Mountain watersheds recently burned by wildfire often experience greater amounts of runoff and increased rates of sediment transport relative to similar unburned areas. Given the sedimentation and debris flow threats caused by increases in erosion, more work is needed to better understand the physical mechanisms responsible for the observed increase in sediment transport in burned environments and the time scale over which a heightened geomorphic response can be expected. In this study, we quantified the relative importance of different hillslope erosion mechanisms during two postwildfire rainstorms at a drainage basin in Southern California by combining terrestrial laser scanner-derived maps of topographic change, field measurements, and numerical modeling of overland flow and sediment transport. Numerous debris flows were initiated by runoff at our study area during a long-duration storm of relatively modest intensity. Despite the presence of a well-developed rill network, numerical model results suggest that the majority of eroded hillslope sediment during this long-duration rainstorm was transported by raindrop-induced sediment transport processes, highlighting the importance of raindrop-driven processes in supplying channels with potential debris flow material. We also used the numerical model to explore relationships between postwildfire storm characteristics, vegetation cover, soil infiltration capacity, and the total volume of eroded sediment from a synthetic hillslope for different end-member erosion regimes. This study adds to our understanding of sediment transport in steep, postwildfire landscapes and shows how data from field monitoring can be combined with numerical modeling of sediment transport to isolate the processes leading to increased erosion in burned areas.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016JF003867","usgsCitation":"McGuire, L., Kean, J.W., Staley, D.M., Rengers, F.K., and Wasklewicz, T.A., 2016, Constraining the relative importance of raindrop- and flow-driven sediment transport mechanisms in postwildfire environments and implications for recovery time scales: Journal of Geophysical Research, v. 121, no. 11, p. 2211-2237, https://doi.org/10.1002/2016JF003867.","productDescription":"27 p.","startPage":"2211","endPage":"2237","ipdsId":"IP-077491","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":470407,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016jf003867","text":"Publisher Index Page"},{"id":342596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Arroyo Seco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.383333,\n 34.441667\n ],\n [\n -117.875,\n 34.441667\n ],\n [\n -117.875,\n 34.2\n ],\n [\n -118.383333,\n 34.2\n ],\n [\n -118.383333,\n 34.441667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-22","publicationStatus":"PW","scienceBaseUri":"5944ee16e4b062508e333607","contributors":{"authors":[{"text":"McGuire, Luke lmcguire@usgs.gov","contributorId":167018,"corporation":false,"usgs":true,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":698465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wasklewicz, Thad A.","contributorId":39275,"corporation":false,"usgs":true,"family":"Wasklewicz","given":"Thad","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":698397,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178537,"text":"70178537 - 2016 - Landscape and flow metrics affecting the distribution of a federally-threatened fish: Improving management, model fit, and model transferability","interactions":[],"lastModifiedDate":"2021-04-26T15:42:46.518202","indexId":"70178537","displayToPublicDate":"2016-11-22T00:00:00","publicationYear":"2016","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":"Landscape and flow metrics affecting the distribution of a federally-threatened fish: Improving management, model fit, and model transferability","docAbstract":"Truncated distributions of pelagophilic fishes have been observed across the Great Plains of North America, with water use and landscape fragmentation implicated as contributing factors. Developing conservation strategies for these species is hindered by the existence of multiple competing flow regime hypotheses related to species persistence. Our primary study objective was to compare the predicted distributions of one pelagophil, the Arkansas River Shiner Notropis girardi, constructed using different flow regime metrics. Further, we investigated different approaches for improving temporal transferability of the species distribution model (SDM). We compared four hypotheses: mean annual flow (a baseline), the 75th percentile of daily flow, the number of zero-flow days, and the number of days above 55th percentile flows, to examine the relative importance of flows during the spawning period. Building on an earlier SDM, we added covariates that quantified wells in each catchment, point source discharges, and non-native species presence to a structured variable framework. We assessed the effects on model transferability and fit by reducing multicollinearity using Spearman’s rank correlations, variance inflation factors, and principal component analysis, as well as altering the regularization coefficient (β) within MaxEnt. The 75th percentile of daily flow was the most important flow metric related to structuring the species distribution. The number of wells and point source discharges were also highly ranked. At the default level of β, model transferability was improved using all methods to reduce collinearity; however, at higher levels of β, the correlation method performed best. Using β = 5 provided the best model transferability, while retaining the majority of variables that contributed 95% to the model. This study provides a workflow for improving model transferability and also presents water-management options that may be considered to improve the conservation status of pelagophils.
Three in-stream experiments were conducted to determine whether sea lamprey, Petromyzon marinus L., tissue extract (alarm cue) and 2-phenylethylamine hydrochloride (PEA HCl, a putative predator cue) influenced the distribution of migrating adult sea lamprey. Experiments evaluated sea lamprey movement when an odour was applied to (1) a tributary of a larger stream; and (2) half of a stream channel. Fewer sea lamprey entered the tributary and side of the river scented with sea lamprey tissue extract compared to the control treatment. Sea lamprey did not avoid the tributary and side of the river scented with PEA HCl. A final laboratory experiment found no difference in the avoidance response of sea lamprey to PEA HCl mixed with river water vs PEA HCl mixed with water from Lake Huron. As such, the lack of sea lamprey response to PEA HCl in the stream was unlikely to have been caused by the presence of the river water. Rather, the difference between laboratory and field results may be attributed to the complexity of the physical environment.
","language":"English","publisher":"Wiley","doi":"10.1111/fme.12198","usgsCitation":"Di Rocco, R., Johnson, N., Brege, L., Imre, I., and Brown, G., 2016, Sea lamprey avoid areas scented with conspecific tissue extract in Michigan streams: Fisheries Management and Ecology, v. 23, no. 6, p. 548-560, https://doi.org/10.1111/fme.12198.","productDescription":"13 p.","startPage":"548","endPage":"560","ipdsId":"IP-077130","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":331157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Ocqueoc River, Silver Creek","volume":"23","issue":"6","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"583415abe4b0070c0abed812","contributors":{"authors":[{"text":"Di Rocco, Richard","contributorId":126735,"corporation":false,"usgs":false,"family":"Di Rocco","given":"Richard","affiliations":[{"id":6585,"text":"Algoma University","active":true,"usgs":false}],"preferred":false,"id":654130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":150983,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas S.","email":"njohnson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":654129,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brege, Linnea 0000-0002-7495-3619 lbrege@usgs.gov","orcid":"https://orcid.org/0000-0002-7495-3619","contributorId":176976,"corporation":false,"usgs":true,"family":"Brege","given":"Linnea","email":"lbrege@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":654131,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Imre, I.","contributorId":25398,"corporation":false,"usgs":true,"family":"Imre","given":"I.","affiliations":[],"preferred":false,"id":654132,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, G.E.","contributorId":58131,"corporation":false,"usgs":true,"family":"Brown","given":"G.E.","email":"","affiliations":[],"preferred":false,"id":654133,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178481,"text":"70178481 - 2016 - Challenge to the model of lake charr evolution: Shallow- and deep-water morphs exist within a small postglacial lake","interactions":[],"lastModifiedDate":"2016-11-21T11:35:07","indexId":"70178481","displayToPublicDate":"2016-11-21T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1019,"text":"Biological Journal of the Linnean Society","active":true,"publicationSubtype":{"id":10}},"title":"Challenge to the model of lake charr evolution: Shallow- and deep-water morphs exist within a small postglacial lake","docAbstract":"All examples of lake charr (Salvelinus namaycush) diversity occur within the largest, deepest lakes of North America (i.e. > 2000 km2). We report here Rush Lake (1.3 km2) as the first example of a small lake with two lake charr morphs (lean and huronicus). Morphology, diet, life history, and genetics were examined to demonstrate the existence of morphs and determine the potential influence of evolutionary processes that led to their formation or maintenance. Results showed that the huronicus morph, caught in deep-water, had a deeper body, smaller head and jaws, higher eye position, greater buoyancy, and deeper peduncle than the shallow-water lean morph. Huronicus grew slower to a smaller adult size, and had an older mean age than the lean morph. Genetic comparisons showed low genetic divergence between morphs, indicating incomplete reproductive isolation. Phenotypic plasticity and differences in habitat use between deep and shallow waters associated with variation in foraging opportunities seems to have been sufficient to maintain the two morphs, demonstrating their important roles in resource polymorphism. Rush Lake expands previous explanations for lake charr intraspecific diversity, from large to small lakes and from reproductive isolation to the presence of gene flow associated with strong ecological drivers.
