Statistical models of nitrate occurrence in the glacial aquifer system of the northern United States, developed by the U.S. Geological Survey, use observed relations between nitrate concentrations and sets of explanatory variables—representing well-construction, environmental, and source characteristics— to predict the probability that nitrate, as nitrogen, will exceed a threshold concentration. However, the models do not explicitly account for the processes that control the transport of nitrogen from surface sources to a pumped well and use area-weighted mean spatial variables computed from within a circular buffer around the well as a simplified source-area conceptualization. The use of models that explicitly represent physical-transport processes can inform and, potentially, improve these statistical models. Specifically, groundwater-flow models simulate advective transport—predominant in many surficial aquifers— and can contribute to the refinement of the statistical models by (1) providing for improved, physically based representations of a source area to a well, and (2) allowing for more detailed estimates of environmental variables.
\nA source area to a well, known as a contributing recharge area, represents the area at the water table that contributes recharge to a pumped well; a well pumped at a volumetric rate equal to the amount of recharge through a circular buffer will result in a contributing recharge area that is the same size as the buffer but has a shape that is a function of the hydrologic setting. These volume-equivalent contributing recharge areas will approximate circular buffers in areas of relatively flat hydraulic gradients, such as near groundwater divides, but in areas with steep hydraulic gradients will be elongated in the upgradient direction and agree less with the corresponding circular buffers.
\nThe degree to which process-model-estimated contributing recharge areas, which simulate advective transport and therefore account for local hydrologic settings, would inform and improve the development of statistical models can be implicitly estimated by evaluating the differences between explanatory variables estimated from the contributing recharge areas and the circular buffers used to develop existing statistical models. The larger the difference in estimated variables, the more likely that statistical models would be changed, and presumably improved, if explanatory variables estimated from contributing recharge areas were used in model development. Comparing model predictions from the two sets of estimated variables would further quantify—albeit implicitly—how an improved, physically based estimate of explanatory variables would be reflected in model predictions. Differences between the two sets of estimated explanatory variables and resultant model predictions vary spatially; greater differences are associated with areas of steep hydraulic gradients. A direct comparison, however, would require the development of a separate set of statistical models using explanatory variables from contributing recharge areas.
\nArea-weighted means of three environmental variables—silt content, alfisol content, and depth to water from the U.S. Department of Agriculture State Soil Geographic (STATSGO) data—and one nitrogen-source variable (fertilizer-application rate from county data mapped to Enhanced National Land Cover Data 1992 (NLCDe 92) agricultural land use) can vary substantially between circular buffers and volume-equivalent contributing recharge areas and among contributing recharge areas for different sets of well variables. The differences in estimated explanatory variables are a function of the same factors affecting the contributing recharge areas as well as the spatial resolution and local distribution of the underlying spatial data. As a result, differences in estimated variables between circular buffers and contributing recharge areas are complex and site specific as evidenced by differences in estimated variables for circular buffers and contributing recharge areas of existing public-supply and network wells in the Great Miami River Basin. Large differences in areaweighted mean environmental variables are observed at the basin scale, determined by using the network of uniformly spaced hypothetical wells; the differences have a spatial pattern that generally is similar to spatial patterns in the underlying STATSGO data. Generally, the largest differences were observed for area-weighted nitrogen-application rate from county and national land-use data; the basin-scale differences ranged from -1,600 (indicating a larger value from within the volume-equivalent contributing recharge area) to 1,900 kilograms per year (kg/yr); the range in the underlying spatial data was from 0 to 2,200 kg/yr. Silt content, alfisol content, and nitrogen-application rate are defined by the underlying spatial data and are external to the groundwater system; however, depth to water is an environmental variable that can be estimated in more detail and, presumably, in a more physically based manner using a groundwater-flow model than using the spatial data. Model-calculated depths to water within circular buffers in the Great Miami River Basin differed substantially from values derived from the spatial data and had a much larger range.
\nDifferences in estimates of area-weighted spatial variables result in corresponding differences in predictions of nitrate occurrence in the aquifer. In addition to the factors affecting contributing recharge areas and estimated explanatory variables, differences in predictions also are a function of the specific set of explanatory variables used and the fitted slope coefficients in a given model. For models that predicted the probability of exceeding 1 and 4 milligrams per liter as nitrogen (mg/L as N), predicted probabilities using variables estimated from circular buffers and contributing recharge areas generally were correlated but differed significantly at the local and basin scale. The scale and distribution of prediction differences can be explained by the underlying differences in the estimated variables and the relative weight of the variables in the statistical models. Differences in predictions of exceeding 1 mg/L as N, which only includes environmental variables, generally correlated with the underlying differences in STATSGO data, whereas differences in exceeding 4 mg/L as N were more spatially extensive because that model included environmental and nitrogen-source variables. Using depths to water from within circular buffers derived from the spatial data and depths to water within the circular buffers calculated from the groundwater-flow model, restricted to the same range, resulted in large differences in predicted probabilities. The differences in estimated explanatory variables between contributing recharge areas and circular buffers indicate incorporation of physically based contributing recharge area likely would result in a different set of explanatory variables and an improved set of statistical models.
\nThe use of a groundwater-flow model to improve representations of source areas or to provide more-detailed estimates of specific explanatory variables includes a number of limitations and technical considerations. An assumption in these analyses is that (1) there is a state of mass balance between recharge and pumping, and (2) transport to a pumped well is under a steady state flow field. Comparison of volumeequivalent contributing recharge areas under steady-state and transient transport conditions at a location in the southeastern part of the basin shows the steady-state contributing recharge area is a reasonable approximation of the transient contributing recharge area after between 10 and 20 years of pumping. The first assumption is a more important consideration for this analysis. A gradient effect refers to a condition where simulated pumping from a well is less than recharge through the corresponding contributing recharge area. This generally takes place in areas with steep hydraulic gradients, such as near discharge locations, and can be mitigated using a finer model discretization. A boundary effect refers to a condition where recharge through the contributing recharge area is less than pumping. This indicates other sources of water to the simulated well and could reflect a real hydrologic process. In the Great Miami River Basin, large gradient and boundary effects—defined as the balance between pumping and recharge being less than half—occurred in 5 and 14 percent of the basin, respectively. The agreement between circular buffers and volume-equivalent contributing recharge areas, differences in estimated variables, and the effect on statisticalmodel predictions between the population of wells with a balance between pumping and recharge within 10 percent and the population of all wells were similar. This indicated process-model limitations did not affect the overall findings in the Great Miami River Basin; however, this would be model specific, and prudent use of a process model needs to entail a limitations analysis and, if necessary, alterations to the model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125001","collaboration":"National Water-Quality Assessment Program","usgsCitation":"Walter, D.A., and Starn, J.J., 2013, The use of process models to inform and improve statistical models of nitrate occurrence, Great Miami River Basin, southwestern Ohio: U.S. Geological Survey Scientific Investigations Report 2012-5001, x, 75 p., https://doi.org/10.3133/sir20125001.","productDescription":"x, 75 p.","numberOfPages":"90","onlineOnly":"N","additionalOnlineFiles":"N","costCenters":[{"id":377,"text":"Massachusetts-Rhode Island Water Science Center","active":false,"usgs":true}],"links":[{"id":272823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20125001.jpg"},{"id":272821,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5001/"},{"id":272822,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5001/pdf/sir2012-5001_report_508.pdf"}],"country":"United States","state":"Ohio","otherGeospatial":"Great Miami River Basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -84.82,38.4 ], [ -84.82,42.0 ], [ -80.52,42.0 ], [ -80.52,38.4 ], [ -84.82,38.4 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"51a4805fe4b064a995b7a0d0","contributors":{"authors":[{"text":"Walter, Donald A. 0000-0003-0879-4477 dawalter@usgs.gov","orcid":"https://orcid.org/0000-0003-0879-4477","contributorId":1101,"corporation":false,"usgs":true,"family":"Walter","given":"Donald","email":"dawalter@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478948,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Starn, J. Jeffrey","contributorId":101617,"corporation":false,"usgs":true,"family":"Starn","given":"J.","email":"","middleInitial":"Jeffrey","affiliations":[],"preferred":false,"id":478949,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70046081,"text":"sir20135007 - 2013 - Relation of watershed setting and stream nutrient yields at selected sites in central and eastern North Carolina, 1997-2008","interactions":[],"lastModifiedDate":"2017-01-17T20:36:54","indexId":"sir20135007","displayToPublicDate":"2013-05-23T00:00:00","publicationYear":"2013","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":"2013-5007","title":"Relation of watershed setting and stream nutrient yields at selected sites in central and eastern North Carolina, 1997-2008","docAbstract":"Data collected between 1997 and 2008 at 48 stream sites were used to characterize relations between watershed settings and stream nutrient yields throughout central and eastern North Carolina. The focus of the investigation was to identify environmental variables in watersheds that influence nutrient export for supporting the development and prioritization of management strategies for restoring nutrient-impaired streams.\n\nNutrient concentration data and streamflow data compiled for the 1997 to 2008 study period were used to compute stream yields of nitrate, total nitrogen (N), and total phosphorus (P) for each study site. Compiled environmental data (including variables for land cover, hydrologic soil groups, base-flow index, streams, wastewater treatment facilities, and concentrated animal feeding operations) were used to characterize the watershed settings for the study sites. Data for the environmental variables were analyzed in combination with the stream nutrient yields to explore relations based on watershed characteristics and to evaluate whether particular variables were useful indicators of watersheds having relatively higher or lower potential for exporting nutrients.\n\nData evaluations included an examination of median annual nutrient yields based on a watershed land-use classification scheme developed as part of the study. An initial examination of the data indicated that the highest median annual nutrient yields occurred at both agricultural and urban sites, especially for urban sites having large percentages of point-source flow contributions to the streams. The results of statistical testing identified significant differences in annual nutrient yields when sites were analyzed on the basis of watershed land-use category. When statistical differences in median annual yields were noted, the results for nitrate, total N, and total P were similar in that highly urbanized watersheds (greater than 30 percent developed land use) and (or) watersheds with greater than 10 percent point-source flow contributions to streamflow had higher yields relative to undeveloped watersheds (having less than 10 and 15 percent developed and agricultural land uses, respectively) and watersheds with relatively low agricultural land use (between 15 and 30 percent). The statistical tests further indicated that the median annual yields for total P were statistically higher for watersheds with high agricultural land use (greater than 30 percent) compared to the undeveloped watersheds and watersheds with low agricultural land use. The total P yields also were higher for watersheds with low urban land use (between 10 and 30 percent developed land) compared to the undeveloped watersheds. The study data indicate that grouping and examining stream nutrient yields based on the land-use classifications used in this report can be useful for characterizing relations between watershed settings and nutrient yields in streams located throughout central and eastern North Carolina.\n\nCompiled study data also were analyzed with four regression tree models as a means of determining which watershed environmental variables or combination of variables result in basins that are likely to have high or low nutrient yields. The regression tree analyses indicated that some of the environmental variables examined in this study were useful for predicting yields of nitrate, total N, and total P. When the median annual nutrient yields for all 48 sites were evaluated as a group (Model 1), annual point-source flow yields had the greatest influence on nitrate and total N yields observed in streams, and annual streamflow yields had the greatest influence on yields of total P. The Model 1 results indicated that watersheds with higher annual point-source flow yields had higher annual yields of nitrate and total N, and watersheds with higher annual streamflow yields had higher annual yields of total P.