs":true,"family":"Kagone","given":"Stefanie","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":873972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Velpuri, Naga Manohar 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Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":873975,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Van der Valk, Michael R.","contributorId":305834,"corporation":false,"usgs":false,"family":"Van der Valk","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":66311,"text":"HYDROLOGY.NL","active":true,"usgs":false}],"preferred":false,"id":873976,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goode, Daniel J. 0000-0002-8527-2456","orcid":"https://orcid.org/0000-0002-8527-2456","contributorId":216750,"corporation":false,"usgs":true,"family":"Goode","given":"Daniel","email":"","middleInitial":"J.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873977,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hantash, Salam Abu","contributorId":305835,"corporation":false,"usgs":false,"family":"Hantash","given":"Salam","email":"","middleInitial":"Abu","affiliations":[{"id":66313,"text":"Palestinian Water Authority, West Bank","active":true,"usgs":false}],"preferred":false,"id":873978,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Al-Momani, Thair M.","contributorId":305836,"corporation":false,"usgs":false,"family":"Al-Momani","given":"Thair","email":"","middleInitial":"M.","affiliations":[{"id":66314,"text":"Ministry of Water and Irrigation, Amman, Jordan","active":true,"usgs":false}],"preferred":false,"id":873979,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Momejian, Nanor","contributorId":305837,"corporation":false,"usgs":false,"family":"Momejian","given":"Nanor","email":"","affiliations":[{"id":66315,"text":"Queens University, Kingston, Canada","active":true,"usgs":false}],"preferred":false,"id":873980,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Eggleston, Jack R. 0000-0001-6633-3041","orcid":"https://orcid.org/0000-0001-6633-3041","contributorId":204628,"corporation":false,"usgs":true,"family":"Eggleston","given":"Jack R.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":873981,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70248047,"text":"70248047 - 2023 - The role of lithology and climate on bedrock river incision and terrace development along the Buffalo National River, Arkansas","interactions":[],"lastModifiedDate":"2023-09-06T16:38:53.2077","indexId":"70248047","displayToPublicDate":"2023-05-26T07:31:49","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3218,"text":"Quaternary Research","active":true,"publicationSubtype":{"id":10}},"title":"The role of lithology and climate on bedrock river incision and terrace development along the Buffalo National River, Arkansas","docAbstract":"The Buffalo National River in northwest Arkansas preserves an extensive Quaternary record of fluvial bedrock incision and aggradation across lithologies of variable resistance. In this work, we apply optically stimulated luminescence (OSL) dating to strath and fill terraces along the Buffalo River to elucidate the role of lithology and climate on the development of the two youngest terrace units (Qtm and Qty). Our OSL ages suggest a minimum strath planation age of ~250 ka for the Qtm terraces followed by a ~200 ka record of aggradation. Qtm incision likely occurred near the LGM, prior to the onset of Qty fill terrace aggradation ~14 ka. Our terrace ages are broadly consistent with other regional fluvial terrace records, and comparison with available paleoclimatic archives suggests that terrace aggradation and incision occurred during drier and wetter hydrological conditions, respectively. Vertical bedrock incision rates were also calculated using OSL-derived estimates of Qtm strath planation and displayed statistically significant spatial variability with bedrock lithology, ranging from 0.04 mm/yr in the higher resistance reaches and 0.02 mm/yr in the lower resistance reaches. In combination with observations of valley width and terrace distribution, these results suggest that vertical processes outpace lateral ones in lithologic reaches with higher resistance.","language":"English","publisher":"Cambridge University Press","doi":"10.1017/qua.2023.16","usgsCitation":"Rodrigues, K., Keen-Zebert, A., Shepherd, S., Hudson, M., Bitting, C.J., Johnson, B.G., and Langston, A., 2023, The role of lithology and climate on bedrock river incision and terrace development along the Buffalo National River, Arkansas: Quaternary Research, v. 115, p. 179-193, https://doi.org/10.1017/qua.2023.16.","productDescription":"15 p.","startPage":"179","endPage":"193","ipdsId":"IP-145049","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":420405,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","otherGeospatial":"Buffalo National River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.45540310278096,\n 36.28717197002243\n ],\n [\n -93.45540310278096,\n 35.819482639248236\n ],\n [\n -92.25296917676606,\n 35.819482639248236\n ],\n [\n -92.25296917676606,\n 36.28717197002243\n ],\n [\n -93.45540310278096,\n 36.28717197002243\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"115","noUsgsAuthors":false,"publicationDate":"2023-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Rodrigues, Kathleen","contributorId":298832,"corporation":false,"usgs":false,"family":"Rodrigues","given":"Kathleen","email":"","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":881630,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keen-Zebert, Amanda","contributorId":224228,"corporation":false,"usgs":false,"family":"Keen-Zebert","given":"Amanda","email":"","affiliations":[{"id":40841,"text":"University of Nevada Reno / Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":881631,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shepherd, Stephanie","contributorId":328899,"corporation":false,"usgs":false,"family":"Shepherd","given":"Stephanie","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":881632,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hudson, Mark R. 0000-0003-0338-6079 mhudson@usgs.gov","orcid":"https://orcid.org/0000-0003-0338-6079","contributorId":1236,"corporation":false,"usgs":true,"family":"Hudson","given":"Mark R.","email":"mhudson@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":881633,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bitting, Charles J.","contributorId":199024,"corporation":false,"usgs":false,"family":"Bitting","given":"Charles","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":881634,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Bradley G.","contributorId":328901,"corporation":false,"usgs":false,"family":"Johnson","given":"Bradley","email":"","middleInitial":"G.","affiliations":[{"id":78522,"text":"Davidson College","active":true,"usgs":false}],"preferred":false,"id":881635,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Langston, Abigail","contributorId":328902,"corporation":false,"usgs":false,"family":"Langston","given":"Abigail","email":"","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":881636,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70244021,"text":"70244021 - 2023 - High voltage: The molecular properties of redox-active dissolved organic matter in northern high-latitude lakes","interactions":[],"lastModifiedDate":"2023-06-28T15:24:10.814471","indexId":"70244021","displayToPublicDate":"2023-05-26T06:41:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"High voltage: The molecular properties of redox-active dissolved organic matter in northern high-latitude lakes","docAbstract":"Redox-active functional groups in dissolved organic matter (DOM) are crucial for microbial electron transfer and methane emissions. However, the extent of aquatic DOM redox properties across northern high-latitude lakes and their relationships with DOM composition have not been thoroughly described. We quantified electron donating capacity (EDC) and electron accepting capacity (EAC) in lake DOM from Canada to Alaska and assessed their relationships with parameters from absorbance, fluorescence, and ultrahigh resolution mass spectrometry (FT-ICR MS) analyses. EDC and EAC are strongly tied to aromaticity and negatively related to aliphaticity and protein-like content. Redox-active formulae spanned a range of aromaticity, including highly unsaturated phenolic formulae, and correlated negatively with many aliphatic N and S-containing formulae. This distribution illustrates the compositional diversity of redox-sensitive functional groups and their sensitivity to ecosystem properties such as local hydrology and residence time. Finally, we developed a reducing index (RI) to predict EDC in aquatic DOM from FT-ICR MS spectra and assessed its robustness using riverine DOM. As the hydrology of the northern high-latitudes continues to change, we expect differences in the quantity and partitioning of EDC and EAC within these lakes, which have implications for local water quality and methane emissions.
Introduction: Designs for safe and effective road crossing structures for small animals are typically under-road microtunnels and culverts which have varying levels of effectiveness reported in the scientific literature. Many species, particularly migratory amphibians, may have limited ability to find and use passages if they are too far apart, resulting in substantial barrier effects.
Methods: We designed a novel open elevated passage (elevated road segment: ERS), similar to a low terrestrial bridge, that could theoretically be built to any length based upon species needs and movement characteristics. A 30 m length prototype ERS was installed along a forest road with a history of amphibian road mortality in Sierra National Forest, Fresno County, CA, USA. From 2018 to 2021, we monitored small animal activity under the ERS in relation to surrounding roadside and forest habitats using active infrared cameras.