","language":"English","publisher":"Wiley","doi":"10.1111/bij.12913","usgsCitation":"Chavarie, L., Muir, A., Zimmerman, M.S., Baillie, S.M., Hansen, M.J., Nate, N.A., Yule, D.L., Middel, T., Bentzen, P., and Krueger, C., 2016, Challenge to the model of lake charr evolution: Shallow- and deep-water morphs exist within a small postglacial lake: Biological Journal of the Linnean Society, https://doi.org/10.1111/bij.12913.","ipdsId":"IP-078858","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":462033,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/bij.12913","text":"Publisher Index Page"},{"id":331153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"583415ace4b0070c0abed814","contributors":{"authors":[{"text":"Chavarie, Louise","contributorId":156227,"corporation":false,"usgs":false,"family":"Chavarie","given":"Louise","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":654136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muir, Andrew M.","contributorId":103933,"corporation":false,"usgs":false,"family":"Muir","given":"Andrew M.","affiliations":[],"preferred":false,"id":654137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zimmerman, Mara S.","contributorId":152687,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Mara","email":"","middleInitial":"S.","affiliations":[{"id":13269,"text":"Washington Department of Fish & Wildlife","active":true,"usgs":false}],"preferred":false,"id":654138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baillie, Shauna M.","contributorId":176176,"corporation":false,"usgs":false,"family":"Baillie","given":"Shauna","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":654139,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hansen, Michael J. 0000-0001-8522-3876 michaelhansen@usgs.gov","orcid":"https://orcid.org/0000-0001-8522-3876","contributorId":5006,"corporation":false,"usgs":true,"family":"Hansen","given":"Michael","email":"michaelhansen@usgs.gov","middleInitial":"J.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":654140,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nate, Nancy A.","contributorId":26626,"corporation":false,"usgs":true,"family":"Nate","given":"Nancy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":654141,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yule, Daniel L. dyule@usgs.gov","contributorId":139525,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","email":"dyule@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":654142,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Middel, Trevor","contributorId":176991,"corporation":false,"usgs":false,"family":"Middel","given":"Trevor","affiliations":[],"preferred":false,"id":654143,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bentzen, Paul","contributorId":176178,"corporation":false,"usgs":false,"family":"Bentzen","given":"Paul","email":"","affiliations":[],"preferred":false,"id":654144,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Krueger, Charles C.","contributorId":73131,"corporation":false,"usgs":true,"family":"Krueger","given":"Charles C.","affiliations":[],"preferred":false,"id":654145,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70178470,"text":"70178470 - 2016 - Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model","interactions":[],"lastModifiedDate":"2018-09-13T14:45:17","indexId":"70178470","displayToPublicDate":"2016-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model","docAbstract":"Reducing uncertainty in data inputs at relevant spatial scales can improve tidal marsh forecasting models, and their usefulness in coastal climate change adaptation decisions. The Marsh Equilibrium Model (MEM), a one-dimensional mechanistic elevation model, incorporates feedbacks of organic and inorganic inputs to project elevations under sea-level rise scenarios. We tested the feasibility of deriving two key MEM inputs—average annual suspended sediment concentration (SSC) and aboveground peak biomass—from remote sensing data in order to apply MEM across a broader geographic region. We analyzed the precision and representativeness (spatial distribution) of these remote sensing inputs to improve understanding of our study region, a brackish tidal marsh in San Francisco Bay, and to test the applicable spatial extent for coastal modeling. We compared biomass and SSC models derived from Landsat 8, DigitalGlobe WorldView-2, and hyperspectral airborne imagery. Landsat 8-derived inputs were evaluated in a MEM sensitivity analysis. Biomass models were comparable although peak biomass from Landsat 8 best matched field-measured values. The Portable Remote Imaging Spectrometer SSC model was most accurate, although a Landsat 8 time series provided annual average SSC estimates. Landsat 8-measured peak biomass values were randomly distributed, and annual average SSC (30 mg/L) was well represented in the main channels (IQR: 29–32 mg/L), illustrating the suitability of these inputs across the model domain. Trend response surface analysis identified significant diversion between field and remote sensing-based model runs at 60 yr due to model sensitivity at the marsh edge (80–140 cm NAVD88), although at 100 yr, elevation forecasts differed less than 10 cm across 97% of the marsh surface (150–200 cm NAVD88). Results demonstrate the utility of Landsat 8 for landscape-scale tidal marsh elevation projections due to its comparable performance with the other sensors, temporal frequency, and cost. Integration of remote sensing data with MEM should advance regional projections of marsh vegetation change by better parameterizing MEM inputs spatially. Improving information for coastal modeling will support planning for ecosystem services, including habitat, carbon storage, and flood protection.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1582","usgsCitation":"Byrd, K.B., Windham-Myers, L., Leeuw, T., Downing, B.D., Morris, J.T., and Ferner, M.C., 2016, Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model: Ecosphere, v. 7, no. 11, e01582; 27 p., https://doi.org/10.1002/ecs2.1582.","productDescription":"e01582; 27 p.","ipdsId":"IP-073438","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470411,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1582","text":"Publisher Index Page"},{"id":438505,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76M34Z1","text":"USGS data release","linkHelpText":"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model"},{"id":331164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335610,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F76M34Z1","text":"Data release for journal article titled, \"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model\""}],"country":"United States","state":"California","otherGeospatial":"Rush Ranch Open Space Preserve, Suisun Slough, Suisun Marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.05501556396483,\n 38.17802085110361\n ],\n [\n -122.05501556396483,\n 38.212288054388175\n ],\n [\n -121.99802398681642,\n 38.212288054388175\n ],\n [\n -121.99802398681642,\n 38.17802085110361\n ],\n [\n -122.05501556396483,\n 38.17802085110361\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"583415ade4b0070c0abed81a","chorus":{"doi":"10.1002/ecs2.1582","url":"http://dx.doi.org/10.1002/ecs2.1582","publisher":"Wiley-Blackwell","authors":"Byrd Kristin B., Windham-Myers Lisamarie, Leeuw Thomas, Downing Bryan, Morris James T., Ferner Matthew C.","journalName":"Ecosphere","publicationDate":"11/2016","auditedOn":"11/29/2016"},"contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":654114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leeuw, Thomas","contributorId":176970,"corporation":false,"usgs":false,"family":"Leeuw","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":654115,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Downing, Bryan D. 0000-0002-2007-5304 bdowning@usgs.gov","orcid":"https://orcid.org/0000-0002-2007-5304","contributorId":1449,"corporation":false,"usgs":true,"family":"Downing","given":"Bryan","email":"bdowning@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":654116,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morris, James T.","contributorId":29118,"corporation":false,"usgs":true,"family":"Morris","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":654117,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ferner, Matthew C.","contributorId":176972,"corporation":false,"usgs":false,"family":"Ferner","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":654118,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178472,"text":"70178472 - 2016 - Intermittent surface water connectivity: Fill and spill vs. fill and merge dynamics","interactions":[],"lastModifiedDate":"2017-01-03T16:04:08","indexId":"70178472","displayToPublicDate":"2016-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Intermittent surface water connectivity: Fill and spill vs. fill and merge dynamics","docAbstract":"Intermittent surface connectivity can influence aquatic systems, since chemical and biotic movements are often associated with water flow. Although often referred to as fill and spill, wetlands also fill and merge. We examined the effects of these connection types on water levels, ion concentrations, and biotic communities of eight prairie pothole wetlands between 1979 and 2015. Fill and spill caused pulsed surface water connections that were limited to periods following spring snow melt. In contrast, two wetlands connected through fill and merge experienced a nearly continuous, 20-year surface water connection and had completely coincident water levels. Fill and spill led to minimal convergence in dissolved ions and macroinvertebrate composition, while these constituents converged under fill and merge. The primary factor determining differences in response was duration of the surface water connection between wetland pairs. Our findings suggest that investigations into the effects of intermittent surface water connections should not consider these connections generically, but need to address the specific types of connections. In particular, fill and spill promotes external water exports while fill and merge favors internal storage. The behaviors of such intermittent connections will likely be accentuated under a future with more frequent and severe climate extremes.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-016-0830-z","usgsCitation":"Leibowitz, S.G., Mushet, D.M., and Newton, W.E., 2016, Intermittent surface water connectivity: Fill and spill vs. fill and merge dynamics: Wetlands, v. 36, no. s2, p. 323-342, https://doi.org/10.1007/s13157-016-0830-z.","productDescription":"20 p.","startPage":"323","endPage":"342","ipdsId":"IP-074809","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":331163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"s2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-12","publicationStatus":"PW","scienceBaseUri":"583415ade4b0070c0abed818","contributors":{"authors":[{"text":"Leibowitz, Scott G.","contributorId":156432,"corporation":false,"usgs":false,"family":"Leibowitz","given":"Scott","email":"","middleInitial":"G.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":654122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":654121,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newton, Wesley E. 0000-0002-1377-043X wnewton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-043X","contributorId":3661,"corporation":false,"usgs":true,"family":"Newton","given":"Wesley","email":"wnewton@usgs.gov","middleInitial":"E.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":654123,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178048,"text":"sir20165085 - 2016 - Documentation and hydrologic analysis of Hurricane Sandy in New Jersey, October 29–30, 2012","interactions":[],"lastModifiedDate":"2016-12-19T13:51:19","indexId":"sir20165085","displayToPublicDate":"2016-11-17T09:16:00","publicationYear":"2016","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":"2016-5085","title":"Documentation and hydrologic analysis of Hurricane Sandy in New Jersey, October 29–30, 2012","docAbstract":"In 2012, a late season tropical depression developed into a tropical storm and later a hurricane. The hurricane, named “Hurricane Sandy,” gained strength to a Category 3 storm on October 25, 2012, and underwent several transitions on its approach to the mid-Atlantic region of the eastern coast of the United States. By October 28, 2012, Hurricane Sandy had strengthened into the largest hurricane ever recorded in the North Atlantic and was tracking parallel to the east coast of United States, heading toward New Jersey. On October 29, 2012, the storm turned west-northwest and made landfall near Atlantic City, N.J. The high winds and wind-driven storm surge caused massive damage along the entire coastline of New Jersey. Millions of people were left without power or communication networks. Many homes were completely destroyed. Sand dunes were eroded, and the barrier island at Mantoloking was breached, connecting the ocean with Barnegat Bay.
Several days before the storm made landfall in New Jersey, the U.S. Geological Survey (USGS) made a decision to deploy a temporary network of storm-tide sensors and barometric pressure sensors from Virginia to Maine to supplement the existing USGS and National Oceanic and Atmospheric Administration (NOAA) networks of permanent tide monitoring stations. After the storm made landfall, the USGS conducted a sensor data recovery and high-water-mark collection campaign in cooperation with the Federal Emergency Management Agency (FEMA).
Peak storm-tide elevations documented at USGS tide gages, tidal crest-stage gages, temporary storm sensor locations, and high-water-mark sites indicate the area from southern Monmouth County, N.J., north through Raritan Bay, N.J., had the highest peak storm-tide elevations during this storm. The USGS tide gages at Raritan River at South Amboy and Raritan Bay at Keansburg, part of the New Jersey Tide Telemetry System, each recorded peak storm-tide elevations of greater than 13 feet (ft)—more than 5 ft higher than the previously recorded period-of-record maximum. A comparison of peak storm-tide elevations to preliminary FEMA Coastal Flood Insurance Study flood elevations indicated that these areas experienced the highest recurrence intervals along the coast of New Jersey. Analysis showed peak storm-tide elevations exceeded the 100-year FEMA flood elevations in many parts of Middlesex, Union, Essex, Hudson, and Bergen Counties, and peak storm-tide elevations at many locations in Monmouth County exceeded the 500-year recurrence interval.