\n\nWhen sites with high point-source flows (greater than 10 percent of total streamflow) were excluded from the regression tree analyses (Models 2–4), the percentage of forested land in the watersheds was identified as the primary environmental variable influencing stream yields for both total N and total P. Models 2, 3 and 4 did not identify any watershed environmental variables that could adequately explain the observed variability in the nitrate yields among the set of sites examined by each of these models. The results for Models 2, 3, and 4 indicated that watersheds with higher percentages of forested land had lower annual total N and total P yields compared to watersheds with lower percentages of forested land, which had higher median annual total N and total P yields. Additional environmental variables determined to further influence the stream nutrient yields included median annual percentage of point-source flow contributions to the streams, variables of land cover (percentage of forested land, agricultural land, and (or) forested land plus wetlands) in the watershed and (or) in the stream buffer, and drainage area. The regression tree models can serve as a tool for relating differences in select watershed attributes to differences in stream yields of nitrate, total N, and total P, which can provide beneficial information for improving nutrient management in streams throughout North Carolina and for reducing nutrient loads to coastal waters.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135007","collaboration":"Prepared in cooperation with the North Carolina Department of Environment and Natural Resources, Division of Water Quality","usgsCitation":"Harden, S.L., Cuffney, T.F., Terziotti, S., and Kolb, K.R., 2013, Relation of watershed setting and stream nutrient yields at selected sites in central and eastern North Carolina, 1997-2008: U.S. Geological Survey Scientific Investigations Report 2013-5007, vii, 47 p.; 4 Appendixes, https://doi.org/10.3133/sir20135007.","productDescription":"vii, 47 p.; 4 Appendixes","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"1997-01-01","temporalEnd":"2008-12-31","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":272761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135007.png"},{"id":272757,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5007/Appendixes/Appendix1"},{"id":272755,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5007/"},{"id":272760,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5007/Appendixes/Appendix4"},{"id":272758,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5007/Appendixes/Appendix2"},{"id":272759,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2013/5007/Appendixes/Appendix3"},{"id":272756,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5007/pdf/sir2013-5007.pdf"}],"country":"United States","state":"North Carolina","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -84.32,33.84 ], [ -84.32,36.59 ], [ -75.46,36.59 ], [ -75.46,33.84 ], [ -84.32,33.84 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"519f2c5de4b0687ba0506b6e","contributors":{"authors":[{"text":"Harden, Stephen L. 0000-0001-6886-0099 slharden@usgs.gov","orcid":"https://orcid.org/0000-0001-6886-0099","contributorId":2212,"corporation":false,"usgs":true,"family":"Harden","given":"Stephen","email":"slharden@usgs.gov","middleInitial":"L.","affiliations":[{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cuffney, Thomas F. 0000-0003-1164-5560 tcuffney@usgs.gov","orcid":"https://orcid.org/0000-0003-1164-5560","contributorId":517,"corporation":false,"usgs":true,"family":"Cuffney","given":"Thomas","email":"tcuffney@usgs.gov","middleInitial":"F.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478849,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Terziotti, Silvia 0000-0003-3559-5844 seterzio@usgs.gov","orcid":"https://orcid.org/0000-0003-3559-5844","contributorId":1613,"corporation":false,"usgs":true,"family":"Terziotti","given":"Silvia","email":"seterzio@usgs.gov","affiliations":[{"id":476,"text":"North Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478850,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kolb, Katharine R. 0000-0002-1663-1662 kkolb@usgs.gov","orcid":"https://orcid.org/0000-0002-1663-1662","contributorId":16299,"corporation":false,"usgs":true,"family":"Kolb","given":"Katharine","email":"kkolb@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":false,"id":478852,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70046019,"text":"sim3255 - 2013 - Flood-inundation maps for the East Fork White River at Columbus, Indiana","interactions":[],"lastModifiedDate":"2013-05-20T13:25:17","indexId":"sim3255","displayToPublicDate":"2013-05-20T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3255","title":"Flood-inundation maps for the East Fork White River at Columbus, Indiana","docAbstract":"Digital flood-inundation maps for a 5.4-mile reach of the East Fork White River at Columbus, Indiana, from where the Flatrock and Driftwood Rivers combine to make up East Fork White River to just upstream of the confluence of Clifty Creek with the East Fork White River, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation, depict estimates of the areal extent of flooding corresponding to selected water levels (stages) at USGS streamgage 03364000, East Fork White River at Columbus, Indiana. Current conditions at the USGS streamgage may be obtained on the Internet from the USGS National Water Information System (http://waterdata.usgs.gov/in/nwis/uv/?site_no=03364000&agency_cd=USGS&). The National Weather Service (NWS) forecasts flood hydrographs for the East Fork White River at Columbus, Indiana at their Advanced Hydrologic Prediction Service (AHPS) flood warning system Website (http://water.weather.gov/ahps/), that may be used in conjunction with the maps developed in this study to show predicted areas of flood inundation. In this study, flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relation at USGS streamgage 03364000, East Fork White River at Columbus, Indiana. The calibrated hydraulic model was then used to determine 15 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum and ranging from bankfull to approximately the highest recorded water level at the streamgage. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from Light Detection and Ranging (LiDAR) data), having a 0.37-ft vertical accuracy and a 1.02 ft horizontal accuracy), in order to delineate the area flooded at each water level. The availability of these maps, along with Internet information regarding current stage from the USGS streamgage at Columbus, Indiana, and forecasted stream stages from the NWS will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for post-flood recovery efforts.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3255","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Lombard, P., 2013, Flood-inundation maps for the East Fork White River at Columbus, Indiana: U.S. Geological Survey Scientific Investigations Map 3255, Pamphlet: vi, 7 p.; Map Sheets: 15 JPEGs, 15 PDFs 17 x 22 inches; Downloads Directory; Readme; Metadata, https://doi.org/10.3133/sim3255.","productDescription":"Pamphlet: vi, 7 p.; Map Sheets: 15 JPEGs, 15 PDFs 17 x 22 inches; Downloads Directory; Readme; Metadata","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"Y","costCenters":[{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":272456,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sim3255.gif"},{"id":272440,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet11_617.7_SIM3255.pdf"},{"id":272444,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet12_618.7_SIM3255.pdf"},{"id":272413,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet03_609.7_SIM3255.pdf"},{"id":272445,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet13_619.7_SIM3255.pdf"},{"id":272453,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3255/Downloads/metadata"},{"id":272451,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3255/Downloads"},{"id":272452,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3255/Downloads/Readme.txt"},{"id":272447,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet14_620.7_SIM3255.pdf"},{"id":272425,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet06_612.7_SIM3255.pdf"},{"id":272428,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet07_613.7_SIM3255.pdf"},{"id":272449,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet15_621.7_SIM3255.pdf"},{"id":272437,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet10_616.7_SIM3255.pdf"},{"id":272434,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet09_615.7_SIM3255.pdf"},{"id":272388,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sim/3255/"},{"id":272389,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3255/pdf/sim3255.pdf"},{"id":272405,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet01_607.7_SIM3255.pdf"},{"id":272418,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet04_610.7_SIM3255.pdf"},{"id":272421,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet05_611.7_SIM3255.pdf"},{"id":272432,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet08_614.7_SIM3255.pdf"},{"id":272409,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sim/3255/pdf/pdf-mapsheets/sheet02_608.7_SIM3255.pdf"}],"projection":"Indiana State Plane Eastern Zone","datum":"North American Datum of 1983","country":"United States","state":"Indiana","city":"Columbus","otherGeospatial":"White River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -85.99617,39.149898 ], [ -85.99617,39.210643 ], [ -85.884247,39.210643 ], [ -85.884247,39.149898 ], [ -85.99617,39.149898 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"519b37dbe4b0e4e151ef5cba","contributors":{"authors":[{"text":"Lombard, Pamela J. 0000-0002-0983-1906","orcid":"https://orcid.org/0000-0002-0983-1906","contributorId":23899,"corporation":false,"usgs":true,"family":"Lombard","given":"Pamela J.","affiliations":[],"preferred":false,"id":478707,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70046014,"text":"sir20135010 - 2013 - Analysis of environmental setting, surface-water and groundwater data, and data gaps for the Citizen Potawatomi Nation Tribal Jurisdictional Area, Oklahoma, through 2011","interactions":[],"lastModifiedDate":"2020-02-26T17:45:28","indexId":"sir20135010","displayToPublicDate":"2013-05-18T00:00:00","publicationYear":"2013","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":"2013-5010","title":"Analysis of environmental setting, surface-water and groundwater data, and data gaps for the Citizen Potawatomi Nation Tribal Jurisdictional Area, Oklahoma, through 2011","docAbstract":"The Citizen Potawatomi Nation Tribal Jurisdictional Area, consisting of approximately 960 square miles in parts of three counties in central Oklahoma, has an abundance of water resources, being underlain by three principal aquifers (alluvial/terrace, Central Oklahoma, and Vamoosa-Ada), bordered by two major rivers (North Canadian and Canadian), and has several smaller drainages. The Central Oklahoma aquifer (also referred to as the Garber-Wellington aquifer) underlies approximately 3,000 square miles in central Oklahoma in parts of Cleveland, Logan, Lincoln, Oklahoma, and Pottawatomie Counties and much of the tribal jurisdictional area. Water from these aquifers is used for municipal, industrial, commercial, agricultural, and domestic supplies.\n\nThe approximately 115,000 people living in this area used an estimated 4.41 million gallons of fresh groundwater, 12.12 million gallons of fresh surface water, and 8.15 million gallons of saline groundwater per day in 2005. Approximately 8.48, 2.65, 2.24, 1.55, 0.83, and 0.81 million gallons per day of that water were used for domestic, livestock, commercial, industrial, crop irrigation, and thermoelectric purposes, respectively. Approximately one-third of the water used in 2005 was saline water produced during petroleum production. Future changes in use of freshwater in this area will be affected primarily by changes in population and agricultural practices. Future changes in saline water use will be affected substantially by changes in petroleum production. Parts of the area periodically are subject to flooding and severe droughts that can limit available water resources, particularly during summers, when water use increases and streamflows substantially decrease.\n\nMost of the area is characterized by rural types of land cover such as grassland, pasture/hay fields, and deciduous forest, which may limit negative effects on water quality by human activities because of lesser emissions of man-made chemicals on such areas than in more urbanized areas. Much of the water in the area is of good quality, though some parts of this area have water quality impaired by very hard surface water and groundwater; large chloride concentrations in some smaller streams; relatively large concentrations of nutrients and counts of fecal-indicator bacteria in the North Canadian River; and chloride, iron, manganese, and uranium concentrations that exceed primary or secondary drinking-water standards in water samples collected from small numbers of wells.\n\nSubstantial amounts of hydrologic and water-quality data have been collected in much of this area, but there are gaps in those data caused by relatively few streamflow-gaging stations, uneven distribution of surface-water quality sampling sites, lack of surface-water quality sampling at high-flow and low-flow conditions, and lack of a regularly measured and sampled groundwater network. This report summarizes existing water-use, climatic, geographic, hydrologic, and water-quality data and describes several means of filling gaps in hydrologic data for this area.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135010","collaboration":"Prepared in cooperation with the Citizen Potawatomi Nation","usgsCitation":"Andrews, W.J., Harich, C.R., Smith, S.J., Lewis, J.M., Shivers, M.J., Seger, C.H., and Becker, C., 2013, Analysis of environmental setting, surface-water and groundwater data, and data gaps for the Citizen Potawatomi Nation Tribal Jurisdictional Area, Oklahoma, through 2011: U.S. Geological Survey Scientific Investigations Report 2013-5010, x, 102 p., https://doi.org/10.3133/sir20135010.","productDescription":"x, 102 p.","numberOfPages":"116","additionalOnlineFiles":"N","temporalStart":"1943-01-01","temporalEnd":"2011-09-30","ipdsId":"IP-041340","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":272365,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135010.gif"},{"id":272363,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5010/"},{"id":272364,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5010/sir2013-5010.pdf"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Potawatomi Nation Tribal Jurisdictional Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.152099609375,\n 34.912962495216966\n ],\n [\n -96.767578125,\n 34.912962495216966\n ],\n [\n -96.767578125,\n 35.46514408578589\n ],\n [\n -97.152099609375,\n 35.46514408578589\n ],\n [\n -97.152099609375,\n 34.912962495216966\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"519894dbe4b0eb382b44ac47","contributors":{"authors":[{"text":"Andrews, William J. 0000-0003-4780-8835 wandrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4780-8835","contributorId":328,"corporation":false,"usgs":true,"family":"Andrews","given":"William","email":"wandrews@usgs.gov","middleInitial":"J.