Results: We documented a total of 8,815 unique use events, using species specific independence criteria, across 22 species of amphibians (3), reptiles (4), and small mammals (15). Poisson regression modeling of taxonomic group activity under the ERS, roadside and forest, showed that amphibian activity was highest in the forest habitat, no differences were observed for reptiles, and small mammal activity was highest under the ERS. However, mean activity estimates under the ERS were equal to or greater than the open roadside habitat for all 22 species, suggesting that adding cover objects, such as downed logs and vegetation may further enhance passage use.
Discussion: Overall, results showed that the design of the ERS crossing has potential to provide high connectivity for a wide range of amphibian, reptile, and small mammal species while reducing road mortality. ERS systems can also be used in areas with challenging terrain and other hydrological and environmental constraints. Incorporating current road ecology science, we provide supplemental ERS concept designs for secondary roads, primary roads and highways to help increase the options available for road mitigation planning for small animals.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2023.1145322","usgsCitation":"Brehme, C.S., Barnes, S., Ewing, B., Gould, P.R., Vaughan, C., Hobbs, M., Tornaci, C., Holm, S., Sheldon, H., Fiutak, J., and Fisher, R., 2023, Elevated road segment (ERS) passage design may provide enhanced connectivity for amphibians, reptiles, and small mammals: Frontiers in Ecology and Evolution, v. 11, 1145322, 16 p., https://doi.org/10.3389/fevo.2023.1145322.","productDescription":"1145322, 16 p.","ipdsId":"IP-152345","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":443347,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2023.1145322","text":"Publisher Index Page"},{"id":417436,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Fresno County","otherGeospatial":"Sierra National Forest, U.S. Forest Service Road 9S09","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.17535466245019,\n 37.14771885929734\n ],\n [\n -119.17501133969637,\n 37.14595717739965\n ],\n [\n -119.17228621533715,\n 37.145084873115294\n ],\n [\n -119.17138499310803,\n 37.139662677742436\n ],\n [\n -119.17361659100861,\n 37.137610010257944\n ],\n [\n -119.17492550900785,\n 37.13629285262114\n ],\n [\n -119.17378825238568,\n 37.13612179150512\n ],\n [\n -119.17063397458409,\n 37.13831134457867\n ],\n [\n -119.1697756676991,\n 37.13990215198771\n ],\n [\n -119.17035502484663,\n 37.145255913964235\n ],\n [\n -119.171513739141,\n 37.146076904653754\n ],\n [\n -119.17361659100861,\n 37.14676105675399\n ],\n [\n -119.1739384560902,\n 37.14782148027888\n ],\n [\n -119.17535466245019,\n 37.14771885929734\n ]\n ]\n ],\n \"type\": 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0000-0001-5540-3905","orcid":"https://orcid.org/0000-0001-5540-3905","contributorId":258242,"corporation":false,"usgs":true,"family":"Ewing","given":"Brittany","email":"","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gould, Philip Robert 0000-0002-8871-0968","orcid":"https://orcid.org/0000-0002-8871-0968","contributorId":294694,"corporation":false,"usgs":true,"family":"Gould","given":"Philip","email":"","middleInitial":"Robert","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873731,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vaughan, Cassie","contributorId":294692,"corporation":false,"usgs":false,"family":"Vaughan","given":"Cassie","email":"","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":873733,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hobbs, Michael","contributorId":258243,"corporation":false,"usgs":false,"family":"Hobbs","given":"Michael","affiliations":[],"preferred":false,"id":873734,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tornaci, Charles","contributorId":294693,"corporation":false,"usgs":false,"family":"Tornaci","given":"Charles","email":"","affiliations":[{"id":63629,"text":"Dokken Engineering","active":true,"usgs":false}],"preferred":false,"id":873735,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Holm, Sarah","contributorId":294695,"corporation":false,"usgs":false,"family":"Holm","given":"Sarah","email":"","affiliations":[{"id":63629,"text":"Dokken Engineering","active":true,"usgs":false}],"preferred":false,"id":873736,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sheldon, Hanna","contributorId":294696,"corporation":false,"usgs":false,"family":"Sheldon","given":"Hanna","email":"","affiliations":[{"id":63629,"text":"Dokken Engineering","active":true,"usgs":false}],"preferred":false,"id":873737,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Fiutak, Jon","contributorId":305732,"corporation":false,"usgs":false,"family":"Fiutak","given":"Jon","email":"","affiliations":[{"id":66276,"text":"Anthony Hardwood Composites & Emtek Matting Solutions, Sheridon, AR","active":true,"usgs":false}],"preferred":false,"id":873738,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873739,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70247942,"text":"70247942 - 2023 - Comparison of historic to future without action (FWOA) land change","interactions":[],"lastModifiedDate":"2023-08-25T13:59:00.018822","indexId":"70247942","displayToPublicDate":"2023-05-25T08:52:55","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"chapter":"Attachment D6","title":"Comparison of historic to future without action (FWOA) land change","docAbstract":"One of the primary purposes of the 2023 Coastal Master Plan Integrated Compartment Model (ICM) is to forecast potential changes in coastal wetland area under varying environmental and restoration scenarios. To validate the model performance, historical analyses of observed wetland changes are needed for comparison to hindcast model runs. To generate these historical analyses, this effort has analyzed satellite imagery from 1985 through 2020 to form a basis of comparison for the hindcast model results.\n\nWetland change is a dynamic process which varies in time and space as a result of multiple compounding and interacting stressors. Wetland area is a fluid concept which can vary depending upon the definition of “wetland” and environmental conditions at the time of acquisition of imagery utilized to estimate land area. Estimates of wetland area are known to vary by more than +/- 5% as a result of nothing more than water level variability.\n\nOften, simplistic wetland change analyses are conducted using simple pre- and post- assessments. These assessments create two wetland classifications at two points in time, difference the two, and assume any change between the two assessments is wetland change. These types of assessments ignore parameters like water level variability, lack temporal resolution to account for said variability, and as such, are often prone to misinterpreting normal environmental variability as wetland change.\n\nThe wetland area change analyses presented here take a multitemporal approach to assessing wetland area change. Annual classifications were created from 1985-2020, and wetland area trends were fit statistically with lines which generalize trends through time. Additionally, the statistical methods utilized here allow for the calculation of confidence intervals with regard to wetland area, which are vital for the validation of model output.\n\nThe analyses presented here provide a comprehensive historical analysis of wetland change by hydrologic compartment from 1985 through 2020. More importantly, these analyses provide a basis by which to assess hindcasting scenarios of the master plan model as it relates to wetland area change. The results from this effort can be utilized to assess model performance and quantify the confidence which may be placed in outputs of projected wetland area.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2023 Coastal Master Plan","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Louisiana Coastal Protection and Restoration Authority","collaboration":"Louisiana Coastal Protection and Restoration Authority","usgsCitation":"Couvillion, B., 2023, Comparison of historic to future without action (FWOA) land change (Version 3), 1300 p.","productDescription":"1300 p.","ipdsId":"IP-144531","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":420153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":420143,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://coastal.la.gov/our-plan/2023-coastal-master-plan/2023-plan-appendices/"}],"country":"United States","state":"Louisiana","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.84855829076659,\n 29.93277612251312\n ],\n [\n -89.59971499975572,\n 30.28731039019054\n ],\n [\n -90.23635934228523,\n 30.560550706997276\n ],\n [\n -91.57502577453536,\n 30.872884459383584\n ],\n [\n -91.89609672665068,\n 30.87383279987627\n ],\n [\n -91.56507428125721,\n 30.30813108484891\n ],\n [\n -93.66683541660686,\n 30.25302437285586\n ],\n [\n -93.94521888014518,\n 29.586089236393093\n ],\n [\n -91.37343665721247,\n 29.06840143481824\n ],\n [\n -89.04531618635953,\n 28.84335507024474\n ],\n [\n -88.84855829076659,\n 29.93277612251312\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Couvillion, Brady 0000-0001-5323-1687","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":222810,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":881152,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70243912,"text":"70243912 - 2023 - The truth is in the stream: Use of tracer techniques and synoptic sampling to evaluate metal loading and remedial options in a hydrologically complex