A level 1 HAZUS (HAZards United States) analysis was done for the counties in New Jersey affected by flooding to estimate total building stock losses. The aggregated total building stock losses estimated by HAZUS for New Jersey, on the basis of the final inundation verified by USGS high-water marks, was almost $19 billion. A comparison of Hurricane Sandy with historic coastal storms showed that peak storm-tide elevations associated with Hurricane Sandy exceeded most of the previously documented elevations associated with the storms of December 1992, March 1962, September 1960, and September 1944 at many coastal communities in New Jersey. This scientific investigation report was prepared in cooperation with FEMA to document flood processes and flood damages resulting from this storm and to assist in future flood mitigation actions in New Jersey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165085","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Suro, T.P., Deetz, Anna, and Hearn, Paul, 2016, Documentation and hydrologic analysis of Hurricane Sandy in New Jersey, October 29–30, 2012: U.S. Geological Survey Scientific Investigations Report 2016–5085, 73 p., https://dx.doi.org/10.3133/sir20165085.","productDescription":"Report: ix, 73 p.; 5 Tables","numberOfPages":"87","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-055579","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":330616,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table4.xls","text":"Table 4","size":"45 KB xls","description":"SIR 2016-5085","linkHelpText":"- Description of U.S. Geological Survey sensors temporarily deployed for Hurricane Sandy with peak storm tide elevations, annual exceedance probabilities, and estimated recurrence intervals in New Jersey, October 29–30, 2012 \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330617,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table5.xls","text":"Table 5","size":"144 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations at 169 high-water-mark sites along the coast of New Jersey during Hurricane Sandy, October 29–30, 2012, and the corresponding Federal Emergency Management Agency flood elevations for the 10-, 50-, 100-, and 500-year recurrence intervals \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330618,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table6.xls","text":"Table 6","size":"61 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations for selected historic coastal floods and peak storm-tide elevations during Hurricane Sandy, October 29–30, 2012, at selected U.S. Geological Survey permanent monitoring tide gages in New Jersey"},{"id":330619,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table7.xls","text":"Table 7","size":"74 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations at 82 high-water-mark sites flagged and surveyed after the December 1992 storm in New Jersey, peak storm-tide elevations from the closest high-water-mark sites flagged and surveyed after Hurricane Sandy, October 29–30, 2012, and peak storm-tide elevations from the nearest U.S. Geological Survey tide gage along the coast of New Jersey during Hurricane Sandy, October 29–30, 2012\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330614,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085.pdf","text":"Report","size":"85.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5085"},{"id":330615,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table3.xls","text":"Table 3","size":"77.5 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak-of-record tide elevations and peak storm-tide elevations at U.S. Geological Survey permanent monitoring tide gages in New Jersey, October 29–30, 2012\t\t\t\t\t\t"},{"id":330613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5085/coverthb.jpg"}],"country":"United States","state":"New 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Jersey\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville NJ, 08648
http://nj.usgs.gov/
Despite widespread and ongoing implementation of conservation practices throughout the Chesapeake Bay watershed, water quality continues to be degraded by excess sediment and nutrient inputs. While the Chesapeake Bay Program has developed and maintains a large-scale and long-term monitoring network to detect improvements in water quality throughout the watershed, fewer resources have been allocated for monitoring smaller watersheds, even though water-quality improvements that may result from the implementation of conservation practices are likely to be first detected at smaller watershed scales.
In 2010, the U.S. Geological Survey partnered with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture to initiate water-quality monitoring in four selected small watersheds that were targeted for increased implementation of conservation practices. Smith Creek watershed is an agricultural watershed in the Shenandoah Valley of Virginia that is dominated by cattle and poultry production, and the Upper Chester River watershed is an agricultural watershed on the Eastern Shore of Maryland that is dominated by row-cropping activities. The Conewago Creek watershed is an agricultural watershed in southeastern Pennsylvania that is characterized by mixed agricultural activities. The fourth watershed, Difficult Run, is a suburban watershed in northern Virginia that is dominated by medium density residential development. The objective of this study was to investigate spatial and temporal variations in water chemistry and suspended sediment in these four relatively small watersheds that represent a range of land-use patterns and underlying geology to (1) characterize current water-quality conditions in these watersheds, and (2) identify the dominant sources, sinks, and transport processes in each watershed.
The general study design involved two components. The first included intensive routine water-quality monitoring at an existing streamgage within each study area (including continuous water-quality monitoring as well as discrete water-quality sampling) to develop a detailed understanding of the temporal and hydrologic variability in stream chemistry and sediment transport in each watershed. The second component involved extensive water-quality monitoring at various sites throughout each watershed to develop a detailed understanding of spatial patterns. Both components were used to improve understanding of sources and transport processes affecting stream chemistry, including nutrients and suspended sediments, and their implications for detecting long-term trends related to best management practices. This report summarizes the results of monitoring that was performed from April 2010 through September 2013.
Summaries for each of the four small watersheds are presented below. Each watershed has a more descriptive and detailed section in the report, but these summaries may be particularly useful for some watershed managers and stakeholders desiring slightly less technical detail.
Smith Creek is a 105.39-mi2 watershed within the Shenandoah Valley that drains to the North Fork Shenandoah River. The long-term Smith Creek base-flow index is 72.3 percent, indicating that on average, approximately 72 percent of Smith Creek flow was base flow, which suggests that Smith Creek streamflow is dominated by groundwater discharge rather than stormwater runoff. A series of cluster and principal components analyses demonstrated that the majority of the variability in Smith Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed for turbidity, suspended sediments, total nitrogen, ammonium, orthophosphate, iron, total phosphorus, and the ratio of calcium to magnesium. Statistically significant inverse correlations with flow were observed for specific conductance, magnesium, δ15N of nitrate, pH, bicarbonate, calcium, and δ18O of nitrate. Of particular note, flow and nitrate were not statistically significantly correlated, likely because of the relatively complex concentration-discharge relationship observed in continuous and discrete datasets. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, turbidity, orthophosphate, total phosphorus, suspended-sediment concentration, and silica were higher during the warm season, but pH, dissolved oxygen, and sulfate were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loads in Smith Creek using the continuous water-quality monitors. The mean Smith Creek in-stream sediment load was approximately 6,900 tons per year, with nearly 90 percent of the sediment load over the 3-year study period contributed during the eight largest storm events during that period. The Smith Creek total phosphorus load was approximately 21,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods, although a substantial phosphorus load still occurs during base-flow conditions. The Smith Creek total nitrogen load was approximately 400,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions (as was the case for sediment and total phosphorus) and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Smith Creek watershed revealed how the complex geology and hydrology interacted to result in variable water chemistry. During relatively dry and low base-flow periods, much of the discharge in Smith Creek was contributed by a single dominant spring—Lacey Spring. During wetter base-flow periods, the flows in Smith Creek were largely generated by a mixture of headwater springs and forested mountain tributaries with very different geochemical composition. The headwater springs generally issued from limestone bedrock and were characterized as having relatively high nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The undeveloped, high-gradient, forested mountain sites were generally characterized by low ionic strength waters with low nutrient concentrations. Nitrate isotope data from the limestone springs generally were consistent with manure-derived nitrogen sources (such as cattle and poultry), although the possibility of other mixed sources cannot be excluded. Nitrate isotope data from the undeveloped, high-gradient forested mountain sites were more consistent with nitrogen from undisturbed soils, atmospheric deposition, or nitrogen fixation. Regardless of the nitrogen source, oxygen isotope data indicate that the nitrate was largely a result of nitrification. Land-use data indicate that manure sources of nitrogen dominated watershed nitrogen inputs. Phosphorus sources were less well studied. The presence of a single point-source discharge near the town of New Market contributed the majority of the phosphorus to Smith Creek under base-flow conditions, but nonpoint sources of phosphorus dominated the loading to Smith Creek during stormflow periods.
Implementation of conservation practices increased in the Smith Creek watershed during the study period, and even though a broad range of practice types was implemented, the most common practices included stream fencing (for cattle exclusion), the development of nutrient management plans, conservation crop rotation, and the planting of cover crops. While the implementation of these conservation practices is encouraging, results indicate small increases in nitrate concentrations at the streamgage over the last 29 years, concurrent with small decreases in nitrate fluxes. It will likely be years before the cumulative effect of these practices can be detected in the Smith Creek water quality, and the magnitude of the effect of these conservation practices detected in Smith Creek will depend largely on whether nutrient loading (of manure and commercial fertilizer) is reduced over time.
The Upper Chester River watershed includes the 36-square-mile (mi2) watershed area around several nontidal tributaries that drain into the tidal Chester River. The streamgage is on Chesterville Branch, the largest nontidal tributary (approximately 6.12 mi2) and is the site for continuous water-quality monitoring for this project. The base-flow index at Chesterville Branch is about 72 percent and indicates that, as in most of the Coastal Plain, groundwater is the greatest contributor to streamflow. As such, more than 90 percent of the nitrogen in the stream is in the form of nitrate from groundwater. Continuous and discrete data collected at Chesterville Branch show the effects of streamflow and season on water quality. Significantly positive correlations with flow were observed for ammonium, dissolved and total phosphorus, sediment, and turbidity as runoff carried these constituents from the land surface into Chesterville Branch. Other constituents that increased significantly with flow include potassium, sulfate, iron, and manganese, which are likely contributed from near-stream areas and ponds with high organic-matter content. Total nitrogen, pH, and specific conductance, along with chemical constituents associated with groundwater inputs including nitrate, calcium, ratio of calcium to magnesium, silica, bicarbonate, and sodium, were negatively correlated with flow because concentrations of these constituents were diluted by runoff.
Seasonal differences in water chemistry, which are most likely related to increased biologic effects on the uptake and release of chemicals in the stream and near-stream areas, also were observed. Water temperature, orthophosphate, δ15N of nitrate, bicarbonate, sodium, and the ratio of sodium to chloride were higher during the warm season, and dissolved oxygen, total nitrogen, nitrate, magnesium, sulfate, and manganese were higher during the cool season.
Surrogate-regression models developed by using continuous water-quality data showed that the annual sediment load for the 2013 water year was about 2,600 tons, with more than 90 percent of this sediment contributed during two storms. The total phosphorus load in 2013 was about 13,000 pounds with more than 90 percent contributed during the same two storms as sediment. The load of total nitrogen, 140,000 pounds, accumulated steadily throughout the 2013 water year as nitrate in groundwater continuously discharged into the stream. The same two large storms that contributed 90 percent of the suspended-sediment and total phosphorus load only contributed about 20 percent of the annual total nitrogen load.
Extensive water-quality monitoring of stream base flow throughout the Upper Chester River watershed identified how differences in land use and hydrogeology affected water chemistry. In parts of the watershed with well-drained soil and thick sandy aquifer sediments, concentrations of nitrate and other chemicals associated with fertilizer and lime application increased in streams as agricultural land use increased. More than 90 percent of the nitrogen in streams from these areas was in the form of nitrate, and concentrations ranged from about 5 milligrams per liter (mg/L) to 8 mg/L as nitrogen in the two largest tributaries. Stream nitrate concentrations were about 1 mg/L as nitrogen where soils were more poorly drained, the surficial aquifer sediments were thinner, and forests and wetlands were more widespread than agriculture. Nitrate isotope data were consistent with inorganic fertilizers ± atmospheric deposition and N2 fixation as sources of nitrogen, and with nitrification as the dominant nitrate-forming process. Nitrate reduction was indicated by elevated δ15N and δ18O values in some samples from streams draining watersheds with poorly drained soils. An analysis of land-use data and SPARROW modeling input data attributed almost 90 percent of the nitrogen sources in the Upper Chester River watershed to inorganic fertilizer and fixation of atmospheric nitrogen by legumes, which is in agreement with the isotopic characteristics of nitrate in this watershed. Local sources of manure are limited in this area. Total phosphorus concentrations during base flow ranged from below detection to about 0.2 mg/L. Stream phosphorus concentrations during base flow were generally lower than those measured during storms because most phosphorus transport likely occurs as phosphorus attached to sediment particles during runoff. Because manure is not widely used in this area, the major source of phosphorus is likely fertilizer.