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478691,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harich, Christopher R. charich@usgs.gov","contributorId":3917,"corporation":false,"usgs":true,"family":"Harich","given":"Christopher","email":"charich@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":478695,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, S. Jerrod 0000-0002-9379-8167 sjsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-9379-8167","contributorId":981,"corporation":false,"usgs":true,"family":"Smith","given":"S.","email":"sjsmith@usgs.gov","middleInitial":"Jerrod","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478692,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lewis, Jason M. 0000-0001-5337-1890 jmlewis@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1890","contributorId":3854,"corporation":false,"usgs":true,"family":"Lewis","given":"Jason","email":"jmlewis@usgs.gov","middleInitial":"M.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478694,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shivers, Molly J. mshivers@usgs.gov","contributorId":4062,"corporation":false,"usgs":true,"family":"Shivers","given":"Molly","email":"mshivers@usgs.gov","middleInitial":"J.","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478696,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Seger, Christian H.","contributorId":34799,"corporation":false,"usgs":true,"family":"Seger","given":"Christian","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":478697,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Becker, Carol 0000-0001-6652-4542 cjbecker@usgs.gov","orcid":"https://orcid.org/0000-0001-6652-4542","contributorId":2489,"corporation":false,"usgs":true,"family":"Becker","given":"Carol","email":"cjbecker@usgs.gov","affiliations":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478693,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70043361,"text":"70043361 - 2013 - Estimating irrigation water demand using an improved method and optimizing reservoir operation for water supply and hydropower generation: a case study of the Xinfengjiang reservoir in southern China","interactions":[],"lastModifiedDate":"2013-05-14T09:17:57","indexId":"70043361","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":680,"text":"Agricultural Water Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating irrigation water demand using an improved method and optimizing reservoir operation for water supply and hydropower generation: a case study of the Xinfengjiang reservoir in southern China","docAbstract":"The ever-increasing demand for water due to growth of population and socioeconomic development in the past several decades has posed a worldwide threat to water supply security and to the environmental health of rivers. This study aims to derive reservoir operating rules through establishing a multi-objective optimization model for the Xinfengjiang (XFJ) reservoir in the East River Basin in southern China to minimize water supply deficit and maximize hydropower generation. Additionally, to enhance the estimation of irrigation water demand from the downstream agricultural area of the XFJ reservoir, a conventional method for calculating crop water demand is improved using hydrological model simulation results. Although the optimal reservoir operating rules are derived for the XFJ reservoir with three priority scenarios (water supply only, hydropower generation only, and equal priority), the river environmental health is set as the basic demand no matter which scenario is adopted. The results show that the new rules derived under the three scenarios can improve the reservoir operation for both water supply and hydropower generation when comparing to the historical performance. Moreover, these alternative reservoir operating policies provide the flexibility for the reservoir authority to choose the most appropriate one. Although changing the current operating rules may influence its hydropower-oriented functions, the new rules can be significant to cope with the increasingly prominent water shortage and degradation in the aquatic environment. Overall, our results and methods (improved estimation of irrigation water demand and formulation of the reservoir optimization model) can be useful for local watershed managers and valuable for other researchers worldwide.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Agricultural Water Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.agwat.2012.10.016","usgsCitation":"Wu, Y., and Chen, J., 2013, Estimating irrigation water demand using an improved method and optimizing reservoir operation for water supply and hydropower generation: a case study of the Xinfengjiang reservoir in southern China: Agricultural Water Management, v. 116, p. 110-121, https://doi.org/10.1016/j.agwat.2012.10.016.","productDescription":"12 p.","startPage":"110","endPage":"121","ipdsId":"IP-041608","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":272201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272200,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.agwat.2012.10.016"}],"country":"China","otherGeospatial":"Xinfengjiang Reservoir","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 114.3728,23.7144 ], [ 114.3728,24.1164 ], [ 114.7686,24.1164 ], [ 114.7686,23.7144 ], [ 114.3728,23.7144 ] ] ] } } ] }","volume":"116","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd5804e4b0b290850f7d16","contributors":{"authors":[{"text":"Wu, Yiping ywu@usgs.gov","contributorId":987,"corporation":false,"usgs":true,"family":"Wu","given":"Yiping","email":"ywu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":473461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Ji","contributorId":101960,"corporation":false,"usgs":true,"family":"Chen","given":"Ji","email":"","affiliations":[],"preferred":false,"id":473462,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70044991,"text":"70044991 - 2013 - Estimating instream constituent loads using replicate synoptic sampling, Peru Creek, Colorado","interactions":[],"lastModifiedDate":"2017-01-17T10:32:25","indexId":"70044991","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating instream constituent loads using replicate synoptic sampling, Peru Creek, Colorado","docAbstract":"The synoptic mass balance approach is often used to evaluate constituent mass loading in streams affected by mine drainage. Spatial profiles of constituent mass load are used to identify sources of contamination and prioritize sites for remedial action. This paper presents a field scale study in which replicate synoptic sampling campaigns are used to quantify the aggregate uncertainty in constituent load that arises from (1) laboratory analyses of constituent and tracer concentrations, (2) field sampling error, and (3) temporal variation in concentration from diel constituent cycles and/or source variation. Consideration of these factors represents an advance in the application of the synoptic mass balance approach by placing error bars on estimates of constituent load and by allowing all sources of uncertainty to be quantified in aggregate; previous applications of the approach have provided only point estimates of constituent load and considered only a subset of the possible errors. Given estimates of aggregate uncertainty, site specific data and expert judgement may be used to qualitatively assess the contributions of individual factors to uncertainty. This assessment can be used to guide the collection of additional data to reduce uncertainty. Further, error bars provided by the replicate approach can aid the investigator in the interpretation of spatial loading profiles and the subsequent identification of constituent source areas within the watershed.
The replicate sampling approach is applied to Peru Creek, a stream receiving acidic, metal-rich effluent from the Pennsylvania Mine. Other sources of acidity and metals within the study reach include a wetland area adjacent to the mine and tributary inflow from Cinnamon Gulch. Analysis of data collected under low-flow conditions indicates that concentrations of Al, Cd, Cu, Fe, Mn, Pb, and Zn in Peru Creek exceed aquatic life standards. Constituent loading within the study reach is dominated by effluent from the Pennsylvania Mine, with over 50% of the Cd, Cu, Fe, Mn, and Zn loads attributable to a collapsed adit near the top of the study reach. These estimates of mass load may underestimate the effect of the Pennsylvania Mine as leakage from underground mine workings may contribute to metal loads that are currently attributed to the wetland area. This potential leakage confounds the evaluation of remedial options and additional research is needed to determine the magnitude and location of the leakage.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2013.02.031","usgsCitation":"Runkel, R.L., Walton-Day, K., Kimball, B.A., Verplanck, P.L., and Nimick, D.A., 2013, Estimating instream constituent loads using replicate synoptic sampling, Peru Creek, Colorado: Journal of Hydrology, v. 489, p. 26-41, https://doi.org/10.1016/j.jhydrol.2013.02.031.","productDescription":"16 p.","startPage":"26","endPage":"41","ipdsId":"IP-044174","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":272199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Peru Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.8287239074707,\n 39.59451160220633\n ],\n [\n -105.8287239074707,\n 39.61144109709137\n ],\n [\n -105.80074310302734,\n 39.61144109709137\n ],\n [\n -105.80074310302734,\n 39.59451160220633\n ],\n [\n -105.8287239074707,\n 39.59451160220633\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"489","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd5804e4b0b290850f7d13","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":476577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walton-Day, Katherine 0000-0002-9146-6193","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":68339,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","affiliations":[],"preferred":false,"id":476579,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kimball, Briant A. bkimball@usgs.gov","contributorId":533,"corporation":false,"usgs":true,"family":"Kimball","given":"Briant","email":"bkimball@usgs.gov","middleInitial":"A.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":476576,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Verplanck, Philip L. 0000-0002-3653-6419 plv@usgs.gov","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":728,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","email":"plv@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":476578,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nimick, David A. dnimick@usgs.gov","contributorId":421,"corporation":false,"usgs":true,"family":"Nimick","given":"David","email":"dnimick@usgs.gov","middleInitial":"A.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":573,"text":"Special Applications Science Center","active":true,"usgs":true}],"preferred":true,"id":476575,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70042353,"text":"70042353 - 2013 - Evaporative losses from soils covered by physical and different types of biological soil crusts","interactions":[],"lastModifiedDate":"2013-05-14T11:23:03","indexId":"70042353","displayToPublicDate":"2013-05-14T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Evaporative losses from soils covered by physical and different types of biological soil crusts","docAbstract":"Evaporation of soil moisture is one of the most important processes affecting water availability in semiarid ecosystems. Biological soil crusts, which are widely distributed ground cover in these ecosystems, play a recognized role on water processes. Where they roughen surfaces, water residence time and thus infiltration can be greatly enhanced, whereas their ability to clog soil pores or cap the soil surface when wetted can greatly decrease infiltration rate, thus affecting evaporative losses. In this work, we compared evaporation in soils covered by physical crusts, biological crusts in different developmental stages and in the soils underlying the different biological crust types. Our results show that during the time of the highest evaporation (Day 1), there was no difference among any of the crust types or the soils underlying them. On Day 2, when soil moisture was moderately low (11%), evaporation was slightly higher in well-developed biological soil crusts than in physical or poorly developed biological soil crusts. However, crust removal did not cause significant changes in evaporation compared with the respective soil crust type. These results suggest that the small differences we observed in evaporation among crust types could be caused by differences in the properties of the soil underneath the biological crusts. At low soil moisture (<6%), there was no difference in evaporation among crust types or the underlying soils. Water loss for the complete evaporative cycle (from saturation to dry soil) was similar in both crusted and scraped soils. Therefore, we conclude that for the specific crust and soil types tested, the presence or the type of biological soil crust did not greatly modify evaporation with respect to physical crusts or scraped soils.