setting","interactions":[],"lastModifiedDate":"2023-05-25T12:35:05.358711","indexId":"70243912","displayToPublicDate":"2023-05-25T07:06:35","publicationYear":"2023","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":"The truth is in the stream: Use of tracer techniques and synoptic sampling to evaluate metal loading and remedial options in a hydrologically complex setting","docAbstract":"Two synoptic sampling campaigns were conducted to quantify metal loading to Illinois Gulch, a\nsmall stream affected by historical mining activities. The first campaign was designed to\ndetermine the degree to which Illinois Gulch loses water to the underlying mine workings, and to\ndetermine the effect of these losses on observed metal loads. The second campaign was designed to evaluate metal loading within Iron Springs, a subwatershed that was responsible for the majority of the metal loading observed during the first campaign. A continuous, constant-rate\ninjection of a conservative tracer was initiated prior to both sampling campaigns and maintained\nthroughout the duration of each study. Tracer concentrations were subsequently used to determine streamflow in gaining stream reaches using the tracer-dilution method, and as an indicator of hydrologic connections between Illinois Gulch and subsurface mine workings. Streamflow losses to the mine workings were quantified during the first campaign using a series of slug additions in which specific conductivity readings were used as a surrogate for tracer concentration. Data from the continuous injections and slug additions were combined to develop spatial streamflow profiles along each study reach. Streamflow estimates were multiplied by observed metal concentrations to yield spatial profiles of metal load that were in turn used to quantify and rank metal sources. Study results indicate that Illinois Gulch loses water to subsurface mine workings and suggest that remedial measures that reduce flow loss (e.g. channel lining) could lessen metal loading from the Iron Springs area. The primary sources of metals to Illinois Gulch include diffuse springs and groundwater, and a draining mine adit. Diffuse sources were determined to have a much larger effect on water quality than other sources that had been the subject of previous investigations due to their visual appearance, supporting the idea that “the truth is in the stream”. The overall approach of combining spatially intensive sampling with a rigorous hydrological characterization is applicable to non-mining constituents such as nutrients and pesticides.","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2023.162458","usgsCitation":"Runkel, R.L., Verplanck, P., Walton-Day, K., McCleskey, R., and Byrne, P., 2023, The truth is in the stream: Use of tracer techniques and synoptic sampling to evaluate metal loading and remedial options in a hydrologically complex setting: Science of the Total Environment, v. 876, 162458, 15 p., https://doi.org/10.1016/j.scitotenv.2023.162458.","productDescription":"162458, 15 p.","ipdsId":"IP-147276","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":498440,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://orcid.org/0000-0002-2699-052X","text":"External Repository"},{"id":417422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Breckenridge District, Illinois Gulch","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.03588092628337,\n 39.47184696761008\n ],\n [\n -106.03586531996221,\n 39.47158193097033\n ],\n [\n -106.0351162165624,\n 39.47134098769499\n ],\n [\n -106.03369604136695,\n 39.47138917641641\n ],\n [\n -106.03197934607566,\n 39.470702483978386\n ],\n [\n -106.02790609633927,\n 39.47112413803276\n ],\n [\n -106.02444149311532,\n 39.4696423139238\n ],\n [\n -106.02359875179056,\n 39.46769059499084\n ],\n [\n -106.02229234667409,\n 39.465750869468195\n ],\n [\n -106.02031034392866,\n 39.4654135203798\n ],\n [\n -106.01873410552521,\n 39.4654376167972\n ],\n [\n -106.01717347344193,\n 39.46540147216794\n ],\n [\n -106.01756363146276,\n 39.46585930275603\n ],\n [\n -106.01898380665817,\n 39.46601592831908\n ],\n [\n -106.02031034392866,\n 39.46594363964175\n ],\n [\n -106.0215588495954,\n 39.466268938097954\n ],\n [\n -106.02276053629916,\n 39.46722072855263\n ],\n [\n -106.02361888394458,\n 39.46970255129088\n ],\n [\n -106.02521072866917,\n 39.47078681499352\n ],\n [\n -106.02773895264343,\n 39.47161807238939\n ],\n [\n -106.03145325700082,\n 39.471124138032025\n ],\n [\n -106.03348867411313,\n 39.47181082630925\n ],\n [\n -106.03588092628337,\n 39.47184696761008\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"876","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873716,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Verplanck, Philip L. 0000-0002-3653-6419","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":212813,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","middleInitial":"L.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":873717,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873718,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":873719,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Byrne, Patrick","contributorId":192845,"corporation":false,"usgs":false,"family":"Byrne","given":"Patrick","affiliations":[],"preferred":false,"id":873720,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243873,"text":"70243873 - 2023 - Sensitivity of the Penman-Monteith reference evapotranspiration equation to meteorological variables for Puerto Rico","interactions":[],"lastModifiedDate":"2023-05-24T19:04:03.81162","indexId":"70243873","displayToPublicDate":"2023-05-24T13:55:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10778,"text":"Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of the Penman-Monteith reference evapotranspiration equation to meteorological variables for Puerto Rico","docAbstract":"Spatiotemporal variations in reference evapotranspiration (ETo) are sensitive to the meteorological data used in its estimation. The sensitivity of the ASCE standardized ETo equation to meteorological variables from GOES-PRWEB dataset was evaluated for the island of Puerto Rico. Island wide, ETo is most sensitive to daily mean relative humidity (RHmean), followed by solar radiation, daily maximum (Tmax) and minimum (Tmin) air temperatures, and wind speed with average absolute relative sensitivity coefficients (SCs) of 0.98, 0.57, 0.50, 0.27, and 0.12, respectively. The derived SCs guided the prioritization of bias correction of meteorological data for ETo estimation from two downscaled climate models (CNRM and CESM). The SCs were applied to evaluate how meteorological variables contribute to model errors and projected future changes in ETo from 1985–2005 to 2040–2060 at irrigated farms in the south. Both models project a 5.6% average increase in annual ETo due to projected increases in Tmax and Tmin and a decrease in RHmean. Despite ETo being most sensitive to relative changes in RHmean, the contributions from RHmean, Tmax, and Tmin to future changes in ETo are similar. CESM projects increases in ETo in March, November, and December, increasing the potential for crop water stress. Study limitations are discussed.
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Rico","active":true,"usgs":false}],"preferred":false,"id":873577,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70244275,"text":"70244275 - 2023 - Per- and polyfluorinated alkyl substances (PFAS) in Pennsylvania surface waters: A statewide assessment, associated sources, and land-use relations","interactions":[],"lastModifiedDate":"2023-06-12T11:38:43.481728","indexId":"70244275","displayToPublicDate":"2023-05-24T06:34:42","publicationYear":"2023","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":"Per- and polyfluorinated alkyl substances (PFAS) in Pennsylvania surface waters: A statewide assessment, associated sources, and land-use relations","docAbstract":"The objectives of this study are to identify per- and polyfluoroalkyl substances (PFAS) in Pennsylvania surface waters, corresponding associations with potential sources of PFAS contamination (PSOC) and other parameters, and compare raw surface water concentrations to human and ecological benchmarks. Surface water samples from 161 streams were collected in September 2019 and were analyzed for 33 target PFAS and water chemistry. Land use and physical attributes in upstream catchments and geospatial counts of PSOC in local catchments are summarized. The hydrologic yield of the sum of 33 PFAS (∑PFAS) for each stream was computed by normalizing each site's load by the drainage area of the upstream catchment. Utilizing conditional inference tree analysis, the percentage of development (>7.58 %) was identified as a primary driver of the ∑PFAS hydrologic yields. When percentage of development was removed from analysis, ∑PFAS yields were closely related to surface water chemistry associated with landscape alteration (e.g., development or agricultural cropland), such as concentrations of total nitrogen, chloride, and ammonia, but also to count of water pollution control facilities (agricultural, industrial, stormwater, and/or municipal waste pollution abatement facilities). In oil and gas development regions, ∑PFAS yields were associated with combined sewage outfalls. Sites surrounded by ≥2 electronic manufacturing facilities had elevated ∑PFAS yields (median = 241 ng/s/km2). Study results are critical to guide future research, regulatory policy, best practices that will mitigate PFAS contamination, and the communication of human health and ecological risks associated with PFAS exposure from surface waters.