The implementation of conservation practices in the Upper Chester River watershed increased substantially during the study period, with a total implementation of 1,194 U.S. Department of Agriculture-compliant practices. The most frequently used practices were oriented towards nutrient and sediment control, including cover crops, nutrient management planning, conservation crop rotation, conservation tillage, and irrigation management. The current Chesapeake Bay model for this area predicts that implementation of best management practices should result in a 13-percent decrease in overall delivery of nitrogen to the Upper Chester River. Because most nitrogen travels through the groundwater system for years to decades before being discharged to streams, the time period of monitoring was not sufficient to see the effects of these practices on water quality. The magnitude of the effect that may eventually be detected will depend on the degree to which nitrate leaching into the groundwater system is reduced over time. Loadings of phosphorus and sediment are primarily transported during large runoff events and are difficult to control and analyze for trends because of their timing and episodic nature.
Conewago Creek has two primary monitoring locations—one near the middle of the 47-mi2 watershed and the other near the outlet just upstream of the Susquehanna River. The base-flow index was 47.3 percent for 2012–2013, indicating that on average, approximately 53 percent of the streamflow in Conewago Creek exited the watershed as surface flow, which suggests that the stormwater runoff was somewhat greater than groundwater discharge (base flow). A series of cluster and principal components analyses demonstrated that the majority of the variability in the Conewago Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed at both monitoring sites for ammonium, total phosphorus, orthophosphate, iron, and manganese; additionally, at the upstream monitoring station, total nitrogen demonstrated a statistically significant positive correlation with flow. Statistically significant inverse correlations with flow were observed at both sites for water temperature, specific conductance (at the downstream site only), sulfate, chloride, calcium, and magnesium. Statistically significant seasonal patterns were observed for several water-quality constituents. Water temperature, phosphorus (upstream site only), and orthophosphate were higher during the warm season, and nitrate and total nitrogen (upstream site only) were higher during the cool season.
Surrogate regression models were developed to compute sediment and nutrient load in Conewago Creek by using the continuous water-quality monitors and water-quality samples. Conewago Creek sediment load was approximately 9,900 tons in 2012 and approximately 18,900 tons in 2013, with nearly 80 percent of the sediment load in 2013 contributed by the three largest storm events. Annual total nitrogen loads could not be estimated due to poor model performance. The addition of continued monitoring or a continuously recording nitrate sensor could improve estimates of total nitrogen loads. During 2012 and 2013, phosphorus loads in Conewago Creek were approximately 50,000 pounds in each year.
Combining data from one high-flow synoptic sampling with the data from routine sampling revealed how the geology and hydrology interact to result in variable water chemistry throughout the Conewago Creek watershed. The areas above the upstream gage in the headwaters are generally underlain by forested non-carbonate bedrock and are characterized by relatively low nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The more developed, agricultural areas below the upstream site were generally characterized by higher ionic strength waters with higher nutrient and metal concentrations. An analysis of land-use data and SPAtially Referenced Regressions On Watershed (SPARROW) modeling data indicates that manure sources of nitrogen dominate the input of nitrogen to the watershed.
Implementation of conservation practices increased in the Conewago Creek watershed during the study period, and while a broad range of practice types were implemented, the most common practices included residue and tillage management, cover crops, nutrient management, terracing, and stream fencing (for animal exclusion or bank restoration). While the implementation of these conservation practices is encouraging, the cumulative effects of these practices probably will not be detected in Conewago Creek water quality for several years. The magnitude of the effects of these conservation practices on water quality in Conewago Creek will depend largely on the extent to which nutrient loading (septic, manure, and commercial fertilizer) and sediment-producing activities are reduced over time.
The Difficult Run watershed is a 57.82-mi2 watershed that drains to the Potomac River. The long-term Difficult Run base-flow index (from 1936 to 2010) was 57.9, indicating that approximately 58 percent of streamflow exited the watershed as base flow and 42 percent as stormflow; however, with continued development and urbanization of the watershed, the base-flow index has decreased to 50 percent during the last 20 years. This base-flow index was less than those of the other watersheds evaluated in this study, likely because the Difficult Run watershed largely is underlain by crystalline piedmont metamorphic rocks and has a greater proportion of impervious urban land cover. A series of cluster and principal components analyses indicated that most of the variability in Difficult Run water quality could be attributed to hydrologic variability and seasonality. Statistically significant positive correlations with flow were observed for turbidity, dissolved oxygen, suspended sediments, ammonium, orthophosphate, iron, and total phosphorus. Statistically significant inverse correlations with flow were observed for water temperature, pH, specific conductance, bicarbonate, calcium, magnesium, nitrate, δ15N of nitrate, and silica. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, ammonium, orthophosphate, and δ15N of nitrate were higher during the warm season, and dissolved oxygen, nitrate, and manganese were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loading rates. The Difficult Run sediment load was approximately 8,000 tons per year, with greater than 95 percent of the sediment load in the 2013 water year contributed by the seven largest storm events. The total phosphorus load in Difficult Run was approximately 14,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods. The total nitrogen load in Difficult Run is estimated to have been approximately 140,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions than that of phosphorus and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Difficult Run watershed revealed relatively uniform generation of flow per unit of watershed area, as well as spatial variation in water quality that is strongly related to land-use activities. Elevated nitrate concentrations were observed in a subset of monitoring sites that are inversely correlated with population density and positively correlated to the septic system density within each subwatershed. The majority of the elevated nitrate concentrations for these sites are hypothesized to be caused by nitrate leaching from septic systems, more so than homeowner fertilizer usage among these subwatersheds that have lower population densities than other parts of the watershed. Nitrate isotope data, temporal patterns in the water-quality data, mass-balance computations, and a separate land-use analysis all generally indicate that leachate from septic systems was the likely source of the elevated nitrate. Another group of water-quality sites have relatively low nitrogen concentrations, are located in areas that are served by city sewer lines, and have experienced stream restoration activities. A final group of sites drained the areas with the highest imperviousness and had strongly elevated specific conductance, chloride, and sodium, which were likely caused by a combination of road salting and other anthropogenic sources draining these urbanized areas in the watershed. A fourth group of sites represents a mixture of water sources and had water quality similar to that at the Difficult Run streamgage. Analysis of the nitrate isotope data generally indicates a broad range of composition indicative of mixed natural and anthropogenic nitrogen sources. Implementation of conservation practices increased in the Difficult Run watershed during the study period, and while a broad range of practice types was implemented, the most common practices included stream restoration. While the implementation of these conservation practices is encouraging, the cumulative effect of these practices probably will not be detected in Difficult Run water quality for several years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165093","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program","usgsCitation":"Hyer, K.E., Denver, J.M., Langland, M.J., Webber, J.S., Böhlke, J.K., Hively, W.D., and Clune, J.W., 2016, Spatial and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013: U.S. Geological Survey Scientific Investigations Report 2016–5093, 211 p., https://dx.doi.org/10.3133/sir20165093.","productDescription":"Report: xix, 211 p.","startPage":"1","endPage":"211","numberOfPages":"236","onlineOnly":"N","ipdsId":"IP-067371","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":330861,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5093/coverthb.jpg"},{"id":330862,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5093/sir20165093.pdf","text":"Report","size":"30.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5093"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia","otherGeospatial":"Conewago Creek watershed, Difficult Run watershed, Smith Creek watershed, Upper Chester River watershed","geographicExtents":"{\n\"id\": \"2434359\",\n\"crs\": {\n\"type\": \"name\",\n\"properties\": {\n\"name\": \"urn:ogc:def:crs:OGC:1.3:CRS84\"\n}\n},\n\"type\": \"Feature\",\n\"geometry\": {\n\"coordinates\": 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U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Digital flood-inundation maps for a 4.9-mile reach of the Yellow River at Plymouth, Indiana (Ind.), were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Office of Community and Rural Affairs. The flood-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 and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage 05516500, Yellow River at Plymouth, Ind. Current conditions for estimating near-real-time areas of inundation using USGS streamgage information may be obtained on the Internet at http://waterdata.usgs.gov/in/nwis/uv?site_no=05516500. In addition, information has been provided to the National Weather Service (NWS) for incorporation into their Advanced Hydrologic Prediction Service (AHPS) flood-warning system (http:/water.weather.gov/ahps/). The NWS AHPS forecasts flood hydrographs at many sites that are often collocated with USGS streamgages, including the Yellow River at Plymouth, Ind. NWS AHPS-forecast peak-stage information may be used in conjunction with the maps developed in this study to show predicted areas of flood and forecasts of flood hydrographs at this site.
For this study, flood profiles were computed for the Yellow River reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the current stage-discharge relations at the Yellow River streamgage, in combination with the flood-insurance study for Marshall County (issued in 2011). The calibrated hydraulic model was then used to determine eight water-surface profiles for flood stages at 1-foot intervals referenced to the streamgage datum and ranging from bankfull to the highest stage of the current stage-discharge rating curve. The 1-percent annual exceedance probability flood profile elevation (flood elevation with recurrence intervals within 100 years) is within the calibrated water-surface elevations for comparison. The simulated water-surface profiles were then used with a geographic information system (GIS) digital elevation model (DEM, derived from Light Detection and Ranging [lidar]) 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 05516500, Yellow River at Plymouth, Ind., and forecast stream stages from the NWS AHPS, provides emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165117","collaboration":"Prepared in cooperation with the Indiana Office of Community and Rural Affairs","usgsCitation":"Menke, C.D., Bunch, A.R., and Kim, M.H., 2016, Flood-inundation maps for the Yellow River at Plymouth, Indiana: U.S. Geological Survey Scientific Investigations Report 2016–5117, 9 p., https://dx.doi.org/10.3133/sir20165117.","productDescription":"Report: vi, 9 p.; Metadata: 2 files; Read Me; Spatial Data: 2 files","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-078607","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":331047,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5117/coverthb.jpg"},{"id":331048,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117.pdf","text":"Report","size":"2.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5117"},{"id":331049,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117_dep_grd.metadata","text":"Metadata Depth Grids","size":"16.5 KB"},{"id":331050,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2016/5117/sir20165117_shapefile.metadata","text":"Metadata Shapefiles","size":"16.7 KB"},{"id":331051,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2016/5117/00Readme.txt","text":"Readme","size":"8.18 KB","linkFileType":{"id":2,"text":"txt"}},{"id":331052,"rank":6,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5117/gis_data/depth_grids.zip","text":"Depth Grids","size":"4.37 MB","linkFileType":{"id":6,"text":"zip"}},{"id":331053,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2016/5117/gis_data/shapefile.zip","text":"Shape File","size":"807 KB","linkFileType":{"id":6,"text":"zip"}}],"country":"United States","state":"Indiana","city":"Plymouth","otherGeospatial":"Yellow River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.4,\n 41.3\n ],\n [\n -86.4,\n 41.5\n ],\n [\n -86.2,\n 41.5\n ],\n [\n -86.2,\n 41.3\n ],\n [\n -86.4,\n 41.3 ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Blvd.