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Hydrological Processes","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/hyp.8421","usgsCitation":"Chamizo, S., Canton, Y., Domingo, F., and Belnap, J., 2013, Evaporative losses from soils covered by physical and different types of biological soil crusts: Hydrological Processes, v. 27, no. 3, p. 324-332, https://doi.org/10.1002/hyp.8421.","productDescription":"9 p.","startPage":"324","endPage":"332","ipdsId":"IP-029706","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":473824,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/hyp.8421","text":"External Repository"},{"id":272222,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272221,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/hyp.8421"}],"volume":"27","issue":"3","noUsgsAuthors":false,"publicationDate":"2012-03-19","publicationStatus":"PW","scienceBaseUri":"53cd588ae4b0b290850f828e","contributors":{"authors":[{"text":"Chamizo, S.","contributorId":49260,"corporation":false,"usgs":true,"family":"Chamizo","given":"S.","affiliations":[],"preferred":false,"id":471367,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Canton, Y.","contributorId":99868,"corporation":false,"usgs":true,"family":"Canton","given":"Y.","email":"","affiliations":[],"preferred":false,"id":471369,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Domingo, F.","contributorId":91776,"corporation":false,"usgs":true,"family":"Domingo","given":"F.","email":"","affiliations":[],"preferred":false,"id":471368,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belnap, J. 0000-0001-7471-2279","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":23872,"corporation":false,"usgs":true,"family":"Belnap","given":"J.","affiliations":[],"preferred":false,"id":471366,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045934,"text":"70045934 - 2013 - Simulating mechanisms for dispersal, production and stranding of small forage fish in temporary wetland habitats","interactions":[],"lastModifiedDate":"2013-05-11T23:50:49","indexId":"70045934","displayToPublicDate":"2013-05-11T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Simulating mechanisms for dispersal, production and stranding of small forage fish in temporary wetland habitats","docAbstract":"Movement strategies of small forage fish (<8 cm total length) between temporary and permanent wetland habitats affect their overall population growth and biomass concentrations, i.e., availability to predators. These fish are often the key energy link between primary producers and top predators, such as wading birds, which require high concentrations of stranded fish in accessible depths. Expansion and contraction of seasonal wetlands induce a sequential alternation between rapid biomass growth and concentration, creating the conditions for local stranding of small fish as they move in response to varying water levels. To better understand how landscape topography, hydrology, and fish behavior interact to create high densities of stranded fish, we first simulated population dynamics of small fish, within a dynamic food web, with different traits for movement strategy and growth rate, across an artificial, spatially explicit, heterogeneous, two-dimensional marsh slough landscape, using hydrologic variability as the driver for movement. Model output showed that fish with the highest tendency to invade newly flooded marsh areas built up the largest populations over long time periods with stable hydrologic patterns. A higher probability to become stranded had negative effects on long-term population size, and offset the contribution of that species to stranded biomass. The model was next applied to the topography of a 10 km × 10 km area of Everglades landscape. The details of the topography were highly important in channeling fish movements and creating spatiotemporal patterns of fish movement and stranding. This output provides data that can be compared in the future with observed locations of fish biomass concentrations, or such surrogates as phosphorus ‘hotspots’ in the marsh.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecological Modelling","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2012.11.001","usgsCitation":"Yurek, S., DeAngelis, D., Trexler, J.C., Jopp, F., and Donalson, D.D., 2013, Simulating mechanisms for dispersal, production and stranding of small forage fish in temporary wetland habitats: Ecological Modelling, v. 250, p. 391-401, https://doi.org/10.1016/j.ecolmodel.2012.11.001.","productDescription":"11 p.","startPage":"391","endPage":"401","ipdsId":"IP-038780","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":272189,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272188,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ecolmodel.2012.11.001"}],"volume":"250","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518f5a51e4b05ebc8f7cc30a","contributors":{"authors":[{"text":"Yurek, Simeon 0000-0002-6209-7915 syurek@usgs.gov","orcid":"https://orcid.org/0000-0002-6209-7915","contributorId":103167,"corporation":false,"usgs":true,"family":"Yurek","given":"Simeon","email":"syurek@usgs.gov","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":478555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Donald L. 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":88015,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Donald L.","affiliations":[],"preferred":false,"id":478554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trexler, Joel C.","contributorId":36267,"corporation":false,"usgs":false,"family":"Trexler","given":"Joel","email":"","middleInitial":"C.","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":478551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jopp, Fred","contributorId":62336,"corporation":false,"usgs":true,"family":"Jopp","given":"Fred","email":"","affiliations":[],"preferred":false,"id":478552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Donalson, Douglas D.","contributorId":74660,"corporation":false,"usgs":true,"family":"Donalson","given":"Douglas","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":478553,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70045932,"text":"ofr20121038 - 2013 - Tidal wetlands of the Yaquina and Alsea River estuaries, Oregon: Geographic Information Systems layer development and recommendations for National Wetlands Inventory revisions","interactions":[],"lastModifiedDate":"2016-05-04T14:44:24","indexId":"ofr20121038","displayToPublicDate":"2013-05-10T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2012-1038","title":"Tidal wetlands of the Yaquina and Alsea River estuaries, Oregon: Geographic Information Systems layer development and recommendations for National Wetlands Inventory revisions","docAbstract":"Geographic Information Systems (GIS) layers of current, and likely former, tidal wetlands in two Oregon estuaries were generated by enhancing the 2010 National Wetlands Inventory (NWI) data with expert local field knowledge, Light Detection and Ranging-derived elevations, and 2009 aerial orthophotographs. Data were generated for two purposes: First, to enhance the NWI by recommending revised Cowardin classifications for certain NWI wetlands within the study area; and second, to generate GIS data for the 1999 Yaquina and Alsea River Basins Estuarine Wetland Site Prioritization study. Two sets of GIS products were generated: (1) enhanced NWI shapefiles; and (2) shapefiles of prioritization sites. The enhanced NWI shapefiles contain recommended changes to the Cowardin classification (system, subsystem, class, and/or modifiers) for 286 NWI polygons in the Yaquina estuary (1,133 acres) and 83 NWI polygons in the Alsea estuary (322 acres). These enhanced NWI shapefiles also identify likely former tidal wetlands that are classified as upland in the current NWI (64 NWI polygons totaling 441 acres in the Yaquina estuary; 16 NWI polygons totaling 51 acres in the Alsea estuary). The former tidal wetlands were identified to assist strategic planning for tidal wetland restoration. Cowardin classifications for the former tidal wetlands were not provided, because their current hydrology is complex owing to dikes, tide gates, and drainage ditches. The scope of this project did not include the field evaluation that would be needed to determine whether the former tidal wetlands are currently wetlands, and if so, determine their correct Cowardin classification. The prioritization site shapefiles contain 49 prioritization sites totaling 2,177 acres in the Yaquina estuary, and 39 prioritization sites totaling 1,045 acres in the Alsea estuary. The prioritization sites include current and former (for example, diked) tidal wetlands, and provide landscape units appropriate for basin-scale wetland restoration and conservation action planning. Several new prioritization sites (not included in the 1999 prioritization) were identified in each estuary, consisting of NWI polygons formerly classified as nontidal wetland or upland. The GIS products of this project improve the accuracy and utility of the NWI data, and provide useful tools for estuarine resource management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20121038","collaboration":"Prepared in cooperation with Green Point Consulting and the U.S. Environmental Protection Agency","usgsCitation":"Brophy, L.S., Reusser, D.A., and Janousek, C.N., 2013, Tidal wetlands of the Yaquina and Alsea River estuaries, Oregon: Geographic Information Systems layer development and recommendations for National Wetlands Inventory revisions: U.S. Geological Survey Open-File Report 2012-1038, vi, 60 p., https://doi.org/10.3133/ofr20121038.","productDescription":"vi, 60 p.","numberOfPages":"68","onlineOnly":"N","additionalOnlineFiles":"Y","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":272177,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ofr20121038.gif"},{"id":272323,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/of/2012/1038/"},{"id":272176,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2012/1038/pdf/ofr2012-1038.pdf","text":"Report","size":"18.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"}],"country":"United States","state":"Oregon","otherGeospatial":"Yaquina And Alsea Estuaries","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.16,44.16 ], [ -124.16,44.5 ], [ -123.5,44.5 ], [ -123.5,44.16 ], [ -124.16,44.16 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518e08f7e4b05ebc8f7cc2de","contributors":{"authors":[{"text":"Brophy, Laura S.","contributorId":47266,"corporation":false,"usgs":false,"family":"Brophy","given":"Laura","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":478548,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reusser, Deborah A. dreusser@usgs.gov","contributorId":2423,"corporation":false,"usgs":true,"family":"Reusser","given":"Deborah","email":"dreusser@usgs.gov","middleInitial":"A.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":547809,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janousek, Christopher N. 0000-0003-2124-6715","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":103951,"corporation":false,"usgs":false,"family":"Janousek","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":478549,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045859,"text":"70045859 - 2013 - Analyzing the water budget and hydrological characteristics and responses to land use in a monsoonal climate river basin in South China","interactions":[],"lastModifiedDate":"2013-06-17T09:24:06","indexId":"70045859","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Analyzing the water budget and hydrological characteristics and responses to land use in a monsoonal climate river basin in South China","docAbstract":"Hydrological models have been increasingly used by hydrologists and water resource managers to understand natural processes and human activities that affect watersheds. In this study, we use the physically based model, Soil and Water Assessment Tool (SWAT), to investigate the hydrological processes in the East River Basin in South China, a coastal area dominated by monsoonal climate. The SWAT model was calibrated using 8-year (1973–1980) record of the daily streamflow at the basin outlet (Boluo station), and then validated using data collected during the subsequent 8 years (1981–1988). Statistical evaluation shows that SWAT can consistently simulate the streamflow of the East River with monthly Nash–Sutcliffe efficiencies of 0.93 for calibration and 0.90 for validation at the Boluo station. We analyzed the model simulations with calibrated parameters, presented the spatiotemporal distribution of the key hydrological components, and quantified their responses to different land uses. Watershed managers can use the results of this study to understand hydrological features and evaluate water resources of the East River in terms of sustainable development and effective management.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Springer","doi":"10.1007/s00267-013-0045-5","usgsCitation":"Wu, Y., and Chen, J., 2013, Analyzing the water budget and hydrological characteristics and responses to land use in a monsoonal climate river basin in South China: Environmental Management, v. 51, no. 6, p. 1174-1186, https://doi.org/10.1007/s00267-013-0045-5.","productDescription":"13 p.","startPage":"1174","endPage":"1186","ipdsId":"IP-042191","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":272031,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272012,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s00267-013-0045-5"}],"country":"China","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 73.5,18.2 ], [ 73.5,53.6 ], [ 134.8,53.6 ], [ 134.8,18.2 ], [ 73.5,18.2 ] ] ] } } ] }","volume":"51","issue":"6","noUsgsAuthors":false,"publicationDate":"2013-04-23","publicationStatus":"PW","scienceBaseUri":"518a1451e4b061e1bd533337","contributors":{"authors":[{"text":"Wu, Yiping ywu@usgs.gov","contributorId":987,"corporation":false,"usgs":true,"family":"Wu","given":"Yiping","email":"ywu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":478445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Ji","contributorId":101960,"corporation":false,"usgs":true,"family":"Chen","given":"Ji","email":"","affiliations":[],"preferred":false,"id":478446,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045851,"text":"sir20135071 - 2013 - Continuous real-time water-quality monitoring and regression analysis to compute constituent concentrations and loads in the North Fork Ninnescah River upstream from Cheney Reservoir, south-central Kansas, 1999–2012","interactions":[],"lastModifiedDate":"2013-05-07T13:25:37","indexId":"sir20135071","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","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":"2013-5071","title":"Continuous real-time water-quality monitoring and regression analysis to compute constituent concentrations and loads in the North Fork Ninnescah River upstream from Cheney Reservoir, south-central Kansas, 1999–2012","docAbstract":"Cheney Reservoir, located in south-central Kansas, is the primary water supply for the city of Wichita. The U.S. Geological Survey has operated a continuous real-time water-quality monitoring station since 1998 on the North Fork Ninnescah River, the main source of inflow to Cheney Reservoir. Continuously measured water-quality physical properties include streamflow, specific conductance, pH, water temperature, dissolved oxygen, and turbidity. Discrete water-quality samples were collected during 1999 through 2009 and analyzed for sediment, nutrients, bacteria, and other water-quality constituents. Regression models were developed to establish relations between discretely sampled constituent concentrations and continuously measured physical properties to compute concentrations of those constituents of interest that are not easily measured in real time because of limitations in sensor technology and fiscal constraints. Regression models were published in 2006 that were based on data collected during 1997 through 2003. This report updates those models using discrete and continuous data collected during January 1999 through December 2009. Models also were developed for four new constituents, including additional nutrient species and indicator bacteria. In addition, a conversion factor of 0.68 was established to convert the Yellow Springs Instruments (YSI) model 6026 turbidity sensor measurements to the newer YSI model 6136 sensor at the North Ninnescah River upstream from Cheney Reservoir site. Newly developed models and 14 years of hourly continuously measured data were used to calculate selected constituent concentrations and loads during January 1999 through December 2012. The water-quality information in this report is important to the city of Wichita because it allows the concentrations of many potential pollutants of interest to Cheney Reservoir, including nutrients and sediment, to be estimated in real time and characterized over conditions and time scales that would not be possible otherwise. In general, model forms and the amount of variance explained by the models was similar between the original and updated models. The amount of variance explained by the updated models changed by 10 percent or less relative to the original models. Total nitrogen, nitrate, organic nitrogen, E. coli bacteria, and total organic carbon