The Muddy River provides habitat for several wildlife and endemic aquatic species protected under the Endangered Species Act. Near Moapa, Nevada, in the Bureau of Land Management’s Muddy River Floodplain Restoration Project Area, a previously constructed levee on the east side of the river alters the natural hydrology and decreases connectivity between the river and its floodplain. The Bureau of Land Management is interested in restoring the project area to a more natural state and proposed removing the existing levee (at the time of this study in 2019) on the east bank of the river and replacing it with a new levee farther away from the river. The 50-, 20-, 10-, 4-, 2-, and 1-percent annual exceedance probability flood streamflows were estimated based on a flood-frequency analysis of a streamgage in the study area. River cross-sections were surveyed and combined with a digital elevation model of the floodplains to create a coupled one- and two-dimensional hydraulic model of the study area. The estimated flood streamflows were used as inputs in the hydraulic model to simulate how flood-inundation extents would change with the proposed restoration. Simulated inundation extents expand with increasing flood magnitudes, with nearly the entire valley inundated by the 2-percent flood streamflow. Within the project area, inundation extents with restoration increased on the east floodplain and decreased on the west floodplain for the 20-, 10-, 4-, 2-, and 1-percent flood streamflows. Outside the Muddy River Floodplain Restoration Project Area, inundation extents decreased with restoration east of the project area for the 20- and 10-percent flood streamflows, but changes in extent for larger streamflows were minor because most of the streamflow leaves the main river channel upstream of the restoration area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235033","usgsCitation":"Morris, C.M., and Childres, H.K., 2023, Flood-inundation maps for the Muddy River, near Moapa, Nevada: U.S. Geological Survey Scientific Investigations Report 2023–5033, 22 p., https://doi.org/10.3133/sir20235033.","productDescription":"Report: viii, 22 p.; Data Release","numberOfPages":"22","onlineOnly":"Y","ipdsId":"IP-104618","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":416999,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5033/sir20235033.pdf","text":"Report","size":"23 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417002,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5033/images"},{"id":416997,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K68IWI","text":"Geospatial data, flood-frequency analysis, and surface-water model archive for flood-inundation maps of the Muddy River, near Moapa, Nevada","description":"Morris, C.M., 2023, Geospatial data, flood-frequency analysis, and surface-water model archive for flood-inundation maps of the Muddy River, near Moapa, Nevada: U.S. Geological Survey data release, at https://doi.org/10.5066/P9K68IWI."},{"id":417000,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5033/covrthb.jpg"},{"id":417001,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5033/sir20235033.xml"},{"id":417003,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235033/full"},{"id":500898,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114737.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Nevada","city":"Moapa","otherGeospatial":"Muddy River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.716667,\n 36.723611\n ],\n [\n -114.716667,\n 36.675\n ],\n [\n -114.675,\n 36.675\n ],\n [\n -114.675,\n 36.723611\n ],\n [\n -114.716667,\n 36.723611\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
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In contrast to many other arid region rivers, streamflow in the South Platte River is heavily augmented by trans-basin water imports and irrigation return flows. Hydrological changes began in the 1880s, resulting in channel narrowing and the development of a continuous Populus-Salix forest by the mid-twentieth century. We assessed the composition, structure and regeneration status of the riparian forest and identified environmental variables affecting annual Populus deltoides tree growth. We sampled forest structure at four sites in 2015, and conducted dendroecological analysis at seven additional sites in 2019. The riparian forest was dominated by P. deltoides, which occurred at all sites, comprising 79% of total tree basal area and 62% of total tree density. Age structure data indicated ongoing though episodic recruitment of P. deltoides, at least over the past ~130 years. We tested 14 linear mixed effects models to describe the effect of climate and streamflow on individual tree growth (modeled as the log of BAI, n = 237 trees). The most parsimonious model selected with AICc explained 28.6% of BAI variability, and included hydrology and climate factors during the growing season (i.e., June–August streamflow, June–July PDSI), some aspects of off-season (i.e., previous November and March) streamflow, along with tree age and study site effects. The riparian forest developed in response to, and has been maintained by, current climate conditions and water management regimes. It may be negatively affected by future climate change and increased urban water demand in the basin.
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reference flow network with improved connectivity to support durable data integration and reproducibility in the coterminous US","interactions":[],"lastModifiedDate":"2023-05-22T13:08:24.434575","indexId":"70243816","displayToPublicDate":"2023-05-22T07:57:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Generating a reference flow network with improved connectivity to support durable data integration and reproducibility in the coterminous US","docAbstract":"This report presents a reference flow network for the conterminous United States that is built from the best available information from the U.S. Geological Survey, the National Oceanic and Atmospheric Administration National Weather Service, and the U.S. Environmental Protection Agency. 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Division","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, J. Michael","contributorId":304963,"corporation":false,"usgs":false,"family":"Johnson","given":"J. Michael","affiliations":[{"id":66193,"text":"NOAA-NWS-OWP","active":true,"usgs":false}],"preferred":false,"id":873361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873362,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243768,"text":"70243768 - 2023 - Watershed carbon yield derived from gauge observations and river network connectivity in the United States","interactions":[],"lastModifiedDate":"2023-05-19T12:28:27.653939","indexId":"70243768","displayToPublicDate":"2023-05-19T07:04:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Watershed carbon yield derived from gauge observations and river network connectivity in the United States","docAbstract":"River networks play a critical role in the global carbon cycle. Although global/continental scale riverine carbon cycle studies demonstrate the significance of rivers and streams for linking land and coastal regions, the lack of spatially distributed riverine carbon load data represents a gap for quantifying riverine carbon net gain or net loss in different regions, understanding mechanisms and factors that influence the riverine carbon cycle, and testing simulations of aquatic carbon cycle models at fine scales. Here, we (1) derive the riverine load of particulate organic carbon (POC) and dissolved organic carbon (DOC) for over 1,000 hydrologic stations across the Conterminous United States (CONUS) and (2) use the river network connectivity information for over 80,000 catchment units within the National Hydrography Dataset Plus (NHDPlus) to estimate riverine POC and DOC net gain or net loss for watersheds controlled between upstream-downstream hydrologic stations. The new riverine carbon load and watershed net gain/loss represent a unique contribution to support future studies for better\nunderstanding and quantification of riverine carbon cycles.","language":"English","publisher":"Springer","doi":"10.1038/s41597-023-02162-7","usgsCitation":"Qiu, H., Zhang, X., Yang, A., Wickland, K., Stets, E.G., and Chen, M., 2023, Watershed carbon yield derived from gauge observations and river network connectivity in the United States: Scientific Data, v. 10, 278, 13 p., https://doi.org/10.1038/s41597-023-02162-7.","productDescription":"278, 13 p.","ipdsId":"IP-150043","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":443465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-023-02162-7","text":"Publisher Index 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2007–18","interactions":[],"lastModifiedDate":"2026-03-06T20:51:31.369117","indexId":"sir20235027","displayToPublicDate":"2023-05-18T10:56:00","publicationYear":"2023","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":"2023-5027","displayTitle":"Evaluating Drivers of Hydrology, Water Quality, and Benthic Macroinvertebrates in Streams of Fairfax County, Virginia, 2007–18","title":"Evaluating drivers of hydrology, water quality, and benthic macroinvertebrates in streams of Fairfax County, Virginia, 2007–18","docAbstract":"In 2007, the U.S. Geological Survey partnered with Fairfax County, Virginia, to establish a long-term water-resources monitoring program to evaluate the hydrology, water quality, and ecology of Fairfax County streams and the watershed-scale effects of management practices. Fairfax County uses a variety of management practices, policies, and programs to protect and restore its water resources, but the effects of such strategies are not well understood. This report used streamflow, water-quality, and ecological monitoring data collected from 20 Fairfax County watersheds from 2007 through 2018 to assess the effects of management practices, landscape factors, and climatic conditions on observed nutrient, sediment, salinity, and benthic-macroinvertebrate community responses.