Indianapolis, IN 46278
http://in.water.usgs.gov/
http://ky.water.usgs.gov/
The Potomac River is a large source of N to Chesapeake Bay, where reducing nutrient loads is a focus of efforts to improve trophic status. Better understanding of NO3– loss, reflected in part by diel variation in NO3– concentrations, may refine model predictions of N loads to the Bay. We analyzed 2 y of high-frequency NO3– sensor data in the Potomac to quantify seasonal variation in the magnitude and timing of diel NO3– loss. Diel patterns were evident, especially during low flow, despite broad seasonal and flow-driven variation in NO3– concentrations. Diel variation was ~0.01 mg N/L in winter and 0.02 to 0.03 mg N/L in summer with intermediate values in spring and autumn, equivalent to <1% of the daily mean NO3– concentration in winter and ~2 to 4% in summer. Maximum diel NO3– values generally occurred in mid- to late morning, with more repeatable patterns in summer and wider variation in autumn and winter. Diel NO3– loss reduced loads by 0.7% in winter and 3% in summer. These losses were less than estimates of total in-stream NO3– load loss across the basin that averaged 33% of the annual groundwater contribution to the river. Water temperature and discharge had stronger relationships to the daily magnitude of diel NO3– variation than did photosynthetically active radiation. Estimated diel areal NO3– loss rates were generally >1000 mg N m–2 d–1, greater than most published values because measurements in this large river integrate over a greater depth/unit stream bottom area than do those from smaller rivers. These diel NO3– patterns are consistent with the influence of photoautotrophic uptake and related denitrification, but we cannot attribute these patterns to assimilation alone because the magnitude and timing of diel dynamics were affected to an unknown extent by processes, such as evapotranspiration, transient storage, and hydrodynamic dispersion. Improvements to diel loss estimates will require additional high-frequency measures, such as dissolved O2, dissolved organic N, and NH4+, and deployment of 2 measurement stations.
","language":"English","publisher":"The University of Chicago Press","doi":"10.1086/688777","usgsCitation":"Burns, D.A., Miller, M.P., Pellerin, B., and Capel, P.D., 2016, Patterns of diel variation in nitrate concentrations in the Potomac River: Freshwater Science, v. 35, no. 4, p. 1117-1132, https://doi.org/10.1086/688777.","productDescription":"16 p.","startPage":"1117","endPage":"1132","ipdsId":"IP-070616","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":438507,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7HT2MD4","text":"USGS data release","linkHelpText":"Water Quality and Hydrologic Data (2011-13) for Freshwater Science Paper titled, "Patterns of Diel Variation in Nitrate Concentrations in the Potomac River""},{"id":331066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Potomac River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.6893310546875,\n 38.052416771864834\n ],\n [\n -79.6893310546875,\n 39.8928799002948\n ],\n [\n -77.080078125,\n 39.8928799002948\n ],\n [\n -77.080078125,\n 38.052416771864834\n ],\n [\n -79.6893310546875,\n 38.052416771864834\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"4","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582dd8e8e4b04d580bd3fa85","contributors":{"authors":[{"text":"Burns, Douglas A. 0000-0001-6516-2869 daburns@usgs.gov","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":1237,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas","email":"daburns@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miller, Matthew P. 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":3919,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653933,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pellerin, Brian A. 0000-0003-3712-7884 bpeller@usgs.gov","orcid":"https://orcid.org/0000-0003-3712-7884","contributorId":147077,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian","email":"bpeller@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":653934,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Capel, Paul D. 0000-0003-1620-5185 capel@usgs.gov","orcid":"https://orcid.org/0000-0003-1620-5185","contributorId":1002,"corporation":false,"usgs":true,"family":"Capel","given":"Paul","email":"capel@usgs.gov","middleInitial":"D.","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":653935,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178387,"text":"70178387 - 2016 - Interannual water-level fluctuations and the vegetation of prairie potholes: Potential impacts of climate change","interactions":[],"lastModifiedDate":"2017-01-03T16:05:22","indexId":"70178387","displayToPublicDate":"2016-11-16T11:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Interannual water-level fluctuations and the vegetation of prairie potholes: Potential impacts of climate change","docAbstract":"Mean water depth and range of interannual water-level fluctuations over wet-dry cycles in precipitation are major drivers of vegetation zone formation in North American prairie potholes. We used harmonic hydrological models, which require only mean interannual water depth and amplitude of water-level fluctuations over a wet–dry cycle, to examine how the vegetation zones in a pothole would respond to small changes in water depth and/or amplitude of water-level fluctuations. Field data from wetlands in Saskatchewan, North Dakota, and South Dakota were used to parameterize harmonic models for four pothole classes. Six scenarios in which small negative or positive changes in either mean water depth, amplitude of interannual fluctuations, or both, were modeled to predict if they would affect the number of zones in each wetland class. The results indicated that, in some cases, even small changes in mean water depth when coupled with a small change in amplitude of water-level fluctuations can shift a prairie pothole wetland from one class to another. Our results suggest that climate change could alter the relative proportion of different wetland classes in the prairie pothole region.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-016-0850-8","usgsCitation":"van der Valk, A., and Mushet, D.M., 2016, Interannual water-level fluctuations and the vegetation of prairie potholes: Potential impacts of climate change: Wetlands, v. 36, no. 2, p. 397-406, https://doi.org/10.1007/s13157-016-0850-8.","productDescription":"10 p.","startPage":"397","endPage":"406","ipdsId":"IP-072077","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470416,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1303&context=eeob_ag_pubs","text":"External Repository"},{"id":331061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"582dd8e9e4b04d580bd3fa87","contributors":{"authors":[{"text":"van der Valk, Arnold","contributorId":145612,"corporation":false,"usgs":false,"family":"van der Valk","given":"Arnold","affiliations":[{"id":15296,"text":"Iowa State University, Ames, IA, USA","active":true,"usgs":false}],"preferred":false,"id":653912,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653911,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70175480,"text":"ofr20161122 - 2016 - Groundwater quality from private domestic water-supply wells in the vicinity of petroleum production in Southwestern Indiana","interactions":[],"lastModifiedDate":"2016-11-23T13:09:40","indexId":"ofr20161122","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","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":"2016-1122","title":"Groundwater quality from private domestic water-supply wells in the vicinity of petroleum production in Southwestern Indiana","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20161122","collaboration":"Agency for Toxic Substances and Disease Registry","usgsCitation":"Risch, M.R., and Silcox, C.A., 2016, Groundwater quality from private domestic water-supply wells in the vicinity of petroleum production in Southwestern Indiana: U.S. Geological Survey Open-File Report 2016-1122, 29 p., https://doi.org/10.3133/ofr20161122.","productDescription":"29 p.","startPage":"1","endPage":"29","numberOfPages":"29","ipdsId":"IP-077952","costCenters":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":331221,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"}],"publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5836b8dfe4b0d9329c801c5f","contributors":{"authors":[{"text":"Risch, Martin R. 0000-0002-7908-7887 mrrisch@usgs.gov","orcid":"https://orcid.org/0000-0002-7908-7887","contributorId":2118,"corporation":false,"usgs":true,"family":"Risch","given":"Martin","email":"mrrisch@usgs.gov","middleInitial":"R.","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":645401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Silcox, Cheryl A. casilcox@usgs.gov","contributorId":5080,"corporation":false,"usgs":true,"family":"Silcox","given":"Cheryl","email":"casilcox@usgs.gov","middleInitial":"A.","affiliations":[{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":645402,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178390,"text":"70178390 - 2016 - Midcontinent Prairie-Pothole wetlands and climate change: An Introduction to the Supplemental Issue","interactions":[],"lastModifiedDate":"2017-01-03T16:06:01","indexId":"70178390","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Midcontinent Prairie-Pothole wetlands and climate change: An Introduction to the Supplemental Issue","docAbstract":"The multitude of wetlands in the Prairie Pothole Region of North America forms one of Earth’s largest wetland complexes. The midcontinent location exposes this ecologically and economically important wetland system to a highly variable climate, markedly influencing ponded-water levels, hydroperiods, chemical characteristics, and biota of individual basins. Given their dominance on the landscape and recognized value, great interest in how projected future changes in climate will affect prairie-pothole wetlands has developed and spawned much scientific research. On June 2, 2015, a special symposium, “Midcontinent Prairie-Pothole Wetlands: Influence of a Changed Climate,” was held at the annual meeting of the Society of Wetland Scientists in Providence, Rhode Island, USA. The symposium’s twelve presenters covered a wide range of relevant topics delivered to a standing-room-only audience. Following the symposium, the presenters recognized the need to publish their presented papers as a combined product to facilitate widespread distribution. The need for additional papers to more fully cover the topic of prairie-pothole wetlands and climate change was also identified. This supplemental issue of Wetlands is the realization of that vision.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-016-0852-6","usgsCitation":"Mushet, D.M., 2016, Midcontinent Prairie-Pothole wetlands and climate change: An Introduction to the Supplemental Issue: Wetlands, v. 36, no. s2, p. 223-228, https://doi.org/10.1007/s13157-016-0852-6.","productDescription":"6 p.","startPage":"223","endPage":"228","ipdsId":"IP-076703","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":488540,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s13157-016-0852-6","text":"Publisher Index Page"},{"id":331060,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Prairie Pothole Region","volume":"36","issue":"s2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"582dd8e9e4b04d580bd3fa8b","contributors":{"authors":[{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653918,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70176433,"text":"fs20163071 - 2016 - Compounds of emerging concern in the San Antonio River Basin, Texas, 2011–12","interactions":[],"lastModifiedDate":"2016-11-16T16:08:41","indexId":"fs20163071","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3071","title":"Compounds of emerging concern in the San Antonio River Basin, Texas, 2011–12","docAbstract":"The City of San Antonio and the surrounding municipalities in Bexar County, Texas, are among the fastest growing cities in the Nation. Increases in residential and commercial development are changing runoff patterns and likely will increase chemical loads into streams. The U.S. Geological Survey, in cooperation with the San Antonio River Authority, evaluated the concentrations and distributional patterns of selected “compounds of emerging concern” (CECs) by collecting and analyzing water-quality samples from 20 sites in the San Antonio River Basin, Tex., during 2011–12. On the basis of their chemical composition or similar uses, the CECs discussed in this fact sheet are wastewater compounds, pharmaceutical compounds (hereinafter referred to as “pharmaceuticals”), and steroidal hormone and sterol compounds (hereinafter referred to as “steroidal hormones and sterols”). Three synoptic sampling events were completed during 2011–12 to analyze for CECs in the San Antonio River Basin. Samples were analyzed for 54 wastewater compounds, 13 pharmaceuticals, 17 steroidal hormones, and 4 sterols. Overall, the concentrations of all CECs analyzed for during this study were low, generally close to or less than the laboratory reporting level.
Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Accuracy of streamflow data depends on the veracity of the rating model used to derive a continuous time series of discharge from the surrogate variables that can readily be collected autonomously at a streamgage. Ratings are typically represented as a simple monotonic increasing function (simple rating), meaning the discharge is a function of stage alone, however this is never truly the case unless the flow is completely uniform at all stages and in transitions from one stage to the next. For example, at some streamflow-monitoring sites the discharge on the rising limb of the hydrograph is discernably larger than the discharge at the same stage on the falling limb of the hydrograph. This is the so-called “loop rating curve” (loop rating). In many cases, these loops are quite small and variation between rising- and falling-limb discharge measurements made at the same stage are well within the accuracy of the measurements. However, certain hydraulic conditions can produce a loop that is large enough to preclude use of a monotonic rating. A detailed data campaign for the Mississippi River at St. Louis, Missouri during a multi-peaked flood over a 56-day period in 2015 demonstrates the rating complexity at this location. The shifting-control method used to deal with complexity at this site matched all measurements within 8%.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"River flow 2016","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Proceedings of the International Conference on Fluvial Hydraulics (River flow 2016)","conferenceDate":"July 11-14, 2016","conferenceLocation":"St. Louis, MO","language":"English","publisher":"CRC Press","usgsCitation":"Holmes, R.R., 2016, River rating complexity, in River flow 2016, St. Louis, MO, July 11-14, 2016, p. 679-686.","productDescription":"8 p.","startPage":"679","endPage":"686","ipdsId":"IP-071265","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":348967,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":348966,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.crcpress.com/River-Flow-2016-Iowa-City-USA-July-11-14-2016/Constantinescu-Garcia-Hanes/p/book/9781138029132","linkFileType":{"id":5,"text":"html"}},{"id":350997,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ja/70193968/70193968.pdf","text":"USGS open-access version of article","size":"507 kB","linkFileType":{"id":1,"text":"pdf"},"description":"USGS open-access version of article"}],"publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60fc9be4b06e28e9c24040","contributors":{"authors":[{"text":"Holmes, Robert R. Jr. 0000-0002-5060-3999 bholmes@usgs.gov","orcid":"https://orcid.org/0000-0002-5060-3999","contributorId":156293,"corporation":false,"usgs":true,"family":"Holmes","given":"Robert","suffix":"Jr.","email":"bholmes@usgs.gov","middleInitial":"R.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":false,"id":721769,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70178266,"text":"sir20165133 - 2016 - Quantifying seepage using heat as a tracer in selected irrigation canals, Walker River Basin, Nevada, 2012 and 2013","interactions":[],"lastModifiedDate":"2025-05-14T18:37:27.795967","indexId":"sir20165133","displayToPublicDate":"2016-11-16T00:00:00","publicationYear":"2016","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":"2016-5133","title":"Quantifying seepage using heat as a tracer in selected irrigation canals, Walker River Basin, Nevada, 2012 and 2013","docAbstract":"The Walker River is an important source of water for western Nevada. The river provides water for agriculture and recharge to local aquifers used by several communities. Farmers began diverting water from the Walker River in the 1860s to support growing agricultural development. Over time, the reduced inflows into Walker Lake from upstream reservoirs and diversions have resulted in 170 feet of lake level decline and increased dissolved-solids concentrations to levels that threaten aquatic ecosystems, including survival of Lahonton cutthroat trout, a native species listed in the Endangered Species Act. Investigations of the water-budget components in the Walker River Basin have revealed uncertainty in the recharge to aquifers from irrigation canals. To address this need, the U.S. Geological Survey conducted an extensive field study from March 2012 through October 2013 to quantify seepage losses in selected canals in the Smith Valley, Mason Valley, and Walker Lake Valley irrigation areas.
The seepage rates estimated for the 2012 and 2013 irrigation seasons in the Smith Valley transect sites (Saroni and Plymouth canals) ranged between 0.01 to 2.5 feet per day (ft/d) (0.01 to 0.68 cubic feet per second per mile [ft3/s-mi]). From 2012 to 2013, the average number of days the canals had flowing water decreased from 190 to 125 due to drier climate and lack of water available for diversion from the Walker River. The nearly 50-percent reductions in volumetric loss rates between 2012 and 2013 were associated with less than average diversions into canals from the Walker River and reductions in infiltration rates following routine canal maintenance.
Models developed for the Saroni canal in 2012 were recalibrated in 2013 to evaluate changes in seepage as a result of siltation. Just prior to the 2012 irrigation season, nearly the entire length of the canal was cleared of vegetation and debris to improve flow conveyance. In 2013, following the first year of maintenance, a 90-percent reduction in seepage was observed at one of the transect sites. The removal of sediment-clogged layers during canal maintenance may have more profound effects on seepage rates beyond what was observed at the transect sites. The seepage rates for the Saroni canal in 2012 ranged from 0.02 to 1.6 ft/d (0.03 to 0.4 ft3/s-mi). The total seepage loss in the Saroni canal for the 2012 and 2013 irrigation seasons was estimated to be 1,100 and 590 acre-feet (acre-ft), respectively.
Seepage rates on the Plymouth canal in Smith Valley in 2012 were among the lowest, ranging from 0.01 to 0.2 ft/d (0.01 to 0.1 ft3/s-mi). In 2013, the seepage rate on the Plymouth canal was similar to 2012; however, the volumetric loss was reduced by 50 percent due to the 50-percent reduction in number of canal flow days. Lower rates of seepage on the Plymouth canal for the 2012 and 2013 irrigation seasons were estimated to be 210 and 130 acre-ft, respectively.
The seepage rates estimated for the 2012 and 2013 irrigation seasons in the Mason Valley transect sites (Fox, Mickey, and Campbell ditches) ranged from 0.1 to 3.3 ft/d (0.2 to 1.3 ft3/s-mi). The influence of water-table declines on seepage was observed at the Mickey and Campbell ditches. In 2012, the estimated seepage on the Mickey ditch was 1.6 ft/d during a period when the water-table altitude was at or above the canal altitude. Following extensive declines in the water table, the hydraulic gradient increased between the canal and the shallow aquifer, thereby increasing the seepage rates to 3.2 ft/d in 2013. During the period of hydraulic disconnection, seepage rates increased to 9.5 ft/d during intermittent periods of canal flow. For the Mickey ditch, the seepage loss in 2013 was 1.5 times the rate estimated in 2012 despite the canal having 45 days less flow. Similarly, the Campbell ditch seepage loss increased slightly from 660 to 700 acre-ft, a factor of 1.1, with 49 days less flow. The seepage loss for the Fox ditch did not exhibit significant year to year variability. The annual seepage loss estimated for 2012 and 2013 in the Fox ditch was 2,100 and 2,200 acre-ft, respectively.
The seepage rates estimated for the 2013 irrigation season in the Walker Lake Valley transect sites (Schurz Lateral Canals 1A and 2A, and Canal 2) ranged from 0.7 to 0.9 ft/d (0.4 to 1.3 ft3/s-mi). In Walker Lake Valley, diversions into Lateral Canals 1A and 2A during the 2013 irrigation season were highly intermittent, a characteristic common of lateral diversions. The annual estimated seepage loss in Walker Lake Valley ranged between 50 and 725 acre-ft among the transect sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165133","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Naranjo, R.C., and Smith, D.W., 2016, Quantifying seepage using heat as a tracer in selected irrigation canals, Walker River Basin, Nevada, 2012 and 2013: U.S. Geological Survey Scientific Investigations Report 2016-5133, 169 p.,\nhttps://dx.doi.org/10.3133/sir20165133.","productDescription":"Report: viii, 169 p.; 2 Appendixes","onlineOnly":"Y","ipdsId":"IP-066495","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":331031,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5133/sir20165133_appendix_6a.xlsx","text":"Appendix 6A","size":"16.4 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5133 Appendix 6A"},{"id":331030,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5133/sir20165133.pdf","text":"Report","size":"11.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5133"},{"id":331032,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5133/sir20165133_appendix_6b.xlsx","text":"Appendix 6B","size":"13.9 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016-5133 Appendix 6B"},{"id":331029,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5133/coverthb.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Walker River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.75,\n 38\n ],\n [\n -119.75,\n 39.25\n ],\n [\n -118.25,\n 39.25\n ],\n [\n -118.25,\n 38\n ],\n [\n -119.75,\n 38\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Rd.
Carson City, NV 89701
http://nv.water.usgs.gov/
This report summarizes the primary sources of potash in the United States. Potash is an essential nutrient that, along with phosphorus and nitrogen, is used as fertilizer for growing crops. Plants require sufficient potash to activate enzymes, which in turn catalyze chemical reactions important for water uptake and photosynthesis. When potassium is available in quantities necessary for healthy plant growth, disease resistance and physical quality are improved and crop yield and shelf life are increased. Potash is a water-soluble compound of potassium formed by geologic and hydrologic processes. The principal potash sources discussed are the large, stratiform deposits that formed during retreat and evaporation of intracontinental seas. The Paradox, Delaware, Holbrook, Michigan, and Williston sedimentary basins in the United States are examples where extensive potash beds were deposited. Ancient marine-type potash deposits that are close to the surface can be mined using conventional underground mining methods. In situ solution mining can be used where beds are too deep, making underground mining cost-prohibitive, or where underground mines are converted to in situ solution mines. Quaternary brine is another source of potash that is recovered by solar evaporation in manmade ponds. Groundwater from Pleistocene Lake Bonneville (Wendover, Utah) and the present-day Great Salt Lake in Utah are sources of potashbearing brine. Brine from these sources pumped to solar ponds is evaporated and potash concentrated for harvesting, processing, and refinement. Although there is sufficient potash to meet near-term demand, the large marine-type deposits are either geographically restricted to a few areas or are too deep to easily mine. Other regions lack sources of potash brine from groundwater or surface water. Thus, some areas of the world rely heavily on potash imports. Political, economic, and global population pressures may limit the ability of some countries from securing potash resources in the future. In this context, a historical perspective on U.S. potash production in a global framework is discussed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161167","isbn":"978-1-4113-4101-2","usgsCitation":"Yager, D.B., 2016, Potash—A vital agricultural nutrient sourced from geologic deposits: U.S. Geological Survey Open-File Report 2016-1167, 28 p., https://dx.doi.org/10.3133/ofr20161167.","productDescription":"v, 28 p.","numberOfPages":"38","onlineOnly":"N","ipdsId":"IP-067057","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":330757,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1167/coverthb.jpg"},{"id":330758,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1167/ofr20161167.pdf","text":"Report","size":"4.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1176"}],"contact":"Center Director
USGS Central Mineral and Environmental Resources Science Center
U.S. Geological Survey
Box 25046, MS 973
Denver, CO 80225
Research into the effects of shade on reservoir fish assemblages is lacking, with most investigations focused on streams. Unlike many streams, the canopy in a reservoir shades only a narrow fringe of water adjacent to the shoreline, and may not have the influential effect on the aquatic environment reported in streams. We compared fish assemblages between shaded and unshaded sites in a shallow reservoir. Overall species richness (gamma diversity) was higher in shaded sites, and fish assemblage composition differed between shaded and unshaded sites. Average light intensity was 66 % lower in shaded sites, and differences in average temperature and dissolved oxygen were small. Unlike streams where shade can have large effects on water physicochemistry, in reservoirs shade-related differences in fish assemblages seemed to be linked principally to differences in light intensity. Diversity in light intensity in shaded and unshaded sites in reservoirs can create various mosaics of light-based habitats that enable diversity of species assemblages. Managing to promote the habitat diversity provided by shade may require coping with the artificial nature of reservoir riparian zones and water level fluctuations.