models were newly developed for this report. Additional data collection over a wider range of hydrological conditions facilitated the development of these models. The nitrate model is particularly important because it allows for comparison to Cheney Reservoir Task Force goals. Mean hourly computed total suspended solids concentration during 1999 through 2012 was 54 milligrams per liter (mg/L). The total suspended solids load during 1999 through 2012 was 174,031 tons. On an average annual basis, the Cheney Reservoir Task Force runoff (550 mg/L) and long-term (100 mg/L) total suspended solids goals were never exceeded, but the base flow goal was exceeded every year during 1999 through 2012. Mean hourly computed nitrate concentration was 1.08 mg/L during 1999 through 2012. The total nitrate load during 1999 through 2012 was 1,361 tons. On an annual average basis, the Cheney Reservoir Task Force runoff (6.60 mg/L) nitrate goal was never exceeded, the long-term goal (1.20 mg/L) was exceeded only in 2012, and the base flow goal of 0.25 mg/L was exceeded every year. Mean nitrate concentrations that were higher during base flow, rather than during runoff conditions, suggest that groundwater sources are the main contributors of nitrate to the North Fork Ninnescah River above Cheney Reservoir. Mean hourly computed phosphorus concentration was 0.14 mg/L during 1999 through 2012. The total phosphorus load during 1999 through 2012 was 328 tons. On an average annual basis, the Cheney Reservoir Task Force runoff goal of 0.40 mg/L for total phosphorus was exceeded in 2002, the year with the largest yearly mean turbidity, and the long-term goal (0.10 mg/L) was exceeded in every year except 2011 and 2012, the years with the smallest mean streamflows. The total phosphorus base flow goal of 0.05 mg/L was exceeded every year. Given that base flow goals for total suspended solids, nitrate, and total phosphorus were exceeded every year despite hydrologic conditions, the established base flow goals are either unattainable or substantially more best management practices will need to be implemented to attain them. On an annual average basis, no discernible patterns were evident in total suspended sediment, nitrate, and total phosphorus concentrations or loads over time, in large part because of hydrologic variability. However, more rigorous statistical analyses are required to evaluate temporal trends. A more rigorous analysis of temporal trends will allow evaluation of watershed investments in best management practices.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135071","collaboration":"Prepared in cooperation with the city of Wichita, Kansas","usgsCitation":"Stone, M.L., Graham, J.L., and Gatotho, J.W., 2013, Continuous real-time water-quality monitoring and regression analysis to compute constituent concentrations and loads in the North Fork Ninnescah River upstream from Cheney Reservoir, south-central Kansas, 1999–2012: U.S. Geological Survey Scientific Investigations Report 2013-5071, viii, 46 p., https://doi.org/10.3133/sir20135071.","productDescription":"viii, 46 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":272007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135071.gif"},{"id":272005,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5071/"},{"id":272006,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5071/sir13-5071.pdf"}],"country":"United States","state":"Kansas","otherGeospatial":"Cheney Reservoir;North Fork Ninnescah River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -99.25,37.5 ], [ -99.25,38.16 ], [ -97.75,38.16 ], [ -97.75,37.5 ], [ -99.25,37.5 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518a145be4b061e1bd53333b","contributors":{"authors":[{"text":"Stone, Mandy L. 0000-0002-6711-1536 mstone@usgs.gov","orcid":"https://orcid.org/0000-0002-6711-1536","contributorId":4409,"corporation":false,"usgs":true,"family":"Stone","given":"Mandy","email":"mstone@usgs.gov","middleInitial":"L.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":478424,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Graham, Jennifer L. 0000-0002-6420-9335 jlgraham@usgs.gov","orcid":"https://orcid.org/0000-0002-6420-9335","contributorId":1769,"corporation":false,"usgs":true,"family":"Graham","given":"Jennifer","email":"jlgraham@usgs.gov","middleInitial":"L.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gatotho, Jackline W.","contributorId":76616,"corporation":false,"usgs":true,"family":"Gatotho","given":"Jackline","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":478425,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045855,"text":"70045855 - 2013 - Parallelization of a hydrological model using the message passing interface","interactions":[],"lastModifiedDate":"2013-05-07T14:33:07","indexId":"70045855","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","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":"Parallelization of a hydrological model using the message passing interface","docAbstract":"With the increasing knowledge about the natural processes, hydrological models such as the Soil and Water Assessment Tool (SWAT) are becoming larger and more complex with increasing computation time. Additionally, other procedures such as model calibration, which may require thousands of model iterations, can increase running time and thus further reduce rapid modeling and analysis. Using the widely-applied SWAT as an example, this study demonstrates how to parallelize a serial hydrological model in a Windows® environment using a parallel programing technology—Message Passing Interface (MPI). With a case study, we derived the optimal values for the two parameters (the number of processes and the corresponding percentage of work to be distributed to the master process) of the parallel SWAT (P-SWAT) on an ordinary personal computer and a work station. Our study indicates that model execution time can be reduced by 42%–70% (or a speedup of 1.74–3.36) using multiple processes (two to five) with a proper task-distribution scheme (between the master and slave processes). Although the computation time cost becomes lower with an increasing number of processes (from two to five), this enhancement becomes less due to the accompanied increase in demand for message passing procedures between the master and all slave processes. Our case study demonstrates that the P-SWAT with a five-process run may reach the maximum speedup, and the performance can be quite stable (fairly independent of a project size). Overall, the P-SWAT can help reduce the computation time substantially for an individual model run, manual and automatic calibration procedures, and optimization of best management practices. In particular, the parallelization method we used and the scheme for deriving the optimal parameters in this study can be valuable and easily applied to other hydrological or environmental models.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Environmental Modelling and Software","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2013.02.002","usgsCitation":"Wu, Y., Li, T., Sun, L., and Chen, J., 2013, Parallelization of a hydrological model using the message passing interface: Environmental Modelling and Software, v. 43, p. 124-132, https://doi.org/10.1016/j.envsoft.2013.02.002.","productDescription":"9 p.","startPage":"124","endPage":"132","ipdsId":"IP-044027","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":272036,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272010,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.envsoft.2013.02.002"}],"volume":"43","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518a145de4b061e1bd533347","contributors":{"authors":[{"text":"Wu, Yiping ywu@usgs.gov","contributorId":987,"corporation":false,"usgs":true,"family":"Wu","given":"Yiping","email":"ywu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":478436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Tiejian","contributorId":25437,"corporation":false,"usgs":true,"family":"Li","given":"Tiejian","email":"","affiliations":[],"preferred":false,"id":478438,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sun, Liqun","contributorId":18249,"corporation":false,"usgs":true,"family":"Sun","given":"Liqun","email":"","affiliations":[],"preferred":false,"id":478437,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Ji","contributorId":101960,"corporation":false,"usgs":true,"family":"Chen","given":"Ji","email":"","affiliations":[],"preferred":false,"id":478439,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045856,"text":"70045856 - 2013 - Investigating the effects of point source and nonpoint source pollution on the water quality of the East River (Dongjiang) in South China","interactions":[],"lastModifiedDate":"2013-05-07T14:25:21","indexId":"70045856","displayToPublicDate":"2013-05-07T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Investigating the effects of point source and nonpoint source pollution on the water quality of the East River (Dongjiang) in South China","docAbstract":"Understanding the physical processes of point source (PS) and nonpoint source (NPS) pollution is critical to evaluate river water quality and identify major pollutant sources in a watershed. In this study, we used the physically-based hydrological/water quality model, Soil and Water Assessment Tool, to investigate the influence of PS and NPS pollution on the water quality of the East River (Dongjiang in Chinese) in southern China. Our results indicate that NPS pollution was the dominant contribution (>94%) to nutrient loads except for mineral phosphorus (50%). A comprehensive Water Quality Index (WQI) computed using eight key water quality variables demonstrates that water quality is better upstream than downstream despite the higher level of ammonium nitrogen found in upstream waters. Also, the temporal (seasonal) and spatial distributions of nutrient loads clearly indicate the critical time period (from late dry season to early wet season) and pollution source areas within the basin (middle and downstream agricultural lands), which resource managers can use to accomplish substantial reduction of NPS pollutant loadings. Overall, this study helps our understanding of the relationship between human activities and pollutant loads and further contributes to decision support for local watershed managers to protect water quality in this region. In particular, the methods presented such as integrating WQI with watershed modeling and identifying the critical time period and pollutions source areas can be valuable for other researchers worldwide.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Ecological Indicators","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2013.04.002","usgsCitation":"Wu, Y., and Chen, J., 2013, Investigating the effects of point source and nonpoint source pollution on the water quality of the East River (Dongjiang) in South China: Ecological Indicators, v. 32, p. 294-304, https://doi.org/10.1016/j.ecolind.2013.04.002.","productDescription":"11 p.","startPage":"294","endPage":"304","ipdsId":"IP-044856","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":272033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272011,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.ecolind.2013.04.002"}],"country":"China","otherGeospatial":"Dongjiang","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 73.5,18.2 ], [ 73.5,53.6 ], [ 134.8,53.6 ], [ 134.8,18.2 ], [ 73.5,18.2 ] ] ] } } ] }","volume":"32","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518a145be4b061e1bd53333f","contributors":{"authors":[{"text":"Wu, Yiping ywu@usgs.gov","contributorId":987,"corporation":false,"usgs":true,"family":"Wu","given":"Yiping","email":"ywu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":478440,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chen, Ji","contributorId":101960,"corporation":false,"usgs":true,"family":"Chen","given":"Ji","email":"","affiliations":[],"preferred":false,"id":478441,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045769,"text":"sir20135084 - 2013 - Groundwater conditions in Georgia, 2010–2011","interactions":[],"lastModifiedDate":"2017-01-17T20:46:02","indexId":"sir20135084","displayToPublicDate":"2013-05-03T00:00:00","publicationYear":"2013","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":"2013-5084","title":"Groundwater conditions in Georgia, 2010–2011","docAbstract":"The U.S. Geological Survey collects groundwater data and conducts studies to monitor hydrologic conditions, better define groundwater resources, and address problems related to water supply, water use, and water quality. In Georgia, water levels were monitored continuously at 186 wells during calendar year 2010 and at 181 wells during calendar year 2011. Because of missing data or short periods of record (less than 3 years) for several of these wells, a total of 168 wells are discussed in this report. These wells include 17 in the surficial aquifer system, 19 in the Brunswick aquifer system and equivalent sediments, 70 in the Upper Floridan aquifer, 16 in the Lower Floridan aquifer and underlying units, 10 in the Claiborne aquifer, 1 in the Gordon aquifer, 11 in the Clayton aquifer, 14 in the Cretaceous aquifer system, 2 in Paleozoic-rock aquifers, and 8 in crystalline-rock aquifers. Data from the well network indicate that water levels generally declined during the 2010 through 2011 calendar-year period, with water levels declining in 158 wells and rising in 10. Water levels declined over the period of record at 106 wells, increased at 56 wells, and remained relatively constant at 6 wells. In addition to continuous water-level data, periodic water-level measurements were collected and used to construct potentiometric-surface maps for the Upper Floridan aquifer in Camden, Charlton, and Ware Counties, Georgia, and adjacent counties in Florida during May–June 2010, and in the following areas in Georgia: the Brunswick area during August 2010 and August 2011, in the Albany–Dougherty County area during November 2010 and November 2011, and in the Augusta–Richmond County area during October 2010 and August 2011. In general, water levels in these areas were lower during 2011 than during 2010; however, the configuration of the potentiometric surfaces in each of the areas showed little change. Groundwater quality in the Floridan aquifer system is monitored in the Albany, Savannah, and Brunswick areas of Georgia. In the Albany area, nitrate as nitrogen concentrations in the Upper Floridan aquifer during 2011 generally decreased from 2010; however, concentrations in two wells remained above the U.S. Environmental Protection Agency (USEPA) 10-milligrams-per-liter (mg/L) drinking-water standard. In the Savannah area, specific conductance and chloride concentrations were measured in water samples from discrete depths in two wells completed in the Upper Floridan aquifer. Data from the two wells indicate that chloride concentrations in the Upper Floridan aquifer showed little change during calendar years 2010 through 2011 and remained below the 250 mg/L USEPA secondary drinking-water standard. During calendar years 2010 through 2011, chloride concentrations in the Lower Floridan aquifer increased slightly at Tybee Island and Skidaway Island, remaining above the drinking-water standard. In the Brunswick area, maps showing the chloride concentration of water in the Upper Floridan aquifer constructed using data collected from 32 wells during August 2010 and from 30 wells during August 2011 indicate that chloride concentrations remained above the USEPA secondary drinking-water standard in an approximately 2-square-mile area. During calendar years 2010 through 2011, chloride concentrations generally decreased in over 70 percent of the wells sampled during 2011, with a maximum decrease of 200 mg/L in a well located in the north-central part of the Brunswick area.