Urbanization, climatic variability, and an increase in management practices occurred within Fairfax County during the study period. Impervious cover, housing units, wastewater infrastructure, and (or) stormwater infrastructure increased in most study watersheds. Climatic conditions varied among study years; countywide estimates of average-annual air temperature differed by about 3 degrees Celsius, and total precipitation ranged from about 34 to 63 inches per year. The effects of the management practices, implemented to reduce nitrogen, phosphorus, and (or) sediment loads, are considered in this study. These management practices primarily consist of stormwater retrofits and stream restorations; however, stream restorations account for most of the financial investment and expected load reductions. Management practices were implemented in half of the study watersheds, and most practices were installed and reductions credited late in the study period.
Changes in hydrologic response during storm events were evaluated over the study period because many management practices that were implemented were designed to achieve nutrient and sediment reductions by slowing or intercepting runoff. The average number and length of storm events was mostly unchanged throughout the monitoring network. Four watersheds with 10 years of streamflow data showed a mixture of trends in stormflow peak, volume, and rate-of-change. Event-mean nutrient and sediment concentrations from these watersheds were evaluated during storm events and generally showed increases in total phosphorus (TP) and suspended sediment and reductions or no changes in total nitrogen (TN).
Landscape inputs of nitrogen and phosphorus and the percentage of inputs delivered to streams were estimated for the study watersheds. Estimated phosphorus from fertilizer and nitrogen from atmospheric deposition represented large nutrient inputs in most watersheds; amounts of other nonpoint sources varied based on land use. Estimated nitrogen inputs declined throughout Fairfax County and in most study watersheds from 2008 through 2018; in comparison, phosphorus input changes were relatively small. Most nonpoint-nutrient inputs were retained on the landscape and did not reach streams, with slightly more nitrogen retention than phosphorus, on average. Retention rates were lower for years with more precipitation and streamflow. After adjusting for streamflow, TN and TP loads were generally higher for years with more nutrient inputs. Calculated as a function of flow-adjusted loads, TP retention declined at most stations from 2009 through 2018, in comparison, TN retention was relatively unchanged.
Landscape and climatic conditions affected spatial differences and changes in Fairfax County stream conditions from 2009 through 2018. TN concentrations were higher and increases over time were larger in watersheds with elevated septic-system density. TP concentrations were higher in watersheds with more turfgrass; concentrations were lower, but had larger increases over time, in watersheds with deeper soils. Suspended-sediment concentrations were higher in watersheds with greater stream densities. Specific conductance was higher in watersheds with more developed land use and shallower soils. Benthic-macroinvertebrate index of biotic integrity (IBI) scores were lower in watersheds with high road density and had larger increases over time in bigger, more developed watersheds. Annual variability in TN and TP concentrations and benthic-macroinvertebrate IBI scores was affected by precipitation; annual variability in suspended sediment concentrations and specific conductance was affected by air temperature.
After accounting for influences from landscape and climatic conditions, expected management-practice effects were not consistently observed in monitored stream responses. These effects were assessed by comparing expected management-practice load reductions with the timing, direction, and magnitude of changes in storm-event hydrology, nutrient and sediment loads, median-annual water-quality conditions, and benthic-macroinvertebrate IBI scores. An important consideration for future investigations of management-practice effects is how to control for water-quality and ecological variability caused by geologic properties, the urban environment, precipitation, and (or) air temperature. The interpretation of management-practice effects in this report was likely influenced by a combination of factors, including (1) the amount, timing, and location of management-practice implementation; (2) unmeasured landscape and climatic factors; (3) uncertain management-practice expectations; (4) hydrologic variability; and (5) analytical assumptions. Through continued data-collection efforts, particularly after management practices have been completed, many of these factors may become less influential in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235027","isbn":"978-1-4113-4516-4","collaboration":"Prepared in cooperation with Fairfax County, Virginia","usgsCitation":"Webber, J.S., Chanat, J.G., Porter, A.J., and Jastram, J.D., 2023, Evaluating drivers of hydrology, water quality, and benthic macroinvertebrates in streams of Fairfax County, Virginia, 2007–18: U.S. Geological Survey Scientific Investigations Report 2023–5027, 198 p., https://doi.org/10.3133/sir20235027.","productDescription":"Report: xv, 198 p.; Data Release","numberOfPages":"198","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-139637","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":417148,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FW7KLH","text":"USGS data release","linkHelpText":"Climate, landscape, and water-quality metrics 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Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Study Region
The groundwater basin underlying the City of Albuquerque, New Mexico, USA.
Study Focus
The study focuses on changes in groundwater storage and how those changes relate to groundwater-level changes. Groundwater storage change was measured using repeat microgravity at 35 stations from 2016 to 2021. Usually, storage is monitored by converting groundwater-level changes to storage changes using the aquifer storage coefficient, a difficult property to measure. With gravity, storage change can be measured directly. The storage coefficient, or specific yield in an unconfined aquifer, was estimated using the gravity method at individual sites, and a map created showing how this property varies over the region.
New Hydrological Insights for the Region
For the first time, a map of specific yield was produced based on interpolated maps of gravity-derived storage change and groundwater-level change, allowing inference of this property over a broad region even without collocated monitoring wells and gravity stations. Gravity data indicate aquifer drawdown and recovery mainly occurs in the central part of the basin where pumping is greatest. This spatial distribution is not captured by the more widely spaced monitoring well network. Because the aquifer is recovering from historically greater-magnitude pumping, net-neutral storage change (inflow=outflow) occurs when pumping from the central part of the well field is about 1.54 × 107 cubic meters (12,500 acre-feet) per year.
Wildfire can alter soil-hydraulic properties, often resulting in an increased prevalence of infiltration-excess overland flow and greater potential for debris-flow hazards. Mini disk tension infiltrometers (MDIs) can be used to estimate soil hydraulic properties, such as field-saturated hydraulic conductivity (Kfs) and wetting front potential (Hf), and their spatial variability following wildfire. However, the small (point-scale) footprint of MDI measurements makes it challenging to use these data to parameterize hydrologic models at the hillslope and watershed scales where hydrologic hazards, such as debris flows, initiate. Here, we designed numerical experiments to estimate spatially constant or watershed-scale effective hydrologic parameters (EHPs) that approximate the response of spatially variable hydrologic parameters with distributions derived from MDI measurements at five sites in the southwestern United States. We found that it is possible to define EHPs for both Kfs and Hf based on the MDI measurements that lead to reasonable approximations of run-off hydrographs at the outlets of small watersheds (<1 km2). We found that watershed EHPs are functions of rainfall characteristics, although they are most sensitive to rainfall intensity and relatively less sensitive to the temporal distribution of rainfall. EHPs are lower than the arithmetic mean of the MDI measurements and are better approximated by the median or geometric mean of the MDI measurements, particularly for storms with recurrence intervals of approximately 1 year or less that commonly initiate post-fire debris flows. This work demonstrated that using the proposed upscaling method to estimate watershed-scale EHPs, as opposed to approximating EHPs based on the arithmetic mean of the MDI measurements, improved the ability of a hydrologic model to identify storms that are likely to produce debris flows. Results improved our ability to link point-scale MDI measurements and watershed-scale EHPs in post-fire settings and helped guide our ability to use MDI data to parameterize post-fire hydrologic models.