","language":"English","publisher":"Springer","doi":"10.1007/s10641-016-0519-4","usgsCitation":"Raines, C.D., and Miranda, L.E., 2016, Role of riparian shade on the fish assemblage of a reservoir littoral: Environmental Biology of Fishes, v. 99, no. 10, p. 753-760, https://doi.org/10.1007/s10641-016-0519-4.","productDescription":"8 p.","startPage":"753","endPage":"760","ipdsId":"IP-076309","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":331009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-19","publicationStatus":"PW","scienceBaseUri":"582c2ce3e4b0c253be072bfc","contributors":{"authors":[{"text":"Raines, C. D.","contributorId":176859,"corporation":false,"usgs":false,"family":"Raines","given":"C.","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":653819,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":653753,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178337,"text":"70178337 - 2016 - Effects of climate and water balance across grasslands of varying C3 and C4 grass cover","interactions":[],"lastModifiedDate":"2016-11-14T12:30:15","indexId":"70178337","displayToPublicDate":"2016-11-14T13:20:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Effects of climate and water balance across grasslands of varying C3 and C4 grass cover","docAbstract":"Climate change in grassland ecosystems may lead to divergent shifts in the abundance and distribution of C3 and C4 grasses. Many studies relate mean climate conditions over relatively long time periods to plant cover, but there is still much uncertainty about how the balance of C3and C4 species will be affected by climate at a finer temporal scale than season (individual events to months). We monitored cover at five grassland sites with co-dominant C3 and C4 grass species or only dominant C3 grass species for 6 yr in national parks across the Colorado Plateau region to assess the influence of specific months of climate and water balance on changes in grass cover. C4 grass cover increased and decreased to a larger degree than C3 grass cover with extremely dry and wet consecutive years, but this response varied by ecological site. Climate and water balance explained 10–49% of the inter-annual variability of cover of C3 and C4 grasses at all sites. High precipitation in the spring and in previous year monsoon storms influenced changes in cover of C4 grasses, with measures of water balance in the same months explaining additional variability. C3 grasses in grasslands where they were dominant were influenced primarily by longer periods of climate, while C3 grasses in grasslands where they were co-dominant with C4 grasses were influenced little by climate anomalies at either short or long periods of time. Our results suggest that future changes in spring and summer climate and water balance are likely to affect cover of both C3 and C4 grasses, but cover of C4 grasses may be affected more strongly, and the degree of change will depend on soils and topography where they are growing and the timing of the growing season.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1577","usgsCitation":"Witwicki, D.L., Munson, S.M., and Thoma, D.P., 2016, Effects of climate and water balance across grasslands of varying C3 and C4 grass cover: Ecosphere, v. 7, no. 11, e01577; 19 p., https://doi.org/10.1002/ecs2.1577.","productDescription":"e01577; 19 p.","ipdsId":"IP-074409","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":470424,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1577","text":"Publisher Index Page"},{"id":330974,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-08","publicationStatus":"PW","scienceBaseUri":"582adb41e4b0c253bdfff08c","chorus":{"doi":"10.1002/ecs2.1577","url":"http://dx.doi.org/10.1002/ecs2.1577","publisher":"Wiley-Blackwell","authors":"Witwicki Dana L., Munson Seth M., Thoma David P.","journalName":"Ecosphere","publicationDate":"11/2016","auditedOn":"11/15/2016"},"contributors":{"authors":[{"text":"Witwicki, Dana L.","contributorId":72473,"corporation":false,"usgs":true,"family":"Witwicki","given":"Dana","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":653649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":653650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thoma, David P.","contributorId":45975,"corporation":false,"usgs":true,"family":"Thoma","given":"David","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":653651,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178339,"text":"70178339 - 2016 - Aquatic-macroinvertebrate communities of Prairie-Pothole wetlands and lakes under a changed climate","interactions":[],"lastModifiedDate":"2017-01-03T16:07:07","indexId":"70178339","displayToPublicDate":"2016-11-14T13:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Aquatic-macroinvertebrate communities of Prairie-Pothole wetlands and lakes under a changed climate","docAbstract":"Understanding how aquatic-macroinvertebrate communities respond to changes in climate is important for biodiversity conservation in the Prairie Pothole Region and other wetland-rich landscapes. We sampled macroinvertebrate communities of 162 wetlands and lakes previously sampled from 1966 to 1976, a much drier period compared to our 2012–2013 sampling timeframe. To identify possible influences of a changed climate and predation pressures on macroinvertebrates, we compared two predictors of aquatic-macroinvertebrate communities: ponded-water dissolved-ion concentration and vertebrate-predator presence/abundance. Further, we make inferences of how macroinvertebrate communities were structured during the drier period when the range of dissolved-ion concentrations was much greater and fish occurrence in aquatic habitats was rare. We found that aquatic-macroinvertebrate community structure was influenced by dissolved-ion concentrations through a complex combination of direct and indirect relationships. Ion concentrations also influenced predator occurrence and abundance, which indirectly affected macroinvertebrate communities. It is important to consider both abiotic and biotic gradients when predicting how invertebrate communities will respond to climate change. Generally, in the wetlands and lakes we studied, freshening of ponded water resulted in more homogenous communities than occurred during a much drier period when salinity range among sites was greater.
","language":"English","publisher":"Wetlands","doi":"10.1007/s13157-016-0848-2","usgsCitation":"McLean, K.I., Mushet, D.M., Renton, D., and Stockwell, C., 2016, Aquatic-macroinvertebrate communities of Prairie-Pothole wetlands and lakes under a changed climate: Wetlands, v. 36, no. s2, p. 423-435, https://doi.org/10.1007/s13157-016-0848-2.","productDescription":"13 p.","startPage":"423","endPage":"435","ipdsId":"IP-071577","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":330972,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"s2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"582adb43e4b0c253bdfff094","contributors":{"authors":[{"text":"McLean, Kyle I. kmclean@usgs.gov","contributorId":147397,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","middleInitial":"I.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":653645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653646,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Renton, David A. drenton@usgs.gov","contributorId":138600,"corporation":false,"usgs":true,"family":"Renton","given":"David A.","email":"drenton@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":653647,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stockwell, Craig A.","contributorId":55257,"corporation":false,"usgs":true,"family":"Stockwell","given":"Craig A.","affiliations":[],"preferred":false,"id":653648,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70178340,"text":"70178340 - 2016 - Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration","interactions":[],"lastModifiedDate":"2016-11-14T12:17:49","indexId":"70178340","displayToPublicDate":"2016-11-14T13:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration","docAbstract":"The Delaware River Basin (DRB) encompasses approximately 0.4 % of the area of the United States (U.S.), but supplies water to 5 % of the population. We studied three forested tributaries to quantify the potential climate-driven change in hydrologic budget for two 25-year time periods centered on 2030 and 2060, focusing on sensitivity to the method of estimating potential evapotranspiration (PET) change. Hydrology was simulated using the Water Availability Tool for Environmental Resources (Williamson et al. 2015). Climate-change scenarios for four Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) and two Representative Concentration Pathways (RCPs) were used to derive monthly change factors for temperature (T), precipitation (PPT), and PET according to the energy-based method of Priestley and Taylor (1972). Hydrologic simulations indicate a general increase in annual (especially winter) streamflow (Q) as early as 2030 across the DRB, with a larger increase by 2060. This increase in Q is the result of (1) higher winter PPT, which outweighs an annual actual evapotranspiration (AET) increase and (2) (for winter) a major shift away from storage of PPT as snow pack. However, when PET change is evaluated instead using the simpler T-based method of Hamon (1963), the increases in Q are small or even negative. In fact, the change of Q depends as much on PET method as on time period or RCP. This large sensitivity and associated uncertainty underscore the importance of exercising caution in the selection of a PET method for use in climate-change analyses.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-016-1782-2","usgsCitation":"Williamson, T., Nystrom, E.A., and Milly, P., 2016, Sensitivity of the projected hydroclimatic environment of the Delaware River basin to formulation of potential evapotranspiration: Climatic Change, v. 139, no. 2, p. 215-228, https://doi.org/10.1007/s10584-016-1782-2.","productDescription":"14 p.","startPage":"215","endPage":"228","ipdsId":"IP-072262","costCenters":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"links":[{"id":330971,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"139","issue":"2","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-09-16","publicationStatus":"PW","scienceBaseUri":"582adb44e4b0c253bdfff09a","chorus":{"doi":"10.1007/s10584-016-1782-2","url":"http://dx.doi.org/10.1007/s10584-016-1782-2","publisher":"Springer Nature","authors":"Williamson Tanja N., Nystrom Elizabeth A., Milly Paul C. D.","journalName":"Climatic Change","publicationDate":"9/16/2016","auditedOn":"2/15/2017","publiclyAccessibleDate":"9/16/2016"},"contributors":{"authors":[{"text":"Williamson, Tanja N. tnwillia@usgs.gov","contributorId":148942,"corporation":false,"usgs":true,"family":"Williamson","given":"Tanja N.","email":"tnwillia@usgs.gov","affiliations":[{"id":354,"text":"Kentucky Water Science Center","active":true,"usgs":true}],"preferred":false,"id":653642,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nystrom, Elizabeth A. 0000-0002-0886-3439 nystrom@usgs.gov","orcid":"https://orcid.org/0000-0002-0886-3439","contributorId":1072,"corporation":false,"usgs":true,"family":"Nystrom","given":"Elizabeth","email":"nystrom@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":653643,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milly, Paul C.D. 0000-0003-4389-3139 cmilly@usgs.gov","orcid":"https://orcid.org/0000-0003-4389-3139","contributorId":2119,"corporation":false,"usgs":true,"family":"Milly","given":"Paul C.D.","email":"cmilly@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":false,"id":653644,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70176823,"text":"ds1023 - 2016 - Quality of surface water in Missouri, water year 2015","interactions":[],"lastModifiedDate":"2016-11-14T12:56:11","indexId":"ds1023","displayToPublicDate":"2016-11-14T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1023","title":"Quality of surface water in Missouri, water year 2015","docAbstract":"The U.S. Geological Survey, in cooperation with the Missouri Department of Natural Resources, designed and operates a series of monitoring stations on streams and springs throughout Missouri known as the Ambient Water-Quality Monitoring Network. During water year 2015 (October 1, 2014, through September 30, 2015), data were collected at 74 stations—72 Ambient Water-Quality Monitoring Network stations and 2 U.S. Geological Survey National Stream Quality Assessment Network stations. Dissolved oxygen, specific conductance, water temperature, suspended solids, suspended sediment, Escherichia coli bacteria, fecal coliform bacteria, dissolved nitrate plus nitrite as nitrogen, total phosphorus, dissolved and total recoverable lead and zinc, and select pesticide compound summaries are presented for 71 of these stations. The stations primarily have been classified into groups corresponding to the physiography of the State, primary land use, or unique station types. In addition, a summary of hydrologic conditions in the State including peak streamflows, monthly mean streamflows, and 7-day low flows is presented.