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135084","usgsCitation":"Peck, M., Gordon, D., and Painter, J.A., 2013, Groundwater conditions in Georgia, 2010–2011: U.S. Geological Survey Scientific Investigations Report 2013-5084, iv, 65 p., https://doi.org/10.3133/sir20135084.","productDescription":"iv, 65 p.","numberOfPages":"71","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":271798,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20135084.gif"},{"id":271796,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5084/"},{"id":271797,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5084/pdf/sir2013-5084.pdf"}],"country":"United States","state":"Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.640625,\n 43.34116005412307\n ],\n [\n -76.640625,\n 44.85586880735725\n ],\n [\n -72.97119140625,\n 44.85586880735725\n ],\n [\n -72.97119140625,\n 43.34116005412307\n ],\n [\n -76.640625,\n 43.34116005412307\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.562255859375,\n 33.96158628979907\n ],\n [\n -82.430419921875,\n 33.87041555094183\n ],\n [\n -82.210693359375,\n 33.779147331286474\n ],\n [\n -82.1337890625,\n 33.660353121928814\n ],\n [\n -82.210693359375,\n 33.62376800118811\n ],\n [\n -82.056884765625,\n 33.55055114384406\n ],\n [\n -81.925048828125,\n 33.44977658311846\n ],\n [\n -81.93603515625,\n 33.358061612778876\n ],\n [\n -81.84814453125,\n 33.293803558346596\n ],\n [\n -81.7822265625,\n 33.211116472416855\n ],\n [\n -81.705322265625,\n 33.17434155100208\n ],\n [\n -81.6064453125,\n 33.100745405144245\n ],\n [\n -81.54052734375,\n 33.04550781490999\n ],\n [\n -81.463623046875,\n 32.90726224488304\n ],\n [\n -81.441650390625,\n 32.80574473290688\n ],\n [\n -81.4306640625,\n 32.69486597787505\n ],\n [\n -81.34277343749999,\n 32.61161640317033\n ],\n [\n -81.298828125,\n 32.54681317351514\n ],\n [\n -81.1669921875,\n 32.43561304116276\n ],\n [\n -81.903076171875,\n 32.03602003973755\n ],\n [\n -83.1884765625,\n 31.82156451492074\n ],\n [\n -83.265380859375,\n 33.00866349457558\n ],\n [\n -82.96875,\n 33.50475906922609\n ],\n [\n -82.781982421875,\n 34.15272698011818\n ],\n [\n -82.6611328125,\n 34.125447565116126\n ],\n [\n -82.584228515625,\n 34.016241889667015\n ],\n [\n -82.562255859375,\n 33.96158628979907\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5184ce60e4b04d6ec94d6299","contributors":{"authors":[{"text":"Peck, Michael F. mfpeck@usgs.gov","contributorId":1467,"corporation":false,"usgs":true,"family":"Peck","given":"Michael F.","email":"mfpeck@usgs.gov","affiliations":[],"preferred":false,"id":478325,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gordon, Debbie W. 0000-0002-5195-6657","orcid":"https://orcid.org/0000-0002-5195-6657","contributorId":79591,"corporation":false,"usgs":true,"family":"Gordon","given":"Debbie W.","affiliations":[],"preferred":false,"id":478326,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Painter, Jaime A. 0000-0001-8883-9158 jpainter@usgs.gov","orcid":"https://orcid.org/0000-0001-8883-9158","contributorId":1466,"corporation":false,"usgs":true,"family":"Painter","given":"Jaime","email":"jpainter@usgs.gov","middleInitial":"A.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478324,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70045740,"text":"70045740 - 2013 - Extending airborne electromagnetic surveys for regional active layer and permafrost mapping with remote sensing and ancillary data, Yukon Flats ecoregion, central Alaska","interactions":[],"lastModifiedDate":"2018-01-12T17:20:50","indexId":"70045740","displayToPublicDate":"2013-05-02T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3032,"text":"Permafrost and Periglacial Processes","active":true,"publicationSubtype":{"id":10}},"title":"Extending airborne electromagnetic surveys for regional active layer and permafrost mapping with remote sensing and ancillary data, Yukon Flats ecoregion, central Alaska","docAbstract":"Machine-learning regression tree models were used to extrapolate airborne electromagnetic resistivity data collected along flight lines in the Yukon Flats Ecoregion, central Alaska, for regional mapping of permafrost. This method of extrapolation (r = 0.86) used subsurface resistivity, Landsat Thematic Mapper (TM) at-sensor reflectance, thermal, TM-derived spectral indices, digital elevation models and other relevant spatial data to estimate near-surface (0–2.6-m depth) resistivity at 30-m resolution. A piecewise regression model (r = 0.82) and a presence/absence decision tree classification (accuracy of 87%) were used to estimate active-layer thickness (ALT) (< 101 cm) and the probability of near-surface (up to 123-cm depth) permafrost occurrence from field data, modelled near-surface (0–2.6 m) resistivity, and other relevant remote sensing and map data. At site scale, the predicted ALTs were similar to those previously observed for different vegetation types. At the landscape scale, the predicted ALTs tended to be thinner on higher-elevation loess deposits than on low-lying alluvial and sand sheet deposits of the Yukon Flats. The ALT and permafrost maps provide a baseline for future permafrost monitoring, serve as inputs for modelling hydrological and carbon cycles at local to regional scales, and offer insight into the ALT response to fire and thaw processes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Permafrost and Periglacial Processes","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/ppp.1775","usgsCitation":"Pastick, N.J., Jorgenson, M., Wylie, B.K., Minsley, B.J., Ji, L., Walvoord, M.A., Smith, B.D., Abraham, J., and Rose, J.R., 2013, Extending airborne electromagnetic surveys for regional active layer and permafrost mapping with remote sensing and ancillary data, Yukon Flats ecoregion, central Alaska: Permafrost and Periglacial Processes, v. 24, no. 3, p. 184-199, https://doi.org/10.1002/ppp.1775.","productDescription":"16 p.","startPage":"184","endPage":"199","ipdsId":"IP-037584","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":271728,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/ppp.1775"},{"id":271729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon Flats","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -149.55,65.47 ], [ -149.55,67.47 ], [ -142.43,67.47 ], [ -142.43,65.47 ], [ -149.55,65.47 ] ] ] } } ] }","volume":"24","issue":"3","noUsgsAuthors":false,"publicationDate":"2013-04-29","publicationStatus":"PW","scienceBaseUri":"51837ce5e4b0a21483941a49","contributors":{"authors":[{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":478219,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgenson, M. Torre","contributorId":40486,"corporation":false,"usgs":true,"family":"Jorgenson","given":"M. Torre","affiliations":[],"preferred":false,"id":478220,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":478216,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":478215,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":2832,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":false,"id":478218,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Walvoord, Michelle Ann 0000-0003-4269-8366 walvoord@usgs.gov","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":147211,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"walvoord@usgs.gov","middleInitial":"Ann","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":478223,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Bruce D. 0000-0002-1643-2997 bsmith@usgs.gov","orcid":"https://orcid.org/0000-0002-1643-2997","contributorId":845,"corporation":false,"usgs":true,"family":"Smith","given":"Bruce","email":"bsmith@usgs.gov","middleInitial":"D.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":478217,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Abraham, Jared D.","contributorId":42630,"corporation":false,"usgs":true,"family":"Abraham","given":"Jared D.","affiliations":[],"preferred":false,"id":478221,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rose, Joshua R.","contributorId":90147,"corporation":false,"usgs":true,"family":"Rose","given":"Joshua","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":478222,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70042887,"text":"70042887 - 2013 - Short-term variability of 7Be atmospheric deposition and watershed response in a Pacific coastal stream, Monterey Bay, California, USA","interactions":[],"lastModifiedDate":"2013-05-10T10:30:22","indexId":"70042887","displayToPublicDate":"2013-05-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2263,"text":"Journal of Environmental Radioactivity","active":true,"publicationSubtype":{"id":10}},"title":"Short-term variability of 7Be atmospheric deposition and watershed response in a Pacific coastal stream, Monterey Bay, California, USA","docAbstract":"Beryllium-7 is a powerful and commonly used tracer for environmental processes such as watershed sediment provenance, soil erosion, fluvial and nearshore sediment cycling, and atmospheric fallout. However, few studies have quantified temporal or spatial variability of 7Be accumulation from atmospheric fallout, and parameters that would better define the uses and limitations of this geochemical tracer. We investigated the abundance and variability of 7Be in atmospheric deposition in both rain events and dry periods, and in stream surface-water samples collected over a ten-month interval at sites near northern Monterey Bay (37°N, 122°W) on the central California coast, a region characterized by a rainy winters, dry summers, and small mountainous streams with flashy hydrology. The range of 7Be activity in rainwater samples from the main sampling site was 1.3–4.4 Bq L−1, with a mean (±standard deviation) of 2.2 ± 0.9 Bq L−1, and a volume-weighted average of 2.0 Bq L−1. The range of wet atmospheric deposition was 18–188 Bq m−2 per rain event, with a mean of 72 ± 53 Bq m−2. Dry deposition fluxes of 7Be ranged from less than 0.01 up to 0.45 Bq m−2 d−1, with an estimated dry season deposition of 7 Bq m−2 month−1. Annualized 7Be atmospheric deposition was approximately 1900 Bq m−2 yr−1, with most deposition via rainwater (>95%) and little via dry deposition. Overall, these activities and deposition fluxes are similar to values found in other coastal locations with comparable latitude and Mediterranean-type climate. Particulate 7Be values in the surface water of the San Lorenzo River in Santa Cruz, California, ranged from <0.01 Bq g−1 to 0.6 Bq g−1, with a median activity of 0.26 Bq g−1. A large storm event in January 2010 characterized by prolonged flooding resulted in the entrainment of 7Be-depleted sediment, presumably from substantial erosion in the watershed. There were too few particulate 7Be data over the storm to accurately model a 7Be load, but the results suggest enhanced watershed export of 7Be from small, mountainous river systems compared to other watershed types.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Environmental Radioactivity","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvrad.2013.02.004","usgsCitation":"Conaway, C., Storlazzi, C., Draut, A.E., and Swarzenski, P.W., 2013, Short-term variability of 7Be atmospheric deposition and watershed response in a Pacific coastal stream, Monterey Bay, California, USA: Journal of Environmental Radioactivity, v. 120, p. 94-103, https://doi.org/10.1016/j.jenvrad.2013.02.004.","startPage":"94","endPage":"103","numberOfPages":"10","ipdsId":"IP-041747","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":272171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":272170,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1016/j.jenvrad.2013.02.004"}],"country":"United States","state":"California","otherGeospatial":"Monterey Bay;San Lorenzo River","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -122.3,36.9 ], [ -122.3,37.3 ], [ -122.9,37.3 ], [ -122.9,36.9 ], [ -122.3,36.9 ] ] ] } } ] }","volume":"120","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"518e16e1e4b05ebc8f7cc2ff","contributors":{"authors":[{"text":"Conaway, Christopher H.","contributorId":52620,"corporation":false,"usgs":true,"family":"Conaway","given":"Christopher H.","affiliations":[],"preferred":false,"id":472506,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":77889,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt D.","affiliations":[],"preferred":false,"id":472507,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Draut, Amy E.","contributorId":92215,"corporation":false,"usgs":true,"family":"Draut","given":"Amy","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":472508,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Swarzenski, Peter W. 0000-0003-0116-0578 pswarzen@usgs.gov","orcid":"https://orcid.org/0000-0003-0116-0578","contributorId":1070,"corporation":false,"usgs":true,"family":"Swarzenski","given":"Peter","email":"pswarzen@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":472505,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70188365,"text":"70188365 - 2013 - Annual modulation of non-volcanic tremor in northern Cascadia","interactions":[],"lastModifiedDate":"2017-06-07T11:42:08","indexId":"70188365","displayToPublicDate":"2013-05-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Annual modulation of non-volcanic tremor in northern Cascadia","docAbstract":"Two catalogs of episodic tremor events in northern Cascadia, one from 2006 to 2012 and the other from 1997 to 2011, reveal two systematic patterns of tremor occurrence in southern Vancouver Island: (1) most individual events tend to occur in the third quarter of the year; (2) the number of events in prolonged episodes (i.e., episodic tremor and slip events), which generally propagate to Vancouver Island from elsewhere along the Cascadia subduction zone, is inversely correlated with the amount of precipitation that occurred in the preceding 2 months. We rationalize these patterns as the product of hydrologic loading of the crust of southern Vancouver Island and the surrounding continental region, superimposed with annual variations from oceanic tidal loading. Loading of the Vancouver Island crust in the winter (when the land surface receives ample precipitation) and unloading in the summer tends to inhibit and enhance downdip shear stress, respectively. Quantitatively, for an annually variable surface load, the predicted stress perturbation depends on mantle viscoelastic rheology. A mechanical model of downdip shear stress on the transition zone beneath Vancouver Island—driven predominantly by the annual hydrologic cycle—is consistent with the 1997–2012 tremor observations, with peak-to-peak downdip shear stress of about 0.4 kPa. This seasonal dependence of tremor occurrence appears to be restricted to southern Vancouver Island because of its unique situation as an elongated narrow-width land mass surrounded by ocean, which permits seasonal perturbations in shear stress at depth.