Wildfire has been shown to increase, decrease, or have no detectable effect on actual evapotranspiration (ETa) fluxes in the western United States. Where disturbance-induced shifts are significant, source-water hydrology may be impacted as ETa constitutes the largest outgoing water flux in much of the arid West. We conducted pixel-scale analysis of 30-m ETa data and various meteorologic and landscape variables at 13 burn scars to understand how wildfire disturbance impacted hillslope-to-burn scar-scale hydrology and vegetation conversion. Significant fire-induced ETa reductions (between approximately −15 to −50%) were detected at nine burn scars through the tenth post-fire year, while ETa recovery rate varied substantially by ecoregion and pre-fire vegetation type. Along elevation gradients, both climate and land disturbance influenced the location of runoff/recharge generation zones, and more net water was generated from a snow-dominated burn scar in dry post-fire years than in wet pre-fire years. However, especially in arid locations where ETa is water-limited, compensatory ETa pathways may be more likely to dampen fire effects on total basin water yield where intact vegetation is located between the disturbance footprint and the basin outlet. Relationships between post-fire ETa shifts and early-successional vegetation conversion were also tracked. The majority of burn scars with significant fire-induced ETa reductions experienced conversion patterns typical of the western United States following stand-replacing disturbance, with forests converting to shrub/scrub and/or grassland/herbaceous cover through at least the end of the study period (eight to 15 years depending on date of the fire event). This could have important implications for high-elevation, snow-dominated watersheds – some of the most critical source water areas - as previous research indicates that wildfire activity is moving upslope and into vegetation communities that have not evolved to withstand fire. Finally, we show that much of the Colorado River Basin’s high-yield source water areas are vulnerable to the fire-induced ETa reductions and vegetation conversion observed herein. As such, water managers in the Colorado River Basin can anticipate changes in burn scar hydrology and snowpack mechanics following fire disturbance.
The Colorado River is often referred to as “the lifeblood of the west.” The basin supplies municipal water to nearly 40 million people and irrigates approximately 22,000 km2 of agricultural lands. Twenty-two major rivers converge with the Colorado after it begins its descent from the Rocky Mountains and winds through the plateaus of Colorado, Utah, and Arizona, onto the deserts of southwestern Arizona, and finally into the Gulf of California, where inflows from the Río Hardy and Río Sonoyta in Mexico complete the drainage. The mainstem Colorado, Green, Yampa, Little Colorado, and Yampa Rivers are described in further detail in the 2005 edition (Blinn and Poff, 2005) of this book. In this edition, we discuss seven other major tributaries in the Colorado River basin: the Gunnison, San Juan, Virgin, Bill Williams, Verde, Black, and Salt Rivers. The water quality and quantity, flora and fauna, and sediment and organic loads of each of these tributaries uniquely alter the mainstem Colorado River and the habitat it provides. Thus, understanding the hydrology, ecology, and human use of these tributaries is critical toward understanding both the complex history and present-day management of the Colorado River Basin as a whole.
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Morgan 0000-0001-5104-9566","orcid":"https://orcid.org/0000-0001-5104-9566","contributorId":221740,"corporation":false,"usgs":true,"family":"Ford","given":"Morgan","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":872249,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kennedy, Theodore 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":221741,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":872248,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243176,"text":"sir20235028 - 2023 - Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico","interactions":[],"lastModifiedDate":"2026-03-06T20:53:37.591262","indexId":"sir20235028","displayToPublicDate":"2023-05-08T11:03:58","publicationYear":"2023","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":"2023-5028","displayTitle":"Development of an Integrated Hydrologic Flow Model of the Rio San Jose Basin and Surrounding Areas, New Mexico","title":"Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico","docAbstract":"The Rio San Jose Integrated Hydrologic Model (RSJIHM) was developed to provide a tool for analyzing the hydrologic system response to historical water use and potential changes in water supplies and demands in the Rio San Jose Basin. The study area encompasses about 6,300 square miles in west-central New Mexico and includes the communities of Grants, Bluewater, and San Rafael and three Native American Tribal lands: the Acoma and Laguna Pueblos and the Navajo Nation. Perennial surface water features are sparse in the study area and most water resources consist of groundwater pumped from sedimentary and basalt aquifers.
Calibration of the RSJIHM was performed using PEST++ (version 4.3.20) and BeoPEST (version 13.6). Model parameter values were adjusted during calibration to fit model simulated values to the measured or estimated values for several observation groups: (1) solar radiation, (2) potential evapotranspiration, (3) actual evapotranspiration, (4) precipitation and minimum and maximum air temperature, (5) snow water equivalent, (6) snow-covered area, (7) streamflow, (8) hydraulic head, (9) springflow at Ojo del Gallo, (10) springflow at Horace Springs, (11) surface-water releases from Bluewater Lake, and (12) surface-water diversions for irrigation within the Bluewater-Toltec Irrigation District.
The simulated average annual hydrologic budget from 1950 through 2018 indicated that the majority (greater than 98 percent) of precipitation within the basin was consumed by evapotranspiration, leaving 1.2 percent to recharge the groundwater system, 0.47 percent to direct runoff to streams, and 0.20 percent to infiltrate the soil zone and interflow to streams. The average annual recharge to the groundwater system and runoff to streams simulated by the RSJIHM was about 28,000 and 11,000 acre-feet, respectively. The RSJIHM simulated about 590,000 acre-feet of cumulative aquifer storage depletion from 1950 through 2018.
Additional work that could improve the simulation capability of the RSJIHM includes (1) further data collection (streamflow, head, springflow) in the southwestern subbasin that includes the El Malpais National Monument, (2) incorporating temporally variable vegetation parameters, (3) spatial downscaling of the hydrometeorological input datasets, (4) incorporating additional spatial variability to hydraulic property parameters on the basis of new data collection, and (5) using environmental tracers to verify and calibrate model parameters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235028","issn":"2328-0328","collaboration":"Prepared in cooperation with the Bureau of Reclamation, Pueblo of Acoma, and Pueblo of Laguna","usgsCitation":"Ritchie, A.B., Chavarria, S.B., Galanter, A.E., Flickinger, A.K., Robertson, A.J., and Sweetkind, D.S., 2023, Development of an integrated hydrologic flow model of the Rio San Jose Basin and surrounding areas, New Mexico: U.S. Geological Survey Scientific Investigations Report 2023–5028, 76 p., 1 pl., https://doi.org/10.3133/sir20235028.","productDescription":"Report: x, 76 p.; 1 Plate: 25.37 x 40.38 inches; Data Release","numberOfPages":"90","onlineOnly":"Y","ipdsId":"IP-111893","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":416632,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5028/coverthb.jpg"},{"id":416635,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235028/full","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5028 HTML"},{"id":416634,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028.XML","size":"482 KB","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5028 XML"},{"id":416638,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028_plate1.pdf","size":"550 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5028 plate 1"},{"id":416633,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5028/sir20235028.pdf","size":"5.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5028"},{"id":416637,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRTKTM","text":"USGS data release—GSFLOW, used to run PRMS and MODFLOW-NWT models, to simulate the effects of natural and anthropogenic impacts on water resources in the Rio San Jose Basin and surrounding areas, New Mexico"},{"id":416636,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5028/Images/"},{"id":500886,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114717.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"New Mexico","otherGeospatial":"Rio San Jose Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.5,\n 36\n ],\n [\n -108.5,\n 36\n ],\n [\n -108.5,\n 34\n ],\n [\n -106.5,\n 34\n ],\n [\n -106.5,\n 36\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
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6700 Edith Blvd. NE
Albuquerque, NM 87113