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U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
The third United States National Climate Assessment emphasized an evaluation of not just the impacts of climate change on species and ecosystems, but also the impacts of climate change on the benefits that people derive from nature, known as ecosystem services. The ecosystems, biodiversity, and ecosystem services component of the assessment largely drew upon the findings of a transdisciplinary workshop aimed at developing technical input for the assessment, involving participants from diverse sectors. A small author team distilled and synthesized this and hundreds of other technical input to develop the key findings of the assessment. The process of developing and ranking key findings hinged on identifying impacts that had particular, demonstrable effects on the U.S. public via changes in national ecosystem services. Findings showed that ecosystem services are threatened by the impacts of climate change on water supplies, species distributions and phenology, as well as multiple assaults on ecosystem integrity that, when compounded by climate change, reduce the capacity of ecosystems to buffer against extreme events. As ecosystems change, such benefits as water sustainability and protection from storms that are afforded by intact ecosystems are projected to decline across the continent due to climate change. An ongoing, sustained assessment that focuses on the co-production of actionable climate science will allow scientists from a range of disciplines to ascertain the capability of their forecasting models to project environmental and ecological change and link it to ecosystem services; additionally, an iterative process of evaluation, development of management strategies, monitoring, and reevaluation will increase the applicability and usability of the science by the U.S. public.
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maps","interactions":[],"lastModifiedDate":"2017-07-19T10:47:31","indexId":"70189628","displayToPublicDate":"2016-11-09T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"St. Louis area earthquake hazards mapping project; seismic and liquefaction hazard maps","docAbstract":"We present probabilistic and deterministic seismic and liquefaction hazard maps for the densely populated St. Louis metropolitan area that account for the expected effects of surficial geology on earthquake ground shaking. Hazard calculations were based on a map grid of 0.005°, or about every 500 m, and are thus higher in resolution than any earlier studies. To estimate ground motions at the surface of the model (e.g., site amplification), we used a new detailed near‐surface shear‐wave velocity model in a 1D equivalent‐linear response analysis. When compared with the 2014 U.S. Geological Survey (USGS) National Seismic Hazard Model, which uses a uniform firm‐rock‐site condition, the new probabilistic seismic‐hazard estimates document much more variability. Hazard levels for upland sites (consisting of bedrock and weathered bedrock overlain by loess‐covered till and drift deposits), show up to twice the ground‐motion values for peak ground acceleration (PGA), and similar ground‐motion values for 1.0 s spectral acceleration (SA). Probabilistic ground‐motion levels for lowland alluvial floodplain sites (generally the 20–40‐m‐thick modern Mississippi and Missouri River floodplain deposits overlying bedrock) exhibit up to twice the ground‐motion levels for PGA, and up to three times the ground‐motion levels for 1.0 s SA. Liquefaction probability curves were developed from available standard penetration test data assuming typical lowland and upland water table levels. A simplified liquefaction hazard map was created from the 5%‐in‐50‐year probabilistic ground‐shaking model. The liquefaction hazard ranges from low (<40% of area expected to liquefy) in the uplands to severe (>60% of area expected to liquefy) in the lowlands. Because many transportation routes, power and gas transmission lines, and population centers exist in or on the highly susceptible lowland alluvium, these areas in the St. Louis region are at significant potential risk from seismically induced liquefaction and associated ground deformation","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220160028","usgsCitation":"Cramer, C.H., Bauer, R.A., Chung, J., Rogers, D., Pierce, L., Voigt, V., Mitchell, B., Gaunt, D., Williams, R., Hoffman, D., Hempen, G.L., Steckel, P., Boyd, O.S., Watkins, C.M., Tucker, K., and McCallister, N., 2016, St. Louis area earthquake hazards mapping project; seismic and liquefaction hazard maps: Seismological Research Letters, v. 88, no. 1, p. 206-223, https://doi.org/10.1785/0220160028.","productDescription":"18 p.","startPage":"206","endPage":"223","ipdsId":"IP-079759","costCenters":[{"id":300,"text":"Geologic Hazards Science 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communities in Essex County, Massachusetts","interactions":[],"lastModifiedDate":"2018-03-08T16:08:01","indexId":"ofr20161187","displayToPublicDate":"2016-11-09T00:00:00","publicationYear":"2016","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":"2016-1187","title":"Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts","docAbstract":"Understanding if and how community exposure to coastal hazards may change over time is crucial information for coastal managers tasked with developing climate adaptation plans. This report summarizes estimates of population and asset exposure to coastal-inundation hazards associated with sea-level-rise and storm scenarios in six coastal communities of the Great Marsh region of Essex County, Massachusetts. This U.S. Geological Survey (USGS) analysis was conducted in collaboration with National Wildlife Federation (NWF) representatives, who are working with local stakeholders to develop local climate adaptation plans for the Towns of Salisbury, Newbury, Rowley, Ipswich, and Essex and the City of Newburyport (hereafter referred to as communities). Community exposure was characterized by integrating various community indicators (land cover and land use, population, economic assets, critical facilities, and infrastructure) with coastal-hazard zones that estimate inundation extents and water depth for three time periods.
Estimates of community exposure are based on the presence of people, businesses, and assets in hazard zones that are calculated from geospatial datasets using geographic-information-system (GIS) tools. Results are based on current distributions of people and assets in hazard zones and do not take into account projections of human population, asset, or land-use changes over time. Results are not loss estimates based on engineering analysis or field surveys for any particular facility and do not take into account aspects of individual and household preparedness before an extreme event, adaptive capacity of a community during an event, or long-term resilience of individuals and communities after an event. Potential losses would match reported inventories only if all residents, business owners, public managers, and elected officials were unaware of what to do if warned of an imminent threat, failed to take protective measures during an extreme event, or failed to implement any long-term strategies to mitigate potential impacts. This analysis is intended to serve as a foundation for additional risk-related studies, plans, and mitigation efforts that are tailored to local needs. After a summary of the geospatial methods used in the analysis, results are organized by community so that local officials can easily use them in their local adaptation planning efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161187","usgsCitation":"Abdollahian, Nina, Jones, J.L., and Wood, N.J., 2016, Community exposure to potential climate-driven changes to coastal-inundation hazards for six communities in Essex County, Massachusetts: U.S. Geological Survey Open-File Report 2016–1187, 87 p., https://dx.doi.org/10.3133/ofr20161187.","productDescription":"ix, 97 p.","onlineOnly":"Y","ipdsId":"IP-076664","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":330895,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1187/ofr20161187.pdf","text":"Report","size":"37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1187"},{"id":330894,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1187/coverthb.jpg"}],"country":"United States","state":"Massachusetts","county":"Essex County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.71006774902344,\n 42.68193062780271\n ],\n [\n -70.68603515625,\n 42.661736441708726\n ],\n [\n -70.67985534667969,\n 42.645576368740564\n ],\n [\n -70.68946838378906,\n 42.61374895431491\n ],\n [\n -70.67779541015624,\n 42.58594981115061\n ],\n [\n -70.70938110351562,\n 42.56926437219384\n ],\n [\n -70.74783325195311,\n 42.5626900946744\n ],\n [\n -70.79315185546875,\n 42.55813826591757\n ],\n [\n -70.81443786621094,\n 42.5530802889558\n ],\n [\n -70.84396362304688,\n 42.549033612225145\n ],\n [\n -70.90164184570312,\n 42.558644041066486\n ],\n [\n -70.92567443847656,\n 42.58443313755392\n ],\n [\n -70.94352722167969,\n 42.61273829368574\n ],\n [\n -70.9771728515625,\n 42.65315192577889\n ],\n [\n -70.98609924316406,\n 42.6970719613491\n ],\n [\n -71.00807189941406,\n 42.74196953252084\n ],\n [\n -71.01837158203125,\n 42.77221922375653\n ],\n [\n -71.026611328125,\n 42.818573401893836\n ],\n [\n -71.03347778320312,\n 42.85834982271733\n ],\n [\n -70.99296569824219,\n 42.86740922288482\n ],\n [\n -70.94627380371094,\n 42.877473663905946\n ],\n [\n -70.92498779296875,\n 42.882505268943774\n ],\n [\n -70.88447570800781,\n 42.882505268943774\n ],\n [\n -70.85975646972656,\n 42.87495770751784\n ],\n [\n -70.83915710449219,\n 42.87042872752173\n ],\n [\n -70.81718444824219,\n 42.87042872752173\n ],\n [\n -70.806884765625,\n 42.835191982798925\n ],\n [\n -70.8013916015625,\n 42.79288135293722\n ],\n [\n -70.77392578125,\n 42.73944805837745\n ],\n [\n -70.73204040527342,\n 42.70110902692345\n ],\n [\n -70.71006774902344,\n 42.68193062780271\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Western Geographic Science Center
U.S. Geological Survey
345 Middlefield Road, MS 531
Menlo Park, CA 94025
http://geography.wr.usgs.gov/