","language":"English","publisher":"AGU","doi":"10.1002/jgrb.50181","usgsCitation":"Pollitz, F., Wech, A.G., Kao, H., and Burgmann, R., 2013, Annual modulation of non-volcanic tremor in northern Cascadia: Journal of Geophysical Research B: Solid Earth, v. 118, no. 5, p. 2445-2459, https://doi.org/10.1002/jgrb.50181.","productDescription":"15 p.","startPage":"2445","endPage":"2459","ipdsId":"IP-045029","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":473858,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jgrb.50181","text":"Publisher Index Page"},{"id":342223,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -128.5400390625,\n 46.28622391806706\n ],\n [\n -121.1572265625,\n 46.28622391806706\n ],\n [\n -121.1572265625,\n 50.958426723359935\n ],\n [\n -128.5400390625,\n 50.958426723359935\n ],\n [\n -128.5400390625,\n 46.28622391806706\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"118","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2013-05-13","publicationStatus":"PW","scienceBaseUri":"593910b4e4b0764e6c5e88e6","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697410,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wech, Aaron G. 0000-0003-4983-1991 awech@usgs.gov","orcid":"https://orcid.org/0000-0003-4983-1991","contributorId":5344,"corporation":false,"usgs":true,"family":"Wech","given":"Aaron","email":"awech@usgs.gov","middleInitial":"G.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":697411,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Kao, Honn","contributorId":105419,"corporation":false,"usgs":true,"family":"Kao","given":"Honn","email":"","affiliations":[],"preferred":false,"id":697412,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Burgmann, Roland","contributorId":192700,"corporation":false,"usgs":false,"family":"Burgmann","given":"Roland","affiliations":[],"preferred":false,"id":697413,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70187305,"text":"70187305 - 2013 - Assessing the potential of reservoir outflow management to reduce sedimentation using continuous turbidity monitoring and reservoir modelling","interactions":[],"lastModifiedDate":"2017-04-27T14:31:57","indexId":"70187305","displayToPublicDate":"2013-05-01T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the potential of reservoir outflow management to reduce sedimentation using continuous turbidity monitoring and reservoir modelling","docAbstract":"In-stream sensors are increasingly deployed as part of ambient water quality-monitoring networks. Temporally dense data from these networks can be used to better understand the transport of constituents through streams, lakes or reservoirs. Data from existing, continuously recording in-stream flow and water quality monitoring stations were coupled with the two-dimensional hydrodynamic CE-QUAL-W2 model to assess the potential of altered reservoir outflow management to reduce sediment trapping in John Redmond Reservoir, located in east-central Kansas. Monitoring stations upstream and downstream from the reservoir were used to estimate 5.6 million metric tons of sediment transported to John Redmond Reservoir from 2007 through 2010, 88% of which was trapped within the reservoir. The two-dimensional model was used to estimate the residence time of 55 equal-volume releases from the reservoir; sediment trapping for these releases varied from 48% to 97%. Smaller trapping efficiencies were observed when the reservoir was maintained near the normal operating capacity (relative to higher flood pool levels) and when average residence times were relatively short. An idealized, alternative outflow management scenario was constructed, which minimized reservoir elevations and the length of time water was in the reservoir, while continuing to meet downstream flood control end points identified in the reservoir water control manual. The alternative scenario is projected to reduce sediment trapping in the reservoir by approximately 3%, preventing approximately 45 000 metric tons of sediment from being deposited within the reservoir annually. This article presents an approach to quantify the potential of reservoir management using existing in-stream data; actual management decisions need to consider the effects on other reservoir benefits, such as downstream flood control and aquatic life.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.9284","usgsCitation":"Lee, C.J., and Foster, G.M., 2013, Assessing the potential of reservoir outflow management to reduce sedimentation using continuous turbidity monitoring and reservoir modelling: Hydrological Processes, v. 27, no. 10, p. 1426-1439, https://doi.org/10.1002/hyp.9284.","productDescription":"14 p.","startPage":"1426","endPage":"1439","ipdsId":"IP-026625","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":340523,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"10","noUsgsAuthors":false,"publicationDate":"2012-04-23","publicationStatus":"PW","scienceBaseUri":"59030329e4b0e862d230f753","contributors":{"authors":[{"text":"Lee, Casey J. 0000-0002-5753-2038 cjlee@usgs.gov","orcid":"https://orcid.org/0000-0002-5753-2038","contributorId":2627,"corporation":false,"usgs":true,"family":"Lee","given":"Casey","email":"cjlee@usgs.gov","middleInitial":"J.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":693240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, Guy M. 0000-0002-9581-057X gfoster@usgs.gov","orcid":"https://orcid.org/0000-0002-9581-057X","contributorId":149145,"corporation":false,"usgs":true,"family":"Foster","given":"Guy","email":"gfoster@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":693241,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045697,"text":"sir20135042 - 2013 - Simulation of groundwater flow, effects of artificial recharge, and storage volume changes in the Equus Beds aquifer near the city of Wichita, Kansas well field, 1935–2008","interactions":[],"lastModifiedDate":"2013-04-30T10:39:05","indexId":"sir20135042","displayToPublicDate":"2013-04-30T00:00:00","publicationYear":"2013","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":"2013-5042","title":"Simulation of groundwater flow, effects of artificial recharge, and storage volume changes in the Equus Beds aquifer near the city of Wichita, Kansas well field, 1935–2008","docAbstract":"The Equus Beds aquifer is a primary water-supply source for Wichita, Kansas and the surrounding area because of shallow depth to water, large saturated thickness, and generally good water quality. Substantial water-level declines in the Equus Beds aquifer have resulted from pumping groundwater for agricultural and municipal needs, as well as periodic drought conditions. In March 2006, the city of Wichita began construction of the Equus Beds Aquifer Storage and Recovery project to store and later recover groundwater, and to form a hydraulic barrier to the known chloride-brine plume near Burrton, Kansas. In October 2009, the U.S. Geological Survey, in cooperation with the city of Wichita, began a study to determine groundwater flow in the area of the Wichita well field, and chloride transport from the Arkansas River and Burrton oilfield to the Wichita well field. Groundwater flow was simulated for the Equus Beds aquifer using the three-dimensional finite-difference groundwater-flow model MODFLOW-2000. The model simulates steady-state and transient conditions. The groundwater-flow model was calibrated by adjusting model input data and model geometry until model results matched field observations within an acceptable level of accuracy. The root mean square (RMS) error for water-level observations for the steady-state calibration simulation is 9.82 feet. The ratio of the RMS error to the total head loss in the model area is 0.049 and the mean error for water-level observations is 3.86 feet. The difference between flow into the model and flow out of the model across all model boundaries is -0.08 percent of total flow for the steady-state calibration. The RMS error for water-level observations for the transient calibration simulation is 2.48 feet, the ratio of the RMS error to the total head loss in the model area is 0.0124, and the mean error for water-level observations is 0.03 feet. The RMS error calculated for observed and simulated base flow gains or losses for the Arkansas River for the transient simulation is 7,916,564 cubic feet per day (91.6 cubic feet per second) and the RMS error divided by (/) the total range in streamflow (7,916,564/37,461,669 cubic feet per day) is 22 percent. The RMS error calculated for observed and simulated streamflow gains or losses for the Little Arkansas River for the transient simulation is 5,610,089 cubic feet per day(64.9 cubic feet per second) and the RMS error divided by the total range in streamflow (5,612,918/41,791,091 cubic feet per day) is 13 percent. The mean error between observed and simulated base flow gains or losses was 29,999 cubic feet per day (0.34 cubic feet per second) for the Arkansas River and -1,369,250 cubic feet per day (-15.8 cubic feet per second) for the Little Arkansas River. Cumulative streamflow gain and loss observations are similar to the cumulative simulated equivalents. Average percent mass balance difference for individual stress periods ranged from -0.46 to 0.51 percent. The cumulative mass balance for the transient calibration was 0.01 percent. Composite scaled sensitivities indicate the simulations are most sensitive to parameters with a large areal distribution. For the steady-state calibration, these parameters include recharge, hydraulic conductivity, and vertical conductance. For the transient simulation, these parameters include evapotranspiration, recharge, and hydraulic conductivity. The ability of the calibrated model to account for the additional groundwater recharged to the Equus Beds aquifer as part of the Aquifer Storage and Recovery project was assessed by using the U.S. Geological Survey subregional water budget program ZONEBUDGET and comparing those results to metered recharge for 2007 and 2008 and previous estimates of artificial recharge. The change in storage between simulations is the volume of water that estimates the recharge credit for the aquifer storage and recovery system. The estimated increase in storage of 1,607 acre-ft in the basin storage area compared to metered recharge of 1,796 acre-ft indicates some loss of metered recharge. Increased storage outside of the basin storage area of 183 acre-ft accounts for all but 6 acre-ft or 0.33 percent of the total. Previously estimated recharge credits for 2007 and 2008 are 1,018 and 600 acre-ft, respectively, and a total estimated recharge credit of 1,618 acre-ft. Storage changes calculated for this study are 4.42 percent less for 2007 and 5.67 percent more for 2008 than previous estimates. Total storage change for 2007 and 2008 is 0.68 percent less than previous estimates. The small difference between the increase in storage from artificial recharge estimated with the groundwater-flow model and metered recharge indicates the groundwater model correctly accounts for the additional water recharged to the Equus Beds aquifer as part of the Aquifer Storage and Recovery project. Small percent differences between inflows and outflows for all stress periods and all index cells in the basin storage area, improved calibration compared to the previous model, and a reasonable match between simulated and measured long-term base flow indicates the groundwater model accurately simulates groundwater flow in the study area. The change in groundwater level through recent years compared to the August 1940 groundwater level map has been documented and used to assess the change of storage volume of the Equus Beds aquifer in and near the Wichita well field for three different areas. Two methods were used to estimate changes in storage from simulation results using simulated change in groundwater levels in layer 1 between stress periods, and using ZONEBUDGET to calculate the change in storage in the same way the effects of artificial recharge were estimated within the basin storage area. The three methods indicate similar trends although the magnitude of storage changes differ. Information about the change in storage in response to hydrologic stresses is important for managing groundwater resources in the study area. The comparison between the three methods indicates similar storage change trends are estimated and each could be used to determine relative increases or decreases in storage. Use of groundwater level changes that do not include storage changes that occur in confined or semi-confined parts of the aquifer will slightly underestimate storage changes; however, use of specific yield and groundwater level changes to estimate storage change in confined or semi-confined parts of the aquifer will overestimate storage changes. Using only changes in shallow groundwater levels would provide more accurate storage change estimates for the measured groundwater levels method. The value used for specific yield is also an important consideration when estimating storage. For the Equus Beds aquifer the reported specific yield ranges between 0.08 and 0.35 and the storage coefficient (for confined conditions) ranges between 0.0004 and 0.16. Considering the importance of the value of specific yield and storage coefficient to estimates of storage change over time, and the wide range and substantial overlap for the reported values for specific yield and storage coefficient in the study area, further information on the distribution of specific yield and storage coefficient within the Equus Beds aquifer in the study area would greatly enhance the accuracy of estimated storage changes using both simulated groundwater level, simulated groundwater budget, or measured groundwater level methods.