Discussed in this chapter are seven significant tributaries of the Lower Mississippi River and its major distributary. As a group, these eight rivers and their basins encompass substantial variation in physical form, hydrology, biota, ecology, and human impacts. The Current River, Ouachita River, and Saline River, flow to the Mississippi out of the U.S. Interior Highlands. The Cache River basin, centered in Arkansas, contains a vast expanse of bottomland hardwood forest and is famous for wintering waterfowl. The Hatchie River, flowing west out of Tennessee, is the longest free-flowing tributary of the Lower Mississippi River and famous for its rich diversity of mussels and fishes. The Wolf River of Tennessee supports a magnificent bald cypress-tupelo swamp and is notable as a protected urban river that flows through Memphis. The Big Sunflower River begins and ends in the alluvial floodplain of the Mississippi River and flows through a basin of intense agriculture and historical human conflict. The Atchafalaya River, the primary distributary of the Mississippi River, supports the nation's largest expanse of bottomland hardwood forest and swamp wetlands. In this chapter, we review the physiography, geomorphology, hydrology, water chemistry, land use, biological diversity, ecological processes, human impacts, and areas of need for research and management of each of these eight rivers and their basins.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Rivers of North America (second edition)","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-818847-7.00002-1","usgsCitation":"Ochs, C., Baustian, J., Harrison, A., Hartfield, P., Johnston, C., Justis, C.A., Larsen, D., Mickelson, A., Piazza, B., and Spurgeon, J.J., 2023, Rivers of the Lower Mississippi Basin, chap. 6 of Rivers of North America (second edition), p. 226-271, https://doi.org/10.1016/B978-0-12-818847-7.00002-1.","productDescription":"46 p.","startPage":"226","endPage":"271","ipdsId":"IP-125987","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433453,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lower Mississippi River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.93450410030157,\n 29.382748469377404\n ],\n [\n -89.33548846730534,\n 30.779284874646976\n ],\n [\n -82.62880013103909,\n 36.5183105334489\n ],\n [\n -99.00599352782979,\n 36.52450752669955\n ],\n [\n -95.77205257921551,\n 32.53534478594656\n ],\n [\n -92.1616938289437,\n 30.17414276402424\n ],\n [\n -91.08124857898872,\n 29.329443449275175\n ],\n [\n -89.11222202428036,\n 29.116282171426377\n ],\n [\n -88.93450410030157,\n 29.382748469377404\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ochs, C.","contributorId":341753,"corporation":false,"usgs":false,"family":"Ochs","given":"C.","email":"","affiliations":[{"id":36508,"text":"University of Mississippi","active":true,"usgs":false}],"preferred":false,"id":908859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baustian, J.J.","contributorId":196475,"corporation":false,"usgs":false,"family":"Baustian","given":"J.J.","email":"","affiliations":[],"preferred":false,"id":908863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harrison, A.","contributorId":341754,"corporation":false,"usgs":false,"family":"Harrison","given":"A.","affiliations":[{"id":81780,"text":"U.S. Army COE","active":true,"usgs":false}],"preferred":false,"id":908862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartfield, P.","contributorId":189996,"corporation":false,"usgs":false,"family":"Hartfield","given":"P.","affiliations":[],"preferred":false,"id":908861,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnston, C.S.","contributorId":317794,"corporation":false,"usgs":false,"family":"Johnston","given":"C.S.","email":"","affiliations":[{"id":39883,"text":"Univ of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":908860,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Justis, Catherine A.","contributorId":343866,"corporation":false,"usgs":false,"family":"Justis","given":"Catherine","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":908865,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Larsen, D.","contributorId":341758,"corporation":false,"usgs":false,"family":"Larsen","given":"D.","affiliations":[{"id":17864,"text":"University of Memphis","active":true,"usgs":false}],"preferred":false,"id":908866,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mickelson, A.","contributorId":341759,"corporation":false,"usgs":false,"family":"Mickelson","given":"A.","email":"","affiliations":[{"id":17864,"text":"University of Memphis","active":true,"usgs":false}],"preferred":false,"id":908867,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Piazza, B.","contributorId":341756,"corporation":false,"usgs":false,"family":"Piazza","given":"B.","email":"","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":908864,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Spurgeon, Jonathan J. 0000-0002-6888-5867","orcid":"https://orcid.org/0000-0002-6888-5867","contributorId":270349,"corporation":false,"usgs":true,"family":"Spurgeon","given":"Jonathan","email":"","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":16610,"text":"University of Nebraska-Lincoln","active":true,"usgs":false}],"preferred":false,"id":908868,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70254178,"text":"70254178 - 2023 - Rivers of Arctic North America","interactions":[],"lastModifiedDate":"2024-05-13T12:32:56.019427","indexId":"70254178","displayToPublicDate":"2023-05-08T07:30:28","publicationYear":"2023","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"20","title":"Rivers of Arctic North America","docAbstract":"This chapter describes the geomorphology, hydrology, chemistry, biodiversity, and ecology of rivers in the North American Arctic. The history, physiography, climate, and land use of the Arctic regions are also described. The chapter includes details on the Kobuk and Colville rivers in Alaska, the Thelon and Kazan rivers in the central Canadian Arctic, Koroc River and Nakvak Brook in the eastern Canadian low Arctic, Thomsen River on Banks Island in the western Canadian Arctic Archipelago, and Ruggles River on Ellesmere Island in the Canadian high Arctic. The rivers are characteristic of the major ecoregions of the North American Arctic, covering a range of geomorphological and physiographic conditions. The history of use of the rivers by Inuit and Dene First Nations Peoples of the north provides the foundation to understand the social, cultural, and economic importance of the river systems, and potential threats to the rivers from climate change are outlined.
The U.S. Geological Survey and University of Wisconsin–Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at mainstem Plum Creek and west Plum Creek in northeastern Wisconsin. These two subbasins cover 88 percent of the Plum Creek Basin (Hydrologic Unit Code 12), which is a subbasin of the lower Fox River Basin. A published total maximum daily load report for the lower Fox River Basin rated Plum Creek as one of the greatest contributors of total suspended solids (TSS) and total phosphorus (TP) draining into the lower Fox River. To reduce TSS and TP exports from Plum Creek, additional cropland conservation practices and watercourse protections were applied between 2012 and 2020. To detect water-quality trends, data were collected during 2010 to 2020 at mainstem Plum Creek and 2013 to 2020 at west Plum Creek.
The project used two methods to evaluate CMP effectiveness. The first method focused on evaluating water-quality changes between initial and post-CMP implementation periods during rain- or snowmelt-induced runoff events (hereafter referred to as “events”). In this approach random-forest models were developed to account for environmental factors which influence water quality. Model residuals from the two time periods were compared to determine the significance of water-quality changes associated with CMP implementation for mainstem and west Plum Creek Basins. The second method used a Weighted Regressions on Time, Discharge, and Season time-series approach to examine changes in water quality during the entire study period in mainstem Plum Creek. Results from both methods indicated there were minimal water-quality changes in TSS concentrations and flow-normalized delivery during runoff events during the 10-year period from 2010 to 2020; however, TP concentrations during low streamflow (less than 3 cubic feet per second [ft3/s]) may have decreased. The lack of observed improvement may be attributable to any of the following: variability in weather and hydrologic conditions, insufficient post-treatment data, additional cropland being converted to corn production, above average rainfall, streambank degradation, acute and legacy sources of phosphorus from farm fields, excessive/vulnerable manure applications and spills, and point-source discharges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235043","collaboration":"Prepared in cooperation with the University of Wisconsin-Green Bay and Outagamie County, Wisconsin","usgsCitation":"Horwatich, J.A., Fermanich, K., Pronschinske, M.A., Robertson, D.M., Kussow, S., Loken, L.C., Reneau, P.C., Freund, J., and Komiskey, M.J., 2023, Assessment of conservation management practices on water quality and observed trends in the Plum Creek Basin, 2010–20: U.S. Geological Survey Scientific Investigations Report 2023–5043, 31 p., https://doi.org/10.3133/sir20235043.","productDescription":"Report: ix, 31 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130579","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5043/coverthb.jpg"},{"id":416707,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":500920,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114718.htm","linkFileType":{"id":5,"text":"html"}},{"id":416709,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92A0H98","text":"USGS data release","linkHelpText":"Water quality and estimated changes in the Plum Creek watershed 2010–2020 (data release and model archive)"},{"id":416708,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5043/images"},{"id":416706,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.pdf","text":"Report","size":"8.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5043"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Plum Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.30723441433527,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.40323167054055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Previously glaciated landscapes often share similar surficial characteristics, including large areas of exposed bedrock, blankets of till deposits, and alluvium-floored valleys. These materials play significant roles in geologic and hydrologic resources, geohazards, and landscape evolution; however, the vast extents of many previously glaciated landscapes have rendered comprehensive, detailed field mapping difficult. While recent advances in remote sensing have facilitated mapping of surficial materials and landforms, manual map creation has remained a time-intensive task.
The development of convolutional neural networks (CNNs) for image classification has provided a new opportunity for rapid characterization of digital elevation models, thus enabling efficient mapping of surficial materials and landforms. We have developed a methodology that leverages existing geologic maps and high-resolution (1–3 m) lidar data to train a U-Net CNN to classify alluvium and exposed bedrock in previously glaciated regions. Coupled with U.S. Geological Survey-developed geomorphometry tools capable of approximating stream incision depths, these classifications can be used to estimate the minimum thicknesses of stream-proximal hillslope sediments in areas where streams have undergone minimal incision into bedrock.