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135042","collaboration":"Prepared in cooperation with the city of Wichita, Kansas, as part of the Equus Beds Groundwater Recharge Project","usgsCitation":"Kelly, B.P., Pickett, L.L., Hansen, C.V., and Ziegler, A., 2013, Simulation of groundwater flow, effects of artificial recharge, and storage volume changes in the Equus Beds aquifer near the city of Wichita, Kansas well field, 1935–2008: U.S. Geological Survey Scientific Investigations Report 2013-5042, Report: viii, 92 p.; Downloads Directory, https://doi.org/10.3133/sir20135042.","productDescription":"Report: viii, 92 p.; Downloads Directory","numberOfPages":"102","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-042806","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":271633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/SIR20135042.gif"},{"id":271632,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2013/5042/downloads/"},{"id":271630,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2013/5042/"},{"id":271631,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5042/sir2013-5042.pdf"}],"country":"United States","state":"Kansas","city":"Wichita","otherGeospatial":"Equus Beds Aquifer","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -98.3,37.6 ], [ -98.3,38.05 ], [ -97.16,38.05 ], [ -97.16,37.6 ], [ -98.3,37.6 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5180d9dce4b0df838b924d35","contributors":{"authors":[{"text":"Kelly, Brian P. 0000-0001-6378-2837 bkelly@usgs.gov","orcid":"https://orcid.org/0000-0001-6378-2837","contributorId":897,"corporation":false,"usgs":true,"family":"Kelly","given":"Brian","email":"bkelly@usgs.gov","middleInitial":"P.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":478069,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pickett, Linda L.","contributorId":108377,"corporation":false,"usgs":true,"family":"Pickett","given":"Linda","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":478070,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hansen, Cristi V. chansen@usgs.gov","contributorId":435,"corporation":false,"usgs":true,"family":"Hansen","given":"Cristi","email":"chansen@usgs.gov","middleInitial":"V.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":478068,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ziegler, Andrew C. aziegler@usgs.gov","contributorId":433,"corporation":false,"usgs":true,"family":"Ziegler","given":"Andrew C.","email":"aziegler@usgs.gov","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":false,"id":478067,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70045583,"text":"sir20125077 - 2013 - Numerical simulation of groundwater and surface-water interactions in the Big River Management Area, central Rhode Island","interactions":[],"lastModifiedDate":"2018-05-17T13:30:55","indexId":"sir20125077","displayToPublicDate":"2013-04-24T00:00:00","publicationYear":"2013","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":"2012-5077","title":"Numerical simulation of groundwater and surface-water interactions in the Big River Management Area, central Rhode Island","docAbstract":"The Rhode Island Water Resources Board is considering use of groundwater resources from the Big River Management Area in central Rhode Island because increasing water demands in Rhode Island may exceed the capacity of current sources. Previous water-resources investigations in this glacially derived, valley-fill aquifer system have focused primarily on the effects of potential groundwater-pumping scenarios on streamflow depletion; however, the effects of groundwater withdrawals on wetlands have not been assessed, and such assessments are a requirement of the State’s permitting process to develop a water supply in this area.\n\nA need for an assessment of the potential effects of pumping on wetlands in the Big River Management Area led to a cooperative agreement in 2008 between the Rhode Island Water Resources Board, the U.S. Geological Survey, and the University of Rhode Island. This partnership was formed with the goal of developing methods for characterizing wetland vegetation, soil type, and hydrologic conditions, and monitoring and modeling water levels for pre- and post-water-supply development to assess potential effects of groundwater withdrawals on wetlands. This report describes the hydrogeology of the area and the numerical simulations that were used to analyze the interaction between groundwater and surface water in response to simulated groundwater withdrawals.\n\nThe results of this analysis suggest that, given the hydrogeologic conditions in the Big River Management Area, a standard 5-day aquifer test may not be sufficient to determine the effects of pumping on water levels in nearby wetlands. Model simulations showed water levels beneath Reynolds Swamp declined by about 0.1 foot after 5 days of continuous pumping, but continued to decline by an additional 4 to 6 feet as pumping times were increased from a 5-day simulation period to a simulation period representative of long-term average monthly conditions. This continued decline in water levels with increased pumping time is related to the shift from the primary source of water to the pumped wells being derived from aquifer storage during the early-time (5 days) simulation to being derived more from induced infiltration from the flooded portion of the Big River (southernmost extent of the Flat River Reservoir) during the months of March through October or from captured groundwater discharge to this portion of the Big River when the downstream Flat River Reservoir is drained for weed control during the months of November through February, as was the case for the long-term monthly conditions.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20125077","collaboration":"Prepared in cooperation with the Rhode Island Water Resources Board","usgsCitation":"Masterson, J., and Granato, G., 2013, Numerical simulation of groundwater and surface-water interactions in the Big River Management Area, central Rhode Island: U.S. Geological Survey Scientific Investigations Report 2012-5077, vi, 53 p., https://doi.org/10.3133/sir20125077.","productDescription":"vi, 53 p.","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":271417,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20125077.jpg"},{"id":271416,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2012/5077/pdf/sir2012-5077_508.pdf"},{"id":271415,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2012/5077/"}],"country":"United States","state":"Rhode Island","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -71.8923,41.1467 ], [ -71.8923,42.0188 ], [ -71.1205,42.0188 ], [ -71.1205,41.1467 ], [ -71.8923,41.1467 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5178f0dfe4b0d842c705f6c0","contributors":{"authors":[{"text":"Masterson, John P. 0000-0003-3202-4413 jpmaster@usgs.gov","orcid":"https://orcid.org/0000-0003-3202-4413","contributorId":1865,"corporation":false,"usgs":true,"family":"Masterson","given":"John P.","email":"jpmaster@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":false,"id":477873,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Granato, Gregory E. 0000-0002-2561-9913 ggranato@usgs.gov","orcid":"https://orcid.org/0000-0002-2561-9913","contributorId":1692,"corporation":false,"usgs":true,"family":"Granato","given":"Gregory E.","email":"ggranato@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":false,"id":477872,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70045518,"text":"70045518 - 2013 - Detecting unfrozen sediments below thermokarst lakes with surface nuclear magnetic resonance","interactions":[],"lastModifiedDate":"2013-04-24T15:22:16","indexId":"70045518","displayToPublicDate":"2013-04-24T00:00:00","publicationYear":"2013","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Detecting unfrozen sediments below thermokarst lakes with surface nuclear magnetic resonance","docAbstract":"A talik is a layer or body of unfrozen ground that occurs in permafrost due to an anomaly in thermal, hydrological, or hydrochemical conditions. Information about talik geometry is important for understanding regional surface water and groundwater interactions as well as sublacustrine methane production in thermokarst lakes. Due to the direct measurement of unfrozen water content, surface nuclear magnetic resonance (NMR) is a promising geophysical method for noninvasively estimating talik dimensions. We made surface NMR measurements on thermokarst lakes and terrestrial permafrost near Fairbanks, Alaska, and confirmed our results using limited direct measurements. At an 8 m deep lake, we observed thaw bulb at least 22 m below the surface; at a 1.4 m deep lake, we detected a talik extending between 5 and 6 m below the surface. Our study demonstrates the value that surface NMR may have in the cryosphere for studies of thermokarst lake hydrology and their related role in the carbon cycle.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geophysical Research Letters","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"AGU","publisherLocation":"Washington, D.C.","doi":"10.1002/grl.50137","usgsCitation":"Parsekian, A.D., Grosse, G., Walbrecker, J.O., Muller-Petke, M., Keating, K., Liu, L., Jones, B.M., and Knight, R., 2013, Detecting unfrozen sediments below thermokarst lakes with surface nuclear magnetic resonance: Geophysical Research Letters, v. 40, no. 3, p. 535-540, https://doi.org/10.1002/grl.50137.","productDescription":"6 p.","startPage":"535","endPage":"540","ipdsId":"IP-043197","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":473864,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/grl.50137","text":"Publisher Index Page"},{"id":271419,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":271418,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/grl.50137"}],"volume":"40","issue":"3","noUsgsAuthors":false,"publicationDate":"2013-02-11","publicationStatus":"PW","scienceBaseUri":"5178f0dbe4b0d842c705f6a8","contributors":{"authors":[{"text":"Parsekian, Andrew D.","contributorId":23829,"corporation":false,"usgs":false,"family":"Parsekian","given":"Andrew","email":"","middleInitial":"D.","affiliations":[{"id":17842,"text":"University of Wyoming, Laramie","active":true,"usgs":false}],"preferred":false,"id":477709,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grosse, Guido","contributorId":101475,"corporation":false,"usgs":true,"family":"Grosse","given":"Guido","affiliations":[{"id":34291,"text":"University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":477715,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walbrecker, Jan O.","contributorId":32061,"corporation":false,"usgs":true,"family":"Walbrecker","given":"Jan","email":"","middleInitial":"O.","affiliations":[],"preferred":false,"id":477710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muller-Petke, Mike","contributorId":80996,"corporation":false,"usgs":true,"family":"Muller-Petke","given":"Mike","email":"","affiliations":[],"preferred":false,"id":477712,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keating, Kristina","contributorId":34018,"corporation":false,"usgs":true,"family":"Keating","given":"Kristina","affiliations":[],"preferred":false,"id":477711,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Liu, Lin","contributorId":92950,"corporation":false,"usgs":false,"family":"Liu","given":"Lin","email":"","affiliations":[{"id":36342,"text":"Earth System Science Programme, Faculty of Science, Chinese University of Hong Kong, Hong Kong, China","active":true,"usgs":false}],"preferred":false,"id":477714,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jones, Benjamin M. 0000-0002-1517-4711 bjones@usgs.gov","orcid":"https://orcid.org/0000-0002-1517-4711","contributorId":2286,"corporation":false,"usgs":true,"family":"Jones","given":"Benjamin","email":"bjones@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":477708,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Knight, Rosemary","contributorId":84245,"corporation":false,"usgs":true,"family":"Knight","given":"Rosemary","email":"","affiliations":[],"preferred":false,"id":477713,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}