We validate this approach in the context of the Neversink River watershed, a subbasin of the Delaware River Basin and significant water source for New York City. Evaluation of deep learning model performance demonstrates substantial agreement with manually drawn maps of alluvium and exposed bedrock. Validation of the minimum sediment thickness map using borehole data and passive seismic measurements shows the greatest performance for shallow materials and decreased performance in deep sediments, as well as in areas where bedrock exposures were too small to be resolved by lidar. To resolve these issues and create more accurate surficial maps, we are training new CNNs with additional geologic data and exploring advanced approaches for estimating depths of stream incision.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.acags.2023.100116","usgsCitation":"Odom, W.E., and Doctor, D.H., 2023, Rapid estimation of minimum depth-to-bedrock from lidar leveraging deep-learning-derived surficial material maps: Applied Computing and Geosciences, v. 18, 100116, 11 p., https://doi.org/10.1016/j.acags.2023.100116.","productDescription":"100116, 11 p.","ipdsId":"IP-146769","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443651,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.acags.2023.100116","text":"Publisher Index Page"},{"id":417128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.31774531759645,\n 38.65648371068133\n ],\n [\n -74.83711116001508,\n 39.00784172038104\n ],\n [\n -74.58034795534637,\n 39.29641683375996\n ],\n [\n -74.84560023131485,\n 39.59878756424641\n ],\n [\n -74.57402157309659,\n 39.81435898583538\n ],\n [\n -74.36164212699806,\n 40.42339954973025\n ],\n [\n -74.7398677925508,\n 40.67483290305228\n ],\n [\n -74.20311730788073,\n 41.49081439956416\n ],\n [\n -73.87900349307475,\n 42.134764710063195\n ],\n [\n -74.40404082594178,\n 42.53787114018144\n ],\n [\n -75.19513563542162,\n 42.478156559974536\n ],\n [\n -75.74165548596034,\n 41.860885020202005\n ],\n [\n -76.19243039302005,\n 41.151909429148475\n ],\n [\n -76.53910969789781,\n 40.49637767696336\n ],\n [\n -76.22016361968275,\n 40.00185213900514\n ],\n [\n -75.6922644432239,\n 39.71427259376151\n ],\n [\n -75.61094038798961,\n 39.383028287456796\n ],\n [\n -75.31774531759645,\n 38.65648371068133\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Odom, William Elijah 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","email":"","middleInitial":"Elijah","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":872923,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70246603,"text":"70246603 - 2023 - Who spawns where? Temperature, elevation, and discharge differentially affect the distribution of breeding by six Pacific salmonids within a large river basin","interactions":[],"lastModifiedDate":"2023-08-08T14:20:51.953441","indexId":"70246603","displayToPublicDate":"2023-05-03T07:03:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Who spawns where? Temperature, elevation, and discharge differentially affect the distribution of breeding by six Pacific salmonids within a large river basin","docAbstract":"Groundwater in the karst groundwater system at Area B of Fort Detrick in Frederick County, Maryland, is contaminated with chlorinated solvents from the past disposal of laboratory wastes. In cooperation with U.S. Army Environmental Command and U.S. Army Garrison Fort Detrick, the U.S. Geological Survey performed a 3-year study to refine the conceptual model of groundwater flow in and around Area B of Fort Detrick at the site- to regional-scale. The investigation was designed to review the geologic setting, assess the temporal variability of the hydrologic system, evaluate the potential for interbasin groundwater flow, determine the degree of vertical connectivity of the aquifer, characterize the sources and timing of groundwater recharge, and identify if dyes from previous tracer tests continue to drain from the aquifer. This study established a continuous hydrologic monitoring network of 12 water level gages, 2 streamgages, a precipitation gage, and in situ fluorometric monitoring. A water budget analysis was performed using hydrologic monitoring data and a soil-water balance model constructed for the study. In this study each individual water budget term is calculated using available data or through modeling, and a water budget residual term is calculated. If the water budget residual term is small relative to the uncertainty of the underlying data, then an additional import or export of water (in other words, interbasin transfer) is not needed to fully describe the hydrologic system. Groundwater and spring samples from 20 locations were collected in a 2019 synoptic geochemical sampling event and analyzed for a suite of analytes that included groundwater age tracer constituents.
The karst groundwater system was found to be highly responsive to hydrologic events, with strong water level and stream base flow responses to individual storm events and a historic wet period in 2017 and 2018. The water budget analysis included historic flooding in May 2018, though more typical hydrologic patterns were observed in 2019 and 2020. During most evaluated intervals, the water budget residual was less than the estimated uncertainty on the residual for the two Carroll Creek watersheds, which suggested no substantial net interbasin flow occurs from these watersheds. The watershed difference area, a region that includes Area B, had a significant negative water budget residual, which may be the result of a net interbasin import of groundwater or the result of focused groundwater recharge not simulated by the soil-water balance model. Geochemical analysis and groundwater age dating reveals shallow groundwater (approximately less than [<] 150 feet deep) appears to be relatively young (approximately <30 years) and to be recharged in the vicinity of Area B. In the deep groundwater sampled in this study (approximately greater than [>] 150 feet deep), older groundwater from a differing recharge source, based on stable isotopes and noble gas analyses, is observed and interpreted to represent less direct connectivity to the surface and increased proportions of water recharged to the north and (or) west of Area B. A clustering analysis to reveal groupings within the suite of geochemical data was used to define seven groups. The groupings generally show that wells in similar depths and lateral aquifer positions generally cluster together, with some exceptions. Although limited by suspended sediments, the in situ fluorometric monitoring at springs did not detect any dye leaving the system above the limit of detection for the method. Dye was only detected above the limit of detection in one well, which was used as an injection well during a previous dye tracer test.
The results of this study support and refine the conceptual site model of groundwater hydrology at Area B. The geologic and geophysical log review in this study agrees with prior assessments of physical controls on groundwater flow. A literature review of mid-Atlantic karst studies identified similar controls reported in these environments. The additional characterization of hydrologic responsiveness in this study suggests that hydrologic conditions and events are important considerations when interpreting potentiometric surfaces and contaminant trends over time and highlights the importance of continuous hydrologic monitoring. There is evidence to suggest that either intense focused groundwater recharge occurs in the vicinity of Area B or net along-valley groundwater interbasin flow from the upper study watershed enters the lower watershed and discharges to Carroll Creek. Geochemical analyses also suggest that water recharged from Catoctin Mountain and the elevated areas to the north and (or) west of the site may be present in the older and deeper Area B groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225054","collaboration":"Prepared in cooperation with U.S. Army Environmental Command and U.S. Army Garrison, Fort Detrick","usgsCitation":"Goodling, P.J., Fleming, B.J., Solder, J., Soroka, A., and Raffensperger, J., 2023, Hydrogeologic characterization of Area B, Fort Detrick, Maryland: U.S. Geological Survey Scientific Investigations Report 2022–5054, 128 p., https://doi.org/10.3133/sir20225054.","productDescription":"Report: xiv, 128 p.; 2 Data Releases","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124092","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":435349,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DUFZY7","text":"USGS data release","linkHelpText":"Supporting Datasets for Hydrogeological Characterization of Ft. Detrick Area B, Maryland"},{"id":500936,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114709.htm","linkFileType":{"id":5,"text":"html"}},{"id":416517,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GTTX8Q","text":"USGS data release","linkHelpText":"Soil water balance model developed for Maryland and Pennsylvania"},{"id":416516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AYWBXU","text":"USGS data release","linkHelpText":"Supporting datasets for hydrogeological characterization of Area B, Fort Detrick, Maryland"},{"id":416515,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.pdf","text":"Report","size":"51.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5054"},{"id":416514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5054/coverthb.jpg"},{"id":416562,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225054/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5054"},{"id":416564,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5054/images/"},{"id":416563,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.XML"}],"country":"United States","state":"Maryland","otherGeospatial":"Fort Detrick","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.4693386578843,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.458154924593\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228